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Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation
Pose estimation is crucial for automating assembly tasks, yet achieving sufficient accuracy for assembly automation remains challenging and part-specific. This paper presents a novel, streamlined approach to pose estimation that facilitates automation of assembly tasks. Our proposed method employs d...
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/PMC10346187/ https://www.ncbi.nlm.nih.gov/pubmed/37447956 http://dx.doi.org/10.3390/s23136107 |
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author | Shi, Debo Rahimpour, Alireza Ghafourian, Amin Naddaf Shargh, Mohammad Mahdi Upadhyay, Devesh Lasky, Ty A. Soltani, Iman |
author_facet | Shi, Debo Rahimpour, Alireza Ghafourian, Amin Naddaf Shargh, Mohammad Mahdi Upadhyay, Devesh Lasky, Ty A. Soltani, Iman |
author_sort | Shi, Debo |
collection | PubMed |
description | Pose estimation is crucial for automating assembly tasks, yet achieving sufficient accuracy for assembly automation remains challenging and part-specific. This paper presents a novel, streamlined approach to pose estimation that facilitates automation of assembly tasks. Our proposed method employs deep learning on a limited number of annotated images to identify a set of keypoints on the parts of interest. To compensate for network shortcomings and enhance accuracy we incorporated a Bayesian updating stage that leverages our detailed knowledge of the assembly part design. This Bayesian updating step refines the network output, significantly improving pose estimation accuracy. For this purpose, we utilized a subset of network-generated keypoint positions with higher quality as measurements, while for the remaining keypoints, the network outputs only serve as priors. The geometry data aid in constructing likelihood functions, which in turn result in enhanced posterior distributions of keypoint pixel positions. We then employed the maximum a posteriori (MAP) estimates of keypoint locations to obtain a final pose, allowing for an update to the nominal assembly trajectory. We evaluated our method on a 14-point snap-fit dash trim assembly for a Ford Mustang dashboard, demonstrating promising results. Our approach does not require tailoring to new applications, nor does it rely on extensive machine learning expertise or large amounts of training data. This makes our method a scalable and adaptable solution for the production floors. |
format | Online Article Text |
id | pubmed-10346187 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103461872023-07-15 Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation Shi, Debo Rahimpour, Alireza Ghafourian, Amin Naddaf Shargh, Mohammad Mahdi Upadhyay, Devesh Lasky, Ty A. Soltani, Iman Sensors (Basel) Article Pose estimation is crucial for automating assembly tasks, yet achieving sufficient accuracy for assembly automation remains challenging and part-specific. This paper presents a novel, streamlined approach to pose estimation that facilitates automation of assembly tasks. Our proposed method employs deep learning on a limited number of annotated images to identify a set of keypoints on the parts of interest. To compensate for network shortcomings and enhance accuracy we incorporated a Bayesian updating stage that leverages our detailed knowledge of the assembly part design. This Bayesian updating step refines the network output, significantly improving pose estimation accuracy. For this purpose, we utilized a subset of network-generated keypoint positions with higher quality as measurements, while for the remaining keypoints, the network outputs only serve as priors. The geometry data aid in constructing likelihood functions, which in turn result in enhanced posterior distributions of keypoint pixel positions. We then employed the maximum a posteriori (MAP) estimates of keypoint locations to obtain a final pose, allowing for an update to the nominal assembly trajectory. We evaluated our method on a 14-point snap-fit dash trim assembly for a Ford Mustang dashboard, demonstrating promising results. Our approach does not require tailoring to new applications, nor does it rely on extensive machine learning expertise or large amounts of training data. This makes our method a scalable and adaptable solution for the production floors. MDPI 2023-07-02 /pmc/articles/PMC10346187/ /pubmed/37447956 http://dx.doi.org/10.3390/s23136107 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 Shi, Debo Rahimpour, Alireza Ghafourian, Amin Naddaf Shargh, Mohammad Mahdi Upadhyay, Devesh Lasky, Ty A. Soltani, Iman Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation |
title | Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation |
title_full | Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation |
title_fullStr | Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation |
title_full_unstemmed | Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation |
title_short | Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation |
title_sort | deep bayesian-assisted keypoint detection for pose estimation in assembly automation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346187/ https://www.ncbi.nlm.nih.gov/pubmed/37447956 http://dx.doi.org/10.3390/s23136107 |
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