Abstract
Unmanned aerial vehicle (UAV) systems have become crucial for gathering information for observation, surveillance, mapping, and 3D modeling tasks. The use of UAVs in close and mid-range sectors has shown potential for cost-effective alternatives to traditional aerial photogrammetry conducted by humans. The research aims to optimize photogrammetric processing for drone-based 3D mapping by examining strategies and applications to increase accuracy and efficiency. The study highlights the significance of image overlap and high-quality camera equipment to obtain reliable outcomes for stereo-photogrammetry and depth measurements. Drones and specialized software like 3DF Zephyr and Pix4D were used to create a 3D map. The software uses automatic structure from motion techniques, including feature extraction, image matching, and bundle block adjustment, to produce a dense point cloud that forms the basis for the 3D model. A DGPS system was implemented to enhance spatial accuracy. The map initially showed inadequate accuracy, with an error rate exceeding 80cm. However, with the use of a DGPS system, the error was reduced to less than 3cm. This study provides suggestions and insights for improving photogrammetric processing for drone-based 3D mapping.
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