Selection of features and evaluation of visual measurements during robotic visual servoing tasks

This paper presents some of the computer vision techniques that were employed in order to automatically select features, measure features' displacements, and evaluate measurements during robotic visual servoing tasks. We experimented with a lot of different techniques, but the most robust proved to be the Sum-of-Squared Differences (SSD) optical flow technique. In addition, several techniques for the evaluation of the measurements are presented. One important characteristic of these techniques is that they can also be used for the selection of features for tracking in conjunction with several numerical criteria that guarantee the robustness of the servoing. These techniques are important aspects of our work since they can be used either on-line or off-line. An extension of the SSD measure to color images is presented and the results from the application of these techniques to real images are discussed. Finally, the derivation of depth maps through the controlled motion of the handeye system is outlined and the important role of the automatic feature selection algorithm in the accurate computation of the depth-related parameters is highlighted.