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Self-generated dataset for category and pose estimation of deformable object

This work considers the problem of garment handling by a general household robot that focuses on the task of classification and pose estimation of a hanging garment in unfolding procedure. Classification and pose estimation of deformable objects such as garment are considered a challenging problem i...

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Main Authors: Hou Y.C., Sahari K.S.M.
Other Authors: 37067465000
Format: Article
Published: Atlantis Press 2023
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spelling my.uniten.dspace-247452023-05-29T15:26:34Z Self-generated dataset for category and pose estimation of deformable object Hou Y.C. Sahari K.S.M. 37067465000 57218170038 This work considers the problem of garment handling by a general household robot that focuses on the task of classification and pose estimation of a hanging garment in unfolding procedure. Classification and pose estimation of deformable objects such as garment are considered a challenging problem in autonomous robotic manipulation because these objects are in different sizes and can be deformed into different poses when manipulating them. Hence, we propose a self-generated synthetic dataset for classifying the category and estimating the pose of garment using a single manipulator. We present an approach to this problem by first constructing a garment mesh model into a piece of garment that crudely spread-out on the flat platform using particle-based modeling and then the parameters such as landmarks and robotic grasping points can be estimated from the garment mesh model. Later, the spread-out garment is picked up by a single robotic manipulator and the 2D garment mesh model is simulated in 3D virtual environment. A dataset of hanging garment can be generated by capturing the depth images of real garment at the robotic platform and also the images of garment mesh model from offline simulation respectively. The synthetic dataset collected from simulation shown the approach performed well and applicable on a different of similar garment. Thus, the category and pose recognition of the garment can be further developed. � 2019 The Authors. Published by Atlantis Press SARL. Final 2023-05-29T07:26:34Z 2023-05-29T07:26:34Z 2019 Article 10.2991/jrnal.k.190220.001 2-s2.0-85079480012 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079480012&doi=10.2991%2fjrnal.k.190220.001&partnerID=40&md5=3d8cb0365a2c5a7366f57e380383df5b https://irepository.uniten.edu.my/handle/123456789/24745 5 4 217 222 All Open Access, Bronze Atlantis Press Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description This work considers the problem of garment handling by a general household robot that focuses on the task of classification and pose estimation of a hanging garment in unfolding procedure. Classification and pose estimation of deformable objects such as garment are considered a challenging problem in autonomous robotic manipulation because these objects are in different sizes and can be deformed into different poses when manipulating them. Hence, we propose a self-generated synthetic dataset for classifying the category and estimating the pose of garment using a single manipulator. We present an approach to this problem by first constructing a garment mesh model into a piece of garment that crudely spread-out on the flat platform using particle-based modeling and then the parameters such as landmarks and robotic grasping points can be estimated from the garment mesh model. Later, the spread-out garment is picked up by a single robotic manipulator and the 2D garment mesh model is simulated in 3D virtual environment. A dataset of hanging garment can be generated by capturing the depth images of real garment at the robotic platform and also the images of garment mesh model from offline simulation respectively. The synthetic dataset collected from simulation shown the approach performed well and applicable on a different of similar garment. Thus, the category and pose recognition of the garment can be further developed. � 2019 The Authors. Published by Atlantis Press SARL.
author2 37067465000
author_facet 37067465000
Hou Y.C.
Sahari K.S.M.
format Article
author Hou Y.C.
Sahari K.S.M.
spellingShingle Hou Y.C.
Sahari K.S.M.
Self-generated dataset for category and pose estimation of deformable object
author_sort Hou Y.C.
title Self-generated dataset for category and pose estimation of deformable object
title_short Self-generated dataset for category and pose estimation of deformable object
title_full Self-generated dataset for category and pose estimation of deformable object
title_fullStr Self-generated dataset for category and pose estimation of deformable object
title_full_unstemmed Self-generated dataset for category and pose estimation of deformable object
title_sort self-generated dataset for category and pose estimation of deformable object
publisher Atlantis Press
publishDate 2023
_version_ 1806427676760604672
score 13.211869

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