Unscented Kalman filter for on-line estimation of Jacobian matrix

Abstract

In image based robot visual servo system,image Jacobian matrix is commonly used for calibration.Using on-line image Jacobian matrix estimation method,the complex system calibration can be avoided without knowing the accurate system models.In this paper,the author proposed to use the Unscented Kalman Filter(UKF) for on-line estimation of total Jacobian matrix for the sake of improving the tracking accuracy of the robots which is tracking a moving object.In order to evaluate the performance,three algorithms using Kalman Filter(KF),Particle Filter(PF),and UKF were used for total Jocobian matrix estimation in a 2-Degree Of Freedom(DOF) robot visual servo platform.The experimental results show that the UKF algorithm outperforms the other two in accuracy while its time cost is very much close to the KF algorithm.