NDLI: Object part segmentation and classification in range images for grasping

Content Provider	IEEE Xplore Digital Library
Author	Varadarajan, K.M. Vincze, M.
Copyright Year	2011
Description	Author affiliation: Vienna University of Technology, Automation and Control Institute, Gusshausstrasse 30 / E376, A-1040 Vienna, Austria (Varadarajan, K.M.; Vincze, M.)
Abstract	Recognition by Components (RBC) has been one of the most conceptually significant frameworks for modeling human visual object recognition. Extension of the model to practical robotic applications have been traditionally limited by the lack of good response in textureless areas in the case of conventional inexpensive stereo cameras as well as by the need for expensive laser based sensor systems to compensate for this deficiency. The recent availability of RGB-D sensors such as the PrimeSense sensor has opened new avenues for practical usage of these sensors for robotic applications such as grasping. In this paper, we present novel algorithms for segmentation of objects and parts from range images with extensions based on semantic cues to yield robust part detection. The detected parts are then parameterized using a superquadric based fitting framework and classified into one of different generic shapes. The categorization of the parts enables rules for grasping the object. This Grasping by Components (GBC) scheme is a natural extension of the RBC framework and provides a scalable framework for grasping of objects. This scheme also permits the grasping of novel objects in the scene, with at least one known grasp affordance. Results of the range segmentation are compared with another scene agnostic graph based segmentation approach.
Starting Page	21
Ending Page	27
File Size	1890230
Page Count	7
File Format	PDF
ISBN	9781457711589
e-ISBN	9781457711596
DOI	10.1109/ICAR.2011.6088647
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	Estonia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Grasping Image edge detection Robot sensing systems Cavity resonators Fitting Image segmentation Cognitive Object Recognition Affordance Recognition by Components Grasping by Components
Content Type	Text
Resource Type	Article

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