Selected aspects of data harmonization from terrestrial laser scanning

Authors

DOI:

https://doi.org/10.24425/ace.2022.141895

Abstract

Periodic inventory and check surveys of the surfaces in engineering structures using terrestrial laser scanning require performing scans from many locations. The survey should be planned so as to obtain full coverage of the measured surface with a point cloud of appropriate density. Due to a variety of terrain obstacles in the close vicinity of the surveyed structure, structural and technical elements, as well as machinery and construction equipment (whose removal is impossible e.g. because of their role in the building and protection of the structure), it is often necessary to combine scans acquired from locations having different measurement geometry of the scene and performed in different lighting conditions. This makes it necessary to fill in blank spots with data of different spectral and geometric quality. This paper presents selected aspects of data harmonization in terrestrial laser scanning. The laser beam incidence angle and the scanning distance are assumed as parameters affecting the quality of the data. Based on the assumed minimum parameters for spectral data, an example of a harmonizing function for the concrete surface of a slurry wall was determined, and the methodology for determining its parameters was described. The presented solution for spectral data harmonization is based on the selection of reference fields representative of a given surface, and their classification with respect to selected geometric parameters of the registered point cloud. For geometric data, possible solutions to the harmonization problem have been analyzed, and criteria for point cloud reduction have been defined in order to obtain qualitatively consistent data. The presented results show that harmonization of point clouds obtained from different stations is necessary before their registration, in order to increase the reliability of analyses performed on the basis of check survey results in the assessment of the technical condition of a surface, its deformation, cracks and scratches.

References

[1] J. Zaczek-Peplinska, M.E. Kowalska, S. Łapiński, M. Grzyb, “Multi-temporal survey of diaphragm wall with terrestrial laser scanning method”, Open Geosciences, 2020, vol. 12, no. 1, pp. 656-667, DOI: 10.1515/geo-2020-0101.

[2] M.E. Kowalska, “Opracowanie metodyki przetwarzania danych z naziemnego skaningu laserowego w pomiarach kontrolnych obiektów inżynierskich (Development of data processing methodology from terrestrial laser scanning in control measurements of engineering structures)”, Ph.D. dissertation, Warsaw University of Technology, Warsaw, Poland, 2020 (in Polish).

[3] Y. Reshetyuk, “Investigation and calibration of pulsed time-of-flight terrestrial laser scanners”, Licentiate thesis in Geodesy, Royal Institute of Technology (KTH), Department of Transport and Economics, Division of Geodesy, Stockholm, 2006.

[4] M.A. Abbas, H. Setan, Z. Majid, A.K. Chong, K.M. Idris, A. Aspuri, “Calibration and Accuracy Assessment of Leica ScanStation C10 Terrestrial Laser Scanner”, in Developments in Multidimensional Spatial Data Models. Lecture Notes in Geoinformation and Cartography. Berlin: Springer, 2013, pp. 33-43, DOI: 10.1007/978-3-642-36379-5_3.

[5] X. Li, Y. Li, X. Xie, L. Xu, “Terrestrial laser scanner autonomous self-calibration with no prior knowledge of point-clouds”, IEEE Sensors Journal, 2018, vol. 18, no.22, pp. 9277-9285, DOI: 10.1109/JSEN.2018.2869559.

[6] J. Wang, H. Kutterer, X. Fang, “External error modelling with combined model in terrestrial laser scanning”, Survey Review, 2016, vol. 48, no. 346, pp. 40-50, DOI: 10.1080/00396265.2015.1097589.

[7] H.A. Jaafar, X. Meng, A. Sowter, “Terrestrial Laser Scanner Error Quantification for the Purpose of Monitoring”, Survey Review, 2018, vol. 50, no. 360, pp. 232-248, DOI: 10.1080/00396265.2016.1259721.

[8] R.A. Hodge, “Using Simulated Terrestrial Laser Scanning to Analyse Errors in High-Resolution Scan Data of Irregular Surfaces”, ISPRS Journal of Photogrammetry and Remote Sensing, 2010, vol. 65, no. 2, pp. 227-240, DOI: 10.1016/j.isprsjprs.2010.01.001.

[9] A.G. Kashani, M.J. Olsen, Ch.E. Parrish, N. Wilson, “A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration”, Sensors (Switzerland), 2015, vol. 15, no. 11, pp. 28099-28128, DOI: 10.3390/s151128099.

[10] K. Tan, X. Cheng, “Correction of Incidence Angle and Distance Effects on TLS Intensity Data Based on Reference Targets”, Remote Sensing, 2016, vol. 8, art. no. 251, DOI: 10.3390/rs8030251.

[11] B. Van Genchten, Theory and Practice on Terrestrial Laser Scanning. Learning tools for advanced three-dimensional surveying in risk awareness project. 2008, pp. 1-241.

[12] S. Kaasalainen, A. Jaakkola, M. Kaasalainen, A. Krooks, A. Kukko, “Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods”, Remote Sensing, 2011, vol. 3, no. 10, pp. 2207-2221, DOI: 10.3390/rs3102207.

[13] W. Boehler, M. Bordas Vicent, A. Marbs, “Investigating Laser Scanner Accuracy”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2003, vol. XXXIVl-5/C15, pp. 696-701.

[14] W. Mukupa, G.V. Roberts, C.M. Hancock, K. Almanasir, “Correction of Terrestrial Lidar Data Using a Hybrid Model”, presented at International Federation of Surveyors (FIG) Working Week 2017, 29 May-2 June 2017, Helsinki, Finland, 2017.

[15] J. Zaczek-Peplinska, “Metodyka oceny stanu powierzchni betonowej budowli pietrzącej na podstawie analizy spektralnej wyników naziemnego skanowania laserowego (Methodology for assessing the condition of the continuation network based on the spectral analysis of the results of terrestrial laser scanning)”, Prace Naukowe Politechniki Warszawskiej. Geodezja, 2018, no. 57 (in Polish).

[16] X.F. Han, J.S. Jin, M.J. Wang, W. Jiang, L. Gao, L. Xiao, “A review of algorithms for filtering the 3D point cloud”, Signal Processing: Image Communication, 2017, vol. 57, pp. 103-112, DOI: 10.1016/j.image.2017.05.009.

[17] D. Lague, N. Brodu, J. Leroux, “Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (NZ)”, ISPRS Journal of Photogrammetry and Remote Sensing, 2013, vol. 82, pp. 10-26, DOI: 10.1016/j.isprsjprs.2013.04.009.

[18] K. Tan, J. Chen, W. Qian, W. Zhang, F. Shen, X. Cheng, “Intensity Data Correction for Long-Range Terrestrial Laser Scanners: A Case Study of Target Differentiation in an Intertidal Zone”, Remote Sensing, 2019, vol. 11, DOI: 10.3390/rs11030331.

[19] D. Bolkas, “Terrestrial laser scanner intensity correction for the incidence angle effect on surfaces with different colours and sheens”, International Journal of Remote Sensing, 2019, vol. 40, no. 18, pp. 7169-7189, DOI: 10.1080/01431161.2019.1601283.

[20] J. Zaczek-Peplinska, K. Osińska-Skotak, “Concrete surface evaluation based on the reflected TLS laser beam’s intensity image classification”, Studia Geotechnica et Mechanica, 2018, vol. 40, no. 1, pp. 56-64, DOI: 10.2478/sgem-2018-0008.

Downloads

Published

2022-09-30

How to Cite

Zaczek-Peplinska, Janina, et al. “Selected Aspects of Data Harmonization from Terrestrial Laser Scanning”. Archives of Civil Engineering, vol. 68, no. 3, Sept. 2022, pp. 429-45, doi:10.24425/ace.2022.141895.

Issue

Section

Articles

Similar Articles

<< < 4 5 6 7 8 9 10 11 12 13 > >> 

You may also start an advanced similarity search for this article.