Online Multi-IMU Calibration Using Visual-Inertial Odometry

This work presents a centralized multi-IMU visual inertial odometry filter framework with online intrinsic and extrinsic calibration for unsynchronized inertial measurement units that is robust against changes in calibration parameters. The novel Extended Kalman Filter-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 Multi-State Constraint Kalman Filter 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.