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Overview

Under construction!!!! moving Wiki from googlecode wiki

Author(s): Mathieu Labbé

Maintainer(s): Mathieu Labbé

License: BSD

Web page: http://introlab.github.io/rtabmap

Source: https://github.com/introlab/rtabmap

Dependencies:

Description: RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D Graph SLAM approach based on a global Bayesian 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.

Visit rtabmap_ros to know how using RTAB-Map under ROS.

Citing

If you use rtabmap in academic context, please cite the following publication:

• RGBD-SLAM

@INPROCEEDINGS{labbe14online, 
  author={Labbe, M. and Michaud, F.}, 
  booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems}, 
  title={{Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM}}, 
  year={2014}, 
  month={Sept}, 
  pages={2661-2666} 
}

• Loop closure detection

@ARTICLE{labbe13appearance,
  author = {Labbe, M. and Michaud, F.},
  title = {{Appearance-Based Loop Closure Detection for Online Large-Scale and
        Long-Term Operation}},
  journal = {IEEE Transactions on Robotics},
  year = {2013},
  volume = {29},
  pages = {734-745},
  number = {3}
}