Depth invariant visual servoing

This paper studies the problem of achieving consistent performance for visual servoing. Given the nonlinearities introduced by the camera projection equations in monocular visual servoing systems, many control algorithms experience non-uniform performance bounds. The variable performance bounds arise from depth dependence in the error rates. In order to guarantee depth invariant performance bounds, the depth nonlinearity must be cancelled, however estimating distance along the optical axis is problematic when faced with an object with unknown geometry. By tracking a planar visual feature on a given target, and measuring the area of the planar feature, a distance invariant input to state stable visual servoing controller is derived. Two approaches are given for achieving the visual tracking. Both of these approaches avoid the need to maintain long-term tracks of individual feature points. Realistic image uncertainty is captured in experimental tests that control the camera motion in a 3D renderer using the observed image data for feedback.