MiNav: Autonomous Drone Navigation Indoors using Millimeter-Waves

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