2026-04-13T01:23:46Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1044072026-01-30T08:38:25Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Ramisa Ayats, Arnau author Alenyà Ribas, Guillem author Moreno-Noguer, Francesc author Torras, Carme author 2016 © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor. In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section. Peer Reviewed Postprint (author's final draft) Àrees temàtiques de la UPC::Informàtica::Robòtica computer vision 3D descriptor Recognition Detection Grasping Manipulation Robotics Classificació INSPEC::Pattern recognition::Computer vision A 3D descriptor to detect task-oriented grasping points in clothing