Abstract
This paper describes a novel omnivision-based Concurrent Map-building and Localization (CML) approach which is able to localize a mobile robot in complex and dynamic environments. The approach extends or improves known CML techniques in essential aspects. For example, a more flexible model of the environment is used to represent experienced observations. By applying an improved learning regime, observations which are not longer of importance for the localization task are actively forgotten to limit complexity. Furthermore, a generalized scheme for hypotheses fusion is presented that enables the integration of further multi-sensory position estimators.
This work is partially supported by TMWFK-Grant # B509-03007 to H.-M. Gross.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Mueller, S., Koenig, A., Gross, H.M. (2005). Neural Architecture for Concurrent Map Building and Localization Using Adaptive Appearance Maps. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_147
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DOI: https://doi.org/10.1007/11550907_147
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