Automated Reconstruction of Railroad Rail Using Helicopter-borne Light Detection and Ranging in a Train Station

Abstract

A coordinate-based 3D model of a railroad rail is essential for the maintenance of railway services. However, automated processing to reconstruct objects from light detection and ranging (LiDAR) data in areas where such facilities are installed in a complex manner is still a challenge. In this study, our objective is to develop a method for the automated reconstruction of rails from LiDAR data in a complex area. Unlike the running sections of a train where one or two rails are present, many rails are installed by joining or branching in a railway station. Three factors, namely, height difference, spatial relationship with other objects, and point density difference, were considered in detecting the rails in this complicated area. For tracking and modeling rails, an iterative random sample consensus (RANSAC) and singular value decomposition (SVD) algorithms were used. The results showed that about 90% of the rail tracks were extracted, and the precision rate was about 99%. All processes were fully automated, and the method proposed in this study was developed to be applicable to various line-type facilities as well as rails.

Pan-Sunet: Utility Corridor Understanding Using Spatial Layout Consistency

“…In order to learn global context, it is essential to include spatial recurring patterns. The performance of tasks like railway lane extractions, road lane recognition, and 3D building modelling has been enhanced by the use of spatial arrangement (Jeon and Kim, 2019). Spatial interactions are crucial for identifying small items that can be overlooked, according to previous studies (Rosman and Ramamoorthy, 2011).…”

Effective Railroad Fragmentation and Infrastructure Recognition Based on Dense Lidar Point Clouds

“…Automated detection of railroad infrastructure has been addressed based on LiDAR point clouds acquired both by mobile terrestrial laser scanning (MLS) (Arastounia, 2015), (Jwa and Sonh, 2015) or low-altitude aerial laser scanning (ALS), obtained usually from helicopters (Zhu and Hyyppa, 2014), (Jeon and Kim, 2019). Beside the generalized approaches, specialized algorithms on some characteristic of the surrounding environment have also been developed, optimizing their results in rural environments (Arastounia, 2015) (Cserép et al, 2018) or in urban environments (Arastounia and Oude Elberink, 2016).…”