Performance limitation analysis in visual servo systems: Bounding the location error introduced by image points matching

Abstract

Visual servoing consists of positioning a robot end-effector based on the matching of some object features in the image. However, due to the presence of image noise, this matching can never be ensured, hence introducing an error on the final location of the robot. This paper addresses the problem of estimating the worst-case location error introduced by image points matching. In particular, we propose some strategies for computing upper bounds and lower bounds of such an error according to several possible measures for certain image noise intensity and camera-object configuration. These bounds provide an admissible region of the sought worst-case location error, and hence allow one to establish performance limitation of visual servo systems. Some examples are reported to illustrate the proposed strategies and their results.