Qiyan Li
Papers
3
Total Citations
26
H-Index
2
About
Qiyan Li is a researcher focused on advancing autonomous navigation and multi-robot coordination, with key contributions in visual SLAM (Simultaneous Localization and Mapping) and pattern formation. Li’s work addresses critical challenges in dynamic and unstructured environments, particularly for mobile robots and intelligent transportation systems. Their most-cited paper, “A robust visual odometry based on RGB-D camera in dynamic indoor environments” (2020, 14 citations), tackles the accuracy limitations of visual odometry in real-world settings with moving objects, proposing a robust method to enhance positioning reliability. This foundational work is complemented by “An iterative optimization approach for multi-robot pattern formation in obstacle environment” (2020, 10 citations), which introduces a novel algorithm for coordinating multiple robots in cluttered spaces, advancing collective robotics. More recently, Li’s “Light-SLAM: A Robust Deep-Learning Visual SLAM System Based on LightGlue under Challenging Lighting Conditions” (2024, 2 citations) integrates deep learning to overcome poor illumination, a persistent hurdle in autonomous driving. With a growing citation impact, Li’s research bridges traditional geometric methods and modern AI, offering practical solutions for robust, real-time navigation. Their work is particularly valuable for students and engineers developing autonomous systems for dynamic indoor and outdoor environments.
Research Focus
Key Achievements
Top Papers
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