针对传统正交迭代算法计算效率低的缺点,提出了一种改进的正交迭代算法以实现基于单目视觉的空间目标位姿测量.首先在传统正交迭代位姿测量算法的基础上,消除迭代过程中的平移向量,使用平行透视模型代替弱透视投影模型进行旋转矩阵初始值的求解,从而实现正交迭代算法的加速求解.其次,利用仿真实验研究了特征点图像坐标提取精度、特征点空间三维坐标精度、相机焦距标定精度、相机主点标定精度及空间特征点数量对算法的精度和运算效率的影响.在此基础上,利用田口算法并结合仿真分析结果定量分析每个因素对算法精度的影响,明确每个因素对算法精度的影响力,找出对位姿测量精度影响最大的因素.最后,通过实物实验验证了改进正交迭代算法的性能,结果表明本算法运行时间短且具有较高的精度,同时,基于正交试验结果,可通过控制影响不同位姿参数的首要因素,使得改进正交迭代算法的精度满足不同的空间目标位姿测量任务的需求.“,”Aiming at the low efficiency of traditional orthogonal iterative algorithm,an improved orthogonal iterative algorithm is proposed to measure the pose of space objects based on monocular vision. Firstly, based on the traditional orthogonal iterative algorithm,the translation vector in the iterative process was eliminated. The initial value of the rotation matrix was solved by using the parallel perspective model instead of the weak perspective projection model,so as to accelerate the solution process of the orthogonal iterative algorithm. Secondly,simulation experiments are used to study the effects of the extraction accuracy of the imaging point,the accuracy of three-dimensional coordinate of the space feature point,the calibration accuracy of the camera principal point,the calibration accuracy of the camera focal length and the number of space feature points on the accuracy and efficiency of the algorithm. Based on the results of the simulation experiments,Taguchi method is used to quantitatively analyze the influence of each factor on the accuracy of the algorithm,and find out the factor that has the greatest influence on the accuracy of the improved orthogonal iteration algorithm. Finally,the performance of the proposed improved orthogonal iteration algorithm is tested by the physical experiments. The physical experiments prove that the proposed method is accurate,and takes shorter run time than that of traditional orthogonal iteration algorithm. Based on the results of the orthogonal experiment,the accuracy of the improved orthogonal iteration algorithm could meet different demand of different space task by controlling the different influence factors.