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A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation System

The number of feature points on the surface of a non-cooperative target satellite used for monocular vision-based relative navigation affects the onboard computational load. A feature point selection method called the quasi-optimal method is proposed to select a subset of feature points with a good...

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Autores principales: Ning, Mingfeng, Zhang, Shijie, Wang, Shiqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877375/
https://www.ncbi.nlm.nih.gov/pubmed/29538294
http://dx.doi.org/10.3390/s18030854
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author Ning, Mingfeng
Zhang, Shijie
Wang, Shiqiang
author_facet Ning, Mingfeng
Zhang, Shijie
Wang, Shiqiang
author_sort Ning, Mingfeng
collection PubMed
description The number of feature points on the surface of a non-cooperative target satellite used for monocular vision-based relative navigation affects the onboard computational load. A feature point selection method called the quasi-optimal method is proposed to select a subset of feature points with a good geometric distribution. This method, with the assumption that all of the feature points are in a plane and have the same variance, is based on the fact that the scattered feature points can provide higher accuracy than that of them grouped together. The cost is defined as a function of the angle between two unit vectors from the projection center to feature points. The redundancy of a feature point is calculated by summing all costs associated with it. Firstly, the feature point with the most redundant information is removed. Then, redundancies are calculated again with the second feature point removed. The procedures above are repeated until the desired number of feature points is reached. Dilution of precision (DOP) represents the mapping relation between the observation variance and the estimated variance. In this paper, the DOP concept is used in a vision-based navigation system to verify the performance of the quasi-optimal method. Simulation results demonstrate the feasibility of calculating the relative position and attitude by using a subset of feature points with a good geometric distribution. It also shows that the feature points selected by the quasi-optimal method can provide a high accuracy with low computation time.
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spelling pubmed-58773752018-04-09 A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation System Ning, Mingfeng Zhang, Shijie Wang, Shiqiang Sensors (Basel) Article The number of feature points on the surface of a non-cooperative target satellite used for monocular vision-based relative navigation affects the onboard computational load. A feature point selection method called the quasi-optimal method is proposed to select a subset of feature points with a good geometric distribution. This method, with the assumption that all of the feature points are in a plane and have the same variance, is based on the fact that the scattered feature points can provide higher accuracy than that of them grouped together. The cost is defined as a function of the angle between two unit vectors from the projection center to feature points. The redundancy of a feature point is calculated by summing all costs associated with it. Firstly, the feature point with the most redundant information is removed. Then, redundancies are calculated again with the second feature point removed. The procedures above are repeated until the desired number of feature points is reached. Dilution of precision (DOP) represents the mapping relation between the observation variance and the estimated variance. In this paper, the DOP concept is used in a vision-based navigation system to verify the performance of the quasi-optimal method. Simulation results demonstrate the feasibility of calculating the relative position and attitude by using a subset of feature points with a good geometric distribution. It also shows that the feature points selected by the quasi-optimal method can provide a high accuracy with low computation time. MDPI 2018-03-14 /pmc/articles/PMC5877375/ /pubmed/29538294 http://dx.doi.org/10.3390/s18030854 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
Ning, Mingfeng
Zhang, Shijie
Wang, Shiqiang
A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation System
title A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation System
title_full A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation System
title_fullStr A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation System
title_full_unstemmed A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation System
title_short A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation System
title_sort non-cooperative satellite feature point selection method for vision-based navigation system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877375/
https://www.ncbi.nlm.nih.gov/pubmed/29538294
http://dx.doi.org/10.3390/s18030854
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