Ju-Hong Park

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In this paper, we suggest a new method of human augmented mapping for indoor environments using only a stereo camera. Through user's help, a robot with a stereo camera can investigate the environment without failure and even more efficiently. Moreover, the user can share the information about the environment with the robot and add semantic information to(More)
In this paper we propose a novel vision-based global localization method based on a hybrid map representation. We employ PCA-SIFT features as visual landmarks and represent the environment with a hybrid map which consists of a global topological map and local metric maps. To localize where a mobile robot is placed, we extract visual features from the(More)
In this paper, we propose a method for global localization using an omni-directional camera. A robot position and angle are estimated by correlation coefficient between topological node-map images and input images. Near-node has the largest correlation coefficient in topological map images. The calculated correlation coefficient makes the mixtures of(More)
In this paper, we suggest a new method of vision-based human augmented mapping for indoor environments. It is a semi-autonomous approach using human-robot interation and can be an alternative to autonomous map building. The advantage of our approach is that the user can share the environments with the robot and insert semantic information to the(More)
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