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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 handheld Kinect or stereo camera for 6DoF RGB-D mapping, and/or on a robot equipped with a laser rangefinder for 3DoF mapping (2D LiDAR) and 6DoF mapping (3D LiDAR).

Visit rtabmap_ros to know how to use RTAB-Map under ROS. The rtabmap package is only for convenient release of the RTAB-Map libraries and standalone application. Visit RTAB-Map's wiki to know how to use the standalone application and tools that come with this package:


If you use rtabmap in academic context, please cite the appropriate publication from http://introlab.github.io/rtabmap

2024-07-13 14:38