  This work presents a centralized multi-IMU filter framework with online
intrinsic and extrinsic calibration for unsynchronized inertial measurement
units that is robust against changes in calibration parameters. The novel
EKF-based method estimates the positional and rotational offsets of the system
of sensors as well as their intrinsic biases without the use of rigid body
geometric constraints. Additionally, the filter is flexible in the total number
of sensors used while leveraging the commonly used MSCKF framework for camera
measurements. The filter framework has been validated using Monte Carlo
simulation as well as experimentally. In both simulations and experiments,
using multiple IMU measurement streams within the proposed filter framework
outperforms the use of a single IMU in a filter prediction step while also
producing consistent and accurate estimates of initial calibration errors.
Compared to current state-of-the-art optimizers, the filter produces similar
intrinsic and extrinsic calibration parameters for each sensor. Finally, an
open source repository has been provided at
https://github.com/unmannedlab/ekf-cal containing both the online estimator and
the simulation used for testing and evaluation.
