Non-Interrupting Rail Track Geometry Measurement System Using UAV and LiDAR
Lihao Qiu, Ming Zhu, JeeWoong Park, Yingtao Jiang, Hualiang (Harry), Teng

TL;DR
This paper introduces a UAV-based LiDAR system combined with machine learning and SLAM algorithms for non-intrusive, accurate, and efficient rail track geometry inspection, reducing operational disruptions and enhancing safety.
Contribution
The paper presents a novel UAV-LiDAR platform with integrated algorithms for seamless, automated rail track geometry measurement without interrupting train operations.
Findings
Achieves sub-inch accuracy in measuring gauge, curvature, and profile.
Operates without disrupting rail services, increasing inspection efficiency.
Offers a cost-effective and safer alternative to traditional methods.
Abstract
The safety of train operations is largely dependent on the health of rail tracks, necessitating regular and meticulous inspection and maintenance. A significant part of such inspections involves geometric measurements of the tracks to detect any potential problems. Traditional methods for track geometry measurements, while proven to be accurate, require track closures during inspections, and consume a considerable amount of time as the inspection area grows, causing significant disruptions to regular operations. To address this challenge, this paper proposes a track geometry measurement system (TGMS) that utilizes an unmanned aerial vehicle (UAV) platform equipped with a light detection and ranging (LiDAR) sensor. Integrated with a state-of-the-art machine-learning-based computer vision algorithm, and a simultaneous localization and mapping (SLAM) algorithm, this platform can conduct…
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Taxonomy
TopicsAdvanced Measurement and Detection Methods · Surface Roughness and Optical Measurements · Remote Sensing and LiDAR Applications
