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Automatic Real-Time Pose Estimation of Machinery from Images
The automatic positioning of machines in a large number of application areas is an important aspect of automation. Today, this is often done using classic geodetic sensors such as Global Navigation Satellite Systems (GNSS) and robotic total stations. In this work, a stereo camera system was develope...
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/PMC9002778/ https://www.ncbi.nlm.nih.gov/pubmed/35408240 http://dx.doi.org/10.3390/s22072627 |
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author | Bertels, Marcel Jutzi, Boris Ulrich, Markus |
author_facet | Bertels, Marcel Jutzi, Boris Ulrich, Markus |
author_sort | Bertels, Marcel |
collection | PubMed |
description | The automatic positioning of machines in a large number of application areas is an important aspect of automation. Today, this is often done using classic geodetic sensors such as Global Navigation Satellite Systems (GNSS) and robotic total stations. In this work, a stereo camera system was developed that localizes a machine at high frequency and serves as an alternative to the previously mentioned sensors. For this purpose, algorithms were developed that detect active markers on the machine in a stereo image pair, find stereo point correspondences, and estimate the pose of the machine from these. Theoretical influences and accuracies for different systems were estimated with a Monte Carlo simulation, on the basis of which the stereo camera system was designed. Field measurements were used to evaluate the actual achievable accuracies and the robustness of the prototype system. The comparison is present with reference measurements with a laser tracker. The estimated object pose achieved accuracies higher than [Formula: see text] with the translation components and accuracies higher than [Formula: see text] with the rotation components. As a result, 3D point accuracies higher than [Formula: see text] were achieved by the machine. For the first time, a prototype could be developed that represents an alternative, powerful image-based localization method for machines to the classical geodetic sensors. |
format | Online Article Text |
id | pubmed-9002778 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90027782022-04-13 Automatic Real-Time Pose Estimation of Machinery from Images Bertels, Marcel Jutzi, Boris Ulrich, Markus Sensors (Basel) Article The automatic positioning of machines in a large number of application areas is an important aspect of automation. Today, this is often done using classic geodetic sensors such as Global Navigation Satellite Systems (GNSS) and robotic total stations. In this work, a stereo camera system was developed that localizes a machine at high frequency and serves as an alternative to the previously mentioned sensors. For this purpose, algorithms were developed that detect active markers on the machine in a stereo image pair, find stereo point correspondences, and estimate the pose of the machine from these. Theoretical influences and accuracies for different systems were estimated with a Monte Carlo simulation, on the basis of which the stereo camera system was designed. Field measurements were used to evaluate the actual achievable accuracies and the robustness of the prototype system. The comparison is present with reference measurements with a laser tracker. The estimated object pose achieved accuracies higher than [Formula: see text] with the translation components and accuracies higher than [Formula: see text] with the rotation components. As a result, 3D point accuracies higher than [Formula: see text] were achieved by the machine. For the first time, a prototype could be developed that represents an alternative, powerful image-based localization method for machines to the classical geodetic sensors. MDPI 2022-03-29 /pmc/articles/PMC9002778/ /pubmed/35408240 http://dx.doi.org/10.3390/s22072627 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 Bertels, Marcel Jutzi, Boris Ulrich, Markus Automatic Real-Time Pose Estimation of Machinery from Images |
title | Automatic Real-Time Pose Estimation of Machinery from Images |
title_full | Automatic Real-Time Pose Estimation of Machinery from Images |
title_fullStr | Automatic Real-Time Pose Estimation of Machinery from Images |
title_full_unstemmed | Automatic Real-Time Pose Estimation of Machinery from Images |
title_short | Automatic Real-Time Pose Estimation of Machinery from Images |
title_sort | automatic real-time pose estimation of machinery from images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002778/ https://www.ncbi.nlm.nih.gov/pubmed/35408240 http://dx.doi.org/10.3390/s22072627 |
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