Yong-Min Shin
Papers
1
Total Citations
2
H-Index
1
About
Yong-Min Shin is a researcher specializing in mobile robotics, computer vision, and autonomous navigation, with a particular focus on enhancing visual odometry for outdoor environments. His most-cited work, "Outdoor Monocular Visual Odometry Enhancement Using Depth Map and Semantic Segmentation" (2020, 2 citations), addresses a critical challenge in robot localization: the degradation of feature matching and triangulation caused by reflective surfaces like building windows and car bodies. By integrating depth maps and semantic segmentation, Shin’s approach improves robustness against such misleading features, advancing the reliability of monocular visual odometry in complex real-world settings. This contribution is vital for autonomous systems operating in urban landscapes, where accurate pose estimation is essential for safe navigation. Though early in his citation impact, Shin’s work demonstrates a keen understanding of practical perception limitations and offers a targeted solution that bridges computer vision and robotics. His research holds promise for future developments in self-driving vehicles, drones, and mobile robots that must function reliably amidst visual clutter and environmental variability.
Research Focus
Key Achievements
Top Papers
- 1