Papers
6
Total Citations
90
H-Index
5
About
Yehu Shen’s research lies at the intersection of computer vision and autonomous robotics, with a focus on enabling robots to perceive, navigate, and map complex, unstructured environments. A central theme in Shen’s work is robust visual perception for navigation, from early monocular obstacle detection (2007) and environment mapping using image sequences (2008) to a highly cited sky region detection algorithm (2013, 41 citations) that provides critical horizontal and background cues for ground robots. Shen has also made significant contributions to stereo vision, developing an efficient normalized cross-correlation method (2011, 16 citations) and, more recently, the DyStSLAM system (2022, 14 citations), which tackles the challenging problem of simultaneous localization and mapping in dynamic environments—a critical step beyond the static-scene assumption of traditional SLAM. Demonstrating a forward-looking approach, Shen’s latest work (2025, 11 citations) explores adaptive soft adhesion for bionic climbing robots, blending structural design with optimization. With a career spanning nearly two decades, Shen’s research consistently addresses real-world robotic challenges, from navigation in unstructured terrain to dynamic scene understanding, making a lasting impact on the field.
Research Focus
Key Achievements
Top Papers
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- 3DyStSLAM: an efficient stereo vision SLAM system in dynamic environment14 citations · 2022
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