  Visual Localization is an essential component in autonomous navigation.
Existing approaches are either based on the visual structure from SLAM/SfM or
the geometric structure from dense mapping. To take the advantages of both, in
this work, we present a complete visual inertial localization system based on a
hybrid map representation to reduce the computational cost and increase the
positioning accuracy. Specially, we propose two modules for data association
and batch optimization, respectively. To this end, we develop an efficient data
association module to associate map components with local features, which takes
only $2$ms to generate temporal landmarks. For batch optimization, instead of
using visual factors, we develop a module to estimate a pose prior from the
instant localization results to constrain poses. The experimental results on
the EuRoC MAV dataset demonstrate a competitive performance compared to the
state of the arts. Specially, our system achieves an average position error in
1.7 cm with 100% recall. The timings show that the proposed modules reduce the
computational cost by 20-30%. We will make our implementation open source at
http://github.com/hyhuang1995/gmmloc.
