Target Model Estimation using Particle Filters for Visual Servoing

In this paper, we present a novel method for model estimation for visual servoing. This method employs a particle filter algorithm to estimate the depth of the image features online. A Gaussian probabilistic model is employed to model the object points in the current camera frame. A set of 3D samples drawn from the model is projected into the image space in the next frame. The 3D sample that maximizes the likelihood is considered to be the most probable real-world 3D point. The variance value of the depth density function converges to very small value within a few iterations. Results show accurate estimate of the depth/model and a high level of stability in the visual servoing process