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<title><string language="fre"><![CDATA[2.7. Grid Localization: an example in 1D]]></string></title>
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<string language="fre"><![CDATA[Now that we have the
equations of the Bayes filter, we need a method in order to implement
in real cases these equations.
So, in the following, I want to
discuss two methods, which are commonly adopted by the Mobile
Robotics Community and, these, if you want, correspond to two
extreme solutions because one is a fully numerical and it
is based on a grid and, for the case of localization, is
known as the grid-localization approach – and the
other one is a fully analytical and it is
known as a the Kalman filter.
So, now, in this video, we
discuss the first method, which is numerical, and we do this
by referring to a simple case – a trivial case, a
one-dimensional case – and this will allow not only to understand these
methods – how it works – but also to better understand
the behavior of the Bayes filter.]]></string></description>
<keyword><string language="fre"><![CDATA[robotics]]></string></keyword><keyword><string language="fre"><![CDATA[autonomous vehicles]]></string></keyword><keyword><string language="fre"><![CDATA[informatics]]></string></keyword><keyword><string language="fre"><![CDATA[mobile robots]]></string></keyword>
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<entity><![CDATA[BEGIN:VCARD
VERSION:3.0
CLASS:PUBLIC
REV:2021-09-16 17:53:44
FN:Agostino Martinelli
N:Martinelli;Agostino;;;
URL;TYPE=work:https://www.canal-u.tv/auteurs/martinelli_agostino
ROLE:author
TZ:+0200
END:VCARD
]]></entity>
<date><dateTime>2015-06-01</dateTime></date>
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<string language="fre"><![CDATA[Droits réservés à l'éditeur et aux auteurs. 
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<string language="fre"><![CDATA[2. Bayes and Kalman Filters]]></string>
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