Yicong Zhou
Papers
4
Total Citations
149
H-Index
4
About
Yicong Zhou is a robotics and computer vision researcher whose work focuses on enabling intelligent autonomous systems through efficient perception and navigation. His primary research areas include real-time semantic segmentation, multi-agent SLAM (Simultaneous Localization and Mapping), bipedal robot locomotion, and autonomous coverage navigation. Zhou’s most impactful contribution is the development of LMFFNet, a well-balanced lightweight network for fast and accurate semantic segmentation, which has garnered 104 citations. This work addresses a critical trade-off in autonomous driving and robotics: achieving high segmentation accuracy without the computational burden of complex models. He also pioneered CVIDS, a collaborative localization and dense mapping framework for multi-agent visual-inertial SLAM (24 citations), advancing the field beyond traditional single-agent systems. In bipedal robotics, Zhou’s research on surface recognition via force-sensory walking-pattern classification (14 citations) enables cost-efficient, safe walking in complex environments. His work on robust autonomous coverage navigation for carlike robots (7 citations) further demonstrates his commitment to practical, real-world robotic applications. Through these contributions, Zhou has established himself as a key figure in developing efficient, deployable solutions for autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 4Towards Robust Autonomous Coverage Navigation for Carlike Robots7 citations · 2021