Search: WFRF:(Andreasson Henrik) >
TBV Radar SLAM - Tr...
TBV Radar SLAM - Trust but Verify Loop Candidates
-
- Adolfsson, Daniel, 1992- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik,MRO Lab of the AASS Research Centre
-
- Karlsson, Mattias (author)
- MRO Lab of the AASS Research Centre, Örebro University, Örebro, Sweden
-
- Kubelka, Vladimír, 1987- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik,MRO Lab of the AASS Research Centre
-
show more...
-
- Magnusson, Martin, Docent, 1977- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik,MRO Lab of the AASS Research Centre
-
- Andreasson, Henrik, 1977- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik,MRO Lab of the AASS Research Centre
-
show less...
-
(creator_code:org_t)
- IEEE, 2023
- 2023
- English.
-
In: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 8:6, s. 3613-3620
- Related links:
-
https://oru.diva-por... (primary) (Raw object)
-
show more...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present TBV (Trust But Verify) Radar SLAM, a method for radar SLAM that introspectively verifies loop closure candidates. TBV Radar SLAM achieves a high correct-loop-retrieval rate by combining multiple place-recognition techniques: tightly coupled place similarity and odometry uncertainty search, creating loop descriptors from origin-shifted scans, and delaying loop selection until after verification. Robustness to false constraints is achieved by carefully verifying and selecting the most likely ones from multiple loop constraints. Importantly, the verification and selection are carried out after registration when additional sources of loop evidence can easily be computed. We integrate our loop retrieval and verification method with a robust odometry pipeline within a pose graph framework. By evaluation on public benchmarks we found that TBV Radar SLAM achieves 65% lower error than the previous state of the art. We also show that it generalizes across environments without needing to change any parameters. We provide the open-source implementation at https://github.com/dan11003/tbv_slam_public
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- SLAM
- localization
- radar
- introspection
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database