Direct Visual Servoing: Vision-Based Estimation and Control Using Only Nonmetric Information

Abstract

This paper addresses the problem of stabilizing a robot at a pose specified via a reference image. Specifically, this paper focuses on six degrees-of-freedom visual servoing techniques that require neither metric information of the observed object nor precise camera and/or robot calibration parameters. Not requiring them improves the flexibility and robustness of servoing tasks. However, existing techniques within the focused class need prior knowledge of the object shape and/or of the camera motion. We present a new visual servoing technique that requires none of the aforementioned information. The proposed technique directly exploits 1) the projective parameters that relate the current image with the reference one and 2) the pixel intensities to obtain these parameters. The level of versatility and accuracy of servoing tasks are, thus, further improved. We also show that the proposed nonmetric scheme allows for path planning. In this way, the domain of convergence is greatly enlarged as well. Theoretical proofs and experimental results demonstrate that visual servoing can, indeed, be highly accurate and robust, despite unknown objects and imaging conditions. This naturally encompasses the cases of color images and illumination changes.