  In this paper, we present the RISE-SLAM algorithm for performing
visual-inertial simultaneous localization and mapping (SLAM), while improving
estimation consistency. Specifically, in order to achieve real-time operation,
existing approaches often assume previously-estimated states to be perfectly
known, which leads to inconsistent estimates. Instead, based on the idea of the
Schmidt-Kalman filter, which has processing cost linear in the size of the
state vector but quadratic memory requirements, we derive a new consistent
approximate method in the information domain, which has linear memory
requirements and adjustable (constant to linear) processing cost. In
particular, this method, the resource-aware inverse Schmidt estimator (RISE),
allows trading estimation accuracy for computational efficiency. Furthermore,
and in order to better address the requirements of a SLAM system during an
exploration vs. a relocalization phase, we employ different configurations of
RISE (in terms of the number and order of states updated) to maximize accuracy
while preserving efficiency. Lastly, we evaluate the proposed RISE-SLAM
algorithm on publicly-available datasets and demonstrate its superiority, both
in terms of accuracy and efficiency, as compared to alternative visual-inertial
SLAM systems.
