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Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal

In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation accuracy. vSLAM can use feature points or a sequence of im...

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Autores principales: Theodorou, Charalambos, Velisavljevic, Vladan, Dyo, Vladimir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571647/
https://www.ncbi.nlm.nih.gov/pubmed/36236652
http://dx.doi.org/10.3390/s22197553
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author Theodorou, Charalambos
Velisavljevic, Vladan
Dyo, Vladimir
author_facet Theodorou, Charalambos
Velisavljevic, Vladan
Dyo, Vladimir
author_sort Theodorou, Charalambos
collection PubMed
description In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation accuracy. vSLAM can use feature points or a sequence of images, as it is the only source of input that can perform localization while simultaneously creating a map of the environment. A vSLAM system based on ORB-SLAM3 and on YOLOR was proposed in this paper. The newly proposed system in combination with an object detection model (YOLOX) applied on extracted feature points is capable of achieving 2–4% better accuracy compared to VPS-SLAM and DS-SLAM. Static feature points such as signs and benches were used to calculate the camera position, and dynamic moving objects were eliminated by using the tracking thread. A specific custom personal dataset that includes indoor and outdoor RGB-D pictures of train stations, including dynamic objects and high density of people, ground truth data, sequence data, and video recordings of the train stations and X, Y, Z data was used to validate and evaluate the proposed method. The results show that ORB-SLAM3 with YOLOR as object detection achieves 89.54% of accuracy in dynamic indoor environments compared to previous systems such as VPS-SLAM.
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spelling pubmed-95716472022-10-17 Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal Theodorou, Charalambos Velisavljevic, Vladan Dyo, Vladimir Sensors (Basel) Article In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation accuracy. vSLAM can use feature points or a sequence of images, as it is the only source of input that can perform localization while simultaneously creating a map of the environment. A vSLAM system based on ORB-SLAM3 and on YOLOR was proposed in this paper. The newly proposed system in combination with an object detection model (YOLOX) applied on extracted feature points is capable of achieving 2–4% better accuracy compared to VPS-SLAM and DS-SLAM. Static feature points such as signs and benches were used to calculate the camera position, and dynamic moving objects were eliminated by using the tracking thread. A specific custom personal dataset that includes indoor and outdoor RGB-D pictures of train stations, including dynamic objects and high density of people, ground truth data, sequence data, and video recordings of the train stations and X, Y, Z data was used to validate and evaluate the proposed method. The results show that ORB-SLAM3 with YOLOR as object detection achieves 89.54% of accuracy in dynamic indoor environments compared to previous systems such as VPS-SLAM. MDPI 2022-10-05 /pmc/articles/PMC9571647/ /pubmed/36236652 http://dx.doi.org/10.3390/s22197553 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
Theodorou, Charalambos
Velisavljevic, Vladan
Dyo, Vladimir
Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal
title Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal
title_full Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal
title_fullStr Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal
title_full_unstemmed Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal
title_short Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal
title_sort visual slam for dynamic environments based on object detection and optical flow for dynamic object removal
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571647/
https://www.ncbi.nlm.nih.gov/pubmed/36236652
http://dx.doi.org/10.3390/s22197553
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