Shibing Yu

Shanghai University, Shanghai University of Engineering Science

Papers

Total Citations

H-Index

About

Shibing Yu is a robotics researcher whose work focuses on mobile robot navigation and visual tracking, with a particular emphasis on sensor fusion and perceptual computing. His most significant contribution is the design of a navigation system that fuses inertial measurement units (IMU) with wheeled encoders, a 2020 paper that has garnered 20 citations for its practical approach to improving robot localization accuracy. Yu also advanced visual tracking for mobile robots through his 2015 work on perceptual image hashing, where he developed a robust tracking algorithm using Discrete Cosine Transform (DCT)-based perceptual hash vectors. This method enables more reliable target tracking by leveraging image fingerprinting techniques, addressing a fundamental challenge in autonomous robotics. While his citation counts reflect a focused, early-career impact, Yu's work demonstrates a clear trajectory in solving real-world robotic perception and navigation problems. His research is particularly relevant for engineers developing cost-effective, reliable autonomous systems, as his fusion approach balances computational efficiency with practical accuracy. Yu's contributions sit at the intersection of sensor integration and computer vision, offering valuable insights for students and researchers working on mobile robot autonomy and embedded navigation systems.