Based on a vehicle-mounted laser track point cloud, this paper develops a rail surface and sleeper detection algorithm. Multiple sets of different grid size comparison experiments are carried out in the process of extracting the rail surface, based on rail data in different regions, and the optimal grid size range is 0.080.1 m. The effect is shown in Fig. 6. On this basis, in contrast with the method proposed by Yang, the rail surface extraction effect of this algorithm is slightly better than that of Yang’s algorithm, and the robustness is better. The extraction quality averages 97.8% and 96.3%, respectively. Several different basic threshold segmentation experiments are carried out during the extraction process to extract sleepers for different areas. In bridge areas and non-bridge areas, the optimal basic thresholds are 0.25 m and 0.28 m, respectively. The maximum value of the z-axis of the point cloud in each segment is used as the upper limit of segmentation, and the difference between it and the corresponding basic threshold value is used as the lower limit of segmentation, which has a better extraction effect. Fig. 7 depicts the effect. The extraction quality is 93.6%. In conclusion, the algorithm proposed in this paper is effective and feasible, has some practical applications, and can provide efficient and accurate measurement data for track maintenance.