Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment
Christos Kampouris, Ioannis Mariolis, Georgia Peleka, Evangelos Skartados, Andreas Kargakos, Dimitra Triantafyllou, Sotiris Malassiotis
Abstract
Perception of garments is a challenging task for robots due to the large variety in shapes, fabric patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric stereo sensors that interacts with the garment through a combination of different basic actions. By applying machine learning techniques on the autonomously acquired data of different modalities we recognize the manipulated garment's type, fabric pattern, and material. Despite the challenges imposed by the unconstrained environment, promising performances are achieved for the majority of the recognition tasks.
BibTeX
@inproceedings{icra2016_multisensorialan,
title = {Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment},
author = {Christos Kampouris and Ioannis Mariolis and Georgia Peleka and Evangelos Skartados and Andreas Kargakos and Dimitra Triantafyllou and Sotiris Malassiotis},
booktitle = {ICRA 2016},
year = {2016}
}