TY - GEN
T1 - LiDAR Feature Outlier Mitigation Aided by Graduated Non-convexity Relaxation for Safety-critical Localization in Urban Canyons https://doi.org/10.1109/PLANS53410.2023.10139983
AU - Zhang, Jiachen
AU - Wen, Weisong
AU - Hsu, Li Ta
AU - Gong, Zheng
AU - Su, Zhongzhe
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/4
Y1 - 2023/4
N2 - Safety-critical localization is essential for unmanned autonomous systems. LiDAR localization gains great popularity in urban canyons due to its high ranging accuracy. Inheriting from the integrity monitoring theory for GNSS, safety-certifiable LiDAR localization first consists in fault detection and exclusion (FDE). In face of numerous LiDAR measurements, conventional chi-square test for FDE is computationally intractable. What's more, inliers could be mistakenly excluded without reconsideration. This paper proposes a computationally tractable and flexible FDE method. It's realized via outlier mitigation aided by graduated non-convexity (GNC) relaxation. The two novel loss functions truncated least square (TLS) and the Geman McClure (GM) are combined respectively. The outlier-mitigated planar-feature-based LiDAR localization is formulated with GNC and TLS or GM. More importantly, a triple-layer optimization method is proposed to solve the localization formulation. Besides the typical GNC relaxation, the control parameter is taken into consideration for tuning the outliers resistance degree. The outlier mitigated pose estimation and the weightings ranging from 0 to 1 for the exploited LiDAR measurements are finally produced. Extensive experiments of the proposed method is conducted on urban dataset. What's more, considering that TSL and GM provides distinct outlier mitigation patterns, the performances from them are investigated and compared.
AB - Safety-critical localization is essential for unmanned autonomous systems. LiDAR localization gains great popularity in urban canyons due to its high ranging accuracy. Inheriting from the integrity monitoring theory for GNSS, safety-certifiable LiDAR localization first consists in fault detection and exclusion (FDE). In face of numerous LiDAR measurements, conventional chi-square test for FDE is computationally intractable. What's more, inliers could be mistakenly excluded without reconsideration. This paper proposes a computationally tractable and flexible FDE method. It's realized via outlier mitigation aided by graduated non-convexity (GNC) relaxation. The two novel loss functions truncated least square (TLS) and the Geman McClure (GM) are combined respectively. The outlier-mitigated planar-feature-based LiDAR localization is formulated with GNC and TLS or GM. More importantly, a triple-layer optimization method is proposed to solve the localization formulation. Besides the typical GNC relaxation, the control parameter is taken into consideration for tuning the outliers resistance degree. The outlier mitigated pose estimation and the weightings ranging from 0 to 1 for the exploited LiDAR measurements are finally produced. Extensive experiments of the proposed method is conducted on urban dataset. What's more, considering that TSL and GM provides distinct outlier mitigation patterns, the performances from them are investigated and compared.
KW - 3D LiDAR
KW - fault detection and exclusion
KW - graduated non-convexity relaxation
KW - satety-critical localization
KW - urban canyons
UR - http://www.scopus.com/inward/record.url?scp=85162923607&partnerID=8YFLogxK
U2 - 10.1109/PLANS53410.2023.10139983
DO - 10.1109/PLANS53410.2023.10139983
M3 - Conference article published in proceeding or book
AN - SCOPUS:85162923607
T3 - 2023 IEEE/ION Position, Location and Navigation Symposium, PLANS 2023
SP - 660
EP - 664
BT - 2023 IEEE/ION Position, Location and Navigation Symposium, PLANS 2023 https://doi.org/10.1109/PLANS53410.2023.101399 83
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/ION Position, Location and Navigation Symposium, PLANS 2023
Y2 - 24 April 2023 through 27 April 2023
ER -