Tracking Intro - Artificial Intelligence for Robotics Udacity English Japanese

Tracking Intro - Artificial Intelligence for RoboticsYouTube↗ IA↗(long shot)
Education 31.9K views 44 likes 0 dislikes 02 Mar 2012

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I'd like to take my students on a little journey to Stanford

スタンフォード大学へのちょっとした旅にお連れして

and show them our self-driving car that uses sensors to sense the environment.

周囲の状況を認識するセンサを使用する自動運転車をお見せしました

So let me dive into the class pretty much right now.

ではさっそく講義を始めます

In our last class, we talked about localization.

レッスン1は位置推定についてでした

We had a robot that lived in an environment and that could use its sensors to determine where in the environment it is.

ある環境に住んでいるロボットがいてその環境のどこにいるのかを判断するためにセンサを使いました

Here you can see the Google self-driving car using a road map localizing itself.

このGoogleの自動運転車は道路地図を使用して自身の位置を推定します

But in addition, what is shown here in red are measurements of other vehicles.

赤で示されるものは他の乗り物の推定位置です

The car uses lasers and radars to track other vehicles and today we're going to talk about how to find other cars.

他の車を追跡するためにレーザとレーダを使いますではどのように他の車を見つけるのかをお話ししましょう

The reason why I would like to find other cars is because we wouldn't want to run into them.

他の車を見つけるのは衝突を避けるためです

We have to understand how to interpret sensor data to make assessments,

判断するためにはセンサ・データの解析方法を理解しなければいけません

not just where these other cars are as in the localization case,

位置推定のように他の車の位置だけでなく

but also how fast they're moving

どれだけの速さで動いているのかも判断し

so you can drive in a way that avoids collisions with them in the future.

起こり得る衝突を避けて運転することができます

That's important--not just for cars.

これは車のためだけでなく

It matters for pedestrians and for bicyclists.

歩行者や自転車にとっても重要なことです

Understand where other cars are an making predictions where they're going to move

他の車がどこにいるのか理解してその動きを予測することは

is absolutely essential for safe driving in the Google car project.

Googleの自動運転車の安全運転には必要不可欠です

ここでは追跡についてお話します

So in this class we're going to talk about tracking. The technique I'd like to teach you is called a Kalman filter.

カルマンフィルタと呼ばれる技術について学びましょう

This is an insanely popular technique for estimating the state of a system.

これはシステムの状態を予測する極めて一般的な技術で

It's actually very similar to the probabilistic localization method

前の講義で説明した確率論的な位置推定手法の

we taught in the previous class--Monte Carlo localization.

モンテカルロ位置推定に非常によく似ています

The primary differences are that Kalman filters estimate a continuous state

主な違いはカルマンフィルタは連続状態を予測するという点です

whereas in Monte Carlo localization we are voiced to chop the world into discrete places.

モンテカルロ位置推定では世界を個々の場所に分割しました

As a result, the Kalman filter happens to give us a unimodal distribution--

つまりカルマンフィルタは単峰型分布です

and I'll tell you in a second what that means--

それが何を意味するのかをお話していきます

whereas Monte Carlo was fine with multimodal distributions.

モンテカルロは多峰型分布でも問題ありませんでしたが

Both of these techniques are applicable to robot localization and tracking other vehicles.

ロボットの位置推定と他の車の追跡にはこれら両方が応用できます

In fact, in a later class, we're going to learn about particle filters,

後々のレッスンでは同様の問題を扱う方法の1つである

which are yet another way to address the same problem,

粒子フィルタについても学びます

and indeed they are actually continuous and multimodal.

しかし今はカルマンフィルタについて見ていきましょう

But for the time being let's look into Kalman filters

他の2つの方法は無視します

and ignore these other two families of methods.

例を出しましょう　ここに車があるとします

Let me start with a example. Consider the car done here. Let's assume the it sees as its measurement, an object here, here, here, here, and here for the times t = 0, t = 1, t = 2, and t = 3.

観測対象とみなすものとして物体をこれらの4ヵ所に配置しますそれぞれの時間はt＝0、t＝1、t＝2、t＝3です t＝4の時物体はどこにあると推測できますか

Where would you assume the object would be at t = 4? Check one of those 3 boxes.

3つのうち1つをチェックしてください

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