  In recent years, prior maps have become a mainstream tool in autonomous
navigation. However, commonly available prior maps are still tailored to
control-and-decision tasks, and the use of these maps for localization remains
largely unexplored. To bridge this gap, we propose a lidar-based localization
and mapping (LOAM) system that can exploit the common HD-maps in autonomous
driving scenarios. Specifically, we propose a technique to extract information
from the drivable area and ground surface height components of the HD-maps to
construct 4DOF pose priors. These pose priors are then further integrated into
the pose-graph optimization problem to create a globally consistent 3D map.
Experiments show that our scheme can significantly improve the global
consistency of the map compared to state-of-the-art lidar-only approaches,
proven to be a useful technology to enhance the system's robustness, especially
in GPS-denied environment. Moreover, our work also serves as a first step
towards long-term navigation of robots in familiar environment, by updating a
map. In autonomous driving this could enable updating the HD-maps without
sourcing a new from a third party company, which is expensive and introduces
delays from change in the world to updated map.
