Going further with direct visual servoing

Abstract

In this thesis we focus on visual servoing (VS) techniques, critical for many robotic vision applications and we focus mainly on direct VS. In order to improve the state-of-the-art of direct methods, we tackle several components of traditional VS control laws. We first propose a method to consider histograms as a new visual servoing feature. It allows the definition of efficient control laws by allowing to choose from any type of his tograms to describe images, from intensity to color histograms, or Histograms of Oriented Gradients. A novel direct visual servoing control law is then proposed, based on a particle filter to perform the optimization part of visual servoing tasks, allowing to accomplish tasks associated with highly non-linear and non-convex cost functions. The Particle Filter estimate can be computed in real-time through the use of image transfer techniques to evaluate camera motions associated to suitable displacements of the considered visual features in the image. Lastly, we present a novel way of modeling the visual servoing problem through the use of deep learning and Convolutional Neural Networks to alleviate the difficulty to model non-convex problems through classical analytic methods. By using image transfer techniques, we propose a method to generate quickly large training datasets in order to fine-tune existing network architectures to solve VS tasks.We shows that this method can be applied both to model known static scenes, or more generally to model relative pose estimations between couples of viewpoints from arbitrary scenes.