Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Matching Feature Points in 3D World
Högskolan i Jönköping, Tekniska Högskolan, JTH, Data- och elektroteknik.
2012 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

This thesis work deals with the most actual topic in Computer Vision field which is scene understanding and this using matching of 3D feature point images. The objective is to make use of Saab’s latest breakthrough in extraction of 3D feature points, to identify the best alignment of at least two 3D feature point images.

The thesis gives a theoretical overview of the latest algorithms used for feature detection, description and matching. The work continues with a brief description of the simultaneous localization and mapping (SLAM) technique, ending with a case study on evaluation of the newly developed software solution for SLAM, called slam6d.

Slam6d is a tool that registers point clouds into a common coordinate system. It does an automatic high-accurate registration of the laser scans. In the case study the use of slam6d is extended in registering 3D feature point images extracted from a stereo camera and the results of registration are analyzed.

In the case study we start with registration of one single 3D feature point image captured from stationary image sensor continuing with registration of multiple images following a trail.

Finally the conclusion from the case study results is that slam6d can register non-laser scan extracted feature point images with high-accuracy in case of single image but it introduces some overlapping results in the case of multiple images following a trail.

sted, utgiver, år, opplag, sider
2012. , s. 45
Emneord [en]
Computer Vision, Edges, Corners, 3D Feature Points, Point Clouds, Simultaneous Localization and Mapping (SLAM), 3D Scene, Iterative Closest Points Algorithm (ICP), Global Matching.
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-23049OAI: oai:DiVA.org:hj-23049DiVA, id: diva2:686457
Eksternt samarbeid
Saab Training Systems
Fag / kurs
JTH, Computer and Electrical Engineering
Veileder
Examiner
Tilgjengelig fra: 2014-01-28 Laget: 2014-01-12 Sist oppdatert: 2014-01-28bibliografisk kontrollert

Open Access i DiVA

fulltext(2606 kB)364 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 2606 kBChecksum SHA-512
53d8ae44ce0f34a731349474ca97a0fd61d1ff9e14e83bcce07b696eba1e5e1d6ae14a2eef17970cba189e991cca8e43d3f6151870d80b9340a2ce19b45964cc
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 364 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 436 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf