Sökning: onr:"swepub:oai:DiVA.org:oru-94463" >
CFEAR Radarodometry...
CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
-
- Adolfsson, Daniel, 1992- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,AASS MRO Lab
-
- Magnusson, Martin, 1977- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,AASS MRO Lab
-
- Alhashimi, Anas, 1978- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,AASS MRO Lab
-
visa fler...
-
- Lilienthal, Achim, 1970- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,AASS MRO Lab
-
- Andreasson, Henrik, 1977- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,AASS MRO Lab
-
visa färre...
-
(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
-
Ingår i: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021). - : IEEE. - 9781665417143 - 9781665417150 ; , s. 5462-5469
- Relaterad länk:
-
https://doi.org/10.4...
-
visa fler...
-
https://oru.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Localization SLAM Mapping Radar
- Computer Science
- Datavetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas