This work presents a fully automated methodology that, in a first step, is able to reliably extract and delineate the position and geometry of the rails from three-dimensional (3D) point cloud data of railway infrastructure by sequentially applying heuristic-based point cloud processing steps, namely railway track segmentation, rough rail estimation, and rail extraction. Then, that information is used to generate the alignment of the surveyed railway lane following the requirements of the Industry Foundation Classes (IFC) Alignment standard. Finally, the proposed method exports an IFC-compliant file that describes the position of the rails with respect to the railway lane alignment. This method has been applied to a 90-km long railway lane, and validated on two one-kilometer subsections of the case study data, obtaining an average rail delineation error of less than 3 cm. Furthermore, the track gauge was measured using the rail alignment data, obtaining errors of a similar magnitude.

这项工作提出了一种完全自动化的方法，第一步，该方法能够通过顺序应用基于启发式的点云处理，从铁路基础设施的三维（3D）点云数据中可靠地提取和描绘轨道的位置和几何形状步骤，即铁路轨道分割，粗轨估计和铁轨提取。然后，该信息将用于遵循行业基础等级（IFC）路线标准的要求来生成被测铁路车道的路线。最后，所提出的方法将导出一个符合IFC的文件，该文件描述了铁轨相对于铁路车道路线的位置。此方法已应用于90公里长的铁路车道，并在案例研究数据的两个一公里分段中得到了验证，获得的平均轨道轮廓误差小于3 cm。此外，使用轨准线数据测量了轨距仪，获得了类似大小的误差。