Martínez, L., Ruiz-del-Solar, J., Sun, L. orcid.org/0000-0002-0393-8665 et al. (2 more authors) (2019) Continuous perception for deformable objects understanding. Robotics and Autonomous Systems, 118. pp. 220-230. ISSN 0921-8890
Abstract
We present a robot vision approach to deformable object classification, with direct application to autonomous service robots. Our approach is based on the assumption that continuous perception provides robots with greater visual competence for deformable objects interpretation and classification. Our approach thus classifies the category of clothing items by continuously perceiving the dynamic interactions of the garment’s material and shape as it is being picked up. Our proposed solution consists of extracting continuously visual features of a RGB-D video sequence and fusing features by means of the Locality Constrained Group Sparse Representation (LGSR) algorithm. To evaluate the performance of our approach, we created a fully annotated database featuring 150 garment videos in random configurations. Experiments demonstrate that by continuously observing an object deform, our approach achieves a classification score of 66.7%, outperforming state-of-the-art approaches by a ∼ 27.3% increase.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier. This is an author produced version of a paper subsequently published in Robotics and Autonomous Systems. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Deformable object classification; Continuous perception; Robot vision |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Dec 2019 14:12 |
Last Modified: | 30 May 2020 00:39 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.robot.2019.05.010 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154468 |
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