- Label UNT : UNIT
- Date de réalisation : 1 Juin 2015
- Durée du programme : 10 min
- Classification Dewey : Applications. Automates, Génie informatique, Informatique - Traitement des données informatiques
- Auteur(s) : Martinelli Agostino
- 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.
3.1. Examples for the Action in the EKF
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.