中文版 | English
Title

Fusing Panoptic Segmentation and Geometry Information for Robust Visual SLAM in Dynamic Environments

Author
Corresponding AuthorJia,Zhenzhong
DOI
Publication Years
2022
ISSN
2161-8070
EISSN
2161-8089
ISBN
978-1-6654-9043-6
Source Title
Volume
2022-August
Pages
1648-1653
Conference Date
20-24 Aug. 2022
Conference Place
Mexico City, Mexico
Abstract
Mobile robots need reliable maps for autonomous operation. Traditional SLAM systems, which are mainly developed for static scenes, often fail in dynamic environments with moving objects present in the scene. Learning based dynamic SLAM systems often suffer from insufficient or inaccurate identification of feature points. This paper proposes a novel real-time RGB-D SLAM system, which is targeted for dynamic environments, can further enhance feature detection and dynamic removal. This is done by fusing panoptic segmentation and geometry information. The system includes four components: dynamic segmentation that reduces the impact of moving objects, pose estimation with dynamic object removal, panoptic mapping, and loop closing. The pose estimation uses coarse-to-fine dynamic/static classification to further reduce the impact of unknown moving objects. Extensive evaluations demonstrate that our system can achieve robust performance in complex dynamic environments.
Keywords
SUSTech Authorship
First ; Corresponding
Language
English
URL[Source Record]
Indexed By
EI Accession Number
20224613111558
Scopus EID
2-s2.0-85141676499
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9926478
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/411877
Affiliation
1.Southern University of Science and Technology (SUSTech),Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems,China
2.Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities,Department of Mechanical and Energy Engineering,Shenzhen,518055,China
First Author AffilicationSouthern University of Science and Technology
Corresponding Author AffilicationSouthern University of Science and Technology
First Author's First AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Zhu,Hu,Yao,Chen,Zhu,Zheng,et al. Fusing Panoptic Segmentation and Geometry Information for Robust Visual SLAM in Dynamic Environments[C],2022:1648-1653.
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