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.

翻译：摘要：随着机器人技术的进步，稠密点云地图的需求日益增长。然而，使用单架无人机的稠密重建受限于飞行速度和电池电量，导致重建速度慢且覆盖范围低。无人机集群系统为地图构建提供了更高的灵活性和更广的覆盖范围。现有集群无人机方法面临精确相对定位、尺度漂移以及高速生成稠密点云地图等挑战。为解决这些问题，本文提出了一种面向大规模稠密重建与实时协同定位的集群框架。该框架前端采用改进的视觉里程计，能有效处理大规模场景。通过两阶段联合优化算法和相对位姿优化算法实现无人机间的协同定位，有效实现无人机精确相对定位并缓解尺度漂移。利用估算位姿实现点云地图的实时稠密重建与融合。为评估所提方法性能，我们在真实数据上进行了定性和定量实验。结果表明，该框架能有效抑制尺度漂移并实时生成大规模稠密点云地图，且随着系统中无人机数量的增加，重建速度也随之提升。