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
- 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
Australia
Datasets
Code
- ORB-SLAM
- LSD-SLAM
- ORB-SLAM2
- DVO: Dense Visual Odometry
- SVO: Semi-Direct Monocular Visual Odometry
- G2O: General Graph Optimization
- RGBD-SLAM
Miscellaneous
Contributing
Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.
