Ressource pédagogique : 2.7. Grid Localization: an example in 1D
Présentation de: 2.7. Grid Localization: an example in 1D
Informations pratiques sur cette ressource
Droits réservés à l'éditeur et aux auteurs. 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.
Description de la ressource pédagogique
Description (résumé)
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.
"Domaine(s)" et indice(s) Dewey
- Applications. Automates (629.89)
- Génie informatique (621.39)
- Informatique - Traitement des données informatiques (004)
Thème(s)
AUTEUR(S)
-
Agostino Martinelli
EN SAVOIR PLUS
-
Identifiant de la fiche
26401 -
Identifiant
oai:canal-u.fr:26401 -
Schéma de la métadonnée
- LOMv1.0
- LOMFRv1.0
- Voir la fiche XML
-
Entrepôt d'origine
Canal-u.fr
