Papers
7
Total Citations
42
H-Index
4
About
Jaehong Park is a robotics researcher whose work centers on mobile robot perception, sensor fusion, and vision-based tracking systems. His most significant contributions emerged around 2010–2011, when he developed a series of innovative vision tracking frameworks designed to solve one of mobile robotics' persistent challenges: maintaining reliable visual lock on a target while the robot itself is in motion. Drawing inspiration from biological systems, Park incorporated the vestibulo-ocular reflex (VOR) and opto-kinetic reflex (OKR) of the human eye into his sensor fusion architectures, blending data from accelerometers, gyroscopes, encoders, and vision sensors using both Kalman filters and fuzzy logic controllers. His most-cited work, a Kalman filter-based fusion system published in 2010 (13 citations), demonstrated high-performance tracking by accurately estimating robot position relative to a moving target despite motion-induced disturbances. Subsequent papers extended this framework to multi-robot tracking scenarios and pan/tilt camera control. More recently, Park has turned toward deep learning, contributing a point-voxel RCNN approach for 3D human leg detection to enable robust human-following in cluttered indoor environments. His body of work reflects a sustained commitment to making mobile robots more perceptually capable and context-aware.
Research Focus
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Top Papers
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