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About

Dexin Li is a robotics and computer vision researcher whose work centers on autonomous mobile systems, visual perception, and human-computer interaction. He is perhaps best known for his contributions to Visual Simultaneous Localization and Mapping (VSLAM) in dynamic indoor environments — a notoriously challenging problem that most existing systems sidestep by assuming static scenes. Li's landmark papers, **SGC-VSLAM** and **MGC-VSLAM** (both 2020), directly confront this limitation by integrating semantic and geometric constraints, as well as meshing-based approaches, to enable robust robot localization even when independently moving objects are present. Together, these works have accumulated over 75 citations, reflecting their meaningful impact on the autonomous robotics community. His earlier research explored intelligent mobile robot navigation using genetic algorithms and dynamical systems theory, demonstrating a long-standing commitment to principled, mathematically grounded approaches to robot autonomy. More recently, Li has expanded into skeleton-based hand action recognition using graph convolutional networks, broadening his contributions toward human-robot interaction and intelligent monitoring. Across more than a decade of research, Dexin Li has consistently tackled real-world complexity in robotic perception — making him a noteworthy contributor to the field of intelligent autonomous systems.