Papers
14
Total Citations
110
H-Index
6
About
Gengyu Ge is a robotics researcher whose work centers on autonomous mobile robot navigation, localization, and simultaneous localization and mapping (SLAM). His research addresses some of the most persistent challenges in indoor robotics, particularly the problem of accurate robot localization in perceptually challenging environments such as symmetrical spaces, long corridors, and large-scale similar surroundings. Ge's most significant contributions lie in enhancing traditional localization frameworks — particularly Monte Carlo Localization (AMCL) — by integrating semantic, visual, and sensor-fusion techniques. His 2022 paper "Text-MCL," his most cited work with 24 citations, introduced a novel text-level semantic information approach to disambiguate similar environments, representing a meaningful advance in robust indoor localization. Complementary work fusing wireless sensor networks, visual features, and laser SLAM further demonstrates his commitment to multi-modal solutions for real-world deployment challenges. Earlier work on Evolutionary Artificial Potential Fields for path planning (22 citations) highlights his broader interest in mobile robot autonomy, while more recent research on dynamic environment VSLAM and semantic topology graphs reflects his evolution toward handling complex, real-world conditions. With over 100 cumulative citations, Ge has established a focused and growing body of work that meaningfully advances practical autonomous robot navigation.
Research Focus
Key Achievements
Top Papers
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- 7Integrating WSN and Laser SLAM for Mobile Robot Indoor Localization6 citations · 2022
- 8An Improved VSLAM for Mobile Robot Localization in Corridor Environment5 citations · 2022
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