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We present a novel optimization-based Visual-Inertial SLAM system designedfor multiple partially overlapped camera systems, named MAVIS. Our frameworkfully 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 theexponential function of an automorphism of SE_2(3), which can effectivelyenhance tracking performance under fast rotational motion and extendedintegration time. Furthermore, we extend conventional front-end tracking andback-end optimization module designed for monocular or stereo setup towardsmulti-camera systems, and introduce implementation details that contribute tothe performance of our system in challenging scenarios. The practical validityof our approach is supported by our experiments on public datasets. Our MAVISwon the first place in all the vision-IMU tracks (single and multi-sessionSLAM) on Hilti SLAM Challenge 2023 with 1.7 times the score compared to thesecond place.

MAVIS: Multi-Camera Augmented Visual-Inertial SLAM using SE2(3) Based Exact IMU Pre-integration