Hyun‐Su Kim
Papers
1
Total Citations
3
H-Index
1
About
Hyun-Su Kim is a robotics researcher whose work centers on computer vision, sensor calibration, and the integration of deep learning into robotic perception systems. His most notable contribution addresses the classic challenge of hand-eye calibration—determining the precise geometric transformation between a robot’s end-effector and an attached camera. In his 2020 paper, Kim proposed a novel method that leverages deep learning to restore degraded images before performing calibration, significantly improving accuracy in industrial settings where fixed-focus cameras are common. This approach bridges traditional robotics calibration with modern AI, offering a practical solution for manufacturing environments. While his citation count remains modest, his work represents a meaningful step toward more robust and automated robotic systems. Kim’s research is particularly relevant for students and engineers working on robot vision, sensor fusion, and the practical deployment of deep learning in real-world automation tasks.
Research Focus
Key Achievements
Top Papers
- 1Hand-eye Calibration using Images Restored by Deep Learning3 citations · 2020