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

TL;DR

The paper tackles the lack of high-fidelity ground truth data for smoke plume dispersion by introducing a five-drone swarm that performs multi-view imaging and NeRF-based 3D reconstruction of plume dynamics. The authors integrate a data acquisition module with a processing pipeline (COLMAP for camera poses, NeRF for dynamic 3D, and YOLOv8 plus Naive Bayes for background removal) to extract plume metrics such as plume volume, angle of deviation, and mean height. Field validation shows an average reconstruction error of 1.18% on a static reference object and 77 time segments of plume dynamics at 1.75 s temporal resolution, enabling detailed, time-resolved analyses. The approach offers a cost-effective, scalable platform for high-resolution plume measurements in wildfires, prescribed burns, volcanic events, and industrial processes, with potential to improve predictive fire models and hazard mitigation.

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.

Figures (8)

• Figure 1: Illustration of the drone swarm system that uses multi-view imaging for 3D smoke plume characterization.
• Figure 2: Drone hardware configuration showing the quadcopter with camera mounted on a 3-axis gimbal and GPS with RTK (left), and the NVIDIA Jetson Orin Nano (right).
• Figure 3: Flowcharts detailing the steps involved in (a) stabilizing the manager drone, (b) collecting data with the worker drone swarm, and (c) processing captured data for 3D plume reconstruction and characterization.
• Figure 4: 3D Reconstruction of truck to validate accuracy (a) Field setup and (b) the corresponding 3D reconstructed point cloud of a 2011 Ford F-350 pickup truck, generated using our multi-view drone swarm imaging system to evaluate its 3D reconstruction accuracy.
• Figure 5: Field deployment setup for data collection, featuring a manager drone positioned above the plume for centralized control and four worker drones encircling the plume to capture multi-angle images for 3D reconstruction.