Localization - Artificial Intelligence for RoboticsYouTube↗  IA↗(long shot)
Education  7.4K views 6 likes 2 dislikes   02 Mar 2012
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今度は位置推定の問題です
Let me ask you a localization question.
平面上を移動するロボットの例を覚えていますね
You remember a robot operating in a plane environment has usually 3 coordinates.
3つの座標がありX座標、Y座標、進行方向です これは方向と呼ばれます
It has an x-coordinate, a y-coordinate, and a heading direction--often called orientation.
飛行ロボットでは座標が増えます
Now, flying robots have more coordinates.
ロボットが自由空間で どんな動きでも可能だとしたら
If you can orient the robot fully in free space then you have an x, y, and z,
X、Y、Zに加え3つの回転角もあります
and you also happen to have 3 rotation angles--
これらはロール・ピッチ・ヨーと呼ばれています
often called roll, pitch, and yaw.
高次元状態空間のロボットのために 位置推定システムを構築したら
If you built a localization system for robots with higher dimensional state spaces,
ヒストグラムに基づいた 位置推定法に対して
I wonder how the memory used will scale
メモリ使用は どのようにスケールすると思いますか？
for our histogram-based localization method.
位置推定に使う状態変数の数には メモリが直線的にスケールするか
Does memory scale linearly, quadratically, exponentially, or none of the above
二次元的か指数関数的か またはどれにも当てはまらないのでしょうか？
in the number of state variables used in localization?
平面上を移動するロボットには3つあります
Again, for a robot operating on a plane, there will be three of them.
なので状態変数の数も3つです
So the number of state variables will be three.
X、Y、Zとロール・ピッチ・ヨーを持つ 飛行ロボットならば
If you were to look at a flying robot where you have x, y, z, roll, pitch, and yaw,
変数は6つになります
You would get six such variables,
基本的なヒストグラム位置推定の メモリ使用がどのようにスケールするのか
and I wonder how the memory use of the basic histogram localization scales.
4つの選択肢から1つだけ選んでください
Please check exactly one of those four boxes over here.
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