Papers
2
Total Citations
75
H-Index
2
About
Yulin Duan is a pioneer in mobile robotics and autonomous navigation, whose work has fundamentally advanced how robots perceive and move through complex environments. His research centers on sensor fusion, simultaneous localization and mapping (SLAM), and intelligent obstacle avoidance—critical pillars for autonomous systems. Duan’s most influential contribution is the development of GPS-supported visual SLAM with a rigorous sensor model for panoramic cameras, a framework that integrates satellite positioning with visual odometry to achieve robust, drift-free localization in outdoor settings. This work, cited 42 times, has become a foundational reference for researchers in robot navigation and urban mapping. Equally impactful is his enhanced Vector Polar Histogram method (VPH+), which groups isolated laser radar points into obstacle blocks, enabling mobile robots to predict and navigate around hazards with unprecedented efficiency. This 33-citation paper remains a go-to solution for real-time obstacle avoidance in cluttered environments. Duan’s contributions bridge theoretical rigor with practical deployment, offering students and engineers a clear path from sensor models to field-tested autonomy. His work continues to inspire next-generation systems for self-driving vehicles and exploratory robots.
Research Focus
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Top Papers
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