Shuguo Pan
Papers
2
Total Citations
4
H-Index
1
About
Shuguo Pan is a rising researcher in intelligent robotics perception and positioning, with a focus on advancing sensor fusion and depth estimation technologies. Their work bridges critical gaps in autonomous navigation by addressing challenges in both visual and LiDAR-based systems. Pan’s most cited paper, “Self-supervised monocular depth estimation via joint attention and intelligent mask loss” (2024), introduces a novel approach that enhances depth perception without requiring labeled data—a key breakthrough for cost-effective robotic and autonomous driving applications. This work has already garnered 3 citations shortly after publication, signaling growing recognition. In their 2025 study on LiDAR point cloud feature matching, Pan systematically analyzes random measurement errors that degrade positioning accuracy in simultaneous localization and mapping (SLAM) systems. By identifying and quantifying these error sources, their research provides foundational insights for improving the reliability of LiDAR-based navigation in real-world environments. Pan’s contributions are particularly valuable for intelligent robotics, where high-precision positioning and perception are essential. Their ongoing work promises to strengthen the robustness of autonomous systems, making them more dependable for complex operational scenarios.
Research Focus
Key Achievements
Top Papers
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- 2