Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Progress on “Pico” Air Vehicles Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

The Antiparticle Filter—An Adaptive Nonlinear Estimator

verfasst von : John Folkesson

Erschienen in: Robotics Research

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Tactile Object Recognition and Localization Using Spatially-Varying Appearance
Nächstes Kapitel Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera
Fußnoten
1
The iterative UKF is an iterative version of the UKF similar to the IEKF.
 
2
We use the threshold 0.01 in our simulations.
 
3
We use 1.0 for this threshold.
 
4
We used N = 21.
 
5
Λ is a matrix from the λ → x spaces and x vector Γ is a symmetric matrix wrt λ.
 
6
α and β are matrices from the λ → x spaces.
 
7
As in Thrun pp. 220–228 (UKF), 250–252 (PF) and 110 (low variance sampling).
 
8
Q was 10−5, 10−4, or 10−3 along the diagonal.
 
9
The simulated distance units are arbitrary but can be thought of as meters while the angles are in radians.
 
Literatur
1.
R. Kalman, A new approach filtering and prediction problems. Trans. ASME J. Basic Eng. 82(1), 35–45 (1960)CrossRef
2.
L. Ljung, Asymptotic behavior of the extended kalman filter as a parameter estimator for linear systems. IEEE Trans. Autom. Control 24(1), 36–51 (1979)MathSciNetCrossRefMATH
3.
S. Huang, G. Dissanayake, Convergence and consistency analysis for extended kalman filter based slam. IEEE Trans. Robot. Autom. 23(5), 1036–1050 (2007)CrossRef
4.
K. Reif, S. Gunther, E. Yaz, R. Unbehauen, Stochastic stability of the discrete-time extended kalman filter. IEEE Trans. Autom. Control 44(4), 714–728 (1999)MathSciNetCrossRefMATH
5.
K. Xiong, H. Zhang, L. Liu, Adaptive robust extended kalman filter for nonlinear stochastic systems. Control Theory Appl. IET 2(3), 239–250 (2008)MathSciNetCrossRef
6.
W. Zhang, B.S. Chen, C. Tseng, Robust H ∞ filtering for nonlinear stochastic systems. IEEE Trans. Signal Process. 53(2), 589–598 (2005)MathSciNetCrossRef
7.
L. Jetto, S. Longhi, G. Venturini, Development and experimental validation of an adaptive extended kalman filter for the localization of mobile robots. IEEE Trans. Robot. Autom. 15(2), 219–229 (1999)CrossRef
8.
S.J. Julier, J.K. Uhlmann, Unscented filtering and nonlinear estimation, in Proceedings of the IEEE, vol. 92 (2004), pp. 401–422
9.
E. Wan, R. Van Der Merwe, The unscented kalman filter for nonlinear estimation, in Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000, vol. 1, no. 1 (2000), pp. 153–158
10.
11.
12.
T. Lefebvre, H. Bruyninck, J.D. Schutter, Kalman filters for nonlinear systems: a comparison of performance. Int. J. Control 77(7), 639–653 (2004)CrossRefMATH
13.
I. Arasaratnam, S. Haykin, R.J. Elliott, Discrete-time nonlinear filtering algorithms using gauss hermite quadrature, in Proceedings of the IEEE, vol. 95 (2007), pp. 953–977
14.
J. Clark, R. Vinter, M. Yaqoob, Shifted rayleigh filter: a new algorithm for bearings-only tracking. IEEE Trans. Aerosp. Electr. Syst. 43(4), 1373–1384 (2007)CrossRef
15.
M. Norgaard, N.K. Poulsen, O. Ravn, New developments in state estimation for nonlinear systems. Automatica 36, 1627–1638 (2000)MathSciNetCrossRefMATH
16.
B. Bell, F. Cathey, The iterated kalman filter update as a gauss-newton method. IEEE Trans. Autom. Control 38(2), 294–297 (1993)MathSciNetCrossRefMATH
17.
Y. Bar-Shalom, T. Fortmann, Tracking and Data Association (Academic Press, New York, NY, 1987)
18.
N. Gordon, D. Salmond, A. Smith, Novel approach to nonlinear/nongaussian bayesian state estimation, in Proceedings of the IEEE for Radar and Signal Processing, vol. 140 (1993), pp. 107–113
19.
M.S. Arulampalam, S. Maskell, N. Gordon, T. Clapp, A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)CrossRef
20.
J. Carpenter, P. Clifford, P. Fearnhead, Improved particle filters for nonlinear problems. IEEE Proc. Radar Sonar Navig. 146(1), 2–7 (1999)CrossRef
21.
F. Dellaert, D. Fox, W. Burgard, S. Thrun, Monte carlo localization for mobile robots, in Proceedings of the IEEE International Conference on Robotics and Automation (1999), pp. 1322–1328
22.
M. Montemerlo et al., FastSLAM: A factored solution to the simultaneous localization and mapping problem, in Proceedings of the National Conference on Artificial Intelligence (AAAI-02) (Edmonton, Canada, 2002)
23.
G. Grisetti, C. Stachniss, W. Burgard, Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling, in Proceedings of the IEEE Int. Conference on Robotics and Automation (2005), pp. 2432–2437
24.
25.
H. Durrant-Whyte, S. Majumder, S. Thrun, M. de Battista1, S. Scheding, A bayesian algorithm for simultaneous localisation and map building, in Springer Tracts in Advanced Robotics, vol. 6 (2003), pp. 49–60
26.
Y. Bresler, A. Macovski, Exact maximum likelihood parameter estimation of superimposed exponential signals in noise. IEEE Trans. Acoust. Speech Signal Process. 34(5), 1081–1089 (1986)CrossRef
27.
K.R. Muske, J.B. Rawlings, Nonlinear Moving Horizon State Estimation (Kluwer, 1995)
28.
C. Rao, J. Rawlings, D. Mayne, Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations. IEEE Trans. Autom. Control 48(2), 246–258 (2003)MathSciNetCrossRef
29.
J. Folkesson, H.I. Christensen, Graphical SLAM: a self-correcting map, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA04), vol. 1 (2004)
30.
F. Dellaert, Square root sam: Simultaneous location and mapping via square root information smoothing, in Robotics: Science and Systems (2005)
31.
M. Briers, A. Doucet, S. Singh, Sequential auxiliary particle belief propagation, in Proceedings of the IEEE International Conference on Information Fusion, vol. 1 (2005), pp. 1–8
32.
J. Folkesson, Robustness of the quadratic antiparticle filter for robot localization, in Proceedings of the European Conference on Mobile Robots, vol. 1 (2011)
33.
S. Thrun, W. Burgard, D. Fox, Probalistic Robotics (MIT Press, 2005)
Metadaten
Titel
The Antiparticle Filter—An Adaptive Nonlinear Estimator
verfasst von
John Folkesson
Copyright-Jahr
2017
Buch
Robotics Research

Print ISBN: 978-3-319-29362-2

Electronic ISBN: 978-3-319-29363-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-29363-9_13

Neuer Inhalt

    Bildnachweise