This paper introduces an implementation of the Polynomial Extended Kalman Filter (PEKF) to solve the Simultaneous Localization and Map building (SLAM) problem. The proposed solution is a filtering algorithm which is a polynomial transformation of state evolution and measurement equations. The performances of the algorithm have been evaluated through two simulation runs. The first ones underline consistency improvement in comparison with the standard Extended Kalman Filter. The other simulation results show the PEKF efficiency when the values of measurement noises are high. At the end, experiments with Victoria Park data are presented too.