Based on the circumstance that the outliers appear sometimes when the pose of target is measured by mono-vision method, a new method is presented for estimating the pose (position & attitude) based on outliers-removal (ORPE). The optimization function is established based on the principle of max-min error. Then the outliers can be detected and removed through eliminating frontier of maximum error. Furthermore the impact of outliers’ error on the accuracy of pose estimation can be eliminated. The pose of 1 m×1 m×1 m cube target is measured by ORPE, which proves the validity of the algorithm. The pose of Boeing plane model is measured by ORPE. As a result, the average attitude error is 2.07°, and the average position error is 1.6%. Comparatively, the average error of ORPE is less than the one of the lest-square pose estimation (LSPE) method. Consequently, it’s demonstrated that ORPE can do outliers-removal effectively, and improve the accuracy of pose estimation simultaneously.