K. Tanaka and E. Kondo (Japan)
SLAM, global localization, partially-incorrect maps, non stationary environments
In this paper, we address the problem of robot self localization in large and non-stationary environments, given a partially-incorrect map. Particle filter -based SLAM is robust technique for localization in stationary en vironments. However, in partially known environments, it becomes inefficient due to multiple initial self-position hy potheses. To solve this problem, we propose a factored par ticle filter represented in local coordinate system defined for each initial self-position hypothesis. Moreover, we uti lize distance filter and object filter to reduce the initial hy potheses. We have tested the method in various environ ments that contain many self-similar movable landmarks and less fixed landmarks.
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