Papers
3
Total Citations
20
H-Index
2
About
Yangfan Zhou is an emerging researcher working at the intersection of computer vision, robotics, and biomechanical engineering. His work primarily focuses on visual odometry (VO) in dynamic environments and soft robotic systems for medical applications. Zhou has made notable contributions to advancing learning-based visual perception, particularly through his development of lightweight yet powerful odometry frameworks. His 2023 paper, "GSL-VO," introduced a geometric-semantic information-enhanced approach to visual odometry, addressing longstanding limitations in perceiving unseen dynamic environments — earning 10 citations since its publication. Building on this foundation, his 2024 work "Fine-MVO" pushed boundaries further by enhancing self-supervised monocular visual odometry through fine-grained feature representations, moving beyond the coarse semantic masks that constrained prior methods, accumulating 8 citations. More recently, Zhou has expanded his research scope into biomedical robotics, developing a thoraco-abdominal biomechanical model and dual-layer control system aimed at respiratory assistance — a clinically meaningful contribution addressing human-robot synchronization challenges for patients with respiratory dysfunction. Across his still-growing body of work, Zhou demonstrates a rare ability to bridge fundamental perception research with impactful real-world applications in both autonomous systems and healthcare robotics.
Research Focus
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