Visual sensors are used to measure the relative state of the chaser spacecraft to the target spacecraft during close range ren- dezvous phases. This article proposes a two-stage iterative algorithm based on an inverse projection ray approach to address the relative position and attitude estimation by using feature points and monocular vision. It consists of two stages： absolute orienta- tion and depth recovery. In the first stage, Umeyama＇s algorithm is used to fit the three-dimensional （3D） model set and estimate the 3D point set while in the second stage, the depths of the observed feature points are estimated. This procedure is repeated until the result converges. Moreover, the effectiveness and convergence of the proposed algorithm are verified through theoreti- cal analysis and mathematical simulation.
以非合作航天器的相对状态确定为研究背景,针对无法在目标航天器上安装测量光标的问题,提出利用目标航天器自然特征的单目视觉测量方案.并针对仅利用非合作航天器自然特征而导致的粗大误差增大等问题,提出了基于Randomized RANdom SAmple Consensus(R-RANSAC)的相对位姿单目视觉确定鲁棒算法,该算法首先采用R-RANSAC剔除粗大误差,然后利用基于特征点的相对位姿确定迭代算法消除其他类型的误差影响,以进一步提高算法确定精度.与航天器交会对接视觉系统不同,该系统无需在目标航天器上安装测量光标,而是充分利用目标航天器的自身结构特征,因此更适用于非合作航天器间的相对状态测量.最后对本文算法进行了数学仿真,结果表明该算法的有效性和可靠性.
