EVALUATING THE POTENTIAL OF RTK-UAV FOR AUTOMATIC POINT CLOUD GENERATION IN 3D RAPID MAPPING
Keywords: Unmanned aerial system, Real time kinematic, Point clouds generation, Bundle adjustment, Digital surface model, Lever arm calibration, Sensor synchronization
Abstract. During disaster and emergency situations, 3D geospatial data can provide essential information for decision support systems. The utilization of geospatial data using digital surface models as a basic reference is mandatory to provide accurate quick emergency response in so called rapid mapping activities. The recipe between accuracy requirements and time restriction is considered critical in this situations. UAVs as alternative platforms for 3D point cloud acquisition offer potentials because of their flexibility and practicability combined with low cost implementations. Moreover, the high resolution data collected from UAV platforms have the capabilities to provide a quick overview of the disaster area. The target of this paper is to experiment and to evaluate a low-cost system for generation of point clouds using imagery collected from a low altitude small autonomous UAV equipped with customized single frequency RTK module. The customized multi-rotor platform is used in this study. Moreover, electronic hardware is used to simplify user interaction with the UAV as RTK-GPS/Camera synchronization, and beside the synchronization, lever arm calibration is done. The platform is equipped with a Sony NEX-5N, 16.1-megapixel camera as imaging sensor. The lens attached to camera is ZEISS optics, prime lens with F1.8 maximum aperture and 24 mm focal length to deliver outstanding images. All necessary calibrations are performed and flight is implemented over the area of interest at flight height of 120 m above the ground level resulted in 2.38 cm GSD. Earlier to image acquisition, 12 signalized GCPs and 20 check points were distributed in the study area and measured with dualfrequency GPS via RTK technique with horizontal accuracy of σ = 1.5 cm and vertical accuracy of σ = 2.3 cm. results of direct georeferencing are compared to these points and experimental results show that decimeter accuracy level for 3D points cloud with proposed system is achievable, that is suitable for 3D rapid mapping applications.