非合作目标在接近的过程中, 将所提取的特征点直接用于相对导航会因特征点数目过多导致计算量大。为减小相对导航的计算量, 需从提取的特征点中选择一个子集用于相对导航。假设所提取的特征点在非合作目标的同平面内,将目标表面特征点选择问题转化为像平面特征点选择。同时, 文中研究了视觉系统的精度因子(DOP), 并利用精度因子与平面特征点构成的面积呈反比的特性, 提出像平面特征点的凸包作为观测量用于相对导航。仿真表明: 将同平面特征点的凸包用于相对导航, 具有较高的精度, 同时能够有效减少特征点数; 计算凸包的时间远小于求解最优PDOP的时间, 能够应用于实时计算。

In the process of approaching a non-cooperative target, it would result in a large amount of calculation using the extracted feature points directly for relative navigation due to the excessive number of feature points. In order to reduce the amount of computation for relative navigation, a subset should be selected from the extracted feature points. Assuming that the extracted feature points were in the same plane of the non-cooperative target, the selection of feature points for non-cooperative target could be transformed as the selection of feature points on the image plane. At the same time, this paper also studied dilution of precision (DOP) for vision-based system and proposed the method that the convex hull of the feature points on the image could be selected as the subset for relative navigation based on the fact that the DOP was inversely proportional to the area of the subset. The simulation shows that the convex hull of feature points can provide high accuracy for relative navigation and reduce the number of feature points effectively. It also shows that the computation time of calculation convex hull is far less than searching the subset with optimal PDOP.