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Distributed Camera Subsystem for Obstacle Detection
This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution ena...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228584/ https://www.ncbi.nlm.nih.gov/pubmed/35746381 http://dx.doi.org/10.3390/s22124588 |
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author | Oščádal, Petr Spurný, Tomáš Kot, Tomáš Grushko, Stefan Suder, Jiří Heczko, Dominik Novák, Petr Bobovský, Zdenko |
author_facet | Oščádal, Petr Spurný, Tomáš Kot, Tomáš Grushko, Stefan Suder, Jiří Heczko, Dominik Novák, Petr Bobovský, Zdenko |
author_sort | Oščádal, Petr |
collection | PubMed |
description | This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras. |
format | Online Article Text |
id | pubmed-9228584 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92285842022-06-25 Distributed Camera Subsystem for Obstacle Detection Oščádal, Petr Spurný, Tomáš Kot, Tomáš Grushko, Stefan Suder, Jiří Heczko, Dominik Novák, Petr Bobovský, Zdenko Sensors (Basel) Article This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras. MDPI 2022-06-18 /pmc/articles/PMC9228584/ /pubmed/35746381 http://dx.doi.org/10.3390/s22124588 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 Oščádal, Petr Spurný, Tomáš Kot, Tomáš Grushko, Stefan Suder, Jiří Heczko, Dominik Novák, Petr Bobovský, Zdenko Distributed Camera Subsystem for Obstacle Detection |
title | Distributed Camera Subsystem for Obstacle Detection |
title_full | Distributed Camera Subsystem for Obstacle Detection |
title_fullStr | Distributed Camera Subsystem for Obstacle Detection |
title_full_unstemmed | Distributed Camera Subsystem for Obstacle Detection |
title_short | Distributed Camera Subsystem for Obstacle Detection |
title_sort | distributed camera subsystem for obstacle detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228584/ https://www.ncbi.nlm.nih.gov/pubmed/35746381 http://dx.doi.org/10.3390/s22124588 |
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