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About

Yingxun Wang is a leading researcher in robotics and autonomous navigation, specializing in multi-robot systems, simultaneous localization and mapping (SLAM), and bio-inspired sensor fusion. Her work addresses critical challenges in persistent coverage, real-time navigation, and robust perception for unmanned aerial vehicles (UAVs) and mobile robots. Wang’s most-cited paper (34 citations) introduces a novel cooperative path planning framework for multi-robot persistent coverage, uniquely integrating coverage period constraints with obstacle avoidance—a significant advance over prior works that overlooked temporal requirements. She also developed a real-time fast incremental SLAM method (10 citations) tailored for micro aerial vehicles, overcoming computational bottlenecks in high-speed indoor navigation. More recently, Wang pioneered REVIO (9 citations), a range- and event-based visual-inertial odometry system that leverages bio-inspired event cameras to eliminate motion drift and blur in challenging lighting and fast-motion scenarios. Her earlier work on stereo vision-based obstacle avoidance (7 citations) using Pioneer3-AT robots laid foundational techniques for rapid obstacle detection and segmentation. With a career spanning over a decade, Wang’s contributions have advanced the reliability and efficiency of autonomous systems, earning her recognition as an innovator at the intersection of robotics, computer vision, and sensor technology.