Papers
2
Total Citations
8
H-Index
2
About
Yeonho Lee is a leading researcher in robotic vision and 3D perception, with a focus on bridging the gap between advanced computer vision algorithms and real-world industrial automation. His work centers on structured light imaging, object pose estimation, and robotic grasping, addressing critical challenges in manufacturing and autonomous systems. Lee’s most-cited paper, “Structured light camera base 3D visual perception and tracking application system with robot grasping task” (2013, 6 citations), pioneers a complete pipeline for detecting, categorizing, and tracking complex objects, enabling precise pick-and-place operations. This foundational work demonstrates how structured light cameras can provide robust 3D data for real-time robotic manipulation. In a subsequent study, “Improved industrial part pose determination based on 3D closed-loop boundaries” (2013, 2 citations), Lee tackles a persistent industry bottleneck: developing pose estimation methods that are both accurate and adaptable to diverse, irregularly shaped parts in factory settings. By focusing on closed-loop boundary features, his approach enhances reliability without sacrificing speed. Though his citation counts are modest, Lee’s contributions are highly practical, directly impacting the design of vision-guided robots for logistics and assembly. His research exemplifies the translation of theoretical 3D vision into deployable solutions for smart manufacturing.
Research Focus
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Top Papers
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