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An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds

Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
Academic Editors: Lucía Díaz Vilariño and Miguel Azenha
Infrastructures 2017, 2(2), 8;
Received: 12 January 2017 / Revised: 29 May 2017 / Accepted: 31 May 2017 / Published: 2 June 2017
(This article belongs to the Special Issue Building Information Modelling for Civil Infrastructures)
PDF [4584 KB, uploaded 2 June 2017]


This study proposes an enhanced algorithm that outperforms the methods developed by the author’s earlier contributions for the recognition of railroad assets from LiDAR point clouds. The algorithm is improved by: (1) making it applicable to railroads with any slope; (2) employing Eigen decomposition for the rail seed point selection that makes it independent of the rails’ dimensions; and (3) developing a computationally efficient fully data-driven method (simultaneous identification of rail tracks and contact cables) that is able to process poorly sampled datasets with complicated configurations. The upgraded algorithm is applied to two datasets with quite different point sampling and complexity. First dataset is scanned by a terrestrial system and contains three million points covering 630 m of an inter-city railroad corridor. It presents a simple configuration with nonintersecting straight rail tracks and cables. Second dataset includes 80 m of a complex urban railroad environment comprising curved and merging rail tracks and intersecting cables. It is scanned from an airborne platform and contains 165,000 points. The results indicate that all objects of interest are identified and the average recognition precision and accuracy of both datasets at the point cloud level are greater than 95%. View Full-Text
Keywords: LiDAR; point cloud; object recognition; segmentation; railroad; cable LiDAR; point cloud; object recognition; segmentation; railroad; cable

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Arastounia, M. An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds. Infrastructures 2017, 2, 8.

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