3.7. Observability Rank Criterion

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.

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.

In this video I want to explain the steps that we have to follow in order to implement an extended Kalman filter in robotics.

In this video we discuss the second two equations of the Kalman filter.

In this video, we are discussing the SLAM problem: simultaneous localization and mapping.

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.

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

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

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

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.

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

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