wuxiaolang/Visual_SLAM_Related_Research

视觉(语义) SLAM 相关研究跟踪

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毕业快乐！最近更新 2021 年 07 月 05 日：论文更新 +104，开源代码 +9

Visual_SLAM_Related_Research

@Author：吴艳敏
@E-mail ：wuyanminmax[AT]gmail.com
@github ：wuxiaolang

前言

以下收集的论文、代码等资料主要与本人的硕士期间的学习方向 视觉 SLAM、增强现实 相关。目前（2019-2021）重点关注 VO、物体级 SLAM 和语义数据关联，对传感器融合、稠密建图也略有关注，所以资料的收集范围也与自己的兴趣比较一致，无法涵盖视觉 SLAM 的所有研究，请谨慎参考，部分整理后发布于知乎。主要内容包括：
1. 开源代码 ：经典、优秀的开源工程
2. 优秀作者与实验室 ：在自己感兴趣领域比较优秀的值得关注的团队或个人
3. SLAM 学习资料 ：SLAM 相关学习资料、视频、数据集、公众号和代码注释
4. 近期论文 ：自己感兴趣方向的最新论文，大概一个月一更新。部分论文的详/泛读笔记放在我的博客/List上。
🌚 本仓库于 2019 年 3 月（研一）开始整理（私密）；
🌝 本仓库于 2020 年 3月（研二）公开，正好一周年；
🎓 2021 年 7 月，研三硕士毕业，此仓库后续可能不会有很频繁的更新，祝各位学习工作顺利，学术交流欢迎邮件联系 wuyanminmax[AT]gmail.com。

1. 开源代码

这一部分整理之后发布在知乎（2020 年 3 月 31 日）：https://zhuanlan.zhihu.com/p/115599978/

😄 1.1 Geometric SLAM

1. PTAM

论文：Klein G, Murray D. Parallel tracking and mapping for small AR workspaces[C]//Mixed and Augmented Reality, 2007. ISMAR 2007. 6th IEEE and ACM International Symposium on. IEEE, 2007: 225-234.
代码：https://github.com/Oxford-PTAM/PTAM-GPL
工程地址：http://www.robots.ox.ac.uk/~gk/PTAM/
作者其他研究：http://www.robots.ox.ac.uk/~gk/publications.html

2. S-PTAM（双目 PTAM）

论文：Taihú Pire,Thomas Fischer, Gastón Castro, Pablo De Cristóforis, Javier Civera and Julio Jacobo Berlles. S-PTAM: Stereo Parallel Tracking and Mapping. Robotics and Autonomous Systems, 2017.
代码：https://github.com/lrse/sptam
作者其他论文：Castro G, Nitsche M A, Pire T, et al. Efficient on-board Stereo SLAM through constrained-covisibility strategies[J]. Robotics and Autonomous Systems, 2019.

3. MonoSLAM

论文：Davison A J, Reid I D, Molton N D, et al. MonoSLAM: Real-time single camera SLAM[J]. IEEE transactions on pattern analysis and machine intelligence, 2007, 29(6): 1052-1067.
代码：https://github.com/hanmekim/SceneLib2

4. ORB-SLAM2

论文：Mur-Artal R, Tardós J D. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras[J]. IEEE Transactions on Robotics, 2017, 33(5): 1255-1262.
代码：https://github.com/raulmur/ORB_SLAM2
作者其他论文：
- 单目半稠密建图：Mur-Artal R, Tardós J D. Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM[C]//Robotics: Science and Systems. 2015, 2015.
- VIORB：Mur-Artal R, Tardós J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803.
- 多地图：Elvira R, Tardós J D, Montiel J M M. ORBSLAM-Atlas: a robust and accurate multi-map system[J]. arXiv preprint arXiv:1908.11585, 2019.

以下5, 6, 7, 8几项是 TUM 计算机视觉组全家桶，官方主页：https://vision.in.tum.de/research/vslam/dso

5. DSO

论文：Engel J, Koltun V, Cremers D. Direct sparse odometry[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(3): 611-625.
代码：https://github.com/JakobEngel/dso
双目 DSO：Wang R, Schworer M, Cremers D. Stereo DSO: Large-scale direct sparse visual odometry with stereo cameras[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 3903-3911.
VI-DSO：Von Stumberg L, Usenko V, Cremers D. Direct sparse visual-inertial odometry using dynamic marginalization[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 2510-2517.

6. LDSO

高翔在 DSO 上添加闭环的工作
论文：Gao X, Wang R, Demmel N, et al. LDSO: Direct sparse odometry with loop closure[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 2198-2204.
代码：https://github.com/tum-vision/LDSO

7. LSD-SLAM

论文：Engel J, Schöps T, Cremers D. LSD-SLAM: Large-scale direct monocular SLAM[C]//European conference on computer vision. Springer, Cham, 2014: 834-849.
代码：https://github.com/tum-vision/lsd_slam

8. DVO-SLAM

论文：Kerl C, Sturm J, Cremers D. Dense visual SLAM for RGB-D cameras[C]//2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2013: 2100-2106.
代码 1：https://github.com/tum-vision/dvo_slam
代码 2：https://github.com/tum-vision/dvo
其他论文：
- Kerl C, Sturm J, Cremers D. Robust odometry estimation for RGB-D cameras[C]//2013 IEEE international conference on robotics and automation. IEEE, 2013: 3748-3754.
- Steinbrücker F, Sturm J, Cremers D. Real-time visual odometry from dense RGB-D images[C]//2011 IEEE international conference on computer vision workshops (ICCV Workshops). IEEE, 2011: 719-722.

9. SVO

苏黎世大学机器人与感知课题组
论文：Forster C, Pizzoli M, Scaramuzza D. SVO: Fast semi-direct monocular visual odometry[C]//2014 IEEE international conference on robotics and automation (ICRA). IEEE, 2014: 15-22.
代码：https://github.com/uzh-rpg/rpg_svo
Forster C, Zhang Z, Gassner M, et al. SVO: Semidirect visual odometry for monocular and multicamera systems[J]. IEEE Transactions on Robotics, 2016, 33(2): 249-265.

10. DSM

论文：Zubizarreta J, Aguinaga I, Montiel J M M. Direct sparse mapping[J]. arXiv preprint arXiv:1904.06577, 2019.
代码：https://github.com/jzubizarreta/dsm ；Video

11. openvslam

论文：Sumikura S, Shibuya M, Sakurada K. OpenVSLAM: A Versatile Visual SLAM Framework[C]//Proceedings of the 27th ACM International Conference on Multimedia. 2019: 2292-2295.
代码：https://github.com/xdspacelab/openvslam ；文档

12. se2lam（地面车辆位姿估计的视觉里程计）

论文：Zheng F, Liu Y H. Visual-Odometric Localization and Mapping for Ground Vehicles Using SE (2)-XYZ Constraints[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 3556-3562.
代码：https://github.com/izhengfan/se2lam
作者的另外一项工作
- 论文：Zheng F, Tang H, Liu Y H. Odometry-vision-based ground vehicle motion estimation with se (2)-constrained se (3) poses[J]. IEEE transactions on cybernetics, 2018, 49(7): 2652-2663.
- 代码：https://github.com/izhengfan/se2clam

13. GraphSfM（基于图的并行大规模 SFM）

论文：Chen Y, Shen S, Chen Y, et al. Graph-Based Parallel Large Scale Structure from Motion[J]. arXiv preprint arXiv:1912.10659, 2019.
代码：https://github.com/AIBluefisher/GraphSfM

14. LCSD_SLAM（松耦合的半直接法单目 SLAM）

论文：Lee S H, Civera J. Loosely-Coupled semi-direct monocular SLAM[J]. IEEE Robotics and Automation Letters, 2018, 4(2): 399-406.
代码：https://github.com/sunghoon031/LCSD_SLAM ；谷歌学术；演示视频
作者另外一篇关于单目尺度的文章代码开源：Lee S H, de Croon G. Stability-based scale estimation for monocular SLAM[J]. IEEE Robotics and Automation Letters, 2018, 3(2): 780-787.

15. RESLAM（基于边的 SLAM）

论文：Schenk F, Fraundorfer F. RESLAM: A real-time robust edge-based SLAM system[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 154-160.
代码：https://github.com/fabianschenk/RESLAM ；项目主页

16. scale_optimization（将单目 DSO 拓展到双目）

论文：Mo J, Sattar J. Extending Monocular Visual Odometry to Stereo Camera System by Scale Optimization[C]. International Conference on Intelligent Robots and Systems (IROS), 2019.
代码：https://github.com/jiawei-mo/scale_optimization

17. BAD-SLAM（直接法 RGB-D SLAM）

论文：Schops T, Sattler T, Pollefeys M. BAD SLAM: Bundle Adjusted Direct RGB-D SLAM[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 134-144.
代码：https://github.com/ETH3D/badslam

18. GSLAM（集成 ORB-SLAM2，DSO，SVO 的通用框架）

论文：Zhao Y, Xu S, Bu S, et al. GSLAM: A general SLAM framework and benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1110-1120.
代码：https://github.com/zdzhaoyong/GSLAM

19. ARM-VO（运行于 ARM 处理器上的单目 VO）

论文：Nejad Z Z, Ahmadabadian A H. ARM-VO: an efficient monocular visual odometry for ground vehicles on ARM CPUs[J]. Machine Vision and Applications, 2019: 1-10.
代码：https://github.com/zanazakaryaie/ARM-VO

20. cvo-rgbd（直接法 RGB-D VO）

论文：Ghaffari M, Clark W, Bloch A, et al. Continuous Direct Sparse Visual Odometry from RGB-D Images[J]. arXiv preprint arXiv:1904.02266, 2019.
代码：https://github.com/MaaniGhaffari/cvo-rgbd

21. Map2DFusion（单目 SLAM 无人机图像拼接）

论文：Bu S, Zhao Y, Wan G, et al. Map2DFusion: Real-time incremental UAV image mosaicing based on monocular slam[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4564-4571.
代码：https://github.com/zdzhaoyong/Map2DFusion

22. CCM-SLAM（多机器人协同单目 SLAM）

论文：Schmuck P, Chli M. CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams[J]. Journal of Field Robotics, 2019, 36(4): 763-781.
代码：https://github.com/VIS4ROB-lab/ccm_slam Video

23. ORB-SLAM3

论文：Carlos Campos, Richard Elvira, et al.ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM[J]. arXiv preprint arXiv:2007.11898, 2020.
代码：https://github.com/UZ-SLAMLab/ORB_SLAM3 | Video

24. OV²SLAM（完全实时在线多功能 SLAM）

论文：Ferrera M, Eudes A, Moras J, et al. OV $^{2} $ SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1399-1406.
代码：https://github.com/ov2slam/ov2slam

25. ESVO（基于事件的双目视觉里程计）

论文：Zhou Y, Gallego G, Shen S. Event-based stereo visual odometry[J]. IEEE Transactions on Robotics, 2021.
代码：https://github.com/HKUST-Aerial-Robotics/ESVO

26. VOLDOR-SLAM（实时稠密非直接法 SLAM）

论文：Min Z, Dunn E. VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough[J]. arXiv preprint arXiv:2104.06800, 2021.
代码：https://github.com/htkseason/VOLDOR

😄 1.2 Semantic / Deep SLAM

1. MsakFusion

论文：Runz M, Buffier M, Agapito L. Maskfusion: Real-time recognition, tracking and reconstruction of multiple moving objects[C]//2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2018: 10-20.
代码：https://github.com/martinruenz/maskfusion

2. SemanticFusion

论文：McCormac J, Handa A, Davison A, et al. Semanticfusion: Dense 3d semantic mapping with convolutional neural networks[C]//2017 IEEE International Conference on Robotics and automation (ICRA). IEEE, 2017: 4628-4635.
代码：https://github.com/seaun163/semanticfusion

3. semantic_3d_mapping

论文：Yang S, Huang Y, Scherer S. Semantic 3D occupancy mapping through efficient high order CRFs[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 590-597.
代码：https://github.com/shichaoy/semantic_3d_mapping

4. Kimera（实时度量与语义定位建图开源库）

论文：Rosinol A, Abate M, Chang Y, et al. Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping[J]. arXiv preprint arXiv:1910.02490, 2019.
代码：https://github.com/MIT-SPARK/Kimera ；演示视频

5. NeuroSLAM（脑启发式 SLAM）

论文：Yu F, Shang J, Hu Y, et al. NeuroSLAM: a brain-inspired SLAM system for 3D environments[J]. Biological Cybernetics, 2019: 1-31.
代码：https://github.com/cognav/NeuroSLAM
第四作者就是 Rat SLAM 的作者，文章也比较了十余种脑启发式的 SLAM

6. gradSLAM（自动分区的稠密 SLAM）

论文：Jatavallabhula K M, Iyer G, Paull L. gradSLAM: Dense SLAM meets Automatic Differentiation[J]. arXiv preprint arXiv:1910.10672, 2019.
代码（预计 20 年 4 月放出）：https://github.com/montrealrobotics/gradSLAM ；项目主页，演示视频

7. ORB-SLAM2 + 目标检测/分割的方案语义建图

https://github.com/floatlazer/semantic_slam
https://github.com/qixuxiang/orb-slam2_with_semantic_labelling
https://github.com/Ewenwan/ORB_SLAM2_SSD_Semantic

8. SIVO（语义辅助特征选择）

论文：Ganti P, Waslander S. Network Uncertainty Informed Semantic Feature Selection for Visual SLAM[C]//2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019: 121-128.
代码：https://github.com/navganti/SIVO

9. FILD（临近图增量式闭环检测）

论文：Shan An, Guangfu Che, Fangru Zhou, Xianglong Liu, Xin Ma, Yu Chen. Fast and Incremental Loop Closure Detection using Proximity Graphs. pp. 378-385, The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)
代码：https://github.com/AnshanTJU/FILD

10. object-detection-sptam（目标检测与双目 SLAM）

论文：Pire T, Corti J, Grinblat G. Online Object Detection and Localization on Stereo Visual SLAM System[J]. Journal of Intelligent & Robotic Systems, 2019: 1-10.
代码：https://github.com/CIFASIS/object-detection-sptam

11. Map Slammer（单目深度估计 + SLAM）

论文：Torres-Camara J M, Escalona F, Gomez-Donoso F, et al. Map Slammer: Densifying Scattered KSLAM 3D Maps with Estimated Depth[C]//Iberian Robotics conference. Springer, Cham, 2019: 563-574.
代码：https://github.com/jmtc7/mapSlammer

12. NOLBO（变分模型的概率 SLAM）

论文：Yu H, Lee B. Not Only Look But Observe: Variational Observation Model of Scene-Level 3D Multi-Object Understanding for Probabilistic SLAM[J]. arXiv preprint arXiv:1907.09760, 2019.
代码：https://github.com/bogus2000/NOLBO

13. GCNv2_SLAM （基于图卷积神经网络 SLAM）

论文：Tang J, Ericson L, Folkesson J, et al. GCNv2: Efficient correspondence prediction for real-time SLAM[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3505-3512.
代码：https://github.com/jiexiong2016/GCNv2_SLAM Video

14. semantic_suma（激光语义建图）

论文：Chen X, Milioto A, Palazzolo E, et al. SuMa++: Efficient LiDAR-based semantic SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 4530-4537.
代码：https://github.com/PRBonn/semantic_suma/ ；Video

15. Neural-SLAM（主动神经 SLAM）

论文：Chaplot D S, Gandhi D, Gupta S, et al. Learning to explore using active neural slam[C]. ICLR 2020.
代码：https://github.com/devendrachaplot/Neural-SLAM

16. TartanVO：一种通用的基于学习的 VO

论文：Wang W, Hu Y, Scherer S. TartanVO: A Generalizable Learning-based VO[J]. arXiv preprint arXiv:2011.00359, 2020.
代码：https://github.com/castacks/tartanvo
数据集：IROS2020 TartanAir: A Dataset to Push the Limits of Visual SLAM，数据集地址

17. DF-VO

论文：Zhan H, Weerasekera C S, Bian J W, et al. DF-VO: What Should Be Learnt for Visual Odometry?[J]. arXiv preprint arXiv:2103.00933, 2021.
- Zhan H, Weerasekera C S, Bian J W, et al. Visual odometry revisited: What should be learnt?[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 4203-4210.
代码：https://github.com/Huangying-Zhan/DF-VO

😄 1.3 Multi-Landmarks / Object SLAM

1. PL-SVO（点线 SVO）

论文：Gomez-Ojeda R, Briales J, Gonzalez-Jimenez J. PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments[C]//Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016: 4211-4216.
代码：https://github.com/rubengooj/pl-svo

2. stvo-pl（双目点线 VO）

论文：Gomez-Ojeda R, Gonzalez-Jimenez J. Robust stereo visual odometry through a probabilistic combination of points and line segments[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 2521-2526.
代码：https://github.com/rubengooj/stvo-pl

3. PL-SLAM（点线 SLAM）

论文：Gomez-Ojeda R, Zuñiga-Noël D, Moreno F A, et al. PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments[J]. arXiv preprint arXiv:1705.09479, 2017.
代码：https://github.com/rubengooj/pl-slam
Gomez-Ojeda R, Moreno F A, Zuñiga-Noël D, et al. PL-SLAM: a stereo SLAM system through the combination of points and line segments[J]. IEEE Transactions on Robotics, 2019, 35(3): 734-746.

4. PL-VIO

论文：He Y, Zhao J, Guo Y, et al. PL-VIO: Tightly-coupled monocular visual–inertial odometry using point and line features[J]. Sensors, 2018, 18(4): 1159.
代码：https://github.com/HeYijia/PL-VIO
VINS + 线段：https://github.com/Jichao-Peng/VINS-Mono-Optimization

5. lld-slam（用于 SLAM 的可学习型线段描述符）

论文：Vakhitov A, Lempitsky V. Learnable line segment descriptor for visual SLAM[J]. IEEE Access, 2019, 7: 39923-39934.
代码：https://github.com/alexandervakhitov/lld-slam ；Video

点线结合的工作还有很多，国内的比如

上交邹丹平老师的 Zou D, Wu Y, Pei L, et al. StructVIO: visual-inertial odometry with structural regularity of man-made environments[J]. IEEE Transactions on Robotics, 2019, 35(4): 999-1013.

浙大的 Zuo X, Xie X, Liu Y, et al. Robust visual SLAM with point and line features[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 1775-1782.

6. PlaneSLAM

论文：Wietrzykowski J. On the representation of planes for efficient graph-based slam with high-level features[J]. Journal of Automation Mobile Robotics and Intelligent Systems, 2016, 10.
代码：https://github.com/LRMPUT/PlaneSLAM
作者另外一项开源代码，没有找到对应的论文：https://github.com/LRMPUT/PUTSLAM

7. Eigen-Factors（特征因子平面对齐）

论文：Ferrer G. Eigen-Factors: Plane Estimation for Multi-Frame and Time-Continuous Point Cloud Alignment[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 1278-1284.
代码：https://gitlab.com/gferrer/eigen-factors-iros2019 ；演示视频

8. PlaneLoc

论文：Wietrzykowski J, Skrzypczyński P. PlaneLoc: Probabilistic global localization in 3-D using local planar features[J]. Robotics and Autonomous Systems, 2019, 113: 160-173.
代码：https://github.com/LRMPUT/PlaneLoc

9. Pop-up SLAM

论文：Yang S, Song Y, Kaess M, et al. Pop-up slam: Semantic monocular plane slam for low-texture environments[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 1222-1229.
代码：https://github.com/shichaoy/pop_up_slam

10. Object SLAM

论文：Mu B, Liu S Y, Paull L, et al. Slam with objects using a nonparametric pose graph[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4602-4609.
代码：https://github.com/BeipengMu/objectSLAM ；Video

11. voxblox-plusplus（物体级体素建图）

论文：Grinvald M, Furrer F, Novkovic T, et al. Volumetric instance-aware semantic mapping and 3D object discovery[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 3037-3044.
代码：https://github.com/ethz-asl/voxblox-plusplus

12. Cube SLAM

论文：Yang S, Scherer S. Cubeslam: Monocular 3-d object slam[J]. IEEE Transactions on Robotics, 2019, 35(4): 925-938.
代码：https://github.com/shichaoy/cube_slam
对，这就是带我入坑的一项工作，2018 年 11 月份看到这篇论文（当时是预印版）之后开始学习物体级 SLAM，个人对 Cube SLAM 的一些注释和总结：链接。
也有很多有意思的但没开源的物体级 SLAM
- Ok K, Liu K, Frey K, et al. Robust Object-based SLAM for High-speed Autonomous Navigation[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 669-675.
- Li J, Meger D, Dudek G. Semantic Mapping for View-Invariant Relocalization[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 7108-7115.
- Nicholson L, Milford M, Sünderhauf N. Quadricslam: Dual quadrics from object detections as landmarks in object-oriented slam[J]. IEEE Robotics and Automation Letters, 2018, 4(1): 1-8.

13. VPS-SLAM（平面语义 SLAM）

论文：Bavle H, De La Puente P, How J, et al. VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems[J]. IEEE Access, 2020.
代码：https://bitbucket.org/hridaybavle/semantic_slam/src/master/

14. Structure-SLAM （低纹理环境下点线 SLAM）

论文：Li Y, Brasch N, Wang Y, et al. Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 6583-6590.
代码：https://github.com/yanyan-li/Structure-SLAM-PointLine

15. PL-VINS

论文：Fu Q, Wang J, Yu H, et al. PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line[J]. arXiv preprint arXiv:2009.07462, 2020.
代码：https://github.com/cnqiangfu/PL-VINS

😄 1.4 Sensor Fusion

1. msckf_vio

论文：Sun K, Mohta K, Pfrommer B, et al. Robust stereo visual inertial odometry for fast autonomous flight[J]. IEEE Robotics and Automation Letters, 2018, 3(2): 965-972.
代码：https://github.com/KumarRobotics/msckf_vio ；Video

2. rovio

论文：Bloesch M, Omari S, Hutter M, et al. Robust visual inertial odometry using a direct EKF-based approach[C]//2015 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2015: 298-304.
代码：https://github.com/ethz-asl/rovio ；Video

3. R-VIO

论文：Huai Z, Huang G. Robocentric visual-inertial odometry[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6319-6326.
代码：https://github.com/rpng/R-VIO ；Video
VI_ORB_SLAM2：https://github.com/YoujieXia/VI_ORB_SLAM2

4. okvis

论文：Leutenegger S, Lynen S, Bosse M, et al. Keyframe-based visual–inertial odometry using nonlinear optimization[J]. The International Journal of Robotics Research, 2015, 34(3): 314-334.
代码：https://github.com/ethz-asl/okvis

5. VIORB

论文：Mur-Artal R, Tardós J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803.
代码：https://github.com/jingpang/LearnVIORB （VIORB 本身是没有开源的，这是王京大佬复现的一个版本）

6. VINS-mono

论文：Qin T, Li P, Shen S. Vins-mono: A robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020.
代码：https://github.com/HKUST-Aerial-Robotics/VINS-Mono
双目版 VINS-Fusion：https://github.com/HKUST-Aerial-Robotics/VINS-Fusion
移动段 VINS-mobile：https://github.com/HKUST-Aerial-Robotics/VINS-Mobile

7. VINS-RGBD

论文：Shan Z, Li R, Schwertfeger S. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots[J]. Sensors, 2019, 19(10): 2251.
代码：https://github.com/STAR-Center/VINS-RGBD ；Video

8. Open-VINS

论文：Geneva P, Eckenhoff K, Lee W, et al. Openvins: A research platform for visual-inertial estimation[C]//IROS 2019 Workshop on Visual-Inertial Navigation: Challenges and Applications, Macau, China. IROS 2019.
代码：https://github.com/rpng/open_vins

9. versavis（多功能的视惯传感器系统）

论文：Tschopp F, Riner M, Fehr M, et al. VersaVIS—An Open Versatile Multi-Camera Visual-Inertial Sensor Suite[J]. Sensors, 2020, 20(5): 1439.
代码：https://github.com/ethz-asl/versavis

10. CPI（视惯融合的封闭式预积分）

论文：Eckenhoff K, Geneva P, Huang G. Closed-form preintegration methods for graph-based visual–inertial navigation[J]. The International Journal of Robotics Research, 2018.
代码：https://github.com/rpng/cpi ；Video

11. TUM Basalt

论文：Usenko V, Demmel N, Schubert D, et al. Visual-inertial mapping with non-linear factor recovery[J]. IEEE Robotics and Automation Letters, 2019.
代码：https://github.com/VladyslavUsenko/basalt-mirror ；Video；Project Page

12. Limo（激光单目视觉里程计）

论文：Graeter J, Wilczynski A, Lauer M. Limo: Lidar-monocular visual odometry[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 7872-7879.
代码：https://github.com/johannes-graeter/limo ； Video

13. LARVIO（多状态约束卡尔曼滤波的单目 VIO）

论文：Qiu X, Zhang H, Fu W, et al. Monocular Visual-Inertial Odometry with an Unbiased Linear System Model and Robust Feature Tracking Front-End[J]. Sensors, 2019, 19(8): 1941.
代码：https://github.com/PetWorm/LARVIO
北航邱笑晨博士的一项工作

14. vig-init（垂直边缘加速视惯初始化）

论文：Li J, Bao H, Zhang G. Rapid and Robust Monocular Visual-Inertial Initialization with Gravity Estimation via Vertical Edges[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 6230-6236.
代码：https://github.com/zju3dv/vig-init
浙大章国峰老师组的一项工作

15. vilib（VIO 前端库）

论文：Nagy B, Foehn P, Scaramuzza D. Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO[J]. arXiv preprint arXiv:2003.13493, 2020.
代码：https://github.com/uzh-rpg/vilib

16. Kimera-VIO

论文：A. Rosinol, M. Abate, Y. Chang, L. Carlone, Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping. IEEE Intl. Conf. on Robotics and Automation (ICRA), 2020.
代码：https://github.com/MIT-SPARK/Kimera-VIO

17. maplab（视惯建图框架）

论文：Schneider T, Dymczyk M, Fehr M, et al. maplab: An open framework for research in visual-inertial mapping and localization[J]. IEEE Robotics and Automation Letters, 2018, 3(3): 1418-1425.
代码：https://github.com/ethz-asl/maplab
多会话建图，地图合并，视觉惯性批处理优化和闭环

18. lili-om：固态雷达惯性里程计与建图

论文：Li K, Li M, Hanebeck U D. Towards high-performance solid-state-lidar-inertial odometry and mapping[J]. arXiv preprint arXiv:2010.13150, 2020.
代码：https://github.com/KIT-ISAS/lili-om

19. CamVox：Lidar 辅助视觉 SLAM

论文：ZHU, Yuewen, et al. CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System. arXiv preprint arXiv:2011.11357, 2020.
代码：https://github.com/ISEE-Technology/CamVox

20. SSL_SLAM：固态 LiDAR 轻量级 3D 定位与建图

论文：Wang H, Wang C, Xie L. Lightweight 3-D Localization and Mapping for Solid-State LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1801-1807.
代码：https://github.com/wh200720041/SSL_SLAM

21. r2live：LiDAR-Inertial-Visual 紧耦合

论文：Lin J, Zheng C, Xu W, et al. R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping[J]. arXiv preprint arXiv:2102.12400, 2021.
代码：https://github.com/hku-mars/r2live

22. GVINS：GNSS-视觉-惯导紧耦合

论文：Cao S, Lu X, Shen S. GVINS: Tightly Coupled GNSS-Visual-Inertial for Smooth and Consistent State Estimation[J]. arXiv e-prints, 2021: arXiv: 2103.07899.
代码：https://github.com/HKUST-Aerial-Robotics/GVINS

23. LVI-SAM：Lidar-Visual-Inertial 建图与定位

论文：Shan T, Englot B, Ratti C, et al. LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping[J]. arXiv preprint arXiv:2104.10831, 2021. (ICRA2021)
代码：https://github.com/TixiaoShan/LVI-SAM

😄 1.5 Dynamic SLAM

1. DynamicSemanticMapping（动态语义建图）

论文：Kochanov D, Ošep A, Stückler J, et al. Scene flow propagation for semantic mapping and object discovery in dynamic street scenes[C]//Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016: 1785-1792.
代码：https://github.com/ganlumomo/DynamicSemanticMapping ；wiki

2. DS-SLAM（动态语义 SLAM）

论文：Yu C, Liu Z, Liu X J, et al. DS-SLAM: A semantic visual SLAM towards dynamic environments[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 1168-1174.
代码：https://github.com/ivipsourcecode/DS-SLAM

3. Co-Fusion（实时分割与跟踪多物体）

论文：Rünz M, Agapito L. Co-fusion: Real-time segmentation, tracking and fusion of multiple objects[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 4471-4478.
代码：https://github.com/martinruenz/co-fusion ； Video

4. DynamicFusion

论文：Newcombe R A, Fox D, Seitz S M. Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 343-352.
代码：https://github.com/mihaibujanca/dynamicfusion

5. ReFusion（动态场景利用残差三维重建）

论文：Palazzolo E, Behley J, Lottes P, et al. ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals[J]. arXiv preprint arXiv:1905.02082, 2019.
代码：https://github.com/PRBonn/refusion ；Video

6. DynSLAM（室外大规模稠密重建）

论文：Bârsan I A, Liu P, Pollefeys M, et al. Robust dense mapping for large-scale dynamic environments[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 7510-7517.
代码：https://github.com/AndreiBarsan/DynSLAM
作者博士学位论文：Barsan I A. Simultaneous localization and mapping in dynamic scenes[D]. ETH Zurich, Department of Computer Science, 2017.

7. VDO-SLAM（动态物体感知的 SLAM）

论文：Zhang J, Henein M, Mahony R, et al. VDO-SLAM: A Visual Dynamic Object-aware SLAM System[J]. arXiv preprint arXiv:2005.11052, 2020.（IJRR Under Review）
- 相关论文
  - IROS 2020 Robust Ego and Object 6-DoF Motion Estimation and Tracking
  - ICRA 2020 Dynamic SLAM: The Need For Speed
代码：https://github.com/halajun/VDO_SLAM | video

😄 1.6 Mapping

1. InfiniTAM（跨平台 CPU 实时重建）

论文：Prisacariu V A, Kähler O, Golodetz S, et al. Infinitam v3: A framework for large-scale 3d reconstruction with loop closure[J]. arXiv preprint arXiv:1708.00783, 2017.
代码：https://github.com/victorprad/InfiniTAM ；project page

2. BundleFusion

论文：Dai A, Nießner M, Zollhöfer M, et al. Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration[J]. ACM Transactions on Graphics (TOG), 2017, 36(4): 76a.
代码：https://github.com/niessner/BundleFusion ；工程地址

3. KinectFusion

论文：Newcombe R A, Izadi S, Hilliges O, et al. KinectFusion: Real-time dense surface mapping and tracking[C]//2011 10th IEEE International Symposium on Mixed and Augmented Reality. IEEE, 2011: 127-136.
代码：https://github.com/chrdiller/KinectFusionApp

4. ElasticFusion

论文：Whelan T, Salas-Moreno R F, Glocker B, et al. ElasticFusion: Real-time dense SLAM and light source estimation[J]. The International Journal of Robotics Research, 2016, 35(14): 1697-1716.
代码：https://github.com/mp3guy/ElasticFusion

5. Kintinuous

ElasticFusion 同一个团队的工作，帝国理工 Stefan Leutenegger 谷歌学术
论文：Whelan T, Kaess M, Johannsson H, et al. Real-time large-scale dense RGB-D SLAM with volumetric fusion[J]. The International Journal of Robotics Research, 2015, 34(4-5): 598-626.
代码：https://github.com/mp3guy/Kintinuous

6. ElasticReconstruction

论文：Choi S, Zhou Q Y, Koltun V. Robust reconstruction of indoor scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 5556-5565.
代码：https://github.com/qianyizh/ElasticReconstruction ；作者主页

7. FlashFusion

论文：Han L, Fang L. FlashFusion: Real-time Globally Consistent Dense 3D Reconstruction using CPU Computing[C]. RSS, 2018.
代码（一直没放出来）：https://github.com/lhanaf/FlashFusion ； Project Page

8. RTAB-Map（激光视觉稠密重建）

论文：Labbé M, Michaud F. RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation[J]. Journal of Field Robotics, 2019, 36(2): 416-446.
代码：https://github.com/introlab/rtabmap ；Video ；project page

9. RobustPCLReconstruction（户外稠密重建）

论文：Lan Z, Yew Z J, Lee G H. Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 9690-9698.
代码：https://github.com/ziquan111/RobustPCLReconstruction ；Video

10. plane-opt-rgbd（室内平面重建）

论文：Wang C, Guo X. Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019: 49-53.
代码：https://github.com/chaowang15/plane-opt-rgbd

11. DenseSurfelMapping（稠密表面重建）

论文：Wang K, Gao F, Shen S. Real-time scalable dense surfel mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 6919-6925.
代码：https://github.com/HKUST-Aerial-Robotics/DenseSurfelMapping

12. surfelmeshing（网格重建）

论文：Schöps T, Sattler T, Pollefeys M. Surfelmeshing: Online surfel-based mesh reconstruction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
代码：https://github.com/puzzlepaint/surfelmeshing

13. DPPTAM（单目稠密重建）

论文：Concha Belenguer A, Civera Sancho J. DPPTAM: Dense piecewise planar tracking and mapping from a monocular sequence[C]//Proc. IEEE/RSJ Int. Conf. Intell. Rob. Syst. 2015 (ART-2015-92153).
代码：https://github.com/alejocb/dpptam
相关研究：基于超像素的单目 SLAM：Using Superpixels in Monocular SLAM ICRA 2014 ；谷歌学术

14. VI-MEAN（单目视惯稠密重建）

论文：Yang Z, Gao F, Shen S. Real-time monocular dense mapping on aerial robots using visual-inertial fusion[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 4552-4559.
代码：https://github.com/dvorak0/VI-MEAN ；Video

15. REMODE（单目概率稠密重建）

论文：Pizzoli M, Forster C, Scaramuzza D. REMODE: Probabilistic, monocular dense reconstruction in real time[C]//2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014: 2609-2616.
原始开源代码：https://github.com/uzh-rpg/rpg_open_remode
与 ORB-SLAM2 结合版本：https://github.com/ayushgaud/ORB_SLAM2 https://github.com/ayushgaud/ORB_SLAM2

16. DeepFactors（实时的概率单目稠密 SLAM）

帝国理工学院戴森机器人实验室
论文：Czarnowski J, Laidlow T, Clark R, et al. DeepFactors: Real-Time Probabilistic Dense Monocular SLAM[J]. arXiv preprint arXiv:2001.05049, 2020.
代码：https://github.com/jczarnowski/DeepFactors （还未放出）
其他论文：Bloesch M, Czarnowski J, Clark R, et al. CodeSLAM—learning a compact, optimisable representation for dense visual SLAM[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 2560-2568.

17. probabilistic_mapping（单目概率稠密重建）

港科沈邵劼老师团队
论文：Ling Y, Wang K, Shen S. Probabilistic dense reconstruction from a moving camera[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6364-6371.
代码：https://github.com/ygling2008/probabilistic_mapping
另外一篇稠密重建文章的代码一直没放出来 Github ：Ling Y, Shen S. Real‐time dense mapping for online processing and navigation[J]. Journal of Field Robotics, 2019, 36(5): 1004-1036.

18. ORB-SLAM2 单目半稠密建图

论文：Mur-Artal R, Tardós J D. Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM[C]//Robotics: Science and Systems. 2015, 2015.
代码（本身没有开源，贺博复现的一个版本）：https://github.com/HeYijia/ORB_SLAM2
加上线段之后的半稠密建图
- 论文：He S, Qin X, Zhang Z, et al. Incremental 3d line segment extraction from semi-dense slam[C]//2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018: 1658-1663.
- 代码：https://github.com/shidahe/semidense-lines
- 作者在此基础上用于指导远程抓取操作的一项工作：https://github.com/atlas-jj/ORB-SLAM-free-space-carving

19. Voxgraph（SDF 体素建图）

论文：Reijgwart V, Millane A, Oleynikova H, et al. Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps[J]. IEEE Robotics and Automation Letters, 2019, 5(1): 227-234.
代码：https://github.com/ethz-asl/voxgraph

20. SegMap（三维分割建图）

论文：Dubé R, Cramariuc A, Dugas D, et al. SegMap: 3d segment mapping using data-driven descriptors[J]. arXiv preprint arXiv:1804.09557, 2018.
代码：https://github.com/ethz-asl/segmap

21. OpenREALM：无人机实时建图框架

论文：Kern A, Bobbe M, Khedar Y, et al. OpenREALM: Real-time Mapping for Unmanned Aerial Vehicles[J]. arXiv preprint arXiv:2009.10492, 2020.
代码：https://github.com/laxnpander/OpenREALM

22. c-blox：可拓展的 TSDF 稠密建图

论文：Millane A, Taylor Z, Oleynikova H, et al. C-blox: A scalable and consistent tsdf-based dense mapping approach[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 995-1002.
代码：https://github.com/ethz-asl/cblox

😄 1.7 Optimization

1. 后端优化库

GTSAM：https://github.com/borglab/gtsam ；官网
g2o：https://github.com/RainerKuemmerle/g2o
ceres：http://ceres-solver.org/

2. ICE-BA

论文：Liu H, Chen M, Zhang G, et al. Ice-ba: Incremental, consistent and efficient bundle adjustment for visual-inertial slam[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 1974-1982.
代码：https://github.com/baidu/ICE-BA

3. minisam（因子图最小二乘优化框架）

论文：Dong J, Lv Z. miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework[J]. arXiv preprint arXiv:1909.00903, 2019.
代码：https://github.com/dongjing3309/minisam ；文档

4. SA-SHAGO（几何基元图优化）

论文：Aloise I, Della Corte B, Nardi F, et al. Systematic Handling of Heterogeneous Geometric Primitives in Graph-SLAM Optimization[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 2738-2745.
代码：https://srrg.gitlab.io/sashago-website/index.html#

5. MH-iSAM2（SLAM 优化器）

论文：Hsiao M, Kaess M. MH-iSAM2: Multi-hypothesis iSAM using Bayes Tree and Hypo-tree[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 1274-1280.
代码：https://bitbucket.org/rpl_cmu/mh-isam2_lib/src/master/

6. MOLA（用于定位和建图的模块化优化框架）

论文：Blanco-Claraco J L. A Modular Optimization Framework for Localization and Mapping[J]. Proc. of Robotics: Science and Systems (RSS), FreiburgimBreisgau, Germany, 2019, 2.
代码：https://github.com/MOLAorg/mola ；Video ；使用文档

2. 优秀作者与实验室

这一部分整理之后发布在知乎（2020 年 4 月 19 日）：https://zhuanlan.zhihu.com/p/130530891

1. 美国卡耐基梅陇大学机器人研究所

研究方向：机器人感知、结构，服务型、运输、制造业、现场机器
研究所主页：https://www.ri.cmu.edu/
下属 Field Robotic Center 主页：https://frc.ri.cmu.edu/
发表论文：https://www.ri.cmu.edu/pubs/
👦 Michael Kaess：个人主页，谷歌学术
👦 Sebastian Scherer：个人主页，谷歌学术
📜 Kaess M, Ranganathan A, Dellaert F. iSAM: Incremental smoothing and mapping[J]. IEEE Transactions on Robotics, 2008, 24(6): 1365-1378.
📜 Hsiao M, Westman E, Zhang G, et al. Keyframe-based dense planar SLAM[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 5110-5117.
📜 Kaess M. Simultaneous localization and mapping with infinite planes[C]//2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015: 4605-4611.

2. 美国加州大学圣地亚哥分校语境机器人研究所

研究方向：多模态环境理解，语义导航，自主信息获取
实验室主页：https://existentialrobotics.org/index.html
发表论文汇总：https://existentialrobotics.org/pages/publications.html
👦 Nikolay Atanasov：个人主页谷歌学术
- 机器人状态估计与感知课程 ppt：https://natanaso.github.io/ece276a2019/schedule.html
📜 语义 SLAM 经典论文：Bowman S L, Atanasov N, Daniilidis K, et al. Probabilistic data association for semantic slam[C]//2017 IEEE international conference on robotics and automation (ICRA). IEEE, 2017: 1722-1729.
📜 实例网格模型定位与建图：Feng Q, Meng Y, Shan M, et al. Localization and Mapping using Instance-specific Mesh Models[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 4985-4991.
📜 基于事件相机的 VIO：Zihao Zhu A, Atanasov N, Daniilidis K. Event-based visual inertial odometry[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 5391-5399.

3. 美国特拉华大学机器人感知与导航组

研究方向：SLAM、VINS、语义定位与建图等
实验室主页：https://sites.udel.edu/robot/
发表论文汇总：https://sites.udel.edu/robot/publications/
Github 地址：https://github.com/rpng?page=2
📜 Geneva P, Eckenhoff K, Lee W, et al. Openvins: A research platform for visual-inertial estimation[C]//IROS 2019 Workshop on Visual-Inertial Navigation: Challenges and Applications, Macau, China. IROS 2019.（代码：https://github.com/rpng/open_vins ）
📜 Huai Z, Huang G. Robocentric visual-inertial odometry[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6319-6326.（代码：https://github.com/rpng/R-VIO ）
📜 Zuo X, Geneva P, Yang Y, et al. Visual-Inertial Localization With Prior LiDAR Map Constraints[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3394-3401.
📜 Zuo X, Ye W, Yang Y, et al. Multimodal localization: Stereo over LiDAR map[J]. Journal of Field Robotics, 2020 （左星星博士谷歌学术）
👦 黄国权教授主页

4. 美国麻省理工学院航空航天实验室

研究方向：位姿估计与导航，路径规划，控制与决策，机器学习与强化学习
实验室主页：http://acl.mit.edu/
发表论文：http://acl.mit.edu/publications （实验室的学位论文也可以在这里找到）
👦 Jonathan P. How 教授：个人主页谷歌学术
👦 Kasra Khosoussi（SLAM 图优化）：谷歌学术
📜 物体级 SLAM：Mu B, Liu S Y, Paull L, et al. Slam with objects using a nonparametric pose graph[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4602-4609.（代码：https://github.com/BeipengMu/objectSLAM）
📜 物体级 SLAM 导航：Ok K, Liu K, Frey K, et al. Robust Object-based SLAM for High-speed Autonomous Navigation[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 669-675.
📜 SLAM 的图优化：Khosoussi, K., Giamou, M., Sukhatme, G., Huang, S., Dissanayake, G., and How, J. P., Reliable Graphs for SLAM [C]//International Journal of Robotics Research (IJRR), 2019.

5. 美国麻省理工学院 SPARK 实验室

研究方向：移动机器人环境感知
实验室主页：http://web.mit.edu/sparklab/
👦 Luca Carlone 教授：个人主页谷歌学术
📜 SLAM 经典综述：Cadena C, Carlone L, Carrillo H, et al. Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age[J]. IEEE Transactions on robotics, 2016, 32(6): 1309-1332.
📜 VIO 流形预积分：Forster C, Carlone L, Dellaert F, et al. On-Manifold Preintegration for Real-Time Visual--Inertial Odometry[J]. IEEE Transactions on Robotics, 2016, 33(1): 1-21.
📜 开源语义 SLAM：Rosinol A, Abate M, Chang Y, et al. Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping[J]. arXiv preprint arXiv:1910.02490, 2019.（代码：https://github.com/MIT-SPARK/Kimera ）

6. 美国麻省理工学院海洋机器人组

研究方向：水下或陆地移动机器人导航与建图
实验室主页：https://marinerobotics.mit.edu/ （隶属于 MIT 计算机科学与人工智能实验室）
👦 John Leonard 教授：谷歌学术
发表论文汇总：https://marinerobotics.mit.edu/biblio
📜 面向物体的 SLAM：Finman R, Paull L, Leonard J J. Toward object-based place recognition in dense rgb-d maps[C]//ICRA Workshop Visual Place Recognition in Changing Environments, Seattle, WA. 2015.
📜 拓展 KinectFusion：Whelan T, Kaess M, Fallon M, et al. Kintinuous: Spatially extended kinectfusion[J]. 2012.
📜 语义 SLAM 概率数据关联：Doherty K, Fourie D, Leonard J. Multimodal semantic slam with probabilistic data association[C]//2019 international conference on robotics and automation (ICRA). IEEE, 2019: 2419-2425.

7. 美国明尼苏达大学多元自主机器人系统实验室

研究方向：视觉、激光、惯性导航系统，移动设备大规模三维建模与定位
实验室主页：http://mars.cs.umn.edu/index.php
发表论文汇总：http://mars.cs.umn.edu/publications.php
👦 Stergios I. Roumeliotis：个人主页，谷歌学术
📜 移动设备 VIO：Wu K, Ahmed A, Georgiou G A, et al. A Square Root Inverse Filter for Efficient Vision-aided Inertial Navigation on Mobile Devices[C]//Robotics: Science and Systems. 2015, 2.（项目主页：http://mars.cs.umn.edu/research/sriswf.php ）
📜 移动设备大规模三维半稠密建图：Guo C X, Sartipi K, DuToit R C, et al. Resource-aware large-scale cooperative three-dimensional mapping using multiple mobile devices[J]. IEEE Transactions on Robotics, 2018, 34(5): 1349-1369. （项目主页：http://mars.cs.umn.edu/research/semi_dense_mapping.php ）
📜 VIO 相关研究：http://mars.cs.umn.edu/research/vins_overview.php

8. 美国宾夕法尼亚大学 Vijay Kumar 实验室

研究方向：自主微型无人机
实验室主页：https://www.kumarrobotics.org/
发表论文：https://www.kumarrobotics.org/publications/
研究成果视频：https://www.youtube.com/user/KumarLabPenn/videos
📜 无人机半稠密 VIO：Liu W, Loianno G, Mohta K, et al. Semi-Dense Visual-Inertial Odometry and Mapping for Quadrotors with SWAP Constraints[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 1-6.
📜 语义数据关联：Liu X, Chen S W, Liu C, et al. Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 2296-2303.

9. Srikumar Ramalingam（美国犹他大学计算机学院）

研究方向：三维重构、语义分割、视觉 SLAM、图像定位、深度神经网络
👦 Srikumar Ramalingam：个人主页谷歌学术
📜 点面 SLAM：Taguchi Y, Jian Y D, Ramalingam S, et al. Point-plane SLAM for hand-held 3D sensors[C]//2013 IEEE international conference on robotics and automation. IEEE, 2013: 5182-5189.
📜 点线定位：Ramalingam S, Bouaziz S, Sturm P. Pose estimation using both points and lines for geo-localization[C]//2011 IEEE International Conference on Robotics and Automation. IEEE, 2011: 4716-4723.（视频）
📜 2D 3D 定位：Ataer-Cansizoglu E, Taguchi Y, Ramalingam S. Pinpoint SLAM: A hybrid of 2D and 3D simultaneous localization and mapping for RGB-D sensors[C]//2016 IEEE international conference on robotics and automation (ICRA). IEEE, 2016: 1300-1307.（视频）

10. Frank Dellaert（美国佐治亚理工学院机器人与智能机器研究中心）

研究方向：SLAM，图像时空重构
👦 个人主页，谷歌学术
📜 因子图：Dellaert F. Factor graphs and GTSAM: A hands-on introduction[R]. Georgia Institute of Technology, 2012. （GTSAM 代码：http://borg.cc.gatech.edu/ ）
📜 多机器人分布式 SLAM：Cunningham A, Wurm K M, Burgard W, et al. Fully distributed scalable smoothing and mapping with robust multi-robot data association[C]//2012 IEEE International Conference on Robotics and Automation. IEEE, 2012: 1093-1100.
📜 Choudhary S, Trevor A J B, Christensen H I, et al. SLAM with object discovery, modeling and mapping[C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2014: 1018-1025.

11. Patricio Vela （美国佐治亚理工学院智能视觉与自动化实验室）

研究方向：机器人控制、定位与导航
实验室主页：http://ivalab.gatech.edu/
👦 Patricio Vela 个人主页
👦 赵轶璞个人主页谷歌学术
📜 Zhao Y, Smith J S, Karumanchi S H, et al. Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy[J]. arXiv preprint arXiv:2003.01317, 2020.
📜 Zhao Y, Vela P A. Good feature selection for least squares pose optimization in VO/VSLAM[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 1183-1189.（代码：https://github.com/ivalab/FullResults_GoodFeature ）
📜 Zhao Y, Vela P A. Good line cutting: Towards accurate pose tracking of line-assisted VO/VSLAM[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 516-531. （代码：https://github.com/ivalab/GF_PL_SLAM ）

12. 加拿大蒙特利尔大学机器人与嵌入式 AI 实验室

研究方向：SLAM，不确定性建模
实验室主页：http://montrealrobotics.ca/
👦 Liam Paull 教授：个人主页谷歌学术
📜 Mu B, Liu S Y, Paull L, et al. Slam with objects using a nonparametric pose graph[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4602-4609.（代码：https://github.com/BeipengMu/objectSLAM）
📜 Murthy Jatavallabhula K, Iyer G, Paull L. gradSLAM: Dense SLAM meets Automatic Differentiation[J]. arXiv preprint arXiv:1910.10672, 2019.（代码：https://github.com/montrealrobotics/gradSLAM ）

13. 加拿大舍布鲁克大学智能、交互、综合、跨学科机器人实验室

研究方向：移动机器人软硬件设计
实验室主页：https://introlab.3it.usherbrooke.ca/
📜 激光视觉稠密重建：Labbé M, Michaud F. RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation[J]. Journal of Field Robotics, 2019, 36(2): 416-446.
- 代码：https://github.com/introlab/rtabmap
- 项目主页：http://introlab.github.io/rtabmap/

14. 瑞士苏黎世大学机器人与感知课题组

研究方向：移动机器人、无人机环境感知与导航，VISLAM，事件相机
实验室主页：http://rpg.ifi.uzh.ch/index.html
发表论文汇总：http://rpg.ifi.uzh.ch/publications.html
Github 代码公开地址：https://github.com/uzh-rpg
📜 Forster C, Pizzoli M, Scaramuzza D. SVO: Fast semi-direct monocular visual odometry[C]//2014 IEEE international conference on robotics and automation (ICRA). IEEE, 2014: 15-22.
📜 VO/VIO 轨迹评估工具 rpg_trajectory_evaluation：https://github.com/uzh-rpg/rpg_trajectory_evaluation
📜 事件相机项目主页：http://rpg.ifi.uzh.ch/research_dvs.html
👦 人物：Davide Scaramuzza 张子潮

15. 瑞士苏黎世联邦理工计算机视觉与几何实验室

研究方向：定位、三维重建、语义分割、机器人视觉
实验室主页：http://www.cvg.ethz.ch/index.php
发表论文：http://www.cvg.ethz.ch/publications/
📜 视觉语义里程计：Lianos K N, Schonberger J L, Pollefeys M, et al. Vso: Visual semantic odometry[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 234-250.
📜 视觉语义定位：CVPR 2018 Semantic visual localization
- 作者博士学位论文：2018 Robust Methods for Accurate and Efficient 3D Modeling from Unstructured Imagery
📜 大规模户外建图：Bârsan I A, Liu P, Pollefeys M, et al. Robust dense mapping for large-scale dynamic environments[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 7510-7517.
- 代码：https://github.com/AndreiBarsan/DynSLAM
- 作者博士学位论文：Barsan I A. Simultaneous localization and mapping in dynamic scenes[D]. ETH Zurich, Department of Computer Science, 2017.
👦 Marc Pollefeys：个人主页，谷歌学术
👦 Johannes L. Schönberger：个人主页，谷歌学术

16. 英国帝国理工学院戴森机器人实验室

研究方向：机器人视觉场景与物体理解、机器人操纵
实验室主页：https://www.imperial.ac.uk/dyson-robotics-lab/
发表论文：https://www.imperial.ac.uk/dyson-robotics-lab/publications/
代表性工作：MonoSLAM、CodeSLAM、ElasticFusion、KinectFusion
- 📜 ElasticFusion：Whelan T, Leutenegger S, Salas-Moreno R, et al. ElasticFusion: Dense SLAM without a pose graph[C]. Robotics: Science and Systems, 2015.（代码：https://github.com/mp3guy/ElasticFusion ）
- 📜 Semanticfusion：McCormac J, Handa A, Davison A, et al. Semanticfusion: Dense 3d semantic mapping with convolutional neural networks[C]//2017 IEEE International Conference on Robotics and automation (ICRA). IEEE, 2017: 4628-4635.（代码：https://github.com/seaun163/semanticfusion ）
- 📜 Code-SLAM：Bloesch M, Czarnowski J, Clark R, et al. CodeSLAM—learning a compact, optimisable representation for dense visual SLAM[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 2560-2568.
👦 Andrew Davison：谷歌学术

17. 英国牛津大学信息工程学

研究方向：SLAM、目标跟踪、运动结构、场景增强、移动机器人运动规划、导航与建图等等等
实验室主页：http://www.robots.ox.ac.uk/
- 主动视觉实验室：http://www.robots.ox.ac.uk/ActiveVision/
- 牛津机器人学院：https://ori.ox.ac.uk/
发表论文汇总：
- 主动视觉实验室：http://www.robots.ox.ac.uk/ActiveVision/Publications/index.html
- 机器人学院：https://ori.ox.ac.uk/publications/papers/
代表性工作：
- 📜 Klein G, Murray D. PTAM: Parallel tracking and mapping for small AR workspaces[C]//2007 6th IEEE and ACM international symposium on mixed and augmented reality. IEEE, 2007: 225-234.
- 📜 RobotCar 数据集：https://robotcar-dataset.robots.ox.ac.uk/
👦 人物（谷歌学术）：David Murray Maurice Fallon
部分博士学位论文可以在这里搜到：https://ora.ox.ac.uk/

18. 德国慕尼黑工业大学计算机视觉组

研究方向：三维重建、机器人视觉、深度学习、视觉 SLAM 等
实验室主页：https://vision.in.tum.de/research/vslam
发表论文汇总：https://vision.in.tum.de/publications
代表作：DSO、LDSO、LSD_SLAM、DVO_SLAM
- 📜 DSO：Engel J, Koltun V, Cremers D. Direct sparse odometry[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(3): 611-625.（代码：https://github.com/JakobEngel/dso ）
- 📜 LSD-SLAM： Engel J, Schöps T, Cremers D. LSD-SLAM: Large-scale direct monocular SLAM[C]//European conference on computer vision. Springer, Cham, 2014: 834-849.（代码：https://github.com/tum-vision/lsd_slam ）2.
Github 地址：https://github.com/tum-vision
👦 Daniel Cremers 教授：个人主页谷歌学术
👦 Jakob Engel（LSD-SLAM，DSO 作者）：个人主页谷歌学术

19. 德国马克斯普朗克智能系统研究所嵌入式视觉组

研究方向：智能体自主环境理解、导航与物体操纵
实验室主页：https://ev.is.tuebingen.mpg.de/
👦 负责人 Jörg Stückler（前 TUM 教授）：个人主页谷歌学术
📜 发表论文汇总：https://ev.is.tuebingen.mpg.de/publications
Kasyanov A, Engelmann F, Stückler J, et al. Keyframe-based visual-inertial online SLAM with relocalization[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 6662-6669.
📜 Strecke M, Stuckler J. EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Association[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 5865-5874.
📜 Usenko, V., Demmel, N., Schubert, D., Stückler, J., Cremers, D. Visual-Inertial Mapping with Non-Linear Factor Recovery IEEE Robotics and Automation Letters (RA-L), 5, 2020

20. 德国弗莱堡大学智能自主系统实验室

研究方向：多机器人导航与协作，环境建模与状态估计
实验室主页：http://ais.informatik.uni-freiburg.de/index_en.php
发表论文汇总：http://ais.informatik.uni-freiburg.de/publications/index_en.php （学位论文也可以在这里找到）
👦 Wolfram Burgard：谷歌学术
开放数据集：http://aisdatasets.informatik.uni-freiburg.de/
📜 RGB-D SLAM：Endres F, Hess J, Sturm J, et al. 3-D mapping with an RGB-D camera[J]. IEEE transactions on robotics, 2013, 30(1): 177-187.（代码：https://github.com/felixendres/rgbdslam_v2 ）
📜 跨季节的 SLAM：Naseer T, Ruhnke M, Stachniss C, et al. Robust visual SLAM across seasons[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015: 2529-2535.
📜 博士学位论文：Robust Graph-Based Localization and Mapping 2015
📜 博士学位论文：Discovering and Leveraging Deep Multimodal Structure for Reliable Robot Perception and Localization 2019
📜 博士学位论文：Robot Localization and Mapping in Dynamic Environments 2019

21. 西班牙萨拉戈萨大学机器人、感知与实时组 SLAM 实验室

研究方向：视觉 SLAM、物体 SLAM、非刚性 SLAM、机器人、增强现实
实验室主页：http://robots.unizar.es/slamlab/
发表论文：http://robots.unizar.es/slamlab/?extra=3 （论文好像没更新，可以访问下面实验室大佬的谷歌学术查看最新论文）
👦 J. M. M. Montiel：谷歌学术
📜 Mur-Artal R, Tardós J D. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras[J]. IEEE Transactions on Robotics, 2017, 33(5): 1255-1262.
Gálvez-López D, Salas M, Tardós J D, et al. Real-time monocular object slam[J]. Robotics and Autonomous Systems, 2016, 75: 435-449.
📜 Strasdat H, Montiel J M M, Davison A J. Real-time monocular SLAM: Why filter?[C]//2010 IEEE International Conference on Robotics and Automation. IEEE, 2010: 2657-2664.
📜 Zubizarreta J, Aguinaga I, Montiel J M M. Direct sparse mapping[J]. arXiv preprint arXiv:1904.06577, 2019.
- Elvira R, Tardós J D, Montiel J M M. ORBSLAM-Atlas: a robust and accurate multi-map system[J]. arXiv preprint arXiv:1908.11585, 2019.

22. 西班牙马拉加大学机器感知与智能机器人课题组

研究方向：自主机器人、人工嗅觉、计算机视觉
实验室主页：http://mapir.uma.es/mapirwebsite/index.php/topics-2.html
发表论文汇总：http://mapir.isa.uma.es/mapirwebsite/index.php/publications-menu-home.html
📜 Gomez-Ojeda R, Moreno F A, Zuñiga-Noël D, et al. PL-SLAM: a stereo SLAM system through the combination of points and line segments[J]. IEEE Transactions on Robotics, 2019, 35(3): 734-746.（代码：https://github.com/rubengooj/pl-slam ）
👦 Francisco-Angel Moreno
👦 Ruben Gomez-Ojeda 点线 SLAM
- 📜 Gomez-Ojeda R, Briales J, Gonzalez-Jimenez J. PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments[C]//Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016: 4211-4216.（代码：https://github.com/rubengooj/pl-svo ）
- 📜 Gomez-Ojeda R, Gonzalez-Jimenez J. Robust stereo visual odometry through a probabilistic combination of points and line segments[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 2521-2526.（代码：https://github.com/rubengooj/stvo-pl ）
- 📜 Gomez-Ojeda R, Zuñiga-Noël D, Moreno F A, et al. PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments[J]. arXiv preprint arXiv:1705.09479, 2017.（代码：https://github.com/rubengooj/pl-slam ）

23. Alejo Concha（Oculus VR，西班牙萨拉戈萨大学）

研究方向：SLAM，单目稠密重建，传感器融合
👦 个人主页：https://sites.google.com/view/alejoconcha/ 谷歌学术
Github：https://github.com/alejocb
📜 IROS 2015 单目平面重建：DPPTAM: Dense piecewise planar tracking and mapping from a monocular sequence （代码：https://github.com/alejocb/dpptam ）
📜 IROS 2017 开源 RGB-D SLAM：RGBDTAM: A Cost-Effective and Accurate RGB-D Tracking and Mapping System（代码：https://github.com/alejocb/rgbdtam ）
📜 ICRA 2016：Visual-inertial direct SLAM
📜 ICRA 2014：Using Superpixels in Monocular SLAM
RSS 2014：Manhattan and Piecewise-Planar Constraints for Dense Monocular Mapping

24. 奥地利格拉茨技术大学计算机图形学与视觉研究所

研究方向：AR/VR，机器人视觉，机器学习，目标识别与三维重建
实验室主页：https://www.tugraz.at/institutes/icg/home/
👦 Friedrich Fraundorfer 教授：团队主页谷歌学术
- 📜 Visual Odometry: Part I The First 30 Years and Fundamentals
- 📜 Visual Odometry: Part II: Matching, Robustness, Optimization, and Applications
- 📜 Schenk F, Fraundorfer F. RESLAM: A real-time robust edge-based SLAM system[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 154-160.（代码：https://github.com/fabianschenk/RESLAM ）
👦 Dieter Schmalstieg 教授：团队主页谷歌学术
- 📜 教科书：Augmented Reality: Principles and Practice
- 📜 Arth C, Pirchheim C, Ventura J, et al. Instant outdoor localization and slam initialization from 2.5 d maps[J]. IEEE transactions on visualization and computer graphics, 2015, 21(11): 1309-1318.
- 📜 Hachiuma R, Pirchheim C, Schmalstieg D, et al. DetectFusion: Detecting and Segmenting Both Known and Unknown Dynamic Objects in Real-time SLAM[J]. arXiv preprint arXiv:1907.09127, 2019.

25. 波兰波兹南工业大学移动机器人实验室

研究方向：SLAM，机器人运动规划，控制
实验室主页：http://lrm.put.poznan.pl/
Github 主页：https://github.com/LRMPUT
📜 Wietrzykowski J. On the representation of planes for efficient graph-based slam with high-level features[J]. Journal of Automation Mobile Robotics and Intelligent Systems, 2016, 10.（代码：https://github.com/LRMPUT/PlaneSLAM ）
📜 Wietrzykowski J, Skrzypczyński P. PlaneLoc: Probabilistic global localization in 3-D using local planar features[J]. Robotics and Autonomous Systems, 2019.（代码：https://github.com/LRMPUT/PlaneLoc ）
📜 PUTSLAM：http://lrm.put.poznan.pl/putslam/

26. Alexander Vakhitov（三星莫斯科 AI 中心）

研究方向：SLAM，几何视觉
👦 个人主页：https://alexandervakhitov.github.io/ ，谷歌学术
📜 点线 SLAM：ICRA 2017 PL-SLAM: Real-time monocular visual SLAM with points and lines
📜 点线定位：Pumarola A, Vakhitov A, Agudo A, et al. Relative localization for aerial manipulation with PL-SLAM[M]//Aerial Robotic Manipulation. Springer, Cham, 2019: 239-248.
📜 学习型线段：IEEE Access 2019 Learnable line segment descriptor for visual SLAM（代码：https://github.com/alexandervakhitov/lld-slam ）

27. 澳大利亚昆士兰科技大学机器人技术中心

研究方向：脑启发式机器人，采矿机器人，机器人视觉
实验室主页：https://www.qut.edu.au/research/centre-for-robotics
开源代码：https://research.qut.edu.au/qcr/open-source-code/
👦 Niko Sünderhauf：个人主页，谷歌学术
- 📜 RA-L 2018 二次曲面 SLAM：QuadricSLAM: Dual quadrics from object detections as landmarks in object-oriented SLAM
- 📜 Nicholson L, Milford M, Sunderhauf N. QuadricSLAM: Dual quadrics as SLAM landmarks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2018: 313-314.
- 📜 Semantic SLAM 项目主页：http://www.semanticslam.ai/
- 📜 IROS 2017：Meaningful maps with object-oriented semantic mapping
👦 Michael Milford：谷歌学术 https://scholar.google.com/citations?user=TDSmCKgAAAAJ&hl=zh-CN&oi=ao
- 📜 ICRA 2012：SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights （代码：https://michaelmilford.com/seqslam/）
- 📜 Ball D, Heath S, Wiles J, et al. OpenRatSLAM: an open source brain-based SLAM system[J]. Autonomous Robots, 2013, 34(3): 149-176.（代码：https://openslam-org.github.io/openratslam.html ）
- 📜 Yu F, Shang J, Hu Y, et al. NeuroSLAM: a brain-inspired SLAM system for 3D environments[J]. Biological Cybernetics, 2019, 113(5-6): 515-545. （代码：https://github.com/cognav/NeuroSLAM ）

28. 澳大利亚机器人视觉中心

研究方向：机器人感知、理解与学习（集合了昆士兰科技大学，澳大利亚国立大学，阿德莱德大学，昆士兰大学等学校机器人领域的研究者）
实验室主页：https://www.roboticvision.org/
人物：https://www.roboticvision.org/rv_person_category/researchers/
发表论文汇总：https://www.roboticvision.org/publications/scientific-publications/
👦 Yasir Latif：个人主页，谷歌学术
- 📜 Latif Y, Cadena C, Neira J. Robust loop closing over time for pose graph SLAM[J]. The International Journal of Robotics Research, 2013, 32(14): 1611-1626.
- 📜 Latif Y, Cadena C, Neira J. Robust loop closing over time[C]//Proc. Robotics: Science Systems. 2013: 233-240.（代码：https://github.com/ylatif/rrr ）
👦 Ian D Reid：谷歌学术：https://scholar.google.com/citations?user=ATkNLcQAAAAJ&hl=zh-CN&oi=sra
- 📜 ICRA 2019：Real-time monocular object-model aware sparse SLAM
- 📜 Reid I. Towards semantic visual SLAM[C]//2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014: 1-1.

29. 日本国立先进工业科学技术研究所

人工智能研究中心：https://www.airc.aist.go.jp/en/intro/
👦 Ken Sakurada：个人主页，谷歌学术
- 📜 Sumikura S, Shibuya M, Sakurada K. OpenVSLAM: A Versatile Visual SLAM Framework[C]//Proceedings of the 27th ACM International Conference on Multimedia. 2019: 2292-2295.（代码：https://github.com/xdspacelab/openvslam ）
👦 Shuji Oishi：谷歌学术
- 📜 极稠密特征点建图：Yokozuka M, Oishi S, Thompson S, et al. VITAMIN-E: visual tracking and MappINg with extremely dense feature points[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2019: 9641-9650.
- 📜 Oishi S, Inoue Y, Miura J, et al. SeqSLAM++: View-based robot localization and navigation[J]. Robotics and Autonomous Systems, 2019, 112: 13-21.

30. Pyojin Kim（韩国首尔大学自主机器人实验室）

研究方向：视觉里程计，定位，AR/VR
👦 个人主页，谷歌学术
📜 平面 SLAM：ECCV 2018：Linear RGB-D SLAM for planar environments
📜 光照变化下的鲁棒 SLAM：ICRA 2017：Robust visual localization in changing lighting conditions
📜 线面 SLAM：CVPR 2018：Indoor RGB-D Compass from a Single Line and Plane
📜 博士学位论文：Low-Drift Visual Odometry for Indoor Robotics

31. 香港科技大学空中机器人实验室

研究方向：空中机器人在复杂环境下的自主运行，包括状态估计、建图、运动规划、多机器人协同以及低成本传感器和计算组件的实验平台开发。
实验室主页：http://uav.ust.hk/
发表论文：http://uav.ust.hk/publications/
👦 沈邵劼教授谷歌学术
代码公开地址：https://github.com/HKUST-Aerial-Robotics
📜 Qin T, Li P, Shen S. Vins-mono: A robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020.（代码：https://github.com/HKUST-Aerial-Robotics/VINS-Mono ）
📜 Wang K, Gao F, Shen S. Real-time scalable dense surfel mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 6919-6925.（代码：https://github.com/HKUST-Aerial-Robotics/DenseSurfelMapping ）

32. 香港科技大学机器人与多感知实验室 RAM-LAB

研究方向：无人车；无人船；室内定位；机器学习。
实验室主页：https://www.ram-lab.com/
发表论文：https://www.ram-lab.com/publication/
👦 刘明教授谷歌学术
📜 Ye H, Chen Y, Liu M. Tightly coupled 3d lidar inertial odometry and mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 3144-3150.（代码：https://github.com/hyye/lio-mapping ）
📜 Zhang J, Tai L, Boedecker J, et al. Neural slam: Learning to explore with external memory[J]. arXiv preprint arXiv:1706.09520, 2017.

33. 香港中文大学天石机器人实验室

研究方向：工业、物流、手术机器人，三维影像，机器学习
实验室主页：http://ri.cuhk.edu.hk/
👦 刘云辉教授：http://ri.cuhk.edu.hk/yhliu
👦 李浩昂：个人主页，谷歌学术
- 📜 Li H, Yao J, Bazin J C, et al. A monocular SLAM system leveraging structural regularity in Manhattan world[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 2518-2525.
- 📜 Li H, Yao J, Lu X, et al. Combining points and lines for camera pose estimation and optimization in monocular visual odometry[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 1289-1296.
- 📜 消失点检测：Lu X, Yaoy J, Li H, et al. 2-Line Exhaustive Searching for Real-Time Vanishing Point Estimation in Manhattan World[C]//Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017: 345-353.（代码：https://github.com/xiaohulugo/VanishingPointDetection ）
👦 郑帆：个人主页，谷歌学术
- 📜 Zheng F, Tang H, Liu Y H. Odometry-vision-based ground vehicle motion estimation with se (2)-constrained se (3) poses[J]. IEEE transactions on cybernetics, 2018, 49(7): 2652-2663.（代码：https://github.com/izhengfan/se2clam ）
- 📜 Zheng F, Liu Y H. Visual-Odometric Localization and Mapping for Ground Vehicles Using SE (2)-XYZ Constraints[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 3556-3562.（代码：https://github.com/izhengfan/se2lam ）

34. 浙江大学 CAD&CG 国家重点实验室

研究方向：SFM/SLAM，三维重建，增强现实
实验室主页：http://www.zjucvg.net/
Github 代码地址：https://github.com/zju3dv
👦 章国峰教授：个人主页，谷歌学术
📜 ICE-BA：Liu H, Chen M, Zhang G, et al. Ice-ba: Incremental, consistent and efficient bundle adjustment for visual-inertial slam[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 1974-1982.（代码：https://github.com/zju3dv/EIBA ）
📜 RK-SLAM：Liu H, Zhang G, Bao H. Robust keyframe-based monocular SLAM for augmented reality[C]//2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2016: 1-10.（项目主页：http://www.zjucvg.net/rkslam/rkslam.html ）
📜 RD-SLAM：Tan W, Liu H, Dong Z, et al. Robust monocular SLAM in dynamic environments[C]//2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2013: 209-218.

35. 邹丹平（上海交通大学）

研究方向：视觉 SLAM，SFM，多源导航，微型无人机
👦 个人主页：http://drone.sjtu.edu.cn/dpzou/index.php ，谷歌学术
📜 Co-SLAM：Zou D, Tan P. Coslam: Collaborative visual slam in dynamic environments[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 35(2): 354-366.（代码：https://github.com/danping/CoSLAM ）
📜 StructSLAM：Zhou H, Zou D, Pei L, et al. StructSLAM: Visual SLAM with building structure lines[J]. IEEE Transactions on Vehicular Technology, 2015, 64(4): 1364-1375.（项目主页：http://drone.sjtu.edu.cn/dpzou/project/structslam.php ）
📜 StructVIO：Zou D, Wu Y, Pei L, et al. StructVIO: visual-inertial odometry with structural regularity of man-made environments[J]. IEEE Transactions on Robotics, 2019, 35(4): 999-1013.

36. 布树辉教授（西北工业大学智能系统实验室）

研究方向：语义定位与建图、SLAM、在线学习与增量学习
👦 个人主页：http://www.adv-ci.com/blog/ 谷歌学术
布老师的课件：http://www.adv-ci.com/blog/course/
实验室 2018 年暑期培训资料：https://github.com/zdzhaoyong/SummerCamp2018
📜 开源的通用 SLAM 框架：Zhao Y, Xu S, Bu S, et al. GSLAM: A general SLAM framework and benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1110-1120.（代码：https://github.com/zdzhaoyong/GSLAM ）
📜 Bu S, Zhao Y, Wan G, et al. Map2DFusion: Real-time incremental UAV image mosaicing based on monocular slam[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4564-4571.（代码：https://github.com/zdzhaoyong/Map2DFusion ）
📜 Wang W, Zhao Y, Han P, et al. TerrainFusion: Real-time Digital Surface Model Reconstruction based on Monocular SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 7895-7902.

+1 Cyrill Stachniss（德国波恩大学摄影测量与机器人实验室）

研究方向：概率机器人、SLAM、自主导航、视觉激光感知、场景分析与分配、无人飞行器
实验室主页：https://www.ipb.uni-bonn.de/
👦 个人主页：https://www.ipb.uni-bonn.de/people/cyrill-stachniss/ 谷歌学术
发表论文：https://www.ipb.uni-bonn.de/publications/
开源代码：https://github.com/PRBonn
📜 IROS 2019 激光语义 SLAM：Chen X, Milioto A, Palazzolo E, et al. SuMa++: Efficient LiDAR-based semantic SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 4530-4537.（代码：https://github.com/PRBonn/semantic_suma/ ）
Cyrill Stachniss 教授 SLAM 公开课：youtube ； bilibili
波恩大学另外一个智能自主系统实验室：http://www.ais.uni-bonn.de/research.html

+1 上海科技大学

Mobile Perception Lab：http://mpl.sist.shanghaitech.edu.cn/
👦 Laurent Kneip：个人主页；谷歌学术
📜 Zhou Y, Li H, Kneip L. Canny-vo: Visual odometry with rgb-d cameras based on geometric 3-d–2-d edge alignment[J]. IEEE Transactions on Robotics, 2018, 35(1): 184-199.
自主移动机器人实验室：https://robotics.shanghaitech.edu.cn/zh
👦 Sören Schwertfeger：个人主页；谷歌学术
📜 Shan Z, Li R, Schwertfeger S. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots[J]. Sensors, 2019, 19(10): 2251.（代码：https://github.com/STAR-Center/VINS-RGBD ）

+1 美国密歇根大学机器人研究所

学院官网：https://robotics.umich.edu/
研究方向：https://robotics.umich.edu/research/focus-areas/
感知机器人实验室（PeRL）
- 实验室主页：http://robots.engin.umich.edu/About/
- 👦 Ryan M. Eustice 谷歌学术
- 📜 激光雷达数据集 Pandey G, McBride J R, Eustice R M. Ford campus vision and lidar data set[J]. The International Journal of Robotics Research, 2011, 30(13): 1543-1552. | 数据集
APRIL robotics lab
- 实验室主页：https://april.eecs.umich.edu/
- 👦 Edwin Olson 个人主页 | 谷歌学术
- 📜 Olson E. AprilTag: A robust and flexible visual fiducial system[C]//2011 IEEE International Conference on Robotics and Automation. IEEE, 2011: 3400-3407. | 代码
- 📜 Wang X, Marcotte R, Ferrer G, et al. ApriISAM: Real-time smoothing and mapping[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 2486-2493. | 代码

+1 瑞士苏黎世联邦理工自主系统实验室

研究方向：复杂多样环境中自主运行的机器人和智能系统
实验室主页：https://asl.ethz.ch/
发表论文：https://asl.ethz.ch/publications-and-sources/publications.html
youtube | Github
👦 Cesar Cadena 个人主页
📜 Schneider T, Dymczyk M, Fehr M, et al. maplab: An open framework for research in visual-inertial mapping and localization[J]. IEEE Robotics and Automation Letters, 2018, 3(3): 1418-1425. | 代码
📜 Dubé R, Cramariuc A, Dugas D, et al. SegMap: 3d segment mapping using data-driven descriptors[J]. arXiv preprint arXiv:1804.09557, 2018. | 代码
📜 Millane A, Taylor Z, Oleynikova H, et al. C-blox: A scalable and consistent tsdf-based dense mapping approach[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 995-1002. | 代码

+1 美国麻省理工学院 Robust Robotics Group

研究方向：MAV 导航与控制；人机交互的自然语言理解；自主海洋机器人的语义理解
实验室主页：http://groups.csail.mit.edu/rrg/index.php
👦 Nicholas Roy：Google Scholar
📜 Greene W N, Ok K, Lommel P, et al. Multi-level mapping: Real-time dense monocular SLAM[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 833-840. video
📜 ICRA 2020 Metrically-Scaled Monocular SLAM using Learned Scale Factors." International Conference on Robotics and Automation | video
📜 ICRA 2019 Robust Object-based SLAM for High-speed Autonomous Navigation

+1 瑞士苏黎世联邦理工 Vision for Robotics Lab

研究方向：机器人视觉，无人机，自主导航，多机器人协同
实验室主页：https://v4rl.ethz.ch/the-group.html
👦 Margarita Chli：个人主页 | Google Scholar
📜 Schmuck P, Chli M. CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams[J]. Journal of Field Robotics, 2019, 36(4): 763-781. code | video
📜 Bartolomei L, Karrer M, Chli M. Multi-robot Coordination with Agent-Server Architecture for Autonomous Navigation in Partially Unknown Environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)(virtual). 2020. code | video
📜 Schmuck P, Chli M. Multi-uav collaborative monocular slam[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 3863-3870.

+1 谢立华教授（南洋理工大学）

研究方向：控制，多智能体，定位
个人主页：https://personal.ntu.edu.sg/elhxie/research.html | Google Scholar
👦 Wang Han：个人主页 | Github
📜 Wang H, Wang C, Xie L. Intensity scan context: Coding intensity and geometry relations for loop closure detection[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 2095-2101. | Code
📜 Wang H, Wang C, Xie L. Lightweight 3-D Localization and Mapping for Solid-State LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1801-1807. | Code
📜 Wang C, Yuan J, Xie L. Non-iterative SLAM[C]//2017 18th International Conference on Advanced Robotics (ICAR). IEEE, 2017: 83-90.

3. SLAM 学习资料

这一部分的内容不太完整，陆续丰富，欢迎补充

3.1 国内资料

1) SLAMcn：http://www.slamcn.org/index.php/
2) SLAM 最新研究更新 Recent_SLAM_Research ：https://github.com/YiChenCityU/Recent_SLAM_Research
3) 西北工大智能系统实验室 SLAM 培训：https://github.com/zdzhaoyong/SummerCamp2018
- 布树辉老师课件：http://www.adv-ci.com/blog/course/
4) IROS 2019 视觉惯导导航的挑战与应用研讨会：http://udel.edu/~ghuang/iros19-vins-workshop/index.html
5) 泡泡机器人 VIO 相关资料：https://github.com/PaoPaoRobot/Awesome-VIO
6) 崔华坤：主流 VIO 论文推导及代码解析：https://github.com/StevenCui/VIO-Doc
7) 李言：SLAM 中的几何与学习方法
8) 黄山老师状态估计视频：bilibili
9) 谭平老师-SLAM 6小时课程：bilibili
10) 2020 年 SLAM 技术及应用暑期学校：视频-bilibili | 课件

3.2 国外资料

1) 事件相机相关研究与发展：https://github.com/uzh-rpg/event-based_vision_resources
2) 加州大学圣地亚哥分校语境机器人研究所 Nikolay Atanasov 教授机器人状态估计与感知课程 ppt：https://natanaso.github.io/ece276a2019/schedule.html
3) 波恩大学 Mobile Sensing and Robotics Course 公开课：youtube ，bilibili

3.3 公众号

泡泡机器人 SLAM：paopaorobot_slam

3.4 代码注释

今天（2020.04.25）刚想到的一个点，就算前面整理了大量的开源工作，但是看原版的代码还是会有很大的困难，感谢国内 SLAM 爱好者的将自己的代码注释分享出来，促进交流，共同进步。这一小节的内容陆续发掘，期待大家的推荐代码注释（可以在 issue 中留言）。

3.5 数据集

泡泡机器人 - SLAM 数据集合集
计算机视觉life - SLAM、重建、语义相关数据集大全
水下 SLAM 相关研究 - 代码、数据集

4. 近期论文（持续更新）

2021 年 6 月论文更新（20 篇）

本期更新于 2021 年 7 月 5 日
共 20 篇论文，其中 7 项（待）开源工作
[4,9,13,14,16,17] LiDAR 相关
[1,2,6,7] Mapping

1. Geometric SLAM

[1] Bokovoy A, Muravyev K, Yakovlev K. MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment[J]. arXiv preprint arXiv:2105.14994, 2021.
- 用于视觉 SLAM 和地图合并质量评估的逼真基准
- 俄罗斯科学院；开源数据集
[2] Demmel N, Sommer C, Cremers D, et al. Square Root Bundle Adjustment for Large-Scale Reconstruction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 11723-11732.
- 大规模重建的平方根 BA
- TUM；代码开源
[3] Chen Y, Zhao L, Zhang Y, et al. Anchor Selection for SLAM Based on Graph Topology and Submodular Optimization[J]. IEEE Transactions on Robotics, 2021.
- 基于图拓扑和子模块优化的SLAM锚点选择
- 悉尼科技大学
[4] Zhou L, Koppel D, Kaess M. LiDAR SLAM with Plane Adjustment for Indoor Environment[J]. IEEE Robotics and Automation Letters, 2021.
- 室内环境中平面调整的 LiDAR SLAM
- Magic Leap，CMU
[5] Liu D, Parra A, Chin T J. Spatiotemporal Registration for Event-based Visual Odometry[J]. arXiv preprint arXiv:2103.05955, 2021.
- 基于事件的视觉里程计的时空配准
- 阿德莱德大学；开源数据集（待公开）

2. Semantic / Deep SLAM / Mapping

[6] Wimbauer F, Yang N, von Stumberg L, et al. MonoRec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 6112-6122.
- MonoRec: 动态环境中从单个移动相机中进行半监督稠密重建
- TUM；项目主页；代码开源
[7] Qin T, Zheng Y, Chen T, et al. RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving[J]. arXiv preprint arXiv:2106.02527, 2021.（ICRA2021）
- 一种用于自动驾驶视觉定位的轻量级语义地图
- 华为
[8] Tschopp F, Nieto J, Siegwart R Y, et al. Superquadric Object Representation for Optimization-based Semantic SLAM[J]. 2021.
- 基于优化的语义 SLAM 的超二次曲面的物体表示
- ETH；Microsoft
[9] Miller I D, Cowley A, Konkimalla R, et al. Any Way You Look at It: Semantic Crossview Localization and Mapping With LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 2397-2404.
- 以任何方式看待它：使用 LiDAR 进行语义交叉视图的定位和建图
- 宾夕法尼亚大学；代码开源
[10] Lu Y, Xu X, Ding M, et al. A Global Occlusion-Aware Approach to Self-Supervised Monocular Visual Odometry[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(3): 2260-2268.
- 自监督单目视觉里程计的全局遮挡感知方法
- 中国人民大学
[11] Li S, Liu S, Zhao Q, et al. Quantized Self-supervised Local Feature for Real-time Robot Indirect VSLAM[J]. IEEE/ASME Transactions on Mechatronics, 2021.
- 实时机器人间接 VSLAM 的量化自监督局部特征
- 上海交大；期刊：中科院二区，JCR Q1，IF 5.3

3. Sensor Fusion

[12] Seiskari O, Rantalankila P, Kannala J, et al. HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry[J]. arXiv preprint arXiv:2106.11857, 2021.
- HybVIO: 突破实时视觉惯性里程计的极限
- Spectacular AI，阿尔托大学，坦佩雷大学
[13] Li L, Kong X, Zhao X, et al. SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure[J]. arXiv preprint arXiv:2106.11516, 2021. （ICRA2021）
- 具有闭环的语义辅助的 LiDAR SLAM
- 浙大
[14] Li K, Ouyang Z, Hu L, et al. Robust SRIF-based LiDAR-IMU Localization for Autonomous Vehicles[J]. 2021. （ICRA2021）
- 用于自动驾驶汽车的鲁棒的基于 SRIF 的 LiDAR-IMU 定位
- 上海科技大学
[15] Kumar H, Payne J J, Travers M, et al. Periodic SLAM: Using Cyclic Constraints to Improve the Performance of Visual-Inertial SLAM on Legged Robots[J].
- 周期性 SLAM：使用循环约束提高腿式机器人视觉惯性 SLAM 的性能
[16] Zhou P, Guo X, Pei X, et al. T-LOAM: Truncated Least Squares LiDAR-Only Odometry and Mapping in Real Time[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021.
- 截断最小二乘法的 LiDAR 实时里程计和建图
- 武汉理工
[17] Jia Y, Luo H, Zhao F, et al. Lvio-Fusion: A Self-adaptive Multi-sensor Fusion SLAM Framework Using Actor-critic Method[J]. arXiv preprint arXiv:2106.06783, 2021.
- Lvio-Fusion：使用Actor-critic方法的自适应多传感器融合SLAM框架
- 北邮，中科院计算机所；代码开源

4. Others

[18] Huang R, Fang C, Qiu K, et al. AR Mapping: Accurate and Efficient Mapping for Augmented Reality[J]. arXiv preprint arXiv:2103.14846, 2021.
- AR Mapping：用于增强现实的准确高效建图
- 阿里巴巴
[19] Kim A, Ošep A, Leal-Taixé L. EagerMOT: 3D Multi-Object Tracking via Sensor Fusion[J]. arXiv preprint arXiv:2104.14682, 2021. (ICRA2021)
- EagerMOT：通过传感器融合进行 3D 多目标跟踪
- TUM；代码开源
[20] Wang J, Zhong Y, Dai Y, et al. Deep Two-View Structure-from-Motion Revisited[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 8953-8962.
- 重新审视两视图 SFM
- 澳大利亚国立、西工大、NVIDIA

2021 年 5 月论文更新（20 篇）

本期更新于 2021 年 6 月 13 日
共 20 篇论文，其中 6 项（待）开源工作
[2] SLAM 中的隐私保护问题近年来受到关注
[4,5,6] 基于线的 SLAM/SFM

1. Geometric SLAM

[1] Geppert M, Larsson V, Speciale P, et al. Privacy Preserving Localization and Mapping from Uncalibrated Cameras[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 1809-1819.
- 未校准相机的隐私保护定位和建图
- ETH, Microsift
[2] Hermann M, Ruf B, Weinmann M. Real-time dense 3D Reconstruction from monocular video data captured by low-cost UAVs[J]. arXiv preprint arXiv:2104.10515, 2021.
- 低成本无人机捕获的单目视频数据实时实时稠密重建
- KIT
[3] Zhang J, Zhu C, Zheng L, et al. ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion[J]. arXiv preprint arXiv:2105.05600, 2021. (SIGGRAPH 2021)
- 快速相机运动下在线稠密重建的随机优化
- 国防科大
[4] Xu B, Wang P, He Y, et al. Leveraging Structural Information to Improve Point Line Visual-Inertial Odometry[J]. arXiv preprint arXiv:2105.04064, 2021.
- 利用结构信息改进点线 VIO
- 武汉大学；东北大学；代码开源
[5] Liu Z, Shi D, Li R, et al. PLC-VIO: Visual-Inertial Odometry Based on Point-Line Constraints[J]. IEEE Transactions on Automation Science and Engineering, 2021.
- PLC-VIO：基于点线约束的 VIO
- 国防科大
[6] Mateus A, Tahri O, Aguiar A P, et al. On Incremental Structure from Motion Using Lines[J]. IEEE Transactions on Robotics, 2021.
- 使用线的增量式 SFM
- 里斯本大学
[7] Patel M, Bandopadhyay A, Ahmad A. Collaborative Mapping of Archaeological Sites using multiple UAVs[J]. arXiv preprint arXiv:2105.07644, 2021.
- 使用多无人机协同绘制考古遗址
- 印度理工学院；数据集&项目主页
[8] Chiu C Y, Sastry S S. Simultaneous Localization and Mapping: A Rapprochement of Filtering and Optimization-Based Approaches[J]. 2021.
- SLAM：滤波和优化方法的结合
- 加州大学伯克利分校硕士学位论文

2. Semantic / Deep SLAM / Mapping

[9] Karkus P, Cai S, Hsu D. Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 2815-2825.
- 可微 SLAM 网络：用于视觉导航的学习型粒子 SLAM
- 新加坡国立大学；项目主页
[10] Ok K, Liu K, Roy N. Hierarchical Object Map Estimation for Efficient and Robust Navigation[C]//Proc. ICRA. 2021
- 用于高效鲁棒导航的层次化物体地图估计
- MIT
[11] Adu-Bredu A, Del Coro N, Liu T, et al. GODSAC: Graph Optimized DSAC for Robot Relocalization**[J]. arXiv preprint arXiv:2105.00546, 2021.
- 用于机器人重定位的图形优化 DSAC*
- 密歇根大学；代码开源
[12] Xu D, Vedaldi A, Henriques J F. Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes[J]. arXiv preprint arXiv:2105.02195, 2021.
- Moving SLAM：非刚性场景中的完全无监督深度学习
- 港科，牛津大学
[13] Ma J, Ye X, Zhou H, et al. Loop-Closure Detection Using Local Relative Orientation Matching[J]. IEEE Transactions on Intelligent Transportation Systems, 2021.
- 使用局部相对方向匹配的闭环检测
- 武汉大学
[14] Çatal O, Jansen W, Verbelen T, et al. LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping[J]. arXiv preprint arXiv:2105.03265, 2021.
- LatentSLAM：用于定位和建图的无监督多传感器表示学习
- 根特大学，安特卫普大学；数据集

3. Sensor Fusion

[15] Zhao S, Zhang H, Wang P, et al. Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments[J]. arXiv preprint arXiv:2104.14938, 2021.
- 超级里程计：用于挑战性环境的以 IMU 为中心的 LiDAR-Visual-Inertial 状态估计器
- CMU
[16] Nguyen T M, Yuan S, Cao M, et al. VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM[J]. arXiv preprint arXiv:2105.03296, 2021.
- VIRAL SLAM：紧耦合相机-IMU-UWB-Lidar SLAM
- 南洋理工大学
[17] Nguyen T M, Yuan S, Cao M, et al. MILIOM: Tightly Coupled Multi-Input Lidar-Inertia Odometry and Mapping[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 5573-5580.
- MILIOM：紧耦合多源激光雷达-惯性里程计和建图
- 南洋理工大学

4. Others

[18] Sakaridis C, Dai D, Van Gool L. ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding[J]. arXiv preprint arXiv:2104.13395, 2021.
- ACDC: 用于语义驾驶场景理解的具有对应关系的不利条件数据集
- ETH；数据集和 benchmark
[19] Huang Z, Zhou H, Li Y, et al. VS-Net: Voting with Segmentation for Visual Localization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 6101-6111.
- VS-Net：通过分割投票进行视觉定位
- 浙大，港中文；代码开源
[20] Kim A, Ošep A, Leal-Taixé L. EagerMOT: 3D Multi-Object Tracking via Sensor Fusion[J]. arXiv preprint arXiv:2104.14682, 2021. (ICRA2021)
- EagerMOT：通过传感器融合进行 3D 多目标跟踪
- TUM；代码开源

2021 年 4 月论文更新（20 篇）

本期更新于 2021 年 5 月 11 日
共 20 篇论文，其中 5 篇来自于 CVPR2021，7 项开源工作
[7, 8, 9] Event-based
[10] VDO-SLAM 作者博士学位论文
[3, 4, 5, 17] 线、平面
[14] NeuralRecon

1. Geometric SLAM

[1] Jang Y, Oh C, Lee Y, et al. Multirobot Collaborative Monocular SLAM Utilizing Rendezvous[J]. IEEE Transactions on Robotics, 2021.
- 利用集合点的多机器人协作单目 SLAM
- 首尔大学
[2] Luo H, Pape C, Reithmeier E. Hybrid Monocular SLAM Using Double Window Optimization[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 4899-4906.
- 使用双窗口优化的混合单目 SLAM
- 汉诺威大学
[3] Yunus R, Li Y, Tombari F. ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames[J]. arXiv preprint arXiv:2103.15068, 2021.
- ManhattanSLAM：利用混合曼哈顿世界的鲁棒平面跟踪与建图
- TUM
[4] Wang Q, Yan Z, Wang J, et al. Line Flow Based Simultaneous Localization and Mapping[J]. IEEE Transactions on Robotics, 2021.
- 基于线流的 SLAM
- 北大（去年的 9 月的 Preprint）
[5] Vakhitov A, Ferraz L, Agudo A, et al. Uncertainty-Aware Camera Pose Estimation From Points and Lines[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 4659-4668.
- 基于点线不确定性感知的相机位姿估计
- SLAMCore；代码开源
[6] Liu D, Parra A, Chin T J. Spatiotemporal Registration for Event-based Visual Odometry[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 4937-4946.
- 基于事件的视觉里程计的时空配准
- 阿德莱德大学
[7] Min Z, Dunn E. Jiao J, Huang H, Li L, et al. Comparing Representations in Tracking for Event Camera-based SLAM[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 1369-1376.
- 比较基于事件相机 SLAM 在跟踪中的表示
- 港科，港大；代码开源
[8] Zhou Y, Gallego G, Shen S. Event-based stereo visual odometry[J]. IEEE Transactions on Robotics, 2021.
- 基于事件的双目视觉里程计
- 港科、柏林工业大学；代码开源,（去年的 8 月的 Preprint）
[9] Min Z, Dunn E. VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough[J]. arXiv preprint arXiv:2104.06800, 2021.
- VOLDOR-SLAM: 当基于特征或直接法不够好时
- 史蒂文斯理工学院；代码开源
[10] Ila V, Henein M, Li H. Meta Information In Graph-based Simultaneous Localisation And Mapping[J]. 2020
- 基于图的 SLAM 中的元信息
- 澳大利亚国立大学 Mina Henein 博士学位论文
- VDO-SLAM: A Visual Dynamic Object-aware SLAM System.
- Dynamic SLAM: The Need for Speed.

2. Semantic / Deep SLAM / Mapping

[11] Li S, Wu X, Cao Y, et al. Generalizing to the Open World: Deep Visual Odometry with Online Adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 13184-13193.
- 推广到开放世界：具有在线适应功能的深度视觉里程计
- 北京大学
- Li S, Wang X, Cao Y, et al. Self-supervised deep visual odometry with online adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 6339-6348.
[12] Qiu K, Chen S, Zhang J, et al. Compact 3D Map-Based Monocular Localization Using Semantic Edge Alignment[J]. arXiv preprint arXiv:2103.14826, 2021.
- 使用语义边缘对齐的基于 3D 紧凑地图的单目定位
- 阿里巴巴
[13] Cheng W, Yang S, Zhou M, et al. Road Mapping and Localization using Sparse Semantic Visual Features[J]. IEEE Robotics and Automation Letters, 2021.
- 使用稀疏语义视觉特征的道路建图和定位
- 阿里巴巴
[14] Sun J, Xie Y, Chen L, et al. NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 15598-15607.
- NeuralRecon：从单目视频中进行实时连贯 3D 重建
- 浙大，商汤；代码开源

3. Sensor Fusion

[15] Shan T, Englot B, Ratti C, et al. LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping[J]. arXiv preprint arXiv:2104.10831, 2021. (ICRA2021)
- LVI-SAM：通过平滑的紧耦合的激光雷达-视觉-惯性里程计和建图框架
- MIT；代码开源
[16] Zhang K, Yang T, Ding Z, et al. The Visual-Inertial-Dynamical UAV Dataset[J]. arXiv preprint arXiv:2103.11152, 2021.
- 视觉-惯性-动力 UAV 数据集
- 浙大；数据集
[17] Amblard V, Osedach T P, Croux A, et al. Lidar-Monocular Surface Reconstruction Using Line Segments[J]. arXiv preprint arXiv:2104.02761, 2021.
- 使用线段的 Lidar - 单目表面重建
- MIT
[18] Wei B, Trigoni N, Markham A. iMag+: An Accurate and Rapidly Deployable Inertial Magneto-Inductive SLAM System[J]. IEEE Transactions on Mobile Computing, 2021.
- iMag+: 一种准确且可快速部署的惯性磁感应 SLAM 系统
- 诺森比亚大学、牛津大学

4. Others

[19] Huang R, Fang C, Qiu K, et al. AR Mapping: Accurate and Efficient Mapping for Augmented Reality[J]. arXiv preprint arXiv:2103.14846, 2021.
- AR Mapping：用于增强现实的准确高效建图
- 阿里巴巴
[20] Wang J, Zhong Y, Dai Y, et al. Deep Two-View Structure-from-Motion Revisited[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 8953-8962.
- 重新审视两视图 DFM
- 澳大利亚国立、西工大、NVIDIA

2021 年 3 月论文更新（23 篇）

本期更新于 2021 年 4 月 15 日
共 23 篇论文，其中 9 项（待）开源工作
[3, 4, 5] 线、面 SLAM
[6, 7] 滤波方法
[10] DynaSLAM 作者博士学位论文
[9, 12, 13, 17, 23] LiDAR

1. Geometric SLAM

[1] Liu P, Zuo X, Larsson V, et al. MBA-VO: Motion Blur Aware Visual Odometry[J]. arXiv preprint arXiv:2103.13684, 2021.
- MBA-VO: 运动模糊感知的视觉里程计
- ETH，Microsoft
[2] Chen H, Hu W, Yang K, et al. Panoramic annular SLAM with loop closure and global optimization[J]. arXiv preprint arXiv:2102.13400, 2021.
- 具有闭环和全局优化的全景环形 SLAM
- 浙大、KIT
[3] Wang X, Christie M, Marchand E. TT-SLAM: Dense Monocular SLAM for Planar Environments[C]//IEEE International Conference on Robotics and Automation, ICRA'21. 2021.
- TT-SLAM: 面向平面环境的单目稠密 SLAM
- 法国雷恩大学
[4] Lim H, Kim Y, Jung K, et al. Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features[J]. arXiv preprint arXiv:2103.01501, 2021. (ICRA2021)
- 使用点线特征避免单目视觉 SLAM 退化
- 韩国科学技术院
[5] Lu J, Fang Z, Gao Y, et al. Line-based visual odometry using local gradient fitting[J]. Journal of Visual Communication and Image Representation, 2021, 77: 103071.
- 使用局部梯度拟合的基于线的视觉里程计
- 上海工程技术大学
[6] Wang J, Meng Z, Wang L. A UPF-PS SLAM Algorithm for Indoor Mobile Robot with Non-Gaussian Detection Model[J]. IEEE/ASME Transactions on Mechatronics, 2021.
- 具有非高斯检测模型的室内移动机器人 UPF-PS SLAM 算法
- 清华大学
[7] Gao L, Battistelli G, Chisci L. PHD-SLAM 2.0: Efficient SLAM in the Presence of Missdetections and Clutter[J]. IEEE Transactions on Robotics, 2021.
- PHD-SLAM 2.0：漏检和杂波污染情况下的高效 SLAM
- 电子科大，佛罗伦萨大学；代码开源
[8] Labbé M, Michaud F. Multi-session visual SLAM for illumination invariant localization in indoor environments[J]. arXiv preprint arXiv:2103.03827, 2021.
- 用于室内环境光照不变定位的多会话视觉 SLAM
- 谢布鲁克大学
[9] Yokozuka M, Koide K, Oishi S, et al. LiTAMIN2: Ultra Light LiDAR-based SLAM using Geometric Approximation applied with KL-Divergence[J]. arXiv preprint arXiv:2103.00784, 2021. (ICRA2021)
- 使用几何近似和 KL 散度的超轻型基于 LiDAR 的 SLAM
- 日本先进工业科学技术研究所

2. Semantic/Deep SLAM

[10] Visual SLAM in dynamic environments. 2020
- 动态环境中视觉 SLAM
- 萨拉戈萨大学 Berta Bescos 博士学位论文，Github
- DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes. 2018
- DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM. 2020
- Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM. 2021
[11] Zhan H, Weerasekera C S, Bian J W, et al. DF-VO: What Should Be Learnt for Visual Odometry?[J]. arXiv preprint arXiv:2103.00933, 2021.
- DF-VO: 视觉里程计应该学习什么？
- 阿德莱德大学；代码开源
- 先关工作：Zhan H, Weerasekera C S, Bian J W, et al. Visual odometry revisited: What should be learnt?[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 4203-4210.
[12] Westfechtel T, Ohno K, Akegawa T, et al. Semantic Mapping of Construction Site From Multiple Daily Airborne LiDAR Data[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 3073-3080.
- 来自多个日常机载激光雷达数据的施工现场语义建图
- 东京大学
[13] Habich T L, Stuede M, Labbé M, et al. Have I been here before? Learning to Close the Loop with LiDAR Data in Graph-Based SLAM[J]. arXiv preprint arXiv:2103.06713, 2021.
- 在图 SLAM 中学习使用 LiDAR 数据的闭环
- 汉诺威大学
[14] Jayasuriya M, Arukgoda J, Ranasinghe R, et al. UV-Loc: A Visual Localisation Strategy for Urban Environments[J]. 2021
- 城市环境中视觉定位策略
- 悉尼科技大学
- pole-like landmarks and ground surface boundaries
[15] Sarlin P E, Unagar A, Larsson M, et al. Back to the Feature: Learning Robust Camera Localization from Pixels to Pose[J]. arXiv preprint arXiv:2103.09213, 2021. （CVPR2021）
- 回归到特征：从像素中学习鲁棒的相机定位
- ETH；代码开源
[16] Zhang J, Sui W, Wang X, et al. Deep Online Correction for Monocular Visual Odometry[J]. arXiv preprint arXiv:2103.10029, 2021. (ICRA2021)
- 单目视觉里程计深度在线校正
- 地平线，华中科大

3. Sensor Fusion

[17] Lin J, Zheng C, Xu W, et al. R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping[J]. arXiv preprint arXiv:2102.12400, 2021.
- R2LIVE：一种鲁棒的的、实时的、LiDAR-惯导-视觉紧耦合的状态估计和建图方法
- 香港大学；代码开源
[18] Cao S, Lu X, Shen S. GVINS: Tightly Coupled GNSS-Visual-Inertial for Smooth and Consistent State Estimation[J]. arXiv e-prints, 2021: arXiv: 2103.07899.
- GVINS: 用于平滑和一致性状态估计的紧耦合 GNSS-视觉-惯性系统
- 港科；代码开源
[19] Zhu P, Geneva P, Ren W, et al. Distributed Visual-Inertial Cooperative Localization[J]. arXiv preprint arXiv:2103.12770, 2021.
- 分布式视觉-惯性协同定位
- 加利福尼亚大学、特拉华大学；video
[20] Peng X, Liu Z, Wang Q, et al. Accurate Visual-Inertial SLAM by Feature Re-identification[J]. arXiv preprint arXiv:2102.13438, 2021.
- 基于特征重识别的视觉惯性 SLAM
- 三星
[21] Reinke A, Chen X, Stachniss C. Simple But Effective Redundant Odometry for Autonomous Vehicles[J]. arXiv preprint arXiv:2105.11783, 2021. (ICRA2021)
- 用于自动驾驶的简易且有效的冗余里程计
- 波恩大学；代码开源（待公开）
[22] Ram K, Kharyal C, Harithas S S, et al. RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments[J]. arXiv preprint arXiv:2103.10400, 2021.
- RP-VIO: 动态环境中鲁棒的基于平面的 VIO
- 印度理工学院海得拉巴机器人研究中心；代码开源（基于 VINS）
[23] Kramer A, Harlow K, Williams C, et al. ColoRadar: The Direct 3D Millimeter Wave Radar Dataset[J]. arXiv preprint arXiv:2103.04510, 2021.
- 直接 3D 毫米波雷达数据集
- 科罗拉多大学博尔德分校；开源数据集

2021 年 2 月论文更新（21 篇）

本期更新于 2021 年 3 月 20 日
共 21 篇论文，其中 8 项开源工作
[3][4] VIO 数据集
[9][10] 杆状特征

1. Geometric SLAM

[1] Ferrera M, Eudes A, Moras J, et al. OV $^{2} $ SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1399-1406.
- 适用于实时应用的完全在线、多功能 SLAM
- IFREMER；代码开源
[2] Gladkova M, Wang R, Zeller N, et al. Tight-Integration of Feature-Based Relocalization in Monocular Direct Visual Odometry[J]. arXiv preprint arXiv:2102.01191, 2021.
- 单目直接法视觉里程计中基于特征重定位的紧耦合
- TUM
[3] Zhang H, Jin L, Ye C. The VCU-RVI Benchmark: Evaluating Visual Inertial Odometry for Indoor Navigation Applications with an RGB-D Camera[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 6209-6214.
- VCU-RVI Benchmark：用于评估 RGB-D 室内导航应用的 VIO
- 弗吉尼亚联邦大学；数据集地址
[4] Minoda K, Schilling F, Wüest V, et al. VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1343-1350.
- VIODE：用于解决 VIO 在动态环境中挑战的仿真数据集
- 东京大学，洛桑联邦理工；数据集地址
[5] Younes G. A Unified Hybrid Formulation for Visual SLAM[D]. , 2021.
- 一种视觉 SLAM 统一混合的架构
- 滑铁卢大学大学，博士学位论文，Google Scholar
[6] Shakeri M, Loo S Y, Zhang H, et al. Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 4512-4519.
- 利用相对深度先验的偏振相机单目密集建图
- 阿尔伯塔大学
[7] Wang H, Wang C, Xie L. Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1715-1721.
- 大规模环境中强度特征辅助的 LiDAR 定位与建图
- NTU, CMU
[8] Doan A, Latif Y, Chin T, et al. HM⁴: hidden Markov model with memory management for visual place recognition[J]. IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 167-174, Jan. 2021.
- 用于视觉位置识别的具有内存管理功能的隐马尔可夫模型
- 阿德莱德大学
[9] Li L, Yang M, Weng L, et al. Robust Localization for Intelligent Vehicles Based on Pole-Like Features Using the Point Cloud[J]. IEEE Transactions on Automation Science and Engineering, 2021.
- 基于杆状点云特征的智能车鲁棒定位
- 上海交大；期刊：中科院一区，JCR Q1，IF 4.9
[10] Tschopp F, von Einem C, Cramariuc A, et al. Hough $^ 2$ Map–Iterative Event-Based Hough Transform for High-Speed Railway Mapping[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 2745-2752.
- 用于高速铁路建图的基于事件相机的迭代式霍夫变换
- ETH；代码开源；Video
[11] Ma X, Liang X. Point-line-based RGB-D SLAM and Bundle Adjustment Uncertainty Analysis[J]. arXiv preprint arXiv:2102.07110, 2021.
- 基于点线的 RGB-D SLAM 和 BA 的不确定性分析
- 上交

2. Semantic/Deep SLAM

[12] Rosinol A, Violette A, Abate M, et al. Kimera: from slam to spatial perception with 3d dynamic scene graphs[J]. arXiv preprint arXiv:2101.06894, 2021.
- Kimera: 从 SLAM 到具有 3D 动态场景图的空间感知
- MIT；项目主页
[13] Liu Y, Liu J, Hao Y, et al. A Switching-Coupled Backend for Simultaneous Localization and Dynamic Object Tracking[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1296-1303.
- 一种同时用于定位与动态物体跟踪的可切换松耦合后端
- 清华大学
[14] Feng Q, Atanasov N. Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning[J]. arXiv preprint arXiv:2101.01844, 2021.
- 使用 2D-3D 联合学习从航空图像进行室外地形建图的 Mesh 重建
- UCSD Nikolay Atanasov
[15] Wong Y S, Li C, Nießner M, et al. RigidFusion: RGB-D Scene Reconstruction with Rigidly-moving Objects[J]. Eurographics, 2021.
- RigidFusion: 移动刚体运动场景的 RGB-D 重建
- UCL, TUM；补充材料
[16] Sun S, Melamed D, Kitani K. IDOL: Inertial Deep Orientation-Estimation and Localization[J]. arXiv preprint arXiv:2102.04024, 2021.(AAAI 2021)
- 基于学习的惯性传感器方向与位置估计
- CMU；代码与数据集
[17] Qin C, Zhang Y, Liu Y, et al. Semantic loop closure detection based on graph matching in multi-objects scenes[J]. Journal of Visual Communication and Image Representation, 2021, 76: 103072.
- 基于图匹配的多目标场景闭环检测
- 东北大学

3. Sensor Fusion

[18] Wang H, Wang C, Xie L. Lightweight 3-D Localization and Mapping for Solid-State LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1801-1807.
- 固态 LiDAR 轻量化 3D 定位与建图
- 南洋理工大学；代码开源
[19] Jiang Z, Taira H, Miyashita N, et al. VIO-Aided Structure from Motion Under Challenging Environments[J]. arXiv preprint arXiv:2101.09657, 2021. (ICIT2021)
- 在具有挑战场景下 VIO 辅助的 SFM
- 东京工业大学

4. Others

[20] Liu Y, Yixuan Y, Liu M. Ground-aware Monocular 3D Object Detection for Autonomous Driving[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 919-926.
- 用于自动驾驶的地面感知单目 3D 目标检测
- 港科；代码开源
[21] Singh G, Akrigg S, Di Maio M, et al. Road: The road event awareness dataset for autonomous driving[J]. arXiv preprint arXiv:2102.11585, 2021.
- 自动驾驶场景的道路事件感知数据集
- ETH；代码开源
- 道路事件由智能体、智能体所执行的动作和智能体所处的环境组成。

2021 年 1 月论文更新（20 篇）

本期更新于 2021 年 2 月 13 日
共 20 篇论文，其中 2 项开源工作
[1] 长期定位
[10] Building Fusion [11] Mesh Reconstruction

1. Geometric SLAM

[1] Rotsidis A, Lutteroth C, Hall P, et al. ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021: 2867-2876.
- 使用指数衰减在动态场景中的长期定位
- 巴斯大学
[2] Wen T, Xiao Z, Wijaya B, et al. High Precision Vehicle Localization based on Tightly-coupled Visual Odometry and Vector HD Map[C]//2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 672-679.
- 基于视觉里程计和矢量高清地图紧耦合的高精度车辆定位
- 清华大学
[3] Lee S J, Kim D, Hwang S S, et al. Local to Global: Efficient Visual Localization for a Monocular Camera[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021: 2231-2240.
- Local to Global：单目相机高效视觉定位
- 人工特征用于实时里程计，基于学习的特征用于定位，地图对齐
[4] Jung K Y, Kim Y E, Lim H J, et al. ALVIO: Adaptive Line and Point Feature-based Visual Inertial Odometry for Robust Localization in Indoor Environments[J]. arXiv preprint arXiv:2012.15008, 2020.
- 基于自适应点线特征的视觉惯性里程计在室内环境中的鲁棒定位
- 韩国高等科学技术学院
[5] Yang A J, Cui C, Bârsan I A, et al. Asynchronous Multi-View SLAM[J]. arXiv preprint arXiv:2101.06562, 2021.
- 异步多视点 SLAM
- 多伦多大学
[6] Lyu Y, Nguyen T M, Liu L, et al. SPINS: Structure Priors aided Inertial Navigation System[J]. arXiv preprint arXiv:2012.14053, 2020.
- 结构先验辅助的惯性导航系统
- 南洋理工
[7] Pan Y, Xu X, Ding X, et al. GEM: online globally consistent dense elevation mapping for unstructured terrain[J]. IEEE Transactions on Instrumentation and Measurement, 2020.
- 非结构化地形的在线全局一致稠密高程图
- 浙大；期刊：中科院三区，ICR Q1，IF 3.6
[8] Tian R, Zhang Y, Zhu D, et al. Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry[J]. arXiv preprint arXiv:2101.05995, 2021.
- 基于平面几何单目视觉里程计的准确鲁棒尺度恢复
- 东北大学，香港中文大学
[9] Fang B, Mei G, Yuan X, et al. Visual SLAM for robot navigation in healthcare facility[J]. Pattern Recognition, 2021: 107822.
- 用于医疗机构机器人导航的视觉 SLAM
- 合肥工业大学；期刊：中科院二区，ICR Q1，IF 7.2

2. Semantic/Deep SLAM

[10] Zheng T, Zhang G, Han L, et al. Building Fusion: Semantic-aware Structural Building-scale 3D Reconstruction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
- Building Fusion：语义感知结构化建筑规模的三维重建
- 清华-伯克利深圳学院，清华大学
[11] Feng Q, Atanasov N. Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning[J]. arXiv preprint arXiv:2101.01844, 2021.
- 使用 2D-3D 联合学习从空中图像进行室外地形的网格重建
- 加州大学圣地亚哥分校 Nikolay A. Atanasov
[12] Ma T, Wang Y, Wang Z, et al. ASD-SLAM: A Novel Adaptive-Scale Descriptor Learning for Visual SLAM[C]//2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 809-816.
- 一种新的视觉 SLAM 自适应尺度描述符学习方法
- 上交；代码开源
[13] Li B, Hu M, Wang S, et al. Self-supervised Visual-LiDAR Odometry with Flip Consistency[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021: 3844-3852.
- 具有翻转一致性的自监督视觉-雷达里程计
- 浙大
[14] Akilan T, Johnson E, Sandhu J, et al. A Hybrid Learner for Simultaneous Localization and Mapping[J]. arXiv preprint arXiv:2101.01158, 2021.
- 用于 SLAM 的混合学习器
- Lakehead University

3. Sensor Fusion

[15] Cowley A, Miller I D, Taylor C J. UPSLAM: Union of Panoramas SLAM[J]. arXiv preprint arXiv:2101.00585, 2021.
- UPSLAM: 全景 SLAM
- 宾夕法尼亚大学
[16] Jiang Z, Taira H, Miyashita N, et al. VIO-Aided Structure from Motion Under Challenging Environments[J]. arXiv preprint arXiv:2101.09657, 2021.
- 挑战场景下 VIO 辅助的 SFM
- 东京工业大学
[17] Zhai C, Wang M, Yang Y, et al. Robust Vision-Aided Inertial Navigation System for Protection Against Ego-Motion Uncertainty of Unmanned Ground Vehicle[J]. IEEE Transactions on Industrial Electronics, 2020.
- 鲁棒的视觉辅助惯性导航系统，避免地面无人车的自我运动估计不确定性
- 北理工；期刊：中科院一区 JCR Q1，IF 7.5
[18] Palieri M, Morrell B, Thakur A, et al. LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time[J]. IEEE Robotics and Automation Letters, 2020, 6(2): 421-428.
- 用于高精度里程计和建图的以雷达为中心的多传感器框架
- 加州理工

4. Others

[19] Chiu H, Li J, Ambrus R, et al. Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving[J]. arXiv preprint arXiv:2012.13755, 2020.
- 自动驾驶场景的 3D 多模态、多目标跟踪
- 斯坦福大学
[20] Deng J, Shi S, Li P, et al. Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection[J]. arXiv preprint arXiv:2012.15712, 2020.（AAAI 2021）
- Voxel R-CNN：面向基于高性能体素的 3D 目标检测
- 中科大，港中文；代码开源

2020 年 12 月论文更新（18 篇）

本期更新于 2021 年 1 月 02 日
共 18 篇论文，其中 6 项（待）开源工作
9 CodeVIO, 10 CamVox

1. Geometric SLAM

[1] Mascaro R, Wermelinger M, Hutter M, et al. Towards automating construction tasks: Large‐scale object mapping, segmentation, and manipulation[J]. Journal of Field Robotics, 2020.
- 【挖掘机抓石头】实现自动化的施工任务：大型物体建图，分割和操纵
- ETH，期刊：中科院二区，JCR Q1
[2] Yang X, Yuan Z, Zhu D, et al. Robust and Efficient RGB-D SLAM in Dynamic Environments[J]. IEEE Transactions on Multimedia, 2020.
- 动态环境中鲁棒高效 RGB-D SLAM
- 华中科大，期刊：中科院二区，JCR Q1，IF 5.5
[3] Yazdanpour M, Fan G, Sheng W. ManhattanFusion: Online Dense Reconstruction of Indoor Scenes from Depth Sequences[J]. IEEE Transactions on Visualization and Computer Graphics, 2020.
- ManhattanFusion：从深度序列中对室内场景进行在线稠密重建
- 北肯塔基大学，期刊：中科院二区，JCR Q1，IF 4.3
[4] Fourie D, Rypkema N R, Teixeira P V, et al. Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation[J]. IROS 2020
- 面向实时非高斯 SLAM 的欠定导航
- MIT

2. Semantic/Deep SLAM

[5] Garg S, Sünderhauf N, Dayoub F, et al. Semantics for Robotic Mapping, Perception and Interaction: A Survey[J]. arXiv preprint arXiv:2101.00443, 2020.
- 用于机器人建图、感知和交互的语义
- 昆士兰科技大学，阿德莱德大学、澳大利亚机器人中心，962 篇论文综述
[6] Nubert J, Khattak S, Hutter M. Self-supervised Learning of LiDAR Odometry for Robotic Applications[J]. arXiv preprint arXiv:2011.05418, 2020.
- 用于机器人应用的基于自监督学习的 LiDAR 里程计
- ETH，代码开源
[7] Thomas H, Agro B, Gridseth M, et al. Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation[J]. arXiv preprint arXiv:2012.05897, 2020.
- 自主室内导航 LIDAR 分割的自监督学习
- 多伦多大学，Apple
[8] Huynh L, Nguyen P, Matas J, et al. Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion[J]. arXiv preprint arXiv:2012.10296, 2020.
- 轻量级 3D 点融合（立体匹配/SLAM）提高单眼深度估计
- 奥卢大学

3. Sensor Fusion

[9] Zuo X, Merrill N, Li W, et al. CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth[J]. arXiv preprint arXiv:2012.10133, 2020.
- CodeVIO: 具有学习可优化稠密深度的 VIO
- ETH，浙大，ICRA2021 投稿论文，video
[10] ZHU, Yuewen, et al. CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System. arXiv preprint arXiv:2011.11357, 2020.
- 低成本、精确的Lidar辅助视觉 SLAM 系统
- 南方科技大学，代码开源，ICRA2021 投稿论文
[11] Gong Z, Liu P, Wen F, et al. Graph-Based Adaptive Fusion of GNSS and VIO Under Intermittent GNSS-Degraded Environment[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-16.
- 间歇性 GNSS 退化环境下的基于图的自适应 GNSS-VIO 融合
- 上海交大
[12] Wu Y, Li Y, Li W, et al. Robust Lidar-Based Localization Scheme for Unmanned Ground Vehicle via Multisensor Fusion[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020.
- 基于激光雷达的多传感器融合无人地面车辆鲁棒定位方法
- 广东工业大学，期刊：中科院一区，JCR Q1, IF 8.8
[13] Zhu P, Ren W. Multi-Robot Joint Visual-Inertial Localization and 3-D Moving Object Tracking[J]. IROS 2020
- 多机器人联合视觉惯性定位和 3D 运动目标跟踪
- 加州大学河滨分校

4. Others

[14] Xu M, Snderhauf N, Milford M. Probabilistic Visual Place Recognition for Hierarchical Localization[J]. IEEE Robotics and Automation Letters, 2020, 6(2): 311-318.
- 用于分层定位的概率视觉场景识别
- 昆士兰科技大学，代码开源
[15] Li D, Miao J, Shi X, et al. RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Keypoints for Indoor Localization[J]. arXiv preprint arXiv:2012.00234, 2020.
- RaP-Net: 区域和点加权网络用于室内定位的鲁棒关键点提取
- 清华、北交大、北航、Intel，代码开源
[16] Bui M, Birdal T, Deng H, et al. 6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference[J]. arXiv preprint arXiv:2004.04807, 2020.(ECCV 2020)
- 通过连续多峰推理在模糊场景中进行 6D 相机重定位
- 斯坦福，TUM，项目主页，代码开源
[17] ALVES, Nelson, et al. Low-latency Perception in Off-Road Dynamical Low Visibility Environments. arXiv preprint arXiv:2012.13014, 2020.
- 低能见度、越野环境下的低延迟感知
- 数据集，代码
[18] Chen C, Al-Halah Z, Grauman K. Semantic Audio-Visual Navigation[J]. arXiv preprint arXiv:2012.11583, 2020.
- 语义视-听导航
- UT Austin, Facebook，项目主页

2020 年 11 月论文更新（20 篇）

本期更新于 2020 年 12 月 07 日
共 20 篇论文，其中 6 项（待）开源工作
其中近一半来自于 IROS 2020 的录用论文和 ICRA 2021 的投稿论文

1. Geometric SLAM

[1] Kim C, Kim J, Kim H J. Edge-based Visual Odometry with Stereo Cameras using Multiple Oriented Quadtrees[J]. IROS 2020
- 使用多个定向四叉树的基于边的双目视觉里程计
- 首尔国立大学
[2] Jaenal A, Zuniga-Noël D, Gomez-Ojeda R, et al. Improving Visual SLAM in Car-Navigated Urban Environments with Appearance Maps[J]. IROS 2020
- 通过外观地图改善城市环境汽车导航的视觉 SLAM
- 马拉加大学；video
[3] Chen L, Zhao Y, Xu S, et al. DenseFusion: Large-Scale Online Dense Pointcloud and DSM Mapping for UAVs[J]. IROS 2020
- DenseFusion：无人机的大规模在线密集点云和 DSM 建图
- 西工大，自动化所
- 前期工作：Wang W, Zhao Y, Han P, et al. TerrainFusion: Real-time Digital Surface Model Reconstruction based on Monocular SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 7895-7902.
[4] Arndt C, Sabzevari R, Civera J. From Points to Planes-Adding Planar Constraints to Monocular SLAM Factor Graphs[J]. IROS 2020
- From Points to Planes：在单目 SLAM 因子图中添加平面约束
- 西班牙萨拉戈萨大学
[5] Giubilato R, Le Gentil C, Vayugundla M, et al. GPGM-SLAM: Towards a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps[C]//IROS Workshop on Planetary Exploration Robots: Challenges and Opportunities (PLANROBO20). ETH Zurich, Department of Mechanical and Process Engineering, 2020.
- GPGM-SLAM：具有高斯过程梯度图的非结构化行星环境的鲁棒 SLAM 系统
- DLR，TUM

2. Semantic/Deep SLAM

[6] Chang Y, Tian Y, How J P, et al. Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping[J]. arXiv preprint arXiv:2011.04087, 2020.
- Kimera-Multi: 分布式多机器人度量语义 SLAM 系统
- MIT
[7] Sharma A, Dong W, Kaess M. Compositional Scalable Object SLAM[J]. arXiv preprint arXiv:2011.02658, 2020.
- 合成可拓展的物体级 SLAM
- CMU；ICRA2021 投稿论文；待开源
[8] Wang W, Hu Y, Scherer S. TartanVO: A Generalizable Learning-based VO[J]. arXiv preprint arXiv:2011.00359, 2020.
- TartanVO：一种通用的基于学习的 VO
- CMU，代码开源
[9] Wimbauer F, Yang N, von Stumberg L, et al. MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera[J]. arXiv preprint arXiv:2011.11814, 2020.
- 动态环境中单个移动相机的半监督稠密重构
- TUM；项目主页
- 相关研究：
  - CVPR 2020 D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
  - ECCV 2018 Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry
[10] Almalioglu Y, Santamaria-Navarro A, Morrell B, et al. Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments[J]. arXiv preprint arXiv:2011.00341, 2020.
- 极端环境下的无监督持久性的单目视觉里程计和深度估计
- 牛津大学，NASA；ICRA2021 投稿论文
[11] Zou Y, Ji P, Tran Q H, et al. Learning monocular visual odometry via self-supervised long-term modeling[J]. arXiv preprint arXiv:2007.10983, 2020. (ECCV 2020)
- 通过自监督长期建模学习单目视觉里程计
- 弗吉尼亚理工大学；项目主页+video
[12] Nubert J, Khattak S, Hutter M. Self-supervised Learning of LiDAR Odometry for Robotic Applications[J]. arXiv preprint arXiv:2011.05418, 2020.
- 应用于机器人的自监督学习 LiDAR 里程计
- ETH；代码开源
[13] Chancán M, Milford M. DeepSeqSLAM: A Trainable CNN+ RNN for Joint Global Description and Sequence-based Place Recognition[J]. arXiv preprint arXiv:2011.08518, 2020.
- 用于联合全局描述和基于序列的位置识别的可训练的CNN + RNN
- 昆士兰科技大学；代码开源，项目主页

3. Sensor Fusion

[14] Zhao S, Wang P, Zhang H, et al. TP-TIO: A Robust Thermal-Inertial Odometry with Deep ThermalPoint[J]. arXiv preprint arXiv:2012.03455, IROS 2020.
- TP-TIO: 一种使用深度 ThermalPoint 网络的红外视觉-惯性里程计
- CMU，东北大学，video
[15] Jaekel J, Mangelson J G, Scherer S, et al. A Robust Multi-Stereo Visual-Inertial Odometry Pipeline[J]. IROS 2020.
- 一种鲁棒的多立体视觉惯性里程计框架
- CMU
[16] Huang H, Ye H, Jiao J, et al. Geometric Structure Aided Visual Inertial Localization[J]. arXiv preprint arXiv:2011.04173, 2020.
- 几何结构辅助的视觉惯性定位
- 港科，ICRA 2021 投稿论文
[17] Ding Z, Yang T, Zhang K, et al. VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation[J]. arXiv preprint arXiv:2011.03993, 2020.
- VID-Fusion: 用于准确外力估计的露露帮视觉惯性里程计
- 浙大 FAST Lab
[18] Li K, Li M, Hanebeck U D. Towards high-performance solid-state-lidar-inertial odometry and mapping[J]. arXiv preprint arXiv:2010.13150, 2020.
- 高性能固态雷达惯性里程计与建图
- 卡尔斯鲁厄理工学院；代码开源

4. Others

[19] Milano F, Loquercio A, Rosinol A, et al. Primal-Dual Mesh Convolutional Neural Networks[J]. Advances in Neural Information Processing Systems, 2020, 33.
- 原始对偶网格卷积神经网络
- ETH；代码开源
[20] Li H, Gordon A, Zhao H, et al. Unsupervised Monocular Depth Learning in Dynamic Scenes[J]. arXiv preprint arXiv:2010.16404, 2020. (CoRL 2020)
- 动态环境中无监督单目深度学习
- Google；代码开源

2020 年 10 月论文更新（22 篇）

本期更新于 2020 年 11 月 09 日
共 22 篇论文，其中 7 项（待）开源工作
9,10,11：SLAM 中动态物体跟踪，动态物体级 SLAM 今年很火
3,7,8,14,18：线段相关

1. Geometric SLAM

[1] Bhutta M, Kuse M, Fan R, et al. Loop-box: Multi-Agent Direct SLAM Triggered by Single Loop Closure for Large-Scale Mapping[J]. arXiv preprint arXiv:2009.13851, 2020. IEEE Transactions on Cybernetics, 2020
- 用于大规模建图的由单闭环触发的多智能体直接 SLAM
- 香港科技大学；项目主页；video
- 期刊：中科院一区，JCR Q1，IF 11.079
[2] Zhou B, He Y, Qian K, et al. S4-SLAM: A real-time 3D LIDAR SLAM system for ground/watersurface multi-scene outdoor applications[J]. Autonomous Robots, 2020: 1-22.
- S4-SLAM：用于地面/水面多场景户外应用的实时 3D LIDAR SLAM 系统
- 东南大学；期刊：中科院三区，JCR Q1，IF 3.6
[3] Li Y, Yunus R, Brasch N, et al. RGB-D SLAM with Structural Regularities[J]. arXiv preprint arXiv:2010.07997, 2020.
- 具有结构规律的 RGB-D SLAM
- TUM
[4] Rodríguez J J G, Lamarca J, Morlana J, et al. SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenes[J]. arXiv preprint arXiv:2010.09409, 2020.
- SD-DefSLAM：适用于可变形和体内场景的半直接法单目 SLAM
- 萨拉戈萨大学；ICRA 2021 投稿论文；Video
[5] Millane A, Oleynikova H, Lanegger C, et al. Freetures: Localization in Signed Distance Function Maps[J]. arXiv preprint arXiv:2010.09378, 2020.
- Freetures：在 SDF 地图中进行定位
- ETH；代码开源（待开源）；Video
[6] Long R, Rauch C, Zhang T, et al. RigidFusion: Robot Localisation and Mapping in Environments with Large Dynamic Rigid Objects[J]. arXiv preprint arXiv:2010.10841, 2020.
- RigidFusion: 在具有动态刚体物体的大型场景中进行机器人定位与建图
- 爱丁堡大学机器人中心
[8] Han J, Dong R, Kan J. A novel loop closure detection method with the combination of points and lines based on information entropy[J]. Journal of Field Robotics. 2020
- 一种新的基于信息熵的点线闭环检测方法
- 北京林业大学；期刊：中科院二区，JCR Q1，IF 3.58

2. Semantic/Deep SLAM

[9] Bescos B, Campos C, Tardós J D, et al. DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM[J]. arXiv preprint arXiv:2010.07820, 2020.
- DynaSLAM II: 多目标跟踪与 SLAM 紧耦合
- 萨拉戈萨大学；一作是 DynaSLAM 的作者，二作是 ORB-SLAM3 的作者
[10] Bescos B, Cadena C, Neira J. Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM[J]. arXiv preprint arXiv:2010.07646, 2020.
- 视觉 SLAM 动态的物体不变空间
- 萨拉戈萨大学、ETH；代码开源
- 个人主页，相关论文：
  - ICRA 2018 Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space
[11] Ballester I, Fontan A, Civera J, et al. DOT: Dynamic Object Tracking for Visual SLAM[J]. arXiv preprint arXiv:2010.00052, 2020.
- 视觉 SLAM 的动态物体跟踪
- 萨拉戈萨大学
[12] Wu S C, Tateno K, Navab N, et al. SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion[J]. arXiv preprint arXiv:2010.13662, 2020.
- SCFusion：具有完整语义的实时增量场景重建
- TUM
[13] Mallick A, Stückler J, Lensch H. Learning to Adapt Multi-View Stereo by Self-Supervision[J]. arXiv preprint arXiv:2009.13278, 2020.
- 通过自监督学习的自适应多视图立体匹配
- 图宾根大学，马普所 Jörg Stückler，BMVC 2020
- 基于 ECCV 2018 MVSNet: Depth Inference for Unstructured Multi-view Stereo，代码

3. Sensor Fusion

[14] Li X, Li Y, Ornek E P, et al. Co-Planar Parametrization for Stereo-SLAM and Visual-Inertial Odometry[J]. IEEE Robotics and Automation Letters, 2020.
- 双目 SLAM 和 VIO 的共面参数化
- 北京大学，代码开源（暂未放出）
[15] Liu Z, Zhang F. BALM: Bundle Adjustment for Lidar Mapping[J]. arXiv preprint arXiv:2010.08215, 2020.
- BALM：激光雷达建图中的 BA 优化
- 香港大学，代码开源
[16] Nguyen T M, Yuan S, Cao M, et al. VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach[J]. arXiv preprint arXiv:2010.12274, 2020.
- VIRAL-Fusion: 视觉-惯性-测距-激光雷达传感器融合方法
- 南洋理工
[17] Liu J, Gao W, Hu Z. Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS Measurements[J]. arXiv preprint arXiv:2010.11675, 2020.
- 基于优化的视觉惯性 SLAM 与原始 GNSS 测量紧耦合
- 中科院自动化所；ICRA 2021 投稿论文

4. Others

[18] Taubner F, Tschopp F, Novkovic T, et al. LCD--Line Clustering and Description for Place Recognition[J]. arXiv preprint arXiv:2010.10867, 2020. (3DV 2020)
- LCD: 用于位置识别的线段聚类和描述
- ETH；代码开源
[19] Triebel R. 3D Scene Reconstruction from a Single Viewport. ECCV 2020
- 单视角进行三维场景重建
- TUM；代码开源
[20] Hidalgo-Carrió J, Gehrig D, Scaramuzza D. Learning Monocular Dense Depth from Events[J]. arXiv preprint arXiv:2010.08350, 2020.(3DV 2020)
- 从事件中学习单目稠密深度
- 苏黎世大学；代码开源；项目主页
[21] Yang B. Learning to reconstruct and segment 3D objects[J]. arXiv preprint arXiv:2010.09582, 2020.
- 学习重建和分割 3D 物体
- 牛津大学 BoYang 博士学位论文
[22] von Stumberg L, Wenzel P, Yang N, et al. LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization[J]. arXiv preprint arXiv:2010.06323, 2020.
- LM-Reloc：基于Levenberg-Marquardt 的直接视觉重定位
- TUM
- 相关论文：von Stumberg L, Wenzel P, Khan Q, et al. Gn-net: The gauss-newton loss for multi-weather relocalization[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 890-897.

2020 年 9 月论文更新（20 篇）

本期更新于 2020 年 9 月 28 日
共 20 篇论文，其中 6 项（待）开源工作
4-5：机器人自主探索
8-11：多路标 SLAM
13: Jan Czarnowski 博士学位论文
17-20：增强现实相关的几项很好玩的工作

1. Geometric SLAM

[1] FZhao Y, Smith J S, Vela P A. Good graph to optimize: Cost-effective, budget-aware bundle adjustment in visual SLAM[J]. arXiv preprint arXiv:2008.10123, 2020.
- Good Graph to Optimize: 视觉 SLAM 中具有成本效益、可感知预算的 BA
- 佐治亚理工学院 Yipu Zhao
- 作者有很多 Good 系列的文章
  - IROS 2018 Good feature selection for least squares pose optimization in VO/VSLAM
  - ECCV 2018 Good line cutting: Towards accurate pose tracking of line-assisted VO/VSLAM
  - T-RO 2020 Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency
[2] Fu Q, Yu H, Wang X, et al. FastORB-SLAM: a Fast ORB-SLAM Method with Coarse-to-Fine Descriptor Independent Keypoint Matching[J]. arXiv preprint arXiv:2008.09870, 2020.
- FastORB-SLAM: 一种 Coarse-to-Fine 描述符独立关键点匹配的快速 ORB-SLAM 方法
- 湖南大学
[3] Wenzel P, Wang R, Yang N, et al. 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving[J]. arXiv preprint arXiv:2009.06364, 2020.
- 4Seasons：自动驾驶中多天气SLAM的跨季节数据集
- 慕尼黑工业大学 Nan Yang
- 数据集网页：http://www.4seasons-dataset.com/
[4] Duong T, Yip M, Atanasov N. Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping[J]. arXiv preprint arXiv:2009.07207, 2020.
- 基于稀疏贝叶斯核占有地图的未知环境自主导航
- UCSD Nikolay A. Atanasov
- 项目主页 | 代码开源
[5] Bartolomei L, Karrer M, Chli M. Multi-robot Coordination with Agent-Server Architecture for Autonomous Navigation in Partially Unknown Environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)(virtual). 2020.
- 在部分未知环境中实现自主导航的多机器人协同代理服务器架构
- ETH | 代码开源 | video
[6] Kern A, Bobbe M, Khedar Y, et al. OpenREALM: Real-time Mapping for Unmanned Aerial Vehicles[J]. arXiv preprint arXiv:2009.10492, 2020.
- OpenREALM：无人机实时建图框架
- 代码开源 | video
[7] Du Z J, Huang S S, Mu T J, et al. Accurate RGB-D SLAM in Dynamic Environment using Observationally Consistent Conditional Random Fields. 2020
- 动态环境中使用观察一致 CRF 的精确 RGB-D SLAM
- 清华大学
[8] Holynski A, Geraghty D, Frahm J M, et al. Reducing Drift in Structure from Motion using Extended Features[J]. arXiv preprint arXiv:2008.12295, 2020.
- 使用拓展特征减小 SFM 中的漂移
- 华盛顿大学，Facebook
[9] Fu Q, Wang J, Yu H, et al. PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line[J]. arXiv preprint arXiv:2009.07462, 2020.
- PL-VINS: 实时点线单目视觉惯性 SLAM
- 湖南大学 | 代码开源
[10] Company-Corcoles J P, Garcia-Fidalgo E, Ortiz A. LiPo-LCD: Combining Lines and Points for Appearance-based Loop Closure Detection[J]. arXiv preprint arXiv:2009.09897, 2020.(BMVC 2020)
- 几何点线的基于外观的闭环检测
- 巴利阿里群岛大学
[11] Wang Q, Yan Z, Wang J, et al. Line Flow based SLAM[J]. arXiv preprint arXiv:2009.09972, 2020.
- 基于 SLAM 的线流
- 北大
[12] Badias A, Alfaro I, Gonzalez D, et al. MORPH-DSLAM: Model Order Reduction for PHysics-based Deformable SLAM[J]. arXiv preprint arXiv:2009.00576, 2020.
- 基于物理可变形 SLAM 降低模型阶数
- 萨拉戈萨大学

2. Semantic/Deep SLAM

[13] Czarnowski J. Learned representations for real-time monocular SLAM[J]. 2020.
- 实时单目 SLAM 的学习表示
- 帝国理工学院 Jan Czarnowski 博士学位论文 | 导师 Andrew Davison
- 代表作
[14] Li J, Pei L, Zou D, et al. Attention-SLAM: A Visual Monocular SLAM Learning from Human Gaze[J]. arXiv preprint arXiv:2009.06886, 2020.
- Attention-SLAM：从人类视线中学习的单目视觉 SLAM
- 上海交通大学
[15] Cremona J, Uzal L, Pire T. WGANVO: Monocular Visual Odometry based on Generative Adversarial Networks[J]. arXiv preprint arXiv:2007.13704, 2020.
- WGANVO: 基于生成对抗网络的单目视觉里程计
- 阿根廷 CIFASIS | 代码开源 | video
[16] Labbé Y, Carpentier J, Aubry M, et al. CosyPose: Consistent multi-view multi-object 6D pose estimation[J]. arXiv preprint arXiv:2008.08465, 2020.（ECCV 2020）
- CosyPose：一致的多视图多物体 6D 位姿估计
- Object SLAM @ 物体位姿估计

3. AR/VR/MR

[17] Yang X, Zhou L, Jiang H, et al. Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone[J]. IEEE Annals of the History of Computing, 2020 (01): 1-1.
- Mobile3DRecon手机上的实时单目重建
- 商汤、浙大
[18] Ungureanu D, Bogo F, Galliani S, et al. HoloLens 2 Research Mode as a Tool for Computer Vision Research[J]. arXiv preprint arXiv:2008.11239, 2020.
- HoloLens 2 研究模式作为计算机视觉研究的工具
- 三星 AI 中心，微软
[19] Mori S, Erat O, Broll W, et al. InpaintFusion: Incremental RGB-D Inpainting for 3D Scenes[J]. IEEE Transactions on Visualization and Computer Graphics, 2020, 26(10): 2994-3007.
- InpaintFusion：3D场景的增量RGB-D修复
- 格拉茨工业大学期刊：中科院二区,JCR Q1, IF 4.558
[20] AAR: Augmenting a Wearable Augmented Reality Display with an Actuated Head-Mounted Projector. 2020
- 使用可驱动的头戴式投影仪增强可穿戴的增强现实显示
- 滑铁卢大学
- 在 AR 眼镜上再装个投影仪。。。。会玩

2020 年 8 月论文更新（30 篇）

本期更新于 2020 年 8 月 27 日
共 30 篇论文，其中 11 项（待）开源工作
这个月公开的论文比较多，且有意思、高质量的工作也不少，多来自于 IROS、RAL(大部分也同步发表于 IROS)，比如融合视觉、惯导、LiDAR 的 LIC-Fusion 2.0 和融合物体语义的视惯里程计 OrcVIO，其他：
4-6、15：多机/多地图
8-13：结构化/室内 SLAM

1. Geometric SLAM

[1] Geppert M, Larsson V, Speciale P, et al. Privacy Preserving Structure-from-Motion[J]. 2020.
- 具有隐私保护的 SFM
- 苏黎世联邦理工
- 相关论文：
  - CVPR 2019 Privacy Preserving Image-Based Localization
  - ECCV 2020 Privacy Preserving Visual SLAM
[2] Zhang Z, Scaramuzza D. Fisher Information Field: an Efficient and Differentiable Map for Perception-aware Planning[J]. arXiv preprint arXiv:2008.03324, 2020.
- Fisher 信息场：一种用于感知规划的高效且可微分的地图
- 苏黎世大学张子潮 | 代码开源
- 相关工作：ICRA 2019 Beyond Point Clouds: Fisher Information Field for Active Visual Localization
[3] Zhou Y, Gallego G, Shen S. Event-based Stereo Visual Odometry[J]. arXiv preprint arXiv:2007.15548, 2020.
- 基于事件相机的双目视觉里程计
- 港科沈邵劼组 | 代码开源（暂未公开）| video
[4] Yue Y, Zhao C, Wu Z, et al. Collaborative Semantic Understanding and Mapping Framework for Autonomous Systems[J]. IEEE/ASME Transactions on Mechatronics, 2020.
- 自治系统协作式语义理解和建图框架
- 南洋理工大学 | 期刊：中科院二区，JCR Q1，IF 5.6
[5] Do H, Hong S, Kim J. Robust Loop Closure Method for Multi-Robot Map Fusion by Integration of Consistency and Data Similarity[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 5701-5708.
- 融合一致性和数据相似性的多机器人地图融合的闭环方法
- 韩国启明大学 | Google Scholar
[6] Zhan Z, Jian W, Li Y, et al. A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph[J]. arXiv preprint arXiv:2007.14592, 2020.
- 基于子图和无向连通图的 SLAM 地图恢复算法
- 武汉大学
[7] Chen H, Zhang G, Ye Y. Semantic Loop Closure Detection with Instance-Level Inconsistency Removal in Dynamic Industrial Scenes[J]. IEEE Transactions on Industrial Informatics, 2020.
- 动态工业场景中具有实例级不一致消除功能的语义闭环检测
- 厦门大学 | 期刊：中科院一区，JCR Q1，IF 9.1
[8] Li Y, Brasch N, Wang Y, et al. Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 6583-6590.
- Structure-SLAM：室内环境中的低漂移单目 SLAM
- TUM | 代码开源
[9] Liu J, Meng Z. Visual SLAM with Drift-Free Rotation Estimation in Manhattan World[J]. IEEE Robotics and Automation Letters, 2020.
- 亚特兰大世界的全局最优和有效消失点估计
- 港中文刘云辉教授组 Haoang Li
[10] Hou J, Yu L, Fei S. A highly robust automatic 3D reconstruction system based on integrated optimization by point line features[J]. Engineering Applications of Artificial Intelligence, 2020, 95: 103879.
- 基于点线联合优化的自动三维重建
- 苏州大学、东南大学 | 期刊：中科院二区，JCR Q1，IF 4.2
[11] Li H, Kim P, Zhao J, et al. Globally Optimal and Efficient Vanishing Point Estimation in Atlanta World[J]. 2020.
- 曼哈顿世界中无漂移旋转估计的视觉 SLAM
- 港中文，西蒙弗雷泽大学
[12] Wang X, Christie M, Marchand E. Relative Pose Estimation and Planar Reconstruction via Superpixel-Driven Multiple Homographies[C]//IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'20. 2020.
- 通过超像素驱动的多个单应性的相对姿势估计和平面重建
- 雷恩大学
[13] Zuñiga-Noël D, Jaenal A, Gomez-Ojeda R, et al. The UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments[J]. The International Journal of Robotics Research, 2020: 0278364920938439.
- UMA-VI 数据集：在低纹理和动态照明环境中的视觉惯性里程计
- 马拉加大学 | PL-SLAM 的作者 | 期刊：中科院二区，JCR Q1，IF 4.0 | 数据集地址

2. Sensor Fusion

[14] Zuo X, Yang Y, Geneva P, et al. LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking[J]. arXiv preprint arXiv:2008.07196, 2020.
- LIC-Fusion 2.0：滑动窗口平面特征跟踪的 LiDAR-惯性-视觉里程计
- 浙大、ETH Google Scholar
- 相关论文：LIC-Fusion: LiDAR-Inertial-Camera Odometry
[15] Alliez P, Bonardi F, Bouchafa S, et al. Real-Time Multi-SLAM System for Agent Localization and 3D Mapping in Dynamic Scenarios[C]//International Confererence on Intelligent Robots and Systems (IROS 2020). 2020.
- 实时 Multi-SLAM 系统，用于动态场景中智能体定位和 3D 建图
- 法国国家信息与自动化研究所
[16] Shao X, Zhang L, Zhang T, et al. A Tightly-coupled Semantic SLAM System with Visual, Inertial and Surround-view Sensors for Autonomous Indoor Parking[J].2020.
- 具有视觉、惯性和全景传感器的紧密耦合语义 SLAM 系统，用于自主室内停车
- 同济大学
[17] Shan M, Feng Q, Atanasov N. OrcVIO: Object residual constrained Visual-Inertial Odometry[J]. arXiv preprint arXiv:2007.15107, 2020.(IROS 2020)
- 物体残差约束的 VIO
- 加州大学圣地亚哥分校 | Nikolay A. Atanasov | Mo Shan
[18] Seok H, Lim J. ROVINS: Robust Omnidirectional Visual Inertial Navigation System[J]. IEEE Robotics and Automation Letters, 2020.
- 鲁棒的全方位视觉惯性导航系统
- 韩国汉阳大学
[19] Liu W, Caruso D, Ilg E, et al. TLIO: Tight Learned Inertial Odometry[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 5653-5660.
- 基于学习的紧耦合的惯性里程计
- 宾夕法尼亚大学 | 代码开源 | 项目主页
[20] Sartipi K, Do T, Ke T, et al. Deep Depth Estimation from Visual-Inertial SLAM[J]. arXiv preprint arXiv:2008.00092, 2020.
- 视觉惯性 SLAM 深度估计
- 明尼苏达大学 | 代码开源

3. Semantic/Deep SLAM

[21] Gomez C, Hernandez A C, Derner E, et al. Object-Based Pose Graph for Dynamic Indoor Environments[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 5401-5408.
- 动态室内环境中基于物体的位姿图
- 马德里卡洛斯三世大学 | Google Scholar（有很多基于物体的机器人导航的工作）
[22] Wang H, Wang C, Xie L. Online Visual Place Recognition via Saliency Re-identification[J]. arXiv preprint arXiv:2007.14549, 2020.（IROS 2020）
- 通过显著性重识别进行视觉场景重识别
- CMU | 代码开源
[23] Jau Y Y, Zhu R, Su H, et al. Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints[J]. arXiv preprint arXiv:2007.15122, 2020.（IROS 2020）
- 具有几何约束的基于深度关键点的相机姿势估计
- 加州大学圣地亚哥分校 | 代码开源
[24] Gong X, Liu Y, Wu Q, et al. An Accurate, Robust Visual Odometry and Detail-preserving Reconstruction System[J]. 2020.
- 准确、鲁棒的视觉里程计和保留细节的重建系统
- 南京航空航天大学 | 代码开源（暂未公开）
[25] Wei P, Hua G, Huang W, et al. Unsupervised Monocular Visual-inertial Odometry Network[J].IJCAI 2020
- 无监督单目视惯里程计网络
- 北大 | 代码开源（暂未公开）
[26] Li D, Shi X, Long Q, et al. DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features[J]. arXiv preprint arXiv:2008.05416, 2020.（IROS 2020）
- 基于深度信息的鲁棒高效视觉 SLAM 系统
- 清华大学 | 代码开源

4. AR/VR/MR

[27] Tahara T, Seno T, Narita G, et al. Retargetable AR: Context-aware Augmented Reality in Indoor Scenes based on 3D Scene Graph[J]. arXiv preprint arXiv:2008.07817, 2020.
- 可重定位的 AR：基于室内 3D 场景图中的情景感知增强现实
- 索尼
[28] Li X, Tian Y, Zhang F, et al. Object Detection in the Context of Mobile Augmented Reality[J]. arXiv preprint arXiv:2008.06655, 2020.（ISMAR 2020）
- 移动增强现实环境下的目标检测
- oppo
[29] Liu C, Shen S. An Augmented Reality Interaction Interface for Autonomous Drone[J]. arXiv preprint arXiv:2008.02234, 2020.（IROS 2020）
- 自主无人机增强现实交互界面
- 港科沈邵劼组
[30] Du R, Turner E L, Dzitsiuk M, et al. DepthLab: Real-time 3D Interaction with Depth Maps for Mobile Augmented Reality[J]. 2020.
- 使用深度地图实时 3D 交互的移动增强现实
- Google

2020 年 7 月论文更新（20 篇）

本期更新于 2020 年 7 月 27 日
共 20 篇论文，其中 8 项（待）开源工作
本月月初 ECCV，IROS 放榜，不少新论文出现
2 隐私保护的视觉 SLAM，11 秦通大佬的 AVP-SLAM
月底 ORB-SLAM3 又制造了大新闻，谷歌学术都没来得及收录，国内公众号都出解析了

1. Geometric SLAM

[1] Carlos Campos, Richard Elvira, et al.ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM[J]. arXiv preprint arXiv:2007.11898, 2020.
- ORB-SLAM3: 集视觉、视惯、多地图 SLAM 于一体
- 西班牙萨拉戈萨大学 | 代码开源 | Video
[2] Shibuya M, Sumikura S, Sakurada K. Privacy Preserving Visual SLAM[J]. arXiv preprint arXiv:2007.10361, 2020.(ECCV 2020)
- 保护隐私的视觉 SLAM
- 日本国立先进工业科学技术研究院；东京工业大学
- 作者 Shinya Sumikura 谷歌学术，OpenVSLAM 的作者 | 项目主页 | Video
- 相关论文：CVPR 2019 Privacy preserving image-based localization
[3] Tompkins A, Senanayake R, Ramos F. Online Domain Adaptation for Occupancy Mapping[J]. arXiv preprint arXiv:2007.00164, 2020.（RSS 2020）
- 在线区域调整以进行占有地图构建
- 悉尼大学 | 代码开源 | Video
[4] Li Y, Zhang T, Nakamura Y, et al. SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes[J]. arXiv preprint arXiv:2007.02108, 2020.(IROS 2020)
- SplitFusion：非刚性场景的 SLAM
- 东京大学
[5] WeiChen Dai, Yu Zhang, Ping Li, and Zheng Fang, Sebastian Scherer. RGB-D SLAM in Dynamic Environments Using Points Correlations. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
- 动态环境中使用点关联的 RGB-D SLAM
- 浙江大学；期刊：PAMI 中科院一区，JCR Q1，IF 17.86
[6] Huang H, Ye H, Sun Y, et al. GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture Models[J]. IEEE Robotics and Automation Letters, 2020.
- 高斯混合模型的结构一致视觉定位
- 香港科技大学 | 代码开源

2. Sensor Fusion

[7] Zuo X, Ye W, Yang Y, et al. Multimodal localization: Stereo over LiDAR map[J]. Journal of Field Robotics, 2020.
- 多模式定位：在 LiDAR 先验地图中使用双目相机定位
- 浙江大学、特拉华大学 | 作者谷歌学术 | 期刊：中科院二区，JCR Q2，IF 4.19
[8] Shan T, Englot B, Meyers D, et al. LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping[J]. arXiv preprint arXiv:2007.00258, 2020.（IROS 2020）
- 平滑紧耦合的激光雷达里程计和建图
- MIT | 代码开源 | Video
[9] Rozenberszki D, Majdik A. LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Maps[J]. arXiv preprint arXiv:2007.01595, 2020.（ICRA 2020）
- 3D 点云地图中仅激光雷达的里程计和定位
- 匈牙利科学院机器感知实验室 | 代码开源 | Video
[10] You R, Hou H. Real-Time Pose Estimation by Fusing Visual and Inertial Sensors for Autonomous Driving[J]. 2020.
- 通过融合视觉和惯性传感器进行自动驾驶的实时位姿估计
- 瑞典查尔默斯理工大学硕士学位论文 | 代码开源

3. Semantic/Deep SLAM

[11] Qin T, Chen T, Chen Y, et al. AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot[J]. arXiv preprint arXiv:2007.01813, 2020.(IROS 2020)
- AVP-SLAM：停车场中自动驾驶车辆的语义 SLAM
- 华为秦通 | 知乎文章
[12] Gomez C, Silva A C H, Derner E, et al. Object-Based Pose Graph for Dynamic Indoor Environments[J]. IEEE Robotics and Automation Letters, 2020.
- 动态室内环境中基于物体的位姿图
- 西班牙马德里卡洛斯三世大学
[13] Costante G, Mancini M. Uncertainty Estimation for Data-Driven Visual Odometry[J]. IEEE Transactions on Robotics, 2020.
- 数据驱动的视觉测程的不确定度估计
- 意大利佩鲁贾大学 | 代码开源（还未放出） | 期刊：中科院二区，JCR Q1，IF 7.0
[14] Min Z, Yang Y, Dunn E. VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR 2020: 4898-4909.
- 对数逻辑密集光流残差的视觉里程计
- 史蒂文斯技术学院 | 代码开源（还未放出）
[15] Zou Y, Ji P, Tran Q H, et al. Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling[J]. arXiv preprint arXiv:2007.10983, 2020.（ECCV 2020）
- 自监督长期建模学习单目视觉里程计
- 弗吉尼亚理工大学 | 项目主页 | 代码 Coming soon
[16] Wei P, Hua G, Huang W, et al. Unsupervised Monocular Visual-inertial Odometry Network[J].2020
- 无监督单目视惯里程计
- 北京大学 | 代码开源（还未放出）

4. AR/VR/MR

[17] Chen Y, Zhang B, Zhou J, et al. Real-time 3D unstructured environment reconstruction utilizing VR and Kinect-based immersive teleoperation for agricultural field robots[J]. Computers and Electronics in Agriculture, 2020, 175: 105579.
- 利用 VR 和基于 Kinect 的沉浸式远程操作技术对农业机器人进行实时 3D 非结构化环境重构
- 南京农业大学 | 期刊：中科院二区，JCR Q1，IF 4.0
[18] Choi J, Son M G, Lee Y Y, et al. Position-based augmented reality platform for aiding construction and inspection of offshore plants[J]. The Visual Computer, 2020: 1-11.
- 基于位置的增强现实平台，用于辅助海上工厂的建设和检查
- 韩国光州科学技术院 | 期刊：中科院四区，JCR Q3

5. Others

[19] Brazil G, Pons-Moll G, Liu X, et al. Kinematic 3D Object Detection in Monocular Video[J]. arXiv preprint arXiv:2007.09548, 2020.(ECCV 2020)
- 单目视频中运动 3D 目标检测
- 密歇根州立大学 | 实验室主页 | [项目主页](

Truncated — view the full README on GitHub.

mapping

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semantic

slam

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See the code

README

毕业快乐！最近更新 2021 年 07 月 05 日：论文更新 +104，开源代码 +9

Visual_SLAM_Related_Research

@Author：吴艳敏
@E-mail ：wuyanminmax[AT]gmail.com
@github ：wuxiaolang

前言

1. 开源代码

这一部分整理之后发布在知乎（2020 年 3 月 31 日）：https://zhuanlan.zhihu.com/p/115599978/

😄 1.1 Geometric SLAM

1. PTAM

论文：Klein G, Murray D. Parallel tracking and mapping for small AR workspaces[C]//Mixed and Augmented Reality, 2007. ISMAR 2007. 6th IEEE and ACM International Symposium on. IEEE, 2007: 225-234.
代码：https://github.com/Oxford-PTAM/PTAM-GPL
工程地址：http://www.robots.ox.ac.uk/~gk/PTAM/
作者其他研究：http://www.robots.ox.ac.uk/~gk/publications.html

2. S-PTAM（双目 PTAM）

论文：Taihú Pire,Thomas Fischer, Gastón Castro, Pablo De Cristóforis, Javier Civera and Julio Jacobo Berlles. S-PTAM: Stereo Parallel Tracking and Mapping. Robotics and Autonomous Systems, 2017.
代码：https://github.com/lrse/sptam
作者其他论文：Castro G, Nitsche M A, Pire T, et al. Efficient on-board Stereo SLAM through constrained-covisibility strategies[J]. Robotics and Autonomous Systems, 2019.

3. MonoSLAM

论文：Davison A J, Reid I D, Molton N D, et al. MonoSLAM: Real-time single camera SLAM[J]. IEEE transactions on pattern analysis and machine intelligence, 2007, 29(6): 1052-1067.
代码：https://github.com/hanmekim/SceneLib2

4. ORB-SLAM2

论文：Mur-Artal R, Tardós J D. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras[J]. IEEE Transactions on Robotics, 2017, 33(5): 1255-1262.
代码：https://github.com/raulmur/ORB_SLAM2
作者其他论文：
- 单目半稠密建图：Mur-Artal R, Tardós J D. Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM[C]//Robotics: Science and Systems. 2015, 2015.
- VIORB：Mur-Artal R, Tardós J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803.
- 多地图：Elvira R, Tardós J D, Montiel J M M. ORBSLAM-Atlas: a robust and accurate multi-map system[J]. arXiv preprint arXiv:1908.11585, 2019.

以下5, 6, 7, 8几项是 TUM 计算机视觉组全家桶，官方主页：https://vision.in.tum.de/research/vslam/dso

5. DSO

论文：Engel J, Koltun V, Cremers D. Direct sparse odometry[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(3): 611-625.
代码：https://github.com/JakobEngel/dso
双目 DSO：Wang R, Schworer M, Cremers D. Stereo DSO: Large-scale direct sparse visual odometry with stereo cameras[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 3903-3911.
VI-DSO：Von Stumberg L, Usenko V, Cremers D. Direct sparse visual-inertial odometry using dynamic marginalization[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 2510-2517.

6. LDSO

高翔在 DSO 上添加闭环的工作
论文：Gao X, Wang R, Demmel N, et al. LDSO: Direct sparse odometry with loop closure[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 2198-2204.
代码：https://github.com/tum-vision/LDSO

7. LSD-SLAM

论文：Engel J, Schöps T, Cremers D. LSD-SLAM: Large-scale direct monocular SLAM[C]//European conference on computer vision. Springer, Cham, 2014: 834-849.
代码：https://github.com/tum-vision/lsd_slam

8. DVO-SLAM

论文：Kerl C, Sturm J, Cremers D. Dense visual SLAM for RGB-D cameras[C]//2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2013: 2100-2106.
代码 1：https://github.com/tum-vision/dvo_slam
代码 2：https://github.com/tum-vision/dvo
其他论文：
- Kerl C, Sturm J, Cremers D. Robust odometry estimation for RGB-D cameras[C]//2013 IEEE international conference on robotics and automation. IEEE, 2013: 3748-3754.
- Steinbrücker F, Sturm J, Cremers D. Real-time visual odometry from dense RGB-D images[C]//2011 IEEE international conference on computer vision workshops (ICCV Workshops). IEEE, 2011: 719-722.

9. SVO

苏黎世大学机器人与感知课题组
论文：Forster C, Pizzoli M, Scaramuzza D. SVO: Fast semi-direct monocular visual odometry[C]//2014 IEEE international conference on robotics and automation (ICRA). IEEE, 2014: 15-22.
代码：https://github.com/uzh-rpg/rpg_svo
Forster C, Zhang Z, Gassner M, et al. SVO: Semidirect visual odometry for monocular and multicamera systems[J]. IEEE Transactions on Robotics, 2016, 33(2): 249-265.

10. DSM

论文：Zubizarreta J, Aguinaga I, Montiel J M M. Direct sparse mapping[J]. arXiv preprint arXiv:1904.06577, 2019.
代码：https://github.com/jzubizarreta/dsm ；Video

11. openvslam

论文：Sumikura S, Shibuya M, Sakurada K. OpenVSLAM: A Versatile Visual SLAM Framework[C]//Proceedings of the 27th ACM International Conference on Multimedia. 2019: 2292-2295.
代码：https://github.com/xdspacelab/openvslam ；文档

12. se2lam（地面车辆位姿估计的视觉里程计）

论文：Zheng F, Liu Y H. Visual-Odometric Localization and Mapping for Ground Vehicles Using SE (2)-XYZ Constraints[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 3556-3562.
代码：https://github.com/izhengfan/se2lam
作者的另外一项工作
- 论文：Zheng F, Tang H, Liu Y H. Odometry-vision-based ground vehicle motion estimation with se (2)-constrained se (3) poses[J]. IEEE transactions on cybernetics, 2018, 49(7): 2652-2663.
- 代码：https://github.com/izhengfan/se2clam

13. GraphSfM（基于图的并行大规模 SFM）

论文：Chen Y, Shen S, Chen Y, et al. Graph-Based Parallel Large Scale Structure from Motion[J]. arXiv preprint arXiv:1912.10659, 2019.
代码：https://github.com/AIBluefisher/GraphSfM

14. LCSD_SLAM（松耦合的半直接法单目 SLAM）

论文：Lee S H, Civera J. Loosely-Coupled semi-direct monocular SLAM[J]. IEEE Robotics and Automation Letters, 2018, 4(2): 399-406.
代码：https://github.com/sunghoon031/LCSD_SLAM ；谷歌学术；演示视频
作者另外一篇关于单目尺度的文章代码开源：Lee S H, de Croon G. Stability-based scale estimation for monocular SLAM[J]. IEEE Robotics and Automation Letters, 2018, 3(2): 780-787.

15. RESLAM（基于边的 SLAM）

论文：Schenk F, Fraundorfer F. RESLAM: A real-time robust edge-based SLAM system[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 154-160.
代码：https://github.com/fabianschenk/RESLAM ；项目主页

16. scale_optimization（将单目 DSO 拓展到双目）

论文：Mo J, Sattar J. Extending Monocular Visual Odometry to Stereo Camera System by Scale Optimization[C]. International Conference on Intelligent Robots and Systems (IROS), 2019.
代码：https://github.com/jiawei-mo/scale_optimization

17. BAD-SLAM（直接法 RGB-D SLAM）

论文：Schops T, Sattler T, Pollefeys M. BAD SLAM: Bundle Adjusted Direct RGB-D SLAM[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 134-144.
代码：https://github.com/ETH3D/badslam

18. GSLAM（集成 ORB-SLAM2，DSO，SVO 的通用框架）

论文：Zhao Y, Xu S, Bu S, et al. GSLAM: A general SLAM framework and benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1110-1120.
代码：https://github.com/zdzhaoyong/GSLAM

19. ARM-VO（运行于 ARM 处理器上的单目 VO）

论文：Nejad Z Z, Ahmadabadian A H. ARM-VO: an efficient monocular visual odometry for ground vehicles on ARM CPUs[J]. Machine Vision and Applications, 2019: 1-10.
代码：https://github.com/zanazakaryaie/ARM-VO

20. cvo-rgbd（直接法 RGB-D VO）

论文：Ghaffari M, Clark W, Bloch A, et al. Continuous Direct Sparse Visual Odometry from RGB-D Images[J]. arXiv preprint arXiv:1904.02266, 2019.
代码：https://github.com/MaaniGhaffari/cvo-rgbd

21. Map2DFusion（单目 SLAM 无人机图像拼接）

论文：Bu S, Zhao Y, Wan G, et al. Map2DFusion: Real-time incremental UAV image mosaicing based on monocular slam[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4564-4571.
代码：https://github.com/zdzhaoyong/Map2DFusion

22. CCM-SLAM（多机器人协同单目 SLAM）

论文：Schmuck P, Chli M. CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams[J]. Journal of Field Robotics, 2019, 36(4): 763-781.
代码：https://github.com/VIS4ROB-lab/ccm_slam Video

23. ORB-SLAM3

论文：Carlos Campos, Richard Elvira, et al.ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM[J]. arXiv preprint arXiv:2007.11898, 2020.
代码：https://github.com/UZ-SLAMLab/ORB_SLAM3 | Video

24. OV²SLAM（完全实时在线多功能 SLAM）

论文：Ferrera M, Eudes A, Moras J, et al. OV $^{2} $ SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1399-1406.
代码：https://github.com/ov2slam/ov2slam

25. ESVO（基于事件的双目视觉里程计）

论文：Zhou Y, Gallego G, Shen S. Event-based stereo visual odometry[J]. IEEE Transactions on Robotics, 2021.
代码：https://github.com/HKUST-Aerial-Robotics/ESVO

26. VOLDOR-SLAM（实时稠密非直接法 SLAM）

论文：Min Z, Dunn E. VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough[J]. arXiv preprint arXiv:2104.06800, 2021.
代码：https://github.com/htkseason/VOLDOR

😄 1.2 Semantic / Deep SLAM

1. MsakFusion

论文：Runz M, Buffier M, Agapito L. Maskfusion: Real-time recognition, tracking and reconstruction of multiple moving objects[C]//2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2018: 10-20.
代码：https://github.com/martinruenz/maskfusion

2. SemanticFusion

论文：McCormac J, Handa A, Davison A, et al. Semanticfusion: Dense 3d semantic mapping with convolutional neural networks[C]//2017 IEEE International Conference on Robotics and automation (ICRA). IEEE, 2017: 4628-4635.
代码：https://github.com/seaun163/semanticfusion

3. semantic_3d_mapping

论文：Yang S, Huang Y, Scherer S. Semantic 3D occupancy mapping through efficient high order CRFs[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 590-597.
代码：https://github.com/shichaoy/semantic_3d_mapping

4. Kimera（实时度量与语义定位建图开源库）

论文：Rosinol A, Abate M, Chang Y, et al. Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping[J]. arXiv preprint arXiv:1910.02490, 2019.
代码：https://github.com/MIT-SPARK/Kimera ；演示视频

5. NeuroSLAM（脑启发式 SLAM）

论文：Yu F, Shang J, Hu Y, et al. NeuroSLAM: a brain-inspired SLAM system for 3D environments[J]. Biological Cybernetics, 2019: 1-31.
代码：https://github.com/cognav/NeuroSLAM
第四作者就是 Rat SLAM 的作者，文章也比较了十余种脑启发式的 SLAM

6. gradSLAM（自动分区的稠密 SLAM）

论文：Jatavallabhula K M, Iyer G, Paull L. gradSLAM: Dense SLAM meets Automatic Differentiation[J]. arXiv preprint arXiv:1910.10672, 2019.
代码（预计 20 年 4 月放出）：https://github.com/montrealrobotics/gradSLAM ；项目主页，演示视频

7. ORB-SLAM2 + 目标检测/分割的方案语义建图

https://github.com/floatlazer/semantic_slam
https://github.com/qixuxiang/orb-slam2_with_semantic_labelling
https://github.com/Ewenwan/ORB_SLAM2_SSD_Semantic

8. SIVO（语义辅助特征选择）

论文：Ganti P, Waslander S. Network Uncertainty Informed Semantic Feature Selection for Visual SLAM[C]//2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019: 121-128.
代码：https://github.com/navganti/SIVO

9. FILD（临近图增量式闭环检测）

论文：Shan An, Guangfu Che, Fangru Zhou, Xianglong Liu, Xin Ma, Yu Chen. Fast and Incremental Loop Closure Detection using Proximity Graphs. pp. 378-385, The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)
代码：https://github.com/AnshanTJU/FILD

10. object-detection-sptam（目标检测与双目 SLAM）

论文：Pire T, Corti J, Grinblat G. Online Object Detection and Localization on Stereo Visual SLAM System[J]. Journal of Intelligent & Robotic Systems, 2019: 1-10.
代码：https://github.com/CIFASIS/object-detection-sptam

11. Map Slammer（单目深度估计 + SLAM）

论文：Torres-Camara J M, Escalona F, Gomez-Donoso F, et al. Map Slammer: Densifying Scattered KSLAM 3D Maps with Estimated Depth[C]//Iberian Robotics conference. Springer, Cham, 2019: 563-574.
代码：https://github.com/jmtc7/mapSlammer

12. NOLBO（变分模型的概率 SLAM）

论文：Yu H, Lee B. Not Only Look But Observe: Variational Observation Model of Scene-Level 3D Multi-Object Understanding for Probabilistic SLAM[J]. arXiv preprint arXiv:1907.09760, 2019.
代码：https://github.com/bogus2000/NOLBO

13. GCNv2_SLAM （基于图卷积神经网络 SLAM）

论文：Tang J, Ericson L, Folkesson J, et al. GCNv2: Efficient correspondence prediction for real-time SLAM[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3505-3512.
代码：https://github.com/jiexiong2016/GCNv2_SLAM Video

14. semantic_suma（激光语义建图）

论文：Chen X, Milioto A, Palazzolo E, et al. SuMa++: Efficient LiDAR-based semantic SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 4530-4537.
代码：https://github.com/PRBonn/semantic_suma/ ；Video

15. Neural-SLAM（主动神经 SLAM）

论文：Chaplot D S, Gandhi D, Gupta S, et al. Learning to explore using active neural slam[C]. ICLR 2020.
代码：https://github.com/devendrachaplot/Neural-SLAM

16. TartanVO：一种通用的基于学习的 VO

论文：Wang W, Hu Y, Scherer S. TartanVO: A Generalizable Learning-based VO[J]. arXiv preprint arXiv:2011.00359, 2020.
代码：https://github.com/castacks/tartanvo
数据集：IROS2020 TartanAir: A Dataset to Push the Limits of Visual SLAM，数据集地址

17. DF-VO

论文：Zhan H, Weerasekera C S, Bian J W, et al. DF-VO: What Should Be Learnt for Visual Odometry?[J]. arXiv preprint arXiv:2103.00933, 2021.
- Zhan H, Weerasekera C S, Bian J W, et al. Visual odometry revisited: What should be learnt?[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 4203-4210.
代码：https://github.com/Huangying-Zhan/DF-VO

😄 1.3 Multi-Landmarks / Object SLAM

1. PL-SVO（点线 SVO）

论文：Gomez-Ojeda R, Briales J, Gonzalez-Jimenez J. PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments[C]//Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016: 4211-4216.
代码：https://github.com/rubengooj/pl-svo

2. stvo-pl（双目点线 VO）

论文：Gomez-Ojeda R, Gonzalez-Jimenez J. Robust stereo visual odometry through a probabilistic combination of points and line segments[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 2521-2526.
代码：https://github.com/rubengooj/stvo-pl

3. PL-SLAM（点线 SLAM）

论文：Gomez-Ojeda R, Zuñiga-Noël D, Moreno F A, et al. PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments[J]. arXiv preprint arXiv:1705.09479, 2017.
代码：https://github.com/rubengooj/pl-slam
Gomez-Ojeda R, Moreno F A, Zuñiga-Noël D, et al. PL-SLAM: a stereo SLAM system through the combination of points and line segments[J]. IEEE Transactions on Robotics, 2019, 35(3): 734-746.

4. PL-VIO

论文：He Y, Zhao J, Guo Y, et al. PL-VIO: Tightly-coupled monocular visual–inertial odometry using point and line features[J]. Sensors, 2018, 18(4): 1159.
代码：https://github.com/HeYijia/PL-VIO
VINS + 线段：https://github.com/Jichao-Peng/VINS-Mono-Optimization

5. lld-slam（用于 SLAM 的可学习型线段描述符）

论文：Vakhitov A, Lempitsky V. Learnable line segment descriptor for visual SLAM[J]. IEEE Access, 2019, 7: 39923-39934.
代码：https://github.com/alexandervakhitov/lld-slam ；Video

点线结合的工作还有很多，国内的比如

上交邹丹平老师的 Zou D, Wu Y, Pei L, et al. StructVIO: visual-inertial odometry with structural regularity of man-made environments[J]. IEEE Transactions on Robotics, 2019, 35(4): 999-1013.

浙大的 Zuo X, Xie X, Liu Y, et al. Robust visual SLAM with point and line features[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 1775-1782.

6. PlaneSLAM

论文：Wietrzykowski J. On the representation of planes for efficient graph-based slam with high-level features[J]. Journal of Automation Mobile Robotics and Intelligent Systems, 2016, 10.
代码：https://github.com/LRMPUT/PlaneSLAM
作者另外一项开源代码，没有找到对应的论文：https://github.com/LRMPUT/PUTSLAM

7. Eigen-Factors（特征因子平面对齐）

论文：Ferrer G. Eigen-Factors: Plane Estimation for Multi-Frame and Time-Continuous Point Cloud Alignment[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 1278-1284.
代码：https://gitlab.com/gferrer/eigen-factors-iros2019 ；演示视频

8. PlaneLoc

论文：Wietrzykowski J, Skrzypczyński P. PlaneLoc: Probabilistic global localization in 3-D using local planar features[J]. Robotics and Autonomous Systems, 2019, 113: 160-173.
代码：https://github.com/LRMPUT/PlaneLoc

9. Pop-up SLAM

论文：Yang S, Song Y, Kaess M, et al. Pop-up slam: Semantic monocular plane slam for low-texture environments[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 1222-1229.
代码：https://github.com/shichaoy/pop_up_slam

10. Object SLAM

论文：Mu B, Liu S Y, Paull L, et al. Slam with objects using a nonparametric pose graph[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4602-4609.
代码：https://github.com/BeipengMu/objectSLAM ；Video

11. voxblox-plusplus（物体级体素建图）

论文：Grinvald M, Furrer F, Novkovic T, et al. Volumetric instance-aware semantic mapping and 3D object discovery[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 3037-3044.
代码：https://github.com/ethz-asl/voxblox-plusplus

12. Cube SLAM

论文：Yang S, Scherer S. Cubeslam: Monocular 3-d object slam[J]. IEEE Transactions on Robotics, 2019, 35(4): 925-938.
代码：https://github.com/shichaoy/cube_slam
对，这就是带我入坑的一项工作，2018 年 11 月份看到这篇论文（当时是预印版）之后开始学习物体级 SLAM，个人对 Cube SLAM 的一些注释和总结：链接。
也有很多有意思的但没开源的物体级 SLAM
- Ok K, Liu K, Frey K, et al. Robust Object-based SLAM for High-speed Autonomous Navigation[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 669-675.
- Li J, Meger D, Dudek G. Semantic Mapping for View-Invariant Relocalization[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 7108-7115.
- Nicholson L, Milford M, Sünderhauf N. Quadricslam: Dual quadrics from object detections as landmarks in object-oriented slam[J]. IEEE Robotics and Automation Letters, 2018, 4(1): 1-8.

13. VPS-SLAM（平面语义 SLAM）

论文：Bavle H, De La Puente P, How J, et al. VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems[J]. IEEE Access, 2020.
代码：https://bitbucket.org/hridaybavle/semantic_slam/src/master/

14. Structure-SLAM （低纹理环境下点线 SLAM）

论文：Li Y, Brasch N, Wang Y, et al. Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 6583-6590.
代码：https://github.com/yanyan-li/Structure-SLAM-PointLine

15. PL-VINS

论文：Fu Q, Wang J, Yu H, et al. PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line[J]. arXiv preprint arXiv:2009.07462, 2020.
代码：https://github.com/cnqiangfu/PL-VINS

😄 1.4 Sensor Fusion

1. msckf_vio

论文：Sun K, Mohta K, Pfrommer B, et al. Robust stereo visual inertial odometry for fast autonomous flight[J]. IEEE Robotics and Automation Letters, 2018, 3(2): 965-972.
代码：https://github.com/KumarRobotics/msckf_vio ；Video

2. rovio

论文：Bloesch M, Omari S, Hutter M, et al. Robust visual inertial odometry using a direct EKF-based approach[C]//2015 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2015: 298-304.
代码：https://github.com/ethz-asl/rovio ；Video

3. R-VIO

论文：Huai Z, Huang G. Robocentric visual-inertial odometry[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6319-6326.
代码：https://github.com/rpng/R-VIO ；Video
VI_ORB_SLAM2：https://github.com/YoujieXia/VI_ORB_SLAM2

4. okvis

论文：Leutenegger S, Lynen S, Bosse M, et al. Keyframe-based visual–inertial odometry using nonlinear optimization[J]. The International Journal of Robotics Research, 2015, 34(3): 314-334.
代码：https://github.com/ethz-asl/okvis

5. VIORB

论文：Mur-Artal R, Tardós J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803.
代码：https://github.com/jingpang/LearnVIORB （VIORB 本身是没有开源的，这是王京大佬复现的一个版本）

6. VINS-mono

论文：Qin T, Li P, Shen S. Vins-mono: A robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020.
代码：https://github.com/HKUST-Aerial-Robotics/VINS-Mono
双目版 VINS-Fusion：https://github.com/HKUST-Aerial-Robotics/VINS-Fusion
移动段 VINS-mobile：https://github.com/HKUST-Aerial-Robotics/VINS-Mobile

7. VINS-RGBD

论文：Shan Z, Li R, Schwertfeger S. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots[J]. Sensors, 2019, 19(10): 2251.
代码：https://github.com/STAR-Center/VINS-RGBD ；Video

8. Open-VINS

论文：Geneva P, Eckenhoff K, Lee W, et al. Openvins: A research platform for visual-inertial estimation[C]//IROS 2019 Workshop on Visual-Inertial Navigation: Challenges and Applications, Macau, China. IROS 2019.
代码：https://github.com/rpng/open_vins

9. versavis（多功能的视惯传感器系统）

论文：Tschopp F, Riner M, Fehr M, et al. VersaVIS—An Open Versatile Multi-Camera Visual-Inertial Sensor Suite[J]. Sensors, 2020, 20(5): 1439.
代码：https://github.com/ethz-asl/versavis

10. CPI（视惯融合的封闭式预积分）

论文：Eckenhoff K, Geneva P, Huang G. Closed-form preintegration methods for graph-based visual–inertial navigation[J]. The International Journal of Robotics Research, 2018.
代码：https://github.com/rpng/cpi ；Video

11. TUM Basalt

论文：Usenko V, Demmel N, Schubert D, et al. Visual-inertial mapping with non-linear factor recovery[J]. IEEE Robotics and Automation Letters, 2019.
代码：https://github.com/VladyslavUsenko/basalt-mirror ；Video；Project Page

12. Limo（激光单目视觉里程计）

论文：Graeter J, Wilczynski A, Lauer M. Limo: Lidar-monocular visual odometry[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 7872-7879.
代码：https://github.com/johannes-graeter/limo ； Video

13. LARVIO（多状态约束卡尔曼滤波的单目 VIO）

论文：Qiu X, Zhang H, Fu W, et al. Monocular Visual-Inertial Odometry with an Unbiased Linear System Model and Robust Feature Tracking Front-End[J]. Sensors, 2019, 19(8): 1941.
代码：https://github.com/PetWorm/LARVIO
北航邱笑晨博士的一项工作

14. vig-init（垂直边缘加速视惯初始化）

论文：Li J, Bao H, Zhang G. Rapid and Robust Monocular Visual-Inertial Initialization with Gravity Estimation via Vertical Edges[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 6230-6236.
代码：https://github.com/zju3dv/vig-init
浙大章国峰老师组的一项工作

15. vilib（VIO 前端库）

论文：Nagy B, Foehn P, Scaramuzza D. Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO[J]. arXiv preprint arXiv:2003.13493, 2020.
代码：https://github.com/uzh-rpg/vilib

16. Kimera-VIO

论文：A. Rosinol, M. Abate, Y. Chang, L. Carlone, Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping. IEEE Intl. Conf. on Robotics and Automation (ICRA), 2020.
代码：https://github.com/MIT-SPARK/Kimera-VIO

17. maplab（视惯建图框架）

论文：Schneider T, Dymczyk M, Fehr M, et al. maplab: An open framework for research in visual-inertial mapping and localization[J]. IEEE Robotics and Automation Letters, 2018, 3(3): 1418-1425.
代码：https://github.com/ethz-asl/maplab
多会话建图，地图合并，视觉惯性批处理优化和闭环

18. lili-om：固态雷达惯性里程计与建图

论文：Li K, Li M, Hanebeck U D. Towards high-performance solid-state-lidar-inertial odometry and mapping[J]. arXiv preprint arXiv:2010.13150, 2020.
代码：https://github.com/KIT-ISAS/lili-om

19. CamVox：Lidar 辅助视觉 SLAM

论文：ZHU, Yuewen, et al. CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System. arXiv preprint arXiv:2011.11357, 2020.
代码：https://github.com/ISEE-Technology/CamVox

20. SSL_SLAM：固态 LiDAR 轻量级 3D 定位与建图

论文：Wang H, Wang C, Xie L. Lightweight 3-D Localization and Mapping for Solid-State LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1801-1807.
代码：https://github.com/wh200720041/SSL_SLAM

21. r2live：LiDAR-Inertial-Visual 紧耦合

论文：Lin J, Zheng C, Xu W, et al. R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping[J]. arXiv preprint arXiv:2102.12400, 2021.
代码：https://github.com/hku-mars/r2live

22. GVINS：GNSS-视觉-惯导紧耦合

论文：Cao S, Lu X, Shen S. GVINS: Tightly Coupled GNSS-Visual-Inertial for Smooth and Consistent State Estimation[J]. arXiv e-prints, 2021: arXiv: 2103.07899.
代码：https://github.com/HKUST-Aerial-Robotics/GVINS

23. LVI-SAM：Lidar-Visual-Inertial 建图与定位

论文：Shan T, Englot B, Ratti C, et al. LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping[J]. arXiv preprint arXiv:2104.10831, 2021. (ICRA2021)
代码：https://github.com/TixiaoShan/LVI-SAM

😄 1.5 Dynamic SLAM

1. DynamicSemanticMapping（动态语义建图）

论文：Kochanov D, Ošep A, Stückler J, et al. Scene flow propagation for semantic mapping and object discovery in dynamic street scenes[C]//Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016: 1785-1792.
代码：https://github.com/ganlumomo/DynamicSemanticMapping ；wiki

2. DS-SLAM（动态语义 SLAM）

论文：Yu C, Liu Z, Liu X J, et al. DS-SLAM: A semantic visual SLAM towards dynamic environments[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 1168-1174.
代码：https://github.com/ivipsourcecode/DS-SLAM

3. Co-Fusion（实时分割与跟踪多物体）

论文：Rünz M, Agapito L. Co-fusion: Real-time segmentation, tracking and fusion of multiple objects[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 4471-4478.
代码：https://github.com/martinruenz/co-fusion ； Video

4. DynamicFusion

论文：Newcombe R A, Fox D, Seitz S M. Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 343-352.
代码：https://github.com/mihaibujanca/dynamicfusion

5. ReFusion（动态场景利用残差三维重建）

论文：Palazzolo E, Behley J, Lottes P, et al. ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals[J]. arXiv preprint arXiv:1905.02082, 2019.
代码：https://github.com/PRBonn/refusion ；Video

6. DynSLAM（室外大规模稠密重建）

论文：Bârsan I A, Liu P, Pollefeys M, et al. Robust dense mapping for large-scale dynamic environments[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 7510-7517.
代码：https://github.com/AndreiBarsan/DynSLAM
作者博士学位论文：Barsan I A. Simultaneous localization and mapping in dynamic scenes[D]. ETH Zurich, Department of Computer Science, 2017.

7. VDO-SLAM（动态物体感知的 SLAM）

论文：Zhang J, Henein M, Mahony R, et al. VDO-SLAM: A Visual Dynamic Object-aware SLAM System[J]. arXiv preprint arXiv:2005.11052, 2020.（IJRR Under Review）
- 相关论文
  - IROS 2020 Robust Ego and Object 6-DoF Motion Estimation and Tracking
  - ICRA 2020 Dynamic SLAM: The Need For Speed
代码：https://github.com/halajun/VDO_SLAM | video

😄 1.6 Mapping

1. InfiniTAM（跨平台 CPU 实时重建）

论文：Prisacariu V A, Kähler O, Golodetz S, et al. Infinitam v3: A framework for large-scale 3d reconstruction with loop closure[J]. arXiv preprint arXiv:1708.00783, 2017.
代码：https://github.com/victorprad/InfiniTAM ；project page

2. BundleFusion

论文：Dai A, Nießner M, Zollhöfer M, et al. Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration[J]. ACM Transactions on Graphics (TOG), 2017, 36(4): 76a.
代码：https://github.com/niessner/BundleFusion ；工程地址

3. KinectFusion

论文：Newcombe R A, Izadi S, Hilliges O, et al. KinectFusion: Real-time dense surface mapping and tracking[C]//2011 10th IEEE International Symposium on Mixed and Augmented Reality. IEEE, 2011: 127-136.
代码：https://github.com/chrdiller/KinectFusionApp

4. ElasticFusion

论文：Whelan T, Salas-Moreno R F, Glocker B, et al. ElasticFusion: Real-time dense SLAM and light source estimation[J]. The International Journal of Robotics Research, 2016, 35(14): 1697-1716.
代码：https://github.com/mp3guy/ElasticFusion

5. Kintinuous

ElasticFusion 同一个团队的工作，帝国理工 Stefan Leutenegger 谷歌学术
论文：Whelan T, Kaess M, Johannsson H, et al. Real-time large-scale dense RGB-D SLAM with volumetric fusion[J]. The International Journal of Robotics Research, 2015, 34(4-5): 598-626.
代码：https://github.com/mp3guy/Kintinuous

6. ElasticReconstruction

论文：Choi S, Zhou Q Y, Koltun V. Robust reconstruction of indoor scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 5556-5565.
代码：https://github.com/qianyizh/ElasticReconstruction ；作者主页

7. FlashFusion

论文：Han L, Fang L. FlashFusion: Real-time Globally Consistent Dense 3D Reconstruction using CPU Computing[C]. RSS, 2018.
代码（一直没放出来）：https://github.com/lhanaf/FlashFusion ； Project Page

8. RTAB-Map（激光视觉稠密重建）

论文：Labbé M, Michaud F. RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation[J]. Journal of Field Robotics, 2019, 36(2): 416-446.
代码：https://github.com/introlab/rtabmap ；Video ；project page

9. RobustPCLReconstruction（户外稠密重建）

论文：Lan Z, Yew Z J, Lee G H. Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 9690-9698.
代码：https://github.com/ziquan111/RobustPCLReconstruction ；Video

10. plane-opt-rgbd（室内平面重建）

论文：Wang C, Guo X. Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019: 49-53.
代码：https://github.com/chaowang15/plane-opt-rgbd

11. DenseSurfelMapping（稠密表面重建）

论文：Wang K, Gao F, Shen S. Real-time scalable dense surfel mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 6919-6925.
代码：https://github.com/HKUST-Aerial-Robotics/DenseSurfelMapping

12. surfelmeshing（网格重建）

论文：Schöps T, Sattler T, Pollefeys M. Surfelmeshing: Online surfel-based mesh reconstruction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
代码：https://github.com/puzzlepaint/surfelmeshing

13. DPPTAM（单目稠密重建）

论文：Concha Belenguer A, Civera Sancho J. DPPTAM: Dense piecewise planar tracking and mapping from a monocular sequence[C]//Proc. IEEE/RSJ Int. Conf. Intell. Rob. Syst. 2015 (ART-2015-92153).
代码：https://github.com/alejocb/dpptam
相关研究：基于超像素的单目 SLAM：Using Superpixels in Monocular SLAM ICRA 2014 ；谷歌学术

14. VI-MEAN（单目视惯稠密重建）

论文：Yang Z, Gao F, Shen S. Real-time monocular dense mapping on aerial robots using visual-inertial fusion[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 4552-4559.
代码：https://github.com/dvorak0/VI-MEAN ；Video

15. REMODE（单目概率稠密重建）

论文：Pizzoli M, Forster C, Scaramuzza D. REMODE: Probabilistic, monocular dense reconstruction in real time[C]//2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014: 2609-2616.
原始开源代码：https://github.com/uzh-rpg/rpg_open_remode
与 ORB-SLAM2 结合版本：https://github.com/ayushgaud/ORB_SLAM2 https://github.com/ayushgaud/ORB_SLAM2

16. DeepFactors（实时的概率单目稠密 SLAM）

帝国理工学院戴森机器人实验室
论文：Czarnowski J, Laidlow T, Clark R, et al. DeepFactors: Real-Time Probabilistic Dense Monocular SLAM[J]. arXiv preprint arXiv:2001.05049, 2020.
代码：https://github.com/jczarnowski/DeepFactors （还未放出）
其他论文：Bloesch M, Czarnowski J, Clark R, et al. CodeSLAM—learning a compact, optimisable representation for dense visual SLAM[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 2560-2568.

17. probabilistic_mapping（单目概率稠密重建）

港科沈邵劼老师团队
论文：Ling Y, Wang K, Shen S. Probabilistic dense reconstruction from a moving camera[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6364-6371.
代码：https://github.com/ygling2008/probabilistic_mapping
另外一篇稠密重建文章的代码一直没放出来 Github ：Ling Y, Shen S. Real‐time dense mapping for online processing and navigation[J]. Journal of Field Robotics, 2019, 36(5): 1004-1036.

18. ORB-SLAM2 单目半稠密建图

论文：Mur-Artal R, Tardós J D. Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM[C]//Robotics: Science and Systems. 2015, 2015.
代码（本身没有开源，贺博复现的一个版本）：https://github.com/HeYijia/ORB_SLAM2
加上线段之后的半稠密建图
- 论文：He S, Qin X, Zhang Z, et al. Incremental 3d line segment extraction from semi-dense slam[C]//2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018: 1658-1663.
- 代码：https://github.com/shidahe/semidense-lines
- 作者在此基础上用于指导远程抓取操作的一项工作：https://github.com/atlas-jj/ORB-SLAM-free-space-carving

19. Voxgraph（SDF 体素建图）

论文：Reijgwart V, Millane A, Oleynikova H, et al. Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps[J]. IEEE Robotics and Automation Letters, 2019, 5(1): 227-234.
代码：https://github.com/ethz-asl/voxgraph

20. SegMap（三维分割建图）

论文：Dubé R, Cramariuc A, Dugas D, et al. SegMap: 3d segment mapping using data-driven descriptors[J]. arXiv preprint arXiv:1804.09557, 2018.
代码：https://github.com/ethz-asl/segmap

21. OpenREALM：无人机实时建图框架

论文：Kern A, Bobbe M, Khedar Y, et al. OpenREALM: Real-time Mapping for Unmanned Aerial Vehicles[J]. arXiv preprint arXiv:2009.10492, 2020.
代码：https://github.com/laxnpander/OpenREALM

22. c-blox：可拓展的 TSDF 稠密建图

论文：Millane A, Taylor Z, Oleynikova H, et al. C-blox: A scalable and consistent tsdf-based dense mapping approach[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 995-1002.
代码：https://github.com/ethz-asl/cblox

😄 1.7 Optimization

1. 后端优化库

GTSAM：https://github.com/borglab/gtsam ；官网
g2o：https://github.com/RainerKuemmerle/g2o
ceres：http://ceres-solver.org/

2. ICE-BA

论文：Liu H, Chen M, Zhang G, et al. Ice-ba: Incremental, consistent and efficient bundle adjustment for visual-inertial slam[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 1974-1982.
代码：https://github.com/baidu/ICE-BA

3. minisam（因子图最小二乘优化框架）

论文：Dong J, Lv Z. miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework[J]. arXiv preprint arXiv:1909.00903, 2019.
代码：https://github.com/dongjing3309/minisam ；文档

4. SA-SHAGO（几何基元图优化）

论文：Aloise I, Della Corte B, Nardi F, et al. Systematic Handling of Heterogeneous Geometric Primitives in Graph-SLAM Optimization[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 2738-2745.
代码：https://srrg.gitlab.io/sashago-website/index.html#

5. MH-iSAM2（SLAM 优化器）

论文：Hsiao M, Kaess M. MH-iSAM2: Multi-hypothesis iSAM using Bayes Tree and Hypo-tree[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 1274-1280.
代码：https://bitbucket.org/rpl_cmu/mh-isam2_lib/src/master/

6. MOLA（用于定位和建图的模块化优化框架）

论文：Blanco-Claraco J L. A Modular Optimization Framework for Localization and Mapping[J]. Proc. of Robotics: Science and Systems (RSS), FreiburgimBreisgau, Germany, 2019, 2.
代码：https://github.com/MOLAorg/mola ；Video ；使用文档

2. 优秀作者与实验室

这一部分整理之后发布在知乎（2020 年 4 月 19 日）：https://zhuanlan.zhihu.com/p/130530891

1. 美国卡耐基梅陇大学机器人研究所

研究方向：机器人感知、结构，服务型、运输、制造业、现场机器
研究所主页：https://www.ri.cmu.edu/
下属 Field Robotic Center 主页：https://frc.ri.cmu.edu/
发表论文：https://www.ri.cmu.edu/pubs/
👦 Michael Kaess：个人主页，谷歌学术
👦 Sebastian Scherer：个人主页，谷歌学术
📜 Kaess M, Ranganathan A, Dellaert F. iSAM: Incremental smoothing and mapping[J]. IEEE Transactions on Robotics, 2008, 24(6): 1365-1378.
📜 Hsiao M, Westman E, Zhang G, et al. Keyframe-based dense planar SLAM[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 5110-5117.
📜 Kaess M. Simultaneous localization and mapping with infinite planes[C]//2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015: 4605-4611.

2. 美国加州大学圣地亚哥分校语境机器人研究所

研究方向：多模态环境理解，语义导航，自主信息获取
实验室主页：https://existentialrobotics.org/index.html
发表论文汇总：https://existentialrobotics.org/pages/publications.html
👦 Nikolay Atanasov：个人主页谷歌学术
- 机器人状态估计与感知课程 ppt：https://natanaso.github.io/ece276a2019/schedule.html
📜 语义 SLAM 经典论文：Bowman S L, Atanasov N, Daniilidis K, et al. Probabilistic data association for semantic slam[C]//2017 IEEE international conference on robotics and automation (ICRA). IEEE, 2017: 1722-1729.
📜 实例网格模型定位与建图：Feng Q, Meng Y, Shan M, et al. Localization and Mapping using Instance-specific Mesh Models[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 4985-4991.
📜 基于事件相机的 VIO：Zihao Zhu A, Atanasov N, Daniilidis K. Event-based visual inertial odometry[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 5391-5399.

3. 美国特拉华大学机器人感知与导航组

研究方向：SLAM、VINS、语义定位与建图等
实验室主页：https://sites.udel.edu/robot/
发表论文汇总：https://sites.udel.edu/robot/publications/
Github 地址：https://github.com/rpng?page=2
📜 Geneva P, Eckenhoff K, Lee W, et al. Openvins: A research platform for visual-inertial estimation[C]//IROS 2019 Workshop on Visual-Inertial Navigation: Challenges and Applications, Macau, China. IROS 2019.（代码：https://github.com/rpng/open_vins ）
📜 Huai Z, Huang G. Robocentric visual-inertial odometry[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6319-6326.（代码：https://github.com/rpng/R-VIO ）
📜 Zuo X, Geneva P, Yang Y, et al. Visual-Inertial Localization With Prior LiDAR Map Constraints[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3394-3401.
📜 Zuo X, Ye W, Yang Y, et al. Multimodal localization: Stereo over LiDAR map[J]. Journal of Field Robotics, 2020 （左星星博士谷歌学术）
👦 黄国权教授主页

4. 美国麻省理工学院航空航天实验室

研究方向：位姿估计与导航，路径规划，控制与决策，机器学习与强化学习
实验室主页：http://acl.mit.edu/
发表论文：http://acl.mit.edu/publications （实验室的学位论文也可以在这里找到）
👦 Jonathan P. How 教授：个人主页谷歌学术
👦 Kasra Khosoussi（SLAM 图优化）：谷歌学术
📜 物体级 SLAM：Mu B, Liu S Y, Paull L, et al. Slam with objects using a nonparametric pose graph[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4602-4609.（代码：https://github.com/BeipengMu/objectSLAM）
📜 物体级 SLAM 导航：Ok K, Liu K, Frey K, et al. Robust Object-based SLAM for High-speed Autonomous Navigation[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 669-675.
📜 SLAM 的图优化：Khosoussi, K., Giamou, M., Sukhatme, G., Huang, S., Dissanayake, G., and How, J. P., Reliable Graphs for SLAM [C]//International Journal of Robotics Research (IJRR), 2019.

5. 美国麻省理工学院 SPARK 实验室

研究方向：移动机器人环境感知
实验室主页：http://web.mit.edu/sparklab/
👦 Luca Carlone 教授：个人主页谷歌学术
📜 SLAM 经典综述：Cadena C, Carlone L, Carrillo H, et al. Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age[J]. IEEE Transactions on robotics, 2016, 32(6): 1309-1332.
📜 VIO 流形预积分：Forster C, Carlone L, Dellaert F, et al. On-Manifold Preintegration for Real-Time Visual--Inertial Odometry[J]. IEEE Transactions on Robotics, 2016, 33(1): 1-21.
📜 开源语义 SLAM：Rosinol A, Abate M, Chang Y, et al. Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping[J]. arXiv preprint arXiv:1910.02490, 2019.（代码：https://github.com/MIT-SPARK/Kimera ）

6. 美国麻省理工学院海洋机器人组

研究方向：水下或陆地移动机器人导航与建图
实验室主页：https://marinerobotics.mit.edu/ （隶属于 MIT 计算机科学与人工智能实验室）
👦 John Leonard 教授：谷歌学术
发表论文汇总：https://marinerobotics.mit.edu/biblio
📜 面向物体的 SLAM：Finman R, Paull L, Leonard J J. Toward object-based place recognition in dense rgb-d maps[C]//ICRA Workshop Visual Place Recognition in Changing Environments, Seattle, WA. 2015.
📜 拓展 KinectFusion：Whelan T, Kaess M, Fallon M, et al. Kintinuous: Spatially extended kinectfusion[J]. 2012.
📜 语义 SLAM 概率数据关联：Doherty K, Fourie D, Leonard J. Multimodal semantic slam with probabilistic data association[C]//2019 international conference on robotics and automation (ICRA). IEEE, 2019: 2419-2425.

7. 美国明尼苏达大学多元自主机器人系统实验室

研究方向：视觉、激光、惯性导航系统，移动设备大规模三维建模与定位
实验室主页：http://mars.cs.umn.edu/index.php
发表论文汇总：http://mars.cs.umn.edu/publications.php
👦 Stergios I. Roumeliotis：个人主页，谷歌学术
📜 移动设备 VIO：Wu K, Ahmed A, Georgiou G A, et al. A Square Root Inverse Filter for Efficient Vision-aided Inertial Navigation on Mobile Devices[C]//Robotics: Science and Systems. 2015, 2.（项目主页：http://mars.cs.umn.edu/research/sriswf.php ）
📜 移动设备大规模三维半稠密建图：Guo C X, Sartipi K, DuToit R C, et al. Resource-aware large-scale cooperative three-dimensional mapping using multiple mobile devices[J]. IEEE Transactions on Robotics, 2018, 34(5): 1349-1369. （项目主页：http://mars.cs.umn.edu/research/semi_dense_mapping.php ）
📜 VIO 相关研究：http://mars.cs.umn.edu/research/vins_overview.php

8. 美国宾夕法尼亚大学 Vijay Kumar 实验室

研究方向：自主微型无人机
实验室主页：https://www.kumarrobotics.org/
发表论文：https://www.kumarrobotics.org/publications/
研究成果视频：https://www.youtube.com/user/KumarLabPenn/videos
📜 无人机半稠密 VIO：Liu W, Loianno G, Mohta K, et al. Semi-Dense Visual-Inertial Odometry and Mapping for Quadrotors with SWAP Constraints[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 1-6.
📜 语义数据关联：Liu X, Chen S W, Liu C, et al. Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 2296-2303.

9. Srikumar Ramalingam（美国犹他大学计算机学院）

研究方向：三维重构、语义分割、视觉 SLAM、图像定位、深度神经网络
👦 Srikumar Ramalingam：个人主页谷歌学术
📜 点面 SLAM：Taguchi Y, Jian Y D, Ramalingam S, et al. Point-plane SLAM for hand-held 3D sensors[C]//2013 IEEE international conference on robotics and automation. IEEE, 2013: 5182-5189.
📜 点线定位：Ramalingam S, Bouaziz S, Sturm P. Pose estimation using both points and lines for geo-localization[C]//2011 IEEE International Conference on Robotics and Automation. IEEE, 2011: 4716-4723.（视频）
📜 2D 3D 定位：Ataer-Cansizoglu E, Taguchi Y, Ramalingam S. Pinpoint SLAM: A hybrid of 2D and 3D simultaneous localization and mapping for RGB-D sensors[C]//2016 IEEE international conference on robotics and automation (ICRA). IEEE, 2016: 1300-1307.（视频）

10. Frank Dellaert（美国佐治亚理工学院机器人与智能机器研究中心）

研究方向：SLAM，图像时空重构
👦 个人主页，谷歌学术
📜 因子图：Dellaert F. Factor graphs and GTSAM: A hands-on introduction[R]. Georgia Institute of Technology, 2012. （GTSAM 代码：http://borg.cc.gatech.edu/ ）
📜 多机器人分布式 SLAM：Cunningham A, Wurm K M, Burgard W, et al. Fully distributed scalable smoothing and mapping with robust multi-robot data association[C]//2012 IEEE International Conference on Robotics and Automation. IEEE, 2012: 1093-1100.
📜 Choudhary S, Trevor A J B, Christensen H I, et al. SLAM with object discovery, modeling and mapping[C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2014: 1018-1025.

11. Patricio Vela （美国佐治亚理工学院智能视觉与自动化实验室）

研究方向：机器人控制、定位与导航
实验室主页：http://ivalab.gatech.edu/
👦 Patricio Vela 个人主页
👦 赵轶璞个人主页谷歌学术
📜 Zhao Y, Smith J S, Karumanchi S H, et al. Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy[J]. arXiv preprint arXiv:2003.01317, 2020.
📜 Zhao Y, Vela P A. Good feature selection for least squares pose optimization in VO/VSLAM[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 1183-1189.（代码：https://github.com/ivalab/FullResults_GoodFeature ）
📜 Zhao Y, Vela P A. Good line cutting: Towards accurate pose tracking of line-assisted VO/VSLAM[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 516-531. （代码：https://github.com/ivalab/GF_PL_SLAM ）

12. 加拿大蒙特利尔大学机器人与嵌入式 AI 实验室

研究方向：SLAM，不确定性建模
实验室主页：http://montrealrobotics.ca/
👦 Liam Paull 教授：个人主页谷歌学术
📜 Mu B, Liu S Y, Paull L, et al. Slam with objects using a nonparametric pose graph[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4602-4609.（代码：https://github.com/BeipengMu/objectSLAM）
📜 Murthy Jatavallabhula K, Iyer G, Paull L. gradSLAM: Dense SLAM meets Automatic Differentiation[J]. arXiv preprint arXiv:1910.10672, 2019.（代码：https://github.com/montrealrobotics/gradSLAM ）

13. 加拿大舍布鲁克大学智能、交互、综合、跨学科机器人实验室

研究方向：移动机器人软硬件设计
实验室主页：https://introlab.3it.usherbrooke.ca/
📜 激光视觉稠密重建：Labbé M, Michaud F. RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation[J]. Journal of Field Robotics, 2019, 36(2): 416-446.
- 代码：https://github.com/introlab/rtabmap
- 项目主页：http://introlab.github.io/rtabmap/

14. 瑞士苏黎世大学机器人与感知课题组

研究方向：移动机器人、无人机环境感知与导航，VISLAM，事件相机
实验室主页：http://rpg.ifi.uzh.ch/index.html
发表论文汇总：http://rpg.ifi.uzh.ch/publications.html
Github 代码公开地址：https://github.com/uzh-rpg
📜 Forster C, Pizzoli M, Scaramuzza D. SVO: Fast semi-direct monocular visual odometry[C]//2014 IEEE international conference on robotics and automation (ICRA). IEEE, 2014: 15-22.
📜 VO/VIO 轨迹评估工具 rpg_trajectory_evaluation：https://github.com/uzh-rpg/rpg_trajectory_evaluation
📜 事件相机项目主页：http://rpg.ifi.uzh.ch/research_dvs.html
👦 人物：Davide Scaramuzza 张子潮

15. 瑞士苏黎世联邦理工计算机视觉与几何实验室

研究方向：定位、三维重建、语义分割、机器人视觉
实验室主页：http://www.cvg.ethz.ch/index.php
发表论文：http://www.cvg.ethz.ch/publications/
📜 视觉语义里程计：Lianos K N, Schonberger J L, Pollefeys M, et al. Vso: Visual semantic odometry[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 234-250.
📜 视觉语义定位：CVPR 2018 Semantic visual localization
- 作者博士学位论文：2018 Robust Methods for Accurate and Efficient 3D Modeling from Unstructured Imagery
📜 大规模户外建图：Bârsan I A, Liu P, Pollefeys M, et al. Robust dense mapping for large-scale dynamic environments[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 7510-7517.
- 代码：https://github.com/AndreiBarsan/DynSLAM
- 作者博士学位论文：Barsan I A. Simultaneous localization and mapping in dynamic scenes[D]. ETH Zurich, Department of Computer Science, 2017.
👦 Marc Pollefeys：个人主页，谷歌学术
👦 Johannes L. Schönberger：个人主页，谷歌学术

16. 英国帝国理工学院戴森机器人实验室

研究方向：机器人视觉场景与物体理解、机器人操纵
实验室主页：https://www.imperial.ac.uk/dyson-robotics-lab/
发表论文：https://www.imperial.ac.uk/dyson-robotics-lab/publications/
代表性工作：MonoSLAM、CodeSLAM、ElasticFusion、KinectFusion
- 📜 ElasticFusion：Whelan T, Leutenegger S, Salas-Moreno R, et al. ElasticFusion: Dense SLAM without a pose graph[C]. Robotics: Science and Systems, 2015.（代码：https://github.com/mp3guy/ElasticFusion ）
- 📜 Semanticfusion：McCormac J, Handa A, Davison A, et al. Semanticfusion: Dense 3d semantic mapping with convolutional neural networks[C]//2017 IEEE International Conference on Robotics and automation (ICRA). IEEE, 2017: 4628-4635.（代码：https://github.com/seaun163/semanticfusion ）
- 📜 Code-SLAM：Bloesch M, Czarnowski J, Clark R, et al. CodeSLAM—learning a compact, optimisable representation for dense visual SLAM[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 2560-2568.
👦 Andrew Davison：谷歌学术

17. 英国牛津大学信息工程学

研究方向：SLAM、目标跟踪、运动结构、场景增强、移动机器人运动规划、导航与建图等等等
实验室主页：http://www.robots.ox.ac.uk/
- 主动视觉实验室：http://www.robots.ox.ac.uk/ActiveVision/
- 牛津机器人学院：https://ori.ox.ac.uk/
发表论文汇总：
- 主动视觉实验室：http://www.robots.ox.ac.uk/ActiveVision/Publications/index.html
- 机器人学院：https://ori.ox.ac.uk/publications/papers/
代表性工作：
- 📜 Klein G, Murray D. PTAM: Parallel tracking and mapping for small AR workspaces[C]//2007 6th IEEE and ACM international symposium on mixed and augmented reality. IEEE, 2007: 225-234.
- 📜 RobotCar 数据集：https://robotcar-dataset.robots.ox.ac.uk/
👦 人物（谷歌学术）：David Murray Maurice Fallon
部分博士学位论文可以在这里搜到：https://ora.ox.ac.uk/

18. 德国慕尼黑工业大学计算机视觉组

研究方向：三维重建、机器人视觉、深度学习、视觉 SLAM 等
实验室主页：https://vision.in.tum.de/research/vslam
发表论文汇总：https://vision.in.tum.de/publications
代表作：DSO、LDSO、LSD_SLAM、DVO_SLAM
- 📜 DSO：Engel J, Koltun V, Cremers D. Direct sparse odometry[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(3): 611-625.（代码：https://github.com/JakobEngel/dso ）
- 📜 LSD-SLAM： Engel J, Schöps T, Cremers D. LSD-SLAM: Large-scale direct monocular SLAM[C]//European conference on computer vision. Springer, Cham, 2014: 834-849.（代码：https://github.com/tum-vision/lsd_slam ）2.
Github 地址：https://github.com/tum-vision
👦 Daniel Cremers 教授：个人主页谷歌学术
👦 Jakob Engel（LSD-SLAM，DSO 作者）：个人主页谷歌学术

19. 德国马克斯普朗克智能系统研究所嵌入式视觉组

研究方向：智能体自主环境理解、导航与物体操纵
实验室主页：https://ev.is.tuebingen.mpg.de/
👦 负责人 Jörg Stückler（前 TUM 教授）：个人主页谷歌学术
📜 发表论文汇总：https://ev.is.tuebingen.mpg.de/publications
Kasyanov A, Engelmann F, Stückler J, et al. Keyframe-based visual-inertial online SLAM with relocalization[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 6662-6669.
📜 Strecke M, Stuckler J. EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Association[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 5865-5874.
📜 Usenko, V., Demmel, N., Schubert, D., Stückler, J., Cremers, D. Visual-Inertial Mapping with Non-Linear Factor Recovery IEEE Robotics and Automation Letters (RA-L), 5, 2020

20. 德国弗莱堡大学智能自主系统实验室

研究方向：多机器人导航与协作，环境建模与状态估计
实验室主页：http://ais.informatik.uni-freiburg.de/index_en.php
发表论文汇总：http://ais.informatik.uni-freiburg.de/publications/index_en.php （学位论文也可以在这里找到）
👦 Wolfram Burgard：谷歌学术
开放数据集：http://aisdatasets.informatik.uni-freiburg.de/
📜 RGB-D SLAM：Endres F, Hess J, Sturm J, et al. 3-D mapping with an RGB-D camera[J]. IEEE transactions on robotics, 2013, 30(1): 177-187.（代码：https://github.com/felixendres/rgbdslam_v2 ）
📜 跨季节的 SLAM：Naseer T, Ruhnke M, Stachniss C, et al. Robust visual SLAM across seasons[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015: 2529-2535.
📜 博士学位论文：Robust Graph-Based Localization and Mapping 2015
📜 博士学位论文：Discovering and Leveraging Deep Multimodal Structure for Reliable Robot Perception and Localization 2019
📜 博士学位论文：Robot Localization and Mapping in Dynamic Environments 2019

21. 西班牙萨拉戈萨大学机器人、感知与实时组 SLAM 实验室

研究方向：视觉 SLAM、物体 SLAM、非刚性 SLAM、机器人、增强现实
实验室主页：http://robots.unizar.es/slamlab/
发表论文：http://robots.unizar.es/slamlab/?extra=3 （论文好像没更新，可以访问下面实验室大佬的谷歌学术查看最新论文）
👦 J. M. M. Montiel：谷歌学术
📜 Mur-Artal R, Tardós J D. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras[J]. IEEE Transactions on Robotics, 2017, 33(5): 1255-1262.
Gálvez-López D, Salas M, Tardós J D, et al. Real-time monocular object slam[J]. Robotics and Autonomous Systems, 2016, 75: 435-449.
📜 Strasdat H, Montiel J M M, Davison A J. Real-time monocular SLAM: Why filter?[C]//2010 IEEE International Conference on Robotics and Automation. IEEE, 2010: 2657-2664.
📜 Zubizarreta J, Aguinaga I, Montiel J M M. Direct sparse mapping[J]. arXiv preprint arXiv:1904.06577, 2019.
- Elvira R, Tardós J D, Montiel J M M. ORBSLAM-Atlas: a robust and accurate multi-map system[J]. arXiv preprint arXiv:1908.11585, 2019.

22. 西班牙马拉加大学机器感知与智能机器人课题组

研究方向：自主机器人、人工嗅觉、计算机视觉
实验室主页：http://mapir.uma.es/mapirwebsite/index.php/topics-2.html
发表论文汇总：http://mapir.isa.uma.es/mapirwebsite/index.php/publications-menu-home.html
📜 Gomez-Ojeda R, Moreno F A, Zuñiga-Noël D, et al. PL-SLAM: a stereo SLAM system through the combination of points and line segments[J]. IEEE Transactions on Robotics, 2019, 35(3): 734-746.（代码：https://github.com/rubengooj/pl-slam ）
👦 Francisco-Angel Moreno
👦 Ruben Gomez-Ojeda 点线 SLAM
- 📜 Gomez-Ojeda R, Briales J, Gonzalez-Jimenez J. PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments[C]//Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016: 4211-4216.（代码：https://github.com/rubengooj/pl-svo ）
- 📜 Gomez-Ojeda R, Gonzalez-Jimenez J. Robust stereo visual odometry through a probabilistic combination of points and line segments[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 2521-2526.（代码：https://github.com/rubengooj/stvo-pl ）
- 📜 Gomez-Ojeda R, Zuñiga-Noël D, Moreno F A, et al. PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments[J]. arXiv preprint arXiv:1705.09479, 2017.（代码：https://github.com/rubengooj/pl-slam ）

23. Alejo Concha（Oculus VR，西班牙萨拉戈萨大学）

研究方向：SLAM，单目稠密重建，传感器融合
👦 个人主页：https://sites.google.com/view/alejoconcha/ 谷歌学术
Github：https://github.com/alejocb
📜 IROS 2015 单目平面重建：DPPTAM: Dense piecewise planar tracking and mapping from a monocular sequence （代码：https://github.com/alejocb/dpptam ）
📜 IROS 2017 开源 RGB-D SLAM：RGBDTAM: A Cost-Effective and Accurate RGB-D Tracking and Mapping System（代码：https://github.com/alejocb/rgbdtam ）
📜 ICRA 2016：Visual-inertial direct SLAM
📜 ICRA 2014：Using Superpixels in Monocular SLAM
RSS 2014：Manhattan and Piecewise-Planar Constraints for Dense Monocular Mapping

24. 奥地利格拉茨技术大学计算机图形学与视觉研究所

研究方向：AR/VR，机器人视觉，机器学习，目标识别与三维重建
实验室主页：https://www.tugraz.at/institutes/icg/home/
👦 Friedrich Fraundorfer 教授：团队主页谷歌学术
- 📜 Visual Odometry: Part I The First 30 Years and Fundamentals
- 📜 Visual Odometry: Part II: Matching, Robustness, Optimization, and Applications
- 📜 Schenk F, Fraundorfer F. RESLAM: A real-time robust edge-based SLAM system[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 154-160.（代码：https://github.com/fabianschenk/RESLAM ）
👦 Dieter Schmalstieg 教授：团队主页谷歌学术
- 📜 教科书：Augmented Reality: Principles and Practice
- 📜 Arth C, Pirchheim C, Ventura J, et al. Instant outdoor localization and slam initialization from 2.5 d maps[J]. IEEE transactions on visualization and computer graphics, 2015, 21(11): 1309-1318.
- 📜 Hachiuma R, Pirchheim C, Schmalstieg D, et al. DetectFusion: Detecting and Segmenting Both Known and Unknown Dynamic Objects in Real-time SLAM[J]. arXiv preprint arXiv:1907.09127, 2019.

25. 波兰波兹南工业大学移动机器人实验室

研究方向：SLAM，机器人运动规划，控制
实验室主页：http://lrm.put.poznan.pl/
Github 主页：https://github.com/LRMPUT
📜 Wietrzykowski J. On the representation of planes for efficient graph-based slam with high-level features[J]. Journal of Automation Mobile Robotics and Intelligent Systems, 2016, 10.（代码：https://github.com/LRMPUT/PlaneSLAM ）
📜 Wietrzykowski J, Skrzypczyński P. PlaneLoc: Probabilistic global localization in 3-D using local planar features[J]. Robotics and Autonomous Systems, 2019.（代码：https://github.com/LRMPUT/PlaneLoc ）
📜 PUTSLAM：http://lrm.put.poznan.pl/putslam/

26. Alexander Vakhitov（三星莫斯科 AI 中心）

研究方向：SLAM，几何视觉
👦 个人主页：https://alexandervakhitov.github.io/ ，谷歌学术
📜 点线 SLAM：ICRA 2017 PL-SLAM: Real-time monocular visual SLAM with points and lines
📜 点线定位：Pumarola A, Vakhitov A, Agudo A, et al. Relative localization for aerial manipulation with PL-SLAM[M]//Aerial Robotic Manipulation. Springer, Cham, 2019: 239-248.
📜 学习型线段：IEEE Access 2019 Learnable line segment descriptor for visual SLAM（代码：https://github.com/alexandervakhitov/lld-slam ）

27. 澳大利亚昆士兰科技大学机器人技术中心

研究方向：脑启发式机器人，采矿机器人，机器人视觉
实验室主页：https://www.qut.edu.au/research/centre-for-robotics
开源代码：https://research.qut.edu.au/qcr/open-source-code/
👦 Niko Sünderhauf：个人主页，谷歌学术
- 📜 RA-L 2018 二次曲面 SLAM：QuadricSLAM: Dual quadrics from object detections as landmarks in object-oriented SLAM
- 📜 Nicholson L, Milford M, Sunderhauf N. QuadricSLAM: Dual quadrics as SLAM landmarks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2018: 313-314.
- 📜 Semantic SLAM 项目主页：http://www.semanticslam.ai/
- 📜 IROS 2017：Meaningful maps with object-oriented semantic mapping
👦 Michael Milford：谷歌学术 https://scholar.google.com/citations?user=TDSmCKgAAAAJ&hl=zh-CN&oi=ao
- 📜 ICRA 2012：SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights （代码：https://michaelmilford.com/seqslam/）
- 📜 Ball D, Heath S, Wiles J, et al. OpenRatSLAM: an open source brain-based SLAM system[J]. Autonomous Robots, 2013, 34(3): 149-176.（代码：https://openslam-org.github.io/openratslam.html ）
- 📜 Yu F, Shang J, Hu Y, et al. NeuroSLAM: a brain-inspired SLAM system for 3D environments[J]. Biological Cybernetics, 2019, 113(5-6): 515-545. （代码：https://github.com/cognav/NeuroSLAM ）

28. 澳大利亚机器人视觉中心

研究方向：机器人感知、理解与学习（集合了昆士兰科技大学，澳大利亚国立大学，阿德莱德大学，昆士兰大学等学校机器人领域的研究者）
实验室主页：https://www.roboticvision.org/
人物：https://www.roboticvision.org/rv_person_category/researchers/
发表论文汇总：https://www.roboticvision.org/publications/scientific-publications/
👦 Yasir Latif：个人主页，谷歌学术
- 📜 Latif Y, Cadena C, Neira J. Robust loop closing over time for pose graph SLAM[J]. The International Journal of Robotics Research, 2013, 32(14): 1611-1626.
- 📜 Latif Y, Cadena C, Neira J. Robust loop closing over time[C]//Proc. Robotics: Science Systems. 2013: 233-240.（代码：https://github.com/ylatif/rrr ）
👦 Ian D Reid：谷歌学术：https://scholar.google.com/citations?user=ATkNLcQAAAAJ&hl=zh-CN&oi=sra
- 📜 ICRA 2019：Real-time monocular object-model aware sparse SLAM
- 📜 Reid I. Towards semantic visual SLAM[C]//2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014: 1-1.

29. 日本国立先进工业科学技术研究所

人工智能研究中心：https://www.airc.aist.go.jp/en/intro/
👦 Ken Sakurada：个人主页，谷歌学术
- 📜 Sumikura S, Shibuya M, Sakurada K. OpenVSLAM: A Versatile Visual SLAM Framework[C]//Proceedings of the 27th ACM International Conference on Multimedia. 2019: 2292-2295.（代码：https://github.com/xdspacelab/openvslam ）
👦 Shuji Oishi：谷歌学术
- 📜 极稠密特征点建图：Yokozuka M, Oishi S, Thompson S, et al. VITAMIN-E: visual tracking and MappINg with extremely dense feature points[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2019: 9641-9650.
- 📜 Oishi S, Inoue Y, Miura J, et al. SeqSLAM++: View-based robot localization and navigation[J]. Robotics and Autonomous Systems, 2019, 112: 13-21.

30. Pyojin Kim（韩国首尔大学自主机器人实验室）

研究方向：视觉里程计，定位，AR/VR
👦 个人主页，谷歌学术
📜 平面 SLAM：ECCV 2018：Linear RGB-D SLAM for planar environments
📜 光照变化下的鲁棒 SLAM：ICRA 2017：Robust visual localization in changing lighting conditions
📜 线面 SLAM：CVPR 2018：Indoor RGB-D Compass from a Single Line and Plane
📜 博士学位论文：Low-Drift Visual Odometry for Indoor Robotics

31. 香港科技大学空中机器人实验室

研究方向：空中机器人在复杂环境下的自主运行，包括状态估计、建图、运动规划、多机器人协同以及低成本传感器和计算组件的实验平台开发。
实验室主页：http://uav.ust.hk/
发表论文：http://uav.ust.hk/publications/
👦 沈邵劼教授谷歌学术
代码公开地址：https://github.com/HKUST-Aerial-Robotics
📜 Qin T, Li P, Shen S. Vins-mono: A robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020.（代码：https://github.com/HKUST-Aerial-Robotics/VINS-Mono ）
📜 Wang K, Gao F, Shen S. Real-time scalable dense surfel mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 6919-6925.（代码：https://github.com/HKUST-Aerial-Robotics/DenseSurfelMapping ）

32. 香港科技大学机器人与多感知实验室 RAM-LAB

研究方向：无人车；无人船；室内定位；机器学习。
实验室主页：https://www.ram-lab.com/
发表论文：https://www.ram-lab.com/publication/
👦 刘明教授谷歌学术
📜 Ye H, Chen Y, Liu M. Tightly coupled 3d lidar inertial odometry and mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 3144-3150.（代码：https://github.com/hyye/lio-mapping ）
📜 Zhang J, Tai L, Boedecker J, et al. Neural slam: Learning to explore with external memory[J]. arXiv preprint arXiv:1706.09520, 2017.

33. 香港中文大学天石机器人实验室

研究方向：工业、物流、手术机器人，三维影像，机器学习
实验室主页：http://ri.cuhk.edu.hk/
👦 刘云辉教授：http://ri.cuhk.edu.hk/yhliu
👦 李浩昂：个人主页，谷歌学术
- 📜 Li H, Yao J, Bazin J C, et al. A monocular SLAM system leveraging structural regularity in Manhattan world[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 2518-2525.
- 📜 Li H, Yao J, Lu X, et al. Combining points and lines for camera pose estimation and optimization in monocular visual odometry[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 1289-1296.
- 📜 消失点检测：Lu X, Yaoy J, Li H, et al. 2-Line Exhaustive Searching for Real-Time Vanishing Point Estimation in Manhattan World[C]//Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017: 345-353.（代码：https://github.com/xiaohulugo/VanishingPointDetection ）
👦 郑帆：个人主页，谷歌学术
- 📜 Zheng F, Tang H, Liu Y H. Odometry-vision-based ground vehicle motion estimation with se (2)-constrained se (3) poses[J]. IEEE transactions on cybernetics, 2018, 49(7): 2652-2663.（代码：https://github.com/izhengfan/se2clam ）
- 📜 Zheng F, Liu Y H. Visual-Odometric Localization and Mapping for Ground Vehicles Using SE (2)-XYZ Constraints[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 3556-3562.（代码：https://github.com/izhengfan/se2lam ）

34. 浙江大学 CAD&CG 国家重点实验室

研究方向：SFM/SLAM，三维重建，增强现实
实验室主页：http://www.zjucvg.net/
Github 代码地址：https://github.com/zju3dv
👦 章国峰教授：个人主页，谷歌学术
📜 ICE-BA：Liu H, Chen M, Zhang G, et al. Ice-ba: Incremental, consistent and efficient bundle adjustment for visual-inertial slam[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 1974-1982.（代码：https://github.com/zju3dv/EIBA ）
📜 RK-SLAM：Liu H, Zhang G, Bao H. Robust keyframe-based monocular SLAM for augmented reality[C]//2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2016: 1-10.（项目主页：http://www.zjucvg.net/rkslam/rkslam.html ）
📜 RD-SLAM：Tan W, Liu H, Dong Z, et al. Robust monocular SLAM in dynamic environments[C]//2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2013: 209-218.

35. 邹丹平（上海交通大学）

研究方向：视觉 SLAM，SFM，多源导航，微型无人机
👦 个人主页：http://drone.sjtu.edu.cn/dpzou/index.php ，谷歌学术
📜 Co-SLAM：Zou D, Tan P. Coslam: Collaborative visual slam in dynamic environments[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 35(2): 354-366.（代码：https://github.com/danping/CoSLAM ）
📜 StructSLAM：Zhou H, Zou D, Pei L, et al. StructSLAM: Visual SLAM with building structure lines[J]. IEEE Transactions on Vehicular Technology, 2015, 64(4): 1364-1375.（项目主页：http://drone.sjtu.edu.cn/dpzou/project/structslam.php ）
📜 StructVIO：Zou D, Wu Y, Pei L, et al. StructVIO: visual-inertial odometry with structural regularity of man-made environments[J]. IEEE Transactions on Robotics, 2019, 35(4): 999-1013.

36. 布树辉教授（西北工业大学智能系统实验室）

研究方向：语义定位与建图、SLAM、在线学习与增量学习
👦 个人主页：http://www.adv-ci.com/blog/ 谷歌学术
布老师的课件：http://www.adv-ci.com/blog/course/
实验室 2018 年暑期培训资料：https://github.com/zdzhaoyong/SummerCamp2018
📜 开源的通用 SLAM 框架：Zhao Y, Xu S, Bu S, et al. GSLAM: A general SLAM framework and benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1110-1120.（代码：https://github.com/zdzhaoyong/GSLAM ）
📜 Bu S, Zhao Y, Wan G, et al. Map2DFusion: Real-time incremental UAV image mosaicing based on monocular slam[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4564-4571.（代码：https://github.com/zdzhaoyong/Map2DFusion ）
📜 Wang W, Zhao Y, Han P, et al. TerrainFusion: Real-time Digital Surface Model Reconstruction based on Monocular SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 7895-7902.

+1 Cyrill Stachniss（德国波恩大学摄影测量与机器人实验室）

研究方向：概率机器人、SLAM、自主导航、视觉激光感知、场景分析与分配、无人飞行器
实验室主页：https://www.ipb.uni-bonn.de/
👦 个人主页：https://www.ipb.uni-bonn.de/people/cyrill-stachniss/ 谷歌学术
发表论文：https://www.ipb.uni-bonn.de/publications/
开源代码：https://github.com/PRBonn
📜 IROS 2019 激光语义 SLAM：Chen X, Milioto A, Palazzolo E, et al. SuMa++: Efficient LiDAR-based semantic SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 4530-4537.（代码：https://github.com/PRBonn/semantic_suma/ ）
Cyrill Stachniss 教授 SLAM 公开课：youtube ； bilibili
波恩大学另外一个智能自主系统实验室：http://www.ais.uni-bonn.de/research.html

+1 上海科技大学

Mobile Perception Lab：http://mpl.sist.shanghaitech.edu.cn/
👦 Laurent Kneip：个人主页；谷歌学术
📜 Zhou Y, Li H, Kneip L. Canny-vo: Visual odometry with rgb-d cameras based on geometric 3-d–2-d edge alignment[J]. IEEE Transactions on Robotics, 2018, 35(1): 184-199.
自主移动机器人实验室：https://robotics.shanghaitech.edu.cn/zh
👦 Sören Schwertfeger：个人主页；谷歌学术
📜 Shan Z, Li R, Schwertfeger S. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots[J]. Sensors, 2019, 19(10): 2251.（代码：https://github.com/STAR-Center/VINS-RGBD ）

+1 美国密歇根大学机器人研究所

学院官网：https://robotics.umich.edu/
研究方向：https://robotics.umich.edu/research/focus-areas/
感知机器人实验室（PeRL）
- 实验室主页：http://robots.engin.umich.edu/About/
- 👦 Ryan M. Eustice 谷歌学术
- 📜 激光雷达数据集 Pandey G, McBride J R, Eustice R M. Ford campus vision and lidar data set[J]. The International Journal of Robotics Research, 2011, 30(13): 1543-1552. | 数据集
APRIL robotics lab
- 实验室主页：https://april.eecs.umich.edu/
- 👦 Edwin Olson 个人主页 | 谷歌学术
- 📜 Olson E. AprilTag: A robust and flexible visual fiducial system[C]//2011 IEEE International Conference on Robotics and Automation. IEEE, 2011: 3400-3407. | 代码
- 📜 Wang X, Marcotte R, Ferrer G, et al. ApriISAM: Real-time smoothing and mapping[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 2486-2493. | 代码

+1 瑞士苏黎世联邦理工自主系统实验室

研究方向：复杂多样环境中自主运行的机器人和智能系统
实验室主页：https://asl.ethz.ch/
发表论文：https://asl.ethz.ch/publications-and-sources/publications.html
youtube | Github
👦 Cesar Cadena 个人主页
📜 Schneider T, Dymczyk M, Fehr M, et al. maplab: An open framework for research in visual-inertial mapping and localization[J]. IEEE Robotics and Automation Letters, 2018, 3(3): 1418-1425. | 代码
📜 Dubé R, Cramariuc A, Dugas D, et al. SegMap: 3d segment mapping using data-driven descriptors[J]. arXiv preprint arXiv:1804.09557, 2018. | 代码
📜 Millane A, Taylor Z, Oleynikova H, et al. C-blox: A scalable and consistent tsdf-based dense mapping approach[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 995-1002. | 代码

+1 美国麻省理工学院 Robust Robotics Group

研究方向：MAV 导航与控制；人机交互的自然语言理解；自主海洋机器人的语义理解
实验室主页：http://groups.csail.mit.edu/rrg/index.php
👦 Nicholas Roy：Google Scholar
📜 Greene W N, Ok K, Lommel P, et al. Multi-level mapping: Real-time dense monocular SLAM[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 833-840. video
📜 ICRA 2020 Metrically-Scaled Monocular SLAM using Learned Scale Factors." International Conference on Robotics and Automation | video
📜 ICRA 2019 Robust Object-based SLAM for High-speed Autonomous Navigation

+1 瑞士苏黎世联邦理工 Vision for Robotics Lab

研究方向：机器人视觉，无人机，自主导航，多机器人协同
实验室主页：https://v4rl.ethz.ch/the-group.html
👦 Margarita Chli：个人主页 | Google Scholar
📜 Schmuck P, Chli M. CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams[J]. Journal of Field Robotics, 2019, 36(4): 763-781. code | video
📜 Bartolomei L, Karrer M, Chli M. Multi-robot Coordination with Agent-Server Architecture for Autonomous Navigation in Partially Unknown Environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)(virtual). 2020. code | video
📜 Schmuck P, Chli M. Multi-uav collaborative monocular slam[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 3863-3870.

+1 谢立华教授（南洋理工大学）

研究方向：控制，多智能体，定位
个人主页：https://personal.ntu.edu.sg/elhxie/research.html | Google Scholar
👦 Wang Han：个人主页 | Github
📜 Wang H, Wang C, Xie L. Intensity scan context: Coding intensity and geometry relations for loop closure detection[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 2095-2101. | Code
📜 Wang H, Wang C, Xie L. Lightweight 3-D Localization and Mapping for Solid-State LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1801-1807. | Code
📜 Wang C, Yuan J, Xie L. Non-iterative SLAM[C]//2017 18th International Conference on Advanced Robotics (ICAR). IEEE, 2017: 83-90.

3. SLAM 学习资料

这一部分的内容不太完整，陆续丰富，欢迎补充

3.1 国内资料

1) SLAMcn：http://www.slamcn.org/index.php/
2) SLAM 最新研究更新 Recent_SLAM_Research ：https://github.com/YiChenCityU/Recent_SLAM_Research
3) 西北工大智能系统实验室 SLAM 培训：https://github.com/zdzhaoyong/SummerCamp2018
- 布树辉老师课件：http://www.adv-ci.com/blog/course/
4) IROS 2019 视觉惯导导航的挑战与应用研讨会：http://udel.edu/~ghuang/iros19-vins-workshop/index.html
5) 泡泡机器人 VIO 相关资料：https://github.com/PaoPaoRobot/Awesome-VIO
6) 崔华坤：主流 VIO 论文推导及代码解析：https://github.com/StevenCui/VIO-Doc
7) 李言：SLAM 中的几何与学习方法
8) 黄山老师状态估计视频：bilibili
9) 谭平老师-SLAM 6小时课程：bilibili
10) 2020 年 SLAM 技术及应用暑期学校：视频-bilibili | 课件

3.2 国外资料

1) 事件相机相关研究与发展：https://github.com/uzh-rpg/event-based_vision_resources
2) 加州大学圣地亚哥分校语境机器人研究所 Nikolay Atanasov 教授机器人状态估计与感知课程 ppt：https://natanaso.github.io/ece276a2019/schedule.html
3) 波恩大学 Mobile Sensing and Robotics Course 公开课：youtube ，bilibili

3.3 公众号

泡泡机器人 SLAM：paopaorobot_slam

3.4 代码注释

今天（2020.04.25）刚想到的一个点，就算前面整理了大量的开源工作，但是看原版的代码还是会有很大的困难，感谢国内 SLAM 爱好者的将自己的代码注释分享出来，促进交流，共同进步。这一小节的内容陆续发掘，期待大家的推荐代码注释（可以在 issue 中留言）。

3.5 数据集

泡泡机器人 - SLAM 数据集合集
计算机视觉life - SLAM、重建、语义相关数据集大全
水下 SLAM 相关研究 - 代码、数据集

4. 近期论文（持续更新）

2021 年 6 月论文更新（20 篇）

本期更新于 2021 年 7 月 5 日
共 20 篇论文，其中 7 项（待）开源工作
[4,9,13,14,16,17] LiDAR 相关
[1,2,6,7] Mapping

1. Geometric SLAM

[1] Bokovoy A, Muravyev K, Yakovlev K. MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment[J]. arXiv preprint arXiv:2105.14994, 2021.
- 用于视觉 SLAM 和地图合并质量评估的逼真基准
- 俄罗斯科学院；开源数据集
[2] Demmel N, Sommer C, Cremers D, et al. Square Root Bundle Adjustment for Large-Scale Reconstruction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 11723-11732.
- 大规模重建的平方根 BA
- TUM；代码开源
[3] Chen Y, Zhao L, Zhang Y, et al. Anchor Selection for SLAM Based on Graph Topology and Submodular Optimization[J]. IEEE Transactions on Robotics, 2021.
- 基于图拓扑和子模块优化的SLAM锚点选择
- 悉尼科技大学
[4] Zhou L, Koppel D, Kaess M. LiDAR SLAM with Plane Adjustment for Indoor Environment[J]. IEEE Robotics and Automation Letters, 2021.
- 室内环境中平面调整的 LiDAR SLAM
- Magic Leap，CMU
[5] Liu D, Parra A, Chin T J. Spatiotemporal Registration for Event-based Visual Odometry[J]. arXiv preprint arXiv:2103.05955, 2021.
- 基于事件的视觉里程计的时空配准
- 阿德莱德大学；开源数据集（待公开）

2. Semantic / Deep SLAM / Mapping

[6] Wimbauer F, Yang N, von Stumberg L, et al. MonoRec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 6112-6122.
- MonoRec: 动态环境中从单个移动相机中进行半监督稠密重建
- TUM；项目主页；代码开源
[7] Qin T, Zheng Y, Chen T, et al. RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving[J]. arXiv preprint arXiv:2106.02527, 2021.（ICRA2021）
- 一种用于自动驾驶视觉定位的轻量级语义地图
- 华为
[8] Tschopp F, Nieto J, Siegwart R Y, et al. Superquadric Object Representation for Optimization-based Semantic SLAM[J]. 2021.
- 基于优化的语义 SLAM 的超二次曲面的物体表示
- ETH；Microsoft
[9] Miller I D, Cowley A, Konkimalla R, et al. Any Way You Look at It: Semantic Crossview Localization and Mapping With LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 2397-2404.
- 以任何方式看待它：使用 LiDAR 进行语义交叉视图的定位和建图
- 宾夕法尼亚大学；代码开源
[10] Lu Y, Xu X, Ding M, et al. A Global Occlusion-Aware Approach to Self-Supervised Monocular Visual Odometry[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(3): 2260-2268.
- 自监督单目视觉里程计的全局遮挡感知方法
- 中国人民大学
[11] Li S, Liu S, Zhao Q, et al. Quantized Self-supervised Local Feature for Real-time Robot Indirect VSLAM[J]. IEEE/ASME Transactions on Mechatronics, 2021.
- 实时机器人间接 VSLAM 的量化自监督局部特征
- 上海交大；期刊：中科院二区，JCR Q1，IF 5.3

3. Sensor Fusion

[12] Seiskari O, Rantalankila P, Kannala J, et al. HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry[J]. arXiv preprint arXiv:2106.11857, 2021.
- HybVIO: 突破实时视觉惯性里程计的极限
- Spectacular AI，阿尔托大学，坦佩雷大学
[13] Li L, Kong X, Zhao X, et al. SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure[J]. arXiv preprint arXiv:2106.11516, 2021. （ICRA2021）
- 具有闭环的语义辅助的 LiDAR SLAM
- 浙大
[14] Li K, Ouyang Z, Hu L, et al. Robust SRIF-based LiDAR-IMU Localization for Autonomous Vehicles[J]. 2021. （ICRA2021）
- 用于自动驾驶汽车的鲁棒的基于 SRIF 的 LiDAR-IMU 定位
- 上海科技大学
[15] Kumar H, Payne J J, Travers M, et al. Periodic SLAM: Using Cyclic Constraints to Improve the Performance of Visual-Inertial SLAM on Legged Robots[J].
- 周期性 SLAM：使用循环约束提高腿式机器人视觉惯性 SLAM 的性能
[16] Zhou P, Guo X, Pei X, et al. T-LOAM: Truncated Least Squares LiDAR-Only Odometry and Mapping in Real Time[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021.
- 截断最小二乘法的 LiDAR 实时里程计和建图
- 武汉理工
[17] Jia Y, Luo H, Zhao F, et al. Lvio-Fusion: A Self-adaptive Multi-sensor Fusion SLAM Framework Using Actor-critic Method[J]. arXiv preprint arXiv:2106.06783, 2021.
- Lvio-Fusion：使用Actor-critic方法的自适应多传感器融合SLAM框架
- 北邮，中科院计算机所；代码开源

4. Others

[18] Huang R, Fang C, Qiu K, et al. AR Mapping: Accurate and Efficient Mapping for Augmented Reality[J]. arXiv preprint arXiv:2103.14846, 2021.
- AR Mapping：用于增强现实的准确高效建图
- 阿里巴巴
[19] Kim A, Ošep A, Leal-Taixé L. EagerMOT: 3D Multi-Object Tracking via Sensor Fusion[J]. arXiv preprint arXiv:2104.14682, 2021. (ICRA2021)
- EagerMOT：通过传感器融合进行 3D 多目标跟踪
- TUM；代码开源
[20] Wang J, Zhong Y, Dai Y, et al. Deep Two-View Structure-from-Motion Revisited[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 8953-8962.
- 重新审视两视图 SFM
- 澳大利亚国立、西工大、NVIDIA

2021 年 5 月论文更新（20 篇）

本期更新于 2021 年 6 月 13 日
共 20 篇论文，其中 6 项（待）开源工作
[2] SLAM 中的隐私保护问题近年来受到关注
[4,5,6] 基于线的 SLAM/SFM

1. Geometric SLAM

[1] Geppert M, Larsson V, Speciale P, et al. Privacy Preserving Localization and Mapping from Uncalibrated Cameras[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 1809-1819.
- 未校准相机的隐私保护定位和建图
- ETH, Microsift
[2] Hermann M, Ruf B, Weinmann M. Real-time dense 3D Reconstruction from monocular video data captured by low-cost UAVs[J]. arXiv preprint arXiv:2104.10515, 2021.
- 低成本无人机捕获的单目视频数据实时实时稠密重建
- KIT
[3] Zhang J, Zhu C, Zheng L, et al. ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion[J]. arXiv preprint arXiv:2105.05600, 2021. (SIGGRAPH 2021)
- 快速相机运动下在线稠密重建的随机优化
- 国防科大
[4] Xu B, Wang P, He Y, et al. Leveraging Structural Information to Improve Point Line Visual-Inertial Odometry[J]. arXiv preprint arXiv:2105.04064, 2021.
- 利用结构信息改进点线 VIO
- 武汉大学；东北大学；代码开源
[5] Liu Z, Shi D, Li R, et al. PLC-VIO: Visual-Inertial Odometry Based on Point-Line Constraints[J]. IEEE Transactions on Automation Science and Engineering, 2021.
- PLC-VIO：基于点线约束的 VIO
- 国防科大
[6] Mateus A, Tahri O, Aguiar A P, et al. On Incremental Structure from Motion Using Lines[J]. IEEE Transactions on Robotics, 2021.
- 使用线的增量式 SFM
- 里斯本大学
[7] Patel M, Bandopadhyay A, Ahmad A. Collaborative Mapping of Archaeological Sites using multiple UAVs[J]. arXiv preprint arXiv:2105.07644, 2021.
- 使用多无人机协同绘制考古遗址
- 印度理工学院；数据集&项目主页
[8] Chiu C Y, Sastry S S. Simultaneous Localization and Mapping: A Rapprochement of Filtering and Optimization-Based Approaches[J]. 2021.
- SLAM：滤波和优化方法的结合
- 加州大学伯克利分校硕士学位论文

2. Semantic / Deep SLAM / Mapping

[9] Karkus P, Cai S, Hsu D. Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 2815-2825.
- 可微 SLAM 网络：用于视觉导航的学习型粒子 SLAM
- 新加坡国立大学；项目主页
[10] Ok K, Liu K, Roy N. Hierarchical Object Map Estimation for Efficient and Robust Navigation[C]//Proc. ICRA. 2021
- 用于高效鲁棒导航的层次化物体地图估计
- MIT
[11] Adu-Bredu A, Del Coro N, Liu T, et al. GODSAC: Graph Optimized DSAC for Robot Relocalization**[J]. arXiv preprint arXiv:2105.00546, 2021.
- 用于机器人重定位的图形优化 DSAC*
- 密歇根大学；代码开源
[12] Xu D, Vedaldi A, Henriques J F. Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes[J]. arXiv preprint arXiv:2105.02195, 2021.
- Moving SLAM：非刚性场景中的完全无监督深度学习
- 港科，牛津大学
[13] Ma J, Ye X, Zhou H, et al. Loop-Closure Detection Using Local Relative Orientation Matching[J]. IEEE Transactions on Intelligent Transportation Systems, 2021.
- 使用局部相对方向匹配的闭环检测
- 武汉大学
[14] Çatal O, Jansen W, Verbelen T, et al. LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping[J]. arXiv preprint arXiv:2105.03265, 2021.
- LatentSLAM：用于定位和建图的无监督多传感器表示学习
- 根特大学，安特卫普大学；数据集

3. Sensor Fusion

[15] Zhao S, Zhang H, Wang P, et al. Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments[J]. arXiv preprint arXiv:2104.14938, 2021.
- 超级里程计：用于挑战性环境的以 IMU 为中心的 LiDAR-Visual-Inertial 状态估计器
- CMU
[16] Nguyen T M, Yuan S, Cao M, et al. VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM[J]. arXiv preprint arXiv:2105.03296, 2021.
- VIRAL SLAM：紧耦合相机-IMU-UWB-Lidar SLAM
- 南洋理工大学
[17] Nguyen T M, Yuan S, Cao M, et al. MILIOM: Tightly Coupled Multi-Input Lidar-Inertia Odometry and Mapping[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 5573-5580.
- MILIOM：紧耦合多源激光雷达-惯性里程计和建图
- 南洋理工大学

4. Others

[18] Sakaridis C, Dai D, Van Gool L. ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding[J]. arXiv preprint arXiv:2104.13395, 2021.
- ACDC: 用于语义驾驶场景理解的具有对应关系的不利条件数据集
- ETH；数据集和 benchmark
[19] Huang Z, Zhou H, Li Y, et al. VS-Net: Voting with Segmentation for Visual Localization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 6101-6111.
- VS-Net：通过分割投票进行视觉定位
- 浙大，港中文；代码开源
[20] Kim A, Ošep A, Leal-Taixé L. EagerMOT: 3D Multi-Object Tracking via Sensor Fusion[J]. arXiv preprint arXiv:2104.14682, 2021. (ICRA2021)
- EagerMOT：通过传感器融合进行 3D 多目标跟踪
- TUM；代码开源

2021 年 4 月论文更新（20 篇）

本期更新于 2021 年 5 月 11 日
共 20 篇论文，其中 5 篇来自于 CVPR2021，7 项开源工作
[7, 8, 9] Event-based
[10] VDO-SLAM 作者博士学位论文
[3, 4, 5, 17] 线、平面
[14] NeuralRecon

1. Geometric SLAM

[1] Jang Y, Oh C, Lee Y, et al. Multirobot Collaborative Monocular SLAM Utilizing Rendezvous[J]. IEEE Transactions on Robotics, 2021.
- 利用集合点的多机器人协作单目 SLAM
- 首尔大学
[2] Luo H, Pape C, Reithmeier E. Hybrid Monocular SLAM Using Double Window Optimization[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 4899-4906.
- 使用双窗口优化的混合单目 SLAM
- 汉诺威大学
[3] Yunus R, Li Y, Tombari F. ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames[J]. arXiv preprint arXiv:2103.15068, 2021.
- ManhattanSLAM：利用混合曼哈顿世界的鲁棒平面跟踪与建图
- TUM
[4] Wang Q, Yan Z, Wang J, et al. Line Flow Based Simultaneous Localization and Mapping[J]. IEEE Transactions on Robotics, 2021.
- 基于线流的 SLAM
- 北大（去年的 9 月的 Preprint）
[5] Vakhitov A, Ferraz L, Agudo A, et al. Uncertainty-Aware Camera Pose Estimation From Points and Lines[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 4659-4668.
- 基于点线不确定性感知的相机位姿估计
- SLAMCore；代码开源
[6] Liu D, Parra A, Chin T J. Spatiotemporal Registration for Event-based Visual Odometry[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 4937-4946.
- 基于事件的视觉里程计的时空配准
- 阿德莱德大学
[7] Min Z, Dunn E. Jiao J, Huang H, Li L, et al. Comparing Representations in Tracking for Event Camera-based SLAM[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 1369-1376.
- 比较基于事件相机 SLAM 在跟踪中的表示
- 港科，港大；代码开源
[8] Zhou Y, Gallego G, Shen S. Event-based stereo visual odometry[J]. IEEE Transactions on Robotics, 2021.
- 基于事件的双目视觉里程计
- 港科、柏林工业大学；代码开源,（去年的 8 月的 Preprint）
[9] Min Z, Dunn E. VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough[J]. arXiv preprint arXiv:2104.06800, 2021.
- VOLDOR-SLAM: 当基于特征或直接法不够好时
- 史蒂文斯理工学院；代码开源
[10] Ila V, Henein M, Li H. Meta Information In Graph-based Simultaneous Localisation And Mapping[J]. 2020
- 基于图的 SLAM 中的元信息
- 澳大利亚国立大学 Mina Henein 博士学位论文
- VDO-SLAM: A Visual Dynamic Object-aware SLAM System.
- Dynamic SLAM: The Need for Speed.

2. Semantic / Deep SLAM / Mapping

[11] Li S, Wu X, Cao Y, et al. Generalizing to the Open World: Deep Visual Odometry with Online Adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 13184-13193.
- 推广到开放世界：具有在线适应功能的深度视觉里程计
- 北京大学
- Li S, Wang X, Cao Y, et al. Self-supervised deep visual odometry with online adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 6339-6348.
[12] Qiu K, Chen S, Zhang J, et al. Compact 3D Map-Based Monocular Localization Using Semantic Edge Alignment[J]. arXiv preprint arXiv:2103.14826, 2021.
- 使用语义边缘对齐的基于 3D 紧凑地图的单目定位
- 阿里巴巴
[13] Cheng W, Yang S, Zhou M, et al. Road Mapping and Localization using Sparse Semantic Visual Features[J]. IEEE Robotics and Automation Letters, 2021.
- 使用稀疏语义视觉特征的道路建图和定位
- 阿里巴巴
[14] Sun J, Xie Y, Chen L, et al. NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 15598-15607.
- NeuralRecon：从单目视频中进行实时连贯 3D 重建
- 浙大，商汤；代码开源

3. Sensor Fusion

[15] Shan T, Englot B, Ratti C, et al. LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping[J]. arXiv preprint arXiv:2104.10831, 2021. (ICRA2021)
- LVI-SAM：通过平滑的紧耦合的激光雷达-视觉-惯性里程计和建图框架
- MIT；代码开源
[16] Zhang K, Yang T, Ding Z, et al. The Visual-Inertial-Dynamical UAV Dataset[J]. arXiv preprint arXiv:2103.11152, 2021.
- 视觉-惯性-动力 UAV 数据集
- 浙大；数据集
[17] Amblard V, Osedach T P, Croux A, et al. Lidar-Monocular Surface Reconstruction Using Line Segments[J]. arXiv preprint arXiv:2104.02761, 2021.
- 使用线段的 Lidar - 单目表面重建
- MIT
[18] Wei B, Trigoni N, Markham A. iMag+: An Accurate and Rapidly Deployable Inertial Magneto-Inductive SLAM System[J]. IEEE Transactions on Mobile Computing, 2021.
- iMag+: 一种准确且可快速部署的惯性磁感应 SLAM 系统
- 诺森比亚大学、牛津大学

4. Others

[19] Huang R, Fang C, Qiu K, et al. AR Mapping: Accurate and Efficient Mapping for Augmented Reality[J]. arXiv preprint arXiv:2103.14846, 2021.
- AR Mapping：用于增强现实的准确高效建图
- 阿里巴巴
[20] Wang J, Zhong Y, Dai Y, et al. Deep Two-View Structure-from-Motion Revisited[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 8953-8962.
- 重新审视两视图 DFM
- 澳大利亚国立、西工大、NVIDIA

2021 年 3 月论文更新（23 篇）

本期更新于 2021 年 4 月 15 日
共 23 篇论文，其中 9 项（待）开源工作
[3, 4, 5] 线、面 SLAM
[6, 7] 滤波方法
[10] DynaSLAM 作者博士学位论文
[9, 12, 13, 17, 23] LiDAR

1. Geometric SLAM

[1] Liu P, Zuo X, Larsson V, et al. MBA-VO: Motion Blur Aware Visual Odometry[J]. arXiv preprint arXiv:2103.13684, 2021.
- MBA-VO: 运动模糊感知的视觉里程计
- ETH，Microsoft
[2] Chen H, Hu W, Yang K, et al. Panoramic annular SLAM with loop closure and global optimization[J]. arXiv preprint arXiv:2102.13400, 2021.
- 具有闭环和全局优化的全景环形 SLAM
- 浙大、KIT
[3] Wang X, Christie M, Marchand E. TT-SLAM: Dense Monocular SLAM for Planar Environments[C]//IEEE International Conference on Robotics and Automation, ICRA'21. 2021.
- TT-SLAM: 面向平面环境的单目稠密 SLAM
- 法国雷恩大学
[4] Lim H, Kim Y, Jung K, et al. Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features[J]. arXiv preprint arXiv:2103.01501, 2021. (ICRA2021)
- 使用点线特征避免单目视觉 SLAM 退化
- 韩国科学技术院
[5] Lu J, Fang Z, Gao Y, et al. Line-based visual odometry using local gradient fitting[J]. Journal of Visual Communication and Image Representation, 2021, 77: 103071.
- 使用局部梯度拟合的基于线的视觉里程计
- 上海工程技术大学
[6] Wang J, Meng Z, Wang L. A UPF-PS SLAM Algorithm for Indoor Mobile Robot with Non-Gaussian Detection Model[J]. IEEE/ASME Transactions on Mechatronics, 2021.
- 具有非高斯检测模型的室内移动机器人 UPF-PS SLAM 算法
- 清华大学
[7] Gao L, Battistelli G, Chisci L. PHD-SLAM 2.0: Efficient SLAM in the Presence of Missdetections and Clutter[J]. IEEE Transactions on Robotics, 2021.
- PHD-SLAM 2.0：漏检和杂波污染情况下的高效 SLAM
- 电子科大，佛罗伦萨大学；代码开源
[8] Labbé M, Michaud F. Multi-session visual SLAM for illumination invariant localization in indoor environments[J]. arXiv preprint arXiv:2103.03827, 2021.
- 用于室内环境光照不变定位的多会话视觉 SLAM
- 谢布鲁克大学
[9] Yokozuka M, Koide K, Oishi S, et al. LiTAMIN2: Ultra Light LiDAR-based SLAM using Geometric Approximation applied with KL-Divergence[J]. arXiv preprint arXiv:2103.00784, 2021. (ICRA2021)
- 使用几何近似和 KL 散度的超轻型基于 LiDAR 的 SLAM
- 日本先进工业科学技术研究所

2. Semantic/Deep SLAM

[10] Visual SLAM in dynamic environments. 2020
- 动态环境中视觉 SLAM
- 萨拉戈萨大学 Berta Bescos 博士学位论文，Github
- DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes. 2018
- DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM. 2020
- Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM. 2021
[11] Zhan H, Weerasekera C S, Bian J W, et al. DF-VO: What Should Be Learnt for Visual Odometry?[J]. arXiv preprint arXiv:2103.00933, 2021.
- DF-VO: 视觉里程计应该学习什么？
- 阿德莱德大学；代码开源
- 先关工作：Zhan H, Weerasekera C S, Bian J W, et al. Visual odometry revisited: What should be learnt?[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 4203-4210.
[12] Westfechtel T, Ohno K, Akegawa T, et al. Semantic Mapping of Construction Site From Multiple Daily Airborne LiDAR Data[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 3073-3080.
- 来自多个日常机载激光雷达数据的施工现场语义建图
- 东京大学
[13] Habich T L, Stuede M, Labbé M, et al. Have I been here before? Learning to Close the Loop with LiDAR Data in Graph-Based SLAM[J]. arXiv preprint arXiv:2103.06713, 2021.
- 在图 SLAM 中学习使用 LiDAR 数据的闭环
- 汉诺威大学
[14] Jayasuriya M, Arukgoda J, Ranasinghe R, et al. UV-Loc: A Visual Localisation Strategy for Urban Environments[J]. 2021
- 城市环境中视觉定位策略
- 悉尼科技大学
- pole-like landmarks and ground surface boundaries
[15] Sarlin P E, Unagar A, Larsson M, et al. Back to the Feature: Learning Robust Camera Localization from Pixels to Pose[J]. arXiv preprint arXiv:2103.09213, 2021. （CVPR2021）
- 回归到特征：从像素中学习鲁棒的相机定位
- ETH；代码开源
[16] Zhang J, Sui W, Wang X, et al. Deep Online Correction for Monocular Visual Odometry[J]. arXiv preprint arXiv:2103.10029, 2021. (ICRA2021)
- 单目视觉里程计深度在线校正
- 地平线，华中科大

3. Sensor Fusion

[17] Lin J, Zheng C, Xu W, et al. R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping[J]. arXiv preprint arXiv:2102.12400, 2021.
- R2LIVE：一种鲁棒的的、实时的、LiDAR-惯导-视觉紧耦合的状态估计和建图方法
- 香港大学；代码开源
[18] Cao S, Lu X, Shen S. GVINS: Tightly Coupled GNSS-Visual-Inertial for Smooth and Consistent State Estimation[J]. arXiv e-prints, 2021: arXiv: 2103.07899.
- GVINS: 用于平滑和一致性状态估计的紧耦合 GNSS-视觉-惯性系统
- 港科；代码开源
[19] Zhu P, Geneva P, Ren W, et al. Distributed Visual-Inertial Cooperative Localization[J]. arXiv preprint arXiv:2103.12770, 2021.
- 分布式视觉-惯性协同定位
- 加利福尼亚大学、特拉华大学；video
[20] Peng X, Liu Z, Wang Q, et al. Accurate Visual-Inertial SLAM by Feature Re-identification[J]. arXiv preprint arXiv:2102.13438, 2021.
- 基于特征重识别的视觉惯性 SLAM
- 三星
[21] Reinke A, Chen X, Stachniss C. Simple But Effective Redundant Odometry for Autonomous Vehicles[J]. arXiv preprint arXiv:2105.11783, 2021. (ICRA2021)
- 用于自动驾驶的简易且有效的冗余里程计
- 波恩大学；代码开源（待公开）
[22] Ram K, Kharyal C, Harithas S S, et al. RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments[J]. arXiv preprint arXiv:2103.10400, 2021.
- RP-VIO: 动态环境中鲁棒的基于平面的 VIO
- 印度理工学院海得拉巴机器人研究中心；代码开源（基于 VINS）
[23] Kramer A, Harlow K, Williams C, et al. ColoRadar: The Direct 3D Millimeter Wave Radar Dataset[J]. arXiv preprint arXiv:2103.04510, 2021.
- 直接 3D 毫米波雷达数据集
- 科罗拉多大学博尔德分校；开源数据集

2021 年 2 月论文更新（21 篇）

本期更新于 2021 年 3 月 20 日
共 21 篇论文，其中 8 项开源工作
[3][4] VIO 数据集
[9][10] 杆状特征

1. Geometric SLAM

[1] Ferrera M, Eudes A, Moras J, et al. OV $^{2} $ SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1399-1406.
- 适用于实时应用的完全在线、多功能 SLAM
- IFREMER；代码开源
[2] Gladkova M, Wang R, Zeller N, et al. Tight-Integration of Feature-Based Relocalization in Monocular Direct Visual Odometry[J]. arXiv preprint arXiv:2102.01191, 2021.
- 单目直接法视觉里程计中基于特征重定位的紧耦合
- TUM
[3] Zhang H, Jin L, Ye C. The VCU-RVI Benchmark: Evaluating Visual Inertial Odometry for Indoor Navigation Applications with an RGB-D Camera[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 6209-6214.
- VCU-RVI Benchmark：用于评估 RGB-D 室内导航应用的 VIO
- 弗吉尼亚联邦大学；数据集地址
[4] Minoda K, Schilling F, Wüest V, et al. VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1343-1350.
- VIODE：用于解决 VIO 在动态环境中挑战的仿真数据集
- 东京大学，洛桑联邦理工；数据集地址
[5] Younes G. A Unified Hybrid Formulation for Visual SLAM[D]. , 2021.
- 一种视觉 SLAM 统一混合的架构
- 滑铁卢大学大学，博士学位论文，Google Scholar
[6] Shakeri M, Loo S Y, Zhang H, et al. Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 4512-4519.
- 利用相对深度先验的偏振相机单目密集建图
- 阿尔伯塔大学
[7] Wang H, Wang C, Xie L. Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1715-1721.
- 大规模环境中强度特征辅助的 LiDAR 定位与建图
- NTU, CMU
[8] Doan A, Latif Y, Chin T, et al. HM⁴: hidden Markov model with memory management for visual place recognition[J]. IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 167-174, Jan. 2021.
- 用于视觉位置识别的具有内存管理功能的隐马尔可夫模型
- 阿德莱德大学
[9] Li L, Yang M, Weng L, et al. Robust Localization for Intelligent Vehicles Based on Pole-Like Features Using the Point Cloud[J]. IEEE Transactions on Automation Science and Engineering, 2021.
- 基于杆状点云特征的智能车鲁棒定位
- 上海交大；期刊：中科院一区，JCR Q1，IF 4.9
[10] Tschopp F, von Einem C, Cramariuc A, et al. Hough $^ 2$ Map–Iterative Event-Based Hough Transform for High-Speed Railway Mapping[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 2745-2752.
- 用于高速铁路建图的基于事件相机的迭代式霍夫变换
- ETH；代码开源；Video
[11] Ma X, Liang X. Point-line-based RGB-D SLAM and Bundle Adjustment Uncertainty Analysis[J]. arXiv preprint arXiv:2102.07110, 2021.
- 基于点线的 RGB-D SLAM 和 BA 的不确定性分析
- 上交

2. Semantic/Deep SLAM

[12] Rosinol A, Violette A, Abate M, et al. Kimera: from slam to spatial perception with 3d dynamic scene graphs[J]. arXiv preprint arXiv:2101.06894, 2021.
- Kimera: 从 SLAM 到具有 3D 动态场景图的空间感知
- MIT；项目主页
[13] Liu Y, Liu J, Hao Y, et al. A Switching-Coupled Backend for Simultaneous Localization and Dynamic Object Tracking[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1296-1303.
- 一种同时用于定位与动态物体跟踪的可切换松耦合后端
- 清华大学
[14] Feng Q, Atanasov N. Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning[J]. arXiv preprint arXiv:2101.01844, 2021.
- 使用 2D-3D 联合学习从航空图像进行室外地形建图的 Mesh 重建
- UCSD Nikolay Atanasov
[15] Wong Y S, Li C, Nießner M, et al. RigidFusion: RGB-D Scene Reconstruction with Rigidly-moving Objects[J]. Eurographics, 2021.
- RigidFusion: 移动刚体运动场景的 RGB-D 重建
- UCL, TUM；补充材料
[16] Sun S, Melamed D, Kitani K. IDOL: Inertial Deep Orientation-Estimation and Localization[J]. arXiv preprint arXiv:2102.04024, 2021.(AAAI 2021)
- 基于学习的惯性传感器方向与位置估计
- CMU；代码与数据集
[17] Qin C, Zhang Y, Liu Y, et al. Semantic loop closure detection based on graph matching in multi-objects scenes[J]. Journal of Visual Communication and Image Representation, 2021, 76: 103072.
- 基于图匹配的多目标场景闭环检测
- 东北大学

3. Sensor Fusion

[18] Wang H, Wang C, Xie L. Lightweight 3-D Localization and Mapping for Solid-State LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1801-1807.
- 固态 LiDAR 轻量化 3D 定位与建图
- 南洋理工大学；代码开源
[19] Jiang Z, Taira H, Miyashita N, et al. VIO-Aided Structure from Motion Under Challenging Environments[J]. arXiv preprint arXiv:2101.09657, 2021. (ICIT2021)
- 在具有挑战场景下 VIO 辅助的 SFM
- 东京工业大学

4. Others

[20] Liu Y, Yixuan Y, Liu M. Ground-aware Monocular 3D Object Detection for Autonomous Driving[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 919-926.
- 用于自动驾驶的地面感知单目 3D 目标检测
- 港科；代码开源
[21] Singh G, Akrigg S, Di Maio M, et al. Road: The road event awareness dataset for autonomous driving[J]. arXiv preprint arXiv:2102.11585, 2021.
- 自动驾驶场景的道路事件感知数据集
- ETH；代码开源
- 道路事件由智能体、智能体所执行的动作和智能体所处的环境组成。

2021 年 1 月论文更新（20 篇）

本期更新于 2021 年 2 月 13 日
共 20 篇论文，其中 2 项开源工作
[1] 长期定位
[10] Building Fusion [11] Mesh Reconstruction

1. Geometric SLAM

[1] Rotsidis A, Lutteroth C, Hall P, et al. ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021: 2867-2876.
- 使用指数衰减在动态场景中的长期定位
- 巴斯大学
[2] Wen T, Xiao Z, Wijaya B, et al. High Precision Vehicle Localization based on Tightly-coupled Visual Odometry and Vector HD Map[C]//2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 672-679.
- 基于视觉里程计和矢量高清地图紧耦合的高精度车辆定位
- 清华大学
[3] Lee S J, Kim D, Hwang S S, et al. Local to Global: Efficient Visual Localization for a Monocular Camera[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021: 2231-2240.
- Local to Global：单目相机高效视觉定位
- 人工特征用于实时里程计，基于学习的特征用于定位，地图对齐
[4] Jung K Y, Kim Y E, Lim H J, et al. ALVIO: Adaptive Line and Point Feature-based Visual Inertial Odometry for Robust Localization in Indoor Environments[J]. arXiv preprint arXiv:2012.15008, 2020.
- 基于自适应点线特征的视觉惯性里程计在室内环境中的鲁棒定位
- 韩国高等科学技术学院
[5] Yang A J, Cui C, Bârsan I A, et al. Asynchronous Multi-View SLAM[J]. arXiv preprint arXiv:2101.06562, 2021.
- 异步多视点 SLAM
- 多伦多大学
[6] Lyu Y, Nguyen T M, Liu L, et al. SPINS: Structure Priors aided Inertial Navigation System[J]. arXiv preprint arXiv:2012.14053, 2020.
- 结构先验辅助的惯性导航系统
- 南洋理工
[7] Pan Y, Xu X, Ding X, et al. GEM: online globally consistent dense elevation mapping for unstructured terrain[J]. IEEE Transactions on Instrumentation and Measurement, 2020.
- 非结构化地形的在线全局一致稠密高程图
- 浙大；期刊：中科院三区，ICR Q1，IF 3.6
[8] Tian R, Zhang Y, Zhu D, et al. Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry[J]. arXiv preprint arXiv:2101.05995, 2021.
- 基于平面几何单目视觉里程计的准确鲁棒尺度恢复
- 东北大学，香港中文大学
[9] Fang B, Mei G, Yuan X, et al. Visual SLAM for robot navigation in healthcare facility[J]. Pattern Recognition, 2021: 107822.
- 用于医疗机构机器人导航的视觉 SLAM
- 合肥工业大学；期刊：中科院二区，ICR Q1，IF 7.2

2. Semantic/Deep SLAM

[10] Zheng T, Zhang G, Han L, et al. Building Fusion: Semantic-aware Structural Building-scale 3D Reconstruction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
- Building Fusion：语义感知结构化建筑规模的三维重建
- 清华-伯克利深圳学院，清华大学
[11] Feng Q, Atanasov N. Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning[J]. arXiv preprint arXiv:2101.01844, 2021.
- 使用 2D-3D 联合学习从空中图像进行室外地形的网格重建
- 加州大学圣地亚哥分校 Nikolay A. Atanasov
[12] Ma T, Wang Y, Wang Z, et al. ASD-SLAM: A Novel Adaptive-Scale Descriptor Learning for Visual SLAM[C]//2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 809-816.
- 一种新的视觉 SLAM 自适应尺度描述符学习方法
- 上交；代码开源
[13] Li B, Hu M, Wang S, et al. Self-supervised Visual-LiDAR Odometry with Flip Consistency[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021: 3844-3852.
- 具有翻转一致性的自监督视觉-雷达里程计
- 浙大
[14] Akilan T, Johnson E, Sandhu J, et al. A Hybrid Learner for Simultaneous Localization and Mapping[J]. arXiv preprint arXiv:2101.01158, 2021.
- 用于 SLAM 的混合学习器
- Lakehead University

3. Sensor Fusion

[15] Cowley A, Miller I D, Taylor C J. UPSLAM: Union of Panoramas SLAM[J]. arXiv preprint arXiv:2101.00585, 2021.
- UPSLAM: 全景 SLAM
- 宾夕法尼亚大学
[16] Jiang Z, Taira H, Miyashita N, et al. VIO-Aided Structure from Motion Under Challenging Environments[J]. arXiv preprint arXiv:2101.09657, 2021.
- 挑战场景下 VIO 辅助的 SFM
- 东京工业大学
[17] Zhai C, Wang M, Yang Y, et al. Robust Vision-Aided Inertial Navigation System for Protection Against Ego-Motion Uncertainty of Unmanned Ground Vehicle[J]. IEEE Transactions on Industrial Electronics, 2020.
- 鲁棒的视觉辅助惯性导航系统，避免地面无人车的自我运动估计不确定性
- 北理工；期刊：中科院一区 JCR Q1，IF 7.5
[18] Palieri M, Morrell B, Thakur A, et al. LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time[J]. IEEE Robotics and Automation Letters, 2020, 6(2): 421-428.
- 用于高精度里程计和建图的以雷达为中心的多传感器框架
- 加州理工

4. Others

[19] Chiu H, Li J, Ambrus R, et al. Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving[J]. arXiv preprint arXiv:2012.13755, 2020.
- 自动驾驶场景的 3D 多模态、多目标跟踪
- 斯坦福大学
[20] Deng J, Shi S, Li P, et al. Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection[J]. arXiv preprint arXiv:2012.15712, 2020.（AAAI 2021）
- Voxel R-CNN：面向基于高性能体素的 3D 目标检测
- 中科大，港中文；代码开源

2020 年 12 月论文更新（18 篇）

本期更新于 2021 年 1 月 02 日
共 18 篇论文，其中 6 项（待）开源工作
9 CodeVIO, 10 CamVox

1. Geometric SLAM

[1] Mascaro R, Wermelinger M, Hutter M, et al. Towards automating construction tasks: Large‐scale object mapping, segmentation, and manipulation[J]. Journal of Field Robotics, 2020.
- 【挖掘机抓石头】实现自动化的施工任务：大型物体建图，分割和操纵
- ETH，期刊：中科院二区，JCR Q1
[2] Yang X, Yuan Z, Zhu D, et al. Robust and Efficient RGB-D SLAM in Dynamic Environments[J]. IEEE Transactions on Multimedia, 2020.
- 动态环境中鲁棒高效 RGB-D SLAM
- 华中科大，期刊：中科院二区，JCR Q1，IF 5.5
[3] Yazdanpour M, Fan G, Sheng W. ManhattanFusion: Online Dense Reconstruction of Indoor Scenes from Depth Sequences[J]. IEEE Transactions on Visualization and Computer Graphics, 2020.
- ManhattanFusion：从深度序列中对室内场景进行在线稠密重建
- 北肯塔基大学，期刊：中科院二区，JCR Q1，IF 4.3
[4] Fourie D, Rypkema N R, Teixeira P V, et al. Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation[J]. IROS 2020
- 面向实时非高斯 SLAM 的欠定导航
- MIT

2. Semantic/Deep SLAM

[5] Garg S, Sünderhauf N, Dayoub F, et al. Semantics for Robotic Mapping, Perception and Interaction: A Survey[J]. arXiv preprint arXiv:2101.00443, 2020.
- 用于机器人建图、感知和交互的语义
- 昆士兰科技大学，阿德莱德大学、澳大利亚机器人中心，962 篇论文综述
[6] Nubert J, Khattak S, Hutter M. Self-supervised Learning of LiDAR Odometry for Robotic Applications[J]. arXiv preprint arXiv:2011.05418, 2020.
- 用于机器人应用的基于自监督学习的 LiDAR 里程计
- ETH，代码开源
[7] Thomas H, Agro B, Gridseth M, et al. Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation[J]. arXiv preprint arXiv:2012.05897, 2020.
- 自主室内导航 LIDAR 分割的自监督学习
- 多伦多大学，Apple
[8] Huynh L, Nguyen P, Matas J, et al. Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion[J]. arXiv preprint arXiv:2012.10296, 2020.
- 轻量级 3D 点融合（立体匹配/SLAM）提高单眼深度估计
- 奥卢大学

3. Sensor Fusion

[9] Zuo X, Merrill N, Li W, et al. CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth[J]. arXiv preprint arXiv:2012.10133, 2020.
- CodeVIO: 具有学习可优化稠密深度的 VIO
- ETH，浙大，ICRA2021 投稿论文，video
[10] ZHU, Yuewen, et al. CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System. arXiv preprint arXiv:2011.11357, 2020.
- 低成本、精确的Lidar辅助视觉 SLAM 系统
- 南方科技大学，代码开源，ICRA2021 投稿论文
[11] Gong Z, Liu P, Wen F, et al. Graph-Based Adaptive Fusion of GNSS and VIO Under Intermittent GNSS-Degraded Environment[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-16.
- 间歇性 GNSS 退化环境下的基于图的自适应 GNSS-VIO 融合
- 上海交大
[12] Wu Y, Li Y, Li W, et al. Robust Lidar-Based Localization Scheme for Unmanned Ground Vehicle via Multisensor Fusion[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020.
- 基于激光雷达的多传感器融合无人地面车辆鲁棒定位方法
- 广东工业大学，期刊：中科院一区，JCR Q1, IF 8.8
[13] Zhu P, Ren W. Multi-Robot Joint Visual-Inertial Localization and 3-D Moving Object Tracking[J]. IROS 2020
- 多机器人联合视觉惯性定位和 3D 运动目标跟踪
- 加州大学河滨分校

4. Others

[14] Xu M, Snderhauf N, Milford M. Probabilistic Visual Place Recognition for Hierarchical Localization[J]. IEEE Robotics and Automation Letters, 2020, 6(2): 311-318.
- 用于分层定位的概率视觉场景识别
- 昆士兰科技大学，代码开源
[15] Li D, Miao J, Shi X, et al. RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Keypoints for Indoor Localization[J]. arXiv preprint arXiv:2012.00234, 2020.
- RaP-Net: 区域和点加权网络用于室内定位的鲁棒关键点提取
- 清华、北交大、北航、Intel，代码开源
[16] Bui M, Birdal T, Deng H, et al. 6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference[J]. arXiv preprint arXiv:2004.04807, 2020.(ECCV 2020)
- 通过连续多峰推理在模糊场景中进行 6D 相机重定位
- 斯坦福，TUM，项目主页，代码开源
[17] ALVES, Nelson, et al. Low-latency Perception in Off-Road Dynamical Low Visibility Environments. arXiv preprint arXiv:2012.13014, 2020.
- 低能见度、越野环境下的低延迟感知
- 数据集，代码
[18] Chen C, Al-Halah Z, Grauman K. Semantic Audio-Visual Navigation[J]. arXiv preprint arXiv:2012.11583, 2020.
- 语义视-听导航
- UT Austin, Facebook，项目主页

2020 年 11 月论文更新（20 篇）

本期更新于 2020 年 12 月 07 日
共 20 篇论文，其中 6 项（待）开源工作
其中近一半来自于 IROS 2020 的录用论文和 ICRA 2021 的投稿论文

1. Geometric SLAM

[1] Kim C, Kim J, Kim H J. Edge-based Visual Odometry with Stereo Cameras using Multiple Oriented Quadtrees[J]. IROS 2020
- 使用多个定向四叉树的基于边的双目视觉里程计
- 首尔国立大学
[2] Jaenal A, Zuniga-Noël D, Gomez-Ojeda R, et al. Improving Visual SLAM in Car-Navigated Urban Environments with Appearance Maps[J]. IROS 2020
- 通过外观地图改善城市环境汽车导航的视觉 SLAM
- 马拉加大学；video
[3] Chen L, Zhao Y, Xu S, et al. DenseFusion: Large-Scale Online Dense Pointcloud and DSM Mapping for UAVs[J]. IROS 2020
- DenseFusion：无人机的大规模在线密集点云和 DSM 建图
- 西工大，自动化所
- 前期工作：Wang W, Zhao Y, Han P, et al. TerrainFusion: Real-time Digital Surface Model Reconstruction based on Monocular SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 7895-7902.
[4] Arndt C, Sabzevari R, Civera J. From Points to Planes-Adding Planar Constraints to Monocular SLAM Factor Graphs[J]. IROS 2020
- From Points to Planes：在单目 SLAM 因子图中添加平面约束
- 西班牙萨拉戈萨大学
[5] Giubilato R, Le Gentil C, Vayugundla M, et al. GPGM-SLAM: Towards a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps[C]//IROS Workshop on Planetary Exploration Robots: Challenges and Opportunities (PLANROBO20). ETH Zurich, Department of Mechanical and Process Engineering, 2020.
- GPGM-SLAM：具有高斯过程梯度图的非结构化行星环境的鲁棒 SLAM 系统
- DLR，TUM

2. Semantic/Deep SLAM

[6] Chang Y, Tian Y, How J P, et al. Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping[J]. arXiv preprint arXiv:2011.04087, 2020.
- Kimera-Multi: 分布式多机器人度量语义 SLAM 系统
- MIT
[7] Sharma A, Dong W, Kaess M. Compositional Scalable Object SLAM[J]. arXiv preprint arXiv:2011.02658, 2020.
- 合成可拓展的物体级 SLAM
- CMU；ICRA2021 投稿论文；待开源
[8] Wang W, Hu Y, Scherer S. TartanVO: A Generalizable Learning-based VO[J]. arXiv preprint arXiv:2011.00359, 2020.
- TartanVO：一种通用的基于学习的 VO
- CMU，代码开源
[9] Wimbauer F, Yang N, von Stumberg L, et al. MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera[J]. arXiv preprint arXiv:2011.11814, 2020.
- 动态环境中单个移动相机的半监督稠密重构
- TUM；项目主页
- 相关研究：
  - CVPR 2020 D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
  - ECCV 2018 Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry
[10] Almalioglu Y, Santamaria-Navarro A, Morrell B, et al. Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments[J]. arXiv preprint arXiv:2011.00341, 2020.
- 极端环境下的无监督持久性的单目视觉里程计和深度估计
- 牛津大学，NASA；ICRA2021 投稿论文
[11] Zou Y, Ji P, Tran Q H, et al. Learning monocular visual odometry via self-supervised long-term modeling[J]. arXiv preprint arXiv:2007.10983, 2020. (ECCV 2020)
- 通过自监督长期建模学习单目视觉里程计
- 弗吉尼亚理工大学；项目主页+video
[12] Nubert J, Khattak S, Hutter M. Self-supervised Learning of LiDAR Odometry for Robotic Applications[J]. arXiv preprint arXiv:2011.05418, 2020.
- 应用于机器人的自监督学习 LiDAR 里程计
- ETH；代码开源
[13] Chancán M, Milford M. DeepSeqSLAM: A Trainable CNN+ RNN for Joint Global Description and Sequence-based Place Recognition[J]. arXiv preprint arXiv:2011.08518, 2020.
- 用于联合全局描述和基于序列的位置识别的可训练的CNN + RNN
- 昆士兰科技大学；代码开源，项目主页

3. Sensor Fusion

[14] Zhao S, Wang P, Zhang H, et al. TP-TIO: A Robust Thermal-Inertial Odometry with Deep ThermalPoint[J]. arXiv preprint arXiv:2012.03455, IROS 2020.
- TP-TIO: 一种使用深度 ThermalPoint 网络的红外视觉-惯性里程计
- CMU，东北大学，video
[15] Jaekel J, Mangelson J G, Scherer S, et al. A Robust Multi-Stereo Visual-Inertial Odometry Pipeline[J]. IROS 2020.
- 一种鲁棒的多立体视觉惯性里程计框架
- CMU
[16] Huang H, Ye H, Jiao J, et al. Geometric Structure Aided Visual Inertial Localization[J]. arXiv preprint arXiv:2011.04173, 2020.
- 几何结构辅助的视觉惯性定位
- 港科，ICRA 2021 投稿论文
[17] Ding Z, Yang T, Zhang K, et al. VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation[J]. arXiv preprint arXiv:2011.03993, 2020.
- VID-Fusion: 用于准确外力估计的露露帮视觉惯性里程计
- 浙大 FAST Lab
[18] Li K, Li M, Hanebeck U D. Towards high-performance solid-state-lidar-inertial odometry and mapping[J]. arXiv preprint arXiv:2010.13150, 2020.
- 高性能固态雷达惯性里程计与建图
- 卡尔斯鲁厄理工学院；代码开源

4. Others

[19] Milano F, Loquercio A, Rosinol A, et al. Primal-Dual Mesh Convolutional Neural Networks[J]. Advances in Neural Information Processing Systems, 2020, 33.
- 原始对偶网格卷积神经网络
- ETH；代码开源
[20] Li H, Gordon A, Zhao H, et al. Unsupervised Monocular Depth Learning in Dynamic Scenes[J]. arXiv preprint arXiv:2010.16404, 2020. (CoRL 2020)
- 动态环境中无监督单目深度学习
- Google；代码开源

2020 年 10 月论文更新（22 篇）

本期更新于 2020 年 11 月 09 日
共 22 篇论文，其中 7 项（待）开源工作
9,10,11：SLAM 中动态物体跟踪，动态物体级 SLAM 今年很火
3,7,8,14,18：线段相关

1. Geometric SLAM

[1] Bhutta M, Kuse M, Fan R, et al. Loop-box: Multi-Agent Direct SLAM Triggered by Single Loop Closure for Large-Scale Mapping[J]. arXiv preprint arXiv:2009.13851, 2020. IEEE Transactions on Cybernetics, 2020
- 用于大规模建图的由单闭环触发的多智能体直接 SLAM
- 香港科技大学；项目主页；video
- 期刊：中科院一区，JCR Q1，IF 11.079
[2] Zhou B, He Y, Qian K, et al. S4-SLAM: A real-time 3D LIDAR SLAM system for ground/watersurface multi-scene outdoor applications[J]. Autonomous Robots, 2020: 1-22.
- S4-SLAM：用于地面/水面多场景户外应用的实时 3D LIDAR SLAM 系统
- 东南大学；期刊：中科院三区，JCR Q1，IF 3.6
[3] Li Y, Yunus R, Brasch N, et al. RGB-D SLAM with Structural Regularities[J]. arXiv preprint arXiv:2010.07997, 2020.
- 具有结构规律的 RGB-D SLAM
- TUM
[4] Rodríguez J J G, Lamarca J, Morlana J, et al. SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenes[J]. arXiv preprint arXiv:2010.09409, 2020.
- SD-DefSLAM：适用于可变形和体内场景的半直接法单目 SLAM
- 萨拉戈萨大学；ICRA 2021 投稿论文；Video
[5] Millane A, Oleynikova H, Lanegger C, et al. Freetures: Localization in Signed Distance Function Maps[J]. arXiv preprint arXiv:2010.09378, 2020.
- Freetures：在 SDF 地图中进行定位
- ETH；代码开源（待开源）；Video
[6] Long R, Rauch C, Zhang T, et al. RigidFusion: Robot Localisation and Mapping in Environments with Large Dynamic Rigid Objects[J]. arXiv preprint arXiv:2010.10841, 2020.
- RigidFusion: 在具有动态刚体物体的大型场景中进行机器人定位与建图
- 爱丁堡大学机器人中心
[8] Han J, Dong R, Kan J. A novel loop closure detection method with the combination of points and lines based on information entropy[J]. Journal of Field Robotics. 2020
- 一种新的基于信息熵的点线闭环检测方法
- 北京林业大学；期刊：中科院二区，JCR Q1，IF 3.58

2. Semantic/Deep SLAM

[9] Bescos B, Campos C, Tardós J D, et al. DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM[J]. arXiv preprint arXiv:2010.07820, 2020.
- DynaSLAM II: 多目标跟踪与 SLAM 紧耦合
- 萨拉戈萨大学；一作是 DynaSLAM 的作者，二作是 ORB-SLAM3 的作者
[10] Bescos B, Cadena C, Neira J. Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM[J]. arXiv preprint arXiv:2010.07646, 2020.
- 视觉 SLAM 动态的物体不变空间
- 萨拉戈萨大学、ETH；代码开源
- 个人主页，相关论文：
  - ICRA 2018 Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space
[11] Ballester I, Fontan A, Civera J, et al. DOT: Dynamic Object Tracking for Visual SLAM[J]. arXiv preprint arXiv:2010.00052, 2020.
- 视觉 SLAM 的动态物体跟踪
- 萨拉戈萨大学
[12] Wu S C, Tateno K, Navab N, et al. SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion[J]. arXiv preprint arXiv:2010.13662, 2020.
- SCFusion：具有完整语义的实时增量场景重建
- TUM
[13] Mallick A, Stückler J, Lensch H. Learning to Adapt Multi-View Stereo by Self-Supervision[J]. arXiv preprint arXiv:2009.13278, 2020.
- 通过自监督学习的自适应多视图立体匹配
- 图宾根大学，马普所 Jörg Stückler，BMVC 2020
- 基于 ECCV 2018 MVSNet: Depth Inference for Unstructured Multi-view Stereo，代码

3. Sensor Fusion

[14] Li X, Li Y, Ornek E P, et al. Co-Planar Parametrization for Stereo-SLAM and Visual-Inertial Odometry[J]. IEEE Robotics and Automation Letters, 2020.
- 双目 SLAM 和 VIO 的共面参数化
- 北京大学，代码开源（暂未放出）
[15] Liu Z, Zhang F. BALM: Bundle Adjustment for Lidar Mapping[J]. arXiv preprint arXiv:2010.08215, 2020.
- BALM：激光雷达建图中的 BA 优化
- 香港大学，代码开源
[16] Nguyen T M, Yuan S, Cao M, et al. VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach[J]. arXiv preprint arXiv:2010.12274, 2020.
- VIRAL-Fusion: 视觉-惯性-测距-激光雷达传感器融合方法
- 南洋理工
[17] Liu J, Gao W, Hu Z. Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS Measurements[J]. arXiv preprint arXiv:2010.11675, 2020.
- 基于优化的视觉惯性 SLAM 与原始 GNSS 测量紧耦合
- 中科院自动化所；ICRA 2021 投稿论文

4. Others

[18] Taubner F, Tschopp F, Novkovic T, et al. LCD--Line Clustering and Description for Place Recognition[J]. arXiv preprint arXiv:2010.10867, 2020. (3DV 2020)
- LCD: 用于位置识别的线段聚类和描述
- ETH；代码开源
[19] Triebel R. 3D Scene Reconstruction from a Single Viewport. ECCV 2020
- 单视角进行三维场景重建
- TUM；代码开源
[20] Hidalgo-Carrió J, Gehrig D, Scaramuzza D. Learning Monocular Dense Depth from Events[J]. arXiv preprint arXiv:2010.08350, 2020.(3DV 2020)
- 从事件中学习单目稠密深度
- 苏黎世大学；代码开源；项目主页
[21] Yang B. Learning to reconstruct and segment 3D objects[J]. arXiv preprint arXiv:2010.09582, 2020.
- 学习重建和分割 3D 物体
- 牛津大学 BoYang 博士学位论文
[22] von Stumberg L, Wenzel P, Yang N, et al. LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization[J]. arXiv preprint arXiv:2010.06323, 2020.
- LM-Reloc：基于Levenberg-Marquardt 的直接视觉重定位
- TUM
- 相关论文：von Stumberg L, Wenzel P, Khan Q, et al. Gn-net: The gauss-newton loss for multi-weather relocalization[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 890-897.

2020 年 9 月论文更新（20 篇）

本期更新于 2020 年 9 月 28 日
共 20 篇论文，其中 6 项（待）开源工作
4-5：机器人自主探索
8-11：多路标 SLAM
13: Jan Czarnowski 博士学位论文
17-20：增强现实相关的几项很好玩的工作

1. Geometric SLAM

[1] FZhao Y, Smith J S, Vela P A. Good graph to optimize: Cost-effective, budget-aware bundle adjustment in visual SLAM[J]. arXiv preprint arXiv:2008.10123, 2020.
- Good Graph to Optimize: 视觉 SLAM 中具有成本效益、可感知预算的 BA
- 佐治亚理工学院 Yipu Zhao
- 作者有很多 Good 系列的文章
  - IROS 2018 Good feature selection for least squares pose optimization in VO/VSLAM
  - ECCV 2018 Good line cutting: Towards accurate pose tracking of line-assisted VO/VSLAM
  - T-RO 2020 Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency
[2] Fu Q, Yu H, Wang X, et al. FastORB-SLAM: a Fast ORB-SLAM Method with Coarse-to-Fine Descriptor Independent Keypoint Matching[J]. arXiv preprint arXiv:2008.09870, 2020.
- FastORB-SLAM: 一种 Coarse-to-Fine 描述符独立关键点匹配的快速 ORB-SLAM 方法
- 湖南大学
[3] Wenzel P, Wang R, Yang N, et al. 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving[J]. arXiv preprint arXiv:2009.06364, 2020.
- 4Seasons：自动驾驶中多天气SLAM的跨季节数据集
- 慕尼黑工业大学 Nan Yang
- 数据集网页：http://www.4seasons-dataset.com/
[4] Duong T, Yip M, Atanasov N. Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping[J]. arXiv preprint arXiv:2009.07207, 2020.
- 基于稀疏贝叶斯核占有地图的未知环境自主导航
- UCSD Nikolay A. Atanasov
- 项目主页 | 代码开源
[5] Bartolomei L, Karrer M, Chli M. Multi-robot Coordination with Agent-Server Architecture for Autonomous Navigation in Partially Unknown Environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)(virtual). 2020.
- 在部分未知环境中实现自主导航的多机器人协同代理服务器架构
- ETH | 代码开源 | video
[6] Kern A, Bobbe M, Khedar Y, et al. OpenREALM: Real-time Mapping for Unmanned Aerial Vehicles[J]. arXiv preprint arXiv:2009.10492, 2020.
- OpenREALM：无人机实时建图框架
- 代码开源 | video
[7] Du Z J, Huang S S, Mu T J, et al. Accurate RGB-D SLAM in Dynamic Environment using Observationally Consistent Conditional Random Fields. 2020
- 动态环境中使用观察一致 CRF 的精确 RGB-D SLAM
- 清华大学
[8] Holynski A, Geraghty D, Frahm J M, et al. Reducing Drift in Structure from Motion using Extended Features[J]. arXiv preprint arXiv:2008.12295, 2020.
- 使用拓展特征减小 SFM 中的漂移
- 华盛顿大学，Facebook
[9] Fu Q, Wang J, Yu H, et al. PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line[J]. arXiv preprint arXiv:2009.07462, 2020.
- PL-VINS: 实时点线单目视觉惯性 SLAM
- 湖南大学 | 代码开源
[10] Company-Corcoles J P, Garcia-Fidalgo E, Ortiz A. LiPo-LCD: Combining Lines and Points for Appearance-based Loop Closure Detection[J]. arXiv preprint arXiv:2009.09897, 2020.(BMVC 2020)
- 几何点线的基于外观的闭环检测
- 巴利阿里群岛大学
[11] Wang Q, Yan Z, Wang J, et al. Line Flow based SLAM[J]. arXiv preprint arXiv:2009.09972, 2020.
- 基于 SLAM 的线流
- 北大
[12] Badias A, Alfaro I, Gonzalez D, et al. MORPH-DSLAM: Model Order Reduction for PHysics-based Deformable SLAM[J]. arXiv preprint arXiv:2009.00576, 2020.
- 基于物理可变形 SLAM 降低模型阶数
- 萨拉戈萨大学

2. Semantic/Deep SLAM

[13] Czarnowski J. Learned representations for real-time monocular SLAM[J]. 2020.
- 实时单目 SLAM 的学习表示
- 帝国理工学院 Jan Czarnowski 博士学位论文 | 导师 Andrew Davison
- 代表作
[14] Li J, Pei L, Zou D, et al. Attention-SLAM: A Visual Monocular SLAM Learning from Human Gaze[J]. arXiv preprint arXiv:2009.06886, 2020.
- Attention-SLAM：从人类视线中学习的单目视觉 SLAM
- 上海交通大学
[15] Cremona J, Uzal L, Pire T. WGANVO: Monocular Visual Odometry based on Generative Adversarial Networks[J]. arXiv preprint arXiv:2007.13704, 2020.
- WGANVO: 基于生成对抗网络的单目视觉里程计
- 阿根廷 CIFASIS | 代码开源 | video
[16] Labbé Y, Carpentier J, Aubry M, et al. CosyPose: Consistent multi-view multi-object 6D pose estimation[J]. arXiv preprint arXiv:2008.08465, 2020.（ECCV 2020）
- CosyPose：一致的多视图多物体 6D 位姿估计
- Object SLAM @ 物体位姿估计

3. AR/VR/MR

[17] Yang X, Zhou L, Jiang H, et al. Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone[J]. IEEE Annals of the History of Computing, 2020 (01): 1-1.
- Mobile3DRecon手机上的实时单目重建
- 商汤、浙大
[18] Ungureanu D, Bogo F, Galliani S, et al. HoloLens 2 Research Mode as a Tool for Computer Vision Research[J]. arXiv preprint arXiv:2008.11239, 2020.
- HoloLens 2 研究模式作为计算机视觉研究的工具
- 三星 AI 中心，微软
[19] Mori S, Erat O, Broll W, et al. InpaintFusion: Incremental RGB-D Inpainting for 3D Scenes[J]. IEEE Transactions on Visualization and Computer Graphics, 2020, 26(10): 2994-3007.
- InpaintFusion：3D场景的增量RGB-D修复
- 格拉茨工业大学期刊：中科院二区,JCR Q1, IF 4.558
[20] AAR: Augmenting a Wearable Augmented Reality Display with an Actuated Head-Mounted Projector. 2020
- 使用可驱动的头戴式投影仪增强可穿戴的增强现实显示
- 滑铁卢大学
- 在 AR 眼镜上再装个投影仪。。。。会玩

2020 年 8 月论文更新（30 篇）

本期更新于 2020 年 8 月 27 日
共 30 篇论文，其中 11 项（待）开源工作
这个月公开的论文比较多，且有意思、高质量的工作也不少，多来自于 IROS、RAL(大部分也同步发表于 IROS)，比如融合视觉、惯导、LiDAR 的 LIC-Fusion 2.0 和融合物体语义的视惯里程计 OrcVIO，其他：
4-6、15：多机/多地图
8-13：结构化/室内 SLAM

1. Geometric SLAM

[1] Geppert M, Larsson V, Speciale P, et al. Privacy Preserving Structure-from-Motion[J]. 2020.
- 具有隐私保护的 SFM
- 苏黎世联邦理工
- 相关论文：
  - CVPR 2019 Privacy Preserving Image-Based Localization
  - ECCV 2020 Privacy Preserving Visual SLAM
[2] Zhang Z, Scaramuzza D. Fisher Information Field: an Efficient and Differentiable Map for Perception-aware Planning[J]. arXiv preprint arXiv:2008.03324, 2020.
- Fisher 信息场：一种用于感知规划的高效且可微分的地图
- 苏黎世大学张子潮 | 代码开源
- 相关工作：ICRA 2019 Beyond Point Clouds: Fisher Information Field for Active Visual Localization
[3] Zhou Y, Gallego G, Shen S. Event-based Stereo Visual Odometry[J]. arXiv preprint arXiv:2007.15548, 2020.
- 基于事件相机的双目视觉里程计
- 港科沈邵劼组 | 代码开源（暂未公开）| video
[4] Yue Y, Zhao C, Wu Z, et al. Collaborative Semantic Understanding and Mapping Framework for Autonomous Systems[J]. IEEE/ASME Transactions on Mechatronics, 2020.
- 自治系统协作式语义理解和建图框架
- 南洋理工大学 | 期刊：中科院二区，JCR Q1，IF 5.6
[5] Do H, Hong S, Kim J. Robust Loop Closure Method for Multi-Robot Map Fusion by Integration of Consistency and Data Similarity[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 5701-5708.
- 融合一致性和数据相似性的多机器人地图融合的闭环方法
- 韩国启明大学 | Google Scholar
[6] Zhan Z, Jian W, Li Y, et al. A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph[J]. arXiv preprint arXiv:2007.14592, 2020.
- 基于子图和无向连通图的 SLAM 地图恢复算法
- 武汉大学
[7] Chen H, Zhang G, Ye Y. Semantic Loop Closure Detection with Instance-Level Inconsistency Removal in Dynamic Industrial Scenes[J]. IEEE Transactions on Industrial Informatics, 2020.
- 动态工业场景中具有实例级不一致消除功能的语义闭环检测
- 厦门大学 | 期刊：中科院一区，JCR Q1，IF 9.1
[8] Li Y, Brasch N, Wang Y, et al. Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 6583-6590.
- Structure-SLAM：室内环境中的低漂移单目 SLAM
- TUM | 代码开源
[9] Liu J, Meng Z. Visual SLAM with Drift-Free Rotation Estimation in Manhattan World[J]. IEEE Robotics and Automation Letters, 2020.
- 亚特兰大世界的全局最优和有效消失点估计
- 港中文刘云辉教授组 Haoang Li
[10] Hou J, Yu L, Fei S. A highly robust automatic 3D reconstruction system based on integrated optimization by point line features[J]. Engineering Applications of Artificial Intelligence, 2020, 95: 103879.
- 基于点线联合优化的自动三维重建
- 苏州大学、东南大学 | 期刊：中科院二区，JCR Q1，IF 4.2
[11] Li H, Kim P, Zhao J, et al. Globally Optimal and Efficient Vanishing Point Estimation in Atlanta World[J]. 2020.
- 曼哈顿世界中无漂移旋转估计的视觉 SLAM
- 港中文，西蒙弗雷泽大学
[12] Wang X, Christie M, Marchand E. Relative Pose Estimation and Planar Reconstruction via Superpixel-Driven Multiple Homographies[C]//IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'20. 2020.
- 通过超像素驱动的多个单应性的相对姿势估计和平面重建
- 雷恩大学
[13] Zuñiga-Noël D, Jaenal A, Gomez-Ojeda R, et al. The UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments[J]. The International Journal of Robotics Research, 2020: 0278364920938439.
- UMA-VI 数据集：在低纹理和动态照明环境中的视觉惯性里程计
- 马拉加大学 | PL-SLAM 的作者 | 期刊：中科院二区，JCR Q1，IF 4.0 | 数据集地址

2. Sensor Fusion

[14] Zuo X, Yang Y, Geneva P, et al. LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking[J]. arXiv preprint arXiv:2008.07196, 2020.
- LIC-Fusion 2.0：滑动窗口平面特征跟踪的 LiDAR-惯性-视觉里程计
- 浙大、ETH Google Scholar
- 相关论文：LIC-Fusion: LiDAR-Inertial-Camera Odometry
[15] Alliez P, Bonardi F, Bouchafa S, et al. Real-Time Multi-SLAM System for Agent Localization and 3D Mapping in Dynamic Scenarios[C]//International Confererence on Intelligent Robots and Systems (IROS 2020). 2020.
- 实时 Multi-SLAM 系统，用于动态场景中智能体定位和 3D 建图
- 法国国家信息与自动化研究所
[16] Shao X, Zhang L, Zhang T, et al. A Tightly-coupled Semantic SLAM System with Visual, Inertial and Surround-view Sensors for Autonomous Indoor Parking[J].2020.
- 具有视觉、惯性和全景传感器的紧密耦合语义 SLAM 系统，用于自主室内停车
- 同济大学
[17] Shan M, Feng Q, Atanasov N. OrcVIO: Object residual constrained Visual-Inertial Odometry[J]. arXiv preprint arXiv:2007.15107, 2020.(IROS 2020)
- 物体残差约束的 VIO
- 加州大学圣地亚哥分校 | Nikolay A. Atanasov | Mo Shan
[18] Seok H, Lim J. ROVINS: Robust Omnidirectional Visual Inertial Navigation System[J]. IEEE Robotics and Automation Letters, 2020.
- 鲁棒的全方位视觉惯性导航系统
- 韩国汉阳大学
[19] Liu W, Caruso D, Ilg E, et al. TLIO: Tight Learned Inertial Odometry[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 5653-5660.
- 基于学习的紧耦合的惯性里程计
- 宾夕法尼亚大学 | 代码开源 | 项目主页
[20] Sartipi K, Do T, Ke T, et al. Deep Depth Estimation from Visual-Inertial SLAM[J]. arXiv preprint arXiv:2008.00092, 2020.
- 视觉惯性 SLAM 深度估计
- 明尼苏达大学 | 代码开源

3. Semantic/Deep SLAM

[21] Gomez C, Hernandez A C, Derner E, et al. Object-Based Pose Graph for Dynamic Indoor Environments[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 5401-5408.
- 动态室内环境中基于物体的位姿图
- 马德里卡洛斯三世大学 | Google Scholar（有很多基于物体的机器人导航的工作）
[22] Wang H, Wang C, Xie L. Online Visual Place Recognition via Saliency Re-identification[J]. arXiv preprint arXiv:2007.14549, 2020.（IROS 2020）
- 通过显著性重识别进行视觉场景重识别
- CMU | 代码开源
[23] Jau Y Y, Zhu R, Su H, et al. Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints[J]. arXiv preprint arXiv:2007.15122, 2020.（IROS 2020）
- 具有几何约束的基于深度关键点的相机姿势估计
- 加州大学圣地亚哥分校 | 代码开源
[24] Gong X, Liu Y, Wu Q, et al. An Accurate, Robust Visual Odometry and Detail-preserving Reconstruction System[J]. 2020.
- 准确、鲁棒的视觉里程计和保留细节的重建系统
- 南京航空航天大学 | 代码开源（暂未公开）
[25] Wei P, Hua G, Huang W, et al. Unsupervised Monocular Visual-inertial Odometry Network[J].IJCAI 2020
- 无监督单目视惯里程计网络
- 北大 | 代码开源（暂未公开）
[26] Li D, Shi X, Long Q, et al. DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features[J]. arXiv preprint arXiv:2008.05416, 2020.（IROS 2020）
- 基于深度信息的鲁棒高效视觉 SLAM 系统
- 清华大学 | 代码开源

4. AR/VR/MR

[27] Tahara T, Seno T, Narita G, et al. Retargetable AR: Context-aware Augmented Reality in Indoor Scenes based on 3D Scene Graph[J]. arXiv preprint arXiv:2008.07817, 2020.
- 可重定位的 AR：基于室内 3D 场景图中的情景感知增强现实
- 索尼
[28] Li X, Tian Y, Zhang F, et al. Object Detection in the Context of Mobile Augmented Reality[J]. arXiv preprint arXiv:2008.06655, 2020.（ISMAR 2020）
- 移动增强现实环境下的目标检测
- oppo
[29] Liu C, Shen S. An Augmented Reality Interaction Interface for Autonomous Drone[J]. arXiv preprint arXiv:2008.02234, 2020.（IROS 2020）
- 自主无人机增强现实交互界面
- 港科沈邵劼组
[30] Du R, Turner E L, Dzitsiuk M, et al. DepthLab: Real-time 3D Interaction with Depth Maps for Mobile Augmented Reality[J]. 2020.
- 使用深度地图实时 3D 交互的移动增强现实
- Google

2020 年 7 月论文更新（20 篇）

本期更新于 2020 年 7 月 27 日
共 20 篇论文，其中 8 项（待）开源工作
本月月初 ECCV，IROS 放榜，不少新论文出现
2 隐私保护的视觉 SLAM，11 秦通大佬的 AVP-SLAM
月底 ORB-SLAM3 又制造了大新闻，谷歌学术都没来得及收录，国内公众号都出解析了

1. Geometric SLAM

[1] Carlos Campos, Richard Elvira, et al.ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM[J]. arXiv preprint arXiv:2007.11898, 2020.
- ORB-SLAM3: 集视觉、视惯、多地图 SLAM 于一体
- 西班牙萨拉戈萨大学 | 代码开源 | Video
[2] Shibuya M, Sumikura S, Sakurada K. Privacy Preserving Visual SLAM[J]. arXiv preprint arXiv:2007.10361, 2020.(ECCV 2020)
- 保护隐私的视觉 SLAM
- 日本国立先进工业科学技术研究院；东京工业大学
- 作者 Shinya Sumikura 谷歌学术，OpenVSLAM 的作者 | 项目主页 | Video
- 相关论文：CVPR 2019 Privacy preserving image-based localization
[3] Tompkins A, Senanayake R, Ramos F. Online Domain Adaptation for Occupancy Mapping[J]. arXiv preprint arXiv:2007.00164, 2020.（RSS 2020）
- 在线区域调整以进行占有地图构建
- 悉尼大学 | 代码开源 | Video
[4] Li Y, Zhang T, Nakamura Y, et al. SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes[J]. arXiv preprint arXiv:2007.02108, 2020.(IROS 2020)
- SplitFusion：非刚性场景的 SLAM
- 东京大学
[5] WeiChen Dai, Yu Zhang, Ping Li, and Zheng Fang, Sebastian Scherer. RGB-D SLAM in Dynamic Environments Using Points Correlations. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
- 动态环境中使用点关联的 RGB-D SLAM
- 浙江大学；期刊：PAMI 中科院一区，JCR Q1，IF 17.86
[6] Huang H, Ye H, Sun Y, et al. GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture Models[J]. IEEE Robotics and Automation Letters, 2020.
- 高斯混合模型的结构一致视觉定位
- 香港科技大学 | 代码开源

2. Sensor Fusion

[7] Zuo X, Ye W, Yang Y, et al. Multimodal localization: Stereo over LiDAR map[J]. Journal of Field Robotics, 2020.
- 多模式定位：在 LiDAR 先验地图中使用双目相机定位
- 浙江大学、特拉华大学 | 作者谷歌学术 | 期刊：中科院二区，JCR Q2，IF 4.19
[8] Shan T, Englot B, Meyers D, et al. LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping[J]. arXiv preprint arXiv:2007.00258, 2020.（IROS 2020）
- 平滑紧耦合的激光雷达里程计和建图
- MIT | 代码开源 | Video
[9] Rozenberszki D, Majdik A. LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Maps[J]. arXiv preprint arXiv:2007.01595, 2020.（ICRA 2020）
- 3D 点云地图中仅激光雷达的里程计和定位
- 匈牙利科学院机器感知实验室 | 代码开源 | Video
[10] You R, Hou H. Real-Time Pose Estimation by Fusing Visual and Inertial Sensors for Autonomous Driving[J]. 2020.
- 通过融合视觉和惯性传感器进行自动驾驶的实时位姿估计
- 瑞典查尔默斯理工大学硕士学位论文 | 代码开源

3. Semantic/Deep SLAM

[11] Qin T, Chen T, Chen Y, et al. AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot[J]. arXiv preprint arXiv:2007.01813, 2020.(IROS 2020)
- AVP-SLAM：停车场中自动驾驶车辆的语义 SLAM
- 华为秦通 | 知乎文章
[12] Gomez C, Silva A C H, Derner E, et al. Object-Based Pose Graph for Dynamic Indoor Environments[J]. IEEE Robotics and Automation Letters, 2020.
- 动态室内环境中基于物体的位姿图
- 西班牙马德里卡洛斯三世大学
[13] Costante G, Mancini M. Uncertainty Estimation for Data-Driven Visual Odometry[J]. IEEE Transactions on Robotics, 2020.
- 数据驱动的视觉测程的不确定度估计
- 意大利佩鲁贾大学 | 代码开源（还未放出） | 期刊：中科院二区，JCR Q1，IF 7.0
[14] Min Z, Yang Y, Dunn E. VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR 2020: 4898-4909.
- 对数逻辑密集光流残差的视觉里程计
- 史蒂文斯技术学院 | 代码开源（还未放出）
[15] Zou Y, Ji P, Tran Q H, et al. Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling[J]. arXiv preprint arXiv:2007.10983, 2020.（ECCV 2020）
- 自监督长期建模学习单目视觉里程计
- 弗吉尼亚理工大学 | 项目主页 | 代码 Coming soon
[16] Wei P, Hua G, Huang W, et al. Unsupervised Monocular Visual-inertial Odometry Network[J].2020
- 无监督单目视惯里程计
- 北京大学 | 代码开源（还未放出）

4. AR/VR/MR

[17] Chen Y, Zhang B, Zhou J, et al. Real-time 3D unstructured environment reconstruction utilizing VR and Kinect-based immersive teleoperation for agricultural field robots[J]. Computers and Electronics in Agriculture, 2020, 175: 105579.
- 利用 VR 和基于 Kinect 的沉浸式远程操作技术对农业机器人进行实时 3D 非结构化环境重构
- 南京农业大学 | 期刊：中科院二区，JCR Q1，IF 4.0
[18] Choi J, Son M G, Lee Y Y, et al. Position-based augmented reality platform for aiding construction and inspection of offshore plants[J]. The Visual Computer, 2020: 1-11.
- 基于位置的增强现实平台，用于辅助海上工厂的建设和检查
- 韩国光州科学技术院 | 期刊：中科院四区，JCR Q3

5. Others

[19] Brazil G, Pons-Moll G, Liu X, et al. Kinematic 3D Object Detection in Monocular Video[J]. arXiv preprint arXiv:2007.09548, 2020.(ECCV 2020)
- 单目视频中运动 3D 目标检测
- 密歇根州立大学 | 实验室主页 | [项目主页](

Truncated — view the full README on GitHub.

mapping

opensource

papers

semantic

slam

visualodometry

Significant stargazers

Terry Yue Zhuo

130 followers · starred Sep 2020

Jingchen Ye

115 followers · starred Jul 2021

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前言

目录

1. 开源代码

😄 1.1 Geometric SLAM

1. PTAM

2. S-PTAM（双目 PTAM）

3. MonoSLAM

4. ORB-SLAM2

5. DSO

6. LDSO

7. LSD-SLAM

8. DVO-SLAM

9. SVO

10. DSM

11. openvslam

12. se2lam（地面车辆位姿估计的视觉里程计）

13. GraphSfM（基于图的并行大规模 SFM）

14. LCSD_SLAM（松耦合的半直接法单目 SLAM）

15. RESLAM（基于边的 SLAM）

16. scale_optimization（将单目 DSO 拓展到双目）

17. BAD-SLAM（直接法 RGB-D SLAM）

18. GSLAM（集成 ORB-SLAM2，DSO，SVO 的通用框架）

19. ARM-VO（运行于 ARM 处理器上的单目 VO）

20. cvo-rgbd（直接法 RGB-D VO）

21. Map2DFusion（单目 SLAM 无人机图像拼接）

22. CCM-SLAM（多机器人协同单目 SLAM）

23. ORB-SLAM3

24. OV²SLAM（完全实时在线多功能 SLAM）

25. ESVO（基于事件的双目视觉里程计）

26. VOLDOR-SLAM（实时稠密非直接法 SLAM）

😄 1.2 Semantic / Deep SLAM

1. MsakFusion

2. SemanticFusion

3. semantic_3d_mapping

4. Kimera（实时度量与语义定位建图开源库）

5. NeuroSLAM（脑启发式 SLAM）

6. gradSLAM（自动分区的稠密 SLAM）

7. ORB-SLAM2 + 目标检测/分割的方案语义建图

8. SIVO（语义辅助特征选择）

9. FILD（临近图增量式闭环检测）

10. object-detection-sptam（目标检测与双目 SLAM）

11. Map Slammer（单目深度估计 + SLAM）

12. NOLBO（变分模型的概率 SLAM）

13. GCNv2_SLAM （基于图卷积神经网络 SLAM）

14. semantic_suma（激光语义建图）

15. Neural-SLAM（主动神经 SLAM）

16. TartanVO：一种通用的基于学习的 VO

17. DF-VO

😄 1.3 Multi-Landmarks / Object SLAM

1. PL-SVO（点线 SVO）

2. stvo-pl（双目点线 VO）

3. PL-SLAM（点线 SLAM）

4. PL-VIO

5. lld-slam（用于 SLAM 的可学习型线段描述符）

6. PlaneSLAM

7. Eigen-Factors（特征因子平面对齐）

8. PlaneLoc

9. Pop-up SLAM

10. Object SLAM

11. voxblox-plusplus（物体级体素建图）

12. Cube SLAM

13. VPS-SLAM（平面语义 SLAM）

14. Structure-SLAM （低纹理环境下点线 SLAM）

15. PL-VINS

😄 1.4 Sensor Fusion

1. msckf_vio

2. rovio

3. R-VIO

4. okvis

5. VIORB

6. VINS-mono

7. VINS-RGBD

8. Open-VINS

9. versavis（多功能的视惯传感器系统）

10. CPI（视惯融合的封闭式预积分）

11. TUM Basalt

12. Limo（激光单目视觉里程计）

12. 加拿大蒙特利尔大学机器人与嵌入式 AI 实验室