Joo Han Lee

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About

Joo Han Lee is a robotics researcher specializing in sensor fusion and localization for autonomous systems, with a particular focus on low-cost, indoor mobile robot navigation. His work centers on developing robust dead reckoning and odometry techniques that integrate multiple sensor modalities to overcome the limitations of individual sensors in challenging environments. Lee's major contributions include the development of sequential batch fusion methods for magnetic anomaly navigation, which enables reliable positioning without GPS, and the creation of DeRO—a novel radar odometry framework that directly incorporates Doppler velocity measurements from 4D Frequency Modulated Continuous Wave radar into a Kalman filter structure, enhanced by accelerometer data. His most cited work, "Sequential batch fusion magnetic anomaly navigation for a low-cost indoor mobile robot" (2023), has garnered 19 citations, while his more recent "DeRO" paper (2024) has already accumulated 7 citations, reflecting growing interest in his innovative approaches. Lee's research addresses critical challenges in robot localization by combining radar, accelerometers, and magnetic field sensing, offering practical solutions for cost-effective autonomous navigation in indoor settings where traditional methods fail.