Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Oščádal, Petr, Spurný, Tomáš, Kot, Tomáš, Grushko, Stefan, Suder, Jiří, Heczko, Dominik, Novák, Petr, Bobovský, Zdenko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784734517058076672
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
work_keys_str_mv AT oscadalpetr distributedcamerasubsystemforobstacledetection
AT spurnytomas distributedcamerasubsystemforobstacledetection
AT kottomas distributedcamerasubsystemforobstacledetection
AT grushkostefan distributedcamerasubsystemforobstacledetection
AT suderjiri distributedcamerasubsystemforobstacledetection
AT heczkodominik distributedcamerasubsystemforobstacledetection
AT novakpetr distributedcamerasubsystemforobstacledetection
AT bobovskyzdenko distributedcamerasubsystemforobstacledetection