Aiming at the localisation drift problem of pure laser SLAM algorithm, a coarse matching loopback detection algorithm based on the global search of point cloud feature descriptor is proposed. The algorithm firstly adopts the fast segmentation method based on image distance to remove ground points from the laser point cloud, implements edge feature extraction and clustering based on the point cloud curvature and key point aggregation algorithm, and obtains the feature descriptor of the point cloud in the current frame through the feature descriptor generation algorithm, then completes the global matching search by calculating the similarity scores of the current frame and the historical frames to achieve the selection of candidate looping frames, and completes the loopback detection. The coarse matching process is completed. Then the NICP algorithm is used to accurately match the current frame with the candidate loopback frame to complete the loopback detection process. Finally, the real vehicle platform of the mobile robot is built to complete the acquisition of the campus dataset to verify the positioning effect of this paper’s algorithm, and through the analysis of the results of the experiments on the real vehicle, it can be seen that the average value of the degree of optimisation of the error on the campus dataset acquired by the real vehicle is 13.15%. In order to further validate the overall performance of this paper’s algorithm, test comparisons are performed on the KITTI dataset, and the results show that compared with the Lego_loam and Lio-sam algorithms, the algorithm proposed in this paper effectively improves the localisation accuracy on the basis of guaranteeing the operational efficiency.