Communications in Information and Systems

Volume 18 (2018)

Number 2

Robust shape estimation for 3D deformable object manipulation

Pages: 107 – 124

DOI: http://dx.doi.org/10.4310/CIS.2018.v18.n2.a3

Authors

Tao Han (Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong)

Xuan Zhao (Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong)

Peigen Sun (Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong)

Jia Pan (Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong)

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.

Full Text (PDF format)

This work was supported by the HKSAR Research Grants Council (RGC) General Research Fund (GRF) CityU 21203216, and by the NSFC/RGC Joint Research Scheme (CityU103/16-NSFC61631166002).

Published 17 October 2018

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