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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.INDEX TERMS Visual servoing, total image Jacobian matrix, unscented particle filter.

Particle filters have been widely used in solving nonlinear filtering problems. Proposal Distribution design is a key issue for these methods and has vital effect on simulation results.  Various proposal distributions have been proposed to improve the performance of particle filters, but practical situations have promoted the researchers to design better candidate for proposal distributions in order to gain better performance. This paper proposes a hybrid proposal distribution designed for particle filtering framework. The algorithm uses hybrid Kalman filter to generate the proposal distribution, which make efficient use of the latest observations and generate more closed approximation of the posterior probability density. The yielded algorithm is named as hybrid Kalman particle filter. In the experiments, we use a scalar estimation model and a real world application problem to evaluate the new algorithm. The experimental results show that the new algorithm outperforms other algorithms with different proposal distributions. In order to reduce the time consumption of the new algorithm, we propose an improvement strategy, namely partition-conquer strategy, in which the particles are partitioned into two parts, with one part drawn from the hybrid Kalman filter, another part from the transition prior. The validity of the strategy is verified through an experiment

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