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Spatial Topological Relation Analysis for Cluttered Scenes

The spatial topological relations are the foundation of robot operation planning under unstructured and cluttered scenes. Defining complex relations and dealing with incomplete point clouds from the surface of objects are the most difficult challenge in the spatial topological relation analysis. In...

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Detalles Bibliográficos
Autores principales: Fu, Yu, Li, Mantian, Zhang, Xinyi, Zhang, Sen, Wei, Chunyu, Guo, Wei, Cai, Hegao, Sun, Lining, Wang, Pengfei, Zha, Fusheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7765252/
https://www.ncbi.nlm.nih.gov/pubmed/33333848
http://dx.doi.org/10.3390/s20247181
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author Fu, Yu
Li, Mantian
Zhang, Xinyi
Zhang, Sen
Wei, Chunyu
Guo, Wei
Cai, Hegao
Sun, Lining
Wang, Pengfei
Zha, Fusheng
author_facet Fu, Yu
Li, Mantian
Zhang, Xinyi
Zhang, Sen
Wei, Chunyu
Guo, Wei
Cai, Hegao
Sun, Lining
Wang, Pengfei
Zha, Fusheng
author_sort Fu, Yu
collection PubMed
description The spatial topological relations are the foundation of robot operation planning under unstructured and cluttered scenes. Defining complex relations and dealing with incomplete point clouds from the surface of objects are the most difficult challenge in the spatial topological relation analysis. In this paper, we presented the classification of spatial topological relations by dividing the intersection space into six parts. In order to improve accuracy and reduce computing time, convex hulls are utilized to represent the boundary of objects and the spatial topological relations can be determined by the category of points in point clouds. We verified our method on the datasets. The result demonstrated that we have great improvement comparing with the previous method.
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spelling pubmed-77652522020-12-27 Spatial Topological Relation Analysis for Cluttered Scenes Fu, Yu Li, Mantian Zhang, Xinyi Zhang, Sen Wei, Chunyu Guo, Wei Cai, Hegao Sun, Lining Wang, Pengfei Zha, Fusheng Sensors (Basel) Article The spatial topological relations are the foundation of robot operation planning under unstructured and cluttered scenes. Defining complex relations and dealing with incomplete point clouds from the surface of objects are the most difficult challenge in the spatial topological relation analysis. In this paper, we presented the classification of spatial topological relations by dividing the intersection space into six parts. In order to improve accuracy and reduce computing time, convex hulls are utilized to represent the boundary of objects and the spatial topological relations can be determined by the category of points in point clouds. We verified our method on the datasets. The result demonstrated that we have great improvement comparing with the previous method. MDPI 2020-12-15 /pmc/articles/PMC7765252/ /pubmed/33333848 http://dx.doi.org/10.3390/s20247181 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fu, Yu
Li, Mantian
Zhang, Xinyi
Zhang, Sen
Wei, Chunyu
Guo, Wei
Cai, Hegao
Sun, Lining
Wang, Pengfei
Zha, Fusheng
Spatial Topological Relation Analysis for Cluttered Scenes
title Spatial Topological Relation Analysis for Cluttered Scenes
title_full Spatial Topological Relation Analysis for Cluttered Scenes
title_fullStr Spatial Topological Relation Analysis for Cluttered Scenes
title_full_unstemmed Spatial Topological Relation Analysis for Cluttered Scenes
title_short Spatial Topological Relation Analysis for Cluttered Scenes
title_sort spatial topological relation analysis for cluttered scenes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7765252/
https://www.ncbi.nlm.nih.gov/pubmed/33333848
http://dx.doi.org/10.3390/s20247181
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