An automatic extraction and segmentation algorithm of underground cable point cloud

In order to solve the problems that it is difficult to separate the cable support from the cable in the point cloud of underground cable tunnel and the cable points need to be extracted manually, an automatic extraction and segmentation algorithm of underground cable point cloud based on regional cylindrical surface fitting and robust adaptive Kalman filter was proposed. Firstly, the radius of the cable area was determined based on the shape of the cylindrical axis; Then, the axis of the initial regional cable was taken as the direction of the regional cable, and the extension of the central axis of the cable was estimated combined with the robust adaptive Kalman filter algorithm to obtain the central axis of a single complete cable. Finally, the single segmentation of the cable point cloud was realized. The results show that the mean square error of unit weight in the initial area of cable is less than 0.015 m, which can effectively distinguish cable points from other types of points; The cable central axis extension has disadvantages in the underground cable point cloud, and the noise still maintains a robust estimation, which has good robustness. This method effectively improves the accuracy and reliability of underground cable extraction.