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
Graphical SLAM using vision and the measurement subspace
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (CAS)ORCID iD: 0000-0002-7796-1438
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-1170-7162
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2005 (English)In: 2005 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-4, IEEE conference proceedings, 2005, 325-330 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we combine a graphical approach for simultaneous localization and mapping, SLAM, with a feature representation that addresses symmetries and constraints in the feature coordinates, the measurement subspace, M-space. The graphical method has the advantages of delayed linearizations and soft commitment to feature measurement matching. It also allows large maps to be built up as a network of small local patches, star nodes. This local map net is then easier to work with. The formation of the star nodes is explicitly stable and invariant with all the symmetries of the original measurements. All linearization errors are kept small by using a local frame. The construction of this invariant star is made clearer by the M-space feature representation. The M-space allows the symmetries and constraints of the measurements to be explicitly represented. We present results using both vision and laser sensors.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2005. 325-330 p.
Keyword [en]
SLAM, vision, graph, features
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-38228DOI: 10.1109/IROS.2005.1545493ISI: 000235632103049Scopus ID: 2-s2.0-79957980708ISBN: 0-7803-8912-3 (print)OAI: oai:DiVA.org:kth-38228DiVA: diva2:436290
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, CANADA. AUG 02-06, 2005
Note

QC 20111010

Available from: 2012-11-16 Created: 2011-08-23 Last updated: 2012-11-16Bibliographically approved

Open Access in DiVA

fulltext(518 kB)155 downloads
File information
File name FULLTEXT02.pdfFile size 518 kBChecksum SHA-512
62a4001f06fac07e2a1676b99fd8d8b10d2703955456b0f79874f2b339961fd0f43ab4e7873d513b8ea55d1cf95c85204e114e7baaa277b9e714231af888c04e
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopusGraphical SLAM using Vision and the Measurement SubspaceIEEEXplore

Authority records BETA

Folkesson, JohnJensfelt, Patric

Search in DiVA

By author/editor
Folkesson, JohnJensfelt, PatricChristensen, Henrik
By organisation
Computer Vision and Active Perception, CVAPCentre for Autonomous Systems, CAS
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 155 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

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 92 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