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An efficient LiDAR-based localization method for self-driving cars in dynamic environments

Published online by Cambridge University Press:  20 April 2021

Yihuan Zhang Open the ORCID record for Yihuan Zhang [Opens in a new window] ,
Liang Wang ,
Xuhui Jiang ,
Yong Zeng  and
Yifan Dai
Yihuan Zhang*
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
Liang Wang
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
Xuhui Jiang
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
Yong Zeng
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
Yifan Dai
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
*
*Corresponding author. Email: [email protected]
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Abstract

Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

Keywords

Self-driving carsCurb detectionMap matchingReal-time localization
Type
Article
Information
Robotica , Volume 40 , Issue 1 , January 2022 , pp. 38 - 55
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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