Nonlinear Optimal Robot Hand-Eye Calibration

Abstract

Nonlinear optimal robot hand-eye calibration based on maximum likelihood estimation is proposed.For the errors in both robot forward position kinematics and camera extrinsic calibration,the classical hand-eye transformation matrix equation often gets violated,thus the existing robot hand-eye calibration algorithms derived from the equation are sensitive to the measurement noise.The proposed strategy estimates the hand-eye transformation and the true measurements according to the noisy measurements to guarantee that the true measurements satisfy the hand-eye equation and the distance to the noisy measurements is minimal.Two classical hand-eye calibration methods are also implemented for comparison.Numerical simulations and real robot hand-eye calibration experiment are performed and the results show the proposed strategy has higher accuracy,sensitivity to motion numbers,and stability.