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Model-Free segmentation and grasp selection of unknown stacked objects

Asif, U., Bennamoun, M. and Sohel, F. (2014) Model-Free segmentation and grasp selection of unknown stacked objects. Lecture Notes in Computer Science, 8693 . pp. 659-674.

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We present a novel grasping approach for unknown stacked objects using RGB-D images of highly complex real-world scenes. Specifically, we propose a novel 3D segmentation algorithm to generate an efficient representation of the scene into segmented surfaces (known as surfels) and objects. Based on this representation, we next propose a novel grasp selection algorithm which generates potential grasp hypotheses and automatically selects the most appropriate grasp without requiring any prior information of the objects or the scene. We tested our algorithms in real-world scenarios using live video streams from Kinect and publicly available RGB-D object datasets. Our experimental results show that both our proposed segmentation and grasp selection algorithms consistently perform superior compared to the state-of-the-art methods.

Item Type: Journal Article
Publisher: Springer Verlag
Copyright: 2014 Springer International Publishing Switzerland
Notes: Book Title: Computer Vision – ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V
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