@article{valimaki_motion_2023,
  author = {Välimäki, Tuomas and Garigipati, Bharath and Ghabcheloo, Reza},
  title = {Motion-Based Extrinsic Sensor-to-Sensor Calibration: Effect of Reference Frame Selection for New and Existing Methods},
  journal = {Sensors},
  volume = {23},
  year = {2023},
  number = {7},
  article-number = {3740},
  url = {https://www.mdpi.com/1424-8220/23/7/3740},
  pubmedid = {37050800},
  issn = {1424-8220},
  doi = {10.3390/s23073740},
  code = {https://github.com/tau-alma/trajectory_calibration_experiments},
  tldr = {Uses hand-eye robot calibration formulation of AX=XB to calibrate sensor pairs in infrastructure context. The paper explores different methods for the calibration, as well as the choice of relative coordinate frame, as for the hand-eye calibration problem, the transformations are relative. I have not seen any treatment of this issue in the literature before, of how to choose the relative transformations when using AX=XB. The answer: 'it depends.'}
}

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. As the sensor trajectories typically contain some composition of noise, the aim is to determine which selection strategies work best under which noise conditions. Different reference selection options are tested under varying noise conditions in simulations, and the findings are validated with real data from the KITTI dataset. The study is conducted for four state-of-the-art methods, as well as two proposed cost functions for nonlinear optimization. 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 the literature, is inferior to other tested options, and that relative error metrics are not reliable for telling which method achieves best calibration performance.

Publisher: http://doi.org/10.3390/s23073740
code: https://github.com/tau-alma/trajectory_calibration_experiments