LiU Electronic Press
Full-text not available in DiVA
Author:
Heintz, Fredrik (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Kvarnström, Jonas (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology) (APD)
Doherty, Patrick (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)
Title:
Stream-Based Hierarchical Anchoring
Department:
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, UASTECH - Autonomous Unmanned Aircraft Systems Technologies
Publication type:
Article in journal (Refereed)
Language:
English
Publisher: Springer
Status:
Published
In:
Künstliche Intelligenz(ISSN 0933-1875)
Volume:
27
Issue:
2
Pages:
119-128
Year of publ.:
2013
URI:
urn:nbn:se:liu:diva-89895
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-89895
Subject category:
Computer and Information Science
Engineering and Technology
Project:
CUAS, CADICS, ELLIIT, NFFP5, CENIIT
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.

Research funder:
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Research funder:
Linnaeus research environment CADICS
Research funder:
Swedish Foundation for Strategic Research
Research funder:
Swedish Research Council
Available from:
2013-03-11
Created:
2013-03-11
Last updated:
2013-08-29
Statistics:
43 hits