Dynamic pose graph SLAM: Long-term mapping in low dynamic environments
Author(s)
Walcott-Bryant, Aisha; Kaess, Michael; Johannsson, Hordur; Leonard, John Joseph
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Maintaining a map of an environment that changes over time is a critical challenge in the development of persistently autonomous mobile robots. Many previous approaches to mapping assume a static world. In this work we incorporate the time dimension into the mapping process to enable a robot to maintain an accurate map while operating in dynamical environments. This paper presents Dynamic Pose Graph SLAM (DPG-SLAM), an algorithm designed to enable a robot to remain localized in an environment that changes substantially over time. Using incremental smoothing and mapping (iSAM) as the underlying SLAM state estimation engine, the Dynamic Pose Graph evolves over time as the robot explores new places and revisits previously mapped areas. The approach has been implemented for planar indoor environments, using laser scan matching to derive constraints for SLAM state estimation. Laser scans for the same portion of the environment at different times are compared to perform change detection; when sufficient change has occurred in a location, the dynamic pose graph is edited to remove old poses and scans that no longer match the current state of the world. Experimental results are shown for two real-world dynamic indoor laser data sets, demonstrating the ability to maintain an up-to-date map despite long-term environmental changes.
Date issued
2013-05-16Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Mechanical EngineeringJournal
Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Walcott-Bryant, Aisha et al. “Dynamic pose graph SLAM: Long-term mapping in low dynamic environments.” Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012): 1871–1878.
Version: Author's final manuscript
ISBN
978-1-4673-1737-5
ISSN
2153-0858