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Multi-scale activity estimation with spatial abstractions
KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.ORCID-id: 0000-0003-1114-6040
2017 (engelsk)Inngår i: 3rd International Conference on Geometric Science of Information, GSI 2017, Springer, 2017, Vol. 10589, s. 273-281Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer, 2017. Vol. 10589, s. 273-281
Serie
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10589
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-218313DOI: 10.1007/978-3-319-68445-1_32ISI: 000440482500032Scopus ID: 2-s2.0-85033660648ISBN: 9783319684444 (tryckt)OAI: oai:DiVA.org:kth-218313DiVA, id: diva2:1160465
Konferanse
3rd International Conference on Geometric Science of Information, GSI 2017, Paris, France, 7 November 2017 through 9 November 2017
Merknad

QC 20171127

Tilgjengelig fra: 2017-11-27 Laget: 2017-11-27 Sist oppdatert: 2018-08-15bibliografisk kontrollert

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Pokorny, Florian T.

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