Eye-in-Hand/Eye-to-Hand Multi-Camera Visual Servoing

A position-based visual servoing algorithm using an hybrid eye-in-hand/eye-to-hand multi-camera configuration is presented in this paper. Based on an extended Kalman filter, this approach exploits the data provided by all the cameras without "a priori" discrimination, allowing real-time object pose estimation. A suitable algorithm is in charge of selecting an optimal subset of image features on the basis of the desired task and of the current configuration of the workspace. Only this subset is considered for feature extraction, thus ensuring a computational cost independent of the number of cameras. Experimental results are reported to demonstrate the feasibility and the effectiveness of the proposed technique.