Xiaoyang Yan

Hong Kong University of Science and Technology

Papers

Total Citations

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About

Xiaoyang Yan is a researcher focused on advancing localization and perception for robotics and autonomous systems, with particular expertise in visual-marker-based methods for challenging environments. Their most-cited work, "Visual-Marker-Based Localization for Flat-Variation Scene" (2024, 8 citations), addresses a critical bottleneck in robotics: maintaining accurate positioning despite appearance variations caused by lighting, weather, or surface wear. Yan’s key contribution lies in leveraging semantic information to filter out invalid data—such as moving vehicles or faded road markings—enabling robust data association in flat-variation scenes where traditional methods fail. This semantic-driven approach enhances reliability in real-world automation tasks, from warehouse robots to autonomous vehicles. Yan’s research bridges the gap between theoretical localization algorithms and practical deployment, offering solutions that are both computationally efficient and resilient to environmental change. With a growing citation footprint, Yan is establishing a reputation for tackling fundamental challenges in robotic perception, making their work essential reading for researchers developing next-generation autonomous systems.