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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7765252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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