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Sensors 2016, 16(1), 132; doi:10.3390/s16010132

Vision-Based Georeferencing of GPR in Urban Areas

Department of Civil and Environmental Engineering (DICA)-Geodesy and Geomatics Section, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan 20133, Italy
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Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 4 September 2015 / Revised: 21 December 2015 / Accepted: 18 January 2016 / Published: 21 January 2016
(This article belongs to the Section Remote Sensors)
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Abstract

Ground Penetrating Radar (GPR) surveying is widely used to gather accurate knowledge about the geometry and position of underground utilities. The sensor arrays need to be coupled to an accurate positioning system, like a geodetic-grade Global Navigation Satellite System (GNSS) device. However, in urban areas this approach is not always feasible because GNSS accuracy can be substantially degraded due to the presence of buildings, trees, tunnels, etc. In this work, a photogrammetric (vision-based) method for GPR georeferencing is presented. The method can be summarized in three main steps: tie point extraction from the images acquired during the survey, computation of approximate camera extrinsic parameters and finally a refinement of the parameter estimation using a rigorous implementation of the collinearity equations. A test under operational conditions is described, where accuracy of a few centimeters has been achieved. The results demonstrate that the solution was robust enough for recovering vehicle trajectories even in critical situations, such as poorly textured framed surfaces, short baselines, and low intersection angles. View Full-Text
Keywords: global positioning system; ground penetrating radar; image processing; photogrammetric positioning global positioning system; ground penetrating radar; image processing; photogrammetric positioning
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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

Barzaghi, R.; Cazzaniga, N.E.; Pagliari, D.; Pinto, L. Vision-Based Georeferencing of GPR in Urban Areas. Sensors 2016, 16, 132.

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