Zhenqi Zheng
Papers
2
Total Citations
15
H-Index
2
About
Zhenqi Zheng is a rising researcher whose work bridges the frontiers of indoor positioning and visual SLAM in dynamic environments. His key research areas include ubiquitous positioning, fingerprint-based localization, and robust feature management for autonomous navigation. Zheng’s major contributions are twofold: first, he demonstrated the critical necessity of modeling location uncertainty in fingerprinting for ubiquitous positioning, a paradigm shift that acknowledges the inherent inaccuracies of crowdsourced data from diverse mobile platforms. Second, he developed GAT-LSTM, an innovative network that integrates graph attention mechanisms with long short-term memory to manage feature points for visual SLAM, enabling reliable operation in highly dynamic settings where traditional methods fail. His most-cited paper, “GAT-LSTM,” has already garnered 9 citations since its 2025 publication, while his foundational work on fingerprint uncertainty has earned 6 citations. These achievements highlight Zheng’s ability to tackle real-world challenges—from smartphone-based indoor navigation to autonomous robots in cluttered spaces—making his research essential reading for students and engineers advancing robust, scalable positioning and mapping technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2