The Simultaneous Localization and Mapping (SLAM) problem, which emerged in the last quarter of the century, has been adapted for territorial, naval and aerial platforms starting from the year of 2000's and some parametric filter approaches such as Kalman Filter based Extended Kalman Filter and Distributed Kalman Filter, the state-estimation methods including nonparametric methods such as Particle Filter, some high level control aspiring, model or graphics-based and particularly image processing techniques has been used along with it. A strong need for performance analysis of the SLAM problem by classification can be mentioned, as it vary considerably in the platform, vehicle, sensor, and media type such as territorial, naval and aerial platforms. The particle flow filter, which put forward in 2009 for the first time, was particularly attractive due to its advantages such as high accuracy and fast convergence. In this research, a Particle Flow Filter based SLAM structure is given including mathematical bases/background of the filter, analysis, an autonomous ground vehicle and a sensor model, for the first time in the literature. According to the simulation results provided with the performance analysis of estimation under uncertainty tools/algorithms, although it has some computational complexity that may cause real time application concerns, the particle flow filter based SLAM performance is superior than other recursive state estimation approaches emerged before in the literature in terms of accuracy. Especially in the measurement environments with less uncertain sensors, it is preferable because it removes the problem of degeneration which arises in the particle filter structure.