  Global localization is essential for robots to perform further tasks like
navigation. In this paper, we propose a new framework to perform global
localization based on a filter-based visual-inertial odometry framework MSCKF.
To reduce the computation and memory consumption, we only maintain the keyframe
poses of the map and employ Schmidt-EKF to update the state. This global
localization framework is shown to be able to maintain the consistency of the
state estimator. Furthermore, we introduce a re-linearization mechanism during
the updating phase. This mechanism could ease the linearization error of
observation function to make the state estimation more precise. The experiments
show that this mechanism is crucial for large and challenging scenes.
Simulations and experiments demonstrate the effectiveness and consistency of
our global localization framework.
