Jinqiang Cui
Papers
7
Total Citations
95
H-Index
6
About
Jinqiang Cui is a dynamic robotics and artificial intelligence researcher whose work spans autonomous navigation, underwater vision, sensor fusion, and embodied AI. His most recognized contribution, a GPU-accelerated incremental Euclidean Distance Transform system for mobile robot motion planning (28 citations), demonstrates his expertise in high-performance volumetric mapping and real-time parallel computing. Cui has also made significant strides in underwater robotics, developing WaterFormer (21 citations), a global–local transformer architecture for underwater image enhancement that advances perception capabilities for aquatic autonomous systems. His research extends into formal methods for robotics, with notable work on two-phase motion planning under Signal Temporal Logic specifications in partially unknown environments (19 citations), bridging theoretical rigor with practical deployment challenges. Complementing this, Cui has addressed critical sensor reliability issues through deep learning-based MEMS gyroscope noise compensation and lightweight attitude estimation networks, improving localization accuracy for cost-constrained platforms. More recently, he has pushed frontiers in multi-robot collaborative perception through 3D scene graph communication and open-vocabulary mapping, and contributed an open-source embodied AI simulation framework integrating large language models. Across roughly 95 total citations, Cui's body of work reflects a researcher steadily shaping the future of intelligent, field-ready robotic systems.
Research Focus
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Top Papers
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