The calibration of the relative pose between rigidly connected cameras with non-overlapping fields of view (FOV) is a prerequisite for many applications. In this paper, we focus on the subtleties of experimental realization of such a calibration optimization method presented in [1]. We evaluate two strategies to adapt a given optimization process to find better local minima. The first strategy is the introduction of a quality measure for the image data used for calibration, which is based on the projection size of known planar calibration patterns on the image. We show, that introducing an additional weighting to the optimization objective chosen as a function of that quality measure improves calibration accuracy and increases robustness against noise. The second strategy to further improve accuracy is a careful data acquisition of pose pairs used for the calibration. We integrate the above strategies into different setups and demonstrate the improvement both in simulation and real-world experiment.

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