Xiule Fan
Papers
2
Total Citations
9
H-Index
2
About
Xiule Fan is a robotics researcher whose work focuses on perception and autonomous navigation for mobile systems. Her key research areas include stereo vision, self-supervised learning, and adaptive path planning for differential drive robots. In her most cited work, "Occlusion-Aware Self-Supervised Stereo Matching with Confidence Guided Raw Disparity Fusion" (2022, 7 citations), Fan addresses a critical challenge in commercial stereo cameras: the raw disparity maps from traditional algorithms often contain errors. She proposes a self-supervised framework that fuses these raw predictions with confidence-guided mechanisms, improving depth estimation accuracy without requiring ground-truth labels—a significant step toward robust perception in real-world robotics. Her second notable paper, "Adaptive Path Following for a Differential Drive Robot with EKF-based Localization" (2022, 2 citations), tackles the practical problem of uncertainty in robot environments. By integrating Extended Kalman Filter localization with adaptive path replanning, Fan enables robots to dynamically avoid unknown obstacles while maintaining precise trajectory tracking. These contributions demonstrate her commitment to bridging the gap between theoretical algorithms and deployable robotic systems, making her work valuable for students and engineers developing intelligent, autonomous platforms.
Research Focus
Key Achievements
Top Papers
- 1
- 2