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Aiming at the problem of anomalous and non-independent distribution of the image errors in the feature-based visual pose estimation, a method of monocular visual pose estimation based on the uncertainty of noise error established by projection vector is proposed. First, by using the covariance matrix to describe the uncertainty of the feature point direction and integrating the uncertainty of the feature point direction into the pose estimation, characteristic point measurement error with different degrees of directional uncertainty can be adapted that can makes the algorithm robust. Then, by introducing the projection vector and combining the depth information of each feature point to represent the collinearity error, the model nonlinear problem caused by the camera perspective projection can be eliminated that can make the algorithm have global convergence. Finally, we use global convergence theorem to prove the global convergence of the proposed algorithm. The results show that the proposed method has good robustness and convergence while adapting to different degrees of error uncertainty, which can meet practical engineering applications.