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About

Qiyang Lyu is an emerging robotics researcher specializing in autonomous localization, mapping, and robust perception for mobile robots and autonomous vehicles. His work addresses some of the most challenging real-world scenarios in robot navigation, particularly in environments where conventional systems struggle — including adverse weather conditions and geometrically repetitive spaces such as corridors, warehouses, and indoor car parks. Lyu's most notable contribution is the **NTU4DRadLM dataset** (33 citations), a pioneering 4D radar-centric multi-modal benchmark that enables robust SLAM in conditions where LiDAR and visual systems typically fail, such as rain, fog, and smoke. This dataset has meaningfully advanced the field's understanding of resilient perception. Complementing this, his research on global localization in repetitive environments (16 citations) tackles the notoriously difficult problem of place disambiguation for autonomous mobile robots operating without reliable GPS. His innovative use of magnetic field signatures as localization cues offers a compelling infrastructure-free, drift-free alternative to traditional approaches. More recently, Lyu has extended his work toward unified LiDAR-based global localization across heterogeneous sensor configurations. With nearly 60 cumulative citations across a compact but focused publication record, Lyu represents a promising voice in resilient robot autonomy research.