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About

Meixiang Quan is a leading researcher in the field of robotic perception and localization, with a primary focus on advancing simultaneous localization and mapping (SLAM) for ground robots. Her work centers on developing tightly coupled, multi-sensor fusion algorithms that integrate monocular vision with inertial sensors, wheel odometry, and MEMS gyroscopes to achieve robust, long-term navigation in challenging environments. Quan’s most impactful contribution is her 2019 paper on tightly coupled monocular visual-odometric SLAM, which has garnered 70 citations for its novel probabilistic approach that significantly improves accuracy and reliability for ground robots. She further advanced the field with a map-assisted EKF-based visual-inertial SLAM system (32 citations), enabling high-frame-rate motion tracking on standard CPUs. Her research also explores innovative parameterization techniques for lines on the ground in monocular visual SLAM, addressing specific challenges in structured environments. Additionally, Quan has contributed to improving feature tracking robustness through her work on an improved inertial-aided KLT tracker. Her achievements demonstrate a sustained commitment to solving real-world localization problems, making her work essential reading for researchers in robotics and autonomous navigation.