Papers
2
Total Citations
21
H-Index
2
About
Xiaoqi Du is an emerging researcher specializing in computer vision, autonomous systems, and deep learning, with a particular focus on Simultaneous Localization and Mapping (SLAM) for intelligent transportation and robotics applications. Du's most notable contribution is the development of Light-SLAM, a robust deep-learning visual SLAM system that leverages the LightGlue feature matching framework to overcome one of the field's most persistent challenges: reliable performance under difficult lighting conditions. Traditional feature-based SLAM methods frequently fail in low-light, overexposed, or rapidly changing illumination environments — a critical limitation for real-world autonomous driving deployment. By integrating deep learning into the SLAM pipeline, Du's work advances the reliability and robustness of localization and mapping in scenarios where conventional approaches struggle. The Light-SLAM system has garnered significant early attention, accumulating 19 citations shortly after its 2025 publication, signaling strong community interest in its practical implications. Du's research sits at the intersection of autonomous driving safety, mobile robotics, and visual perception, making it highly relevant to researchers and engineers working to bring more dependable autonomous systems into challenging real-world environments.
Research Focus
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Top Papers
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