Kwangsoo Kim
Papers
12
Total Citations
125
H-Index
7
About
Kwangsoo Kim is a robotics researcher whose work centers on mobile robot localization, sensor data fusion, and vision tracking systems — areas that sit at the intersection of control engineering, computer vision, and autonomous systems. His most significant contribution lies in developing robust frameworks that enable mobile robots to accurately perceive and navigate their environments by intelligently combining data from multiple sensors, including gyroscopes, encoders, accelerometers, and cameras. Kim's most cited work (39 citations) demonstrates the effectiveness of the Unscented Kalman Filter (UKF) for sensor fusion, achieving reliable real-time localization for mobile robots — a foundational challenge in autonomous robotics. He extended this approach across numerous studies, applying Kalman filtering and fuzzy logic control to vision tracking systems that allow robots to continuously follow moving targets despite motion disturbances. His bio-inspired work, drawing on the vestibulo-ocular reflex of the human eye, reflects an innovative cross-disciplinary perspective. With contributions spanning stereo vision integration, pan/tilt camera control, and line-of-sight stabilization, Kim has built a coherent and practical research portfolio. Collectively accumulating over 120 citations, his work provides valuable engineering solutions for researchers and developers advancing mobile robotics and autonomous navigation systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7Mobile robot vision tracking system using Unscented Kalman Filter7 citations · 2011
- 8
- 9
- 10Stable Vision System for Indoor Moving Robot Using Encoder Information4 citations · 2009