Papers
6
Total Citations
106
H-Index
4
About
Sunhyo Kim is a leading researcher in robotics, whose work has significantly advanced the fields of simultaneous localization and mapping (SLAM) and visual servoing for mobile robots. Kim’s most influential contribution is the development of novel SLAM techniques, including a landmark approach using omni-directional vertical and horizontal line features for indoor environments, which has garnered 49 citations. To overcome the limitations of traditional sensor-based methods, Kim pioneered a neuro-evolutionary optimization framework, NeoSLAM, which reframes SLAM as a global optimization problem, enabling robust map building and localization even with sonar readings. In visual servoing, Kim introduced a hybrid fuzzy control method that integrates position and image-based approaches, solving the critical challenge of keeping a target in the camera’s field of view while achieving optimal path planning and robot pose control. This work, with 23 citations, demonstrates a practical fusion of control theory and computer vision. Through these contributions, Kim has established a reputation for creating intelligent, learning-based solutions that push the boundaries of autonomous navigation, making complex robotic tasks more reliable and efficient.
Research Focus
Key Achievements
Top Papers
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- 3Hybrid Position and Image Based Visual Servoing for mobile robots23 citations · 2007
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