This paper studies the effect of reference frame selection in sensor-to-sensor extrinsic calibration when formulated as a motion-based hand-eye calibration problem. Different reference selection options are tested under varying noise conditions in simulation, and the findings are validated with real data from the KITTI dataset. We propose two nonlinear cost functions for optimization and compare them with four state-of-the-art methods. One of the proposed cost functions incorporates outlier rejection to improve calibration performance and was shown to significantly improve performance in the presence of outliers, and either match or outperform the other algorithms in other noise conditions. However, the performance gain from reference frame selection was deemed larger than that from algorithm selection. In addition, we show that with realistic noise, the reference frame selection method commonly used in literature is inferior to other tested options, and that relative error metrics are not reliable for telling which method achieves best calibration performance.

翻译：本文研究了在将传感器间外部标定问题表述为基于运动的手眼标定问题时，参考框架选择所带来的影响。在仿真中，针对不同类型的噪声条件，测试了多种参考框架选择方案，并利用KITTI数据集中的真实数据对研究结果进行了验证。我们提出了两种用于优化的非线性代价函数，并将其与四种当前最先进的方法进行了比较。其中一种提出的代价函数通过引入异常值剔除来提升标定性能，在存在异常值时显著改善了标定效果，并在其他噪声条件下与其余算法性能相当或更优。然而，参考框架选择带来的性能提升被认为大于算法选择所带来的效果。此外，我们证明，在存在实际噪声的情况下，文献中常用的参考框架选择方法劣于其他测试方案，并且相对误差度量无法可靠地判断何种方法能达到最佳标定性能。