@inproceedings{096bb08f223a4b72a1138475e539ace0,
title = "Anemoi: A Low-cost Sensorless Indoor Drone System for Automatic Mapping of 3D Airflow Fields",
abstract = "Mapping 3D airflow fields is important for many HVAC, industrial, medical, and home applications. However, current approaches are expensive and time-consuming. We present Anemoi, a sub-\$100 drone-based system for autonomously mapping 3D airflow fields in indoor environments. Anemoi leverages the effects of airflow on motor control signals to estimate the magnitude and direction of wind at any given point in space. We introduce an exploration algorithm for selecting optimal waypoints that minimize overall airflow estimation uncertainty. We demonstrate through microbenchmarks and real deployments that Anemoi is able to estimate wind speed and direction with errors up to 0.41 m/s and 25.1° lower than the existing state of the art and map 3D airflow fields with an average RMS error of 0.73 m/s.",
keywords = "drone, environmental sensing, micro aerial vehicles, mobile sensing, path planning, sensor, sensorless",
author = "Stephen Xia and Minghui Zhao and Charuvahan Adhivarahan and Kaiyuan Hou and Yuyang Chen and Jingping Nie and Eugene Wu and Karthik Dantu and Xiaofan Jiang",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 29th Annual International Conference on Mobile Computing and Networking, MobiCom 2023 ; Conference date: 02-10-2023 Through 06-10-2023",
year = "2023",
doi = "10.1145/3570361.3613292",
language = "English",
series = "Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM",
publisher = "Association for Computing Machinery",
pages = "1166--1181",
booktitle = "Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, ACM MobiCom 2023",
address = "United States",
}