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This paper studies the effect of reference frame selection insensor-to-sensor extrinsic calibration when formulated as a motion-basedhand-eye calibration problem. Different reference selection options are testedunder varying noise conditions in simulation, and the findings are validatedwith real data from the KITTI dataset. We propose two nonlinear cost functionsfor optimization and compare them with four state-of-the-art methods. One ofthe proposed cost functions incorporates outlier rejection to improvecalibration performance and was shown to significantly improve performance inthe presence of outliers, and either match or outperform the other algorithmsin other noise conditions. However, the performance gain from reference frameselection was deemed larger than that from algorithm selection. In addition, weshow that with realistic noise, the reference frame selection method commonlyused in literature is inferior to other tested options, and that relative errormetrics are not reliable for telling which method achieves best calibrationperformance.

Motion-based extrinsic sensor-to-sensor calibration: Effect of reference frame selection for new and existing methods