Hyongjin Kim

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This paper proposes a GPU (graphics processing unit)-based real-time RGB-D (red-green-blue depth) 3D SLAM (simultaneous localization and mapping) system. RGB-D data contain 2D image and per-pixel depth information. First, 6-DOF (degree-of-freedom) visual odometry is obtained through the 3D-RANSAC (three-dimensional random sample consensus) algorithm with(More)
In this paper we describe a method for solving a mobile robot localization problem using prior data. By matching 2D image features to a 3D point cloud, the robot position is estimated in the prior point cloud. We prove our method by testing at specific locations over the whole point clod data.
This paper represents a novel autonomous jellyfish removal robot system, called JEROS (Jellyfish Elimination RObotic Swarm). The JEROS consists of an autonomous surface vehicle (ASV), a grid for jellyfish removal, and an autonomous navigation system. Once jellyfish are detected using a camera, efficient path to remove the jellyfish is generated. Then, the(More)
The aim of this paper is to propose an image and map data-based localization method applicable to a variety of environments. For the localization, we use prior map database, image-based localization method, and MCL (Monte Carlo Localization). The results were confirmed by open data set in a variety of environments. The experimental results show the(More)
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