Papers
12
Total Citations
191
H-Index
7
About
Fei Han is a leading researcher in robotics and artificial intelligence, with key contributions spanning visual place recognition, human-robot interaction, and autonomous navigation. His work addresses fundamental challenges in long-term robot autonomy, particularly in enabling robots to recognize locations despite dramatic environmental changes. Han’s most cited paper, "SRAL: Shared Representative Appearance Learning for Long-Term Visual Place Recognition" (49 citations), introduces a novel approach to loop closure detection that significantly improves visual SLAM robustness across different times of day and seasons. His research on multimodal loop closure detection using structured sparsity (40 citations) further advances place recognition under perceptual aliasing conditions. In human-robot interaction, Han has pioneered skeleton-based activity prediction for real-time collaboration (30 citations) and developed graph-embedded learning for team behavior recognition (11 citations). His recent work on deep reinforcement learning for active SLAM with snake robots (20 citations) demonstrates his continued innovation in multi-sensor fusion and adaptive navigation. With over 180 total citations across his publications, Han’s research has established new paradigms for long-term autonomous operation and intelligent human-robot teaming.
Research Focus
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