Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5917-5924 |
| Number of pages | 8 |
| ISBN (electronic) | 9798350399462 |
| Publication status | Published - 2023 |
| Event | 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023 - Bilbao, Spain Duration: 24 Sept 2023 → 28 Sept 2023 |
Publication series
| Name | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC |
|---|---|
| ISSN (Print) | 2153-0009 |
| ISSN (electronic) | 2153-0017 |
Abstract
A core task of autonomous vehicles is the ego-localization. On the one hand, it needs to be accurate and robust towards outliers, and on the other hand, it needs to provide a high integrity. For an accurate localization, usually the likelihood as the most common objective function is maximized with respect to the sensor measurements. More precisely, based on the assumption of normally distributed random variables, usually a least squares minimization is conducted, e. g. in a recursive manner in a Kalman Filter. However, this approach severely lacks robustness since least squares is inherently sensitive to outliers, which, using LiDAR data, arise in a high proportion due to changing environments and dynamic traffic participants. Therefore, more robust loss functions have been introduced with the outlier count, also referred to as maximum consensus optimization, as the most robust one since the estimation result is independent of the outlier magnitude. Whereas localization itself has been investigated thoroughly, localization integrity, which describes the ability of a system to correctly and timely warn the user when specific error limits are exceeded, has received comparatively few attention in the domain of autonomous vehicles. In this paper, on the one hand, we propose a robust localization approach based on the maximum consensus criterion to ensure a high reliability even in challenging environments, and on the other hand, we propose an estimation of a grid-based probability distribution as a first step towards a new integrity framework for LiDAR based localization. Both outcomes are based on the same pipeline using GPU computed range images for the expected measurements. The introduced localization and probability estimation are tested for structured, semi-structured, and unstructured environments using overall around 2,500 epochs from two different LiDARs.
Keywords
- Beam model, Integrity, LiDAR, Localization, Maximum Consensus, Protection Level, Range image rendering, Robust Estimation
ASJC Scopus subject areas
- Engineering(all)
- Automotive Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 5917-5924 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Maximum Consensus based Localization and Protection Level Estimation using Synthetic LiDAR Range Images
AU - Axmann, Jeldrik
AU - Brenner, Claus
PY - 2023
Y1 - 2023
N2 - A core task of autonomous vehicles is the ego-localization. On the one hand, it needs to be accurate and robust towards outliers, and on the other hand, it needs to provide a high integrity. For an accurate localization, usually the likelihood as the most common objective function is maximized with respect to the sensor measurements. More precisely, based on the assumption of normally distributed random variables, usually a least squares minimization is conducted, e. g. in a recursive manner in a Kalman Filter. However, this approach severely lacks robustness since least squares is inherently sensitive to outliers, which, using LiDAR data, arise in a high proportion due to changing environments and dynamic traffic participants. Therefore, more robust loss functions have been introduced with the outlier count, also referred to as maximum consensus optimization, as the most robust one since the estimation result is independent of the outlier magnitude. Whereas localization itself has been investigated thoroughly, localization integrity, which describes the ability of a system to correctly and timely warn the user when specific error limits are exceeded, has received comparatively few attention in the domain of autonomous vehicles. In this paper, on the one hand, we propose a robust localization approach based on the maximum consensus criterion to ensure a high reliability even in challenging environments, and on the other hand, we propose an estimation of a grid-based probability distribution as a first step towards a new integrity framework for LiDAR based localization. Both outcomes are based on the same pipeline using GPU computed range images for the expected measurements. The introduced localization and probability estimation are tested for structured, semi-structured, and unstructured environments using overall around 2,500 epochs from two different LiDARs.
AB - A core task of autonomous vehicles is the ego-localization. On the one hand, it needs to be accurate and robust towards outliers, and on the other hand, it needs to provide a high integrity. For an accurate localization, usually the likelihood as the most common objective function is maximized with respect to the sensor measurements. More precisely, based on the assumption of normally distributed random variables, usually a least squares minimization is conducted, e. g. in a recursive manner in a Kalman Filter. However, this approach severely lacks robustness since least squares is inherently sensitive to outliers, which, using LiDAR data, arise in a high proportion due to changing environments and dynamic traffic participants. Therefore, more robust loss functions have been introduced with the outlier count, also referred to as maximum consensus optimization, as the most robust one since the estimation result is independent of the outlier magnitude. Whereas localization itself has been investigated thoroughly, localization integrity, which describes the ability of a system to correctly and timely warn the user when specific error limits are exceeded, has received comparatively few attention in the domain of autonomous vehicles. In this paper, on the one hand, we propose a robust localization approach based on the maximum consensus criterion to ensure a high reliability even in challenging environments, and on the other hand, we propose an estimation of a grid-based probability distribution as a first step towards a new integrity framework for LiDAR based localization. Both outcomes are based on the same pipeline using GPU computed range images for the expected measurements. The introduced localization and probability estimation are tested for structured, semi-structured, and unstructured environments using overall around 2,500 epochs from two different LiDARs.
KW - Beam model
KW - Integrity
KW - LiDAR
KW - Localization
KW - Maximum Consensus
KW - Protection Level
KW - Range image rendering
KW - Robust Estimation
UR - http://www.scopus.com/inward/record.url?scp=85186517132&partnerID=8YFLogxK
U2 - 10.1109/ITSC57777.2023.10421977
DO - 10.1109/ITSC57777.2023.10421977
M3 - Conference contribution
AN - SCOPUS:85186517132
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 5917
EP - 5924
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
Y2 - 24 September 2023 through 28 September 2023
ER -