Papers
3
Total Citations
12
H-Index
2
About
Shuyi Tan is a robotics researcher whose work focuses on enabling intelligent mobile robots to perceive and navigate complex, real-world environments. Her primary research areas include Visual Simultaneous Localization and Mapping (VSLAM), semantic mapping, and robust feature extraction, with a particular emphasis on overcoming the limitations of traditional systems in dynamic and low-light conditions. Tan's major contributions address critical gaps in robotic perception. She developed a Semantic Topology Graph to detect re-localization and loop closure in dynamic environments, a significant advancement over conventional VSLAM systems that assume static surroundings. Her work on improved feature point extraction methods for low-light dynamic environments further extends the operational capabilities of VSLAM. Additionally, she has explored salient semantic segmentation using RGB-D cameras to create more comprehensive semantic maps that can identify obstacles with semantic meaning. With her most-cited paper accumulating 6 citations since 2023, Tan's research is gaining recognition for its practical relevance to autonomous navigation. Her integrated approach—combining network models for semantic segmentation with robust VSLAM frameworks—represents a meaningful step toward robots that can truly understand and interact with the unpredictable, real-world environments they are deployed in.
Research Focus
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