  Adding more cameras to SLAM systems improves robustness and accuracy but
complicates the design of the visual front-end significantly. Thus, most
systems in the literature are tailored for specific camera configurations. In
this work, we aim at an adaptive SLAM system that works for arbitrary
multi-camera setups. To this end, we revisit several common building blocks in
visual SLAM. In particular, we propose an adaptive initialization scheme, a
sensor-agnostic, information-theoretic keyframe selection algorithm, and a
scalable voxel-based map. These techniques make little assumption about the
actual camera setups and prefer theoretically grounded methods over heuristics.
We adapt a state-of-the-art visual-inertial odometry with these modifications,
and experimental results show that the modified pipeline can adapt to a wide
range of camera setups (e.g., 2 to 6 cameras in one experiment) without the
need of sensor-specific modifications or tuning.
