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A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments

A static environment is a prerequisite for the stable operation of most visual SLAM systems, which limits the practical use of most existing systems. The robustness and accuracy of visual SLAM systems in dynamic environments still face many complex challenges. Only relying on semantic information or...

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Autores principales: Chen, Weifeng, Shang, Guangtao, Hu, Kai, Zhou, Chengjun, Wang, Xiyang, Fang, Guisheng, Ji, Aihong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695233/
https://www.ncbi.nlm.nih.gov/pubmed/36422435
http://dx.doi.org/10.3390/mi13112006
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author Chen, Weifeng
Shang, Guangtao
Hu, Kai
Zhou, Chengjun
Wang, Xiyang
Fang, Guisheng
Ji, Aihong
author_facet Chen, Weifeng
Shang, Guangtao
Hu, Kai
Zhou, Chengjun
Wang, Xiyang
Fang, Guisheng
Ji, Aihong
author_sort Chen, Weifeng
collection PubMed
description A static environment is a prerequisite for the stable operation of most visual SLAM systems, which limits the practical use of most existing systems. The robustness and accuracy of visual SLAM systems in dynamic environments still face many complex challenges. Only relying on semantic information or geometric methods cannot filter out dynamic feature points well. Considering the problem of dynamic objects easily interfering with the localization accuracy of SLAM systems, this paper proposes a new monocular SLAM algorithm for use in dynamic environments. This improved algorithm combines semantic information and geometric methods to filter out dynamic feature points. Firstly, an adjusted Mask R-CNN removes prior highly dynamic objects. The remaining feature-point pairs are matched via the optical-flow method and a fundamental matrix is calculated using those matched feature-point pairs. Then, the environment’s actual dynamic feature points are filtered out using the polar geometric constraint. The improved system can effectively filter out the feature points of dynamic targets. Finally, our experimental results on the TUM RGB-D and Bonn RGB-D Dynamic datasets showed that the proposed method could improve the pose estimation accuracy of a SLAM system in a dynamic environment, especially in the case of high indoor dynamics. The performance effect was better than that of the existing ORB-SLAM2. It also had a higher running speed than DynaSLAM, which is a similar dynamic visual SLAM algorithm.
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spelling pubmed-96952332022-11-26 A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments Chen, Weifeng Shang, Guangtao Hu, Kai Zhou, Chengjun Wang, Xiyang Fang, Guisheng Ji, Aihong Micromachines (Basel) Article A static environment is a prerequisite for the stable operation of most visual SLAM systems, which limits the practical use of most existing systems. The robustness and accuracy of visual SLAM systems in dynamic environments still face many complex challenges. Only relying on semantic information or geometric methods cannot filter out dynamic feature points well. Considering the problem of dynamic objects easily interfering with the localization accuracy of SLAM systems, this paper proposes a new monocular SLAM algorithm for use in dynamic environments. This improved algorithm combines semantic information and geometric methods to filter out dynamic feature points. Firstly, an adjusted Mask R-CNN removes prior highly dynamic objects. The remaining feature-point pairs are matched via the optical-flow method and a fundamental matrix is calculated using those matched feature-point pairs. Then, the environment’s actual dynamic feature points are filtered out using the polar geometric constraint. The improved system can effectively filter out the feature points of dynamic targets. Finally, our experimental results on the TUM RGB-D and Bonn RGB-D Dynamic datasets showed that the proposed method could improve the pose estimation accuracy of a SLAM system in a dynamic environment, especially in the case of high indoor dynamics. The performance effect was better than that of the existing ORB-SLAM2. It also had a higher running speed than DynaSLAM, which is a similar dynamic visual SLAM algorithm. MDPI 2022-11-17 /pmc/articles/PMC9695233/ /pubmed/36422435 http://dx.doi.org/10.3390/mi13112006 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Weifeng
Shang, Guangtao
Hu, Kai
Zhou, Chengjun
Wang, Xiyang
Fang, Guisheng
Ji, Aihong
A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments
title A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments
title_full A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments
title_fullStr A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments
title_full_unstemmed A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments
title_short A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments
title_sort monocular-visual slam system with semantic and optical-flow fusion for indoor dynamic environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695233/
https://www.ncbi.nlm.nih.gov/pubmed/36422435
http://dx.doi.org/10.3390/mi13112006
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