Unmanned Aerial Vehicle (UAV) is used in a variety of fields because there are no people on board. In particular, technologies for 3D modeling using UAV are being researched and developed. In 3D modeling using such an UAV, in order to save time and cost and improve the quality of output, a flight plan must be established in advance with the characteristics of the object to be model. However, it is difficult to find a simulator that can check the flight path and the product in advance with the characteristics of object. Therefore, in this study, a pre-flight simulator was developed to improve the efficiency of 3D modeling by applying point cloud data and LOD. In addition, to verify the performance of the simulator, the estimated product of the simulator and the actual product were compared.

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In unmanned aerial vehicle (UAV) photogrammetry, the qualities of three-dimensional (3D) models, including ground sample distance (GSD) and shaded areas, are strongly affected by flight planning. However, during flight planning, the quality of the output cannot be estimated, as it depends on the experience of the operator. Therefore, to reduce the time and cost incurred by repetitive work required to obtain satisfactory quality, a simulator, which can automatically generate a route, acquire images through simulation, and analyze the shaded areas without real flight, has been required. While some simulators have been developed, there are some limitations. Furthermore, evaluating the performance of the simulator is difficult owing to the lack of a validation method. Therefore, to overcome the limitations, target functions, which can plan flights and can detect shaded areas, were set, developed, and validated in this study. As a result, a simulator successfully planned a flight and detected shaded areas. In this way, the simulator was validated to determine the applicability of its performance. Furthermore, the outputs of this study can be applied to not only UAV photogrammetry simulators but also other 3D modeling simulators.

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