Calibration-free and model-independent method for high-DOF image-based visual servoing

This paper presents a novel method to improve the performance of high-DOF image base visual servoing (IBVS) with an uncalibrated camera. Firstly, analysis and comparison between point-based and moment-based features are carried out with respect to a 4-DOF positioning task. Then, an extended interaction matrix (IM) related to the digital image, and a Kalman filter (KF)-based estimation algorithm of the extended IM without calibration and IM model are proposed. Finally, the KF-based algorithm is extended to realize an approximation to decoupled control scheme. Experimental results conducted on an industrial robot show that our proposed methods can provide accurate estimation of IM, and achieve similar performance compared with traditional calibration-based method. Therefore, the proposed methods can be applied to any robot control system in variational environments, and can realize instant operation to planar object with complex and unknown shape at large displacement.