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Intuitively Trained Segmentation of Industrial Workpieces for Robotic Manipulation

Möller, Pascal (2017) Intuitively Trained Segmentation of Industrial Workpieces for Robotic Manipulation. Master's. DLR-Interner Bericht. DLR-IB-RM-OP-2017-18.

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Abstract

The research and application field robotics consists of a wide range of complex subtasks that all need to work together as a unit to produce a secure, reliable and efficient machine humans can work with. This thesis presents a solution to robotic vision using an intuitively trained segmentation as basis for robotic manipulation of industrial workpieces. Features, such as sharp edges and corners, are common keypoints to look for if the objects provide surface textures. However, industrial workpieces lack of those textures and are often metallic what causes the reflection of light to have a big impact on the objects appearance. Hence, the method described in this thesis is not based on edges but uses geometrical primitives to detect an object. The relations among those primitives play a key role in detecting the position of an object. They build a so called similarity matrix which represents the matching behaviour of primitive pairs. This matrix is then used in a subsequent segmentation process to assign detected primitives to corresponding objects in the image. Although most detection approaches depend on a high number of training data to achieve good results, the method proposed in this thesis gets a long with a few images. The results in chapter 5 show that two arbitrarily positioned objects can be clustered correctly with only 9 training images.

Item URL in elib:https://elib.dlr.de/117764/
Document Type:Monograph (DLR-Interner Bericht, Master's)
Title:Intuitively Trained Segmentation of Industrial Workpieces for Robotic Manipulation
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Möller, PascalUNSPECIFIEDUNSPECIFIED
Date:2017
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:image processing, segmentation, edge map
Institution:Technische Universität München
Department:Fakultät für Informatik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Marton, Dr. Zoltan-Csaba
Deposited On:02 Jan 2018 13:32
Last Modified:02 Jan 2018 13:32

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