3D Characterization of Smoke Plume Dispersion Using Multi-View Drone Swarm

TL;DR

This paper introduces a multi-view drone swarm system that captures high-resolution 3D data of smoke plume dispersion dynamics, providing valuable insights for fire management and environmental monitoring.

Contribution

It presents a novel autonomous drone swarm system using NeRF for 3D reconstruction of smoke plumes, enabling detailed temporal and spatial analysis.

Findings

Successfully captured 3D plume dynamics at 1-second intervals

Demonstrated accurate volume and directional shift measurements

Enhanced predictive modeling of smoke dispersion

Abstract

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.