Notice
3.5. Simultaneous Localization and Mapping (SLAM)
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Descriptif
In this video, we are discussing theSLAM problem: simultaneous localization and mapping.
Avec les mêmes intervenants et intervenantes
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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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3.8. 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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3.1. 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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3.7. Observability Rank Criterion
MartinelliAgostinoIn 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
MartinelliAgostinoIn this video we discuss the second two equations of the Kalman filter.
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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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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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2.1. Localization process in a probabilistic framework: basic concepts
MartinelliAgostinoIn 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
MartinelliAgostinoIn 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
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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5.9. Other approaches: Planning-based approaches
Vasquez GoveaAlejandro DizanIn 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 GoveaAlejandro DizanIn 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
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5.5. From trajectories to discrete time-state models
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5.8. Other approaches: Social Forces
Vasquez GoveaAlejandro DizanIn 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 GoveaAlejandro DizanIn 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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4.1. Robot Perception for Dynamic environments: Outline and DP-Grids concept
LaugierChristianThe fourth part of the course addresses perception, situation awareness and decision making. In this first video, we're giving an outline of the problem and introducing the new concept of dynamic
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5.2. Expectation-Maximization Clustering
Vasquez GoveaAlejandro DizanIn this video, we are going to start working towards building a motion model.
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4.6. Detection and Tracking of Mobile Objects – Model and Grid based approaches
LaugierChristianThis video addresses the question of model-based and grid-based approaches.
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4.2. Dynamic Probabilistic Grids – Bayesian Occupancy Filter concept
LaugierChristianThis video will show how to describe Bayesian occupancy filter concept.
