Stephen Nogar

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About

Stephen Nogar is a leading researcher in autonomous aerial robotics, with a focus on enabling micro aerial vehicles (MAVs) to operate in GPS-denied and infrastructure-poor environments. His work bridges the gap between perception, control, and practical deployment, particularly for search and rescue and surveillance missions. Nogar’s most cited paper (23 citations) presents a system for the autonomous landing of a UAV on a moving ground vehicle without GPS, demonstrating a robust, minimalist approach that maximizes modern robotics tools. In another influential work (20 citations), he tackles aggressive visual perching on inclined surfaces, allowing quadrotors to conserve energy and extend mission endurance by staring from a fixed vantage point. His recent contribution (19 citations) introduces AZTR, a novel aerial video action recognition method that integrates auto zoom and temporal reasoning, enabling real-time, edge-deployable human activity analysis from UAVs. Nogar’s research is distinguished by its focus on practical, computationally efficient solutions that push the boundaries of what small aerial robots can achieve autonomously in challenging, real-world scenarios.