  Accuracy and computational efficiency are the most important metrics to
Visual Inertial Navigation System (VINS). The existing VINS algorithms with
either high accuracy or low computational complexity, are difficult to provide
the high precision localization in resource-constrained devices. To this end,
we propose a novel filter-based VINS framework named SchurVINS, which could
guarantee both high accuracy by building a complete residual model and low
computational complexity with Schur complement. Technically, we first formulate
the full residual model where Gradient, Hessian and observation covariance are
explicitly modeled. Then Schur complement is employed to decompose the full
model into ego-motion residual model and landmark residual model. Finally,
Extended Kalman Filter (EKF) update is implemented in these two models with
high efficiency. Experiments on EuRoC and TUM-VI datasets show that our method
notably outperforms state-of-the-art (SOTA) methods in both accuracy and
computational complexity. The experimental code of SchurVINS is available at
https://github.com/bytedance/SchurVINS.
