Abstract: In this study, we propose an improved back loop detection TDE-LIO-SAM (lidar inertial odometry-smoothing and mapping based on time distance-entropy reduction strategy) algorithm to resolve the issues of false detection and repeated back loop detection in LIO-SAM algorithm in small-scale outdoor mapping. The proposed algorithm is based on the distance and time threshold of the LIO-SAM algorithm. It preliminarily selects the optional loop through the distance threshold and time threshold and then determines the final matching loop according to the entropy reduction of the loop point cloud. With respect to the application of TDE-LIO-SAM in practical engineering, a fast gravity alignment method for a six-axis inertial measurement unit is introduced so that the algorithm can quickly align gravity. This further improves the applicability of the algorithm to different sensor specifications. Our findings of the experiments on the open-source KITTI dataset and