Optimizing Spatial Accuracy for Photogrammetric Processing of Drone-based 3D Mapping

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.