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Multi-scale activity estimation with spatial abstractions
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.ORCID iD: 0000-0003-1114-6040
2017 (English)In: 3rd International Conference on Geometric Science of Information, GSI 2017, Springer, 2017, Vol. 10589, p. 273-281Conference paper, Published paper (Refereed)
Abstract [en]

Estimation and forecasting of dynamic state are fundamental to the design of autonomous systems such as intelligent robots. State-of-the-art algorithms, such as the particle filter, face computational limitations when needing to maintain beliefs over a hypothesis space that is made large by the dynamic nature of the environment. We propose an algorithm that utilises a hierarchy of such filters, exploiting a filtration arising from the geometry of the underlying hypothesis space. In addition to computational savings, such a method can accommodate the availability of evidence at varying degrees of coarseness. We show, using synthetic trajectory datasets, that our method achieves a better normalised error in prediction and better time to convergence to a true class when compared against baselines that do not similarly exploit geometric structure.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10589, p. 273-281
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10589
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-218313DOI: 10.1007/978-3-319-68445-1_32ISI: 000440482500032Scopus ID: 2-s2.0-85033660648ISBN: 9783319684444 OAI: oai:DiVA.org:kth-218313DiVA, id: diva2:1160465
Conference
3rd International Conference on Geometric Science of Information, GSI 2017, Paris, France, 7 November 2017 through 9 November 2017
Note

QC 20171127

Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-08-15Bibliographically approved

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Pokorny, Florian T.

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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