Efficient Extrinsic Self-Calibration of Multiple IMUs using Measurement Subset Selection
<|reference_start|>Efficient Extrinsic Self-Calibration of Multiple IMUs using Measurement Subset Selection: This paper addresses the problem of choosing a sparse subset of measurements for quick calibration parameter estimation. A standard solution to this is selecting a measurement only if its utility -- the difference between posterior (with the measurement) and prior information (without the measurement) -- exceeds some threshold. Theoretically, utility, a function of the parameter estimate, should be evaluated at the estimate obtained with all measurements selected so far, hence necessitating a recalibration with each new measurement. However, we hypothesize that utility is insensitive to changes in the parameter estimate for many systems of interest, suggesting that evaluating utility at some initial parameter guess would yield equivalent results in practice. We provide evidence supporting this hypothesis for extrinsic calibration of multiple inertial measurement units (IMUs), showing the reduction in calibration time by two orders of magnitude by forgoing recalibration for each measurement.<|reference_end|>
@article{lee2024efficient,
    title={Efficient Extrinsic Self-Calibration of Multiple IMUs using Measurement
  Subset Selection},
    author={Jongwon Lee, David Hanley, Timothy Bretl},
    journal={arXiv preprint arXiv:2407.02232},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02232},
    primaryClass={cs.RO}
}