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Sensors 2017, 17(7), 1465; doi:10.3390/s17071465

Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data

1
Departmento de Explotación de Minas, Grupo de Investigación en Geomática y Computación Gráfica (GEOGRAPH), Universidad de Oviedo, 33004 Oviedo, Spain
2
Grupo de Investigación en Geomática e Ingeniería Cartográfica (GEOINCA), Universidad de León, Avenida de Astorga, s/n, 24001 Ponferrada, Spain
*
Author to whom correspondence should be addressed.
Received: 23 May 2017 / Revised: 12 June 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
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Abstract

Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds. This work is focused on establishing a methodology and developing an algorithm to detect pole-like objects and classify them into several categories using MLS datasets. The developed procedure starts with the discretization of the point cloud by means of a voxelization, in order to simplify and reduce the processing time in the segmentation process. In turn, a heuristic segmentation algorithm was developed to detect pole-like objects in the MLS point cloud. Finally, two supervised classification algorithms, linear discriminant analysis and support vector machines, were used to distinguish between the different types of poles in the point cloud. The predictors are the principal component eigenvalues obtained from the Cartesian coordinates of the laser points, the range of the Z coordinate, and some shape-related indexes. The performance of the method was tested in an urban area with 123 poles of different categories. Very encouraging results were obtained, since the accuracy rate was over 90%. View Full-Text
Keywords: Mobile Laser Scanner (MLS); point cloud; pole-like objects; automatic feature detection; principal component analysis Mobile Laser Scanner (MLS); point cloud; pole-like objects; automatic feature detection; principal component analysis
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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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MDPI and ACS Style

Ordóñez, C.; Cabo, C.; Sanz-Ablanedo, E. Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data. Sensors 2017, 17, 1465.

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