Hierarchical Estimation-Based LiDAR Odometry With Scan-to-Map Matching and Fixed-Lag Smoothing

doi:10.1109/TIV.2022.3173665

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	Hierarchical Estimation-Based LiDAR Odometry With Scan-to-Map Matching and Fixed-Lag Smoothing
	Liang, Shuang 1; Cao, Zhiqiang 1; Wang, Chengpeng 1; Yu, Junzhi 2
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2023-02-01
卷号	8 期号:2 页码:1607-1623
关键词	Feature extraction Laser radar Smoothing methods Pose estimation Point cloud compression Real-time systems Three-dimensional displays LiDAR odometry hierarchical estimation scan-to-map matching fixed-lag smoothing
ISSN号	2379-8858
DOI	10.1109/TIV.2022.3173665
通讯作者	Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn)
英文摘要	LiDAR odometry (LO) has gained popularity in recent years due to accurate depth measurement and robustness to illumination. Typically, the solutions based on scan-to-map matching mainly optimize current pose. To further reduce the accumulated error of pose estimation, the fixed-lag smoothing that optimizes fixed-size poses simultaneously by matching corresponding point features of multiple frames becomes necessary. The integration of fixed-lag smoothing with LO still needs further exploration. In this paper, a general fixed-lag smoothing module is proposed, which can be appended to existing LO framework to improve the consistency of trajectory. Also, a fast scan-to-map matching module based on sparse features is developed to guarantee the real-time performance. Besides, the feature-centric feature management strategy is adopted in both scan-to-map matching and fixed-lag smoothing modules, which makes the proposed LO efficient. On this basis, a hierarchical estimation-based LiDAR odometry is presented, where low-level scan-to-map matching estimates pose of each frame by aligning associated features in the frame and corresponding surrounding map with high efficiency, and high-level fixed-lag smoothing further optimizes keyframe poses in a sliding window by matching associated features among multiple frames with high accuracy. As a result, a fast and accurate pose estimation is achieved, which is verified by experiments on the KITTI dataset, Newer College dataset, and an actual outdoor scenario.
资助项目	National Natural Science Foundation of China[62073322] ; National Natural Science Foundation of China[61836015]
WOS关键词	LOCALIZATION ; POINT ; SLAM ; FEATURES ; LOAM ; NDT
WOS研究方向	Computer Science ; Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001006888500049
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53690]
专题	多模态人工智能系统全国重点实验室
通讯作者	Cao, Zhiqiang
作者单位	1.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Peking Univ, Coll Engn, Dept Adv Mfg & Robot, State Key Lab Turbulence & Complex Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liang, Shuang,Cao, Zhiqiang,Wang, Chengpeng,et al. Hierarchical Estimation-Based LiDAR Odometry With Scan-to-Map Matching and Fixed-Lag Smoothing[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(2):1607-1623.
APA	Liang, Shuang,Cao, Zhiqiang,Wang, Chengpeng,&Yu, Junzhi.(2023).Hierarchical Estimation-Based LiDAR Odometry With Scan-to-Map Matching and Fixed-Lag Smoothing.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(2),1607-1623.
MLA	Liang, Shuang,et al."Hierarchical Estimation-Based LiDAR Odometry With Scan-to-Map Matching and Fixed-Lag Smoothing".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.2(2023):1607-1623.