Zhiqiang Zhan

Papers

Total Citations

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About

Zhiqiang Zhan is a researcher whose work centers on advancing visual simultaneous localization and mapping (SLAM), a foundational technology in robotics and autonomous navigation. His major contribution lies in addressing critical limitations of traditional point-feature-based SLAM, particularly in challenging low-texture or weak-matching environments where conventional methods suffer from insufficient features, motion jitter, and poor localization accuracy. Zhan’s most cited paper, “A visual SLAM method based on point-line fusion in weak-matching scene” (2020, 19 citations), introduces an innovative approach that integrates both point and line features to enhance robustness and precision. This fusion method significantly improves SLAM performance in real-world scenarios, such as indoor corridors or feature-sparse landscapes, where point-only systems often fail. By tackling these practical challenges, Zhan’s work has direct implications for robotics, augmented reality, and autonomous vehicles. His research not only advances algorithmic stability but also provides a scalable solution for deployment in dynamic, low-texture environments. With a growing citation impact, Zhan is recognized for bridging theoretical gaps and delivering tangible improvements to visual SLAM, making his contributions essential reading for students and researchers exploring robust perception systems.