  We present a novel optimization-based Visual-Inertial SLAM system designed
for multiple partially overlapped camera systems, named MAVIS. Our framework
fully exploits the benefits of wide field-of-view from multi-camera systems,
and the metric scale measurements provided by an inertial measurement unit
(IMU). We introduce an improved IMU pre-integration formulation based on the
exponential function of an automorphism of SE_2(3), which can effectively
enhance tracking performance under fast rotational motion and extended
integration time. Furthermore, we extend conventional front-end tracking and
back-end optimization module designed for monocular or stereo setup towards
multi-camera systems, and introduce implementation details that contribute to
the performance of our system in challenging scenarios. The practical validity
of our approach is supported by our experiments on public datasets. Our MAVIS
won the first place in all the vision-IMU tracks (single and multi-session
SLAM) on Hilti SLAM Challenge 2023 with 1.7 times the score compared to the
second place.
