A Hierarchical LiDAR Odometry via Maximum Likelihood Estimation With Tightly Associated Distributions

doi:10.1109/TVT.2022.3183202

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	A Hierarchical LiDAR Odometry via Maximum Likelihood Estimation With Tightly Associated Distributions
	Wang, Chengpeng 2,3; Cao, Zhiqiang 2,3; Li, Jianjie 2,3; Liang, Shuang 2,3; Tan, Min 2,3; Yu, Junzhi 1
刊名	IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
	2022-10-01
卷号	71 期号:10 页码:10254-10268
关键词	Point cloud compression Laser radar Feature extraction Smoothing methods Three-dimensional displays Optimization Simultaneous localization and mapping 3D LiDAR odometry fixed-lag smoothing hierarchical optimization maximum likelihood estimation
ISSN号	0018-9545
DOI	10.1109/TVT.2022.3183202
通讯作者	Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn)
英文摘要	LiDAR odometry has gained popularity due to accurate depth measurement with the robustness to illuminations. However, existing distribution-based methods do not sufficiently exploit the information from source point cloud, which affects the odometry performance. In this paper, a novel distribution-to-distribution matching method is proposed based on maximum likelihood estimation to solve relative transformation, where source and target point sets are tightly jointed to represent the sampling distribution in the objective function. On this basis, a hierarchical 3D LiDAR odometry with the low-level scan-to-map matching and high-level fixed-lag smoothing is designed. With the decoupling strategy, the matching method is extended to a fixed-lag smoothing module and the heavy computation burden is overcome. Our smoothing module is universal, which can be attached to LiDAR odometry framework for performance improvement. The experiments on KITTI dataset, Newer College dataset, and large-scale KITTI-360 dataset verify the effectiveness of the proposed method.
资助项目	National Natural Science Foundation of China[62073322] ; National Natural Science Foundation of China[61633020] ; National Natural Science Foundation of China[61836015]
WOS关键词	SCAN REGISTRATION ; SLAM ; DISTANCE ; POINT
WOS研究方向	Engineering ; Telecommunications ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000870332400006
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/173211/50693]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Cao, Zhiqiang
作者单位	1.Peking Univ, Coll Engn, Dept Mech & Engn Sci, BIC ESAT,State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China 2.Univ Chinese Acad Sci, 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	Wang, Chengpeng,Cao, Zhiqiang,Li, Jianjie,et al. A Hierarchical LiDAR Odometry via Maximum Likelihood Estimation With Tightly Associated Distributions[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2022,71(10):10254-10268.
APA	Wang, Chengpeng,Cao, Zhiqiang,Li, Jianjie,Liang, Shuang,Tan, Min,&Yu, Junzhi.(2022).A Hierarchical LiDAR Odometry via Maximum Likelihood Estimation With Tightly Associated Distributions.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,71(10),10254-10268.
MLA	Wang, Chengpeng,et al."A Hierarchical LiDAR Odometry via Maximum Likelihood Estimation With Tightly Associated Distributions".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 71.10(2022):10254-10268.