Enabling grasping of unknown objects through a synergistic use of edge and surface information

Authors:
Gert Kootstra;Mila Popović;Jimmy Alison Jørgensen;Kamil Kuklinski;Konstantsin Miatliuk;Danica Kragic;Norbert Krüger
Affiliations:
The Computer Vision and Active Perception Lab and the Centre for Autonomous Systems, CSC, Royal Institute of Technology (KTH), Stockholm, Sweden;The Cognitive Vision Lab, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark;The Robotics Lab, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark;Automation and Robotics Department, Białystok University of Technology, Poland;Automation and Robotics Department, Białystok University of Technology, Poland;The Computer Vision and Active Perception Lab and the Centre for Autonomous Systems, CSC, Royal Institute of Technology (KTH), Stockholm, Sweden;The Cognitive Vision Lab, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark
Venue:
International Journal of Robotics Research
Year:
2012

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Abstract

Grasping unknown objects based on visual input, where no a priori knowledge about the objects is used, is a challenging problem. In this paper, we present an Early Cognitive Vision system that builds a hierarchical representation based on edge and texture information which provides a sparse but powerful description of the scene. Based on this representation, we generate contour-based and surface-based grasps. We test our method in two real-world scenarios, as well as on a vision-based grasping benchmark providing a hybrid scenario using real-world stereo images as input and a simulator for extensive and repetitive evaluation of the grasps. The results show that the proposed method is able to generate successful grasps, and in particular that the contour and surface information are complementary for the task of grasping unknown objects. This allows for dealing with rather complex scenes.