RSS: Robust Stereo SLAM With Novel Extraction and Full Exploitation of Plane Features

doi:10.1109/LRA.2024.3388854

	RSS: Robust Stereo SLAM With Novel Extraction and Full Exploitation of Plane Features
	Wang, Haolin1,2 ; Wei, Hao1 ; Xu, Zewen 1,2; Lv, Zeren 3; Zhang, Pengju1 ; An, Ning4 ; Tang, Fulin1 ; Wu, Yihong1,2
发表期刊	IEEE ROBOTICS AND AUTOMATION LETTERS
ISSN	2377-3766
	2024-06-01
卷号	9 期号:6 页码:5158-5165
通讯作者	Wei, Hao(weihao2019@ia.ac.cn) ; Wu, Yihong(yihong.wu@ia.ac.cn)
摘要	Planar structures, prevalent in man-made environments, can be observed by a camera for significant periods of time due to their large spatial presence. These structures provide strong planar regularities for Simultaneous Localization and Mapping (SLAM) systems, facilitating long-term navigation. Therefore, we propose a novel point-plane-based stereo SLAM system, fully regularized by plane features within a unified non-linear optimization framework. The core of our method is an accurate and efficient stereo plane extraction algorithm with strict 2D and 3D outlier rejection mechanisms, effectively extracting main planes from robust stereo correspondences and enabling real-time point-plane association. Furthermore, we introduce a novel optimization formulation, incorporating geometric feature (point and plane) and across-feature (point-on-plane) constraints that promote each other through the mutual constraints between associated point and plane features, which fully exploits plane constraints to improve the performance of SLAM system. The proposed plane extraction algorithm is evaluated on the EuRoC MAV dataset, achieving significant improvements in number, accuracy, reliability, and efficiency over the state-of-the-art (SOTA) stereo point-plane-based system (Zhang et al., 2021). The results of an ablation study on two public datasets show that the proposed SLAM system outperforms (Zhang et al., 2021) in both accuracy and robustness, and further demonstrate the mutual enhancement between the two types of constraints.
关键词	SLAM Localization
DOI	10.1109/LRA.2024.3388854
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
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:001209593700009
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/57045
专题	多模态人工智能系统全国重点实验室_机器人视觉
通讯作者	Wei, Hao; Wu, Yihong
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 3.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China 4.China Coal Res Inst, Res Inst Mine Big Data, Beijing 100013, 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, Haolin,Wei, Hao,Xu, Zewen,et al. RSS: Robust Stereo SLAM With Novel Extraction and Full Exploitation of Plane Features[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2024,9(6):5158-5165.
APA	Wang, Haolin.,Wei, Hao.,Xu, Zewen.,Lv, Zeren.,Zhang, Pengju.,...&Wu, Yihong.(2024).RSS: Robust Stereo SLAM With Novel Extraction and Full Exploitation of Plane Features.IEEE ROBOTICS AND AUTOMATION LETTERS,9(6),5158-5165.
MLA	Wang, Haolin,et al."RSS: Robust Stereo SLAM With Novel Extraction and Full Exploitation of Plane Features".IEEE ROBOTICS AND AUTOMATION LETTERS 9.6(2024):5158-5165.