Abstract
The main purpose of this paper is to examine the upper limits of self-localization ability using a local-vision system in the RoboCup Small Size League. Using an omni-directional vision system on a mobile robot, we originally developed a self-localization method based on imaging of the floor region. However, we could not explore its full potential because of a quantization error in the images. Therefore, we developed a self-localization method that allowed for better estimates of values than by individual methods, by integrating omni-directional vision and dead reckoning with the Kalman filter. This paper describes the algorithms and experimental results with an actual robot. In addition, we examine error recovery when the robot position changes due to collision with other objects and human intervention in the RoboCup competition.
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Sekimori, D., Usui, T., Masutani, Y., Miyasaki, F. (2003). Evaluation of Self-localization Performance for a Local Vision Robot in the Small Size League. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds) RoboCup 2002: Robot Soccer World Cup VI. RoboCup 2002. Lecture Notes in Computer Science(), vol 2752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45135-8_4
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DOI: https://doi.org/10.1007/978-3-540-45135-8_4
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