Simultaneous localization and mapping (SLAM) technique is sensitive to the environments with laser interference or structural similarity, which usually cause the closed-loop misdetection. To solve this problem, this study proposed a closed-loop coarse matching and geomagnetic feature screening closed-loop detection algorithm. By adding a geomagnetic matching algorithm to the closed-loop detection link to further filter the candidate closed-loop detection pose node set, the false detection phenomenon of traditional lidar closed-loop detection can be reduced. It can also correct the false detection and reflection caused by reflection and transmission interference in the positioning and mapping environment, as well as the map image distortion. This study verified the performance of the algorithm through the lidar point cloud and geomagnetic signal data sets collected in the real environment. Compared with traditional lidar SLAM methods, the proposed method outperformed in both matching speed and accuracy. Compared with Google Cartographer algorithm, the algorithm can improve the closed-loop detection speed by 31%, and the false detection rate of closed-loop detection can be reduced by 23% under the condition of 0.8 recall rate. This research expands the application scenarios of SLAM technology, so that the lidar SLAM has better positioning and mapping effects in the scene contains laser interference.