In this paper we propose a practical and efficient method for finding the globally optimal solution to the problem of camera pose estimation for calibrated cameras. While traditional methods may get trapped in local minima, due to the non-convexity of the problem, we have developed an approach that guarantees global optimality.

The scheme is based on ideas from global optimization theory, in particular, convex under-estimators in combination with branch and bound. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data.