Recognition of grasp points for clothes manipulation under unconstrained conditions
Author
dc.contributor.author
Martínez, Luz María
Author
dc.contributor.author
Ruiz del Solar, Javier
Admission date
dc.date.accessioned
2019-05-31T15:21:11Z
Available date
dc.date.available
2019-05-31T15:21:11Z
Publication date
dc.date.issued
2017
Cita de ítem
dc.identifier.citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volumen 11175 LNAI, 2017
Identifier
dc.identifier.issn
16113349
Identifier
dc.identifier.issn
03029743
Identifier
dc.identifier.other
10.1007/978-3-030-00308-1_29
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/169527
Abstract
dc.description.abstract
In this work a system for recognizing grasp points in RGB-D
images is proposed. This system is intended to be used by a domestic
robot when deploying clothes lying at a random position on a table. By
taking into consideration that the grasp points are usually near key parts
of clothing, such as the waist of pants or the neck of a shirt. The proposed
system attempts to detect these key parts first, using a local multivariate
contour that adapts its shape accordingly. Then, the proposed system
applies the Vessel Enhancement filter to identify wrinkles in the clothes,
allowing to compute a roughness index for the clothes. Finally, by mixing
(i) the key part contours and (ii) the roughness information obtained by
the vessel filter, the system is able to recognize grasp points for unfolding
a piece of clothing. The recognition system is validated using realistic
RGB-D images of different cloth types.