3D Feature-Based Sampled-Data Visual Tracking*

Abstract

3D feature-based Visual Servoing (VS) on the one hand shows attractive peculiarities, on the other hand it suffers from drawbacks related to the existence of local minima, which may affect the convergence character of the VS control loop. Furthermore, the performance of the visual tracking module may constitute a bottleneck enforcing severe constraints on the workspace and visual task execution speed. In this paper we introduce a novel sampled-data model of the 3D feature-based VS, and, in order to avoid drawbacks due to local minima, we plan the target reference trajectory in the feature space with the aim to constraint the feature error dynamics to remain close to the desired equilibrium point. Then, we propose a novel feature generation based on the homography provided by a template matching algorithm based on the Zero mean Normalized Cross Correlation (ZNCC) and the design of a visual tracking scheme by resorting to the Extended Kalman Filter (EKF) and Lyapunov direct method, which explicitly takes into account the camera velocity limits, while ensuring stability.