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arXiv · 1809.09802

Robust Shape Estimation for 3D Deformable Object Manipulation

Tao Han·Xuan Zhao·Peigen Sun·Jia Pan
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Abstract

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/

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Tao Han, Xuan Zhao, Peigen Sun, Jia Pan. 2018-09-26. Robust Shape Estimation for 3D Deformable Object Manipulation. https://arxiv.org/abs/1809.09802

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