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Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target
This paper proposes an autonomous algorithm to determine the relative pose between the chaser spacecraft and the uncooperative space target, which is essential in advanced space applications, e.g., on-orbit serving missions. The proposed method, named Congruent Tetrahedron Align (CTA) algorithm, use...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948898/ https://www.ncbi.nlm.nih.gov/pubmed/29597323 http://dx.doi.org/10.3390/s18041009 |
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author | Yin, Fang Chou, Wusheng Wu, Yun Yang, Guang Xu, Song |
author_facet | Yin, Fang Chou, Wusheng Wu, Yun Yang, Guang Xu, Song |
author_sort | Yin, Fang |
collection | PubMed |
description | This paper proposes an autonomous algorithm to determine the relative pose between the chaser spacecraft and the uncooperative space target, which is essential in advanced space applications, e.g., on-orbit serving missions. The proposed method, named Congruent Tetrahedron Align (CTA) algorithm, uses the very sparse unorganized 3D point cloud acquired by a LIDAR sensor, and does not require any prior pose information. The core of the method is to determine the relative pose by looking for the congruent tetrahedron in scanning point cloud and model point cloud on the basis of its known model. The two-level index hash table is built for speeding up the search speed. In addition, the Iterative Closest Point (ICP) algorithm is used for pose tracking after CTA. In order to evaluate the method in arbitrary initial attitude, a simulated system is presented. Specifically, the performance of the proposed method to provide the initial pose needed for the tracking algorithm is demonstrated, as well as their robustness against noise. Finally, a field experiment is conducted and the results demonstrated the effectiveness of the proposed method. |
format | Online Article Text |
id | pubmed-5948898 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59488982018-05-17 Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target Yin, Fang Chou, Wusheng Wu, Yun Yang, Guang Xu, Song Sensors (Basel) Article This paper proposes an autonomous algorithm to determine the relative pose between the chaser spacecraft and the uncooperative space target, which is essential in advanced space applications, e.g., on-orbit serving missions. The proposed method, named Congruent Tetrahedron Align (CTA) algorithm, uses the very sparse unorganized 3D point cloud acquired by a LIDAR sensor, and does not require any prior pose information. The core of the method is to determine the relative pose by looking for the congruent tetrahedron in scanning point cloud and model point cloud on the basis of its known model. The two-level index hash table is built for speeding up the search speed. In addition, the Iterative Closest Point (ICP) algorithm is used for pose tracking after CTA. In order to evaluate the method in arbitrary initial attitude, a simulated system is presented. Specifically, the performance of the proposed method to provide the initial pose needed for the tracking algorithm is demonstrated, as well as their robustness against noise. Finally, a field experiment is conducted and the results demonstrated the effectiveness of the proposed method. MDPI 2018-03-28 /pmc/articles/PMC5948898/ /pubmed/29597323 http://dx.doi.org/10.3390/s18041009 Text en © 2018 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 Yin, Fang Chou, Wusheng Wu, Yun Yang, Guang Xu, Song Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target |
title | Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target |
title_full | Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target |
title_fullStr | Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target |
title_full_unstemmed | Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target |
title_short | Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target |
title_sort | sparse unorganized point cloud based relative pose estimation for uncooperative space target |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948898/ https://www.ncbi.nlm.nih.gov/pubmed/29597323 http://dx.doi.org/10.3390/s18041009 |
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