Global Performance Evaluation of Image Features for Visual Servo Control

摘要

The performance of a visual servo control system depends on the set of image features used in the control loop. Although some local performance measures have been used for evaluating the image features, their usage in the process of feature selection requires on-line computation that is difficult to realize in real-time, especially with a large number of candidate features. In this article, we introduce a global measure for evaluating the performance of image features for visual servo tasks. This measure can be computed off-line and it takes into account several desirable properties of the image features, including minimization of singularities in the image Jacobian, linearity of feature variation, and maximization of feature resolution. For a given kinematic and imaging model of a robot/camera setup, the measure can be used for a variety of visual servo tasks. A numerical approximation scheme is presented along with several computed examples to illustrate the utility of this measure.