Research to establish 3D model of mine industrial site area from terrestrial laser scanning and Unmanned aerial vehicle data

  • Affiliations:

    1 Hanoi University of Mining and Geology, Hanoi, Vietnam
    2 Vietnam Academy of Science and Technology, Hanoi, Vietnam
    3 Hung Phat A Chau Company, Hanoi, Vietnam

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  • Received: 13th-Mar-2022
  • Revised: 29th-June-2022
  • Accepted: 1st-Aug-2022
  • Online: 31st-Oct-2022
Pages: 25 - 36
Views: 3151
Downloads: 2140
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Abstract:

In recent years, three-dimensional (3D) models are being built in many fields including mining. These products are often used to develop a database of smart mines which in terms can be used in the management of production in underground coal mines. Unmanned aerial vehicle (UAV) and terrestrial laser scanning (TLS) technologies are known as the two main technologies that quickly and accurately collect 3D point cloud (PC) data. This article presents the integration of a 3D point cloud produced from UAV photos and TLS to build a detailed 3D model for the ground plant at the level of +35 m in the Nui Beo underground coal mine. To collect data, a DJI Phantom 4 Advanced drone was used to take photos in three modes: a shot angle of 900, a 3D grid with a 450 angle, and a circular flight orbit with 450 and 600 shooting angles. A Faro Focus3D X130 laser scanner was used for scanning the mine shaft’s tower to fill the missing point cloud of the UAV. The PC established by both methods was evaluated for accuracy based on the control points measured by a Leica TS09 total station, which was merged by the Iterative Closest Point (ICP) algorithm. The integrated PC met the accuracy requirement of establishing a 3D model of the study area with the level of detail 3 in the CityGML standard.

How to Cite
Le, C.Van, Cao, C.Xuan, Tong, S.Si and Dinh, H.Van 2022. Research to establish 3D model of mine industrial site area from terrestrial laser scanning and Unmanned aerial vehicle data (in Vietnamese). Journal of Mining and Earth Sciences. 63, 5 (Oct, 2022), 25-36. DOI:https://doi.org/10.46326/JMES.2022.63(5).03.
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