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MiNav: Autonomous Drone Navigation Indoors Using Millimeter-Waves

Maisy Lam,Joshua Herrera,Sayed Saad Afzal,Kaichen Zhou,Fadel M. Adib

Published 2025 in Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

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

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

  • Publication date

    2025-09-03

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1145/3749464
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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