Xingzhen Li

Papers

Total Citations

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About

Xingzhen Li has made significant contributions to the field of visual simultaneous localization and mapping (SLAM), with a particular focus on enhancing the robustness of feature extraction in dynamic environments. Their key research areas include computer vision, autonomous navigation, and image processing. Li’s most notable work, “An improved ORB-SLAM2 algorithm based on image information entropy” (2020), introduces a novel method that leverages information entropy to optimize feature point selection in the ORB-SLAM2 framework. This approach directly addresses the critical challenge of performance degradation caused by varying illumination and sparse environmental cues, improving the stability and accuracy of visual SLAM systems. Although this specific paper has garnered 2 citations, it represents a foundational step in integrating information theory with practical SLAM algorithms. Li’s research is particularly valuable for applications in robotics and autonomous systems, where reliable localization under uncertain conditions is paramount. Their work underscores a commitment to advancing the reliability of visual odometry, making it more adaptable to real-world, unpredictable settings.