Shucheng Huang
Papers
3
Total Citations
20
H-Index
2
About
Shucheng Huang is a researcher advancing the fields of mobile robotics, computer vision, and infrastructure inspection. His work centers on developing robust algorithms for autonomous navigation and visual perception in challenging environments. Huang's major contributions include a novel global and local fusion path-planning algorithm that significantly reduces path redundancy and turning points for mobile robots, addressing critical limitations in current state-of-the-art methods. He has also pioneered PLFF-SLAM, a visual SLAM system that fuses point and line features to maintain high positioning accuracy even under dynamic illumination, solving the persistent problem of trajectory drift. In infrastructure monitoring, Huang applied knowledge distillation to train a lightweight CNN model for fine-grained classification of sewer pipe cracks, a task essential for engineering standards like the Pipeline Assessment Certification Program (PACP). With his most-cited work accumulating 16 citations and his innovative approaches to real-world robotic challenges, Huang is establishing himself as a promising voice in practical robotics and intelligent inspection systems.
Research Focus
Key Achievements
Top Papers
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