多无人机协同快速重建复杂场景，用激光雷达探索+相机拍摄分工协作。
SOAR: Simultaneous Exploration and Photographing with Heterogeneous UAVs for Fast Autonomous Reconstruction
- 分角色协作：激光雷达无人机负责探索未知区域，相机无人机负责拍摄。
- 高效规划：通过优化路径算法，在10分钟内完成复杂环境的表面覆盖。
- 适合高精度重建需求者，如城市测绘、灾害评估等场景应用。
无人飞行器（UAV）在场景重建中备受关注。本文提出SOAR，一种专为快速自主重建复杂环境设计的激光雷达-视觉异构多无人机系统。系统包含配备大视场（FoV）激光雷达的探索型无人机，以及搭载摄像头的拍摄型无人机。为实现场景表面几何的快速获取，探索无人机采用基于表面前沿的探索策略，逐步识别未覆盖区域并生成增量视角。这些视角通过求解一致性的多重起点多旅行商问题（Consistent-MDMTSP）分配给拍摄无人机，以优化扫描效率并保证任务一致性。最后，拍摄无人机利用分配的视角规划最优覆盖路径以获取图像。我们在真实感仿真环境中进行了大量基准测试，验证了SOAR相较于经典及先进方法的优越性能。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) have gained significant popularity in scene reconstruction. This paper presents SOAR, a LiDAR-Visual heterogeneous multi-UAV system specifically designed for fast autonomous reconstruction of complex environments. Our system comprises a LiDAR-equipped explorer with a large field-of-view (FoV), alongside photographers equipped with cameras. To ensure rapid acquisition of the scene's surface geometry, we employ a surface frontier-based exploration strategy for the explorer. As the surface is progressively explored, we identify the uncovered areas and generate viewpoints incrementally. These viewpoints are then assigned to photographers through solving a Consistent Multiple Depot Multiple Traveling Salesman Problem (Consistent-MDMTSP), which optimizes scanning efficiency while ensuring task consistency. Finally, photographers utilize the assigned viewpoints to determine optimal coverage paths for acquiring images. We present extensive benchmarks in the realistic simulator, which validates the performance of SOAR compared with classical and state-of-the-art methods. For more details, please see our project page at https://sysu-star.github.io/SOAR}{sysu-star.github.io/SOAR.
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