This paper presents a Graph Matching Pose SLAM to build the perception map which is a prerequisite of localization and environment perception of the intelligent vehicle. Graph-based simultaneous localization and mapping (SLAM) is widely used to build a map with global consistency and requires loop closure to eliminate the accumulative errors. However, loop closure forces the vehicle to re-visit a previously entered area. In this paper, a road network-based graph SLAM method without the requirement of loop closure is proposed to build a consistent map for the real environment. The estimated poses from the original SLAM are linked with the available road network based on the graph matching. Thus the available road network introduces the real topology of the environment to Pose SLAM regularly. In this way, the map is optimized by using the factor graph inference frequently. The proposed method is validated on the KITTI dataset and the real-world experiments. This method outperforms the state-of-the-art methods.