Xiaoli Hu

Inner Mongolia University of Science and Technology

Papers

Total Citations

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About

Xiaoli Hu is a leading researcher in indoor positioning technologies, with a primary focus on visible-light positioning (VLP) systems and their integration with artificial intelligence. Her most notable contribution is the development of a groundbreaking indoor visible-light 3D positioning system that leverages Gated Recurrent Unit (GRU) neural networks, published in 2023 and already garnering 20 citations. This work addresses a critical challenge in robotics and smart environments: achieving high-accuracy, three-dimensional positioning indoors where GPS signals are unavailable. By pioneering the application of GRU networks to VLP, Hu has demonstrated how deep learning can significantly enhance positioning precision and reliability, outperforming traditional methods. Her research bridges the gap between optical communication and AI-driven localization, offering practical solutions for autonomous robots, augmented reality, and IoT applications. Hu’s work is recognized for its innovative approach to combining temporal sequence learning with optical signal processing, marking her as a key contributor to the next generation of indoor navigation systems. Her findings continue to influence both academic research and industrial implementations in smart space technologies.