Wind-aware UAV photogrammetry planning: minimising motion blur for effective terrain surveying

In recent years, the use of Unmanned Aerial Vehicles (UAVs) for remote sensing and aerial photogrammetry has surged, owing to their affordability and ability to capture high-resolution data in hard-to-reach areas. However, the effectiveness of these platforms can be limited by environmental factors such as turbulence and wind gusts, which may destabilize the aircraft and compromise the proper exposure of images. To address this issue, this work presents a terrain survey planner that considers the environmental conditions derived from a meteorological forecast. The system employs Computational Fluid Dynamics (CFD) simulations to generate high-resolution wind predictions for a given study area. This data is integrated into a state-of-the-art UAV simulator to estimate aircraft behaviour at various locations. Additionally, it incorporates an empirically calibrated camera model to predict sensor performance based on solar radiation estimates. With this information, a multi-objective optimization is performed, computing the optimal path and camera settings to mitigate the impact of wind on photogrammetry. Results highlight the significant impact of wind and poor lighting on motion blur, emphasizing the need to carefully plan not only the inspection path but also the time and date for correct image exposure.