Abstract
We discuss recently published models of neural information processing under uncertainty and a SLAM system that was inspired by the neural structures underlying mammalian spatial navigation. We summarize the derivation of a novel filter scheme that captures the important ideas of the biologically inspired SLAM approach, but implements them on a higher level of abstraction. This leads to a new and more efficient approach to biologically inspired filtering which we successfully applied to real world urban SLAM challenge of 66 km length.
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Sünderhauf, N., Protzel, P. (2010). From Neurons to Robots: Towards Efficient Biologically Inspired Filtering and SLAM. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds) KI 2010: Advances in Artificial Intelligence. KI 2010. Lecture Notes in Computer Science(), vol 6359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_39
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DOI: https://doi.org/10.1007/978-3-642-16111-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16110-0
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