Efficient Extrinsic Self-Calibration of Multiple IMUs using Measurement Subset Selection

TL;DR

This paper proposes a method for fast extrinsic calibration of multiple IMUs by selecting measurement subsets based on utility, avoiding repeated recalibration, and achieving significant time savings.

Contribution

It introduces a measurement subset selection approach that reduces calibration time by ignoring recalibration at each step, supported by evidence for IMU calibration.

Findings

Calibration time reduced by two orders of magnitude.

Utility evaluation at initial guess is sufficient for accurate subset selection.

Method maintains calibration accuracy while significantly speeding up the process.

Abstract

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.