Yuangang Fan

Papers

Total Citations

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About

Yuangang Fan is a researcher specializing in computer vision and autonomous robotics, with a focused emphasis on visual Simultaneous Localization and Mapping (SLAM) systems. His most notable contribution is the development of RWT-SLAM, a robust visual SLAM framework specifically engineered to address one of the field's most persistent challenges: reliable navigation and mapping in highly weak-textured environments. Traditional visual SLAM systems frequently falter in feature-sparse settings such as plain walls, low-light corridors, or uniform surfaces — scenarios common in real-world robotic deployment. Fan's RWT-SLAM system directly confronts this limitation through novel modifications to the SLAM pipeline, pushing the boundaries of what intelligent robots can perceive and navigate. His 2022 and 2023 publications on this system have collectively garnered 12 citations, reflecting growing interest from the robotics and computer vision communities in solving texture-deficient mapping problems. For students and researchers working at the intersection of autonomous systems, robot perception, and SLAM technology, Fan's work represents an important step toward making visual SLAM viable across the full spectrum of real-world environmental conditions that robots are increasingly expected to handle.