OSM-Assisted Extraction of 3D Vector Boundary from Mobile Laser Scanning Point Cloud

高精度的三维道路信息在智能交通、城市规划与管理等领域具有重要的作用。车载激光点云数据包含了道路及其周围地物的三维信息，但存在数据量大、场景复杂和地物遮挡等问题，为道路边界信息的准确提取带来挑战。OpenStreetMap (OSM)作为一种众源数据，提供了基础道路信息。本文提出一种基于OSM数据辅助，从车载激光点云数据中提取道路三维边界的方法。首先分析车载点云的空间分布特征，构建车载点云特征图，然后以OSM数据作为初始位置，通过改进的活动轮廓模型算法进行道路边界提取，得到三维矢量道路边界。本文采用StreetMapper数据进行实验，结果表明，本文提出的算法能够修复点云缺损导致的边界信息缺失，准确且完整的提取道路三维边界信息，具有较强的稳健性和适用性。 High-precision 3D road information plays an important role in intelligent transportation, urban planning and management. The mobile laser scanning system can quickly obtain the 3D informa-tion of the street scene, but it is difficult to directly extract the complete and accurate road boun-dary from the original point cloud due to the large amount of data, occlusion and complicated urban street scenes. OpenStreetMap is a kind of crowd source geographic data. It can be used to assist road extraction of mobile laser point clouds. This paper proposes a road 3D boundary extraction algorithm that integrates two-dimensional vector data OpenStreetMap and vehicle-borne laser point cloud data. Firstly, the point cloud feature map is constructed by analyzing the spatial distribution characteristics of the Scanning points. The OSM provides the initial position, and then the road boundary extraction is performed on the feature map of the point cloud by the improved active contour model. We use StreetMapper data to carry out experiments. The results show that the proposed algorithm can repair the lack of boundary information caused by point cloud defects, and accurately and completely extract road three-dimensional boundary information, which proves strong robustness and applicability.