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About

Sheng Fu is a robotics researcher whose work sits at the intersection of autonomous navigation, computer vision, and human-robot interaction. His research has made meaningful contributions to two core areas: simultaneous localization and mapping (SLAM) for mobile robots, and trust-based teleoperation frameworks. Fu's early work, concentrated around 2007, advanced SLAM methodologies by integrating diverse sensing modalities — including laser range finders, monocular vision, binocular stereo vision, and sonar — enabling autonomous mobile robots to navigate complex indoor environments without prior maps. These contributions collectively represent a comprehensive exploration of sensor fusion strategies for robust localization, with his laser and monocular vision paper alone garnering 32 citations. Expanding beyond autonomous systems, Fu later addressed the human element of robotics with his 2016 work on trust-based mixed-initiative teleoperation, which proposed an innovative framework for dynamically sharing control between human operators and autonomous robot controllers in bilateral haptic teleoperation — earning 27 citations and reflecting growing interest in collaborative human-robot systems. With a cumulative citation count exceeding 80 across his key works, Fu's research offers valuable foundations for students and engineers working on mobile robotics, sensor integration, and adaptive control architectures.