Suqing Yan

Guilin University of Electronic Technology

Papers

Total Citations

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About

Suqing Yan is a researcher at the forefront of integrating deep learning with robotic perception, specializing in visual simultaneous localization and mapping (SLAM) and object detection. Their most notable contribution is the development of a dynamic visual SLAM algorithm that synergizes with YOLO, a state-of-the-art real-time object detection framework. This work addresses a critical challenge in autonomous navigation: robustly handling dynamic environments where moving objects can corrupt traditional SLAM systems. By fusing YOLO’s detection capabilities with visual SLAM, Yan’s algorithm enables robots and autonomous vehicles to accurately map and localize themselves even in cluttered, changing scenes—a breakthrough with implications for service robotics, autonomous driving, and augmented reality. While their 2025 paper has garnered early citations, reflecting growing interest from the robotics community, Yan’s research continues to push boundaries in real-time perception and sensor fusion. Their work stands as a promising bridge between classical geometric methods and modern learning-based approaches, offering practical solutions for machines to understand and navigate the unpredictable real world.