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2.7. Grid Localization: an example in 1D

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Auteur(s) :
Martinelli Agostino

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2.7. Grid Localization: an example in 1D

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.

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Label UNT : UNIT
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Date de réalisation : 1 Juin 2015
Durée du programme : 12 min
Classification Dewey : Applications. Automates, Génie informatique, Informatique - Traitement des données informatiques
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Catégorie : Vidéocours
Niveau : niveau Master (LMD), 2ieme cycle
Disciplines : Informatique, Informatique
Collections : 2. Bayes and Kalman Filters
ficheLom : Voir la fiche LOM
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Auteur(s) : Martinelli Agostino
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Langue : Anglais
Mots-clés : robotics, autonomous vehicles, informatics, mobile robots
Conditions d’utilisation / Copyright : This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.

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