Qing Dai

Luoyang Institute of Science and Technology

Papers

Total Citations

H-Index

About

Qing Dai is a leading researcher in precise GNSS positioning and its integration with robotic state estimation. Their work bridges the gap between high-accuracy satellite navigation and modern factor graph optimization techniques, a cornerstone of SLAM (Simultaneous Localization and Mapping) in robotics. Dai’s most-cited paper, "PPP ambiguity resolution based on factor graph optimization" (2024, 44 citations), introduces a novel framework that applies factor graph optimization—traditionally used for camera, LiDAR, and inertial navigation systems—to resolve integer ambiguities in Precise Point Positioning (PPP). This contribution enables centimeter-level GNSS accuracy within a unified estimation framework, overcoming a key limitation in deploying high-precision GNSS for autonomous systems. By adapting SLAM-inspired algorithms to satellite-based positioning, Dai has opened new pathways for robust, real-time navigation in challenging environments. Their work is particularly impactful for researchers developing multi-sensor fusion systems, demonstrating how cross-domain optimization techniques can enhance GNSS performance. With growing citations and a focus on practical integration, Qing Dai is establishing themselves as a pivotal figure at the intersection of satellite geodesy and robotic perception.