Abstract
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.
Highlights
Relative navigation of a non-cooperative target satellite is an important part of space missions such as space offense and defense, on-orbit maintenance and orbital debris removal [1,2]
If all of the feature points extracted from the surface of the target satellite are used for the relative navigation, the computational load will be very large
Relative navigation based on vision sensors increasingly becomes significant because of the advantage of accuracy
Summary
Relative navigation of a non-cooperative target satellite is an important part of space missions such as space offense and defense, on-orbit maintenance and orbital debris removal [1,2]. Since satellites are artificial objects, they usually have obvious feature points such as edges and corners on their surfaces, and these feature points can be used to obtain the relative navigation information [5]. If all of the feature points extracted from the surface of the target satellite are used for the relative navigation, the computational load will be very large. It is a great challenge for the chaser satellite with limited computing ability or a high real-time requirement. A compromise between the computational load and the performance should be explored in practice From this point of view, a subset of feature points can be selected for the relative navigation. The accuracy with the selected feature points should meet the navigation requirement
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