A Novel Sparse Geometric 3-D LiDAR Odometry Approach
文献类型：期刊论文
| 作者 | Liang, Shuang2,3 ; Cao, Zhiqiang2,3; Guan, Peiyu2,3; Wang, Chengpeng2,3; Yu, Junzhi1,3; Wang, Shuo2,3
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| 刊名 | IEEE SYSTEMS JOURNAL
 |
| 出版日期 | 2021-03-01 |
| 卷号 | 15期号:1页码:1390-1400 |
| 关键词 | Laser radar Feature extraction Simultaneous localization and mapping Three-dimensional displays Computational complexity Distance measurement Lighting Line and plane features line-to-line and plane-to-plane associations sparse geometric map 3-D light detection and ranging (LiDAR) odometry |
| ISSN号 | 1932-8184 |
| DOI | 10.1109/JSYST.2020.2995727 |
| 通讯作者 | Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn) |
| 英文摘要 | Localization is a fundamental prerequisite, no matter whether a single robot or multirobot system, where light detection and ranging (LiDAR) odometry has attracted great interest with accurate depth information and robustness to illumination variations. In this article, a novel 3-D LiDAR odometry approach based on sparse geometric information is proposed. Different from geometric map-based 3-D LiDAR odometry methods with point features, we concern significant line and plane features based on eigenvalues of neighboring points. Furthermore, line-to-line and plane-to-plane associations instead of point-to-line and point-to-plane associations are adopted, and the problem of high computation complexity for scan-to-map matching module caused by point feature is solved. The proposed approach can not only guarantee the accuracy of pose estimation but also reduce computation complexity. Experiments on the public KITTI dataset and an outdoor scenario demonstrate the effectiveness of our approach in terms of accuracy and efficiency. |
| 资助项目 | National Natural Science Foundation of China[61633017] ; National Natural Science Foundation of China[61633020] ; National Natural Science Foundation of China[61836015] ; Beijing Advanced Innovation Center for Intelligent Robots and Systems[2018IRS21] ; Key Research and Development Program of Shandong Province[2017CXGC0925] |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000628985900137 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Beijing Advanced Innovation Center for Intelligent Robots and Systems ; Key Research and Development Program of Shandong Province |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44167]  |
| 专题 | 智能机器人系统研究 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, Beijing Innovat Ctr Engn Sci & Adv Technol, Dept Mech & Engn Sci, Coll Engn,State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, 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 | Liang, Shuang,Cao, Zhiqiang,Guan, Peiyu,et al. A Novel Sparse Geometric 3-D LiDAR Odometry Approach[J]. IEEE SYSTEMS JOURNAL,2021,15(1):1390-1400. |
| APA | Liang, Shuang,Cao, Zhiqiang,Guan, Peiyu,Wang, Chengpeng,Yu, Junzhi,&Wang, Shuo.(2021).A Novel Sparse Geometric 3-D LiDAR Odometry Approach.IEEE SYSTEMS JOURNAL,15(1),1390-1400. |
| MLA | Liang, Shuang,et al."A Novel Sparse Geometric 3-D LiDAR Odometry Approach".IEEE SYSTEMS JOURNAL 15.1(2021):1390-1400. |
入库方式： OAI收割
来源：自动化研究所
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