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About

Sung Woo Noh is a robotics researcher whose work focuses on autonomous navigation and localization for mobile and underwater robots. His key research areas include probabilistic state estimation, sensor fusion, and terrain-based positioning. Noh’s most impactful contribution is his 2011 paper on Monte Carlo Localization for underwater robots using both internal and external sensor data, which has accumulated 20 citations. This work pioneered the use of particle filters to predict robot pose by fusing thruster, inertial, and compass data with acoustic range measurements. He has also advanced object-following methods using Ultra Wide Band (UWB) sensors for differential mobile robots and compared the performance of unscented Kalman filters and particle filters for seabed terrain-based underwater localization. Noh’s research on integrating elementary navigation functions—including map building via ICP, path planning, and obstacle avoidance—has provided practical frameworks for indoor mobile robot autonomy. His work is notable for bridging theoretical Bayesian filters with real-world experimental validation, addressing challenges like sensor uncertainty and computational efficiency. With a cumulative citation count exceeding 40, Noh’s contributions continue to inform the development of robust, sensor-driven navigation systems for challenging environments.