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.
Funding statement: This work is partially funded by the Sustainability Center Freiburg as part of the project “Erhöhung des Automatisierungsgrades für die Bewertung der Standsicherheit von Brücken” (ErfASst).
Acknowledgments
We thank Elmar Strobach from IMP Bautest AG in Switzerland for all the support. Furthermore, we thank 3D-Radar for the GPR system and all the support.
References
[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).Search in Google Scholar
[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/B978-0-08-096532-1.01315-710.1016/B978-0-08-096532-1.01315-7Search in Google Scholar
[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).Search in Google Scholar
[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/10.1117/12.221934510.1117/12.2219345Search in Google Scholar
[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.2006.88225310.1109/TGRS.2006.882253Search in Google Scholar
[6] Wolf, J., Discher, S., Masopust, L., Schulz, S., Richter, R., & Döllner, J. (2018). Combined visual exploration of 2d ground radar and 3d point cloud data for road environments. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 6210, 231–236. https://doi.org/10.5194/isprs-archives-XLII-4-W10-231-201810.5194/isprs-archives-XLII-4-W10-231-2018Search in Google Scholar
[7] De Smet, T. S., Everett, M. E., Warden, R. R., Komas, T., Hagin, J. N., Gavette, P., Martini, J. A., & Barker, L. (2019). The fate of the historic fortifications at Alcatraz island based on terrestrial laser scans and ground-penetrating radar interpretations from the recreation yard. Near Surface Geophysics, 17(2), 151–168. https://doi.org/10.1002/nsg.1203110.1002/nsg.12031Search in Google Scholar
[8] Puente, I., Solla, M., Lagüela-Lopez, S., & Sanjurjo-Pinto, J. (2018). Reconstructing the Roman site “Aquis Querquennis” (Bande, Spain) from GPR, T-LiDAR and IRT data fusion. Remote Sensing, 10(3), 379. https://doi.org/10.3390/rs1003037910.3390/rs10030379Search in Google Scholar
[9] Kamp, N., Russ, S., Sass, O., Tiefengraber, G., & Tiefengraber, S. (2014). A Fusion of GPR- and LiDAR-Data for Surveying and Visualisation of Archaeological Structures – a case example of an archaeological site in Strettweg, District of Murtal, Austria. EGU General Assembly Conference Abstracts, 12621.Search in Google Scholar
[10] Leica Geosystems AG (2020, July 22). Leica Pegasus:Stream. https://www.gefos-leica.cz/data/original/skenery/mobilni-mapovani/stream/leica_pegasusstream_bro.pdfSearch in Google Scholar
[11] Leica Geosystems AG (2020, July 22). Leica Pegasus:Stream specifications. https://leica-geosystems.com/-/media/files/leicageosystems/products/datasheets/leica_pegasusstream_ds.ashx?la=en&hash=5063BA7630D2EB4E5AAD9740CA7E6B2FSearch in Google Scholar
[12] Fraunhofer IPM (2020, July 22). Automated 3D-Data Interpretation 3D-AI. https://www.ipm.fraunhofer.de/content/dam/ipm/en/PDFs/product-information/OF/MTS/3D-AI-data-interpretation-automated.pdfSearch in Google Scholar
[13] Daniels, D. J. (Ed.) (2007). IEE Radar, Sonar, Navigation and Avionics Series: Vol. 15. Ground Penetrating Radar (2 ed., repr.). Inst. of Electrical Engineers.Search in Google Scholar
[14] Vilbig, R. A. (2013). Air-coupled and ground-coupled ground penetrating radar techniques. https://repository.library.northeastern.edu/files/neu:848Search in Google Scholar
[15] 3D-Radar (2019, August 1). Step Frequency Radar. http://3d-radar.com/step-frequency-radar/Search in Google Scholar
[16] Fraunhofer IPM (2019, June 3). Clearance Profile Scanner CPS. https://www.ipm.fraunhofer.de/content/dam/ipm/en/PDFs/product-information/OF/MTS/Clearance-Profile-Scanner-CPS.pdfSearch in Google Scholar
[17] Fraunhofer IPM (2020, July 22). Pavement Profile Scanner PPS/PPS-PLUS. https://www.ipm.fraunhofer.de/content/dam/ipm/de/PDFs/produktblaetter/OF/MTS/Pavement-Profile-Scanner-de.pdfSearch in Google Scholar
[18] Applanix (2020, July 22). POSLV Specifications. https://www.applanix.com/downloads/products/specs/POS-LV-Datasheet.pdfSearch in Google Scholar
[19] 3D-Radar (2020, July 22). Placement of the antenna elements in a DX1821 array. http://3d-radar.com/technology/Search in Google Scholar
[20] Bundesamt für Kartographie und Geodäsie (2020, July 22). WMS WebAtlasDE.light. https://gdz.bkg.bund.de/index.php/default/wms-webatlasde-light-wms-webatlasde-light.htmlSearch in Google Scholar
[21] regioDATA (2020, July 22). planSERVICE. https://www.regiodata-gmbh.de/de-de/produkte/planserviceSearch in Google Scholar
[22] LEO-BW (2020, July 22). Franziskaner Kloster. https://www.leo-bw.de/web/guest/detail-gis/-/Detail/details/DOKUMENT/labw_kloester/348/Ehemaliges+Franziskanerkloster+mit+St+Martinskirche+um+1840Search in Google Scholar
[23] ACADEMIC (2020, July 22). Grundriss von Kirche und Kloster vor dem Teilabriss des Kreuzgangs. https://deacademic.com/dic.nsf/dewiki/2365216Search in Google Scholar
[24] Jol, H. M. (Ed.) (2009). Ground Penetrating Radar Theory and Applications. Oxford: Elsevier Science. https://doi.org/10.1016/B978-0-444-53348-7.X0001-410.1016/B978-0-444-53348-7.X0001-4Search in Google Scholar
[25] FreiGIS (2020, July 22). Orthobilder Freiburg. https://stadtplan.freiburg.de/mapbender/frames/index.php?&gui_id=stadtplanSearch in Google Scholar
[26] Hofmann-Wellenhof, B., Lichtenegger, H., & Wasle, E. (2008). GNSS – Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and More. Springer-Verlag Wien. http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10490534, https://doi.org/10.1007/978-3-211-73017-110.1007/978-3-211-73017-1Search in Google Scholar
[27] Sala, J., Penne, H., & Eide E. (2012). Time-frequency dependent filtering of step-frequency ground penetrating radar data. In: 14th International Conference on Ground Penetrating Radar (GPR).Search in Google Scholar
[28] Xavier Neto, P., & de Medeiros, W. E. (2006). A practical approach to correct attenuation effects in GPR data. Journal of Applied Geophysics, 59(2), 140–151. https://doi.org/10.1016/j.jappgeo.2005.09.00210.1016/j.jappgeo.2005.09.002Search in Google Scholar
[29] Hoegh K., Thompkins D., & Khazanovich L. (2016). Evaluation, Development, and Implementation of 3D GPR for Assessment of Minnesota Infrastructure. Center for Transportation Studies, University of Minnesota. Retrieved from the University of Minnesota Digital Conservancy, http://hdl.handle.net/11299/184895Search in Google Scholar
[30] Yu, Y., Li, J., Guan, H., & Wang, C. (2015). Automated extraction of urban road facilities using mobile laser scanning data. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2167–2181. https://doi.org/10.1109/TITS.2015.239949210.1109/TITS.2015.2399492Search in Google Scholar
[31] Rashidi, P., & Rastiveis, H. (2018). Extraction of ground points from LiDAR data based on slope and progressive window thresholding (SPWT). Earth Observation and Geomatics Engineering, 2(1), 36–44. https://doi.org/10.22059/eoge.2018.240284.1012Search in Google Scholar
[32] GPR-SLICE Ground Penetrating Radar Imaging Software (2020, July 22). GPR-Slice. https://www.gpr-survey.com/Search in Google Scholar
[33] 3D-Radar (2020, July 22). 3DR-Examiner Software. http://3d-radar.com/system/Search in Google Scholar
[34] Zheng, L., Liu, Z., Wang G., & Zhang Z. (2016/06). Research on application of Hilbert transform in radar signal simulation. In: Proceedings of the 7th ICEEG (pp. 347–349).10.2991/iceeg-16.2016.93Search in Google Scholar
[35] Sharma, P., Kumar, B., & Singh, D. (2018). Development of adaptive threshold and data smoothening algorithm for GPR imaging. Defence Science Journal, 68, 316–325. https://doi.org/10.14429/dsj.68.1235410.14429/dsj.68.12354Search in Google Scholar
[36] Gargouri, F. (2020). Thresholding the maximum entropy. https://www.mathworks.com/matlabcentral/fileexchange/35158-thresholding-the-maximum-entropy, MATLAB Central File Exchange. Retrieved July 12, 2019.Search in Google Scholar
[37] Korsawe, J. (2015). Minimal Bounding Box. https://de.mathworks.com/matlabcentral/fileexchange/18264-minimal-bounding-box. Retrieved July 12, 2019.Search in Google Scholar
[38] Park, B., Kim, J., Lee, J., Kang, M.-S., & An, Y.-K. (2018). Underground object classification for urban roads using instantaneous phase analysis of ground-penetrating radar (GPR) data. Remote Sensing, 10(9), 1417. https://doi.org/10.3390/rs1009141710.3390/rs10091417Search in Google Scholar
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