UAV LiDAR Data-Based Lane-Level Road Network Generation for Urban Scene HD Maps

High-definition (HD) maps offer precise positioning and dependable navigation capabilities, which are essential to guarantee the safety of autonomous vehicles. Lane-level road network, as a crucial component of HD maps, can provide perception, positioning, local planning, and vehicle control services. In urban scenarios, the effectiveness of using sensor-equipped mapping vehicles to construct HD maps on a large scale is hindered by complex road conditions, heavy traffic flow, and limited sensor measurement range. In this letter, we propose a method for generating a lane-level road network from unmanned aerial vehicle (UAV) LiDAR data, which is flexible, maneuverable, and not limited by the constraints of road traffic conditions. The proposed method employs a SegFormer model to acquire road areas and eliminates pavement interferential objects through a density-based spatial clustering of applications with noise (DBSCAN) clustering and random sample consensus (RANSAC) plane fitting algorithm. Subsequently, the PP-LiteSeg model is utilized to extract road symbols from a relatively clean pavement point clouds, and the lane-level road network is generated. We tested our method on the inner ring elevated road section of Yangpu District, Shanghai. The experimental results demonstrate the effectiveness and robustness of our method for generating lane-level road network in high-density urban scene.