Coarse-to-Fine Lane Boundary Extraction for Large-Scale HD Mapping

Lane boundaries, as the main component of high definition maps (HD maps), are difficult to auto-generate accurately in various scenarios. In this paper, a general lane boundary extraction method is proposed for HD mapping in both highway and urban scenarios. Firstly, a learning-based heatmap regression network is applied to estimate the center of lane boundaries in bird’s eye view (BEV) images from light detection and ranging (LiDAR). Secondly, the geometry of various lane boundaries is extracted accurately in a coarse-to-fine strategy. Given the regression results, the geometry generation method initially extracts kinds of lane boundaries coarsely, including highway boundaries and complex cases in urban scenarios, such as splitting lane boundaries, lane boundaries in arbitrary directions, etc. Subsequently, the fine adjustment method increases the accuracy of the lane boundary geometry by inserting and adjusting the keypoints recursively according to the regression heatmap. To handle large-scale mapping, additional methods are presented to merge the same lane boundary including the connection priority strategy and adaptive lane vertex downsampling. Experiments demonstrate that the proposed method manages to generate accurate lane boundaries in both highway and urban scenarios with limited storage consumption, and therefore is an effective and storage-saving method for large-scale HD mapping.