  High definition (HD) maps have demonstrated their essential roles in enabling
full autonomy, especially in complex urban scenarios. As a crucial layer of the
HD map, lane-level maps are particularly useful: they contain geometrical and
topological information for both lanes and intersections. However, large scale
construction of HD maps is limited by tedious human labeling and high
maintenance costs, especially for urban scenarios with complicated road
structures and irregular markings. This paper proposes an approach based on
semantic-particle filter to tackle the automatic lane-level mapping problem in
urban scenes. The map skeleton is firstly structured as a directed cyclic graph
from online mapping database OpenStreetMap. Our proposed method then performs
semantic segmentation on 2D front-view images from ego vehicles and explores
the lane semantics on a birds-eye-view domain with true topographical
projection. Exploiting OpenStreetMap, we further infer lane topology and
reference trajectory at intersections with the aforementioned lane semantics.
The proposed algorithm has been tested in densely urbanized areas, and the
results demonstrate accurate and robust reconstruction of the lane-level HD
map.
