  Accurate rail location is a crucial part in the railway support driving
system for safety monitoring. LiDAR can obtain point clouds that carry 3D
information for the railway environment, especially in darkness and terrible
weather conditions. In this paper, a real-time rail recognition method based on
3D point clouds is proposed to solve the challenges, such as disorderly, uneven
density and large volume of the point clouds. A voxel down-sampling method is
first presented for density balanced of railway point clouds, and pyramid
partition is designed to divide the 3D scanning area into the voxels with
different volumes. Then, a feature encoding module is developed to find the
nearest neighbor points and to aggregate their local geometric features for the
center point. Finally, a multi-scale neural network is proposed to generate the
prediction results of each voxel and the rail location. The experiments are
conducted under 9 sequences of 3D point cloud data for the railway. The results
show that the method has good performance in detecting straight, curved and
other complex topologies rails.
