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 parametricfilter approaches such as Kalman Filter based Extended Kalman Filter and Distributed Kalman Filter, the stateestimation 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 needfor performance analysis of the SLAM problem by classification can be mentioned, as it vary considerably in theplatform, 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 accuracyand fast convergence. In this research, a Particle Flow Filter based SLAM structure is given including mathematicalbases/background of the filter, analysis, an autonomous ground vehicle and a sensor model, for the first time in theliterature. According to the simulation results provided with the performance analysis of estimation underuncertainty tools/algorithms, although it has some computational complexity that may cause real time applicationconcerns, the particle flow filter based SLAM performance is superior than other recursive state estimationapproaches emerged before in the literature in terms of accuracy. Especially in the measurement environments withless uncertain sensors, it is preferable because it removes the problem of degeneration which arises in the particlefilter structure