Shin‐Dug Kim
Papers
2
Total Citations
109
H-Index
2
About
Shin‐Dug Kim is a leading researcher in computer vision and augmented reality, with a primary focus on advancing simultaneous localization and mapping (SLAM) technologies for mobile and real-time applications. His major contributions lie in developing adaptive, visual-inertial SLAM systems that overcome critical challenges in mobile AR, such as rapid camera motion estimation and true-scale reconstruction. Kim’s 2019 work on “Real-Time Visual–Inertial SLAM Based on Adaptive Keyframe Selection” (71 citations) introduced a novel keyframe selection strategy that significantly improves tracking stability and computational efficiency, making it highly suitable for resource-constrained mobile devices. His earlier 2017 paper, “Adaptive Monocular Visual–Inertial SLAM for Real-Time Augmented Reality Applications” (38 citations), further established his expertise by addressing the fusion of visual and inertial data to achieve robust, drift-free localization. Collectively, his research has been cited over 100 times, reflecting its practical impact on fields ranging from autonomous navigation to drone and robot control. Kim’s work is particularly notable for bridging the gap between theoretical SLAM algorithms and deployable AR systems, enabling more immersive and responsive user experiences on everyday smartphones and tablets.
Research Focus
Key Achievements
Top Papers
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