最小化重投影误差的手眼标定优化算法

为了减小图像噪声对手眼标定精度的影响,在传统的手眼标定算法的基础上,利用基于最小化重投影误差优化算法求解手眼标定方程。以基于矩阵直积参数化方法计算出的手眼关系矩阵为初值,以摄影测量光束平差为优化模型,最小化重投影误差为代价函数,获得最优的手眼关系标定值。实验结果证明了该方法的具有较好的稳定性和精确性,算法求解了10次实验结果,手眼标定矩阵的最大重投影误差为1.037个像素,波动值为0.007像素。与最小化代数误差的优化方法和线性优化分步估计方法相比,提高了手眼标定的求解精度。

Hand-eye calibration optimization algorithm based on minimizing reprojection error

In order to reduce the influence of image noise on the accuracy of hand eye calibration,based on the traditional hand eye calibration algorithm,the hand eye calibration equation is solved by the optimization algorithm based on the minimum reprojection error.The hand-eye relation matrix calculated based on the direct product parameterization method of matrix is taken as the initial value,the photogrammetric beam adjustment is used as the optimization model,and the cost function is used to minimize the reprojection error.Experimental results show that this method has good stability and accuracy.The algorithm solves the results of 10 experiments,and the maximum reprojection error of the hand-eye calibration matrix is 1.037 pixels,and the fluctuation value is 0.007 pixels.Compared with the optimization method of minimizing the algebraic error and the linear stepwise estimation method,the accuracy of hand-eye calibration is improved.