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Framework for extracting rails and setting-out railway line axis based on UAV photogrammetric measurements
Abstract
Technical diagnostics enables assessing the current technical condition of a railway line and adjacent infrastructure, and forecasting its changes over a specific time horizon. One of its elements is the periodic monitoring of rail position and their geometry. This article presents a new framework for the setting-out of a railway track axis. The process presented in the manuscript is based on the specific filtration and extraction of rails from a point cloud originating from the photogrammetric process. Data was acquired using a small unmanned aerial vehicle (UAV). A railway track axis setting-out using the process described herein was related to dynamic satellite track measurement. An average accuracy achieved in the horizontal plane is 1.6 cm which corresponds to the accuracy of the source photogrammetric product. The achieved accuracy enables using this method to be suitable for technical track monitoring, geoinformation, and cartographic work.
Citations
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Authors (6)
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Cezary Specht prof. dr hab. inż.
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Mariusz Specht dr inż.
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Pawel S. Dabrowski dr inż
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Oktawia Lewicka mgr
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/23248378.2022.2140713
- License
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Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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International Journal of Rail Transportation
no. 12,
pages 200 - 216,
ISSN: 2324-8378 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Burdziakowski P., Specht C., Stateczny A., Specht M., Dabrowski P. S., Lewicka O.: Framework for extracting rails and setting-out railway line axis based on UAV photogrammetric measurements// International Journal of Rail Transportation -Vol. 12,iss. 1 (2024), s.200-216
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/23248378.2022.2140713
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
Referenced datasets
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