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.

翻译：本文研究了如何选择稀疏测量子集以实现快速标定参数估计的问题。该问题的标准解决方案是：仅当某测量的效用——即包含该测量时的后验信息与不包含该测量时的先验信息之差——超过特定阈值时才予以选择。理论上，作为参数估计函数的效用值应在当前已选全部测量所得估计值处进行计算，这意味着每新增一个测量都需重新标定。然而，我们提出假设：对于许多目标系统而言，效用值对参数估计的变化不敏感，这表明在实际应用中基于初始参数猜测评估效用值可获得等效结果。我们为多惯性测量单元（IMU）的外部标定提供了支持该假设的证据，结果表明通过避免对每个测量进行重新标定，可将标定时间缩短两个数量级。