We will talk today of localization.

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.

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

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

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.

Now, localization is nothing else but the iteration of sense and move.

If you understand this step, you understand localization.

Thrun Here is the application of HMM to a real robot localization example.

One of our largest sources of community volunteer work is localization, which is basically translation.

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.

The probability of getting a head and then another head.

But what you've implemented is the core of Google's self driving car localization idea.

So localization has a lot of math, but before I dive into mathematical detail,

Let's talk about localization of a car like our DARPA Urban Challenge car Junior.

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.

The head of a Gump.

I want to spend a few minutes and go over the formal definition of localization.

Head tail head overlaps itself.

That's 1 head, 1 head.

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.

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

Head

You've done exactly what I gave you intuitively in the beginning as the secret of localization.

We have ten head of cattle.

I have 1,500 head of cattle.

So get out of your head!

At the head of the arroyo.

Professor Snape, head of Slytherin house.

Before we're done with localization, I'd like to talk about robot motion.

This representation is absolutely core to probability and to mobile robot localization.

Head of a lion, body of a human.

Thrun I want to show you a little animation of hidden Markov models used for robot localization.

If you went head tail head tail head, you can cunningly get two occurrences of the pattern in only five tosses.