Details
| Original language | English |
|---|---|
| Pages (from-to) | 9-16 |
| Number of pages | 8 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 5 |
| Issue number | 2 |
| Publication status | Published - 17 Jun 2021 |
| Event | 2021 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II - Nice, France Duration: 5 Jul 2021 → 9 Jul 2021 |
Abstract
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.
Keywords
- Integrity, LiDAR, Localization, Maximum Consensus, Point Cloud Registration, Robust Estimation
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Instrumentation
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 5, No. 2, 17.06.2021, p. 9-16.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Maximum consensus localization using lidar sensors
AU - Axmann, J.
AU - Brenner, C.
N1 - Funding Information: This project is supported by the German Research Foundation (DFG), as part of the Research Training Group i.c.sens, GRK 2159, ‘Integrity and Collaboration in Dynamic Sensor Networks’.
PY - 2021/6/17
Y1 - 2021/6/17
N2 - 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.
AB - 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.
KW - Integrity
KW - LiDAR
KW - Localization
KW - Maximum Consensus
KW - Point Cloud Registration
KW - Robust Estimation
UR - http://www.scopus.com/inward/record.url?scp=85119654126&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-V-2-2021-9-2021
DO - 10.5194/isprs-annals-V-2-2021-9-2021
M3 - Conference article
AN - SCOPUS:85119654126
VL - 5
SP - 9
EP - 16
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 2
T2 - 2021 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II
Y2 - 5 July 2021 through 9 July 2021
ER -