Pipeline elevation measurement method, system and equipment based on laser point cloud and medium

Abstract

The invention discloses a pipeline elevation measurement method, system and equipment based on laser point cloud and a medium, and relates to the technical field of pipeline elevation, and the method comprises the steps: carrying out the preprocessing of three-dimensional point cloud data of a pipe trench region; extracting center line point cloud data of the pipeline and point cloud data of the pipeline from the preprocessed three-dimensional point cloud data; generating a ground model based on the center line point cloud data of the pipeline; and projecting the point cloud data of the pipeline to the ground model to elevate the pipeline. Due to the characteristics of good reliability, high accuracy and the like of the laser point cloud technology, the laser point cloud technology is applied to the elevation measurement of the oil and gas pipeline, the accuracy of the elevation measurement can be improved, and the labor and time cost is reduced.

Description

Background Technology

[0002] In recent years, with the development of science and technology and the advancement of measurement techniques, the oil and gas industry has placed higher demands on the scientific rigor, accuracy, and reliability of elevation measurements for oil and gas pipelines. Previously, elevation measurements of oil and gas pipelines mainly relied on surveyors using levels, a method that was not only time-consuming and labor-intensive, but whose accuracy was directly linked to the surveyors' skill and experience. Today, with the rapid development of network information technology and the continuous updating and iteration of measurement methods, elevation measurements of oil and gas pipelines are becoming increasingly intelligent. For example, methods such as using drones for aerial photogrammetry, employing ultrasonic measuring devices, and utilizing GPS positioning technology are more convenient and efficient than previous methods, but their cost is relatively high. Therefore, there is a need for a cost-effective, reliable, and efficient method for measuring the elevation of oil and gas pipelines.

[0003] The existing technology is as follows:

[0004] 1) In the utility model patent with authorization announcement number "CN212460613U" and subject name "A Trench Measurement System for Pipeline Engineering Construction", the trench measurement system includes a drone, a model building device, and an engineering parameter acquisition device. The drone is configured to acquire trench videos, which include a first video and a second video. The first video includes a video before trench excavation, and the second video includes a video of the trench awaiting backfilling. The model building device is connected to the drone and configured to acquire trench videos. It is also configured to build a first trench model and a second trench model based on the first and second videos, respectively. The first trench model includes a model before trench excavation, and the second trench model includes a model of the trench awaiting backfilling. The engineering parameter acquisition device is connected to the model building device and configured to acquire the first and second trench models. It is also configured to compare the elevations of the first and second trench models to obtain the engineering parameters of the trench, including trench dimensions and earthwork volume.

[0005] 2) The utility model patent with authorization announcement number "CN218955634U" and subject name "A soil backfill elevation measuring device suitable for foundation pits or trenches" includes a accommodating cavity, a measuring device, and a compaction part. The measuring device includes a first measuring part and a second measuring part. The first measuring part is sleeved on the accommodating cavity. The top surface and side surface of the accommodating cavity are completely fitted with the inner wall of the first measuring part. A through hole is opened at the center of the top surface of the first measuring part. The second measuring part passes through the through hole and is connected to the compaction part. The second measuring part is slidably connected to the through hole. The side surface of the compaction part is completely fitted with the inner wall of the accommodating cavity. A scale is provided on the first measuring part and the second measuring part respectively. It can use the accommodating cavity as a mold to manufacture one or more standard columns. When backfilling foundation pits or trenches, it is only necessary to first compare the elevation of the first measuring part with the design height of the backfill, then pull out the measuring device and the accommodating cavity to expose the standard column, and then compare the height with the standard column to compact to the design height. No multiple measurements are required, which makes it easy to control the backfill height, ensure construction quality, and improve construction efficiency.

[0006] As can be seen from the existing technology described above, traditional measurement methods mainly involve surveyors using measuring tools to take measurements on-site. This is not only time-consuming and labor-intensive, but also prone to errors that are difficult to eliminate. Therefore, there is a need for a method that can improve measurement efficiency and accuracy. Summary of the Invention

[0007] The technical problem to be solved by this invention is to address the shortcomings of existing technologies. Specifically, it provides a method, system, equipment, and medium for measuring pipeline elevation based on laser point clouds, as detailed below:

[0008] 1) In a first aspect, the present invention provides a method for measuring pipeline elevation based on laser point clouds, the specific technical solution of which is as follows:

[0009] Preprocess the 3D point cloud data of the trench area;

[0010] Extract the centerline point cloud data and the point cloud data of the pipeline from the preprocessed 3D point cloud data;

[0011] A ground model is generated based on the centerline point cloud data of the pipeline.

[0012] The point cloud data of the pipeline is projected onto the ground model to determine the elevation of the pipeline.

[0013] The beneficial effects of the pipeline elevation measurement method based on laser point clouds provided by this invention are as follows:

[0014] Due to the high reliability and accuracy of laser point cloud technology, its application in the elevation measurement of oil and gas pipelines can improve the accuracy of elevation measurement and reduce labor and time costs.

[0015] Based on the above scheme, the pipeline elevation measurement method based on laser point cloud of the present invention can be further improved as follows.

[0016] Furthermore, the process of acquiring 3D point cloud data includes:

[0017] The laser sensor carried by the drone is used to scan the trench area to obtain three-dimensional point cloud data of the trench area. Alternatively, the laser sensor carried by the drone is used to scan the trench area to obtain two-dimensional point cloud data of the trench area. The two-dimensional point cloud data of the trench area is then processed to obtain three-dimensional point cloud data of the trench area.

[0018] Furthermore, two-dimensional point cloud data of the trench area is acquired, and the two-dimensional point cloud data of the trench area is processed to obtain three-dimensional point cloud data of the trench area, including:

[0019] The two-dimensional point cloud data of the trench area is preprocessed, and the amount of two-dimensional point cloud data is reduced by combining the construction pile number to obtain the processed two-dimensional point cloud data.

[0020] The three-dimensional point cloud data of the trench area is obtained by processing the two-dimensional point cloud data after data processing using the three-dimensional RGB point cloud reconstruction method.

[0021] Furthermore, the two-dimensional point cloud data of the trench area is preprocessed, including:

[0022] The two-dimensional point cloud data of the trench area is filtered, sampled, registered, segmented, feature extracted and denoised.

[0023] 2) Secondly, the present invention also provides a pipeline elevation measurement system based on laser point clouds, the specific technical solution of which is as follows:

[0024] The system includes a 3D point cloud data preprocessing module, a point cloud data extraction module, a ground model generation module, and an elevation module.

[0025] The 3D point cloud data preprocessing module is used to preprocess the 3D point cloud data of the trench area;

[0026] The point cloud data extraction module is used to extract the centerline point cloud data and the point cloud data of the pipeline from the preprocessed 3D point cloud data.

[0027] The ground model generation module is used to generate a ground model based on the centerline point cloud data of the pipeline.

[0028] The elevation module is used to project the point cloud data of the pipeline onto the ground model to determine the elevation of the pipeline.

[0029] Based on the above scheme, the pipeline elevation measurement system based on laser point cloud of the present invention can be further improved as follows.

[0030] Furthermore, it also includes a 3D point cloud data acquisition module, which is used to: scan the trench area using a laser sensor carried by the UAV to acquire 3D point cloud data of the trench area, or scan the trench area using a laser sensor carried by the UAV to acquire 2D point cloud data of the trench area, and process the 2D point cloud data of the trench area to obtain 3D point cloud data of the trench area.

[0031] Furthermore, the 3D point cloud data acquisition module is specifically used to: preprocess the 2D point cloud data of the trench area, and reduce the amount of 2D point cloud data by combining the construction pile number, so as to obtain the 2D point cloud data after data processing;

[0032] The three-dimensional point cloud data of the trench area is obtained by processing the two-dimensional point cloud data after data processing using the three-dimensional RGB point cloud reconstruction method.

[0033] Furthermore, the 3D point cloud data acquisition module is also specifically used for:

[0034] The two-dimensional point cloud data of the trench area is filtered, sampled, registered, segmented, feature extracted and denoised.

[0035] 3) In a third aspect, the present invention also provides a computer device, the computer device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to enable the computer device to implement any of the above-mentioned pipeline elevation measurement methods based on laser point clouds.

[0036] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above-mentioned pipeline elevation measurement methods based on laser point clouds.

[0037] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0038] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0039] Figure 1This is a flowchart illustrating a pipeline elevation measurement method based on laser point clouds according to an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of a pipeline elevation measurement system based on laser point cloud according to an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0043] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for measuring pipeline elevation based on laser point clouds, comprising the following steps:

[0044] S1. Preprocess the 3D point cloud data of the trench area;

[0045] S2. Extract the centerline point cloud data and the point cloud data of the pipeline from the preprocessed 3D point cloud data;

[0046] S3. Generate a ground model based on the centerline point cloud data of the pipeline;

[0047] S4. Project the point cloud data of the pipeline onto the ground model to determine the elevation of the pipeline.

[0048] Optionally, in the above technical solution, the process of acquiring 3D point cloud data includes:

[0049] S01. Use the laser sensor carried by the drone to scan the trench area and obtain the three-dimensional point cloud data of the trench area; or, use the laser sensor carried by the drone to scan the trench area and obtain the two-dimensional point cloud data of the trench area. Process the two-dimensional point cloud data of the trench area to obtain the three-dimensional point cloud data of the trench area.

[0050] Optionally, in the above technical solution, acquiring two-dimensional point cloud data of the trench area and processing the two-dimensional point cloud data of the trench area to obtain three-dimensional point cloud data of the trench area includes:

[0051] S010. Preprocess the two-dimensional point cloud data of the trench area, and reduce the amount of two-dimensional point cloud data by combining the construction pile number to obtain the processed two-dimensional point cloud data.

[0052] The preprocessing of the two-dimensional point cloud data in the trench area includes: filtering, sampling, registration, segmentation, feature extraction, and denoising of the two-dimensional point cloud data in the trench area. Specifically:

[0053] 1) Data filtering refers to the removal of noise and outliers from two-dimensional point cloud data;

[0054] 2) Sampling refers to downsampling two-dimensional point cloud data to reduce the amount of data and computation.

[0055] 3) Registration refers to aligning multiple point cloud data in a two-dimensional point cloud dataset for subsequent processing.

[0056] 4) Segmentation refers to dividing two-dimensional point cloud data into different parts for more refined processing;

[0057] 5) Feature extraction refers to extracting key features from two-dimensional point cloud data in order to perform target recognition and classification;

[0058] 6) Noise removal refers to the process of removing noise from point cloud data to improve the quality and accuracy of the data.

[0059] Because the point cloud volume corresponding to the two-dimensional point cloud data is large, it is necessary to combine the point cloud with the construction station number to downsample the point cloud and only retain the point cloud around the station line for subsequent calculations.

[0060] S011. The three-dimensional RGB point cloud reconstruction method is used to process the two-dimensional point cloud data after data processing to obtain the three-dimensional point cloud data of the trench area.

[0061] Among them, the three-dimensional RGB point cloud reconstruction method refers to: using an RGB-D camera to extract images of the trench area from multiple perspectives, and converting them into three-dimensional point cloud data through a 2D-3D transformation matrix.

[0062] The technical solution of the present invention will be described through the following embodiments:

[0063] S10. Obtain the three-dimensional point cloud data of the trench area:

[0064] A drone carrying a lidar device scans the trench area to acquire initial two-dimensional point cloud data, which mainly includes information about the trench excavation area. This initial point cloud data includes both pre-excavation and post-excavation point cloud data.

[0065] Among them, the two-dimensional point cloud data before trench excavation mainly refers to the two-dimensional point cloud data obtained by the UAV scanning the trench area before the target area is excavated, and the two-dimensional point cloud data after trench excavation mainly refers to the two-dimensional point cloud data obtained by the UAV when the trench excavation is completed but the pipe has not yet been laid or the pipe has been laid and the trench is waiting to be backfilled.

[0066] The optimal flight altitude for the drone is 40-70 meters. The more accurate the 2D point cloud data collected from the trench area, the better the resulting 3D model. If the drone flies too high, the collected 2D point cloud data will be blurry and its accuracy will be greatly reduced. If the drone flies too low, the collected 2D point cloud data will be incomplete.

[0067] The preprocessing steps for 2D point clouds mainly include:

[0068] 1) Screening and processing of point cloud data scanned by UAV lidar: Since the collected point cloud data may have blurry or unclear points at the trench boundary, it is necessary to screen the point cloud data at the trench boundary. Specifically, the two-dimensional point cloud data in the trench area is filtered, sampled, registered, segmented, feature extracted and denoised.

[0069] 2) Due to the large volume of point cloud data collected, information processing during the creation of the 3D model will be time-consuming and labor-intensive. Therefore, it is necessary to reduce the amount of point cloud data collected by combining the construction station number, and only retain the point cloud around the station number for subsequent calculations.

[0070] The three-dimensional point cloud data of the trench area is obtained by processing the two-dimensional point cloud data after data processing using the three-dimensional RGB point cloud reconstruction method.

[0071] S11. Extract the centerline point cloud data and the point cloud data of the pipeline:

[0072] 1) The intensity information of the ground area is obtained by using lidar. The intensity value of the pipeline area is significantly different from that of other areas. The point cloud needs to be processed in batches. The default number of points to be extracted is 1000.

[0073] 2) Screen the point clouds input in batches by intensity value to achieve the purpose of preliminary segmentation of pipelines according to intensity value, and point cloud areas with intensity value less than 0.1.

[0074] 3) The processed red area represents a point cloud with a scan intensity less than 0.1. Most points fall on the pipeline, but many noise points still exist at the edges. Due to the structural characteristics of the pipeline, the height variation of the point cloud on the pipeline should be greater than or equal to its radius. Furthermore, since the pipeline is buried in a trench, there should be a significant gradient change at the pipeline edges. Based on this, most noise points can be filtered out considerably.

[0075] 4) The processed portion still contains some contiguous noise, but the main continuous areas (pipeline areas) are quite clear, while the noise areas are scattered. We can use machine learning clustering techniques to cluster the 3D point cloud, extracting the largest cluster and removing the rest.

[0076] 5) After the above processing, the pipeline area has been accurately segmented. Then, block-by-block straight line fitting is performed on the pipeline point cloud to obtain a fitted straight line that is approximately at the center of the pipeline. Further block fitting utilizes the slope variations of the segmented straight lines to effectively screen out curved areas. Finally, blocks with similar slopes are merged, and the highest point is searched at the endpoints of the straight lines. The lines are then fitted again to obtain the pipeline centerline point cloud data.

[0077] 6) Generate the ground model:

[0078] Based on the previously processed point cloud data of the pipeline centerline, a three-dimensional ground model is built from it. The main purpose of this is to facilitate the elevation measurement of oil and gas pipelines. This is because only by performing surface fitting on the point cloud near the ground and using it as a reference can the pipeline elevation measurement be completed more easily.

[0079] 7) Elevation measurement:

[0080] By projecting the pipeline point cloud onto the established ground model, relevant information about the oil and gas pipeline can be obtained simply by measuring the height difference between the pipeline point cloud and the ground model.

[0081] This method for measuring the elevation of oil and gas pipelines based on laser point clouds has the functions of automatically calibrating the height of the pipeline relative to the top and bottom of the trench, and automatically measuring the main parameters of the trench when the trench contains the pipeline. Moreover, each cross-section can be extracted and analyzed separately to obtain the trench parameters (height of the upper and lower surfaces, trench height) and the pipeline height (distance of the pipeline from the upper and lower surfaces of the trench and the elevation of the pipeline itself).

[0082] In another embodiment, it includes:

[0083] 1) Data Acquisition: A drone equipped with a lidar or other laser sensor is used to scan the trench area and acquire 3D point cloud data of the trench. This point cloud data mainly contains information about the excavated area (including point cloud information before and after trench excavation).

[0084] 2) 3D Reconstruction: The acquired point cloud data is preprocessed to generate a 3D point cloud model of the scene, mainly using the 3D RGB point cloud reconstruction method for 3D modeling. The 3D RGB point cloud reconstruction method refers to using an RGB-D camera to extract images of the trench from multiple perspectives and converting them into 3D point cloud data through a 2D-3D transformation matrix.

[0085] 3) Point Cloud Preprocessing: The purpose of point cloud preprocessing is to optimize and process point cloud data for better application in fields such as 3D modeling, robot navigation, and virtual reality. The main tasks of point cloud preprocessing include data filtering, sampling, registration, segmentation, feature extraction, and denoising. Specifically, data filtering removes noise and outliers from the point cloud data; sampling downsamples the point cloud data to reduce data volume and computational load; registration aligns multiple point cloud datasets for subsequent processing; segmentation divides the point cloud data into different parts for more refined processing; feature extraction extracts key features from the point cloud data for target recognition and classification; and denoising removes noise from the point cloud data to improve data quality and accuracy. Due to the large volume of point clouds, it is necessary to downsample the point cloud data in conjunction with the construction station lines, retaining only the point cloud data around the station lines for subsequent calculations.

[0086] 4) Pipeline Point Cloud Extraction: Features are extracted using the geometric and topological properties of the point cloud. Common methods include shape-based features, normal-based features, and surface curvature-based features. These features can be used to describe the shape, size, orientation, and other attributes of the target object in the point cloud. Using the same extraction method as for the pipeline centerline, the pipeline centerline point cloud is ultimately obtained.

[0087] 5) Ground Model Generation: A ground model is generated from point cloud data for pipeline elevation measurement. This is achieved by surface fitting of the point cloud near the ground. Features related to channel morphology are detected in the ground point cloud. These features may include elevation, slope, curvature, etc. By calculating these features, the topographic morphology, such as the shapes of ditches and riverbeds, can be better described. Based on the morphological results, the point cloud data is segmented to separate channel areas from non-channel areas. Then, methods such as clustering and surface fitting analysis are used to optimize the smoothness of the segmented edges.

[0088] 6) Elevation Calculation: The elevation of the extracted pipeline point cloud is calculated. This is achieved by projecting the pipeline point cloud onto the ground model and measuring the height difference between the pipeline point cloud and the ground, ultimately obtaining information such as pipeline burial height and depth.

[0089] A laser point cloud is a collection of three-dimensional coordinates of ground reflection points obtained by a lidar system scanning the ground. This data is characterized by its disorder, asymmetry, and uneven distribution. Laser point cloud technology is often used for data acquisition of 3D building scenes. By scanning the target area with a lidar system to obtain point cloud data, analyzing and processing this data, effective geometric information can ultimately be extracted.

[0090] Laser point cloud-based measurement methods are now quite mature and widely used in water conservancy, road surveying, and power line inspection. In water conservancy, laser point cloud technology typically utilizes drones equipped with lidar to acquire cloud data through laser scanning, forming raw ground data. This data is then processed to perform runoff and rainfall analysis for the entire region's watershed. In road surveying, laser point cloud technology also uses drones equipped with lidar to scan the area around the road, obtaining high-density laser point clouds. Analyzing this point cloud data, combined with GPS and drone-captured images, can effectively improve the accuracy of road measurements. In power line inspection, laser point cloud technology can be used to convert power line towers, transmission lines, and the surrounding environment into three-dimensional point cloud data. This allows for a more intuitive observation of the spatial location and outline of transmission lines and surrounding facilities, enabling the determination of distances between power lines and ground structures and vegetation. In power line inspection, laser point cloud technology can achieve full coverage of the location, structure, and attribute status of transmission lines, making the inspection results digitized and analyzable. Surveying oil and gas pipelines is very similar to road surveying; both require accurate mapping of the surrounding environment to minimize the impact of trees, buildings, and topography. Laser point cloud technology is often implemented using drones equipped with lidar, and drone technology is widely used in oil and gas pipeline inspection, oil and gas station equipment monitoring, and emergency response. Therefore, a laser point cloud-based method for measuring the elevation of oil and gas pipelines becomes possible.

[0091] The main technical advantages of this invention are as follows: First, it achieves non-contact measurement of oil and gas pipeline elevations. Traditional oil and gas pipeline elevation measurement mainly relies on manual measurement by surveyors using measuring tools. This invention, however, utilizes a drone equipped with a lidar system to collect point cloud data of the oil and gas pipelines. Elevation measurement can be completed simply by analyzing the point cloud data, demonstrating progress and innovation. Second, it significantly improves the reliability and accuracy of oil and gas pipeline elevation measurement results. Manual measurement and other methods for measuring oil and gas pipeline elevations often suffer from measurement errors and unsatisfactory results. Compared to existing methods, this invention, by utilizing a drone lidar system and computer network technology for point cloud data analysis, improves the accuracy and reliability of the measurement results. Third, it significantly increases the efficiency of oil and gas pipeline elevation measurement. Traditional measurement methods are time-consuming, labor-intensive, and inefficient; this invention is far more convenient and efficient. In this era of rapid development of network information, we can go further by riding the wave of emerging technologies. This invention utilizes these emerging technologies and has a certain degree of innovation. It is believed that this invention can provide some convenience for the elevation measurement of oil and gas pipelines.

[0092] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0093] like Figure 2 As shown, an embodiment of the present invention provides a pipeline elevation measurement system 200 based on laser point clouds, comprising: a three-dimensional point cloud data preprocessing module 201, a point cloud data extraction module 202, a ground model generation module 203, and an elevation module 204.

[0094] The 3D point cloud data preprocessing module 201 is used to preprocess the 3D point cloud data of the trench area;

[0095] The point cloud data extraction module 202 is used to: extract the centerline point cloud data and the point cloud data of the pipeline from the preprocessed 3D point cloud data;

[0096] Ground model generation module 203 is used to: generate a ground model based on the centerline point cloud data of the pipeline;

[0097] The elevation module 204 is used to project the point cloud data of the pipeline onto the ground model to determine the elevation of the pipeline.

[0098] Optionally, the above technical solution also includes a three-dimensional point cloud data acquisition module, which is used to: scan the trench area using a laser sensor carried by the UAV to acquire three-dimensional point cloud data of the trench area, or scan the trench area using a laser sensor carried by the UAV to acquire two-dimensional point cloud data of the trench area, and process the two-dimensional point cloud data of the trench area to obtain three-dimensional point cloud data of the trench area.

[0099] Optionally, in the above technical solution, the three-dimensional point cloud data acquisition module is specifically used to: preprocess the two-dimensional point cloud data of the trench area, and reduce the amount of two-dimensional point cloud data by combining the construction pile number, so as to obtain the two-dimensional point cloud data after data processing.

[0100] The three-dimensional point cloud data of the trench area is obtained by processing the two-dimensional point cloud data after data processing using the three-dimensional RGB point cloud reconstruction method.

[0101] Optionally, in the above technical solution, the 3D point cloud data acquisition module is also specifically used for:

[0102] The two-dimensional point cloud data of the trench area is filtered, sampled, registered, segmented, feature extracted and denoised.

[0103] In another embodiment, the pipeline elevation measurement system based on laser point clouds includes a drone, a lidar system, a 3D model building system, and a data analysis and processing system. The 3D model building system mainly consists of a point cloud preprocessing module, a model information acquisition module, and a modeling module. The input of the point cloud preprocessing module is directly connected to the output of the drone's lidar, allowing the module to preprocess the point cloud data scanned by the drone's lidar.

[0104] It should be noted that the beneficial effects of the pipeline elevation measurement system 200 based on laser point clouds provided in the above embodiments are the same as those of the pipeline elevation measurement method based on laser point clouds described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0105] like Figure 3 As shown, an embodiment of the present invention provides a computer device 300, which includes a processor 320 coupled to a memory 310. The memory 310 stores at least one computer program 330, which is loaded and executed by the processor 320 to enable the computer device 300 to implement any of the above-mentioned pipeline elevation measurement methods based on laser point clouds. Specifically:

[0106] The computer device 300 can vary considerably due to differences in configuration or performance. It may include one or more processors 320 (Central Processing Units, CPUs) and one or more memories 310. The one or more memories 310 store at least one computer program 330, which is loaded and executed by the one or more processors 320 to enable the computer device 300 to implement any of the laser point cloud-based pipeline elevation measurement methods provided in the above embodiments. Of course, the computer device 300 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The computer device 300 may also include other components for implementing device functions, which will not be elaborated upon here.

[0107] An embodiment of the present invention provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above-described pipeline elevation measurement methods based on laser point clouds.

[0108] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0109] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the aforementioned pipeline elevation measurement methods based on laser point clouds.

[0110] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0111] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0112] Any combination of one or more computer-readable media can be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0113] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A pipeline elevation measurement method based on laser point cloud, characterized in that: include: Preprocess the 3D point cloud data of the trench area; Extracting the centerline point cloud data and the point cloud data of the pipeline from the preprocessed three-dimensional point cloud data; generating a ground model based on the centerline point cloud data of the pipeline; The point cloud data of the pipeline is projected onto the ground model to elevate the pipeline.

2. The method for measuring pipeline elevation based on laser point cloud according to claim 1, characterized in that: The process of acquiring the three-dimensional point cloud data includes: Use the laser sensor carried by the drone to scan the trench area to obtain three-dimensional point cloud data of the trench area, or use the laser sensor carried by the drone to scan the trench area to obtain two-dimensional point cloud data of the trench area, and process the two-dimensional point cloud data of the trench area to obtain three-dimensional point cloud data of the trench area.

3. The pipeline elevation measurement method based on laser point cloud according to claim 2 is characterized in that: Acquiring two-dimensional point cloud data of the pipe trench area, and processing the two-dimensional point cloud data of the pipe trench area to obtain three-dimensional point cloud data of the pipe trench area, including: Preprocess the two-dimensional point cloud data of the trench area, and reduce the data volume of the two-dimensional point cloud data in combination with the construction pile number to obtain the two-dimensional point cloud data after data processing; The two-dimensional point cloud data after data processing is processed using a three-dimensional RGB point cloud reconstruction method to obtain three-dimensional point cloud data of the trench area.

4. The pipeline elevation measurement method based on laser point cloud according to claim 3 is characterized in that: Preprocess the 2D point cloud data of the trench area, including: The two-dimensional point cloud data of the trench area is filtered, sampled, aligned, segmented, feature extracted and denoised.

5. A pipeline elevation measurement system based on laser point cloud, characterized in that: include: 3D point cloud data preprocessing module, point cloud data extraction module, ground model generation module and elevation module; The three-dimensional point cloud data preprocessing module is used to: preprocess the three-dimensional point cloud data of the trench area; The point cloud data extraction module is used to extract the centerline point cloud data of the pipeline and the point cloud data of the pipeline from the preprocessed three-dimensional point cloud data; The ground model generation module is used to: generate a ground model based on the centerline point cloud data of the pipeline; The elevation module is used to project the point cloud data of the pipeline onto the ground model to perform elevation on the pipeline.

6. The pipeline elevation measurement system based on laser point cloud according to claim 5 is characterized in that: It also includes a three-dimensional point cloud data acquisition module, which is used to: use the laser sensor carried by the drone to scan the trench area to obtain three-dimensional point cloud data of the trench area, or use the laser sensor carried by the drone to scan the trench area to obtain two-dimensional point cloud data of the trench area, and process the two-dimensional point cloud data of the trench area to obtain three-dimensional point cloud data of the trench area.

7. The pipeline elevation measurement system based on laser point cloud according to claim 6 is characterized in that: The three-dimensional point cloud data acquisition module is specifically used to: pre-process the two-dimensional point cloud data of the trench area, and reduce the data volume of the two-dimensional point cloud data in combination with the construction pile number to obtain the two-dimensional point cloud data after data processing; The two-dimensional point cloud data after data processing is processed using a three-dimensional RGB point cloud reconstruction method to obtain three-dimensional point cloud data of the trench area.

8. The pipeline elevation measurement system based on laser point cloud according to claim 7 is characterized in that: The three-dimensional point cloud data acquisition module is also specifically used for: The two-dimensional point cloud data of the trench area is filtered, sampled, aligned, segmented, feature extracted and denoised.

9. A computer device, characterized in that: The computer device includes a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the computer device implements a pipeline elevation measurement method based on laser point cloud as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor so that the computer implements the pipeline elevation measurement method based on laser point cloud as described in any one of claims 1 to 4.

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