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.

The deterrent principle.

i can't do it. it's the principle... principle of it.

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.

That's the principle objective.

Here's an economic principle.

Consider the following principle, which I will modestly call Wilson's Principle Number One

If you understand this step, you understand localization.

He stuck to his principle.

He figured out a principle.

Carelessness is his principle feature.

The principle is the same.

There are three principle actions.

Well, there's two principle ways.

We use the Premack principle.

So Wilson's Principle Number Three

What is it, this principle?

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.

That doesn't accord with my principle.

Now, who remembers Heisenberg's uncertainty principle?

Now, it's the exact same principle.

But you need understand this principle.

Now that you know the principle,

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.

Everyone will accept that idea in principle.

I agree to the proposal in principle.

Years ago this principle was widely recognized.

Here are two examples of this principle.

So let's apply this principle to Siberia.

But nevertheless the principle is the same.

The management principle is dollars and cents.

However, all machine follow the same principle.

This is in principle our next belief.

The principle root is a positive root.