Robust Shape Estimation for 3D Deformable Object Manipulation
This work addresses the need for precise and reliable shape estimation in robotic manipulation of deformable objects, which is incremental by improving upon existing methods that are off-line, model-dependent, or sensitive to noise and occlusion.
The paper tackles the problem of real-time shape estimation for 3D deformable object manipulation by developing a model-free, robust method that achieves high precision in physical robot tasks, as demonstrated through evaluations.
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/