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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MDPI AG
2018-03-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/3/854 |
| _version_ | 1798038470101303296 |
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| author | Mingfeng Ning Shijie Zhang Shiqiang Wang |
| author_facet | Mingfeng Ning Shijie Zhang Shiqiang Wang |
| author_sort | Mingfeng Ning |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T21:40:38Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:40:38Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bf3f34aa10e34aa58e58705d0e36847f2022-12-22T04:01:36ZengMDPI AGSensors1424-82202018-03-0118385410.3390/s18030854s18030854A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation SystemMingfeng Ning0Shijie Zhang1Shiqiang Wang2Research Center of Satellite Technology, Harbin Institute of Technology, Harbin 150080, ChinaResearch Center of Satellite Technology, Harbin Institute of Technology, Harbin 150080, ChinaResearch Center of Satellite Technology, Harbin Institute of Technology, Harbin 150080, ChinaThe 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.http://www.mdpi.com/1424-8220/18/3/854non-cooperative satellitefeature point selectionvision-based navigation systemquasi-optimal method |
| spellingShingle | Mingfeng Ning Shijie Zhang Shiqiang Wang A Non-Cooperative Satellite Feature Point Selection Method for Vision-Based Navigation System Sensors non-cooperative satellite feature point selection vision-based navigation system quasi-optimal method |
| 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 | non-cooperative satellite feature point selection vision-based navigation system quasi-optimal method |
| url | http://www.mdpi.com/1424-8220/18/3/854 |
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