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Square root unscented filter based FastSLAM approach for SLAM problem solution | IEEE Conference Publication | IEEE Xplore

Square root unscented filter based FastSLAM approach for SLAM problem solution


Abstract:

There are different Bayesian based approaches proposed for the solution of simultaneous localization and mapping (SLAM) problem in the literature. In this study, square r...Show More

Abstract:

There are different Bayesian based approaches proposed for the solution of simultaneous localization and mapping (SLAM) problem in the literature. In this study, square root unscented Kalman based (Sru)-FastSLAM and square root unscented particle filter based (SruPf) - FastSLAM were proposed for the SLAM problem solution. The first method used Sru - Kalman filter for estimating the robot position, the landmarks location and particle weights. The second method with the help of FastSlam II uses Sru-Kalman filter for each particle. FastSLAM II, unscented particle filter based (Upf) FastSlam II, unscented (U) FastSlam, unscented Kalman aided (UAided) FastSLAM, Sru- FastSlam and SruPf - FastSLAM were used for comparison of filter performance in the experimental results. It is seen that Sru - FastSlam and SruPf-FastSLAM are alternative to solving the problem of SLAM. The best results for heading, position error of robot/ vehicle and uncertainty of position of landmarks were obtained by Sru-FastSlam II.
Date of Conference: 24-26 April 2013
Date Added to IEEE Xplore: 13 June 2013
ISBN Information:
Conference Location: Haspolat, Turkey

References

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