Visit rtabmap_ros wiki page for nodes documentation, demos and tutorials on ROS.For the loop closure detection approach, visit RTAB-Map on IntRoLab website.Ask a question on RTAB-Map Forum ( New address! August 9, 2021).
See also SetupOnYourRobot to know how to integrate RTAB-Map on your robot. For ROS users, take a look to rtabmap page on the ROS wiki for a package overview.Visit RTAB-Map’s page on IntRoLab for detailed information on the loop closure detection approach and related datasets.Michaud, “ Memory management for real-time appearance-based loop closure detection,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. Michaud, “ Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation,” in IEEE Transactions on Robotics, vol. Results shown in this paper can be reproduced by the Multi-session mapping tutorial.Michaud, “ Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014. Michaud, “ Long-term online multi-session graph-based SPLAM with memory management,” in Autonomous Robots, vol. Simultaneous Planning, Localization and Mapping (SPLAM) Michaud, “ RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation,” in Journal of Field Robotics, vol. 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. 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. 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. 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. 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.