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About

Ruihang Miao is a leading researcher in autonomous navigation and robotic perception, whose work addresses the critical challenge of enabling robots and autonomous vehicles to operate reliably in complex, unstructured, and intermittently degraded environments. His primary research areas span multi-sensor fusion, 3D scene understanding, and intelligent path planning. Miao’s most impactful contribution is the development of a graph-based adaptive fusion system that seamlessly integrates GNSS and visual-inertial odometry (VIO), achieving robust global positioning even under intermittent GNSS degradation—a work that has garnered 60 citations and is foundational for real-world autonomous driving. He also pioneered OccDepth, a depth-aware method for 3D semantic scene completion that enhances geometric and semantic reconstruction from visual images, accumulating 39 citations and advancing dense scene representation for autonomous systems. Additionally, Miao introduced semantic probabilistic traversable maps for robot path planning in unstructured terrains, and designed a reconfigurable multi-sensor testbed that provides a safe, versatile development platform for autonomous vehicle research. His integration of SLAM-based topological mapping with reinforcement learning-based local planners further demonstrates his innovative approach to navigation. With a portfolio of highly cited papers, Miao’s work is instrumental in pushing the boundaries of autonomous navigation and perception.