Notice
3.7. Observability Rank Criterion
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Descriptif
In this video, we discuss anautomatic method which is analytical and allows us toanswer the question if a state is observable or not: this method is theObservability Rank Criterion which has been introduced byHerman Krener in 1977.
Thème
Documentation
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Avec les mêmes intervenants et intervenantes
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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.
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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.
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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.
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3.2. Examples for the Perception in the EKF
MARTINELLI Agostino
In this video we discuss the second two equations of the Kalman filter.
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3.5. Simultaneous Localization and Mapping (SLAM)
MARTINELLI Agostino
In this video, we are discussing the SLAM problem: simultaneous localization and mapping.
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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.
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3.1. Examples for the Action in the EKF
MARTINELLI Agostino
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
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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
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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
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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.
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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
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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
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In video 5.2 we showed how to apply the expectation maximization clustering algorithm to two-dimensional data. In this video we will learn how to apply it to trajectory data. And then we will be
