  Visual-inertial navigation systems are powerful in their ability to
accurately estimate localization of mobile systems within complex environments
that preclude the use of global navigation satellite systems. However, these
navigation systems are reliant on accurate and up-to-date temporospatial
calibrations of the sensors being used. As such, online estimators for these
parameters are useful in resilient systems. This paper presents an extension to
existing Kalman Filter based frameworks for estimating and calibrating the
extrinsic parameters of multi-camera IMU systems. In addition to extending the
filter framework to include multiple camera sensors, the measurement model was
reformulated to make use of measurement data that is typically made available
in fiducial detection software. A secondary filter layer was used to estimate
time translation parameters without closed-loop feedback of sensor data.
Experimental calibration results, including the use of cameras with
non-overlapping fields of view, were used to validate the stability and
accuracy of the filter formulation when compared to offline methods. Finally
the generalized filter code has been open-sourced and is available online.
