Awesome SLAM

Simultaneous Localization and Mapping, also known as SLAM, is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.

News

• For researchers, please read the recent review paper, Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age, from Cesar Cadena, Luca Carlone et al.

Table of Contents

• Books

• Courses, Lectures and Workshops

• Papers

• Researchers

• Datasets

• Code

• Miscellaneous

• Contributing

Books

• State Estimation for Robotic -- A Matrix Lie Group Approach by Timothy D. Barfoot, 2018
• Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods by Juan-Antonio Fernández-Madrigal and José Luis Blanco Claraco, 2012
• Simultaneous Localization and Mapping: Exactly Sparse Information Filters by Zhan Wang, Shoudong Huang and Gamini Dissanayake, 2011
• Probabilistic Robotics by Dieter Fox, Sebastian Thrun, and Wolfram Burgard, 2005
• An Invitation to 3-D Vision -- from Images to Geometric Models by Yi Ma, Stefano Soatto, Jana Kosecka and Shankar S. Sastry, 2005
• Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman, 2004
• Numerical Optimization by Jorge Nocedal and Stephen J. Wright, 1999

Courses, Lectures and Workshops

• SLAM Tutorial@ICRA 2016
• Geometry and Beyond - Representations, Physics, and Scene Understanding for Robotics at Robotics: Science and Systems (2016)
• Robotics - UPenn on Coursera by Vijay Kumar (2016)
• Robot Mapping - UniFreiburg by Gian Diego Tipaldi and Wolfram Burgard (2015-2016)
• Robot Mapping - UniBonn by Cyrill Stachniss (2016)
• Introduction to Mobile Robotics - UniFreiburg by Wolfram Burgard, Michael Ruhnke and Bastian Steder (2015-2016)
• Computer Vision II: Multiple View Geometry - TUM by Daniel Cremers ( Spring 2016)
• Advanced Robotics - UCBerkeley by Pieter Abbeel (Fall 2015)
• Mapping, Localization, and Self-Driving Vehicles at CMU RI seminar by John Leonard (2015)
• The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM sponsored by Australian Centre for Robotics and Vision (2015)
• Robotics - UPenn by Philip Dames and Kostas Daniilidis (2014)
• Autonomous Navigation for Flying Robots on EdX by Jurgen Sturm and Daniel Cremers (2014)
• Robust and Efficient Real-time Mapping for Autonomous Robots at CMU RI seminar by Michael Kaess (2014)
• KinectFusion - Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera by David Kim (2012)
• SLAM Summer School organized by Australian Centre for Field Robotics (2009)
• SLAM Summer School organized by University of Oxford and Imperial College London (2006)
• SLAM Summer School organized by KTH Royal Institute of Technology (2002)

Papers

• Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age (2016)
• Direct Sparse Odometry (2016)
• Modelling Uncertainty in Deep Learning for Camera Relocalization (2016)
• Large-Scale Cooperative 3D Visual-Inertial Mapping in a Manhattan World (2016)
• Towards Lifelong Feature-Based Mapping in Semi-Static Environments (2016)
• Tree-Connectivity: Evaluating the Graphical Structure of SLAM (2016)
• Visual-Inertial Direct SLAM (2016)
• A Unified Resource-Constrained Framework for Graph SLAM (2016)
• Multi-Level Mapping: Real-time Dense Monocular SLAM (2016)
• Lagrangian duality in 3D SLAM: Verification techniques and optimal solutions (2015)
• A Solution to the Simultaneous Localization and Map Building (SLAM) Problem
• Simulataneous Localization and Mapping with the Extended Kalman Filter
• Robotic Mapping: A Survey

Researchers

United States

• John Leonard
• Sebastian Thrun
• Frank Dellaert
• Dieter Fox
• Stergios I. Roumeliotis
• Vijay Kumar
• Ryan Eustice
• Michael Kaess
• Guoquan (Paul) Huang
• Gabe Sibley
• Luca Carlone
• Andrea Censi

Europe

• Paul Newman
• Roland Siegwart
• Juan Nieto
• Wolfram Burgard
• Jose Neira
• Davide Scaramuzza

Australia

• Cesar Cadena
• Ian Reid
• Tim Bailey
• Gamini Dissanayake
• Shoudong Huang

Datasets

1. Intel Research Lab (Seattle)

Code

1. ORB-SLAM
2. LSD-SLAM
3. ORB-SLAM2
4. DVO: Dense Visual Odometry
5. SVO: Semi-Direct Monocular Visual Odometry
6. G2O: General Graph Optimization
7. RGBD-SLAM

Miscellaneous

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Contributing

Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.

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License