Robust Shape Estimation for 3D Deformable Object Manipulation

TL;DR

This paper introduces a real-time, model-free shape estimation method for deformable objects that is robust to noise and occlusion, enabling precise robotic manipulation.

Contribution

The proposed joint tracking and reconstruction framework achieves high-quality, real-time shape estimation without relying on pre-defined models or offline processing.

Findings

Effective in real-world robotic manipulation tasks

Robust to noise and occlusion

Operates in real-time

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/