Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Direction estimation using visual odometry
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesisAlternative title
Uppskattning av riktning med visuell odometri (Swedish)
Abstract [en]

This Master thesis tackles the problem of measuring objects’ directions from a motionless observation point. A new method based on a single rotating camera requiring the knowledge of only two (or more) landmarks’ direction is proposed. In a first phase, multi-view geometry is used to estimate camera rotations and key elements’ direction from a set of overlapping images. Then in a second phase, the direction of any object can be estimated by resectioning the camera associated to a picture showing this object. A detailed description of the algorithmic chain is given, along with test results on both synthetic data and real images taken with an infrared camera.

Abstract [sv]

Detta masterarbete behandlar problemet med att mäta objekts riktningar från en fast observationspunkt. En ny metod föreslås, baserad på en enda roterande kamera som kräver endast två (eller flera) landmärkens riktningar. I en första fas används multiperspektivgeometri, för att uppskatta kamerarotationer och nyckelelements riktningar utifrån en uppsättning överlappande bilder. I en andra fas kan sedan riktningen hos vilket objekt som helst uppskattas genom att kameran, associerad till en bild visande detta objekt, omsektioneras. En detaljerad beskrivning av den algoritmiska kedjan ges, tillsammans med testresultat av både syntetisk data och verkliga bilder tagen med en infraröd kamera.

Place, publisher, year, edition, pages
2015. , 83 p.
Keyword [en]
direction, estimation, visual odometry, camera, computer vision, bundle adjustment, SLAM
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-168307OAI: oai:DiVA.org:kth-168307DiVA: diva2:816098
External cooperation
THALES OPTRONIQUE SAS
Subject / course
Computer Science
Educational program
Master of Science - Systems, Control and Robotics
Presentation
2015-04-02, 304, KTH Campus, Teknikringen 14, Stockholm, 09:30 (English)
Supervisors
Examiners
Available from: 2015-06-29 Created: 2015-06-01 Last updated: 2015-06-29Bibliographically approved

Open Access in DiVA

MasterThesis_ClementMasson(4586 kB)79 downloads
File information
File name FULLTEXT01.pdfFile size 4586 kBChecksum SHA-512
c71fad90ffcc226e5a458d3da8af5265cd5b63ee7330fc3e6278205e4479548bccc2a957a18cc8305b18dbaa6a0779c36b6aa3e704c3a4c9303e3c15cf66fda3
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Vision and Robotics (Autonomous Systems)Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 79 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 89 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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