Knowledge Commons of Institute of Automation,CAS
A Novel 3D LiDAR SLAM Based on Directed Geometry Point and Sparse Frame | |
Liang, Shuang1,2; Cao, Zhiqiang1,2; Wang, Chengpeng1,2; Yu, Junzhi1,3 | |
发表期刊 | IEEE ROBOTICS AND AUTOMATION LETTERS |
ISSN | 2377-3766 |
2021-04-01 | |
卷号 | 6期号:2页码:374-381 |
通讯作者 | Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn) |
摘要 | Simultaneous localization and mapping is an indispensable yet challenging direction for mobile robots. Attracted by 3D LiDAR with accurate depth information and robustness to illumination variations, many 3D LiDAR SLAM methods based on scan-to-map matching have been developed. However, there is a critical issue of existing approaches, where a large and dense map is generally required to achieve satisfactory localization accuracy, leading to low efficiency of scan-to-map matching. To solve this problem, in this letter, we propose a novel 3D LiDAR SLAM based on directed geometry point (DGP) and sparse frame. The former is used to provide a sparse distribution of points in the spatial dimension and the latter gives rise to a sparse distribution of frames in the temporal sequence. The sparsity of points and frames impove the efficiency of 3D LiDAR SLAM, and the strict data association based on directed geometric points also brings in good accuracy of pose estimation. To compensate the accuracy loss of the localization and mapping caused by frame sparsity, point propagation is proposed to improve the quality of directed geometric points in the map and the accuracy of scan-to-map matching. Also, loop detection and pose graph optimization are conducted for global consistency. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and efficiency. |
关键词 | 3D LiDAR SLAM directed geometric point sparse frame point propagation |
DOI | 10.1109/LRA.2020.3043200 |
关键词[WOS] | REGISTRATION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62073322] ; National Natural Science Foundation of China[61633020] ; National Natural Science Foundation of China[61633017] ; National Natural Science Foundation of China[61836015] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Robotics |
WOS类目 | Robotics |
WOS记录号 | WOS:000602951000008 |
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/173211/42808 |
专题 | 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, Coll Engn, BIC ESAT,Dept Mech & Engn Sci, 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,Wang, Chengpeng,et al. A Novel 3D LiDAR SLAM Based on Directed Geometry Point and Sparse Frame[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2021,6(2):374-381. |
APA | Liang, Shuang,Cao, Zhiqiang,Wang, Chengpeng,&Yu, Junzhi.(2021).A Novel 3D LiDAR SLAM Based on Directed Geometry Point and Sparse Frame.IEEE ROBOTICS AND AUTOMATION LETTERS,6(2),374-381. |
MLA | Liang, Shuang,et al."A Novel 3D LiDAR SLAM Based on Directed Geometry Point and Sparse Frame".IEEE ROBOTICS AND AUTOMATION LETTERS 6.2(2021):374-381. |
条目包含的文件 | 条目无相关文件。 |
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