Vidéo pédagogique
Langue :
Agostino Martinelli (Intervention)
Conditions d'utilisation
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.
DOI : 10.60527/7yn8-ps64
Citer cette ressource :
Agostino Martinelli. Inria. (2015, 1 juin). 3.1. Examples for the Action in the EKF , in 3. Extended Kalman Filters. [Vidéo]. Canal-U. (Consultée le 22 juillet 2024)

3.1. Examples for the Action in the EKF

Réalisation : 1 juin 2015 - Mise en ligne : 3 janvier 2017
  • document 1 document 2 document 3
  • niveau 1 niveau 2 niveau 3

In part 2, we have seen theequations of the Bayes filter, which are the generalequations which allow us to update the probabilitydistribution, as the data from both proprioceptive sensors andexteroceptive sensors are delivered.We have seen a possibleimplementation 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 theextended Kalman filter.

In part 3, we want to better explain theseequations starting from a very simple example in 1D.Then we willconsider problems like simultaneous localizationmapping, and othertheoretical issue about estimation.

In this video, westart to discuss the first two equations of the Kalman filter.


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