Zhengyang Xiao

Papers

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About

Zhengyang Xiao is pioneering the fusion of high-precision GNSS positioning with robotic state estimation, bridging a critical gap between satellite navigation and autonomous systems. His core research focuses on precise point positioning (PPP), factor graph optimization, and multi-sensor fusion for simultaneous localization and mapping (SLAM). Xiao’s major contribution lies in reimagining PPP—traditionally reliant on Kalman filtering—through the lens of factor graph optimization, a technique proven in robotic SLAM but rarely applied to GNSS. His 2024 paper on PPP based on factor graph optimization (52 citations) demonstrated that this approach reduces linearization errors and improves robustness, while his follow-up work on PPP ambiguity resolution (44 citations) extended the method to integer ambiguity fixing, a key step for centimeter-level accuracy. Most recently, his 2025 paper on loosely coupled PPP/Inertial/LiDAR SLAM (1 citation) integrates GNSS with inertial and LiDAR sensors in a unified graph optimization framework, addressing the challenge of maintaining continuous positioning in signal-degraded environments. With over 97 total citations in just two years, Xiao’s work is rapidly shaping how autonomous vehicles and mobile robots achieve reliable, high-precision localization in complex real-world conditions.