An effective approach for 3D point cloud registration in railway contexts

This paper presents an accurate and robust processing pipeline for merging tridimensional datasets related to railway contexts and thus producing a comprehensive 3D model of the monitored scenario. The method is made of multiple modules able to detect the rail tracks and achieve consecutive point cloud registration. A preliminary stage is aimed at filtering out those outlier points and selecting only specific regions of interest from the point cloud. Afterwards, the procedure detects the 3D points belonging to the rail tracks, which can be considered as good candidates for attaining the final point cloud registration. A local analysis for each 3D point is performed by considering a parallelepiped-shaped voxel opportunely centered at the point under investigation. The evaluation of the spatial distributions of points inside the considered volume voxel is performed in order to establish if a seed point lies on the rail head. Further checks enable to reject false candidate points from previous steps by taking advantage of the knowledge about the rail track gauge. Finally, a hierarchical clustering completes the extraction of potential rails. The registration module uses the IterativeClosest-Point method, combined with an algorithm that iteratively reduces the overlapping regions between two consecutive point clouds, for merging the data by using the rail points. The methodology is validated on two different datasets collected by using a stereo camera developed at our laboratory. Final outcomes prove as the proposed approach enables to attain robust and accurate global 3D registration in railway contexts.