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Tytuł artykułu

Generating 3D point-cloud based on combining adjacent multi-station scanning data in 2D laser scanning: A case study of Hokuyo UTM 30lxk

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Using a lower-cost laser scanner for generating accuracy in 3D point-cloud has been a concern because of economic issues; therefore, this study aims to create a 3D point cloud of a target object using a low-cost 2D laser scanner, Hokuyo UTM 30LX. The experiment was carried out in November 2019 with 16 single scans from 8 different view points to capture the surface information of a structure object with many intricate details. The device was attached to a rail, and it could move with stable velocity thanks to an adjustable speed motor. The corresponding 16 point-clouds were generated by using the R language. Then, they were combined one by one to make a completed 3D point cloud in the united coordinate system. The resulted point cloud consisted of 1.4 million points with high accuracy (RMSE = ±1:5 cm) is suitable for visualizing and assessing the target object thanks to high dense point-cloud data. Both small details and characters on the object surface can be recognized directly from the point cloud. This result confirms the ability of generated the accuracy point cloud from the low-cost 2D laser scanner Hokuyo UTM 30LX for 3D visualizing or indirectly evaluating the current situation of the target object.
Rocznik
Strony
451--467
Opis fizyczny
Bibliogr. 28 poz., il., tab.
Twórcy
  • Department of Geomatics Engineering, Faculty of Civil Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam
  • Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
  • Department of Bridge and Highway Engineering, Faculty of Civil Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam
  • Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
Bibliografia
  • [1] A. Berenyi, T. Lovasand, and A. Barsi, “Terrestrial laser scanning - civil engineering applications, International Archives of Photogrammetry”. Remote Sensing and Spatial Information Sciences, vol. 38, Part 5, Commission V Symposium, Newcastle upon Tyne, UK. 2010.
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  • [4] Y. Yin and Y. Antonio, “Application of 3D laser scanning technology for image data processing in the protection of ancient building sites through deep learning”. Image and Vision Computing, vol. 102, 2020, DOI: 10.1016/j.imavis.2020.103969.
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  • [9] F. Bosché, et al., “The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components”. Automation in Construction, vol. 49, part B, pp. 201-213, 2015, DOI: 10.1016/j.autcon.2014.05.014.
  • [10] D. Rebolj, et al., “Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring”. Automation in Construction, vol. 84, pp. 323-334, 2017, DOI: 10.1016/j.autcon.2017.09.021.
  • [11] G. Rocha, et al., “A Scan-to-BIM methodology applied to heritage buildings”. Heritage, vol. 3, no. 1, pp. 47-67, 2020, DOI: 10.3390/heritage3010004.
  • [12] M.E. Esfahani, et al., “Quantitative investigation on the accuracy and precision of Scan-to-BIM under different modelling scenarios”. Automation in Construction, vol. 126, 2021.
  • [13] Q. Wangand and M.K. Kim, “Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018”. Advanced Engineering Informatics, vol. 39, pp. 306-319, 2019.
  • [14] T.Wang and Z. Xiong, “Methods of As-is BIM reconstruction using point cloud data for existing buildings”. IOP Conference Series: Earth and Environmental Science, Bristol, vol. 676, no. 1, 2021.
  • [15] Y. He, et al., “Real-time 3D reconstruction of thin surface based on laser line scanner”. Sensors, vol. 20, no. 2, p. 534, 2020, DOI: 10.3390/s20020534.
  • [16] A.A. Al-Temeemyand S.A. Al-Saqal, “Laser-based structured light technique for 3D reconstruction using extreme laser stripes extraction method with global information extraction”. Optics and Laser Technology, vol. 138, 2021, DOI: 10.1016/j.optlastec.2020.106897.
  • [17] F. Javadnejad, et al., “Dense point cloud quality factor as proxy for accuracy assessment of image-based 3D reconstruction”. Journal of Surveying Engineering, vol. 147, no. 1, February 2021.
  • [18] J.C. White, et al., “Comparing ALS and image-based point cloud metrics and modelled forest inventory attributes in a complex coastal forest environment”. Forests, vol. 6, no. 10, 2015, DOI: 10.3390/f6103704.
  • [19] J.L.R. Jensen and A.J. Mathews, “Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem”. Remote Sens, vol. 8, no. 1, p. 50, 2016, DOI: 10.3390/rs8010050.
  • [20] F. Javadnejad, et al., “A photogrammetric approach to fusing natural colour and thermal infrared UAS imagery in 3D point cloud generation”. International Journal of Remote Sensing, vol. 41, no. 1, pp. 211-237, 2020.
  • [21] G. Karakas, et al., “Derivation of earthquake-induced landslide distribution using aerial photogrammetry: the January 24, 2020, Elazig (Turkey) earthquake”. Landslides, vol. 18, pp. 2193-2209, 2021, DOI: 10.1007/s10346-021-01660-2.
  • [22] N. Cenni, S. Fiaschi, and M. Fabris, “Integrated use of archival aerial photogrammetry, GNSS, and InSAR data for the monitoring of the Patigno landslide (Northern Apennines, Italy)”, Landslides, vol. 18, 2021, DOI: 10.1007/s10346-021-01635-3.
  • [23] A. Guarnieri, N. Milanand , and A. Vettore, “Monitoring of complex structure for structural control using Terrestrial Laser Scanning (TLS) and photogrammetry”. International Journal of Architectural Heritage, vol. 7, no. 1, pp. 54-67, 2013.
  • [24] I. Aicardi, et al., “Integration between TLS and UAV photogrammetry techniques for forestry applications”. iForest vol. 10, no. 1, pp. 41-47, 2016.
  • [25] F. Alidoost and H. Arefi, “Comparison of UAS-based photogrammetry software for 3D point cloud generation: a survey over a historical site”. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. IV-4/W4, the 4th International GeoAdvances Workshop, Safranbolu, Karabuk, Turkey, 14-15 October 2017.
  • [26] D. Moon, et al., “Comparison and utilization of point cloud generated from photogrammetry and laser scanning: 3D world model for smart heavy equipment planning”. Automation in Construction, vol. 98, pp. 322-331, 2019.
  • [27] G. Kermarrec, B. Kargolland, and H. Alkhatib, “Deformation analysis using B-spline surface with correlated terrestrial laser scanner observations - a bridge under load”. Remote Sens, vol. 12, no. 5, p. 829, 2020, DOI: 10.3390/rs12050829.
  • [28] K. Tan, et al., “Estimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner”. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 159, pp. 129-139, 2020, DOI: 10.1016/j.isprsjprs.2019.11.003.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-605dfd80-7076-4ca7-be7b-989f3750bc6d
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