Hang Guo
Papers
7
Total Citations
161
H-Index
7
About
Hang Guo is a researcher specializing in indoor robot navigation, multi-sensor fusion, and mobile robot localization systems. His work addresses one of the most persistent challenges in robotics: achieving accurate, robust positioning in GPS-denied indoor environments where single-sensor approaches inevitably suffer from error accumulation and reduced reliability. Guo's most significant contributions center on integrating LiDAR, inertial navigation systems (INS), and visual sensors through advanced filtering techniques. His 2019 paper introducing a cascaded finite-impulse response (FIR) filter for INS/LiDAR-based robot localization has garnered 45 citations, demonstrating its considerable influence in the field. Complementing this, his work combining visual and inertial sensors for indoor positioning (39 citations) showcases his ability to leverage complementary sensing modalities to overcome individual sensor limitations. His exploration of federated filtering frameworks and SLAM-based approaches further reflects his commitment to developing practical, scalable navigation solutions. Across his body of work, which spans from 2018 to 2021, Guo has accumulated over 160 citations, reflecting growing recognition within the robotics and navigation communities. His research offers valuable algorithmic foundations for engineers and researchers developing autonomous mobile robots, warehouse systems, and other applications where precise indoor localization is critical.
Research Focus
Key Achievements
Top Papers
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- 3Indoor multi-sensor fusion positioning based on federated filtering24 citations · 2020
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- 5Research into Kinect/Inertial Measurement Units Based on Indoor Robots11 citations · 2018
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