Xiaoxue Jia

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About

Xiaoxue Jia’s research lies at the intersection of precise positioning, sensor fusion, and intelligent navigation, with a focus on advancing indoor and outdoor localization for autonomous systems. Her major contributions include pioneering the application of factor graph optimization (FGO) to precise point positioning (PPP), demonstrating that FGO can outperform traditional Kalman filtering methods by reducing linearization errors and improving estimation consistency—a breakthrough with 52 citations since 2024. She also developed a novel deep learning framework that fuses 5G channel state information, geomagnetism, and visual-inertial odometry (VIO) for robust indoor localization, addressing VIO’s vulnerability to lighting and cumulative drift (21 citations). This work bridges the gap between communication signals and classical navigation, offering resilient positioning for long-term mobile robot tasks. Jia’s research has been recognized for its practical impact on SLAM and autonomous navigation, with her FGO-based PPP approach gaining rapid traction in the robotics and geodesy communities. Her work exemplifies how cross-domain innovation—combining optimization theory, deep learning, and multi-sensor integration—can solve real-world localization challenges, making her a rising figure in positioning and navigation research.