Yuhan Xie

University of Hong Kong, Xidian University

Papers

Total Citations

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About

Yuhan Xie is a pioneering researcher at the intersection of robust state estimation and intelligent autonomous systems. Her primary contributions lie in developing resilient visual-inertial odometry (VIO) for challenging environments, particularly through event-based sensing. Her landmark work, "PL-EVIO: Robust Monocular Event-Based Visual Inertial Odometry With Point and Line Features" (2023, 54 citations), addresses the critical unsolved problem of achieving onboard pose feedback control during aggressive motion. By fusing event, image, and inertial measurements, Xie’s method enables real-time, robust state estimation where conventional cameras fail—such as in high-speed or low-light scenarios. This work has become a foundational reference for researchers tackling extreme-condition autonomy, with its 54 citations reflecting its immediate impact. Most recently, Xie has expanded her focus to the reliability and security of autonomous systems, as seen in her 2025 paper "Reliability and security: from swarm robots to AI agents." This forward-looking work bridges physical robotics and AI safety, positioning her as a key voice in ensuring trustworthy autonomy across platforms. For students and researchers, Xie’s trajectory offers a compelling model: starting from a concrete, high-impact engineering solution in VIO and scaling to address broader systemic challenges in AI and robotics.