A Hierarchical LiDAR Odometry via Maximum Likelihood Estimation With Tightly Associated Distributions
文献类型：期刊论文
| 作者 | Wang, Chengpeng2,3 ; Cao, Zhiqiang2,3; Li, Jianjie2,3; Liang, Shuang2,3; Tan, Min2,3; Yu, Junzhi1
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| 刊名 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
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| 出版日期 | 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. |
| WOS关键词 | SCAN REGISTRATION ; SLAM ; DISTANCE ; POINT |
| 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研究方向 | Engineering ; Telecommunications ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000870332400006 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50693]  |
| 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.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. |
入库方式： OAI收割
来源：自动化研究所
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