liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Stream-Based Hierarchical Anchoring
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology. (APD)ORCID iD: 0000-0002-5500-8494
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies. Linköping University, The Institute of Technology.
2013 (English)In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 27, no 2, p. 119-128Article in journal (Refereed) Published
Abstract [en]

Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols denoting physical objects and sensor data being collected about them, a process called anchoring.In this paper we present a stream-based hierarchical anchoring framework. A classification hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives.  Symbols can be associated with an object at any level of classification, permitting symbolic reasoning on different levels of abstraction. The approach is integrated in the DyKnow knowledge processing middleware and has been applied to an unmanned aerial vehicle traffic monitoring application.

Place, publisher, year, edition, pages
Springer, 2013. Vol. 27, no 2, p. 119-128
National Category
Computer and Information Sciences Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-89895DOI: 10.1007/s13218-013-0239-2OAI: oai:DiVA.org:liu-89895DiVA, id: diva2:610375
Projects
CUASCADICSELLIITNFFP5CENIIT
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICSSwedish Foundation for Strategic Research Swedish Research CouncilAvailable from: 2013-03-11 Created: 2013-03-11 Last updated: 2018-01-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Heintz, FredrikKvarnström, JonasDoherty, Patrick

Search in DiVA

By author/editor
Heintz, FredrikKvarnström, JonasDoherty, Patrick
By organisation
KPLAB - Knowledge Processing LabThe Institute of TechnologyUASTECH - Autonomous Unmanned Aircraft Systems Technologies
In the same journal
Künstliche Intelligenz
Computer and Information SciencesEngineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 324 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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