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Calibration is a crucial step to obtaining 3D measurement using video camera-based stereo systems. All the parameters can be determined except for the pair of principal points, which poses a considerable drawback. Whereas in low-accuracy systems such points can be assumed to lie at the image center without degrading the overall 3D accuracy; in high-accuracy systems their true position must be computed accurately. In this case, all the calibration parameters can still be estimated through epipolar geometry, but it is necessary to minimize a highly nonlinear cost function. It is shown here that by combining two evolutionary optimization strategies this minimization can be carried out, both efficiently and reliably. The resulting strategy, which we call enhanced evolutionary search (EES), allows the full calibration of a stereo system using only a rigid bar. Moreover, EES can be applied to a wide range of applications where the cost function contains complex nonlinear relationships among the optimization variables.
Cerveri et al. (Fri,) studied this question.