To address the issues of inaccurate mapping and position drift in 3D autonomous driving maps， LIDAR odometry was utilized to counteract cumulative errors of the inertial measurement unit（IMU）， and corrections for LIDAR point cloud distortions were made through IMU pre-integration. This approach enabled the creation of a mapping system where LIDAR and IMU were tightly integrated. Subsequently， the back-end map was enhanced by the incorporation of loopback detection， LIDAR odometry， and IMU pre-integration factors， aiming to bolster the global consistency of the positioning map and minimize cumulative drift errors. The optimization of the back-end map sought to enhance global localization consistency， reduce positioning errors， and curtail cumulative drift. Experimental validation was conducted in a school campus environment and with the use of the KITTI open-source dataset. The results demonstrate that in the school campus scenario， an 11.04% reduction in average APE error and a 17.35% decrease in RMSE are achieved by the refined algorithm compared to the baseline algorithm. For the KITTI dataset scenario， a reduction of 10.04% in both average APE error and RMSE， and a 12.04% decrease in mean square error are observed， underscoring the efficacy of the enhanced mapping technique in elevating position estimation and map construction precision.