Automatic feature planning for robust visual servoing

Automatic specification of desired motions and planning of optimal feature sets are fundamental problems which must be addressed in order to ensure accurate and robust execution of complex tasks using visual servoing. In this paper we introduce a simulation platform for generation of visual servoing tasks as well as a new method for automatic selection of object features. This method considers several aspects related to the robustness of the tracking system to select features which ensure optimal performance of the robot control system. Finally we present experiments which compare the performance of a visual servoing system employing the proposed feature planning technique to that of a servoing system based on features selected using traditional methods. These experiments demonstrate that our technique not only improves the robustness of the visual tracking system but also significantly increases the accuracy of the visual servoing control loop.