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About

Yilong Zhu is a robotics researcher specializing in simultaneous localization and mapping (SLAM), sensor fusion, and autonomous navigation. His work addresses some of the most pressing challenges in robotic perception, particularly how robots can reliably understand and navigate complex, dynamic environments using multiple sensing modalities. Zhu's most influential contribution is his multi-LiDAR SLAM framework, which enables robust odometry, mapping, and real-time extrinsic calibration across multiple LiDAR sensors simultaneously — a paper that has garnered over 124 citations and represents a significant advance in scalable robotic perception. Complementing this, he spearheaded the FusionPortable and FusionPortableV2 benchmark datasets, providing the research community with richly diverse, multi-sensor evaluation environments spanning varied platforms and scales, collectively accumulating over 80 citations. Beyond sensor integration, Zhu has made notable theoretical contributions, including globally optimal solutions to pose estimation and hand-eye calibration problems, tackling their inherently nonconvex nature with rigorous mathematical frameworks. His work on equipment-free IMU calibration and 3D LiDAR-based long-term localization further demonstrates his commitment to practical, deployable robotics solutions. With nearly 300 total citations across a focused and rapidly growing body of work, Yilong Zhu is emerging as a distinctive voice in robust, generalizable robotic navigation research.