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Nombre de programmes trouvés : 2817

Label UNT Vidéocours

le 01/06/2015 (9m48s)

1.9. Intelligent Vehicles: Technical Challenges & Driving Skills

This video presents the technical challenges and driving skills for intelligent vehicles.

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Label UNT Vidéocours

le 01/06/2015 (7m48s)

2.3. Wheel encoders for a differential drive vehicle

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.

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Label UNT Vidéocours

le 01/06/2015 (7m45s)

2.4. Sensor statistical models

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 configuration S, this regards the exteroceptive sensors.

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Label UNT Vidéocours

le 01/06/2015 (6m8s)

2.5. Reminds on probability

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.

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Label UNT Vidéocours

le 01/06/2015 (7m36s)

2.6. The Bayes Filter

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.

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Label UNT Vidéocours

le 01/06/2015 (8m47s)

3.2. Examples for the Perception in the EKF

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

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Label UNT Vidéocours

le 01/06/2015 (7m22s)

3.3. The EKF is a weight mean

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.

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Label UNT Vidéocours

le 01/06/2015 (10m36s)

3.4. The use of the EKF in robotics

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

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Label UNT Vidéocours

le 01/06/2015 (6m13s)

3.5. Simultaneous Localization and Mapping (SLAM)

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

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Label UNT Vidéocours

le 01/06/2015 (8m11s)

3.8. Applications of the Observability Rank Criterion

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.

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