LAUSR

In the maintenance work of tunnels, images are often used to detect diseases, but collections of tunnel images are limited by the tunnel environment and working time. Three-dimensional laser scanning technology can acquire high-precision tunnel information efficiently, and the main problem to be solved by using this technology to collect tunnel inner wall images is the dimensionality reduction of the laser tunnel point cloud data. This letter proposes a tunnel surface parameterization algorithm based on a harmonic map, where a 3-D tunnel point cloud is used as a data source to reconstruct a triangle mesh model of the tunnel and then generate a harmonic map depth map of the tunnel inner wall on the triangle mesh. We can obtain the spatial distribution and position information of the appendages and detect whether there are cracks, water leakage, falling pieces, and other diseases by the depth images. The results of this study indicate that the proposed algorithm is suitable for tunnels of various shapes and has low area distortion, which can better avoid the loss of information during dimensionality reduction. Compared with other existing methods, the algorithm has higher efficiency and applicability.