  Robust and accurate state estimation remains a challenge in robotics,
Augmented, and Virtual Reality (AR/VR), even as Visual-Inertial Simultaneous
Localisation and Mapping (VI-SLAM) getting commoditised. Here, a full VI-SLAM
system is introduced that particularly addresses challenges around long as well
as repeated loop-closures. A series of experiments reveals that it achieves and
in part outperforms what state-of-the-art open-source systems achieve. At the
core of the algorithm sits the creation of pose-graph edges through
marginalisation of common observations, which can fluidly be turned back into
landmarks and observations upon loop-closure. The scheme contains a realtime
estimator optimising a bounded-size factor graph consisting of observations,
IMU pre-integral error terms, and pose-graph edges -- and it allows for
optimisation of larger loops re-using the same factor-graph asynchronously when
needed.
