Abstract
Abstract. This study introduces a new method to reconstruct 3D model of railway tracks from a railway corridor scene captured by mobile LiDAR data. The proposed approach starts to approximate the orientation of railway track trajectory from LiDAR point clouds and extract a strip, which direction is orthogonal to the trajectory of railway track. Within the strip, a track region and its track points are detected based on the Bayesian decision process. Once the main track region is localized, rail head points are segmented based on the region growing approach from the detected track points and then initial track models are reconstructed using a third-degree polynomial function. Based on the initial modelling result, a potential track region with varying lengths is dynamically predicted and the model parameters are updated in the Kalman Filter framework. The key aspect is that the proposed approach is able to enhance the efficiency of the railway tracking process by reducing the complexity for detecting track points and reconstructing track models based on the use of the track model previously reconstructed. An evaluation of the proposed method is performed over an urban railway corridor area containing multiple railway track pairs.
Highlights
A precise and effective maintenance of railway infrastructure should be guaranteed to find a solution for high operating safety and low maintenance costs
Various techniques related to the railway track model reconstruction from Imagery and LiDAR data have been proposed in literature
The reported algorithms using images which are in b/w or color often depend on the following key assumptions to obtain a tractable practical solution to railway scene complexity. (a) As metals such as iron and steel are usually used for the track material, railway tracks are a distinctive object with uniform brightness
Summary
A precise and effective maintenance of railway infrastructure should be guaranteed to find a solution for high operating safety and low maintenance costs. Compared to the image-based approaches, the LiDAR as an active remote sensing system enables detailed capture of a 3D railway scene with high point density for example in a number of hundreds per square meter. This allows us to readily detect track objects in the 3D space by compensating the weaknesses derived from image-based techniques. This study integrated track point detection with track modelling in the Kalman filter framework It enhances the efficiency of the track modelling process by simultaneously capturing track points along the predicted track models and updating the previous track models.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
