  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.
