Template generation for self-position estimation by Genetic Algorithm with indirect fitness inference

We have proposed about an image template generation method for the self-position estimation of an autonomous mobile robot based on anytime algorithm. In this method, the time for the self-position estimation can be varied by changing the size of image templates. Moreover, the stable self-position estimation can be realized even if the size of image templates is changed. However, image templates are generated with genetic algorithm in this method. Therefore, the time for the template generation is enormous. In this paper, we propose a new image template generation method based on genetic algorithm with the fitness inference system to reduce the template generation time. In our method, the values of some parameters in the evaluation function are inferred instead of inferring the evaluation value directly. Then, the evaluation value are calculated with the inferred parameters by using the evaluation function. The time for the image template generation can be reduced drastically by the proposed method. The usefulness of the image templates generated by the proposed method is shown through some experimental results of the self-position estimation using a real-robot.