In outdoor large-scene environment measurements, when laser simultaneous loca lization and mapping (SLAM) is used for three-dimensional (3D) map construction, the odometer position based on the LiDAR sensor can easily produce cumulative errors, leading to dislocation drift and even mapping failure of the 3D point cloud map, which seriously affects the accuracy and application of LiDAR SLAM 3D map construction. Hardware description language (HDL)-Graph-SLAM is a lightweight laser SLAM mapping algorithm that adds a loopback detection module in the mapping process but only takes distance as a constraint. In large scenes or corridors and other similar scenes with a single environment, the interaction between the cumulative error of the laser odometer and the single environmental feature can easily lead to an error in the closed loop, whereby the distance-based association cannot find the correct correspondence between the current point cloud frame and historical point cloud frame, leading to a dislocation drift in the point cloud map. To improve the calibration accuracy of large loop detection, this study proposes a hybrid loop calibration laser SLAM algorithm, which uses the fusion processing of two methods based on the spatial position method (distance threshold) and appearance similarity method (bag of words model), to search for and obtain candidate loop frames, which effectively improve the robustness of the loop detection algorithm. Experimental results show that compared with the simple HDL-Graph-SLAM with only distance threshold algorithm, the hybrid loopback calibration method proposed in this study significantly improves the accuracy of laser odometer pose estimation in large outdoor environments, increasing absolute trajectory estimation accuracy by 16%, thus effectively improving 3D mapping accuracy.