To address the challenges of robot trajectory drift in dynamic and complex environments, and overcome the limitations of conventional static map construction, we propose a robust multi-sensor fusion SLAM algorithm integrated with dynamic point cloud removal. Our front-end processing employs IMU pre-integration to compensate for point cloud distortion and utilizes an iterative error state Kalman filter (IESKF) for refined initial pose estimation. Furthermore, we introduce a novel dynamic point cloud removal strategy that combines ground segmentation with spatio-temporal normal vector analysis. It effectively eliminates moving objects and preserves static structures to ensure global map consistency. On the back end, our method leverages factor graph optimization, fusing laser-inertial odometry, IMU pre-integration, and wheel encoder data to enhance trajectory estimation. In addition, we incorporate ground plane constraints to suppress cumulative errors and mitigate z-axis drift. Experimental validation in a complex campus environment demonstrates that our method significantly reduces positioning root mean square error (RMSE) by 46.2%, 49.4%, and 35.9% compared to LeGO-LOAM, FAST-LIO, and LIO-SAM, respectively. Moreover, our method successfully removes dynamic point clouds from the constructed maps, showcasing superior robustness in dynamic scenarios. These advancements provide reliable support for autonomous robot navigation and high-precision mapping in complex dynamic environments.