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**RTAB-Map** (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo and Lidar Graph-Based SLAM approach based on an incremental appearance-based loop closure detector. The loop closure detector uses a bag-of-words approach to determinate how likely a new image comes from a previous location or a new location. When a loop closure hypothesis is accepted, a new constraint is added to the map's graph, then a graph optimizer minimizes the errors in the map. A memory management approach is used to limit the number of locations used for loop closure detection and graph optimization, so that real-time constraints on large-scale environnements are always respected. RTAB-Map can be used alone with a handheld Kinect, a stereo camera or a 3D lidar for 6DoF mapping, or on a robot equipped with a laser rangefinder for 3DoF mapping. **RTAB-Map** (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo and Lidar Graph-Based SLAM approach based on an incremental appearance-based loop closure detector. The loop closure detector uses a bag-of-words approach to determinate how likely a new image comes from a previous location or a new location. When a loop closure hypothesis is accepted, a new constraint is added to the map's graph, then a graph optimizer minimizes the errors in the map. A memory management approach is used to limit the number of locations used for loop closure detection and graph optimization, so that real-time constraints on large-scale environnements are always respected. RTAB-Map can be used alone with a handheld Kinect, a stereo camera or a 3D lidar for 6DoF mapping, or on a robot equipped with a laser rangefinder for 3DoF mapping.
#### Illumination-Invariant Visual Re-Localization #### Illumination-Invariant Visual Re-Localization
* M. Labbé and F. Michaud, “[Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments](https://arxiv.org/abs/2103.03827),” in _Frontiers in Robotics and AI_, vol. 9, 2022. ([Frontiers](https://doi.org/10.3389/frobt.2022.801886)) ([Dataset link](https://github.com/introlab/rtabmap/tree/master/archive/2022-IlluminationInvariant)) * M. Labbé and F. Michaud, “[Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments](https://arxiv.org/abs/2103.03827),” in _Frontiers in Robotics and AI_, vol. 9, 2022. ([Frontiers](https://doi.org/10.3389/frobt.2022.801886)) ([Dataset link](https://github.com/introlab/rtabmap/tree/master/archive/2022-IlluminationInvariant)) ([Google Scholar](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=G3BrBkMAAAAJ&citation_for_view=G3BrBkMAAAAJ:ufrVoPGSRksC))
#### Lidar and Visual SLAM #### Lidar and Visual SLAM
* M. Labbé and F. Michaud, “[RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/7/7a/Labbe18JFR_preprint.pdf),” in _Journal of Field Robotics_, vol. 36, no. 2, pp. 416–446, 2019. ([Wiley](https://doi.org/10.1002/rob.21831)) * M. Labbé and F. Michaud, “[RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/7/7a/Labbe18JFR_preprint.pdf),” in _Journal of Field Robotics_, vol. 36, no. 2, pp. 416–446, 2019. ([Wiley](https://doi.org/10.1002/rob.21831)) ([Google Scholar](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=G3BrBkMAAAAJ&citation_for_view=G3BrBkMAAAAJ:Y0pCki6q_DkC))
#### Simultaneous Planning, Localization and Mapping (SPLAM) #### Simultaneous Planning, Localization and Mapping (SPLAM)
* M. Labbé and F. Michaud, “[Long-term online multi-session graph-based SPLAM with memory management](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/8/87/LabbeAURO2017.pdf),” in _Autonomous Robots_, vol. 42, no. 6, pp. 1133-1150, 2018. ([Springer](http://dx.doi.org/10.1007/s10514-017-9682-5)) * M. Labbé and F. Michaud, “[Long-term online multi-session graph-based SPLAM with memory management](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/8/87/LabbeAURO2017.pdf),” in _Autonomous Robots_, vol. 42, no. 6, pp. 1133-1150, 2018. ([Springer](http://dx.doi.org/10.1007/s10514-017-9682-5)) ([Google Scholar](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=G3BrBkMAAAAJ&citation_for_view=G3BrBkMAAAAJ:Tyk-4Ss8FVUC))
#### Multi-session SLAM #### Multi-session SLAM
* M. Labbé and F. Michaud, “[Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/e/eb/Labbe14-IROS.pdf),” in _Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems_, 2014. ([IEEE Xplore](http://ieeexplore.ieee.org/document/6942926/)) * M. Labbé and F. Michaud, “[Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/e/eb/Labbe14-IROS.pdf),” in _Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems_, 2014. ([IEEE Xplore](http://ieeexplore.ieee.org/document/6942926/)) ([Google Scholar](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=G3BrBkMAAAAJ&citation_for_view=G3BrBkMAAAAJ:9yKSN-GCB0IC))
* Results shown in this paper can be reproduced by the [Multi-session mapping](https://github.com/introlab/rtabmap/wiki/Multi-session) tutorial. * Results shown in this paper can be reproduced by the [Multi-session mapping](https://github.com/introlab/rtabmap/wiki/Multi-session) tutorial.
#### Loop closure detection #### Loop closure detection
* M. Labbé and F. Michaud, “[Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/b/bc/TRO2013.pdf),” in _IEEE Transactions on Robotics_, vol. 29, no. 3, pp. 734-745, 2013. ([IEEE Xplore](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6459608)) * M. Labbé and F. Michaud, “[Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/b/bc/TRO2013.pdf),” in _IEEE Transactions on Robotics_, vol. 29, no. 3, pp. 734-745, 2013. ([IEEE Xplore](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6459608)) ([Google Scholar](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=G3BrBkMAAAAJ&citation_for_view=G3BrBkMAAAAJ:u-x6o8ySG0sC))
* M. Labbé and F. Michaud, “[Memory management for real-time appearance-based loop closure detection](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/f/f0/Labbe11memory.pdf),” in _Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems_, 2011, pp. 1271–1276. ([IEEE Xplore](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6094602)) * M. Labbé and F. Michaud, “[Memory management for real-time appearance-based loop closure detection](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/f/f0/Labbe11memory.pdf),” in _Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems_, 2011, pp. 1271–1276. ([IEEE Xplore](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6094602)) ([Google Scholar](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=G3BrBkMAAAAJ&citation_for_view=G3BrBkMAAAAJ:u5HHmVD_uO8C))
* Visit [RTAB-Map's page on IntRoLab](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/index.php/RTAB-Map) for detailed information on the loop closure detection approach and related datasets. * Visit [RTAB-Map's page on IntRoLab](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/index.php/RTAB-Map) for detailed information on the loop closure detection approach and related datasets.
## Install ## Install