Fully automated methodology for the delineation of railway lanes and the generation of IFC alignment models using 3D point cloud data

Abstract

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.