Linyang Li
Papers
1
Total Citations
21
H-Index
1
About
Linyang Li is a leading researcher in indoor localization and sensor fusion, whose work addresses critical challenges in mobile robot navigation. His key research areas include deep learning for positioning systems, multi-sensor fusion (combining 5G channel state information, geomagnetism, and visual-inertial odometry), and robust localization in GPS-denied environments. Li's major contribution lies in developing novel deep learning approaches that overcome the limitations of traditional VIO-based positioning, which suffers from light sensitivity and cumulative errors during long-term navigation. His most-cited paper (2023, 21 citations) introduces a fusion framework that integrates complementary sensor modalities, achieving superior accuracy and reliability for indoor mobile robots. This work has significant implications for autonomous systems in warehouses, hospitals, and smart factories. Li's research demonstrates how deep learning can effectively merge heterogeneous data sources, setting a new standard for resilient indoor positioning. His achievements highlight the growing importance of multi-sensor fusion in enabling robust, long-duration autonomous navigation in complex indoor environments.
Research Focus
Key Achievements
Top Papers
- 1