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Main Title: Probabilistic multi-class segmentation for the Amazon picking challenge
Author(s): Jonschkowski, Rico
Eppner, Clemens
Höfer, Sebastian
Martín-Martín, Roberto
Brock, Oliver
Type: Research Paper
Language Code: en
Abstract: We present a method for multi-class segmentation from RGB-D data in a realistic warehouse picking setting. The method computes pixel-wise probabilities and combines them to find a coherent object segmentation. It reliably segments objects in cluttered scenarios, even when objects are translucent, reflective, highly deformable, have fuzzy surfaces, or consist of loosely coupled components. The robust performance results from the exploitation of problem structure inherent to the warehouse setting. The proposed method proved its capabilities as part of our winning entry to the 2015 Amazon Picking Challenge. We present a detailed experimental analysis of the contribution of different information sources, compare our method to standard segmentation techniques, and assess possible extensions that further enhance the algorithm’s capabilities. We release our software and data sets as open source.
Issue Date: Feb-2016
Date Available: 17-Mar-2016
DDC Class: 600 Technik, Technologie
Subject(s): robotics
multi-class segmentation
Series: Technical Report of the Robotics and Biology Laboratory, Department of Computer Engineering and Microelectronics, Technische Universität Berlin
Series Number: RBO-2016-01
Appears in Collections:Inst. Technische Informatik und Mikroelektronik » Publications

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