In this paper, we present a novel technique for calibrating central omnidirectional cameras. The proposed procedure is very fast and completely automatic, as the user is only asked to collect a few images of a checker board, and click on its corner points. In contrast with previous approaches, this technique does not use any specific model of the omnidirectional sensor. It only assumes that the imaging function can be described by a Taylor series expansion whose coefficients are estimated by solving a four-step least-squares linear minimization problem, followed by a non-linear refinement based on the maximum likelihood criterion. To validate the proposed technique, and evaluate its performance, we apply the calibration on both simulated and real data. Moreover, we show the calibration accuracy by projecting the color information of a calibrated camera on real 3D points extracted by a 3D sick laser range finder. Finally, we provide a Toolbox which implements the proposed calibration procedure.