Non-Interrupting Rail Track Geometry Measurement System Using UAV and LiDAR
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917818102972416 |
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| author | Qiu, Lihao Zhu, Ming Park, JeeWoong Jiang, Yingtao Hualiang Teng |
| author_facet | Qiu, Lihao Zhu, Ming Park, JeeWoong Jiang, Yingtao Hualiang Teng |
| contents | 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 rail geometry inspections seamlessly over a larger area without interrupting rail operations. In particular, this semi- or fully automated measurement is found capable of measuring critical rail geometry irregularities in gauge, curvature, and profile with sub-inch accuracy. Cross-level and warp are not measured due to the absence of gravity data. By eliminating operational interruptions, our system offers a more streamlined, cost-effective, and safer solution for inspecting and maintaining rail infrastructure. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_10832 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Non-Interrupting Rail Track Geometry Measurement System Using UAV and LiDAR Qiu, Lihao Zhu, Ming Park, JeeWoong Jiang, Yingtao Hualiang Teng Robotics Image and Video Processing 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 rail geometry inspections seamlessly over a larger area without interrupting rail operations. In particular, this semi- or fully automated measurement is found capable of measuring critical rail geometry irregularities in gauge, curvature, and profile with sub-inch accuracy. Cross-level and warp are not measured due to the absence of gravity data. By eliminating operational interruptions, our system offers a more streamlined, cost-effective, and safer solution for inspecting and maintaining rail infrastructure. |
| title | Non-Interrupting Rail Track Geometry Measurement System Using UAV and LiDAR |
| topic | Robotics Image and Video Processing |
| url | https://arxiv.org/abs/2410.10832 |