  In GPS-denied scenarios, a robust environmental perception and localization
system becomes crucial for autonomous driving. In this paper, a LiDAR-based
online localization system is developed, incorporating road marking detection
and registration on a high-definition (HD) map. Within our system, a road
marking detection approach is proposed with real-time performance, in which an
adaptive segmentation technique is first introduced to isolate high-reflectance
points correlated with road markings, enhancing real-time efficiency. Then, a
spatio-temporal probabilistic local map is formed by aggregating historical
LiDAR scans, providing a dense point cloud. Finally, a LiDAR bird's-eye view
(LiBEV) image is generated, and an instance segmentation network is applied to
accurately label the road markings. For road marking registration, a semantic
generalized iterative closest point (SG-ICP) algorithm is designed. Linear road
markings are modeled as 1-manifolds embedded in 2D space, mitigating the
influence of constraints along the linear direction, addressing the
under-constrained problem and achieving a higher localization accuracy on HD
maps than ICP. Extensive experiments are conducted in real-world scenarios,
demonstrating the effectiveness and robustness of our system.
