基于PCA和欧式聚类的埋地排水管道声呐点云去噪技术OA

With the progress of urbanization,the phenomenon of underground drainage pipes running with full water is increasing,and the traditional detection methods are difficult to adapt to the health assessment of pipes in the environment with full water.Sonar detection technology provides a new solution for health assessment of high-water-level and full-water-drainage pipes,but the generated point cloud data is often contaminated with noise.In this paper,a point cloud denoising method based on PCA and adaptive European clustering is proposed.First,PCA was used to identify and fit the principal components of the inner wall of the pipeline;then,noise was removed,and deposition lines were extracted using adaptive European-style clustering,which retained the pipeline's key structural features.Through field application of the Dongguan drainage pipeline,the results show that this method can effectively improve the quality of sonar point cloud data,accurately extract the pipe wall and sediment line,and meet engineering requirements.