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DOPESLAM: High-Precision ROS-Based Semantic 3D SLAM in a Dynamic Environment
Recent advancements in deep learning techniques have accelerated the growth of robotic vision systems. One way this technology can be applied is to use a mobile robot to automatically generate a 3D map and identify objects within it. This paper addresses the important challenge of labeling objects a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181773/ https://www.ncbi.nlm.nih.gov/pubmed/37177568 http://dx.doi.org/10.3390/s23094364 |
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author | Roch, Jesse Fayyad, Jamil Najjaran, Homayoun |
author_facet | Roch, Jesse Fayyad, Jamil Najjaran, Homayoun |
author_sort | Roch, Jesse |
collection | PubMed |
description | Recent advancements in deep learning techniques have accelerated the growth of robotic vision systems. One way this technology can be applied is to use a mobile robot to automatically generate a 3D map and identify objects within it. This paper addresses the important challenge of labeling objects and generating 3D maps in a dynamic environment. It explores a solution to this problem by combining Deep Object Pose Estimation (DOPE) with Real-Time Appearance-Based Mapping (RTAB-Map) through means of loose-coupled parallel fusion. DOPE’s abilities are enhanced by leveraging its belief map system to filter uncertain key points, which increases precision to ensure that only the best object labels end up on the map. Additionally, DOPE’s pipeline is modified to enable shape-based object recognition using depth maps, allowing it to identify objects in complete darkness. Three experiments are performed to find the ideal training dataset, quantify the increased precision, and evaluate the overall performance of the system. The results show that the proposed solution outperforms existing methods in most intended scenarios, such as in unilluminated scenes. The proposed key point filtering technique has demonstrated an improvement in the average inference speed, achieving a speedup of 2.6× and improving the average distance to the ground truth compared to the original DOPE algorithm. |
format | Online Article Text |
id | pubmed-10181773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101817732023-05-13 DOPESLAM: High-Precision ROS-Based Semantic 3D SLAM in a Dynamic Environment Roch, Jesse Fayyad, Jamil Najjaran, Homayoun Sensors (Basel) Article Recent advancements in deep learning techniques have accelerated the growth of robotic vision systems. One way this technology can be applied is to use a mobile robot to automatically generate a 3D map and identify objects within it. This paper addresses the important challenge of labeling objects and generating 3D maps in a dynamic environment. It explores a solution to this problem by combining Deep Object Pose Estimation (DOPE) with Real-Time Appearance-Based Mapping (RTAB-Map) through means of loose-coupled parallel fusion. DOPE’s abilities are enhanced by leveraging its belief map system to filter uncertain key points, which increases precision to ensure that only the best object labels end up on the map. Additionally, DOPE’s pipeline is modified to enable shape-based object recognition using depth maps, allowing it to identify objects in complete darkness. Three experiments are performed to find the ideal training dataset, quantify the increased precision, and evaluate the overall performance of the system. The results show that the proposed solution outperforms existing methods in most intended scenarios, such as in unilluminated scenes. The proposed key point filtering technique has demonstrated an improvement in the average inference speed, achieving a speedup of 2.6× and improving the average distance to the ground truth compared to the original DOPE algorithm. MDPI 2023-04-28 /pmc/articles/PMC10181773/ /pubmed/37177568 http://dx.doi.org/10.3390/s23094364 Text en © 2023 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 Roch, Jesse Fayyad, Jamil Najjaran, Homayoun DOPESLAM: High-Precision ROS-Based Semantic 3D SLAM in a Dynamic Environment |
title | DOPESLAM: High-Precision ROS-Based Semantic 3D SLAM in a Dynamic Environment |
title_full | DOPESLAM: High-Precision ROS-Based Semantic 3D SLAM in a Dynamic Environment |
title_fullStr | DOPESLAM: High-Precision ROS-Based Semantic 3D SLAM in a Dynamic Environment |
title_full_unstemmed | DOPESLAM: High-Precision ROS-Based Semantic 3D SLAM in a Dynamic Environment |
title_short | DOPESLAM: High-Precision ROS-Based Semantic 3D SLAM in a Dynamic Environment |
title_sort | dopeslam: high-precision ros-based semantic 3d slam in a dynamic environment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181773/ https://www.ncbi.nlm.nih.gov/pubmed/37177568 http://dx.doi.org/10.3390/s23094364 |
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