Yonggang Zhang
Papers
5
Total Citations
90
H-Index
4
About
Yonggang Zhang is a leading researcher in multi-robot systems, with a primary focus on decentralized cooperative localization (DCL) and autonomous navigation in complex environments. His work addresses critical challenges in ensuring accurate and robust pose estimation for robot teams operating without absolute positioning infrastructure. Zhang’s major contributions include developing adaptive recursive DCL algorithms that handle time-varying measurement accuracy, robust methods to mitigate the impact of sensor measurement outliers, and distributed consensus learning techniques to solve unknown process noise uncertainty—problems that previously degraded localization consistency. His most cited paper (47 citations) on adaptive recursive DCL for multirobot systems has established a foundational framework for resilient cooperative localization. Beyond localization, Zhang has advanced LiDAR-based place recognition for urban environments with the novel OSK method, improving feature extraction from sparse point clouds. His research, published in top venues, demonstrates significant impact in enabling scalable, fault-tolerant multi-robot operations for applications ranging from search-and-rescue to autonomous driving.
Research Focus
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