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The Antiparticle Filter: an Adaptive Nonlinear Estimator
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-7796-1438
2011 (English)In: International Symposium of Robotics Research, 2011Conference 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 theuncertainty 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
National Category
Robotics Control Engineering Signal Processing
URN: urn:nbn:se:kth:diva-48845OAI: diva2:458708
15th International Symposium on Robotics Research, Flagstaff, AZ — 28 August - 1 September 2011
QC 20120109Available from: 2012-01-09 Created: 2011-11-23 Last updated: 2012-01-17Bibliographically approved

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