Chen Siyuan, Zeng Xiangding, Laefer Debra F, Truong-Hong Linh, Mangina Eleni
School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414015, China.
School of Civil Engineering, University College Dublin, D04C1P1 Dublin, Ireland.
Sensors (Basel). 2023 Aug 14;23(16):7159. doi: 10.3390/s23167159.
Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.
无人机图像可用于生成三维（3D）点云模型。然而，最终的数据质量会受到飞行高度、相机角度、重叠率和数据处理策略的影响。通常，既要收集全景图像，也要收集冗余的近景图像，这会显著增加数据收集和处理时间。为了研究输入资源与输出质量之间的关系，提出了一套七个指标，包括总点数、平均点密度、均匀性、产率、覆盖率、几何精度和时间效率。当在实地应用于一个全尺寸结构时，无人机高度和相机角度对数据密度和均匀性的影响最大。成功进行三维重建需要66%的重叠率。与增加重叠率相比，采用多条飞行路径能更好地提高局部几何精度。由于在运动恢复结构过程中，点群之间形成了半不规则网格间隙，覆盖率在77%时达到最高。对于每个数据收集目标，没有一组飞行参数是最优的。因此，了解飞行路径参数的影响对于优化无人机数据收集至关重要。
