Next Article in Journal
The Potential of Active Contour Models in Extracting Road Edges from Mobile Laser Scanning Data
Previous Article in Journal
Highway Bridge Infrastructure in the Province of British Columbia (BC), Canada
Previous Article in Special Issue
Improving MMS Performance during Infrastructure Surveys through Geometry Aided Design
Article Menu

Export Article

Open AccessArticle
Infrastructures 2017, 2(2), 8; doi:10.3390/infrastructures2020008

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
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)

Abstract

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
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Infrastructures EISSN 2412-3811 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top