2. Bayes and Kalman Filters

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Mise en ligne : 01 juin 2015
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Descriptif

Contents of this second part:

2.1. Localization process in a probabilistic framework: basic concepts2.2. Characterization of proprioceptive and exteroceptive sensors 2.3. Wheel encoders for a differential drive vehicle 2.4. Sensor statistical models 2.5. Reminds on probability 2.6. The Bayes Filter 2.7. Grid Localization: an example in 1D 2.8. The Extended Kalman Filter (EKF)

Vidéos

Vidéo pédagogique
00:07:47

2.3. Wheel encoders for a differential drive vehicle

Martinelli
Agostino

In this video, we want to discuss the case of a wheel encoders in 2D, and in particular the case of a robot equipped with a differential drive which is very popular in mobile robotics.

Vidéo pédagogique
00:08:04

2.2. Characterization of proprioceptive and exteroceptive sensors

Before deriving the equations of the Bayes filter, I want to remind you a few concepts in the theory of probability, and also some mathematical characterization for the statistical error of the

Vidéo pédagogique
00:08:07

﻿2.1. Localization process in a probabilistic framework: basic concepts

Martinelli
Agostino

In this part, we will talk about localization which is a fundamental problem that a robot has to be able to solve in order to accomplish almost any tasks. In particular, we will start by

Vidéo pédagogique
00:07:44

2.4. Sensor statistical models

Martinelli
Agostino

So far in the characterization of our sensor measurements, we didn't talk about the errors. This is precisely what we want to do in this video. In particular, we want to compute two probability

Vidéo pédagogique
00:06:07

2.5. Reminds on probability

Martinelli
Agostino

In this sequence I want to remind you a few concepts in the theory of probability and then in the next one we finally derive the equations of the Bayes filter. So the concept that I want to

Vidéo pédagogique
00:07:35

2.6. The Bayes Filter

Martinelli
Agostino

The equations of the Bayes filters are the equation that allow us to update the probability distribution for the robot to be in a given configuration by integrating the information that are in the

Vidéo pédagogique
00:11:52

2.7. Grid Localization: an example in 1D

Martinelli
Agostino

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

Vidéo pédagogique
00:08:01

2.8. The Extended Kalman Filter (EKF)

Martinelli
Agostino

We have seen the grid localization, and the advantage of this approach is that we can deal with any kind of probability distribution; in particular we don't need to do a Gaussian assumption. The

Intervenants et intervenantes

Martinelli Agostino

Directeur de thèse à l'Université de Grenoble