Change search
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
The antiparticle filter—an adaptive nonlinear estimator
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-7796-1438
2017 (English)In: 15th International Symposium of Robotics Research, 2011, Springer, 2017, 219-234 p.Conference paper, Published paper (Refereed)
Abstract [en]

We introduce the antiparticle filter, AF, a new type of recursive Bayesian estimator that is unlike either the extended Kalman Filter, EKF, unscented Kalman Filter, UKF or the particle filter PF. We show that for a classic problem of robot localization the AF can substantially outperform these other filters in some situations. The AF estimates the posterior distribution as an auxiliary variable Gaussian which gives an analytic formula using no random samples. It adaptively changes the complexity of the posterior distribution as the uncertainty changes. It is equivalent to the EKF when the uncertainty is low while being able to represent non-Gaussian distributions as the uncertainty increases. The computation time can be much faster than a particle filter for the same accuracy. We have simulated comparisons of two types of AF to the EKF, the iterative EKF, the UKF, an iterative UKF, and the PF demonstrating that AF can reduce the error to a consistent accurate value.

Place, publisher, year, edition, pages
Springer, 2017. 219-234 p.
Keyword [en]
Bandpass filters, Kalman filters, Monte Carlo methods, Robot applications, Robotics, Target tracking, Analytic formula, Auxiliary variables, Non-gaussian distribution, Nonlinear estimator, Posterior distributions, Recursive Bayesian estimators, Robot localization, Unscented Kalman Filter, Extended Kalman filters
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-195123DOI: 10.1007/978-3-319-29363-9_13ISI: 000405326800013Scopus ID: 2-s2.0-84984843544ISBN: 9783319293622 (print)OAI: oai:DiVA.org:kth-195123DiVA: diva2:1044390
Conference
9 December 2011 through 12 December 2011
Note

Funding Details: 501100002367, CAS, Stiftelsen för Strategisk Forskning. QC 20161103

Available from: 2016-11-03 Created: 2016-11-02 Last updated: 2017-08-01Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Folkesson, John
By organisation
Centre for Autonomous Systems, CAS
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 28 hits
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