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About

Zeyu Wan is a robotics researcher whose work bridges the gap between human-inspired perception and autonomous navigation. His primary research areas include tactile sensing for 3D reconstruction, bio-inspired simultaneous localization and mapping (SLAM), and colorized point cloud mapping. Wan’s most notable contribution is **Tac2Structure** (2023, 9 citations), which tackles the challenge of object surface reconstruction using only tactile feedback—a critical capability for robots operating in visually occluded or low-light environments. This work draws inspiration from human haptic perception, enabling robots to “feel” unfamiliar objects without relying on vision. In SLAM, Wan proposed **LFVB-BioSLAM** (2023, 4 citations), a bionic system combining a lightweight LiDAR front end with a bio-inspired visual back end to reduce power consumption while maintaining accuracy. He also introduced **Observation Contribution Theory** (2021, 3 citations), a framework for quantifying how individual feature points affect pose estimation accuracy, offering a principled method to improve robotic localization. His more recent work, **ER-Mapping** (2024, 2 citations) and **RISED** (2025, 1 citation), advances robust colorized mapping through intelligent image selection and point cloud densification. Wan’s research is distinguished by its integration of biological principles with practical robotic systems, making his work highly relevant for students and researchers interested in embodied intelligence, sensor fusion, and autonomous exploration.