Papers
11
Total Citations
525
H-Index
6
About
Young-Sik Shin is a leading researcher in field robotics, specializing in perception, navigation, and autonomy for robots operating in challenging, real-world environments. His work is defined by a focus on all-weather, all-terrain, and multi-modal sensing, addressing the critical gap between controlled lab settings and the messy diversity of the natural and built world. Shin’s most impactful contribution is the creation of benchmark datasets that have become foundational resources for the robotics community. His "Complex urban dataset with multi-level sensors" (295 citations) and the "ViViD++ : Vision for Visibility Dataset" (77 citations) provide essential, high-diversity data for developing and testing algorithms under extreme lighting and structural conditions. He has also pioneered novel sensor fusion techniques, such as using low-cost mmWave radars for 3D ego-motion estimation in fog and smoke (61 citations), and has extended his expertise to agricultural robotics, analyzing the efficiency of heterogeneous robot teams for smart greenhouse harvesting. With recent works like "DiTer++" and "Uni-Mapper" pushing the boundaries of multi-session, multi-robot SLAM, Shin continues to shape the future of robust, field-deployable autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2ViViD++ : Vision for Visibility Dataset77 citations · 2022
- 3
- 4Complex Urban LiDAR Data Set53 citations · 2018
- 5Online depth estimation and application to underwater image dehazing10 citations · 2016
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- 7
- 8Comparative Study of Sonar Image Processing for Underwater Navigation5 citations · 2016
- 9Complex Urban LiDAR Data Set4 citations · 2018
- 10