3D coverage path planning for UAVs is a crucial problem in diverse practical applications. However, existing methods have shown unsatisfactory system simplicity, computation efficiency, and path quality in large and complex scenes. To address these challenges, we propose FC-Planner, a skeleton-guided planning framework that can achieve fast aerial coverage of complex 3D scenes without pre-processing. We decompose the scene into several simple subspaces by a skeleton-based space decomposition (SSD). Additionally, the skeleton guides us to effortlessly determine free space. We utilize the skeleton to efficiently generate a minimal set of specialized and informative viewpoints for complete coverage. Based on SSD, a hierarchical planner effectively divides the large planning problem into independent sub-problems, enabling parallel planning for each subspace. The carefully designed global and local planning strategies are then incorporated to guarantee both high quality and efficiency in path generation. We conduct extensive benchmark and real-world tests, where FC-Planner computes over 10 times faster compared to state-of-the-art methods with shorter path and more complete coverage. The source code will be made publicly available to benefit the community. Project page: https://hkust-aerial-robotics.github.io/FC-Planner.

翻译：无人机三维覆盖路径规划是众多实际应用中的关键问题。然而，现有方法在大规模复杂场景中表现出系统简洁性、计算效率和路径质量欠佳的问题。针对这些挑战，我们提出FC-Planner——一种无需预处理的骨架引导式规划框架，可实现复杂三维场景的快速空中覆盖。通过基于骨架的空间分解（SSD），我们将场景分解为多个简单子空间。同时，骨架引导我们轻松确定自由空间。我们利用骨架高效生成最小规模的专门化信息视点集以实现完整覆盖。基于SSD，分层规划器将大型规划问题有效分解为独立子问题，从而支持各子空间的并行规划。精心设计的全局与局部规划策略相结合，保证了路径生成的高质量与高效率。通过广泛的基准测试与真实场景实验，FC-Planner相比现有最优方法计算速度提升超10倍，同时路径更短、覆盖更完整。源代码将公开以惠及社区。项目页面：https://hkust-aerial-robotics.github.io/FC-Planner。