OrthoSwarm: Orthoimagery Drone Swarms

Abstract

This paper addresses the urgent need for rapid synthesis of georeferenced orthoimages in post-disaster scenarios, where pre-disaster satellite maps cannot be directly reused due to significant urban changes. Drone swarms offer advantages of large scale, wide aerial view and rapid coverage, of disaster-stricken areas. However, synthesizing georeferenced orthoimages within limited time remains challenging without camera calibration, primarily due to inevitable inconsistencies in intrinsics and extrinsics across different cameras, as well as sensor errors. To tackle this issue, we propose OrthoSwarm, a parallelizable calibration-free system architecture that leverages drone swarms rectilinear path planning and pre-disaster satellite maps for efficient orthoimage synthesis. OrthoSwarm's performance is validated on a self-constructed benchmark dataset, generated by drone swarms in a digital twin city covering 3 natural disaster scenarios(debris, waterlogging, haze), with real-world validation using real single-drone aerial videos split into segments to simulate swarm acquisition. Experimental results from both simulated and real-captured data confirm the effectiveness of the proposed approach, enabling fast and visually consistent georeferenced orthoimage synthesis in stable post-disaster environments to support first responders promptly.