Uncalibrated Image-Based Visual Servoing Control with Maximum Correntropy Kalman Filter

Abstract

A major challenge in solving robot visual servoing problems with unstructured environments is to obtain image Jacobian matrix, especially non-Gaussian noise always exist in the whole process. However, the standard Kalman Filter (KF) is exhausted to find the optimal value under Gaussian noise assumption. In this paper, a Kalman filter which adopted the maximum correntropy criterion (MCC) instead of the minimum mean square error (MMSE) criterion is proposed to solve the approximation issue of the image Jacobian. The simulation and experiment results using a conventional 6R manipulator are conducted to verify the effectiveness of the proposed method.