ACTIVE SELF-CALIBRATION OF ROBOTIC EYES AND HAND-EYE RELATIONSHIPS WITH MODEL IDENTIFICATION

In this short paper, we first review research results of camera self-c alibration achieved in photogrammetry, robotics and computer vision. T hen we propose a method for self-calibration of robotic hand cameras b y means of active motion. Through tracking a set of world points of un known coordinates during robot motion, the internal parameters of the cameras (including distortions), the mounting parameters as well as th e coordinates of the world points are estimated. The approach is fully autonomous, in that no initial guesses of the unknown parameters are to be provided from the outside by humans for the solution of a set of nonlinear equations. Sufficient conditions for a unique solution are derived in terms of controlled motion sequences. Methods to improve ac curacy and robustness are proposed by means of best model identificati on and motion planning. Experimental results in both a simulated and a real environments are reported.