Yue Pan is a PhD student and algorithm engineer with eight years of experience in LiDAR and SLAM research, currently at the Photogrammetry & Robotics Lab, University of Bonn and a recent visiting researcher at ETH Zürich. His work spans core SLAM algorithm development, point-cloud processing, and sensor fusion, with practical impact demonstrated by MULLS—an ICRA '21 LiDAR SLAM system he helped implement and maintain that ranked on the KITTI leaderboard. He combines strong academic credentials (ETH Zürich MS, Wuhan University BS) with industrial experience at Hesai and SenseTime, applying research to real sensors like Pandar LiDARs and HD mapping pipelines. Known for improving visualization, tooling, and reproducible releases, he bridges full-stack implementation and rigorous evaluation in robotics projects. Colleagues can expect a researcher who balances high-accuracy algorithm design with production-minded engineering and dataset curation.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS, Geomatics Engineering, 3.8/4.0, Bachelor of Science - BS, Geomatics Engineering, 3.8/4.0 at Wuhan University
Exchange student, GIS, 3.8/4.0, Exchange student, GIS, 3.8/4.0 at The Hong Kong Polytechnic University
Secondary School, high school, Secondary School, high school at High School Affiliated to Fudan University
Master of Science - MS, Geomatics Engineering, 5.7/6.0, Master of Science - MS, Geomatics Engineering, 5.7/6.0 at ETH Zürich
MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square [ICRA '21]
Role in this project:
Full-stack Developer
Contributions:39 commits, 1 PR, 36 pushes in 1 year 9 months
Contributions summary:Yue appears to have focused on the development and maintenance of the MULLS LiDAR SLAM system. Their contributions include implementing core algorithms for LiDAR odometry and registration, as demonstrated by their initial release commit. Subsequent commits show the user updating scripts for running the system and modifying the codebase for improved functionality and potential bug fixes. They also updated documentation and configuration files, and improved the visualization tools.
Contributions:14 commits, 13 pushes, 1 branch in 1 month
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