LiDAR-Based Localization on Highways Using Raw Data and Pole-Like Object Features

Sheng Cheng Lee*, Victor Lu, Chieh Chih Wang, Wen Chieh Lin

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

Poles on highways provide important cues for how a scan should be localized onto a map. However existing point cloud scan matching algorithms do not fully leverage such cues, leading to suboptimal matching accuracy in highway environments. To improve the ability to match in such scenarios, we include pole-like objects for lateral information and add this information to the current matching algorithm. First, we classify the points from the LiDAR sensor using the Random Forests classifier to find the points that represent poles. Each detected pole point will then generate a residual by the distance to the nearest pole in map. The pole residuals are later optimized along with the point-to-distribution residuals proposed in the normal distributions transform (NDT) using a nonlinear least squares optimization to get the localization result. Compared to the baseline (NDT), our proposed method obtains a 34% improvement in accuracy on highway scenes in the localization problem. In addition, our experiment shows that the convergence area is significantly enlarged, increasing the usability of the self-driving car localization algorithm on highway scenarios.

原文English
主出版物標題Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
發行者IEEE Computer Society
頁面230-237
頁數8
ISBN(電子)9798350302493
DOIs
出版狀態Published - 2023
事件2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 - Vancouver, Canada
持續時間: 18 6月 202322 6月 2023

出版系列

名字IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
2023-June
ISSN(列印)2160-7508
ISSN(電子)2160-7516

Conference

Conference2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
國家/地區Canada
城市Vancouver
期間18/06/2322/06/23

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