Structure-from-motion based hand-eye calibration using L∞ minimization

Abstract

This paper presents a novel method for so-called hand-eye calibration. Using a calibration target is not possible for many applications of hand-eye calibration. In such situations Structure-from-Motion approach of hand-eye calibration is commonly used to recover the camera poses up to scaling. The presented method takes advantage of recent results in the L∞-norm optimization using Second-Order Cone Programming (SOCP) to recover the correct scale. Further, the correctly scaled displacement of the hand-eye transformation is recovered solely from the image correspondences and robot measurements, and is guaranteed to be globally optimal with respect to the L∞-norm. The method is experimentally validated using both synthetic and real world datasets.