  In this paper, we present BAMF-SLAM, a novel multi-fisheye visual-inertial
SLAM system that utilizes Bundle Adjustment (BA) and recurrent field transforms
(RFT) to achieve accurate and robust state estimation in challenging scenarios.
First, our system directly operates on raw fisheye images, enabling us to fully
exploit the wide Field-of-View (FoV) of fisheye cameras. Second, to overcome
the low-texture challenge, we explore the tightly-coupled integration of
multi-camera inputs and complementary inertial measurements via a unified
factor graph and jointly optimize the poses and dense depth maps. Third, for
global consistency, the wide FoV of the fisheye camera allows the system to
find more potential loop closures, and powered by the broad convergence basin
of RFT, our system can perform very wide baseline loop closing with little
overlap. Furthermore, we introduce a semi-pose-graph BA method to avoid the
expensive full global BA. By combining relative pose factors with loop closure
factors, the global states can be adjusted efficiently with modest memory
footprint while maintaining high accuracy. Evaluations on TUM-VI, Hilti-Oxford
and Newer College datasets show the superior performance of the proposed system
over prior works. In the Hilti SLAM Challenge 2022, our VIO version achieves
second place. In a subsequent submission, our complete system, including the
global BA backend, outperforms the winning approach.
