面对需要实时计算的相机位姿估计问题，针对经典的广泛应用的正交迭代算法，提出了一种加速正交迭代算法。其关键思想是将每一次迭代过程规整化，从而提炼出每一次迭代的重复计算，若将此重复计算在迭代开始前提前计算，则可以大幅度的减少迭代过程中的计算量，使得每一次迭代的计算复杂度从O(n)降低为O(1)。因此，可以在更短的时间内迭代更多的次数，从而获得更高的精度。进行了对比实验，结果显示本加速算法计算精度更高，速度更快。并通过实验提出了选择稳健n 点透视(RPnP)计算初值，再使用加速正交迭代算法进行迭代运算的方法，在控制点不多的情况下，是一种精度接近最大似然估计，计算速度最快的算法。

An accelerative orthogonal iteration algorithm about the classical and wildly used orthogonal iteration algorithm for camera pose estimation is proposed for real time computation. The key idea is to integrate the steps in each iteration. The repetitive computation in each iteration can be abstracted and done before iteration. The computational complexity of each iteration is reduced from O(n) to O(1). So that, more iteration can be done in short time, and the accuracy is improved as well. The contrastive simulation and real data experiments show the efficiency and accuracy of the accelerative algorithm. Experimentally, the accelerative algorithm with the robust perspective n point (RPnP) initialization has nearly the same accuracy as maximum likelihood estimation (MLE), and is the fastest algorithm when there are few control points.