MiNav: Autonomous Drone Navigation Indoors using Millimeter-Waves
Author(s)
Lam, Maisy
Herrera, Joshua
Afzal, Sayed Saad
Zhou, Kaichen
Adib, Fadel
Date Issued
September 3, 2025
Journal
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Publisher
ACM
Citation
Maisy Lam, Joshua Herrera, Sayed Saad Afzal, Kaichen Zhou, and Fadel Adib. 2025. MiNav: Autonomous Drone Navigation Indoors Using Millimeter-Waves. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 9, 3, Article 97 (September 2025), 32 pages.
Version
Final published version
Abstract
We present the design, implementation, and evaluation of MiNav, a system capable of accurate, efficient and fully autonomous
drone navigation in challenging indoor environments, including those where vision-based systems fail. MiNav builds on
recent literature in millimeter-wave (mmWave) backscatter localization and makes the leap to full end-to-end autonomous
mmWave-based navigation.
MiNav leverages a mmWave radar mounted on a drone and one or more mmWave backscatter tags deployed in the environment.
To enable autonomous navigation, our design introduces key innovations. First, MiNav derives a novel Joint DOP-SNR
formulation to probabilistically model uncertainty in localization, and uses this uncertainty to generate an RF-Navigation Map
that maximizes the accuracy and reliability of mmWave backscatter localization throughout an environment. It then applies a
RF-aware Autonomous Path Planning technique that jointly optimizes for navigation efficiency and localization performance.
We built an end-to-end real-time implementation of MiNav consisting of a custom built drone and mmWave backscatter
tags. We tested it in practical indoor environments. We run over 165 successful autonomous missions across different tag
deployments and demonstrate a median 3D navigation error of 9.1 cm. Our results also show that in comparison to baseline
implementations that rely on more classical uncertainty metrics, MiNav achieves a 20% increase in navigation reliability and
nearly 3x improvement in self-tracking in millimeter-wave backscatter localization. Finally, we demonstrate first of its kind
capabilities, such as fully autonomous, end-to-end mmWave-based drone navigation and path planning in featureless and dark
environments. Demo video: http://y2u.be/EpnWibRcxBI
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3749464
