Papers
4
Total Citations
358
H-Index
3
About
Lu Yu is a leading researcher in computer vision, specializing in semantic segmentation for autonomous systems. Her work focuses on integrating multimodal data—particularly RGB, thermal, and depth images—to enable robots and autonomous vehicles to understand complex urban and indoor environments. Yu’s most impactful contribution is the **GMNet** (2021), a graded-feature multilabel-learning network for RGB-thermal urban scene semantic segmentation, which has garnered **318 citations** for its novel approach to cross-modal fusion. She further advanced the field with **THCANet** (2023), a two-layer hop cascaded asymptotic network for robot-driving road-scene segmentation in RGB-D images, and **AMCFNet** (2023), designed for indoor service robots. Yu also addressed practical robotics challenges with a real-time SLAM algorithm based on improved point-line feature fusion (2023). Her work consistently pushes the boundaries of how robots perceive and navigate their surroundings, bridging the gap between high-level semantic understanding and real-time operational demands. With growing citation impact and a clear focus on deployable AI, Yu is a rising figure in embodied vision and autonomous navigation research.
Research Focus
Key Achievements
Top Papers
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