Enhanced Road Marking Point Cloud Extraction Method Using Improved RandLA-Net

This paper proposes a road marking point cloud extraction method based on enhanced RandLA-Net to address the issue of inadequate accuracy in road marking extraction within high-precision maps. Road markings possess spatial characteristics such as smoothness, minimal undulation, approximate parallelism to the horizontal plane, and high echo intensity. To differentiate road markings from other features, we used total variance, flatness, perpendicularity, and echo intensity, thereby enhancing the distinction and similarity in RandLA-Net neighborhood point clouds. First, we calculated the three covariance features of the point cloud. Second, we applied the improved RandLA-Net feature fusion module to realize feature fusion and semantic segmentation. The segmentation results were then refined by Euclidean clustering to derive the final road marking point cloud. The proposed method was validated on the publicly available Toronto-3D and WHU-MLS datasets and compared with prevalent point cloud semantic segmentation methods and traditional thresholding techniques at the semantic segmentation and road marking extraction stages. The experimental results demonstrate that the proposed method provides more complete and accurate road marking point clouds.