With the rapid development of automatic driving and artificial intelligence, SLAM has been paid more and more attention. However, more research on SLAM has not been widely used in life and production because of errors in the algorithms and sensors involved in SLAM. The errors accumulate over time and space that directly results the deformation of the map, which severely limits the practical application of SLAM. Based on the loop closure detection of visual SLAM and the advantages of lidar, correcting ICP-SLAM strategy with double loop closure detection is presented to deal with the accumulated errors. The strategy firstly extracts and matches the point cloud data collected by lidar, and then analyzes the transformation matrix produced by ICP to determine loop closure detection, and finally, uses the mean distribution method to correct the map deformation. Experimental verification shows that: the accuracy of the closed-loop detection is improved, which is about 13.33% higher than that of the depth neural network; this strategy can calculate the accumulated error that leading to the deformation of the map; it can effectively correct the map deformation caused by ICP-SLAM, and the deformation has a 54.61% improvement in the rotation direction.