Underground pipeline extraction method, system and equipment based on point cloud and storage medium

Abstract

The embodiment of the invention provides an underground pipeline extraction method, system and device based on point cloud and a storage medium, and belongs to the technical field of point cloud space geometry extraction. According to the scheme, underground pipeline point cloud data are obtained; segmenting the pipeline point cloud data to obtain a segmentation result; when the segmentation result is a single-pipe scene, principal component analysis and cylinder fitting are carried out based on all pipeline point cloud data to obtain a normal vector and a pipeline radius of each data point; performing plane cutting according to the normal vector to obtain a tangential plane, and obtaining a pipeline center line based on the tangential plane; determining a distance threshold parameter according to the radius of the pipeline; based on the distance threshold parameter, performing segmented straight line fitting on the pipeline center line through a random sampling consistency algorithm to obtain a center line point set; performing feature extraction on the center line point set to obtain feature points; determining a connection relationship of adjacent feature points according to the geometrical relationship; the pipeline reconstruction model obtained based on the connection relation can be suitable for complex scenes, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of point cloud spatial geometry extraction, and in particular to a point cloud-based underground pipeline extraction method, system, device and storage medium. Background Art

[0002] In the maintenance and update of underground pipeline facility information systems, the detection of pipeline spatial positions and information extraction are important links. With the acceleration of urbanization, the complexity and scale of underground pipeline systems continue to increase, which puts higher requirements on the accuracy and efficiency of pipeline information extraction. Due to the complex background of underground pipelines and the limitations of laser scanning technology in obtaining data, some data will be missing in the collection of pipeline information.

[0003] At present, the commonly used pipeline detection and information extraction methods mainly include region growing, Hough transform, Gaussian fitting and random sampling consensus (RANSAC) algorithm. However, most of the related technologies focus on the information extraction of industrial plant pipelines. The usage scenarios are relatively simple and unified, and the data collection quality requirements are high. The data collection scenarios of underground pipeline facilities systems are relatively complex, the integrity of pipeline facility point cloud collection is low, and the geographic spatial distribution area of ​​pipelines is much larger. Although region growing and Hough transform can extract pipeline information, they are sensitive to noise and have high computational complexity. Although Gaussian fitting can provide accurate cylinder parameters, it has high requirements for initial conditions and data quality. The RANSAC algorithm performs well when processing noisy data, but the uncertainty of parameter selection is its main disadvantage. Therefore, in the case of incomplete point cloud data, the current information extraction method has low computational efficiency and it is difficult to extract underground pipelines. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a point cloud-based underground pipeline extraction method, system, equipment and storage medium, aiming to realize the automatic extraction of underground pipeline information, accurately capture the geometric structure of the pipeline when the point cloud data is incomplete, and improve computing efficiency.

[0005] To achieve the above purpose, one aspect of an embodiment of the present application proposes a method for extracting underground pipelines based on point clouds, the method comprising:

[0006] Obtain underground pipeline point cloud data;

[0007] Performing scene object segmentation on the pipeline point cloud data to obtain a segmentation result, wherein the segmentation result includes a single-pipeline scene and a multi-pipeline scene;

[0008] When the segmentation result is a single-pipe scene, principal component analysis is performed based on all the pipeline point cloud data to obtain the normal vector of each data point in the pipeline point cloud data, and cylindrical fitting is performed on the pipeline point cloud data to obtain the pipeline radius;

[0009] Performing plane cutting according to the normal vector to obtain a tangent plane, and obtaining a pipeline centerline based on the tangent plane;

[0010] Determining a distance threshold parameter according to the pipeline radius;

[0011] Based on the distance threshold parameter, performing piecewise straight line fitting on the pipeline centerline by a random sampling consistency algorithm to obtain a centerline point set;

[0012] Performing feature extraction on the centerline point set to obtain feature points of the pipeline centerline, wherein the feature points include endpoints and inflection points;

[0013] Determine the connection relationship between adjacent feature points according to the geometric relationship;

[0014] A pipeline reconstruction model is obtained based on the connection relationship.

[0015] In some embodiments, the step of obtaining underground pipeline point cloud data includes the following steps:

[0016] Obtain underground spatial point cloud data;

[0017] The spatial point cloud data is semantically segmented through a deep learning semantic segmentation network, and the pipeline is segmented from the background to obtain pipeline point cloud data.

[0018] In some embodiments, performing scene object segmentation on the pipeline point cloud data to obtain a segmentation result includes the following steps:

[0019] Dividing the pipeline point cloud data into a number of clusters of pipeline subset data by using a clustering algorithm and pipeline prediction labels;

[0020] When the number of the pipeline subset data is equal to one cluster, determining that the segmentation result is a single-pipe scene;

[0021] Alternatively, when the number of the pipeline subset data is greater than one cluster, determining that the segmentation result is a multi-pipeline scene;

[0022] Among them, in the clustering algorithm processing process, the clusters that do not meet the constraints are removed according to the volume threshold constraint and the quantity threshold constraint;

[0023] The underground pipeline extraction method based on point cloud also includes the following steps:

[0024] When the segmentation result is a multi-pipe scene, principal component analysis is performed on each cluster of the pipeline subset data to obtain a normal vector of each data point in the pipeline subset data.

[0025] In some embodiments, performing principal component analysis based on all the pipeline point cloud data to obtain a normal vector of each data point in the pipeline point cloud data includes the following steps:

[0026] For each data point in the pipeline point cloud data, determine neighboring points with the data point as the center to obtain a neighboring point set;

[0027] The covariance matrix of the neighboring point set is calculated, and the eigenvector corresponding to the minimum eigenvalue of the covariance matrix is ​​used as the normal vector of the data point.

[0028] In some embodiments, the step of performing plane cutting according to the normal vector to obtain a tangent plane, and obtaining a pipeline centerline based on the tangent plane, comprises the following steps:

[0029] Performing plane cutting along a direction perpendicular to the normal vector to obtain a plurality of tangent planes;

[0030] Determine the center point of each of the tangential planes, and obtain the rotational symmetry axis according to the position information of all the center points;

[0031] The rotational symmetry axis is determined as the pipeline centerline.

[0032] In some embodiments, determining the distance threshold parameter according to the pipeline radius comprises the following steps:

[0033] The distance threshold parameter of the random sampling consensus algorithm is set to β multiples of the pipeline radius, where β∈[0.1,1].

[0034] In some embodiments, before the step of obtaining the centerline point set, the method further includes the following steps:

[0035] The cylindrical neighborhood filtering method is used to take the pipeline centerline obtained by fitting as the axis, and α times the pipeline radius as the cylindrical neighborhood radius to remove noise points within the cylindrical range, where α∈[0.5,5].

[0036] To achieve the above object, another aspect of the embodiment of the present application provides a point cloud-based underground pipeline extraction system, the system comprising:

[0037] The first module is used to obtain underground pipeline point cloud data;

[0038] The second module is used to perform scene object segmentation on the pipeline point cloud data to obtain a segmentation result, wherein the segmentation result includes a single-pipeline scene and a multi-pipeline scene;

[0039] The third module is used for performing principal component analysis based on all the pipeline point cloud data to obtain the normal vector of each data point in the pipeline point cloud data when the segmentation result is a single-pipe scene, and performing cylindrical fitting on the pipeline point cloud data to obtain the pipeline radius;

[0040] A fourth module is used to perform plane cutting according to the normal vector to obtain a tangent plane, and obtain a pipeline centerline based on the tangent plane;

[0041] A fifth module is used to determine a distance threshold parameter according to the pipeline radius;

[0042] A sixth module is used to perform piecewise straight line fitting on the pipeline centerline through a random sampling consistency algorithm based on the distance threshold parameter to obtain a centerline point set;

[0043] The seventh module is used to extract features from the centerline point set to obtain feature points of the pipeline centerline, wherein the feature points include endpoints and inflection points;

[0044] An eighth module is used to determine the connection relationship between adjacent feature points according to the geometric relationship;

[0045] The ninth module is used to obtain a pipeline reconstruction model based on the connection relationship.

[0046] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0047] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0048] The embodiments of the present application include at least the following beneficial effects: the present application provides a point cloud-based underground pipeline extraction method, system, device and storage medium, the scheme obtains underground pipeline point cloud data; performs scene object segmentation on the pipeline point cloud data to obtain segmentation results, wherein the segmentation results include single-pipe scenes and multi-pipe scenes; when the segmentation result is a single-pipe scene, principal component analysis is performed based on all pipeline point cloud data to obtain the normal vector of each data point in the pipeline point cloud data, and cylinder fitting is performed on the pipeline point cloud data to obtain the pipeline radius; plane cutting is performed according to the normal vector to obtain a tangent plane, and the pipeline centerline is obtained based on the tangent plane, which can simplify the three-dimensional geometric shape into a one-dimensional center line, reducing data complexity and improving computing efficiency. Even when point cloud data is incomplete or noisy, it can still accurately capture the geometric structure of the pipeline and enhance robustness. The distance threshold parameter is determined according to the radius of the pipeline, and the parameter can be adaptively adjusted according to actual conditions to improve extraction accuracy and stability. Based on the distance threshold parameter, the pipeline centerline is segmented and fitted with a straight line through a random sampling consistency algorithm to obtain a centerline point set. Feature extraction is performed on the centerline point set to obtain feature points of the pipeline centerline. The connection relationship between adjacent feature points is determined based on the geometric relationship. Based on the connection relationship, a pipeline reconstruction model is obtained, which can be applied to complex application scenarios and realize the automatic extraction of underground pipeline information. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of an underground pipeline extraction method based on point cloud provided in an embodiment of the present application;

[0050] Figure 2 is a schematic diagram of the original point cloud of a single-tube scene provided in an embodiment of the present application;

[0051] Figure 3 Schematic diagram of the segmentation result of the single-tube scene semantic segmentation provided in the embodiment of the present application;

[0052] Figure 4 is a schematic diagram of the original point cloud of the multi-tube scene provided in the embodiment of the present application;

[0053] Figure 5 This is a schematic diagram of the segmentation result of the multi-tube scene semantic segmentation provided in the embodiment of the present application

[0054] Figure 6 is a schematic diagram of reconstruction results of a single-tube scene reconstruction pipeline provided in an embodiment of the present application;

[0055] Figure 7 is a schematic diagram of reconstruction results of a multi-tube scene reconstruction pipeline provided in an embodiment of the present application;

[0056] Figure 8 is a flow chart of an underground pipeline extraction method based on point cloud provided by another embodiment of the present application;

[0057] Fig. 9 It is a structural schematic diagram of an underground pipeline extraction system based on point cloud provided in an embodiment of the present application;

[0058] Fig.10 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0060] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0061] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0063] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0064] (1) Branch: The basic shape of the pipeline consists of a cylinder, which is called a branch in this article.

[0065] (2) Joint: The connecting part of a branch (such as a tee) is called a joint.

[0066] (3) Rotational axis of symmetry (ROSA): The axis of symmetry in which the overall shape and structure of the pipeline remain unchanged after it is rotated around a certain axis by any angle in three-dimensional space.

[0067] Next, some related technologies involved in the embodiments of the present application are explained.

[0068] The region growing method starts from one or more seed points and gradually merges adjacent pixels or voxels similar to the seed points to form a complete region, thereby detecting pipeline geometry.

[0069] Hough transform is a shape detection method based on parameter space. It identifies the parameters that conform to the pipeline model by converting points in space into parameter space.

[0070] The Gaussian fitting method assumes that the data points follow a Gaussian distribution and fits the center line and radius of the cylinder through an optimization algorithm.

[0071] The RANSAC algorithm is an iterative method that finds the best pipeline model by randomly selecting a subset of data for model fitting. The maxDistance parameter in the algorithm defines the maximum distance threshold for a point to be considered an inlier, which is used to determine whether a data point conforms to the fitted model.

[0072] However, the region growing method is simple and intuitive, and the Hough transform is suitable for detection of obvious shapes. These two methods are definitely sensitive to noise and have high computational complexity. Gaussian fitting can provide accurate cylinder parameters, but it has high requirements on initial conditions and data quality. The RANSAC algorithm performs well when processing noisy data, but the uncertainty of parameter selection is its main disadvantage. For example, the inlier threshold maxDistance is the error threshold for determining whether a data point is an inlier. If the distance from the data point to the model is less than this threshold, the point is considered to be an inlier. If the threshold is set too large, many noise points may be mistakenly regarded as inliers, affecting the accuracy of the model; if it is set too small, the real inliers may be mistaken for noise points. Therefore, a more computationally efficient method is needed.

[0073] In view of this, an embodiment of the present application provides a point cloud-based underground pipeline extraction method, system, equipment and storage medium. The pipeline information extraction method based on the center line simplifies complex three-dimensional geometric shapes into one-dimensional defects, reduces the complexity of the data, and facilitates subsequent analysis and processing.

[0074] The underground pipeline extraction method based on point cloud provided in the embodiment of the present application relates to the technical field of point cloud spatial geometry extraction. The underground pipeline extraction method based on point cloud provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the underground pipeline extraction method based on point cloud, etc., but is not limited to the above forms.

[0075] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0076] Figure 1 is an optional flow chart of the underground pipeline extraction method based on point cloud provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S109.

[0077] Step S101, obtaining underground pipeline point cloud data.

[0078] Specifically, step S101 may include but is not limited to steps S201 to S202.

[0079] Step S201, obtaining underground spatial point cloud data.

[0080] Exemplarily, a laser scanning device (such as a laser scanner or lidar) is used to emit a laser beam to measure an underground scene. Based on the time from emission to return of the laser beam, as well as the angle and intensity of reflection, the three-dimensional coordinates and surface properties of objects in the scene are calculated to generate underground spatial point cloud data.

[0081] Step S202: perform semantic segmentation on the spatial point cloud data through a deep learning semantic segmentation network, separate the pipeline from the background, and obtain pipeline point cloud data.

[0082] Optionally, the spatial point cloud data is semantically segmented by an advanced deep learning semantic segmentation network (such as PointNet, PointNet++, RandLA-Net, PointConv, etc.), and the acquired underground spatial point cloud data is input into the deep learning semantic segmentation network. For example, the point cloud data is processed by the RandLA-Net network. After training, the network can automatically segment the underground pipelines from the complex background. The segmentation result includes all the points belonging to the pipelines, thereby obtaining the pipeline point cloud data.

[0083] In some embodiments, reference Figure 2 , the original point cloud data of the single-tube scene is input into the deep learning semantic segmentation network, and the semantic segmentation result of the single-tube scene is obtained as follows Figure 3 As shown, blue represents pipelines.

[0084] In another embodiment, referring to Figure 4 , the original point cloud data of the multi-tube scene is input into the deep learning semantic segmentation network, and the semantic segmentation results of the multi-tube scene are obtained as follows Figure 5 As shown, blue represents pipelines.

[0085] Step S102, performing scene object segmentation on the pipeline point cloud data to obtain a segmentation result, wherein the segmentation result includes a single-pipeline scene and a multi-pipeline scene.

[0086] Specifically, scene object segmentation of pipeline point cloud data refers to the use of specific algorithms and techniques to divide the pipeline point cloud data according to its spatial, geometric, texture and other characteristics, so that the point cloud data in the same division have similar characteristics.

[0087] It can be understood that the single-pipe scene indicates that the pipeline point cloud data sets exhibit similar characteristics. When performing scene segmentation on the pipeline point cloud data, all pipeline point cloud data in this scenario are regarded as coming from one pipeline.

[0088] A multi-pipeline scene means that the pipeline point cloud data is segmented into multiple independent areas. There are differences in spatial position, geometric shape, texture and other features between areas. When performing scene segmentation on the pipeline point cloud data, the pipeline point cloud data located in the same area in this scenario are regarded as the same pipeline, and the pipeline point cloud data located in different areas are regarded as coming from different pipelines.

[0089] In some embodiments, step S102 may include but is not limited to steps S301 to S303.

[0090] Step S301 : dividing pipeline point cloud data into several clusters of pipeline subset data by using a clustering algorithm and pipeline prediction labels.

[0091] Specifically, based on the HDBSACN clustering algorithm and pipeline prediction labels, the distribution characteristics, cluster shape and quantity of pipeline point cloud data are comprehensively considered, and the pipeline point cloud data with similar characteristics are divided into a cluster of pipeline subset data, thereby obtaining several clusters of independent pipeline subset data.

[0092] Step S302: When the number of pipeline subset data is equal to one cluster, it is determined that the segmentation result is a single-pipe scene.

[0093] It can be understood that when there is only one cluster of pipeline subset data after division, it means that the pipelines exhibit similar characteristics and all pipeline point cloud data come from one pipeline. Therefore, the segmentation result is determined to be a single-pipeline scene.

[0094] Step S303, or, when the number of pipeline subset data is greater than one cluster, it is determined that the segmentation result is a multi-pipe scene.

[0095] Alternatively, when the number of pipeline subset data is greater than one cluster after division, for example, the pipeline point cloud data is divided into three clusters according to the characteristics of spatial distribution, it may mean that the pipeline point cloud data comes from three different pipelines. Therefore, the segmentation result is determined to be a multi-pipeline scene.

[0096] In the process of clustering algorithm processing, clusters that do not meet the constraints are removed according to the volume threshold constraint and the quantity threshold constraint.

[0097] Optionally, considering the problems of noise and erroneous segmentation in point cloud data, clusters that do not meet the constraints can be removed according to the volume threshold constraint and the number threshold constraint during the clustering algorithm processing. For example, the volume threshold is set to 0.5 cubic meters and the number threshold is set to 500. During the clustering algorithm processing, clusters with a volume less than 0.5 cubic meters are removed according to the preset volume threshold constraint, and clusters with a number less than 500 are removed according to the number threshold constraint.

[0098] Furthermore, the underground pipeline extraction method based on point cloud may also include but is not limited to the following steps: performing principal component analysis on each pipeline subset data to obtain the normal vector of each data point in the pipeline subset data.

[0099] Step S103, when the segmentation result is a single-pipe scene, principal component analysis is performed based on all pipeline point cloud data to obtain the normal vector of each data point in the pipeline point cloud data, and cylindrical fitting is performed on the pipeline point cloud data to obtain the pipeline radius.

[0100] Specifically, when the segmentation result is a single-pipe scene, first, the normal vector of each data point in the pipeline point cloud data is estimated by performing principal component analysis (PCA) on the neighborhood point set of each data point in the pipeline point cloud data.

[0101] Then, a cylinder is fitted to the pipeline based on the RANSAC algorithm, and the radius parameter of the fitted cylinder is the radius of the pipeline. Optionally, when calculating the radius, multiple iterations are used, and the radius of the pipeline with the most iterations is taken as the radius of the pipeline.

[0102] It is understandable that in order to further reduce the calculation errors introduced by noise points caused by laser point cloud scanning, it is also possible to limit the reasonable range of pipe diameters based on the existing pipeline facility library, filter unreasonable radius parameters, reduce the errors introduced by underground pipeline laser scanning, and thus optimize the radius calculation of the pipeline and improve accuracy.

[0103] In some embodiments, the step of performing principal component analysis based on all pipeline point cloud data to obtain the normal vector of each data point in the pipeline point cloud data may include but is not limited to steps S401 to S402.

[0104] Step S401 : for each data point in the pipeline point cloud data, neighboring points are determined with the data point as the center to obtain a neighboring point set.

[0105] Exemplarily, a k-nearest neighbor algorithm may be used to search based on a fixed number, and for each data point in the pipeline point cloud data, k nearest neighbor points (eg, k=20) are selected with the data point as the center to obtain a neighbor point set.

[0106] Alternatively, a radius search method is used to search based on a preset radius, and for each data point in the pipeline point cloud data, all points within a given radius are selected with the data point as the center to obtain a neighboring point set.

[0107] Step S402, calculating the covariance matrix of the neighboring point set, and taking the eigenvector corresponding to the minimum eigenvalue of the covariance matrix as the normal vector of the data point.

[0108] For example, let the neighboring point set of a data point be N. For a three-dimensional point cloud, the coordinates of each neighboring point are (x i ,y i ,z i ), calculate the mean of the coordinates of each neighboring point in each dimension for the selected neighboring point set, and obtain the centroid coordinates. Subtract the centroid coordinates from each neighboring point coordinate to obtain the corresponding offset vector, and then construct the covariance matrix based on all the offset vectors.

[0109] Next, the covariance matrix of the neighboring point set is calculated, and the eigenvector corresponding to the minimum eigenvalue of the covariance matrix is ​​obtained through PCA analysis. Usually, the eigenvector corresponding to the smallest eigenvalue represents the direction with the smallest change in the data distribution, that is, the normal direction of the local surface. Therefore, this eigenvector is used as the normal vector of the data point.

[0110] Step S104, performing plane cutting according to the normal vector to obtain a tangent plane, and obtaining the pipeline centerline based on the tangent plane.

[0111] In some embodiments, step S104 may include but is not limited to steps S501 to S503.

[0112] Step S501, performing plane cutting along a direction perpendicular to the normal vector to obtain a plurality of tangent planes.

[0113] Specifically, several local optimal cutting planes (ie, tangent planes) are obtained through continuous iteration and plane cutting along the direction perpendicular to the normal vector of each data point.

[0114] Step S502, determining the center point of each tangential plane, and obtaining the rotational symmetry axis according to the position information of all the center points.

[0115] For example, when the tangent plane intersects with the pipeline point cloud data, a series of intersection points will be generated. These intersection points are roughly located on the cross section of the pipeline, and theoretically should be distributed on the surface of a cylinder with the rotational symmetry axis as the center line. Through continuous iteration and plane cutting, the center point of the tangent plane is obtained. After determining the center point of each tangent plane, the center points of each tangent plane are connected according to the position information of all the center points to obtain the rotational symmetry axis that can reflect the direction and shape of the pipeline.

[0116] Step S503: determine the rotational symmetry axis as the pipeline centerline.

[0117] It can be understood that the ROSA algorithm is an algorithm for extracting center lines from incomplete point clouds. The algorithm is implemented based on the generalized rotational symmetry axis of the directed point set. Given a subset S of oriented points, the algorithm introduces a variational definition of the oriented point that is most rotationally symmetric relative to the subset S. Based on the ROSA idea, the normal vector information is effectively used to compensate for missing data, and the axis that minimizes the change after the subset S is rotated relative to a certain axis is found, thereby determining the rotational symmetry axis, which is the center line of the pipeline.

[0118] In this embodiment, even when the pipeline point cloud data is incomplete or noisy, the geometric structure of the pipeline can still be accurately captured, it has a certain degree of robustness against point cloud noise and data missing, and can reliably extract pipeline information even in the face of complex and imperfect data.

[0119] Step S105, determining a distance threshold parameter according to the pipeline radius.

[0120] Specifically, step S105 may include but is not limited to step S601.

[0121] Step S601, setting the distance threshold parameter of the random sampling consensus algorithm to β multiples of the pipeline radius, where β∈[0.1,1].

[0122] Considering that the uncertainty of RANSAC algorithm parameter selection is its main disadvantage, when the distance threshold parameter for judging whether it is an inlier is set too large, it is easy to cause noise points to be mistakenly regarded as inliers, affecting the accuracy of the model; but when the distance threshold is set too small, it is easy for the real inliers to be mistakenly regarded as noise points. Therefore, in order to dynamically adjust the distance threshold parameter of the RANSAC algorithm according to the actual situation, the distance threshold parameter is adaptively adjusted based on the pipeline radius.

[0123] Specifically, the distance threshold parameter in the RANSAC algorithm is set to be a multiple of the pipeline radius, where β∈[0.1,1]. It should be noted that β and the pipeline radius are two independent parameters. The setting of the β value is affected by the noise points in the centerline point set. The more noise points there are, the larger the β value is set. Optionally, the specific value of the β value can be set based on experience. By combining the experience library to eliminate the incorrect pipe diameter fitting results caused by noise, a more suitable pipeline radius under the majority is obtained.

[0124] It should be noted that in addition to the mode, the mean, median, etc. can also be used as long as the error caused by noise can be reduced.

[0125] Step S106, based on the distance threshold parameter, a random sampling consistency algorithm is used to perform piecewise straight line fitting on the pipeline centerline to obtain a centerline point set.

[0126] Specifically, firstly, linear fitting is performed on every five consecutive points on the roughly extracted pipeline centerline to obtain the direction of the local line segment. Secondly, the cosine value between adjacent straight line segments is calculated, the angle between them is evaluated, and the fitted curve segment is optimized according to the cosine value. When traversing the point cloud data, this operation is repeated many times to gradually purify and smooth the entire curve to improve the accuracy and robustness of subsequent curve fitting. More accurate results are obtained by fitting and optimizing local features to better adapt to the shape and noise of complex curves.

[0127] Next, based on the distance threshold parameter determined according to the pipeline radius, the RANSAC algorithm is used to construct a candidate model by randomly sampling data points, and then the internal structure of the data is estimated by identifying internal points (data points that fit the model well) and external points (noise or outliers) to find the best fitting model. Its main goal is to find a model that can best describe the data in a dataset with noise and outliers and eliminate the interference of outliers.

[0128] Finally, all identified internal points of the model are connected to obtain the centerline point set on the pipeline centerline.

[0129] In some embodiments, before the step of obtaining the centerline point set, the process may also include but is not limited to step S701.

[0130] Step S701, using the cylindrical neighborhood filtering method, the pipeline centerline obtained by fitting is used as the axis, and the α multiple of the pipeline radius is used as the cylindrical neighborhood radius to remove noise points within the cylindrical range, where α∈[0.5,5].

[0131] Specifically, considering the characteristics of internal pipeline field scanning, when extracting the centerline of the pipeline, the noise and abnormal points of the point cloud mostly exist in the space inside the pipeline, and the closer to the rotational symmetry axis of the pipeline, the denser the noise distribution. Therefore, the parameter adaptive adjustment strategy based on the pipeline radius can be used to adjust the parameter setting of the cylindrical neighborhood filter. In the iterative optimization process based on the RANSAC segmented extraction algorithm, the straight line obtained each time is used as the axis, and the multiple of the pipeline radius α is used as the cylindrical neighborhood radius to remove the noise points within the cylindrical range, where α∈[0.5,5].

[0132] Step S107, extracting features from the centerline point set to obtain feature points of the pipeline centerline, wherein the feature points include endpoints and inflection points.

[0133] Specifically, feature points are extracted from the centerline point set to obtain feature points whose geometric features are more consistent with the geometric characteristics of pipeline straight lines or broken lines. In view of the geometric characteristics of pipelines, the start and end point extraction method, the filtering processing feature method, and the feature point extraction method without filtering are proposed for the centerline point set to extract feature points (endpoints, inflection points, etc.) from the centerline point set. In addition, in the feature point extraction calculation, the number is introduced as a limiting factor to avoid the extraction method of only one feature point.

[0134] Exemplarily, the feature point extraction mode provides three situations for the user to choose according to the actual situation, including the pipeline start and end point extraction method, the noise filter extraction method, and the filter-free feature point extraction method.

[0135] When the pipeline is a straight line, the start and end endpoints extraction method is used to extract the start and end endpoints of the pipeline as feature points.

[0136] When the number of feature points obtained after the centerline point set feature extraction is greater than two, one case is that the pipeline is a straight pipe but the data is interfered by noise, and the other case is that there is a curved part in the pipeline, so the feature points also include inflection points.

[0137] Considering that the filter can have a good denoising effect on straight pipes, but will cause deviations in the pipeline trajectory for curved pipes, in the case where there may be curved parts in the pipeline, the system uses a filter-free feature point extraction method without filtering the feature points to retain the inflection points in the feature points.

[0138] In case that the data may be interfered by noise, the noise point filtering extraction method can be used to filter the feature points and remove the noise points in the feature points.

[0139] It should be noted that when the noise filtering extraction method is used to filter the feature points, if there is only one feature point in the end, the pipeline information cannot be restored. Therefore, in the feature point extraction process, it is necessary to judge the number of feature points and introduce a quantity restriction condition. If the number of feature points after filtering is 1, the system automatically adjusts to the feature point extraction method without filtering.

[0140] Step S108: determining the connection relationship between adjacent feature points according to the geometric relationship.

[0141] Exemplarily, for the calculated feature points, their main directions are determined by principal component analysis, and the adjacent relationship between the feature points is determined in sequence according to the main directions, and the lines between the feature points are connected.

[0142] Step S109, obtaining a pipeline reconstruction model based on the connection relationship.

[0143] Exemplarily, the spatial length of the pipeline is calculated according to the connection relationship to generate the pipeline reconstruction model.

[0144] In some embodiments, according to Figure 2 The pipeline centerline and pipeline radius are extracted from the original point cloud data of the single-pipe scene, and feature points are obtained based on the pipeline centerline and pipeline radius. The spatial length of the pipeline is calculated based on the connection relationship between adjacent feature points to obtain the pipeline reconstruction model after the single-pipe scene is reconstructed, as shown in Figure 6 As shown in Figure 1, the red lines represent the reconstructed pipeline. Figure 6 It can be seen that in the single-pipe scenario, even if some point cloud data is missing, the system can still re-establish the complete pipeline reconstruction model of a single-segment pipe after calculating the spatial length based on the connection relationship between adjacent feature points.

[0145] according to Figure 4 The pipeline centerline and pipeline radius are extracted from the original point cloud data of the multi-pipe scene, and the feature points are obtained according to the pipeline centerline and pipeline radius. The spatial length of the pipeline is calculated based on the connection relationship between adjacent feature points, and the pipeline reconstruction model after the multi-pipe scene is reconstructed is obtained, such as Figure 7 As shown, the red lines represent the reconstructed pipeline.

[0146] It should be noted that due to the limitations of laser scanning data in complex underground spaces, some single pipelines have intermittent scanning, resulting in incomplete point cloud data. At this time, there is a straight pipe that is mistakenly identified as a multi-pipe scene due to missing point cloud data. In order to reduce the impact of missing data on the accuracy of pipeline reconstruction, in some embodiments, when the segmentation result is a multi-pipe scene, when reconstructing the pipeline, the system will also provide a single-pipe scene reconstruction result that treats all pipeline point cloud data as a cluster. When multiple pipelines are identified, the system simultaneously outputs pipeline reconstruction models for single-pipe and multi-pipe scenes to the user, so that the user can choose a reconstruction effect that is more in line with reality.

[0147] The following is a detailed introduction and description of the solution of the embodiment of the present invention in conjunction with a specific application example.

[0148] Reference Figure 8 , Figure 8 A flowchart of a point cloud-based underground pipeline extraction method provided in another embodiment of the present application.

[0149] In this embodiment, semantic segmentation is first performed to segment the underground pipelines from the complex background through a deep learning semantic segmentation network.

[0150] Next, the pipeline objects are clustered and the pipeline point cloud data is processed for scene object segmentation, including single-pipeline scenes and multi-pipeline scenes. When there are multiple pipelines in the same scene, the pipeline set can be divided into independent subsets through clustering algorithms and pipeline prediction labels. During the implementation process, two processing methods are provided for users to choose: single pipeline (i.e. no clustering) and multiple pipelines (i.e. clustering).

[0151] For multi-pipeline scenarios, certain threshold rules are also combined to remove the influence of noise clusters. After clustering, considering that incorrect segmentation often occurs during object segmentation, volume and quantity threshold constraints can be implemented to remove clusters with too small volume or too sparse point distribution.

[0152] Then, the normal vector is estimated. Based on the analyzed single pipeline scene, the normal vector of the pipeline point cloud data is estimated by performing principal component analysis on the neighborhood point set of each data point.

[0153] Furthermore, the radius of the obtained pipeline point cloud data is calculated, and the cylinder radius of the pipeline is fitted multiple times based on the RANSAC algorithm. The incorrect pipe diameter fitting results caused by noise are eliminated by combining the experience library, and the preset threshold library of underground pipeline facilities is introduced to eliminate the abnormal values ​​of the fitting. Optionally, the average value is taken through iterative optimization to finally determine the radius parameter of the pipeline.

[0154] The centerline extraction step mainly includes the centerline extraction of branches and the processing of joints.

[0155] Specifically, based on the normal vector of the pipeline point cloud data, the ROSA algorithm is used to extract the center line of the pipeline branch through continuous iteration and plane cutting.

[0156] Then, the point cloud data at the joints is smoothed based on the Laplacian Smoothing algorithm, which is designed to reduce noise and enhance the smoothness of the shape. The basic idea of ​​Laplacian smoothing is to iteratively optimize the position of the point so that it is closer to the average position of the surrounding neighboring points. In this way, the point cloud surface becomes smoother and the high-frequency components caused by noise are reduced.

[0157] Next, the smoothed point cloud data is processed using the Moving Lest Squares (MLS) algorithm to determine the center point of the joint (i.e., the intersection point). Moving Least Squares is a technique for smoothing point cloud data by fitting a local surface within the neighborhood of each point to reduce noise and irregularities.

[0158] After extracting the centerline of the pipeline, noise removal is performed. Optionally, during the implementation process, a variety of noise removal methods are provided for users to choose from to improve the data quality of the segmented pipeline. Among them, the noise removal method provides filtering algorithms including statistical filtering, mean filtering, median filtering, Gaussian filtering, etc., for users to switch and select according to the three-dimensional reconstruction effect during implementation.

[0159] It should be noted that among the noise removal methods provided, Gaussian filtering, mean filtering, median filtering, etc. have a smoothing effect. These filters can have a good denoising effect on straight pipes, but will cause deviations in the pipeline trajectory for curved pipes. Therefore, the system will provide two methods for users to choose: whether to use filtering or not.

[0160] Based on the filtered pipeline centerline, the RANSAC segmented straight line fitting algorithm is used to iteratively optimize the centering, so as to accurately extract the feature points (endpoints, inflection points, etc.) of the centerline. In order to dynamically adjust the optimization process of the pipeline centerline feature points according to the actual situation, a parameter adaptive adjustment mechanism based on the pipeline radius is introduced. The maxDsitance parameter in the RANSAC algorithm is adaptively adjusted based on the calculated pipeline radius. After each fitting, the cylindrical neighborhood filter is used to remove the noise around the fitting line.

[0161] The main direction of the calculated feature points is determined by principal component analysis, the connection relationship between the feature points is determined, and the connection between the feature points is realized.

[0162] Finally, the length is calculated based on the obtained centerline and radius parameters, and the pipeline type attributes are assigned to generate a pipeline reconstruction model. In view of the uneven quality of on-site laser scanning, two situations can be provided for user reference: multi-segment pipe spatial information output through cluster analysis, and single-segment pipe spatial information output under the assumption of a single pipeline, so as to select a pipeline reconstruction model that meets the actual situation and realize underground pipeline extraction.

[0163] The underground pipeline extraction method based on point cloud provided in this embodiment obtains underground pipeline point cloud data; identifies and analyzes pipeline objects in the scanning scene, including single-pipeline scene predetermined assumptions and multi-pipeline object predetermined assumptions. Then, principal component analysis is performed on the pipeline point cloud data of the single-pipeline scene to obtain the normal vector of each data point in the pipeline point cloud data, and cylindrical fitting is performed on the pipeline point cloud data. At the same time, the unreasonable pipeline radius judgment is filtered out by considering the combination of the pipeline radius experience library, and the pipeline radius is obtained by combining multiple iterative calculations using the majority analysis method. The plane is cut according to the normal vector to obtain the tangent plane, and the pipeline centerline is obtained based on the tangent plane; the distance threshold parameter is determined according to the pipeline radius; based on the distance threshold parameter, the pipeline centerline is segmented and straight-line fitted by a random sampling consistency algorithm to obtain a centerline point set; the centerline point set is feature extracted to obtain the feature points of the pipeline centerline, and different filtering models can be strategically proposed to realize feature point filtering analysis and reduce the influence of noise points on pipeline centerline extraction. The connection relationship between adjacent feature points is determined according to the geometric relationship; the pipeline reconstruction model is obtained based on the connection relationship, which overcomes the influence of the complex background of underground pipelines and the limitations of laser scanning technology in acquiring data. Through the adaptive parameter adjustment mechanism, it is ensured that the algorithm can flexibly adapt to different pipe diameters and complex backgrounds, thereby improving the overall extraction accuracy and stability, thereby efficiently and automatically extracting pipeline information from complex underground environments, and having strong robustness to noise and data missing.

[0164] Secondly, this embodiment uses the concept of ROSA and normal vector information to extract the centerline of the curve. Even if the point cloud data is incomplete or noisy, it can still accurately capture the geometric structure of the pipeline and has a certain robustness against point cloud noise and data missing. This robustness ensures that in practical applications, in the face of complex and imperfect data, pipeline information can still be reliably extracted, thereby enhancing robustness.

[0165] At the same time, it can also realize the automatic extraction of underground pipeline information, and extract and optimize the pipeline centerline without human intervention, which improves efficiency and practicality and has a high degree of automation.

[0166] In addition, this embodiment simplifies the complex three-dimensional pipeline geometry into a one-dimensional centerline, reduces data complexity, improves calculation efficiency, and is suitable for large-scale underground pipeline systems.

[0167] When dealing with challenges such as complex backgrounds, data noise and missing data of underground pipelines, the method provided in this embodiment can demonstrate high adaptability and robustness, achieve efficient and automated extraction of pipeline information, and has significant application advantages.

[0168] See also Fig. 9The embodiment of the present application further provides a point cloud-based underground pipeline extraction system, which can implement the above-mentioned point cloud-based underground pipeline extraction method, and the system includes:

[0169] The first module is used to obtain underground pipeline point cloud data.

[0170] The second module is used to perform scene object segmentation on the pipeline point cloud data to obtain segmentation results, wherein the segmentation results include single-pipe scenes and multi-pipe scenes.

[0171] The third module is used to perform principal component analysis based on all pipeline point cloud data when the segmentation result is a single-pipe scene, obtain the normal vector of each data point in the pipeline point cloud data, perform cylindrical fitting on the pipeline point cloud data, and obtain the pipeline radius.

[0172] The fourth module is used to obtain a tangent plane by performing plane cutting according to a normal vector, and obtain a pipeline centerline based on the tangent plane.

[0173] The fifth module is used to determine the distance threshold parameter according to the pipeline radius.

[0174] The sixth module is used to perform segmented straight line fitting on the pipeline centerline based on the distance threshold parameter through a random sampling consistency algorithm to obtain a centerline point set.

[0175] The seventh module is used to extract features from the centerline point set to obtain feature points of the pipeline centerline, wherein the feature points include endpoints and inflection points.

[0176] The eighth module is used to determine the connection relationship between adjacent feature points based on geometric relationships.

[0177] The ninth module is used to obtain a pipeline reconstruction model based on the connection relationship.

[0178] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0179] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned underground pipeline extraction method based on point cloud when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0180] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0181] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0182] The processor 111 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0183] The memory 112 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 112 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 112, and the processor 111 calls and executes the underground pipeline extraction method based on point cloud in the embodiment of this application.

[0184] The input / output interface 113 is used to implement information input and output.

[0185] The communication interface 114 is used to realize communication interaction between the device and other devices. Communication can be realized through wired methods (such as USB, network cable, etc.) or wireless methods (such as mobile network, WIFI, Bluetooth, etc.).

[0186] The bus 115 transmits information between various components of the device (eg, the processor 111 , the memory 112 , the input / output interface 113 , and the communication interface 114 ).

[0187] The processor 111 , the memory 112 , the input / output interface 113 and the communication interface 114 are connected to each other in communication within the device via a bus 115 .

[0188] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned point cloud-based underground pipeline extraction method is implemented.

[0189] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0190] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0191] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0192] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0193] The system embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0194] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0195] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0196] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A method for extracting underground pipelines based on point cloud, characterized in that: The method comprises the following steps: Obtain underground pipeline point cloud data; Performing scene object segmentation on the pipeline point cloud data to obtain a segmentation result, wherein the segmentation result includes a single-pipeline scene and a multi-pipeline scene; When the segmentation result is a single-pipe scene, principal component analysis is performed based on all the pipeline point cloud data to obtain the normal vector of each data point in the pipeline point cloud data, and cylindrical fitting is performed on the pipeline point cloud data to obtain the pipeline radius; Performing plane cutting according to the normal vector to obtain a tangent plane, and obtaining a pipeline centerline based on the tangent plane; Determining a distance threshold parameter according to the pipeline radius; Based on the distance threshold parameter, performing piecewise straight line fitting on the pipeline centerline by a random sampling consistency algorithm to obtain a centerline point set; Performing feature extraction on the centerline point set to obtain feature points of the pipeline centerline, wherein the feature points include endpoints and inflection points; Determine the connection relationship between adjacent feature points according to the geometric relationship; A pipeline reconstruction model is obtained based on the connection relationship.

2. The method according to claim 1, characterized in that The step of obtaining underground pipeline point cloud data includes the following steps: Obtain underground spatial point cloud data; The spatial point cloud data is semantically segmented through a deep learning semantic segmentation network, and the pipeline is segmented from the background to obtain pipeline point cloud data.

3. The method according to claim 1, characterized in that The step of performing scene object segmentation on the pipeline point cloud data to obtain a segmentation result comprises the following steps: Dividing the pipeline point cloud data into a number of clusters of pipeline subset data by using a clustering algorithm and pipeline prediction labels; When the number of the pipeline subset data is equal to one cluster, determining that the segmentation result is a single-pipe scene; Alternatively, when the number of the pipeline subset data is greater than one cluster, determining that the segmentation result is a multi-pipeline scene; Among them, in the clustering algorithm processing process, the clusters that do not meet the constraints are removed according to the volume threshold constraint and the quantity threshold constraint; The underground pipeline extraction method based on point cloud also includes the following steps: When the segmentation result is a multi-pipe scene, principal component analysis is performed on each cluster of the pipeline subset data to obtain a normal vector of each data point in the pipeline subset data.

4. The method according to claim 1, characterized in that: The principal component analysis is performed based on all the pipeline point cloud data to obtain the normal vector of each data point in the pipeline point cloud data, including the following steps: For each data point in the pipeline point cloud data, determine neighboring points with the data point as the center to obtain a neighboring point set; The covariance matrix of the neighboring point set is calculated, and the eigenvector corresponding to the minimum eigenvalue of the covariance matrix is ​​used as the normal vector of the data point.

5. The method according to claim 1, characterized in that The step of performing plane cutting according to the normal vector to obtain a tangent plane, and obtaining a pipeline centerline based on the tangent plane, comprises the following steps: Performing plane cutting along a direction perpendicular to the normal vector to obtain a plurality of tangent planes; Determine the center point of each of the tangential planes, and obtain the rotational symmetry axis according to the position information of all the center points; The rotational symmetry axis is determined as the pipeline centerline.

6. The method according to claim 1, characterized in that Determining the distance threshold parameter according to the pipeline radius includes the following steps: The distance threshold parameter of the random sampling consensus algorithm is set to β multiples of the pipeline radius, where β∈[0.1,1].

7. The method according to claim 1, characterized in that Before the step of obtaining the center line point set, the method further comprises the following steps: The cylindrical neighborhood filtering method is used to take the pipeline centerline obtained by fitting as the axis, and the α times of the pipeline radius as the cylindrical neighborhood radius to remove noise points within the cylindrical range, where α∈[0.5,5].

8. A point cloud-based underground pipeline extraction system, characterized in that: The system comprises: The first module is used to obtain underground pipeline point cloud data; The second module is used to perform scene object segmentation on the pipeline point cloud data to obtain a segmentation result, wherein the segmentation result includes a single-pipeline scene and a multi-pipeline scene; The third module is used for performing principal component analysis based on all the pipeline point cloud data to obtain the normal vector of each data point in the pipeline point cloud data when the segmentation result is a single-pipe scene, and performing cylindrical fitting on the pipeline point cloud data to obtain the pipeline radius; A fourth module is used to perform plane cutting according to the normal vector to obtain a tangent plane, and obtain a pipeline centerline based on the tangent plane; A fifth module is used to determine a distance threshold parameter according to the pipeline radius; A sixth module is used to perform piecewise straight line fitting on the pipeline centerline through a random sampling consistency algorithm based on the distance threshold parameter to obtain a centerline point set; The seventh module is used to extract features from the centerline point set to obtain feature points of the pipeline centerline, wherein the feature points include endpoints and inflection points; An eighth module is used to determine the connection relationship between adjacent feature points according to the geometric relationship; The ninth module is used to obtain a pipeline reconstruction model based on the connection relationship.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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