AIGC-Enhanced UAV-Based 3D Mapping and Trajectory Planning for Rapid Disaster Response

摘要

Three-dimensional (3D) mapping is vital in modern remote sensing. Satellites provide map data but are limited by cloud cover, especially during natural disasters (e.g., earthquakes, tsunamis), where rapid response is crucial and damaged infrastructure often renders digital maps unusable. Although Unmanned Aerial Vehicles (UAVs) present a viable alternative, the generation of precise 3D maps using monocular camera systems remains technically challenging. This work introduces an innovative approach for fast 3D mapping with intelligent trajectory planning. The method employs 2D Gaussian Splatting (2DGS) with block-based parallel optimization, integrating a monocular depth prior, depth filter, and a novel dense gradient strategy to reconstruct 3D maps from 2D images. To address operational reliability, we implement a multi-agent planning system which integrates artificial intelligence generated content (AIGC) models as agents. Each UAV's trajectory is managed by the agents to optimize paths dynamically. Experiments demonstrate the method's superiority in speed and effectiveness, offering a robust solution for disaster response and reconstruction.