Adaptive Sampling-Based Next Best View for UAVs in Large-Scale Mapping
Recent developments in 3D scene reconstruction and autonomous inspection have significantly benefited from Next-Best-View (NBV) planning. NBV planning is one of the main ways for viewpoint optimization to obtain high-quality 3D data efficiently. NBV planning aims at minimizing data redundancy and operating costs while maximizing model quality. Adaptive Sampling-Based NBV for UAVs in Large-Scale Surface Mapping focuses on optimizing UAV trajectories for efficiently mapping surfaces like terrains, solar farms, or roofs. By integrating voxel-based adaptive sampling, the approach dynamically prioritizes high-information areas while minimizing redundancy. This method is expected to ensure accurate surface reconstruction, reduce computational load, and improve flight efficiency.
The objective is to develop an adaptive sampling-based NBV framework for UAVs to optimize large-scale mapping. The methodology involves:
- Integrating VSLAM for real-time UAV localization and mapping.
- Designing a voxel-based representation to dynamically evaluate surface coverage.
- Implementing adaptive sampling algorithms to identify high-information viewpoints.
- Planning efficient UAV trajectories while minimizing redundancy.
- Validating performance through simulation platforms like Blender, AirSim, Isaac SIM, or another suitable tool.
Take Your Best Shot: Sampling-Based Next-Best-View Planning for Autonomous Photography & Inspection
Shijie Gao, Lauren Bramblett, Nicola Bezzo