Adaptive visual servoing using point and line features with an uncalibrated eye-in-hand camera

This paper presents a novel approach for image-based visual servoing of a robot manipulator with an eye-in-hand camera when the camera parameters are not calibrated and the 3-D coordinates of the features are not known. Both point and line features are considered. This paper extends the concept of depth-independent interaction (or image Jacobian) matrix, developed in earlier work for visual servoing using point features and fixed cameras, to the problem using eye-in-hand cameras and point and line features. By using the depth-independent interaction matrix, it is possible to linearly parameterize, by the unknown camera parameters an the unknown coordinates of the features, the closed-loop dynamics of the system. A new algorithm is developed to estimate unknown parameters online by combining the Slotine-Li method with the idea of structure from motion in computer vision. By minimizing the errors between the real and estimated projections of the feature on multiple images captured during motion of the robot, this new adaptive algorithm can guarantee the convergence of the estimated parameters to the real values up to a scale. On the basis of the nonlinear robot dynamics, we proved asymptotic convergence of the image errors by the Lyapunov theory. Experiments have been conducted to demonstrate the performance of the proposed controller.