Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) |
| Subtitle of host publication | July 12-16, 2021, Delft, The Netherlands |
| Pages | 504-510 |
| Number of pages | 7 |
| ISBN (electronic) | 9781665441391 |
| Publication status | Published - 11 Mar 2021 |
| Event | 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics - Delft, Netherlands Duration: 12 Jul 2021 → 16 Jul 2021 |
Abstract
Keywords
- cs.RO
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
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Proceedings of the 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM): July 12-16, 2021, Delft, The Netherlands. 2021. p. 504-510.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Have I been here before?
T2 - 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
AU - Habich, Tim-Lukas
AU - Stuede, Marvin
AU - Labbé, Mathieu
AU - Spindeldreier, Svenja
N1 - Accepted at AIM Conference 2021
PY - 2021/3/11
Y1 - 2021/3/11
N2 - This work presents an extension of graph-based SLAM methods to exploit the potential of 3D laser scans for loop detection. Every high-dimensional point cloud is replaced by a compact global descriptor, whereby a trained detector decides whether a loop exists. Searching for loops is performed locally in a variable space to consider the odometry drift. Since closing a wrong loop has fatal consequences, an extensive verification is performed before acceptance. The proposed algorithm is implemented as an extension of the widely used state-of-the-art library RTAB-Map, and several experiments show the improvement: During SLAM with a mobile service robot in changing indoor and outdoor campus environments, our approach improves RTAB-Map regarding total number of closed loops. Especially in the presence of significant environmental changes, which typically lead to failure, localization becomes possible by our extension. Experiments with a car in traffic (KITTI benchmark) show the general applicability of our approach. These results are comparable to the state-of-the-art LiDAR method LOAM. The developed ROS package is freely available.
AB - This work presents an extension of graph-based SLAM methods to exploit the potential of 3D laser scans for loop detection. Every high-dimensional point cloud is replaced by a compact global descriptor, whereby a trained detector decides whether a loop exists. Searching for loops is performed locally in a variable space to consider the odometry drift. Since closing a wrong loop has fatal consequences, an extensive verification is performed before acceptance. The proposed algorithm is implemented as an extension of the widely used state-of-the-art library RTAB-Map, and several experiments show the improvement: During SLAM with a mobile service robot in changing indoor and outdoor campus environments, our approach improves RTAB-Map regarding total number of closed loops. Especially in the presence of significant environmental changes, which typically lead to failure, localization becomes possible by our extension. Experiments with a car in traffic (KITTI benchmark) show the general applicability of our approach. These results are comparable to the state-of-the-art LiDAR method LOAM. The developed ROS package is freely available.
KW - cs.RO
UR - http://www.scopus.com/inward/record.url?scp=85112759656&partnerID=8YFLogxK
U2 - 10.1109/AIM46487.2021.9517565
DO - 10.1109/AIM46487.2021.9517565
M3 - Conference contribution
SP - 504
EP - 510
BT - Proceedings of the 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
Y2 - 12 July 2021 through 16 July 2021
ER -