Abstract
Underground sewer pipeline inspection is essential for ensuring the safety and sustainability of urban infrastructure and improving residents’ quality of life. Traditional sewer pipeline inspection methods incur high manual costs. Therefore, this paper proposes an automated siltation depth identification and three-dimensional pipeline model reconstruction using an iterative farthest point removal fitting process based on a sonar point cloud. This method includes interpreting raw sonar data into point cloud data, point cloud preprocessing, iterative farthest point removal fitting process to fit pipeline profile and reverse interpolation of pipeline slices for three-dimensional model reconstruction. This method is validated in real-world applications, successfully calculating and annotating silt depth for pipeline slice profiles. The quantitative analysis shows an error in the estimated siltation proportion fluctuating within approximately 1%. This method also reconstructs the three-dimensional spatial model of siltation in the sewer pipeline, thereby providing effective technical support for engineering applications and reducing manual effort.
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