Quality Assessment of UAV Data using Multiple RTK Reference Stations

UAV (Unmanned Aerial Vehicle) mapping with GNSS-assisted photogrammetry is highly efficient in surveying small or medium-sized regions. On the other hand, the mapping quality is not intuitively predictable, especially over undulating terrain, where the quality of the real-time kinematic (RTK) positioning fluctuates. We offer a technique for predicting mapping quality based on post-flight data such as flight data, reference station, a digital surface model (DSM), and embedded sensor characteristics. We propose the concept of global precision in the context of minimum and efficient ground control point deployment in a complex terrain after outlining the critical considerations. Finally, we use scientifically rigorous testing to evaluate the proposed methodology against various experiments conducted under different mapping conditions. Photogrammetric blocks flown by drones with onboard receivers capable of RTK (real-time kinematic) positioning, and camera stations at exposure time can be determined with cmlevel accuracy and used to georeferenced the block and control its deformations. The repeatability of DSM production from many blocks acquired with an RTK-enabled drone was investigated in this article, where differential corrections were transmitted from different reference stations. Testing has been used to conduct two separate flights for each RTK mode flight, all of which followed the same flight plan. Bundle Block Adjustment (BBA) was used for photogrammetric image processing to generate DSM using Pix4DMapper software. The results reveal that Images georeferenced with SP-60 GNSS reference station in RTK mode produce RMS Error 0.024 m, which is less than the DJI Phantom D-RTK RMSE 0.031 m.