An efficient LiDAR-based localization method for self-driving cars in dynamic environments. Issue 1 (20th January 2022)

• Record Type: Journal Article
• Authors: Zhang, Yihuan; Wang, Liang; Jiang, Xuhui; Zeng, Yong; Dai, Yifan
• Is Part Of: Robotica. Volume 40:Issue 1(2022)
• Page Start: 38
• Page End: 55
• Publication Date: 2022-01-20
• Subjects: Self-driving cars -- Curb detection -- Map matching -- Real-time localization; Robots -- Periodicals; 629.89205
• DOI: 10.1017/S0263574721000369
• Languages: English
• ISSNs: 0263-5747

Abstract

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.