Peiyuan Zhou

PLA Information Engineering University, University of Arizona

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About

Peiyuan Zhou is pioneering the integration of high-precision Global Navigation Satellite System (GNSS) positioning with robotic state estimation, bridging a critical gap between autonomous navigation and satellite geodesy. His primary research focuses on precise point positioning (PPP) and factor graph optimization (FGO), a technique traditionally dominant in robotic SLAM systems. Zhou’s seminal 2024 paper, “PPP based on factor graph optimization” (52 citations), demonstrated that FGO can outperform conventional Kalman filtering for GNSS, reducing linearization errors and enabling smoother sensor fusion. He further advanced the field with “PPP ambiguity resolution based on factor graph optimization” (44 citations), which resolved integer ambiguities within the FGO framework—a breakthrough for achieving centimeter-level accuracy in dynamic environments. This work directly enables robust GNSS integration into camera/LiDAR/INS SLAM systems, a long-standing challenge in robotics. Earlier in his career, Zhou contributed to flexible manipulator design (2002), showcasing his versatility across control theory and mechanical engineering. With over 100 combined citations on his core GNSS-FGO publications, Zhou is shaping the future of autonomous navigation, where seamless GNSS-RTK performance meets the flexibility of graph-based optimization.