Hierarchical Estimation-Based LiDAR Odometry With Scan-to-Map Matching and Fixed-Lag Smoothing
文献类型：期刊论文
| 作者 | Liang, Shuang1 ; Cao, Zhiqiang1; Wang, Chengpeng1; Yu, Junzhi2
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
 |
| 出版日期 | 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. |
| WOS关键词 | LOCALIZATION ; POINT ; SLAM ; FEATURES ; LOAM ; NDT |
| 资助项目 | National Natural Science Foundation of China[62073322] ; National Natural Science Foundation of China[61836015] |
| WOS研究方向 | Computer Science ; Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001006888500049 |
| 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/173211/53690]  |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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