Monocular pose estimation is a basic and important problem in computer vision, being widely used in robot positioning, virtual reality and image precision measurement. In practical application, the coordinates of reference points inevitably contain outliers which may lead to an estimating result far from the true value. Therefore, an adaptive weighted robust orthogonal iteration algorithm is proposed. To improve robustness, this algorithm uses a robust estimation method to find out the outliers and suppress their impaction by allocating them smaller weights. The experiment results show that the proposed algorithm is robust with high accuracy. This algorithm can effectively restrain the influence of outliers with different number and levels. When there are 8 outliers of 60 pixel in 20 reference points, the accuracy of this method is 2 and 1 orders of magnitude higher than that of classical orthogonal iteration algorithm and weighted orthogonal iteration algorithm, respectively.