RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation
By Mathieu Labbé, François Michaud. J. Field Robotics, 2018
946 citations recorded by the source corpus.
Distributed as an open‐source library since 2013, real‐time appearance‐based mapping (RTAB‐Map) started as an appearance‐based loop closure detection approach with memory management to deal with large‐scale and long‐term online operation. It then grew to implement simultaneous localization and mapping (SLAM) on various robots and mobile platforms. As each application brings its own set of constraints on sensors, processing capabilities, and locomotion, it raises the question of which SLAM approach is the most appropriate to use in terms of cost, accuracy, computation power, and ease of integration. Since most of SLAM approaches are either visual‐ or lidar‐based, comparison is difficult. Therefore, we decided to extend RTAB‐Map to support both visual and lidar SLAM, providing in one package a tool allowing users to implement and compare a variety of 3D and 2D solutions for a wide range of applications with different robots and sensors. This paper presents this extended version of RTAB‐Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real‐world datasets (e.g., KITTI, EuRoC, TUM RGB‐D, MIT Stata Center on PR2 robot), outlining strengths, and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications.
Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Geology
View Paper: https://www.semanticscholar.org/paper/e28ebcc38b8735a679a18362bdb0d008913f0ec3
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Citations in the corpus
- ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning (2023, methodology): “In both cases, we map the scene with an Azure Kinect Camera and rely on RTAB-Map [69] to obtain camera poses and the scene point cloud.”
- Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “Direct matching [112], [113], [114] is called dense visual SLAM, which adopts original information for matching that is dense in the image,”
- A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “[69] M. Labbé and F. Michaud, “RTAB-Map as an open-source LiDAR and visual simultaneous localization and mapping library for large-scale and long-term online operation,” J. Field Robot., vol. 36, no. 2, pp. 416–446, 2019.”
- Combining Optimal Control and Learning for Visual Navigation in Novel Environments (2019, methodology): “Geometric mapping and planning is implemented using the RTAB-Map package [45].”
- Visual Language Maps for Robot Navigation (2022, methodology): “During inference, we also use the global localization module of RTAB-Map to initialize the robot pose.”