Yikun Chen
Papers
1
Total Citations
4
H-Index
1
About
Yikun Chen is a researcher advancing the field of indoor robotics localization and sensor fusion. Their primary research areas include ultra-wideband (UWB) positioning, odometry integration, and graph optimization techniques for autonomous mobile robots. Chen’s most notable contribution is the development of a fusion positioning method that combines UWB and odometry data through graph optimization, directly addressing the critical challenge of accurate indoor localization for intelligent omnidirectional robots. This work, published in 2023, has already garnered 4 citations, signaling its relevance to the growing demand for robust, real-time navigation in GPS-denied environments. By improving localization precision, Chen’s research supports the practical deployment of autonomous systems in complex indoor settings, such as warehouses and smart factories. Their work stands out for its practical engineering approach, bridging theoretical optimization with real-world robotic applications. As indoor robotics continues to expand, Chen’s contributions offer a valuable foundation for future innovations in autonomous navigation and sensor integration.
Research Focus
Key Achievements
Top Papers
- 1