Online image Jacobian identification using optimal adaptive robust Kalman filter for uncalibrated visual servoing

Abstract

Dynamic image Jacobian matrix identification is proved complicated and tough in uncalibrated visual servoing. Comparing with standard Kalman Filter (KF), which is exhausted to find the optimal value of unknown noise covariance, the adaptive robust KF is developed to deal with uncertainty noise covariance information. The state model and measurement of noise covariance matrices are adopted recursive estimation to tune unknown variation respectively, and an adaptive factor is employed to adjust the estimated state vector by using residual sequence. The simulation results show that the proposed algorithm has better performance for using a robotic manipulator with eye-in-hand configuration when the noise variances statistical information of the system is indeterminate.