Papers
18
Total Citations
685
H-Index
10
About
Younggun Cho is a robotics and computer vision researcher whose work centers on autonomous navigation, sensor fusion, dataset development, and image enhancement for challenging real-world environments. His most significant contribution is the creation of large-scale, multimodal datasets that have become essential benchmarks for the robotics community. His 2019 "Complex Urban Dataset," garnering nearly 300 citations, provides researchers with richly diverse urban sensor data spanning multiple cities and conditions — a foundational resource for autonomous driving and mobile robotics research. Complementing this, his Complex Urban LiDAR Data Set and ViViD++ dataset address perception under constrained sensing conditions, including limited fields of view and varying luminance. Beyond dataset contributions, Cho has advanced image enhancement techniques for degraded visual environments, tackling haze, underwater turbidity, and illumination variance through both model-based and deep learning approaches. His work on proactive camera control using Bayesian optimization is particularly innovative, shifting the paradigm from passive post-capture correction to intelligent, real-time camera attribute management. More recently, his SOLiD descriptor and DiTer dataset reflect a growing focus on robust place recognition and field robot autonomy. With over 650 cumulative citations, Cho's research meaningfully bridges perception robustness with practical autonomous systems deployment.
Research Focus
Key Achievements
Top Papers
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- 3ViViD++ : Vision for Visibility Dataset77 citations · 2022
- 4Complex Urban LiDAR Data Set53 citations · 2018
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- 10Online depth estimation and application to underwater image dehazing10 citations · 2016