# A New Clustering-based View Planning Method for Building Inspection with Drone

arXiv: https://arxiv.org/abs/2408.01435 · HTML: https://mlsanity.com/paper/2408.01435/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Online Next-Best-View Planner for 3D-Exploration and Inspection With a Mobile Manipulator Robot](https://mlsanity.com/paper/2203.10113/) (2022)
- [A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction](https://mlsanity.com/paper/2205.03716/) (2022)
- [Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization](https://mlsanity.com/paper/2007.02084/) (2020)

## Built on by (most cited first, 1 of 1)

- [Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions](https://mlsanity.com/paper/2607.10649/) (2026)

## Abstract (arXiv)

With the rapid development of drone technology, the application of drones equipped with visual sensors for building inspection and surveillance has attracted much attention. View planning aims to find a set of near-optimal viewpoints for vision-related tasks to achieve the vision coverage goal. This paper proposes a new clustering-based two-step computational method using spectral clustering, local potential field method, and hyper-heuristic algorithm to find near-optimal views to cover the target building surface. In the first step, the proposed method generates candidate viewpoints based on spectral clustering and corrects the positions of candidate viewpoints based on our newly proposed local potential field method. In the second step, the optimization problem is converted into a Set Covering Problem (SCP), and the optimal viewpoint subset is solved using our proposed hyper-heuristic algorithm. Experimental results show that the proposed method is able to obtain better solutions with fewer viewpoints and higher coverage.