M2DGR-plus: Extension and update of M2DGR, a novel Multi-modal and Multi-scenario SLAM Dataset for Ground Robots (ICRA2022 & ICRA2024)

Figure 1. Acquisition Platform and Diverse Scenarios.

News & Updates

• 🔥2024/10/11: Introducing M2DGR-benchmark, benchmarking newest SOTA LiDAR-visual SLAM alrogithms on both M2DGR and M2DGR-plus!

• 2024/07/15: Introducing a list of LiDAR-Visual SLAM systems at awesome-LiDAR-Visual-SLAM, wheel-based SLAM systems at awesome-wheel-slam, and Isaac Sim resources at awesome-isaac-sim (keep updating)

This dataset is based on M2DGR. And the algorithm code is Ground-Fusion. The preprint version of this paper is arxiv.

1.LICENSE

This work is licensed under GPL-3.0 license. International License and is provided for academic purpose. If you are interested in our project for commercial purposes, please contact us on [email protected] for further communication.

If you use this work in an academic work, please cite:

@article{yin2021m2dgr,
  title={M2dgr: A multi-sensor and multi-scenario slam dataset for ground robots},
  author={Yin, Jie and Li, Ang and Li, Tao and Yu, Wenxian and Zou, Danping},
  journal={IEEE Robotics and Automation Letters},
  volume={7},
  number={2},
  pages={2266--2273},
  year={2021},
  publisher={IEEE}
}

@INPROCEEDINGS{yin2024ground,
  author={Yin, Jie and Li, Ang and Xi, Wei and Yu, Wenxian and Zou, Danping},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases}, 
  year={2024},
  volume={},
  number={},
  pages={8603-8609},
  keywords={Location awareness;Visualization;Simultaneous localization and mapping;Accuracy;Wheels;Sensor fusion;Land vehicles},
  doi={10.1109/ICRA57147.2024.10610070}}

2.SENSOR SETUP

The calibration results are here. All the sensors and track devices and their most important parameters are listed as below:

• LIDAR Robosense 16, 360 Horizontal Field of View (FOV),-30 to +10 vertical FOV,5Hz,Max Range 200 m,Range Resolution 3 cm, Horizontal Angular Resolution 0.2°.
• GNSS Ublox F9p, GPS/BeiDou/Glonass/Galileo, 1Hz
• V-I Sensor,Realsense d435i,RGB/Depth 640*480,69H-FOV,42.5V-FOV,15Hz;IMU 6-axix, 200Hz
• IMU,wheeltec,9-axis,100Hz;
• GNSS-IMU Xsens Mti 680G. GNSS-RTK,localization precision 2cm,100Hz;IMU 9-axis,100 Hz;
• Motion-capture System Vicon Vero 2.2, localization accuracy 1mm, 50 Hz;

The rostopics of our rosbag sequences are listed as follows:

• 3D LIDAR: /rslidar_points

• 2D LIDAR: /scan

• Odom: /odom

• GNSS Ublox F9p:
  /ublox_driver/ephem ,

/ublox_driver/glo_ephem ,

/ublox_driver/range_meas ,

/ublox_driver/receiver_lla ,

/ublox_driver/receiver_pvt

• V-I Sensor:
  /camera/color/image_raw,
  /camera/imu

• IMU: /imu

3.DATASET SEQUENCES

[!TIP] We are delighted to see that many users enjoy our M2DGR-plus dataset. However, due to an overwhelming number of download requests, OneDrive has become unstable. If you encounter any issues, please wait for about 10 minutes and try again.

4. EXPERIMENTAL RESULTS

We test methods with diverse senser settings to validate our benchmark dataset. Results shown that our dataset is a valid and effective testfield for localization methods.

And in some cases, our Ground-Fusion achieves comparable performance to Lidar SLAM!

Figure 2. The ATE RMSE (m) result on some sequences.

Figure 3. The visualized trajectory.

5. Configuration Files

We provide configuration files for several cutting-edge baseline methods, including VINS-RGBD,TartanVO,VINS-Mono and VIW-Fusion and GVINS.