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15 résultats
section 1 sur 2
résultats 1 à 12
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2.3. Wheel encoders for a differential drive vehicle  Droits d'accès : non autorisé
Description : 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.
Date : 01-06-2015
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2.3. Wheel encoders for a differential drive vehicle
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2.4. Sensor statistical models  Droits d'accès : non autorisé
Description : 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 distributions which are these: P of having an exteroceptive measurement Z by knowing the state, so the co ...
Date : 01-06-2015
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2.4. Sensor statistical models
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2.5. Reminds on probability  Droits d'accès : non autorisé
Description : 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 remind you are 3: the Markov assumption, the theorem of a total probability and the Bayes theorem.
Date : 01-06-2015
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2.5. Reminds on probability
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2.6. The Bayes Filter  Droits d'accès : non autorisé
Description : 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 measurements provided by the robot.
Date : 01-06-2015
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2.6. The Bayes Filter
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2.7. Grid Localization: an example in 1D  Droits d'accès : non autorisé
Description : 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 beca ...
Date : 01-06-2015
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2.7. Grid Localization: an example in 1D
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2.8. The Extended Kalman Filter (EKF)  Droits d'accès : non autorisé
Description : 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 drawback is that the solution becomes very expensive. There exists another solution that is the Kalman fi ...
Date : 01-06-2015
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2.8. The Extended Kalman Filter (EKF)
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3.1. Examples for the Action in the EKF  Droits d'accès : non autorisé
Description : 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 ...
Date : 01-06-2015
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3.1. Examples for the Action in the EKF
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3.2. Examples for the Perception in the EKF  Droits d'accès : non autorisé
Description : In this video we discuss the second two equations of the Kalman filter.
Date : 01-06-2015
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3.2. Examples for the Perception in the EKF
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3.3. The EKF is a weight mean  Droits d'accès : non autorisé
Description : 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.
Date : 01-06-2015
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3.3. The EKF is a weight mean
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3.4. The use of the EKF in robotics  Droits d'accès : non autorisé
Description : In this video I want to explain the steps that we have to follow in order to implement an extended Kalman filter in robotics.
Date : 01-06-2015
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3.4. The use of the EKF in robotics
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3.5. Simultaneous Localization and Mapping (SLAM)  Droits d'accès : non autorisé
Description : In this video, we are discussing the SLAM problem: simultaneous localization and mapping.
Date : 01-06-2015
 Description complète
3.5. Simultaneous Localization and Mapping (SLAM)
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3.6. Observability in robotics  Droits d'accès : non autorisé
Description : 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.
Date : 01-06-2015
 Description complète
3.6. Observability in robotics
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