Papers
47
Total Citations
481
H-Index
10
About
Kang-Hyun Jo is a leading figure in robotics and autonomous systems, with a career spanning foundational work in mobile robot control to cutting-edge deep learning for autonomous driving. His research primarily focuses on perception, navigation, and human-robot interaction (HRI). Jo has made significant contributions to real-time vision systems, notably developing a fast CPU-based facial expression detector using sequential attention networks for HRI, a paper that has garnered 56 citations. His work on camera and 2D laser rangefinder calibration provides a simple yet efficient method widely used in sensor fusion. In autonomous navigation, he has advanced path planning through heuristic searching and road map images, with several papers accumulating over 20 citations each. Jo’s comprehensive survey on deep learning-based perception for autonomous driving, with 99 citations, underscores his impact in synthesizing and advancing the field. His earlier work on hybrid neural control systems for mobile robots and vision-based heading estimation laid the groundwork for modern autonomous navigation. With a publication record that demonstrates both breadth and depth, Kang-Hyun Jo continues to shape intelligent robotics, from real-time face tracking to robust vehicle license plate detection.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Facet-based multiple building analysis for robot intelligence26 citations · 2008
- 4
- 5Real-time Face Tracking for Human-Robot Interaction25 citations · 2018
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- 7Global path planning for unmanned ground vehicle based on road map images20 citations · 2014
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- 9Hybrid neural-based control system for mobile robot15 citations · 2005
- 10