Real-time Rail Recognition Based on 3D Point Clouds

TL;DR

This paper introduces a real-time method for rail recognition using 3D point clouds from LiDAR, addressing challenges like uneven density and large data volume to improve safety monitoring in railway systems.

Contribution

It proposes a novel density balancing technique, pyramid partitioning, and a multi-scale neural network for accurate rail detection in complex environments.

Findings

Effective detection of straight and curved rails

Robust performance under various environmental conditions

High accuracy in complex railway topologies

Abstract

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.