A Novel Sparse Geometric 3-D LiDAR Odometry Approach

doi:10.1109/JSYST.2020.2995727

	A Novel Sparse Geometric 3-D LiDAR Odometry Approach
	Liang, Shuang 2,3; Cao, Zhiqiang 2,3; Guan, Peiyu 2,3; Wang, Chengpeng 2,3; Yu, Junzhi 1,3; Wang, Shuo 2,3
刊名	IEEE SYSTEMS JOURNAL
	2021-03-01
卷号	15 期号:1 页码:1390-1400
关键词	Laser radar Feature extraction Simultaneous localization and mapping Three-dimensional displays Computational complexity Distance measurement Lighting Line and plane features line-to-line and plane-to-plane associations sparse geometric map 3-D light detection and ranging (LiDAR) odometry
ISSN号	1932-8184
DOI	10.1109/JSYST.2020.2995727
通讯作者	Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn)
英文摘要	Localization is a fundamental prerequisite, no matter whether a single robot or multirobot system, where light detection and ranging (LiDAR) odometry has attracted great interest with accurate depth information and robustness to illumination variations. In this article, a novel 3-D LiDAR odometry approach based on sparse geometric information is proposed. Different from geometric map-based 3-D LiDAR odometry methods with point features, we concern significant line and plane features based on eigenvalues of neighboring points. Furthermore, line-to-line and plane-to-plane associations instead of point-to-line and point-to-plane associations are adopted, and the problem of high computation complexity for scan-to-map matching module caused by point feature is solved. The proposed approach can not only guarantee the accuracy of pose estimation but also reduce computation complexity. Experiments on the public KITTI dataset and an outdoor scenario demonstrate the effectiveness of our approach in terms of accuracy and efficiency.
资助项目	National Natural Science Foundation of China[61633017] ; National Natural Science Foundation of China[61633020] ; National Natural Science Foundation of China[61836015] ; Beijing Advanced Innovation Center for Intelligent Robots and Systems[2018IRS21] ; Key Research and Development Program of Shandong Province[2017CXGC0925]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000628985900137
资助机构	National Natural Science Foundation of China ; Beijing Advanced Innovation Center for Intelligent Robots and Systems ; Key Research and Development Program of Shandong Province
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44167]
专题	智能机器人系统研究
通讯作者	Cao, Zhiqiang
作者单位	1.Peking Univ, Beijing Innovat Ctr Engn Sci & Adv Technol, Dept Mech & Engn Sci, Coll Engn,State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liang, Shuang,Cao, Zhiqiang,Guan, Peiyu,et al. A Novel Sparse Geometric 3-D LiDAR Odometry Approach[J]. IEEE SYSTEMS JOURNAL,2021,15(1):1390-1400.
APA	Liang, Shuang,Cao, Zhiqiang,Guan, Peiyu,Wang, Chengpeng,Yu, Junzhi,&Wang, Shuo.(2021).A Novel Sparse Geometric 3-D LiDAR Odometry Approach.IEEE SYSTEMS JOURNAL,15(1),1390-1400.
MLA	Liang, Shuang,et al."A Novel Sparse Geometric 3-D LiDAR Odometry Approach".IEEE SYSTEMS JOURNAL 15.1(2021):1390-1400.