Globally Optimal Hand-Eye Calibration Using Branch-and-Bound

This paper introduces a novel solution to the hand-eye calibration problem. It uses camera measurements directly and, at the same time, requires neither prior knowledge of the external camera calibrations nor a known calibration target. Our algorithm uses branch-and-bound approach to minimize an objective function based on the epipolar constraint. Further, it employs Linear Programming to decide the bounding step of the algorithm. Our technique is able to recover both the unknown rotation and translation simultaneously and the solution is guaranteed to be globally optimal with respect to the L-infinity-norm.