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About

Jinhui Fan is a robotics researcher whose work spans mobile robot navigation, simultaneous localization and mapping (SLAM), and intelligent assistive systems. His research focuses on developing robust perception and control frameworks for autonomous mobile robots, integrating sensors such as laser range finders and binocular stereo vision to enable reliable indoor navigation and map building. Fan's most notable contributions lie in multi-sensor SLAM methodologies, where he pioneered efficient techniques combining Rao-Blackwellized Particle Filters with distributed middleware architectures, demonstrating practical approaches to handling measurement uncertainty in complex environments. His 2011 series of papers on Bayesian map building and consistent map construction established foundational approaches to occupancy grid modeling that attracted sustained citation interest. Beyond navigation, Fan extended his expertise to intelligent assistive robotics, proposing a position-based visual servo control system for wheelchair-bed docking in 2018, addressing real-world accessibility challenges. His 2015 work on direct adaptive control using RBF neural networks for omnidirectional mobile robots further showcases his breadth in nonlinear control systems. With a cumulative citation count reflecting steady influence across robotics and autonomous systems communities, Fan's research offers valuable contributions to students exploring sensor fusion, probabilistic robotics, and intelligent mobility solutions.