Traditional Simultaneous Localization and Mapping(SLAM) algorithm is lack of the ability to describe multiple sensor information accurately in a clutter environment, and it is prone to false data association. Therefore, this paper proposes a SLAM algorithm based on Probability Hypothesis Density (PHD) filter to deal with these problems. By taking the sensor observation and environmental map as random finite sets in every time step, a joint target state variable is constructed. Then, with the Probability Hypothesis Density(PHD) filtering, the poses and environmental map of the robot are estimated simultaneously and the PHD filter is realized by a particle filter. To avoid the error caused by cluster, a time-delay particle set outputting approach is proposed for joint target state extracting. The new algorithm can describe the observation uncertainty, loss detecting, false alarm due to a clutter and other sensor information accurately, and also can avoid the data association, by which the system state estimation is closer to real values. The simulation results show that the accuracy of the new algorithm in the vehicle localization and mapping is improved by more than 50% as compared with that of traditional SLAM algorithm. It provides a new solution for SLAM problems in the clutter environment.