  As robotics technology advances, dense point cloud maps are increasingly in
demand. However, dense reconstruction using a single unmanned aerial vehicle
(UAV) suffers from limitations in flight speed and battery power, resulting in
slow reconstruction and low coverage. Cluster UAV systems offer greater
flexibility and wider coverage for map building. Existing methods of cluster
UAVs face challenges with accurate relative positioning, scale drift, and
high-speed dense point cloud map generation. To address these issues, we
propose a cluster framework for large-scale dense reconstruction and real-time
collaborative localization. The front-end of the framework is an improved
visual odometry which can effectively handle large-scale scenes. Collaborative
localization between UAVs is enabled through a two-stage joint optimization
algorithm and a relative pose optimization algorithm, effectively achieving
accurate relative positioning of UAVs and mitigating scale drift. Estimated
poses are used to achieve real-time dense reconstruction and fusion of point
cloud maps. To evaluate the performance of our proposed method, we conduct
qualitative and quantitative experiments on real-world data. The results
demonstrate that our framework can effectively suppress scale drift and
generate large-scale dense point cloud maps in real-time, with the
reconstruction speed increasing as more UAVs are added to the system.
