In this dissertation, architecture of two-layer particle filters incorporating particle swarm optimization (PSO) is proposed and applied on the localization and mapping of mobile robot. For the robot localization, the particle filter is modified by integrating a particle swarm optimization algorithm, where the excellent performance in global optimization of the PSO is used to improve the localization performance. In comparison with conventional particle filters, the proposed particle filter can better determine the robot’s position. For the map building, the particle filter is applied to estimate position of landmarks in the environment, in which the prediction step in the filter is modified by adding small random perturbations into the particles. As a result, the proposed method can better determine position of landmarks. By combining these two functionalities, the architecture of two-layer particle filters is proposed to investigate the localization and mapping of the mobile robot simultaneously. Due to the incorporation of the PSO, the proposed architecture is capable of reducing the localization error of the robot while improving the mapping accuracy of the landmarks. As a result, the robot can better explore an unknown environment with the proposed architecture. Simulation and experimental results show that the proposed approach has a better performance for the localization and mapping of the mobile robot.