Joint Probabilistic Data Association (JPDA) based FastSLAM Approach to Slam Problem
Approaches to the solution of the Kalman filter-based SLAM problem maintain its popularity. However, in some cases, the desired results have not been obtained. In this study, unlike previous studies, a Joint probabilistic data association (JPDA) based FastSLAM has been presented. The method has been also compared with improved approaches based on differential evolution (DE) and particle swarm optimization (PSO). When the results are examined, it is seen that the proposed method gives faster and more accurate estimation results than the optimized methods.
Keywords - Simultaneous Localization and Mapping, Kalman Filter, Joint Probabilistic Data Association.