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About

Zehuan Yu is an emerging researcher specializing in robotics perception, simultaneous localization and mapping (SLAM), and 3D scene reconstruction, with a particular focus on bridging efficient map representations with real-world autonomous systems. His most-cited work, "H₂-Mapping" (2023, 35 citations), demonstrates his ability to push the boundaries of NeRF-based dense mapping, achieving high-quality real-time reconstruction critical for robotics, AR/VR, and digital twin applications. Beyond neural representations, Yu has made notable contributions to LiDAR-based mapping, proposing lightweight semantic line-and-plane frameworks that address the scalability and memory challenges of traditional point cloud systems, as evidenced by his SLIM series of papers. His research extends into monocular lane mapping, global point cloud registration under low-overlap conditions, and innovative integration of LiDAR with Building Information Modeling (BIM). A particularly distinctive contribution is the SLABIM dataset, which uniquely couples SLAM with architectural BIM data, filling a critical gap in indoor robotics benchmarking. Yu's work on locomotion mode prediction further illustrates his interdisciplinary reach into wearable robotics. Collectively accumulating nearly 100 citations across recent publications, Yu is rapidly establishing himself as a versatile and impactful voice in the robotics and spatial intelligence community.