Efficient UAV flight planning for LOD2 city model improvement

Geometric errors in LoD2 building models can be caused by the modeling algorithm but are often related to the quality of input data. One approach to tackling the modeling errors caused by the quality of input data is to collect additional data with a UAV and remodel the buildings. However, no flight planning approach exists specifically designed for efficient data recollection for model improvement. In this paper, we propose an innovative flight planning approach for this purpose. Contrary to the conventional method that recollects the data covering the entire building roof, our approach only collects the data over the erroneous region and uses it to improve the erroneous model part later. Our algorithm utilizes the existing LiDAR survey data to automatically detect model errors and design the camera networks by considering the roof geometry. We optimize the trajectory that connects the viewpoints with a genetic algorithm and develops an obstacle avoidance function with ray-casting to ensure a collision-free path. The proposed flight plan is implemented in a real-world scene. Our result shows an improved point cloud created through dense image matching with the collected UAV image data. The generated point cloud is successfully used for creating partial building models for improving the original models.