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Anglais
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Agostino Martinelli (Intervention)
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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. https://doi.org/10.60527/7yn8-ps64. (Consultée le 22 juillet 2024)

# 3.1. Examples for the Action in the EKF

Réalisation : 1 juin 2015 - Mise en ligne : 3 janvier 2017
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Descriptif

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.

Intervention
Thème
Documentation

## Avec les mêmes intervenants et intervenantes

• Vidéo pédagogique
00:07:21

### 3.6. Observability in robotics

Martinelli
Agostino

In this video we discuss a fundamental issue which arises when we deal with an estimation problem: understanding if the system contains enough information to perform the estimation of the state.

• Vidéo pédagogique
00:08:10

### 3.8. Applications of the Observability Rank Criterion

Martinelli
Agostino

In this video we want to apply the observability rank criterion to understand the observability properties of the system that we saw in the previous videos.

• Vidéo pédagogique
00:10:35

### 3.4. The use of the EKF in robotics

Martinelli
Agostino

In this video I want to explain the steps that we have to follow in order to implement an extended Kalman filter in robotics.

• Vidéo pédagogique
00:08:46

### 3.2. Examples for the Perception in the EKF

Martinelli
Agostino

In this video we discuss the second two equations of the Kalman filter.

• Vidéo pédagogique
00:06:12

### 3.5. Simultaneous Localization and Mapping (SLAM)

Martinelli
Agostino

In this video, we are discussing the SLAM problem: simultaneous localization and mapping.

• Vidéo pédagogique
00:06:56

### 3.7. Observability Rank Criterion

Martinelli
Agostino

In this video, we discuss an automatic method which is analytical and allows us to answer the question if a state is observable or not: this method is the Observability Rank Criterion which has

• Vidéo pédagogique
00:07:21

### 3.3. The EKF is a weight mean

Martinelli
Agostino

In this video I want to discuss the second two equations of the Kalman filter. And in particular I want to show that these actually perform a kind of weight mean.

• 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
00:08:07

### ﻿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
00:06:07

### 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
00:07:47

### 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.

## Sur le même thème

• Conférence
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### 5.6. Predicting Human Motion

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### 5.9. Other approaches: Planning-based approaches

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In this video we are going to study a second, and probably the most promising alternative for motion prediction: planning-based algorithms.

• Vidéo pédagogique
00:04:13

### 5.4. Bayesian filter inference

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In this video we will review the base filter and we will study a particular instance of the Bayesian filter called Hidden Markov models which is a discrete version of a Bayesian filter.

• Vidéo pédagogique
00:05:32

### 5.7. Typical Trajectories: drawbacks

Vasquez Govea
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In previous videos we have discussed how to implement the typical trajectories and motion patterns approach. In this video we are going to discuss what are the drawbacks of such an approach,

• Vidéo pédagogique
00:04:39

### 5.5. From trajectories to discrete time-state models

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In this video we are going to apply the concepts we have reviewed in the video 5.4 into real trajectories.

• Vidéo pédagogique
00:05:35

### 5.8. Other approaches: Social Forces

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In this video we will review one of the alternatives we are proposing to the use of Hidden Markov models and typical trajectories: the Social Force model.

• Vidéo pédagogique
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### 4.5. Detection and Tracking of Mobile Objects – Problem and Approaches

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This video adresses the Detection and Tracking of Mobile Objects (DATMO) problem.

• Vidéo pédagogique
00:04:46

### 5.3b. Learning typical trajectories 2/2

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In this video we are aiming to improve on the results we obtained in video 5.3a, in particular with respect to the greyed-out trajectories that are badly represented.

• Vidéo pédagogique
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### 4.9. Situation Awareness – Collision Risk Assessment and Decision (Object level)

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This video addresses the problem of collision risk assessment and decision.

• Vidéo pédagogique
00:07:39

### 4.1. Robot Perception for Dynamic environments: Outline and DP-Grids concept

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The fourth part of the course addresses perception, situation awareness and decision making. In this first video, we're giving an outline of the problem and introducing the new concept of dynamic