  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.
