Guojun Dai

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About

Guojun Dai is a researcher whose work bridges robotics, autonomous navigation, and intelligent perception. His primary contributions lie in trajectory tracking control for mobile robots and monocular vision-based simultaneous localization and mapping (SLAM) for large-scale outdoor environments. In a foundational 2007 paper (12 citations), Dai proposed a variable structure control algorithm for nonholonomic mobile robots, combining backstepping methods with a virtual feedback parameter and signum function to achieve robust trajectory tracking. This work laid important groundwork for motion control in differentially driven platforms. He further advanced autonomous navigation with a 2009 study (3 citations) on monocular vision SLAM, which eliminated the need for traditional odometry by leveraging Structure from Motion (SFM) for step-to-step motion estimation in outdoor settings. More recently, Dai’s 2024 work on multi-scale feature fusion with attention mechanisms for crowded road object detection (13 citations) demonstrates a shift toward perception in complex environments, integrating deep learning to improve detection accuracy. With over 28 citations across his most-cited papers, Dai’s research has influenced both control theory and computer vision, offering practical solutions for mobile robotics and autonomous systems.