Automatic Rail Extraction in Terrestrial and Airborne LiDAR Data

Datasets of stretches of railway tracks are collected using both Airborne and Terrestrial LiDAR scanners having varying density, resolution and provide different views of the railway track. Manual feature extraction from such datasets is tedious and labour intensive. Therefore, automatic extraction of desired features is highly desirable. In this work, we propose a technique to extract the rails from these two types of datasets. Our rail extraction technique models the a railway track as a dynamic system of local pairs of parallel line segments and uses the Kalman filter to predict and monitor the state of the system. The system's state is composed of the two centroids of the parallel line segments as well as their common direction. Additionally, we augment the Kalman filter process to deal with special cases such as missing railway track segments, sensor noise, and data sparseness. Our technique is effective on both types of data sets as we achieve a precision of 91% and a recall of 99% on the high resolution Terrestrial dataset and a precision of 94% and a recall of 91% on the Airborne dataset.