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Robot based 3D scanning and recognition of workpieces
Örebro University, School of Science and Technology.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Quality inspection of a product is central of many manufacturing processes.

While inspection on flat surfaces can be made fairly autonomous today, highly

reflective free-form objects is problematic in many ways. This thesis is one part

out of a two-part project investigating in an autonomous way to recognize,

model, store relevant information and inspect these kind of work pieces. This

part will focus on the recognition, modeling and database design. The system,

established in this thesis will use a robotic manipulator, an industrial camera

and the handheld 3-D scanner Exascanner. We present a methodology for

preparing a work piece to be inspected autonomously and a simple implementation

of the proposed methodology. The implementation recognizes workpieces

with a support vector machine trained on histogram of oriented gradients features.

These features are extracted from several pictures taken from different

angles around the workpiece. The use of different angles are to make the classifier

more versatile and robust to object being rotated or moved. If the workpeice

is not recognized a spiral shaped dome path is created, scaled with the help of

the pictures already taken. This shape helps ensuring a high quality scan of objects

were there is no shape information to be used. The robotic manipulator is

used to move the scanner along the path around the object, creating a surface

profile of the object. This profile is built up of triangular facets of various size

and needs to be processed before inspection of the surface can be made. A recursive

splitting algorithm is used to make the facets as equilateral as possible

and to make their size more suitable for the viewing range of the surface inspection

camera. As a final step this information is stored in a database to be

used later as support during inspection.

 

Place, publisher, year, edition, pages
2011.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-21276ISRN: ORU-NAT/DVE-AS-2012/1--SEOAI: oai:DiVA.org:oru-21276DiVA, id: diva2:482261
Subject / course
Computer Engineering
Uppsok
Technology
Supervisors
Examiners
Available from: 2012-01-30 Created: 2012-01-23 Last updated: 2018-01-12Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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