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Robust and accurate state estimation remains a challenge in robotics,Augmented, and Virtual Reality (AR/VR), even as Visual-Inertial SimultaneousLocalisation and Mapping (VI-SLAM) getting commoditised. Here, a full VI-SLAMsystem is introduced that particularly addresses challenges around long as wellas repeated loop-closures. A series of experiments reveals that it achieves andin part outperforms what state-of-the-art open-source systems achieve. At thecore of the algorithm sits the creation of pose-graph edges throughmarginalisation of common observations, which can fluidly be turned back intolandmarks and observations upon loop-closure. The scheme contains a realtimeestimator optimising a bounded-size factor graph consisting of observations,IMU pre-integral error terms, and pose-graph edges -- and it allows foroptimisation of larger loops re-using the same factor-graph asynchronously whenneeded.

OKVIS2: Realtime Scalable Visual-Inertial SLAM with Loop Closure