Since the solution of SLAM problem is achieved about a decade ago. But some difficulties left behind realizing more general solutions. Here making and using perceptually good maps will help in generalizing it. The purpose is to provide a broad knowledge to this rapidly growing field. The paper begins by providing a brief background of early developments in SLAM. Then we have three sections, i.e., formulation section, solution section and application section. The formulation section uses the structure the SLAM problem in present standard Bayesian form, and explains evolution of this process. Next, the solution section describes the two key computational solutions to the SLAM problem through the use of the extended Kalman filter (EKF-SLAM) which is one of the popular approximate solution methods and through the use of Rao-Blackwellized particle filters (FastSLAM).