A branch-and-bound algorithm for globally optimal hand-eye calibration

摘要

This paper introduces a novel solution to hand-eye calibration problem. It is the first method that uses camera measurements directly and at the same time requires neither prior knowledge of the external camera calibrations nor a known calibration device. 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. The presented 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∞-norm.