Knowledge Commons of Institute of Automation,CAS
Hierarchical Distribution-Based Tightly-Coupled LiDAR Inertial Odometry | |
Wang, Chengpeng1,2; Cao, Zhiqiang1,2; Li, Jianjie1,2; Yu, Junzhi3; Wang, Shuo1,2 | |
发表期刊 | IEEE TRANSACTIONS ON INTELLIGENT VEHICLES |
ISSN | 2379-8858 |
2024-01 | |
卷号 | 9期号:1页码:1423-1435 |
摘要 | LiDAR inertial odometry (LIO) has attracted much attention due to the complementarity of LiDAR and IMU measurements. In the distribution-based LIO, the components related to distribution covariance in the residual and residual uncertainty from the LiDAR measurement noise is neutralized. And the resultant point cloud constraint degeneration problem severely affects the accuracy of pose estimation. In this article, a hierarchical tightly-coupled LIO based on distribution is proposed. By excluding the eigenvalue elements in the distribution covariance component with the designed loss function, the uncertainty of corresponding residual is rectified.As a result, the degeneration problem is solved.With anti-degeneration point-to-distribution constraints, a LiDAR inertial odometry based on iterated extended Kalman filter and a factor graph optimization are designed and organized in a hierarchical way to achieve coarse-to-fine pose estimation, where LiDAR and IMU measurements are tightly coupled in both layers. In this way, the respective advantages of high efficiency and high accuracy from filtering and optimization are combined, which offers high-fidelity estimation results in real time. The effectiveness of the proposed method is verified through experiments on the public NC and ENC datasets. |
关键词 | 3D LiDAR inertial odometry, distribution filtering optimization point cloud constraint degeneration |
DOI | https://doi.org/10.1109/TIV.2023.3273288 |
七大方向——子方向分类 | 智能机器人 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56546 |
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 Automat, State Key Lab Multimodal Artificial Intelligence 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 |
推荐引用方式 GB/T 7714 | Wang, Chengpeng,Cao, Zhiqiang,Li, Jianjie,et al. Hierarchical Distribution-Based Tightly-Coupled LiDAR Inertial Odometry[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2024,9(1):1423-1435. |
APA | Wang, Chengpeng,Cao, Zhiqiang,Li, Jianjie,Yu, Junzhi,&Wang, Shuo.(2024).Hierarchical Distribution-Based Tightly-Coupled LiDAR Inertial Odometry.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,9(1),1423-1435. |
MLA | Wang, Chengpeng,et al."Hierarchical Distribution-Based Tightly-Coupled LiDAR Inertial Odometry".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 9.1(2024):1423-1435. |
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