A fast and accurate new algorithm for hand-eye calibration on SO(3)xR3

In this paper, we propose a new solution for an old problem in robotics related to the calibration of the hand, i.e., the robot's end-effector, and the eye, i.e., a stereo vision camera. The problem is formulated as a point set matching problem and a nonlinear estimator on manifold SO(3)xR(3) used for obtaining the solution. The main advantage of the proposed approach is that it allows to decouple the error associated with the rotational estimation on Lie group SO(3) from the error of the translational estimation on R-3, which subsequently allows to tune the learning rates for the rotation and translation estimation, separately. This will result in significant increase in the convergence speed of the proposed approach. To show the advantages of our approach, we compare the results with those obtained from other conventional hand-eye calibration solutions as well as those based on point set matching. The experimental results will demonstrate that our proposed hand-eye calibration approach outperforms other approaches in terms of accuracy and computational speed.