FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
Most 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.
CitationRamisa, A. [et al.]. FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation. A: IEEE/RSJ International Conference on Intelligent Robots and Systems. "IROS 2013: New Horizon Conference Digest: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems: Tokyo, Big Sight, Tokyo, Japan, November 3-8, 2013". Tokyo: Institute of Electrical and Electronics Engineers (IEEE), 2013, p. 824-830.