Yulong Huang
Papers
5
Total Citations
94
H-Index
5
About
Yulong Huang is a leading researcher in multi-robot systems, specializing in decentralized cooperative localization (DCL), sensor fusion, and robust state estimation. His work addresses critical challenges in enabling robot teams to operate accurately in GPS-denied or uncertain environments. Huang’s major contributions include developing adaptive algorithms that dynamically handle time-varying measurement accuracy and unknown process noise, significantly improving the consistency and reliability of distributed localization. His 2021 paper on adaptive recursive DCL for multirobot systems has garnered 47 citations, establishing a foundational method for real-world deployment. He further advanced the field with robust approaches against measurement outliers (2024, 19 citations) and distributed consensus learning for unknown noise uncertainty (2024, 12 citations), directly tackling practical sensor imperfections. Beyond localization, Huang has innovated in LiDAR-based place recognition with the OSK method (2024, 8 citations) and efficient pose estimation via the SE(n)++ framework (2020, 8 citations). His work is widely cited for its practical impact on autonomous driving and robotics, offering scalable, fault-tolerant solutions that bridge theoretical estimation theory with real-world multi-robot operations.
Research Focus
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