使用多视角无人机集群对烟羽扩散进行三维表征
3D Characterization of Smoke Plume Dispersion Using Multi-View Drone Swarm
AI总结：
提出由1架管理机与4架工作机组成的多视角无人机集群，结合同步环绕成像和NeRF三维重建，以约1 s分辨率表征烟羽扩散动态。
摘要：
本研究提出了一种先进的多视角无人机集群成像系统，用于对烟羽扩散动力学进行三维表征。该系统由一架管理无人机和四架工作无人机组成，每架均配备高分辨率相机和精密GPS模块。管理无人机利用图像反馈自主探测烟羽并定位至其上方，随后指挥工作无人机以同步圆形飞行模式环绕该区域飞行，捕捉多角度图像。首先估计这些图像的相机位姿，然后将图像分批分组，并使用Neural Radiance Fields（NeRF，神经辐射场）进行处理，以生成随时间变化的烟羽动力学高分辨率三维重建。现场测试表明，该系统能够以约1 s的时间分辨率捕捉关键烟羽特征，包括体积动力学、风力驱动的方向偏移以及抬升行为。该系统生成的三维重建提供了独特的现场数据，可用于增强烟羽扩散和火势蔓延的预测模型。从更广泛的意义上说，该无人机集群系统为高分辨率测量野火、火山喷发、计划烧除和工业过程中的污染物排放与传输提供了一个多功能平台，最终支持更有效的火灾控制决策并降低野火风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
