A Hierarchical LiDAR Odometry via Maximum Likelihood Estimation With Tightly Associated Distributions
Wang, Chengpeng1,2; Cao, Zhiqiang1,2; Li, Jianjie1,2; Liang, Shuang1,2; Tan, Min1,2; Yu, Junzhi3
发表期刊IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
ISSN0018-9545
2022-10-01
卷号71期号:10页码:10254-10268
通讯作者Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn)
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.
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
DOI10.1109/TVT.2022.3183202
关键词[WOS]SCAN REGISTRATION ; SLAM ; DISTANCE ; POINT
收录类别SCI
语种英语
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]
项目资助者National Natural Science Foundation of China
WOS研究方向Engineering ; Telecommunications ; Transportation
WOS类目Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS记录号WOS:000870332400006
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/50694
专题复杂系统管理与控制国家重点实验室_先进机器人
通讯作者Cao, Zhiqiang
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Peking Univ, Coll Engn, Dept Mech & Engn Sci, BIC ESAT,State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
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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.
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