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)
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)
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.
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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