Search: onr:"swepub:oai:DiVA.org:oru-108799" >
Oriented surface po...
Oriented surface points for efficient and accurate radar odometry
-
- Adolfsson, Daniel, 1992- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik,MRO Lab of the AASS Research Centre
-
- Magnusson, Martin, Docent, 1977- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik,MRO Lab of the AASS Research Centre
-
- Alhashimi, Anas, 1978- (author)
- School of Science and Technology, Örebro University, Örebro, Sweden,MRO Lab of the AASS Research Centre
-
show more...
-
- Lilienthal, Achim, 1970- (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)
- 2021
- 2021
- English.
- Related links:
-
https://doi.org/10.4...
-
show more...
-
https://urn.kb.se/re...
-
show less...
Abstract
Subject headings
Close
- This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Publication and Content Type
- ref (subject category)
- kon (subject category)
To the university's database