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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...

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Autores principales: Shi, Debo, Rahimpour, Alireza, Ghafourian, Amin, Naddaf Shargh, Mohammad Mahdi, Upadhyay, Devesh, Lasky, Ty A., Soltani, Iman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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