Pose estimation problem in computer vision is often solved by minimizing cost functions that combine the reprojection errors in the 2D images,but the cost functions are based on the assumption that measurement noises are isotropic,independent and identically distributed(i.i.d.) at every 2D feature point,so the solution of pose becomes statistically meaningful and optimal in the sense of maximum likelihood.However,this assumption is rarely the case in real applications,where the positional noise not only varies at different features,but also has strong directionality.In this situation,we propose a new pose estimation method that incorporates the directional uncertainty of the points into reprojection errors and constructs a new uncertainty-weighted cost function.The method can adapt to varying degrees of directional uncertainty and guarantee more accurate results.In particular,unlike other reprojection-error-based methods,our method does not degrade with increase in directionality of uncertainty.Finally we demonstrate the effectiveness of the method by experiments on synthetic data that contains high directional uncertainty.