Qiguang Zhu

Papers

Total Citations

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About

Qiguang Zhu is an emerging researcher working at the intersection of computer vision and robotics, with a focus on Simultaneous Localization and Mapping (SLAM) — a foundational challenge in enabling autonomous systems to navigate and understand their environments in real time. His notable work, "A Dynamic SLAM Algorithm Based on Improved YOLOv9S" (2025), demonstrates a forward-thinking approach to addressing one of SLAM's most persistent limitations: performance degradation in dynamic environments populated by moving objects. By integrating an optimized version of the YOLOv9S object detection architecture into the SLAM pipeline, Zhu's research enhances the system's ability to distinguish static scene elements from dynamic ones, improving both mapping accuracy and localization robustness. Though early in his research career — with the work having accumulated 2 citations shortly after publication — his contributions reflect a timely and technically sophisticated response to real-world demands in autonomous navigation, robotics, and intelligent systems. Students and researchers working in autonomous vehicles, mobile robotics, or deep learning-based perception will find Zhu's evolving body of work a valuable reference point for dynamic scene understanding.