Yejin Roh
Papers
1
Total Citations
2
H-Index
1
About
Yejin Roh is a robotics researcher whose work focuses on autonomous navigation and sensor fusion for mobile robots in complex, real-world environments. Her key research areas include LiDAR-visual mapping, path planning, and the integration of perception systems for autonomous mobile robots (AMRs) in logistics settings. Roh’s most notable contribution is the development of a loosely coupled LiDAR-visual mapping and navigation framework, which enables robust localization and obstacle avoidance without relying on tightly integrated sensor systems. This approach improves the reliability of AMRs in dynamic, cluttered environments, addressing critical challenges in warehouse and industrial automation. Her work builds on classic path deformation techniques, such as the elastic band method, but advances them by incorporating dynamic constraints and real-time sensor feedback. With her 2022 paper already garnering early citations, Roh is establishing herself as an emerging voice in field robotics. Her research holds practical significance for the future of autonomous logistics, where safe and efficient navigation remains a bottleneck. For students and researchers, Roh’s work offers a clear example of how foundational planning algorithms can be adapted for modern, sensor-rich robotic systems.
Research Focus
Key Achievements
Top Papers
- 1