MAXIMUM CONSENSUS LOCALIZATION USING LIDAR SENSORS Article Swipe
Jeldrik Axmann
,
Claus Brenner
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-annals-v-2-2021-9-2021
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-annals-v-2-2021-9-2021
Real world localization tasks based on LiDAR usually face a high proportion of outliers arising from erroneous measurements and changing environments. However, applications such as autonomous driving require a high integrity in all of their components, including localization. Standard localization approaches are often based on (recursive) least squares estimation, for example, using Kalman filters. Since least squares minimization shows a strong susceptibility to outliers, it is not robust.In this paper, we focus on high integrity vehicle localization and investigate a maximum consensus localization strategy. For our work, we use 2975 epochs from a Velodyne VLP-16 scanner (representing the vehicle scan data), and map data obtained using a Riegl VMX-250 mobile mapping system. We investigate the effects of varying scene geometry on the maximum consensus result by exhaustively computing the consensus values for the entire search space. We analyze the deviations in position and heading for a circular course in a downtown area by comparing the estimation results to a reference trajectory, and show the robustness of the maximum consensus localization.
Related Topics
Concepts
Robustness (evolution)
Outlier
Computer science
Simultaneous localization and mapping
Heading (navigation)
Artificial intelligence
Kalman filter
Lidar
RANSAC
Consensus
Computer vision
Extended Kalman filter
Trajectory
Remote sensing
Geography
Mobile robot
Geodesy
Image (mathematics)
Chemistry
Physics
Astronomy
Biochemistry
Gene
Robot
Multi-agent system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-annals-v-2-2021-9-2021
- https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2021/9/2021/isprs-annals-V-2-2021-9-2021.pdf
- OA Status
- diamond
- Cited By
- 4
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3176334722
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3176334722Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-annals-v-2-2021-9-2021Digital Object Identifier
- Title
-
MAXIMUM CONSENSUS LOCALIZATION USING LIDAR SENSORSWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-17Full publication date if available
- Authors
-
Jeldrik Axmann, Claus BrennerList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-annals-v-2-2021-9-2021Publisher landing page
- PDF URL
-
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2021/9/2021/isprs-annals-V-2-2021-9-2021.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2021/9/2021/isprs-annals-V-2-2021-9-2021.pdfDirect OA link when available
- Concepts
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Robustness (evolution), Outlier, Computer science, Simultaneous localization and mapping, Heading (navigation), Artificial intelligence, Kalman filter, Lidar, RANSAC, Consensus, Computer vision, Extended Kalman filter, Trajectory, Remote sensing, Geography, Mobile robot, Geodesy, Image (mathematics), Chemistry, Physics, Astronomy, Biochemistry, Gene, Robot, Multi-agent systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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