The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation
Open accessAbstract
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.
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Cited by
Visual Language Maps for Robot Navigation
Huang, C., Mees, O., Zeng, A., & Burgard, W. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160969 · Cited by 376
Hu, H., Zhang, K., Tan, A. H., Ruan, M., Agia, C. G., & Nejat, G. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3093551 · Cited by 93
Embodied navigation with multi-modal information: A survey from tasks to methodology
Wu, Y., Zhang, P., Gu, M., Zheng, J., & Bai, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102532 · Cited by 34
Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation
Schmalstieg, F., Honerkamp, D., Welschehold, T., & Valada, A. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3329619 · Cited by 21
Learning to Infer Kinematic Hierarchies for Novel Object Instances
Abdul-Rashid, H., Freeman, M., Abbatematteo, B., Konidaris, G., & Ritchie, D. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811968 · Cited by 9
Metadata
- Authors
- Labbé, M., & Michaud, F.
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2018-10-27
- Volume
- 36
- Issue
- 2
- Pages
- 416-446
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W2898231767
- Cited by
- 989
- References
- 75
- FWCI
- 50.38
- Type
- journal article
- Language
- en
- Open access
- green
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- Labbé, M., & Michaud, F. (2019). RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation. Journal of Field Robotics. https://doi.org/10.1002/rob.21831
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