Accuracy analysis on the estimation of camera parameters for active vision systems

In camera calibration, due to the correlations between certain camera parameters, an estimate of a set of camera parameters which minimizes a given criterion does not guarantee that the estimates of the physical camera parameter are themselves accurate. This problem has not drawn much attention from our computer vision society because conventional computer vision applications require only accurate 3D measurements and do not care much about the values of the physical parameters as long as their composite effect is satisfactory. However, when dealing with an active vision system, accuracy of the physical parameters is very critical because we need that accuracy to establish the relation between the motor positions and the camera parameters (both intrinsic and extrinsic). The contribution of this work is mainly in error analysis of camera calibration, especially in the accuracy of the physical camera parameters themselves, for four different types of calibration problems. With our error analysis, the most suitable camera calibration technique and calibration configuration for providing accurate camera parameters can be determined. Based on this error analysis, we have developed a method for calibrating our eight-degree-of-freedom binocular system. Real experiments have shown that this method can achieve accuracy of one pixel prediction error and 0.2 pixel epipolar error, even when all the eight joints, including the focus motors, are moved simultaneously. This high accuracy greatly alleviates the difficulties encountered in applying the active vision approach to automatic reconstruction of 3D objects and environments, which is our current endeavor