Knowledge Commons of Institute of Automation,CAS
A Novel Sparse Geometric 3-D LiDAR Odometry Approach | |
Liang, Shuang1,2![]() ![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE SYSTEMS JOURNAL
![]() |
ISSN | 1932-8184 |
2021-03-01 | |
卷号 | 15期号:1页码:1390-1400 |
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. |
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 |
DOI | 10.1109/JSYST.2020.2995727 |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Natural Science Foundation of China ; Beijing Advanced Innovation Center for Intelligent Robots and Systems ; Key Research and Development Program of Shandong Province |
WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Operations Research & Management Science ; Telecommunications |
WOS记录号 | WOS:000628985900137 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 机器人感知与决策 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44168 |
专题 | 多模态人工智能系统全国重点实验室_智能机器人系统研究 复杂系统认知与决策实验室_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, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.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 |
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 | 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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A_Novel_Sparse_Geome(4597KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论