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Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes

Simultaneous localization and mapping (SLAM) is a fundamental problem in robotics and computer vision. It involves the task of a robot or an autonomous system navigating an unknown environment, simultaneously creating a map of the surroundings, and accurately estimating its position within that map....

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Autores principales: Zhang, Xiao Ya, Abd Rahman, Abdul Hadi, Qamar, Faizan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588701/
https://www.ncbi.nlm.nih.gov/pubmed/37869467
http://dx.doi.org/10.7717/peerj-cs.1628
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author Zhang, Xiao Ya
Abd Rahman, Abdul Hadi
Qamar, Faizan
author_facet Zhang, Xiao Ya
Abd Rahman, Abdul Hadi
Qamar, Faizan
author_sort Zhang, Xiao Ya
collection PubMed
description Simultaneous localization and mapping (SLAM) is a fundamental problem in robotics and computer vision. It involves the task of a robot or an autonomous system navigating an unknown environment, simultaneously creating a map of the surroundings, and accurately estimating its position within that map. While significant progress has been made in SLAM over the years, challenges still need to be addressed. One prominent issue is robustness and accuracy in dynamic environments, which can cause uncertainties and errors in the estimation process. Traditional methods using temporal information to differentiate static and dynamic objects have limitations in accuracy and applicability. Nowadays, many research trends have leaned towards utilizing deep learning-based methods which leverage the capabilities to handle dynamic objects, semantic segmentation, and motion estimation, aiming to improve accuracy and adaptability in complex scenes. This article proposed an approach to enhance monocular visual odometry’s robustness and precision in dynamic environments. An enhanced algorithm using the semantic segmentation algorithm DeeplabV3+ is used to identify dynamic objects in the image and then apply the motion consistency check to remove feature points belonging to dynamic objects. The remaining static feature points are then used for feature matching and pose estimation based on ORB-SLAM2 using the Technical University of Munich (TUM) dataset. Experimental results show that our method outperforms traditional visual odometry methods in accuracy and robustness, especially in dynamic environments. By eliminating the influence of moving objects, our method improves the accuracy and robustness of visual odometry in dynamic environments. Compared to the traditional ORB-SLAM2, the results show that the system significantly reduces the absolute trajectory error and the relative pose error in dynamic scenes. Our approach has significantly improved the accuracy and robustness of the SLAM system’s pose estimation.
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spelling pubmed-105887012023-10-21 Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes Zhang, Xiao Ya Abd Rahman, Abdul Hadi Qamar, Faizan PeerJ Comput Sci Artificial Intelligence Simultaneous localization and mapping (SLAM) is a fundamental problem in robotics and computer vision. It involves the task of a robot or an autonomous system navigating an unknown environment, simultaneously creating a map of the surroundings, and accurately estimating its position within that map. While significant progress has been made in SLAM over the years, challenges still need to be addressed. One prominent issue is robustness and accuracy in dynamic environments, which can cause uncertainties and errors in the estimation process. Traditional methods using temporal information to differentiate static and dynamic objects have limitations in accuracy and applicability. Nowadays, many research trends have leaned towards utilizing deep learning-based methods which leverage the capabilities to handle dynamic objects, semantic segmentation, and motion estimation, aiming to improve accuracy and adaptability in complex scenes. This article proposed an approach to enhance monocular visual odometry’s robustness and precision in dynamic environments. An enhanced algorithm using the semantic segmentation algorithm DeeplabV3+ is used to identify dynamic objects in the image and then apply the motion consistency check to remove feature points belonging to dynamic objects. The remaining static feature points are then used for feature matching and pose estimation based on ORB-SLAM2 using the Technical University of Munich (TUM) dataset. Experimental results show that our method outperforms traditional visual odometry methods in accuracy and robustness, especially in dynamic environments. By eliminating the influence of moving objects, our method improves the accuracy and robustness of visual odometry in dynamic environments. Compared to the traditional ORB-SLAM2, the results show that the system significantly reduces the absolute trajectory error and the relative pose error in dynamic scenes. Our approach has significantly improved the accuracy and robustness of the SLAM system’s pose estimation. PeerJ Inc. 2023-10-10 /pmc/articles/PMC10588701/ /pubmed/37869467 http://dx.doi.org/10.7717/peerj-cs.1628 Text en © 2023 Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Zhang, Xiao Ya
Abd Rahman, Abdul Hadi
Qamar, Faizan
Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes
title Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes
title_full Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes
title_fullStr Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes
title_full_unstemmed Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes
title_short Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes
title_sort semantic visual simultaneous localization and mapping (slam) using deep learning for dynamic scenes
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588701/
https://www.ncbi.nlm.nih.gov/pubmed/37869467
http://dx.doi.org/10.7717/peerj-cs.1628
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