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<title><string language="fre"><![CDATA[3.1. Examples for the Action in the EKF]]></string></title>
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<string language="fre"><![CDATA[In part 2, we have seen the
equations of the Bayes filter, which are the general
equations which allow us to update the probability
distribution, as the data from both proprioceptive sensors and
exteroceptive sensors are delivered.
We have seen a possible
implementation of these equations, based on a numerical solution: the grid localization.
We have also started to see the equations of the Kalman filter, or better the
extended Kalman filter.
In part 3, we want to better explain these
equations starting from a very simple example in 1D.
Then we will
consider problems like simultaneous localization
mapping, and other
theoretical issue about estimation.
In this video, we
start to discuss the first two equations of the Kalman 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 17:51:03
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[3. Extended Kalman Filters]]></string>
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<string language="eng">DDC 22nd ed.</string>
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