Towards Image-Grade LiDAR Odometry and Mapping

被引:0
作者
Wang, Yusheng [1 ]
Song, Weiwei [1 ]
Lou, Yidong [1 ]
Yu, Huan [2 ,3 ]
机构
[1] Wuhan Univ, GNSS Res Ctr, 129 Luoyu Rd, Wuhan 430079, Peoples R China
[2] Wuhan Univ, Sch Geodesy & Geomat, 129 Luoyu Rd, Wuhan 430079, Peoples R China
[3] INDRIVING Co Ltd, Cent China Intellectual Valley, Wuhan 430076, Peoples R China
来源
2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | 2022年
关键词
D O I
10.1109/ITSC55140.2022.9921943
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The recent developments in LiDAR technologies has promoted several super high intensity image-grade LiDAR sensors, receiving great interest in the intelligent transportation applications. Although objects detection and high-resolution map construction can benefit from this characteristic, there are several underlying challenges arising from such LiDARs. In this contribution, we present a robust and accurate odometry and mapping scheme for image-grade LiDARs, which addresses many key issues including outliers rejection, moving vehicles filtering, feature extraction and selection. Results from extensive experiments performed in various environments, including feature-poor tunnels and busy urban roads, demonstrate the high accuracy and robustness of the proposed method for state estimation in the real applications.
引用
收藏
页码:1542 / 1547
页数:6
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