用无人机群实现烟雾扩散的高精度三维动态捕捉
3D Characterization of Smoke Plume Dispersion Using Multi-View Drone Swarm
- 四架无人机环绕飞行，结合视觉反馈与NeRF重建3D烟流
- 每秒可捕捉烟雾体积、方向变化和抬升行为等关键特征
- 适合火灾监测、火山喷发和工业排放等场景的高精度测量
本研究提出一种基于多视角无人机群的烟雾扩散三维表征系统。系统由一架管控无人机和四架作业无人机组成，均配备高分辨率相机和精准GPS模块。管控机通过图像反馈自主定位至烟流上方，指挥作业机以同步环形航线环绕拍摄多角度图像。首先估计相机位姿，再将图像分批输入神经辐射场（NeRF）生成随时间演变的高分辨率3D烟流重构。实地测试表明，该系统可在约1秒的时间分辨率下捕捉烟雾体积动态、风驱动方向偏移及抬升行为。生成的3D重构提供了独特的现场数据，有助于提升烟雾扩散与火势蔓延预测模型。该无人机群系统为野火、火山喷发、计划性焚烧及工业过程中的污染物排放与传输提供了高分辨率测量平台，最终支持更有效的火灾防控决策与风险减缓。
原文摘要 · Abstract (English)
This study presents an advanced multi-view drone swarm imaging system for the three-dimensional characterization of smoke plume dispersion dynamics. The system comprises a manager drone and four worker drones, each equipped with high-resolution cameras and precise GPS modules. The manager drone uses image feedback to autonomously detect and position itself above the plume, then commands the worker drones to orbit the area in a synchronized circular flight pattern, capturing multi-angle images. The camera poses of these images are first estimated, then the images are grouped in batches and processed using Neural Radiance Fields (NeRF) to generate high-resolution 3D reconstructions of plume dynamics over time. Field tests demonstrated the ability of the system to capture critical plume characteristics including volume dynamics, wind-driven directional shifts, and lofting behavior at a temporal resolution of about 1 s. The 3D reconstructions generated by this system provide unique field data for enhancing the predictive models of smoke plume dispersion and fire spread. Broadly, the drone swarm system offers a versatile platform for high resolution measurements of pollutant emissions and transport in wildfires, volcanic eruptions, prescribed burns, and industrial processes, ultimately supporting more effective fire control decisions and mitigating wildfire risks.
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