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 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.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: A. H. Abdul Hafez, C. V. Jawahar
• Keywords: Computer Vision, Gaussian Probabilistic Model, ICPR 2006, Real-world 3D Point, Visual Servoing Process