Korea Advanced Institute of Science and Technology · 工学
Professor Hyun Myung's research lab specializes in intelligent sensing and autonomous systems for civil infrastructure and robotics applications. The lab focuses on developing low-cost, robust sensing solutions—such as structured light systems and RGB-D SLAM—for structural health monitoring and autonomous inspection in challenging environments. Key research directions include real-time human pose estimation using depth sensors, GPS-denied navigation for UAVs in confined spaces like bridges, and hybrid optimization techniques for complex engineering problems. The lab emphasizes practical, embedded solutions that enable real-time operation on low-cost hardware platforms.
Figures are computed from collected data and may differ slightly.
The displacement measurement in structural health monitoring (SHM), though important, was not popular due to inaccessibility of the civil infrastructures and high installation cost. The currently popular approaches use accelerometer, strain gauge, PZT, GPS, etc., most of which indirectly measure the displacement and require high cost to install and maintain. Thus the development of SHM system that directly measures the displacement of the structure using low-cost sensors is urgently needed. In t
With the increasing demand for autonomous systems in the field of inspection, the use of unmanned aerial vehicles (UAVs) to replace human labor is becoming more frequent. However, the Global Positioning System (GPS) signal is usually denied in environments near or under bridges, which makes the manual operation of a UAV difficult and unreliable in these areas. This paper addresses a novel hierarchical graph-based simultaneous localization and mapping (SLAM) method for fully autonomous bridge ins
In this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which the positions of objects change over long intervals. Therefore, in the low dynamic environments, robots have difficulty recognizing the repositioning of objects unlike in highly dynamic environments in which relatively fast-moving objects can be detec
In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine) and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from
A hybrid of evolutionary programming and a deterministic optimization procedure is applied to a series of nonlinear and quadratic optimization problems. The results indicate that the hybrid method can outperform the exclusive use of evolutionary programming when addressing constrained optimization problems with finite penalty functions. Directions for future research are outlined.
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