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Polynomial Extended Kalman Filter in a SLAM framework

Abstract : This paper introduces an implementation of the Polynomial Extended Kalman Filter (PEKF) to solve the Simultaneous Localization and Map building (SLAM) problem. The proposed solution is a filtering algorithm which is a polynomial transformation of state evolution and measurement equations. The performances of the algorithm have been evaluated through two simulation runs. The first ones underline consistency improvement in comparison with the standard Extended Kalman Filter. The other simulation results show the PEKF efficiency when the values of measurement noises are high. At the end, experiments with Victoria Park data are presented too.
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Contributor : Paul Checchin Connect in order to contact the contributor
Submitted on : Wednesday, December 3, 2008 - 11:01:17 AM
Last modification on : Wednesday, April 21, 2021 - 8:34:02 AM


  • HAL Id : hal-00343930, version 1



François Chanier, Paul Checchin, Christophe Blanc, Laurent Trassoudaine. Polynomial Extended Kalman Filter in a SLAM framework. IEEE IROS08 2nd Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Sep 2008, Nice, France. pp.P05. ⟨hal-00343930⟩



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