Abstract
Solving the global localization problem for a humanoid robot in a fully symmetric environment, such as the soccer field of the RoboCup games under the most recent rules, may be a difficult task. It requires to maintain the robot position distribution multi-modality whenever it is necessary, for ensuring that the correct position is among the distribution modes. We describe a three-level approach for handling this problem, where (1) a particle filter is run to implement the Bayes filter and integrate elegantly our probabilistic knowledge about the visual observations and the one on the robot motion, (2) a mode management system maintains explicitly the distribution modes and allows to guarantee the satisfaction of constraints such as unresolved symmetry, and (3) a discrete state machine over the modes is used to determine the most pertinent observation models. We present very promising results of our strategy both in simulated environments and in real configurations.
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García Vazquez, M.J., Madrigal, J.F., Mar, O., Esteves, C., Hayet, JB. (2013). Robust Visual Localization of a Humanoid Robot in a Symmetric Space. In: Batyrshin, I., González Mendoza, M. (eds) Advances in Artificial Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37807-2_20
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DOI: https://doi.org/10.1007/978-3-642-37807-2_20
Publisher Name: Springer, Berlin, Heidelberg
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