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Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud.

Shima Sahebdivani¹, Hossein Arefi¹, Mehdi Maboudi²

¹School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Tehran 1439957131, Iran.

Sensors (Basel, Switzerland)

|September 16, 2020

Summary

This study introduces a novel 3D modeling technique for railway tracks using unmanned aerial vehicle (UAV) data. The method achieves high accuracy, with a median distance of approximately 3 cm, demonstrating its effectiveness for railway infrastructure.

Keywords:

3D modeling RANSAC UAV-based point cloud projection-based modeling railroad railway detection

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Area of Science:

Geomatics Engineering
Civil Engineering
Computer Vision

Background:

The railway industry's expansion necessitates advanced 3D modeling of tracks.
Unmanned Aerial Vehicle (UAV) technology offers significant advantages for data acquisition in this domain.

Discussion:

A projection-based approach is proposed for 3D railway track modeling using UAV-derived dense point clouds.
Key steps include height jump analysis for rail point extraction, RANSAC for line fitting, and Fourier curve refinement for continuous modeling.

Key Insights:

The developed algorithm accurately models railway tracks, achieving a median geometric distance of about 3 cm.
This demonstrates the high quality and precision of the proposed 3D modeling method.

Outlook:

The method shows promise for enhancing railway infrastructure monitoring and management.
Further research could explore integration with other geospatial data for comprehensive railway analysis.