The antiparticle filter—an adaptive nonlinear estimator
2017 (English)In: 15th International Symposium of Robotics Research, 2011, Springer, 2017, 219-234 p.Conference paper (Refereed)
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. © Springer International Publishing Switzerland 2017.
Place, publisher, year, edition, pages
Springer, 2017. 219-234 p.
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
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-195123DOI: 10.1007/978-3-319-29363-9_13ScopusID: 2-s2.0-84984843544ISBN: 9783319293622OAI: oai:DiVA.org:kth-195123DiVA: diva2:1044390
9 December 2011 through 12 December 2011
Funding Details: 501100002367, CAS, Stiftelsen för Strategisk Forskning. QC 201611032016-11-032016-11-022016-11-03Bibliographically approved