Abstract
The urban drainage system is an important part of the urban water cycle. However, with the aging of drainage pipelines and other external reasons, damages such as cracks, corrosion, and deformation of underground pipelines can cause serious consequences such as urban waterlogging and road collapse. At present, the detection of underground drainage pipelines mostly focuses on the qualitative identification of pipeline damage, and it is impossible to quantitatively analyze pipeline damage. Therefore, a method to quantify the damage volume of concrete pipes that combines surface segmentation and reconstruction is proposed. An RGB-D sensor is used to collect the damage information of the drainage pipeline, and the collected depth frame is registered to generate the pipeline’s surface point cloud. Voxel sampling and Gaussian filtering are used to improve data processing efficiency and reduce noise, respectively, and the RANSAC algorithm is used to remove the pipeline’s surface information. The ball-pivoting algorithm is used to reconstruct the surface of the segmented damage data and pipe’s surface information, and finally to obtain the damage volume. In order to evaluate, we conducted our research on real-world materials. The measurement results show that the method proposed in this paper measures an average relative error of 7.17% for the external damage volume of concrete pipes and an average relative error of 5.22% for the internal damage measurements of concrete pipes.
Highlights
As an important guarantee for the construction of urban civilization and healthy human life, urban drainage systems can isolate sewage and clean water, thereby improving the quality of human life [1]
In order to quantify the damage volume of underground pipelines under the interference of a complex environment, we propose a quantitative method of assessing the damage volume of underground drainage pipelines integrating 3D point cloud surface segmentation and reconstruction
The method mainly consisted of four parts: (1) conversion from 2D depth frames to 3D point gathering was completed according to the conversion relationship between the internal coordinates of the acquisition instrument and the world coordinates; (2) the data set was preprocessed by integrating voxel sampling and a Gaussian filter; (3) the parameters of the surface model were estimated by the random sampling consensus algorithm, and the point cloud of the pipeline surface was removed; (4) after the damage data were reconstructed with the surface point cloud, the ballpivoting algorithm (BPA) algorithm was used to complete the surface reconstruction in order to obtain the real damage volume
Summary
As an important guarantee for the construction of urban civilization and healthy human life, urban drainage systems can isolate sewage and clean water, thereby improving the quality of human life [1]. In order to quantify the damage volume of underground pipelines under the interference of a complex environment, we propose a quantitative method of assessing the damage volume of underground drainage pipelines integrating 3D point cloud surface segmentation and reconstruction. The method mainly consisted of four parts: (1) conversion from 2D depth frames to 3D point gathering was completed according to the conversion relationship between the internal coordinates of the acquisition instrument and the world coordinates; (2) the data set was preprocessed by integrating voxel sampling and a Gaussian filter; (3) the parameters of the surface model were estimated by the random sampling consensus algorithm, and the point cloud of the pipeline surface was removed; (4) after the damage data were reconstructed with the surface point cloud, the BPA algorithm was used to complete the surface reconstruction in order to obtain the real damage volume.
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