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Article

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Department of Geoinformation, Photogrammetry and Environmental Remote Sensing, AGH University of Science and Technology, Al. A.Mickiewicza 30, 30-059 Kraków, Poland
Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 18 July 2016 / Revised: 19 September 2016 / Accepted: 22 September 2016 / Published: 27 September 2016
This paper presents a new method for detecting, locating and classifying overhead contact systems (catenary systems) in point clouds collected by mobile mapping systems (MMS) on rail roads. Contrary to many other application types, railway embankments are highly regulated and standardized. Railway infrastructure geometric relations remain roughly unchanged within established regions and have similarities between them. The newly-developed method exploits both these characteristics, as well as the survey process. There are several steps in this approach. Firstly, it restricts the search for catenaries relative to the distance to registered MMS trajectory, then finds possible support structures according to the density of points above the track. Subsequently, the method verifies the structures’ presence and classifies the points with the use of the RANSAC algorithm. It establishes the presence of cantilevers, as well as poles or structural beams, depending on the type of detected support structure. The method also determines the coordinates of the identified object on the ground. Finally, a classification is clarified with the use of a modified DBSCAN algorithm. The design method has been verified with data collected in four surveys where the cumulative length of the route was almost 90 km. Over 97% of support structures were correctly detected, and out of these, over 95% were completely classified. View Full-Text
Keywords: LiDAR; mobile mapping; point cloud classification; railway monitoring; object recognition LiDAR; mobile mapping; point cloud classification; railway monitoring; object recognition
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MDPI and ACS Style

Pastucha, E. Catenary System Detection, Localization and Classification Using Mobile Scanning Data. Remote Sens. 2016, 8, 801. https://0-doi-org.brum.beds.ac.uk/10.3390/rs8100801

AMA Style

Pastucha E. Catenary System Detection, Localization and Classification Using Mobile Scanning Data. Remote Sensing. 2016; 8(10):801. https://0-doi-org.brum.beds.ac.uk/10.3390/rs8100801

Chicago/Turabian Style

Pastucha, Elżbieta. 2016. "Catenary System Detection, Localization and Classification Using Mobile Scanning Data" Remote Sensing 8, no. 10: 801. https://0-doi-org.brum.beds.ac.uk/10.3390/rs8100801

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