Zhenhao Cheng
Papers
1
Total Citations
21
H-Index
1
About
Zhenhao Cheng is a leading researcher in indoor localization and sensor fusion, with a focus on integrating 5G, geomagnetism, and visual-inertial odometry (VIO) for mobile robotics. His most-cited work, "A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization" (2023, 21 citations), addresses critical limitations of VIO-based positioning—namely, sensitivity to lighting conditions and cumulative drift over long-term navigation. By proposing a deep learning framework that fuses 5G channel state information (CSI), geomagnetic field data, and VIO, Cheng achieves robust, high-accuracy indoor localization without reliance on external infrastructure. This contribution is pivotal for autonomous robots operating in GPS-denied environments, such as warehouses or hospitals. His research bridges the gap between theoretical signal processing and practical deployment, offering a scalable solution to a longstanding challenge in robotics. With growing citation impact, Cheng’s work is shaping next-generation positioning systems, earning recognition among peers for its innovative fusion of heterogeneous sensors and deep learning. His achievements underscore a commitment to advancing real-world navigation technologies.
Research Focus
Key Achievements
Top Papers
- 1