Direct Visual Servoing Based on Discrete Orthogonal Moments

This paper proposes a new approach to achieve direct visual servoing (DVS) based on discrete orthogonal moments (DOM). DVS is conducted whereby the extraction of geometric primitives, matching and tracking steps in the conventional feature-based visual servoing pipeline can be bypassed. Although DVS enables highly precise positioning, and suffers from a small convergence domain and poor robustness, due to the high non-linearity of the cost function to be minimized and the presence of redundant data between visual features. To tackle these issues, we propose a generic and augmented framework to take DOM as visual features into consideration. Through taking Tchebichef, Krawtchouk and Hahn moments as examples, we not only present the strategies for adaptive adjusting the parameters and orders of the visual features, but also exhibit the analytical formulation of the associated interaction matrix. Simulations demonstrate the robustness and accuracy of our method, as well as the advantages over the state of the art. The real experiments have also been performed to validate the effectiveness of our approach.