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About

Yonghan Lee is a robotics and computer vision researcher whose work centers on autonomous navigation, teleoperation, and 3D scene understanding for mobile robots. His research spans multi-robot coordination, self-supervised depth estimation, visual localization, and novel-view synthesis. Lee’s most influential work proposes a teleoperation framework for a platoon of distributed wheeled mobile robots using predictive display and peer-to-peer communication, enabling a human operator to control a leader robot while followers maintain formation autonomously—a contribution with 16 citations that addresses critical challenges in multi-robot teleoperation. He also developed SelfTune, a self-supervised learning algorithm that resolves scale ambiguity in monocular depth estimation by integrating monocular SLAM with proprioceptive sensors, achieving metric-scale depth without ground-truth labels. Additionally, Lee contributed large-scale indoor localization datasets for crowded spaces, supporting augmented reality and robot navigation where GPS fails. His most recent work, Mode-GS, introduces anchored 3D Gaussian splatting for robust ground-view scene rendering, overcoming splat drift in neural rendering. With publications spanning 2018 to 2024, Lee’s research demonstrates a clear trajectory toward enabling reliable, scalable perception and control for real-world robotic systems.