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COMPARISON OF THE GRAPH-OPTIMIZATION FRAMEWORKS G2O AND SBA
Mälardalen University, School of Innovation, Design and Engineering.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis starts with an introduction to Simulataneous Localization and Mapping (SLAM) and more background on Visual SLAM (VSLAM). The goal of VSLAM is to map the world with a camera, and at the same time localize the camera in that world. One important step is to optimize the acquired map, which can be done in several different ways. In this thesis, two state-of-the-art optimization algorithms are identified and compared, namely the g2o package and the SBA package. The results show that SBA is better on smaller datasets, and g2o on larger. It is also discovered that there is an error in the implementation of the pinhole camera model in the SBA package.

Place, publisher, year, edition, pages
2016. , 25 p.
Keyword [en]
SLAM, g2o, SBA, graph-optimization, new college, venice, mono-slam, VSLAM
National Category
Robotics
Identifiers
URN: urn:nbn:se:mdh:diva-31764OAI: oai:DiVA.org:mdh-31764DiVA: diva2:934179
Subject / course
Computer Science
Supervisors
Examiners
Available from: 2016-06-21 Created: 2016-06-08 Last updated: 2016-06-21Bibliographically approved

Open Access in DiVA

fulltext(2352 kB)102 downloads
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8c197f1e70f5320bf8c76df1f513d1387c243779f2caf361efeaef7d429a8ea879d65e4b8e552545bae2aed2ba7e380ada5d5c6c8db293bd171e4384cf1e9ac1
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School of Innovation, Design and Engineering
Robotics

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf