基于飞行高度变化评估无人机衍生产品的空间精度

S. Akay, Orkan Özcan, F. B. Sanli, B. Bayram, T. Gorum
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引用次数: 2

摘要

无人驾驶飞行器(uav),能够携带多种有效载荷,并与地面控制站自动或手动操作。目前,无人机可以利用现有的传感器系统制定摄影测量飞行计划并获取摄影测量数据。与其他测量方法相比,自动数据采集过程可以在短时间内提供成本更低、空间和时间分辨率更高的图像。因此,产生了正形图,密集点云和数字表面模型(dsm),这些无人机衍生的数据用于各种学科,如建筑,地理信息学,地球科学等。在本研究中,使用不同高度的无人机实现相同的飞行计划,并使用同一集成数码相机获得所有航拍图像。通过对不同高度的图像进行处理,得到了正形图、dsm和点云。在本研究中,旨在比较一个小型地球静止物体的长度、面积和体积差异。地面控制点(GCPs),这些数据是由RTK-GPS(实时运动)收集的,与飞行结合到数据生产过程中,以获得高度精确的产品。最后,将产生的数据与地面测量数据进行了互相关。结果表明,物体的尺寸随飞行高度的变化而变化,但由于原始点云数据的不确定性,物体的体积变化会发生变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ASSESSING THE SPATIAL ACCURACY OF UAV-DERIVED PRODUCTS BASED ON VARIATION OF FLIGHT ALTITUDES
Unmanned Aerial Vehicles (UAVs), which can carry a variety of payloads, and be operated automatically or manually with ground control stations. Nowadays, UAVs can make photogrammetric flight plans and obtain photogrammetric data with existing sensor systems. Automatic data acquisition processes provide lower cost, and high spatial and temporal resolution images in a short period of time compared to other measurement methods. As a result, orthomosaics, dense point clouds and digital surface models (DSMs) are produced and these UAV-derived data are used in various disciplines such as constructions, geomatics, earth sciences, etc. In this study, the same flight plans were realized with an UAV at different altitudes and all aerial images were obtained with the same integrated digital camera. As a result of the processing of images acquired from different altitudes, orthomosaics, DSMs and point cloud were produced. In this study, it is aimed to compare the length, areal and volumetric differences of a small geostationary object. Ground control points (GCPs), which were collected by RTK-GPS (Real-Time Kinematic) in conjunction with the flight integrated into data production process in order to highly accurate product. Ultimately, cross-correlation has been done with the produced data and the terrestrial measurement. Results show that the dimension of the object depend on the flight altitude as expected, however the volumetric changes vary due to the uncertainties in the raw point cloud data.
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