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<img src="https://raw.githubusercontent.com/introlab/rtabmap/master/guilib/src/images/RTAB-Map.png" alt="RTAB-Map logo" title="RTAB-Map" align="left" width="120"> <img src="https://raw.githubusercontent.com/introlab/rtabmap/master/guilib/src/images/RTAB-Map.png" alt="RTAB-Map logo" title="RTAB-Map" align="left" width="120">
**RTAB-Map** (Real-Time Appearance-Based Mapping) is a RGB-D 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 hand-held Kinect or stereo camera for 6DoF RGB-D mapping, or on a robot equipped with a laser rangefinder for 3DoF mapping. **RTAB-Map** (Real-Time Appearance-Based Mapping) is a RGB-D 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 hand-held Kinect or stereo camera for 6DoF RGB-D mapping, or on a robot equipped with a laser rangefinder for 3DoF mapping.
#### Simultaneous Planning, Localization and Mapping (SPLAM)
* M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” in ''Autonomous Robots'', accepted, 2017. ([[Media:LabbeAURO2017.pdf|pdf]]) ([http://dx.doi.org/10.1007/s10514-017-9682-5 Springer])
#### RGB-D mapping #### RGB-D mapping
* 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/))
* 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.