  Accurate localization is fundamental to a variety of applications, such as
navigation, robotics, autonomous driving, and Augmented Reality (AR). Different
from incremental localization, global localization has no drift caused by error
accumulation, which is desired in many application scenarios. In addition to
GPS used in the open air, 3D maps are also widely used as alternative global
localization references. In this paper, we propose a compact 3D map-based
global localization system using a low-cost monocular camera and an IMU
(Inertial Measurement Unit). The proposed compact map consists of two types of
simplified elements with multiple semantic labels, which is well adaptive to
various man-made environments like urban environments. Also, semantic edge
features are used for the key image-map registration, which is robust against
occlusion and long-term appearance changes in the environments. To further
improve the localization performance, the key semantic edge alignment is
formulated as an optimization problem based on initial poses predicted by an
independent VIO (Visual-Inertial Odometry) module. The localization system is
realized with modular design in real time. We evaluate the localization
accuracy through real-world experimental results compared with ground truth,
long-term localization performance is also demonstrated.
