Square root unscented filter based FastSLAM approach for SLAM problem solution

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.