Just to recap, in the class we learned about localization. Localization

That's the localization question.

You learned about localization.

Here is a localization quiz.

We will talk today of localization.

Let's get back to Monte Carlo localization.

Here is the critical step for localization.

So congratulations. You understand probability and localization.

Let me ask you a localization question.

If you understand this step, you understand localization.

So localization, for example, it's clearly an approximate filter.

You should say to yourself, I just implemented localization.

What it really means is simultaneous localization and mapping.

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It did localization similar to what I taught you before.

The next question we had was about internationalization and localization.

You wrote an algorithm that implements what's called Markov localization.

This process is a very calculated process.

The very first problem I'm trying to solve is called localization.

In the Google self driving car, localization plays a key role.

If you understood this, you understand probability, and you understand localization.

You've programmed the core of Google's self driving car localization methods.

Sebastian Localization does depend on having a map of the environment.

Well, it was really easy to compute in our localization example.

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