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Título

FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation

Autor Ramisa, Arnau; Alenyà, Guillem ; Moreno-Noguer, Francesc; Torras, Carme
Fecha de publicación 2013
EditorInstitute of Electrical and Electronics Engineers
Citación IEEE/RSJ International Conference on Intelligent Robots and Systems: 824-830 (2013)
ResumenMost current depth sensors provide 2.5D range images in which depth values are assigned to a rectangular 2D array. In this paper we take advantage of this structured information to build an efficient shape descriptor which is about two orders of magnitude faster than competing approaches, while showing similar performance in several tasks involving deformable object recognition. Given a 2D patch surrounding a point and its associated depth values, we build the descriptor for that point, based on the cumulative distances between their normals and a discrete set of normal directions. This processing is made very efficient using integral images, even allowing to compute descriptors for every range image pixel in a few seconds. The discriminative power of our descriptor, dubbed FINDDD, is evaluated in three different scenarios: recognition of specific cloth wrinkles, instance recognition from geometry alone, and detection of reliable and informed grasping points.
Descripción Trabajo presentado al IROS celebrado en Tokyo del 3 al 7 de noviembre de 2013.
Versión del editorhttp://dx.doi.org/10.1109/IROS.2013.6696446
URI http://hdl.handle.net/10261/96393
DOI10.1109/IROS.2013.6696446
Identificadoresdoi: 10.1109/IROS.2013.6696446
issn: 2153-0858
e-issn: 2153-0866
Aparece en las colecciones: (IRII) Artículos
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