To address the limitation of single-sensor performance in positioning and mapping tasks in large-scale urban environments,particularly the influence of dynamic objects on positioning accuracy and map construction,this paper proposes a semantic simultaneous localization and mapping method including semantic segmentation and laser inertial odometry (SLI-SLAM). First, the image semantic segmentation model employs an improved lightweight semantic segmentation network (D3p-S) to segment images and achieves point cloud semantic segmentation through spatiotemporal synchronization between sensors. Additionally, a geometric space consistency based on a facet model is designed to detect and eliminate dynamic obstacles. Second, inertial measurement unit (IMU) preintegration is used to eliminate the motion distortion caused by LiDAR, while point cloud ground segmentation and denoising help reduce the computational complexity. Finally, a factor graph is used to optimize the trajectory, enabling fast and accurate positioning of unmanned vehicles in urban road environments and the construction of three-dimensional semantic maps. Experimental results show that the proposed SLI-SLAM method reduces the root mean square error (RMSE) of absolute trajectory error by 30.33% compared with the classical laser SLAM algorithm (LIO-SAM) in highly dynamic urban road scenes.