Xinyi Zhou

China University of Mining and Technology

Papers

Total Citations

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About

Xinyi Zhou is a researcher whose work lies at the intersection of robotics, artificial intelligence, and sensor data fusion. Her primary research focuses on developing intelligent localization systems for mobile robots operating in complex indoor environments. Zhou’s most notable contribution is a neural network-based data fusion approach that significantly enhances both the real-time performance and accuracy of indoor robot localization. By integrating odometry data with environmental measurements, her method effectively mitigates errors caused by sensor noise and environmental uncertainties—a critical challenge in autonomous navigation. Her pioneering paper on this topic, published in 2020, has already garnered 17 citations, reflecting its growing influence in the robotics community. This work not only advances theoretical understanding but also offers practical solutions for deploying reliable robots in settings like warehouses, hospitals, and smart homes. Zhou’s research stands out for its pragmatic approach to bridging the gap between neural computation and real-world robotic systems, making her a promising voice in the field of intelligent autonomous navigation.