Active self-calibration of hand cameras and hand-eye relationships with motion planning

In this paper we propose a method for self-calibration of robotic hand cameras by means of active motion of the robot. Through tracking a set of world points of unknown coordinates, the internal parameters of the cameras (including lens distortions), the mounting parameters as well as the 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. To improve robustness of the calibration, we propose to identify the best lens-distortion model by using F-test. Furthermore, a method is proposed to minimize the effect of robot motion uncertainties by motion planning. Experimental results in both a simulated and a real environments are reported.

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