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In this paper, we propose an efficient feature matching method for scene recognition and global localization. The proposed method enables mobile robots to autonomously navigate through the dynamic environment where the robot frequently encounters visual occlusion and kidnapping. For this purpose, we present a scale optimization method to enhance the matching performance with the combination of the...
Vision-based robotic applications such as Simultaneous Localization and Mapping (SLAM), global localization, and autonomous navigation have suffered from problems related to dynamic environments involving moving objects and kidnapping. One of the possible solutions to these problems is to establish robust correspondences when obtaining images from static scenes. Therefore we propose an efficient technique...
This paper addresses a navigation method which copes with dynamic environments, e.g. objects moving in the environment and environment changes. Because many vision-based navigation methods mainly focus on finding consistent corresponding parts with one of the database images, dynamic environments can cause the failure of autonomous navigation due to visual occlusion. To solve these problems, we propose...
Recently, many vision-based robotic applications such as visual SLAM (Simultaneous Localization And Mapping) and autonomous navigation have achieved good performance using visual features. In these applications, robust feature tracking plays an important role, e.g., in scene recognition for autonomous navigation and in data association for visual SLAM. In this paper, we propose a hierarchical outlier...
Recently, many vision-based navigation methods have been introduced as an intelligent robot application. However, many of these methods mainly focus on finding an image in the database corresponding to a query image. Thus, if the environment changes, for example, objects moving in the environment, a robot is unlikely to find consistent corresponding points with one of the database images. To handle...
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