Fast and globally convergent pose estimation from video images

Determining the rigid transformation relating 2D images to known 3D geometr y is a classical problem in photogrammetry and computer vision. Heretofore, the best methods for solving the problem have relied on iterative optimiza tion methods which cannot be proven to converge and/or which do not effecti vely account for the orthonormal structure of rotation matrices. We show th at the pose estimation problem can be formulated as that of minimizing an e rror metric based on collinearity in object (as opposed to image) space. Us ing object space collinearity error, we derive an iterative algorithm which directly computes orthogonal rotation matrices and which is globally conve rgent. Experimentally, we show that the method is computationally efficient , that ii is no less accurate than the best currently employed optimization methods. and that it outperforms all tested methods in robustness to outli ers.