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Navigating in GPS-denied environments is a critical challenge for drone systems, particularly in urban areas with tall buildings, indoor spaces with complex layouts, and remote locations with weak GPS signals. Traditional approaches rely on cameras or Light Detection and Ranging (LiDAR) sensors. However, their performance can deteriorate or even fail in challenging lighting conditions or when there are airborne obscurants like dust and fog. Our solution is to propose a novel Millimeter Wave (mmWave) radar aided drone navigation system. This system offers robust and dependable perceptual information about the environment, particularly in scenarios with degraded visibility and harsh conditions. Notably, our proposed system enables agile navigation with high precision. Additionally, the integrated hardware is designed in a compact, cost-effective form factor, making it well-suited for drone systems.

Lead Researcher

Prof. Tao Gu

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