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HSRを用いた自動走行
2019年5月12日
瀬戸内ROS勉強会第1回
河田尚孝
自己紹介
氏名:河田 尚孝(かわた なおたか)
所属:岡山県立大学大学院2年
研究:情報検索、機械翻訳
ロボット歴:
WRS2018(3位):eR@sers
レスコン2016(優勝):メヒャニカ
2
講演内容
1. 生活支援ロボットHSR
2. インタラクティブ清掃
3. 指差し認識
4. 姿勢推定
5. 自己位置推定及びマップ生成
6. まとめ
3
講演内容
1.生活支援ロボットHSR
2. インタラクティブ清掃
3. 指差し認識
4. 姿勢推定
5. 自己位置推定及びマップ生成
6. まとめ
4
HSR(Human Support Robot)
介助・自立・生活面の幅広いサポートを
目指す生活支援ロボット。
物を運ぶ・拾うなどができる
介護予防や健康管理への応用
5
講演内容
1. 生活支援ロボットHSR
2.インタラクティブ清掃
3. 指差し認識
4. 姿勢推定
5. 自己位置推定及びマップ生成
6. まとめ
6
インタラクティブ清掃
課題:
アバターの指差し動作から指定された物体の
推測、把持、移動を行う
7
講演内容
1. 生活支援ロボットHSR
2. インタラクティブ清掃
3.指差し認識
4. 姿勢推定
5. 自己位置推定及びマップ生成
8
OpenPose
動画像から二次元の骨格情報を推定
高速な計算が可能(GTX1080で8.8FPS)
9
Realtime Multi-Person 2D Pose
Estimation using Part Affinity
Fields, Zhe Cao et al., 2017.
指差し画像
10
OpenPose使用結果
11
講演内容
1. 生活支援ロボットHSR
2. インタラクティブ清掃
3. 指差し認識
4.姿勢推定
5. 自己位置推定及びマップ生成
6. まとめ
12
Point Cloud
3次元座標(x,y,z)と色(RGB)が
格納されたデータ
→RGBD画像
13
姿勢推定の流れ
14
プログラム本体 OpenPose外部フォルダ
指差し時の画像&
PointCloudの取得
指差し画像の解析
アバターの姿勢を
推定
画像
アバター
座標
アバターの座標と
PointCloudから
物体の位置を推定
講演内容
1. 生活支援ロボットHSR
2. インタラクティブ清掃
3. 指差し認識
4. 姿勢推定
5.自己位置推定及びマップ生成
6. まとめ
15
SLAM(Simultaneous Localization
and Mapping)
自己位置推定とマップ生成を
同時に行うこと
自分で調べて納得してください
(キーワード:SLAM、amcl)
参考文献
https://qiita.com/MoriKen/items/0b75ab2
91ab0d95c37c2
16
SLAMの例
17https://www.youtube.com/watch?v=cK6s7soVwws
実際に生成したマップ
18
なぜSLAMを使うのか(自動運転)
19
従来
人間の目で環境を判断
自動運転
センサなどから環境情報を取り込み、
経路探索を行う必要があるため
自己位置推定とマップ生成が不可欠
まとめ
SLAMによって現実空間のシュミレーショ
ンが出来る
ただし障害物かどうかまでは分からないた
め、画像処理技術と組み合わせたりする
必要がある
20
appendix
21
成功例(左下のチーム)
22https://www.youtube.com/watch?v=PTyfEskbK2I&list=PL5Oq_p2DlThHZSWVsbo4_X97Wd1oK
-vTV&index=3&t=7176s
ROSを動かしているところ
23
Yoloの識別結果
24

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SetoROS_0512

Editor's Notes

  1. 先ほどの紹介からタイトルが変わっております。ご容赦ください。