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SLAM process using Polynomial extended Kalman filter: Experimental Assessment

Abstract : This paper deals with the Simultaneous Localization and Map building (SLAM) problem using an implementation of the Polynomial Extended Kalman Filter (PEKF). The proposed PEKF implementation is a filtering algorithm which is a polynomial transformation of state evolution and measurement equations. The performances of the algorithm have been evaluated through simulations. The comparison with the standard Extended Kalman Filter shows that the PEKF provides more consistent estimates in a SLAM framework. Experiments on real data are presented too.
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Contributor : Paul Checchin Connect in order to contact the contributor
Submitted on : Monday, December 1, 2008 - 6:01:53 PM
Last modification on : Wednesday, April 21, 2021 - 8:34:02 AM


  • HAL Id : hal-00343515, version 1



François Chanier, Paul Checchin, Christophe Blanc, Laurent Trassoudaine. SLAM process using Polynomial extended Kalman filter: Experimental Assessment. 10th IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), Dec 2008, Hanoi, Vietnam. pp.P0978. ⟨hal-00343515⟩



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