Multi-Scale activity estimation with spatial abstractions

Majd Hawasly, Florian T. Pokorny, Subramanian Ramamoorthy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProceedings of 3rd Conference on Geometric Science of Information (GSI 2017)
PublisherSpringer, Cham
Number of pages8
ISBN (Electronic)978-3-319-68445-1
ISBN (Print)978-3-319-68444-4
Publication statusPublished - 24 Oct 2017
EventInternational Conference on Geometric Science of Information 2017 - Mines ParisTech, Paris, France
Duration: 7 Nov 20179 Nov 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Geometric Science of Information 2017
Abbreviated titleGSI 2017
Internet address


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