  It holds great implications for practical applications to enable
centimeter-accuracy positioning for mobile and wearable sensor systems. In this
paper, we propose a novel, high-precision, efficient visual-inertial (VI)-SLAM
algorithm, termed Schmidt-EKF VI-SLAM (SEVIS), which optimally fuses IMU
measurements and monocular images in a tightly-coupled manner to provide 3D
motion tracking with bounded error. In particular, we adapt the Schmidt Kalman
filter formulation to selectively include informative features in the state
vector while treating them as nuisance parameters (or Schmidt states) once they
become matured. This change in modeling allows for significant computational
savings by no longer needing to constantly update the Schmidt states (or their
covariance), while still allowing the EKF to correctly account for their
cross-correlations with the active states. As a result, we achieve linear
computational complexity in terms of map size, instead of quadratic as in the
standard SLAM systems. In order to fully exploit the map information to bound
navigation drifts, we advocate efficient keyframe-aided 2D-to-2D feature
matching to find reliable correspondences between current 2D visual
measurements and 3D map features. The proposed SEVIS is extensively validated
in both simulations and experiments.
