Robot Hand-Eye Calibration Using Unscented Kalman Filtering

An online robot hand-eye calibration method using unscented Kalman filtering(UKF) is presented to solve the homogeneous transformation equations of the form AX=XB.The hidden Markov model(HMM) of the hand-eye calibration is constructed,based on which UKF is performed to estimate calibration parameters recursively according to the Bayesian theory,and the evolution of calibration parameters can be visualized in real time.Monte Carlo simulation shows that the proposed algorithm possesses better accuracy than the traditional least squares(LS) based method under low isotropic,high isotropic and anisotropic Gaussian noises.Real robot hand-eye calibration experiment is also performed and the result shows stable convergence of the proposed algorithm with better accuracy comparing with the LS based method.