Notice
3.6. Observability in robotics
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Descriptif
In this video we discuss a fundamental issue which arises when we dealwith an estimation problem: understandingif the system contains enough information to performthe estimation of the state.
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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
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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3.5. 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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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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3.7. 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
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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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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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2.4. Sensor statistical models
MARTINELLI Agostino
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
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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
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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.
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5.6. Predicting Human Motion
VASQUEZ GOVEA Alejandro Dizan
In video 5.5 we have defined an HMM in Python. In this video we are going to learn how to use it to estimate and predict 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.
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5.4. Bayesian filter inference
VASQUEZ GOVEA Alejandro Dizan
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.
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5.7. Typical Trajectories: drawbacks
VASQUEZ GOVEA Alejandro Dizan
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,
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5.5. From trajectories to discrete time-state models
VASQUEZ GOVEA Alejandro Dizan
In this video we are going to apply the concepts we have reviewed in the video 5.4 into real trajectories.
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4.3. Dynamic Probabilistic Grids – Implementation approaches
LAUGIER Christian
This video addresses the problem of the practical implementation for the dynamic probabilistic grid.
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5.1. Introduction
VASQUEZ GOVEA Alejandro Dizan
In this part of the course we are going to go deeper into situation awareness and in particular we are going to study how to model and learn human behavior which is a crucial task for social-aware
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4.5. Detection and Tracking of Mobile Objects – Problem and Approaches
LAUGIER Christian
This video adresses the Detection and Tracking of Mobile Objects (DATMO) problem.
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5.3b. Learning typical trajectories 2/2
VASQUEZ GOVEA Alejandro Dizan
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.
