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3.1. Examples for the Action in the EKF
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Descriptif
In part 2, we have seen theequations of the Bayes filter, which are the generalequations which allow us to update the probabilitydistribution, as the data from both proprioceptive sensors andexteroceptive sensors are delivered.We have seen a possibleimplementation of these equations, based on a numerical solution: the grid localization.We have also started to see the equations of the Kalman filter, or better theextended Kalman filter.
In part 3, we want to better explain theseequations starting from a very simple example in 1D.Then we willconsider problems like simultaneous localizationmapping, and othertheoretical issue about estimation.
In this video, westart to discuss the first two equations of the Kalman filter.
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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
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3.4. The use of the EKF 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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3.5. Simultaneous Localization and Mapping (SLAM)
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3.7. Observability Rank Criterion
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2.3. Wheel encoders for a differential drive vehicle
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2.8. The Extended Kalman Filter (EKF)
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2.4. Sensor statistical models
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