The number of feature points on the surface of a non-cooperative target satellite used for\nmonocular vision-based relative navigation affects the onboard computational load. A feature point\nselection method called the quasi-optimal method is proposed to select a subset of feature points\nwith a good geometric distribution. This method, with the assumption that all of the feature points\nare in a plane and have the same variance, is based on the fact that the scattered feature points can\nprovide higher accuracy than that of them grouped together. The cost is defined as a function of\nthe angle between two unit vectors from the projection center to feature points. The redundancy\nof a feature point is calculated by summing all costs associated with it. Firstly, the feature point\nwith the most redundant information is removed. Then, redundancies are calculated again with\nthe second feature point removed. The procedures above are repeated until the desired number\nof feature points is reached. Dilution of precision (DOP) represents the mapping relation between\nthe observation variance and the estimated variance. In this paper, the DOP concept is used in a\nvision-based navigation system to verify the performance of the quasi-optimal method. Simulation\nresults demonstrate the feasibility of calculating the relative position and attitude by using a subset\nof feature points with a good geometric distribution. It also shows that the feature points selected by\nthe quasi-optimal method can provide a high accuracy with low computation time.
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