Square root central difference-based FastSLAM approach improved by differential evolution

Authors : HAYDAR ANKIŞHAN, FİKRET ARI, EMRE ÖNER TARTAN, AHMET GÜNGÖR PAKFİLİZ

Pages : 994-1013

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Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This study presents a new approach to improve the performance of FastSLAM. The aim of the study is to obtain a more robust algorithm for FastSLAM applications by using a Kalman filter that uses Stirling`s polynomial interpolation formula. In this paper, some new improvements have been proposed; the first approach is the square root central difference Kalman filter-based FastSLAM, called SRCD-FastSLAM. In this method, autonomous vehicle insert ignore into journalissuearticles values(or robot); position, landmarks` position estimations, and importance weight calculations of the particle filter are provided by the SRCD-Kalman filter. The second approach is an improved version of the SRCD-FastSLAM in which particles are improved by a differential evolution insert ignore into journalissuearticles values(DE); algorithm for reducing the risk of the particle depletion problem. Simulation results are given as a comparison of FastSLAM II, unscented insert ignore into journalissuearticles values(U);-FastSLAM, SRCD-Kalman filter-aided FastSLAM, SRCD particle filter-based FastSLAM, SRCD-FastSLAM, and DE-SRCD-FastSLAM. The results show that SRCD-based FastSLAM approaches accurately compute mean and precise uncertainty of the robot position in comparison with FastSLAM II and U-FastSLAM methods. However, the best results are obtained by DE-SRCD-FastSLAM, which provides significantly more accurate and robust estimation with the help of DE with fewer particles. Moreover, consistency of the DE-SRCD-FastSLAM is more prolonged than that of FastSLAM II, U-FastSLAM, and SRCD-FastSLAM.
Keywords : Simultaneous localization and mapping, square root central difference Kalman filter, Stirling`s polynomial interpolation, differential evolution