Affiliation:
1. 9174 University of Freiburg , Department of Sustainable Systems Engineering - INATECH , Freiburg , Germany
2. 28468 Fraunhofer Institute for Physical Measurement Techniques IPM , Freiburg , Germany
Abstract
Abstract
Mobile mapping vehicles, equipped with cameras, laser scanners (in this paper referred to as light detection and ranging, LiDAR), and positioning systems are limited to acquiring surface data. However, in this paper, a method to fuse both LiDAR and 3D ground penetrating radar (GPR) data into consistent georeferenced point clouds is presented, allowing imaging both the surface and subsurface. Objects such as pipes, cables, and wall structures are made visible as point clouds by thresholding the GPR signal’s Hilbert envelope. The results are verified with existing utility maps. Varying soil conditions, clutter, and noise complicate a fully automatized approach. Topographic correction of the GPR data, by using the LiDAR data, ensures a consistent ground height. Moreover, this work shows that the LiDAR point cloud, as a reference, increases the interpretability of GPR data and allows measuring distances between above ground and subsurface structures.
Subject
Earth and Planetary Sciences (miscellaneous),Engineering (miscellaneous),Modelling and Simulation
Reference38 articles.
1. Sato, M. (2016). The state of the art in Ground Penetrating Radar and the regulation of electromagnetic wave. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
2. Benedetto, A., & Benedetto, F. (2014). Application Field Specific Synthesizing of Sensing Technology: Civil Engineering Application of Ground Penetrating Radar Sensing Technology (Vol. 13). Amsterdam: Elsevier Science Publisher B.V., https://iris.uniroma3.it/handle/11590/171256#.XxXGg0FCSM8, https://doi.org/10.1016/B978-0-08-096532-1.01315-7
3. Pham, M., & Lefèvre, S. (2016). Buried object detection from B-scan ground penetrating radar data using faster-RCNN. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
4. Zhang, Y., Huston, D., & Xia, T. (2016). Underground object characterization based on neural networks for ground penetrating radar data. In: Proc. SPIE 9804, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016, p. 980403. International Society for Optics and Photonics. https://doi.org/10.1117/12.2219345
5. Grasmueck, M., & Viggiano, D. A. (2007). Integration of ground-penetrating radar and laser position sensors for real-time 3-D data fusion. IEEE Transactions on Geoscience and Remote Sensing, 45(1), 130–137. https://doi.org/10.1109/TGRS.2006.882253
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