Improved Cubature Kalman Filtering-Based Estimation of the Jacobian Matrix for Environment Mutation

To improve the accuracy of online estimation of Jacobian matrix (JCM) in image-based visual servo, an improved cubature Kalman filtering (CKF) algorithm is proposed to estimate the Jacobian matrix online. The key idea of the proposed method is to introduce the fading factor into the measurement update process, which can improve the capability when coping with the environment mutation. In addition, to avoid the emergence of the non-positive covariance matrix, the Cholesky decomposition of the estimation is replaced by the singular value decomposition (SVD). To verify the effect of the proposed method, two sets of experiments are carried out. Three-dimensional motion tracking is employed to prove tracking effect and the eye in hand manipulator positioning based on the Puma560 simulation is adopted to testify stability of this method. Simulation results show that the proposed method is feasible and effective.