RGBD画像からの3D人間姿勢推定によるロボットタスク学習
3D Human Pose Estimation in RGBD Images for Robotic Task Learning
単一のRGBD画像から実世界スケールで3D人間姿勢を推定し、マーカなしでPR2ロボットに人間の操作動作を模倣させる手法を提案した論文。
著者: Christian Zimmermann, Tim Welschehold, Christian Dornhege, Wolfram Burgard, Thomas Brox
分類: cs.CV, cs.RO
原文アブストラクト
We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.
