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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 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...
Recently, many vision-based SLAM methods have achieved good results using visual features. However, most algorithms suffer from the accumulated error that inevitably occurs. In this paper, we propose a robust loop detection method by matching image features between the incoming image and key-frame images saved in SLAM. Loop detection is a task of deciding whether a robot has returned to a previously...
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