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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...

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Detalles Bibliográficos
Autores principales: Yin, Fang, Chou, Wusheng, Wu, Yun, Yang, Guang, Xu, Song
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
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.
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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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