  The monocular visual-inertial system (VINS), which consists one camera and
one low-cost inertial measurement unit (IMU), is a popular approach to achieve
accurate 6-DOF state estimation. However, such locally accurate visual-inertial
odometry is prone to drift and cannot provide absolute pose estimation.
Leveraging history information to relocalize and correct drift has become a hot
topic. In this paper, we propose a monocular visual-inertial SLAM system, which
can relocalize camera and get the absolute pose in a previous-built map. Then
4-DOF pose graph optimization is performed to correct drifts and achieve global
consistent. The 4-DOF contains x, y, z, and yaw angle, which is the actual
drifted direction in the visual-inertial system. Furthermore, the proposed
system can reuse a map by saving and loading it in an efficient way. Current
map and previous map can be merged together by the global pose graph
optimization. We validate the accuracy of our system on public datasets and
compare against other state-of-the-art algorithms. We also evaluate the map
merging ability of our system in the large-scale outdoor environment. The
source code of map reuse is integrated into our public code, VINS-Mono.
