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.
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Documentation
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Avec les mêmes intervenants et intervenantes
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3.8. Applications of the Observability Rank Criterion
MartinelliAgostinoIn 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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3.6. Observability in robotics
MartinelliAgostinoIn 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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3.5. Simultaneous Localization and Mapping (SLAM)
MartinelliAgostinoIn this video, we are discussing the SLAM problem: simultaneous localization and mapping.
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3.3. The EKF is a weight mean
MartinelliAgostinoIn 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
MartinelliAgostinoIn 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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3.4. The use of the EKF in robotics
MartinelliAgostinoIn 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
MartinelliAgostinoIn this video we discuss the second two equations of the Kalman filter.
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2.3. Wheel encoders for a differential drive vehicle
MartinelliAgostinoIn 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)
MartinelliAgostinoWe 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
MartinelliAgostinoNow 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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2.4. Sensor statistical models
MartinelliAgostinoSo 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
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2.6. The Bayes Filter
MartinelliAgostinoThe 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
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