Denoising algorithm for inclined tunnel point cloud data based on irregular contour features

Abstract

Abstract In response to the problem where current traditional denoising algorithms cannot effectively remove noise from inclined tunnel point cloud data with irregular contours. This paper proposes a denoising algorithm for inclined tunnel point cloud data based on irregular contour features. The algorithm combines the DBSCAN clustering algorithm with polynomial curve fitting to obtain sequential point cloud slices along the perpendicular direction to the centerline of the inclined tunnel. By identifying and extracting irregular contour feature points from these slices, it achieves the extraction of irregular wall shapes inside the tunnel. Based on these irregular wall shape features, noise points are effectively removed using distance iteration calculations. Experimental results demonstrate that the proposed algorithm can effectively handle the irregular shapes and elevation variations in inclined tunnel point cloud data and achieve good denoising performance for various types of noise within the tunnel. This algorithm lays a solid foundation for subsequent three-dimensional modeling of tunnels with high precision.