Square root unscented based FastSlam optimized by particle swarm optimization passive congregation

Simultaneous localization and mapping (SLAM) is known to be a problem for autonomous vehicles/robots. Different solutions have recently been proposed on this subject. The best known of these are FastSlam based approaches. In this study, two improved FastSlam based methods are proposed to solve the SLAM problem. In the first method, square root unscented (Sru) Kalman filter is used instead of extended Kalman filter in robot position prediction/update for each particle filter samples and feature updates. The second method uses Sru - Kalman filter with particle swarm optimization passive congregation (PSO-PC) for robot/feature position estimations. In the second method, particle swarm optimization passive congregation (PSO-PC) is used to optimize particle samples in case of sampling stage. The experimental results were compared with FastSlamII and unscented U-FastSlam. It is seen that proposed methods are an alternative for the solution of SLAM problem. The best results were obtained by Sru - based PSO-PC optimized FastSlam approach for the vehicle position and heading angle mean square errors.