FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2023.
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Citing papers per year: 2025: 2; 2026: 4.
6 papers.
- 2025GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstructioncited by 15
- 2025ExploreGS: a vision-based low overhead framework for 3D scene reconstructioncited by 0
- 2026Hierarchical Dual-Change Collaborative Learning for UAV Scene Change Captioningcited by 0
- 2026A 3D Reconstruction Benchmark for Asset Inspectioncited by 0
- 2026Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directionscited by 0
- 2026AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstructioncited by 0
- 2026AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction8 Sep 2026
- 2026Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions12 Jul 2026
- 2026A 3D Reconstruction Benchmark for Asset Inspection18 Mar 2026
- 2026Hierarchical Dual-Change Collaborative Learning for UAV Scene Change Captioning13 Mar 2026
- 2025ExploreGS: a vision-based low overhead framework for 3D scene reconstruction14 May 2025
- 2025GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstruction29 Apr 2025