Globally optimal hand-eye calibration under free choice of cost-function

This paper introduces globally optimal hand-eye self-calibration of camera orientation under a wide range of cost-functions, which includes L₂ and Blake-Zisserman. The main contributions are new feasibility tests to integrate this hand-eye calibration problem into a branch-and-bound parameter space search. The presented method constitutes the first guaranteed globally optimal estimator, which is not limited to L_∞-type cost-functions. The algorithm directly minimizes the desired cost-function based on reprojection errors of interest point correspondences and does not depend on any structure and motion preprocessing to estimate camera poses. The system is evaluated in both synthetic and real world scenarios. Typical cost-functions are exemplarily integrated to validate the algorithm´s flexibility.