K. Doki, N. Isetani, A. Torii, and A. Ueda (Japan)
Image template generation, Self-position identification, Autonomous mobile robot, Genetic algorithm, Normalized correlation coefficient
We propose a new image template generation method for the self-position identification of an autonomous mobile robot. In the proposed method, an image template is gen erated with Genetic Algorithm. Then, in the process of the self-position identification, the size of the image tem plate can be varied in order to change the time for the self position identification according to the situation around the robot. Therefore, a suitable image template is searched by GA search as the size of the image is varied. The position of the robot is identified by matching the input image at the current situation with the stored image templates which indicate certain positions. As a criterion of the template matching, the normalized correlation coefficient is applied. This method is sensitive to the position shift of the image. Therefore, in order to realize the robust self-position iden tification for the position shift, the amount of the position shift between the image template and the input image is compensated before the template matching. The usefulness of the proposed method is shown through some experimen tal results.
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