Chaoqun Yu

Papers

Total Citations

H-Index

About

Chaoqun Yu is a leading researcher at the intersection of Global Navigation Satellite Systems (GNSS) and robotic state estimation, with a primary focus on advancing precise positioning through factor graph optimization. His most cited work, "PPP Ambiguity Resolution based on Factor Graph Optimization" (2024, 44 citations), bridges a critical gap between the robotics SLAM community and high-precision GNSS. While factor graph optimization has revolutionized camera, LiDAR, and INS-based SLAM, its application to GNSS positioning remained limited—until Yu’s contribution. By introducing factor graph optimization to Precise Point Positioning (PPP) ambiguity resolution, he enables centimeter-level GNSS accuracy to be seamlessly integrated into robotic systems, unlocking new potential for autonomous navigation in challenging environments. This work has quickly garnered attention, reflecting its timely impact on both the GNSS and robotics fields. Yu’s research is pivotal for enabling robust, real-time state estimation in autonomous vehicles, drones, and mobile robots, where reliable GNSS positioning is essential. His achievements mark a significant step toward unifying high-precision GNSS with modern sensor fusion frameworks.