Mengyun Liu
Papers
2
Total Citations
63
H-Index
2
About
Mengyun Liu is a leading researcher in indoor localization and visual positioning systems, with a focus on advancing location-based services (LBS) for seamless navigation, robotics, and precision marketing. Her work bridges computer vision and edge-cloud collaborative IoT, addressing critical challenges in real-world deployment. Liu’s most cited paper, “Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free” (2018, 60 citations), introduced a groundbreaking approach that eliminates the need for extensive training or 3D modeling, leveraging convolutional neural networks for efficient image retrieval. This work has been widely recognized for its practicality in enabling robust indoor positioning without costly infrastructure. Her recent contribution, “Efficient and precise visual location estimation by effective priority matching-based pose verification in edge-cloud collaborative IoT” (2024), further advances the field by optimizing pose verification for low-latency, high-accuracy localization in distributed systems. Liu’s research has significant implications for autonomous robotics and smart environments, with her citation impact underscoring its influence. Her innovative, training-free methods have set a new standard for accessible and scalable visual positioning, making her a key figure in the evolution of LBS technologies.
Research Focus
Key Achievements
Top Papers
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