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This paper describes self-localization of a mobile robot from the multiple candidates of landmarks. Our robot uses omni-directional vision system for efficient self-localization. This vision system acquires the visible information of all direction views. Our robot utilizes feature of landmarks that size of feature is bigger than that of others in image such as building, symbol tower, hanging banner etc. Our robot uses vertical edges and those merged region as feature. In our previous work, we found the problem that landmark matching is difficult when selected candidates of landmark belong to region of repeating the vertical edges in image. To overcome these problems, robot uses the merged region of vertical edges. If interval of vertical edges is short then robot bundles them regarding as the same region. Thus, these features are candidates of landmarks. In other words, candidates of landmark are vertical edges and those merged regions. Therefore, the extracted merged region of vertical edge reduces the ambiguity of landmark matching. Robot compares with the candidates of landmark between previous and current image. Then, robot is able to find the same landmark between image sequences using the proposed feature and method. The experiments implemented our campus and achieved the efficient self-localization result using robust landmark matching method.
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