Online global loop closure detection for large-scale multi-session graph-based SLAM
By Mathieu Labbé, François Michaud. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014
406 citations recorded by the source corpus.
For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has to deal with unknown initial positioning caused by either the kidnapped robot problem or multi-session mapping. This paper addresses these problems by tying the SLAM system with a global loop closure detection approach, which intrinsically handles these situations. However, online processing for global loop closure detection approaches is generally influenced by the size of the environment. The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements. The approach is tested and demonstrated using five indoor mapping sessions of a building using a robot equipped with a laser rangefinder and a Kinect.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/533d8c11c8e533c34774eff76fcbab6559ee67e4
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In Aerospace Engineering
In Computer Vision and Pattern Recognition
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Citations in the corpus
- Deep Visual Teach and Repeat on Path Networks (2018, background): “This process is fused in SLAM, which has has been successfully applied in numerous applications, including indoor navigation and autonomous driving [32, 22, 17, 16].”