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With the rapid development of high-speed railway, there are many problems with the traditional railway slab assessment method. The traditional method is slow, and its precision is limited by the precision of specified tools for railway slab inspection. Scholars have developed a variety of inspection systems for railway slab geometry. Since those systems’ precision assessment relies on railway slab testing tools that are complex for operation, this paper proposes a novel method to assess the precision of an intelligent slab inspection system itself by using the spatial position deviation between the point cloud of a benchmark slab and the corresponding digital 3D model. The proposed method takes the RMSE of the deviation value of points in the key surfaces as the evaluation index. The key surfaces are the two shoulder surfaces and the rail-bearing surface of the rail-bearing platform, which can be extracted by the region growing algorithm associated with surface normals. Based on the real point cloud processed by an intelligent slab inspection system, the experimental results show that the system can align the slab point cloud to its corresponding 3D digital model. The deviation is distributed on the model uniformly, and its precision is 0.1 mm. In addition, this procedure is consistent with that of general slab inspection and can be used as a self-verification tool for daily precision evaluation of the system itself.
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