Unscented Particle Filter for Online Total Image Jacobian Matrix Estimation in Robot Visual Servoing

The main purpose of visual servoing is to control the motion of a robot system based on visual information provided by one or more cameras. It is an important research topic in the robotics community. In uncalibrated visual servoing, the image Jacobian matrix estimation is of great importance to the success of visual servoing control. This paper addresses the online estimation of the total Jacobian matrix for robot visual servoing using the unscented particle filter. We first give the definition of the total Jacobian matrix and formulate the total Jacobian matrix estimation problem into Bayesian filtering framework. Then, we propose to estimate the total Jacobian matrix using the unscented particle filter. Each particle is propagated and updated using the unscented Kalman filter equations. Such an update can make full use of the image feature measurements and consequently generate more accurate estimation results. The simulation results on a 2DOF robot visual servoing platform show that the proposed method provides accurate and reliable performance in the object tracking task.