Photometric Gaussian mixtures based visual servoing

Abstract

The advantages of using the entire photometric image information as visual feature are: it does not require any feature detections, matching or tracking process. To enlarge the convergence domain, we propose to accomplish visual servoing based on the analytical formulation of Gaussian mixtures to model the images. During the servoing, we consider the optimization of the Gaussian spreads allowing the camera to converge to a desired pose even from a far initial one. Simulation that overcomes the state-of-the-art and real experiments highlight the success of our approach.