3次元変形物体操作のためのロバスト形状推定
Robust Shape Estimation for 3D Deformable Object Manipulation
RGB-Dカメラの入力と参照形状モデルを同時に追跡・更新する枠組みにより、実時間・モデルフリーでノイズや遮蔽に頑健な変形物体の形状推定手法を提案し、実機ロボットで評価した。
著者: Tao Han, Xuan Zhao, Peigen Sun, Jia Pan
分類: cs.RO
原文アブストラクト
Existing shape estimation methods for deformable object manipulation suffer from the drawbacks of being off-line, model dependent, noise-sensitive or occlusion-sensitive, and thus are not appropriate for manipulation tasks requiring high precision. In this paper, we present a real-time shape estimation approach for autonomous robotic manipulation of 3D deformable objects. Our method fulfills all the requirements necessary for the high-quality deformable object manipulation in terms of being real-time, model-free and robust to noise and occlusion. These advantages are accomplished using a joint tracking and reconstruction framework, in which we track the object deformation by aligning a reference shape model with the stream input from the RGB-D camera, and simultaneously upgrade the reference shape model according to the newly captured RGB-D data. We have evaluated the quality and robustness of our real-time shape estimation pipeline on a set of deformable manipulation tasks implemented on physical robots. Videos are available at https://lifeisfantastic.github.io/DeformShapeEst/
