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Autonomous vehicle self-localization in urban environments based on 3D curvature feature points – Monte Carlo localization

Published online by Cambridge University Press:  23 June 2021

Qi Liu Open the ORCID record for Qi Liu [Opens in a new window] ,
Xiaoguang Di Open the ORCID record for Xiaoguang Di [Opens in a new window]  and
Binfeng Xu
Qi Liu
Affiliation:
Control and Simulation Center, Harbin Institute of Technology, Harbin, China
Xiaoguang Di*
Affiliation:
Control and Simulation Center, Harbin Institute of Technology, Harbin, China
Binfeng Xu
Affiliation:
Department of Intelligent Driving, Horizon Robotics, Shanghai, China
*
*Corresponding author. Email: dixiaoguang@hit.edu.cn
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Abstract

This paper proposes a map-based localization system for autonomous vehicle self-localization in urban environments, which is composed of a pose graph mapping method and 3D curvature feature points – Monte Carlo Localization algorithm (3DCF-MCL). The advantage of 3DCF-MCL is that it combines the high accuracy of the 3D feature points registration and the robustness of particle filter. Experimental results show that 3DCF-MCL can provide an accurate localization for autonomous vehicles with the 3D point cloud map that generated by our mapping method. Compared with other map-based localization algorithms, it demonstrates that 3DCF-MCL outperforms them.

Keywords

Autonomous vehicle localizationPose graphInformation matrixFeature pointsParticle filter

Information

Type
Article
Information
Robotica , Volume 40 , Issue 3 , March 2022 , pp. 817 - 833
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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