Jun Hyeong Jo
Papers
2
Total Citations
5
H-Index
1
About
Jun Hyeong Jo is a robotics researcher focused on advancing autonomous navigation and human-robot interaction, particularly for mobile robots operating in cluttered, real-world environments. His work centers on two key challenges: robust state estimation and reliable human detection. Jo’s most cited paper, “Development of a Practical ICP Outlier Rejection Scheme for Graph-based SLAM Using a Laser Range Finder” (2019, 4 citations), addresses a critical bottleneck in simultaneous localization and mapping (SLAM) by improving the accuracy of scan matching, a foundational task for robot self-localization. His more recent contribution, “Development of a human-following scheme using point-voxel RCNN-based 3D human leg detection for the robust human-following of mobile robots in cluttered environments” (2024, 1 citation), tackles the practical problem of enabling robots to safely follow humans in dynamic settings like warehouses and offices. By integrating a deep learning-based 3D leg detector into a complete tracking and control pipeline, Jo’s work aims to reduce collision risks and improve the stability of human-robot following. Though early in his career, his research directly addresses the gap between theoretical algorithms and deployable robotic systems, making his contributions valuable for engineers building autonomous service robots.
Research Focus
Key Achievements
Top Papers
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- 2