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SLAMっぽい方法で動きを測る
@kiy (入江清)
Twitter: kiy_at_dev
Github: kiyoshiiriemon
Web: http://furo.org/irie/
2018/06/19 ROS Japan UG #24 勉強会
ワールドカップ,始まりましたね!
kiy 「SLAMっぽい方法で動きを測る」
もちろん,ロボットの方です!
kiy 「SLAMっぽい方法で動きを測る」
Robocup 2018 モントリオールで開催中
CIT Brainsを応援してください!
試合経過
kiy 「SLAMっぽい方法で動きを測る」
https://www.robocuphumanoid.org/hl-2018/results/ より
 CIT Brainsはグループリーグを2位通過
話は変わってSLAM
 SLAM: センサデータをつないで地図を作る
 自分の位置がわからないと綺麗につながらない
kiy 「SLAMっぽい方法で動きを測る」
友納正裕「SLAM入門」のサンプルプログラム LittleSLAM より
https://github.com/furo-org/LittleSLAM
ROS対応LittleSLAM処理の様子
kiy 「SLAMっぽい方法で動きを測る」
Sensor log data courtesy by Yoshitaka Hara
ポーズ調整
kiy 「SLAMっぽい方法で動きを測る」
 逐次的なSLAM処理で累積した誤差を修正
⁃ 各時刻のロボット位置と拘束(観測)をグラフで表現
⁃ 誤差が小さくなるよう過去の経路全体を修正する
ノード (i,j)間の観測誤差
LittleSLAMのROS対応
 ソースはGitHubで公開
⁃ https://github.com/kiyoshiiriemon/little_slam/
 設定を変えて実験できる(設定AからIまで)
kiy 「SLAMっぽい方法で動きを測る」
ポーズ調整あり(設定H)ポーズ調整なし(設定I)
ここから本題
kiy 「SLAMっぽい方法で動きを測る」
卓球のスイングを分析したい
kiy 「SLAMっぽい方法で動きを測る」
IMUを使ってスイングの軌跡を推定
 単純に加速度と角速度を積分するだけではダメ
⁃ 加速度を2回積分すると誤差は急速に累積
kiy 「SLAMっぽい方法で動きを測る」
 各時刻の状態をノード,拘束をエッジ
⁃ 拘束: ノード間の状態差分 (姿勢,速度,位置)
 拘束に関する誤差の総和を最小化
グラフベース最適化で誤差修正
時系列的拘束
(IMU観測から)
ループを閉じる拘束
(運動の規則などから)
は状態の差分を取る演算子
kiy 「SLAMっぽい方法で動きを測る」
修正すると
kiy 「SLAMっぽい方法で動きを測る」
 打球の左右をPCA第一主成分で分離できる
⁃ 修正前は時系列が第一主成分 (つまりドリフト)
 今後の取り組み
⁃ コースの回帰予測,ミスの原因推定など
⁃ 処理時間の短縮(現在数十秒かかる)
インパクトの瞬間の状態を分析
修正前 修正後
第一主成分
14
まとめ
 ポーズ調整に似た方法でIMUの累積誤差を修正
⁃ 論文はRobomech Journalに掲載
⁃ K. Irie, “A Graph Optimization Approach for Motion
Estimation Using Inertial Measurement Unit Data”
⁃ https://rdcu.be/QmDw
 実際の卓球データの分析を進めてます
 おまけ:
⁃ LittleSLAMのROS版を公開しました
⁃ CIT Brainsを応援してください!
kiy 「SLAMっぽい方法で動きを測る」

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SLAMっぽ方法で動きを測る (ROS Japan UG #24)