CN109035224B - Submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud - Google Patents
Submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud Download PDFInfo
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Abstract
本发明涉及多波束声呐水下目标检测和点云数据建模领域，具体涉及一种基于多波束点云的海底管道检测与三维重建方法。根据多波束测深声呐探测管道得到的水下声呐图像采用阈值法对图像像素点进行分类和提取，得到三维点云数据；然后采用基于密度分析的点云去噪滤波方法，得到滤波去噪后的管道的三维点云数据；然后采用线性拟合方法对管道每个截面的点云数据进行圆拟合，将得到拟合圆的半径以及线性变化的圆心点进行三维重建，得到所述管道的三维图；相对于通过测深点得到点云数据，本发明直接从声呐图像中提取点云数据，依然能够获得较为精确的点云模型，且计算量小，适用于水下各类管道的检测与三维重建。
The invention relates to the field of multi-beam sonar underwater target detection and point cloud data modeling, in particular to a method for detecting and three-dimensional reconstruction of submarine pipelines based on multi-beam point clouds. According to the underwater sonar image obtained by the multi-beam sounding sonar detection pipeline, the threshold method is used to classify and extract the image pixels to obtain the three-dimensional point cloud data; The three-dimensional point cloud data of the pipeline is obtained; then the linear fitting method is used to perform circle fitting on the point cloud data of each section of the pipeline, and the radius of the fitted circle and the linearly changing center point are three-dimensionally reconstructed to obtain the pipeline. Three-dimensional map; compared with the point cloud data obtained through the sounding points, the present invention directly extracts the point cloud data from the sonar image, still can obtain a relatively accurate point cloud model, and has a small amount of calculation, and is suitable for the detection of various underwater pipelines with 3D reconstruction.
Description
Technical Field
The invention relates to the field of multi-beam sonar underwater target detection and point cloud data modeling, in particular to a multi-beam point cloud-based submarine pipeline detection and three-dimensional reconstruction method.
Background
In recent decades, the development of global marine oil and gas resources has been rapid, and in the development of oil and gas resources, a submarine oil and gas pipeline is the most rapid, safe and economic way for conveying oil and gas resources, and is called as a 'life line' of marine oil and gas engineering. With the continuous deep development of ocean oil and gas resources, the laying scale of submarine pipelines is larger and larger, but under the influence of the abnormal complex environment of the seabed and human construction operation, the submarine oil and gas pipelines are easy to be damaged and damaged, oil and gas leakage can occur when the damage is accumulated to a certain degree, resource waste and ecological environment damage can be caused, even explosion can be caused by oil and gas leakage, personnel casualties and larger property loss can be caused, and therefore, the daily routing inspection of the submarine pipelines is particularly important. The operation condition of the submarine pipeline is checked regularly, the safety state of the submarine pipeline is mastered in time, and the method becomes an important guarantee measure for offshore oil and gas production, so that the pipeline corrosion can be prevented, the safe operation of the pipeline can be guaranteed, and the service life of the pipeline is prolonged.
Because the detection of the submarine pipeline mostly adopts internal detection and external detection, and in the existing submarine oil pipeline external detection technology, the mode that the underwater robot carries the multi-beam system is more and more widely applied. In order to better observe the state of the pipeline, three-dimensional information of the pipeline needs to be further extracted, the point cloud technology is widely applied to the extraction of the three-dimensional information of the target surface at present, and the point cloud technology is applied to the detection and three-dimensional reconstruction of the submarine pipeline, so that the three-dimensional information of the submarine pipeline can be accurately extracted.
The existing multi-beam sounding system can give water depth values of dozens or even hundreds of seabed measured points perpendicular to a track direction at one time, accurately and quickly obtain the size, shape and height change of an underwater target in a certain width along the track direction, and further obtain good point cloud data, so that the system has the characteristics of large measurement range, high measurement speed, high accuracy and high efficiency. However, for a pipeline with a smaller radius, the number of point clouds on the surface of the pipeline obtained by the multi-beam sounding system is very small, and the detection and three-dimensional reconstruction of the pipeline cannot be met.
Disclosure of Invention
The invention aims to provide a submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud so as to improve the three-dimensional reconstruction efficiency of underwater pipeline point cloud data.
The embodiment of the invention provides a submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud, which comprises the following steps:
the method comprises the following steps: classifying and extracting image pixel points by adopting a threshold value method and a canny edge detection method according to an underwater sonar image obtained by detecting a pipeline by using a multi-beam sounding sonar to obtain three-dimensional point cloud data of the pipeline;
step two: setting different initial radiuses R and minimum neighborhood number k by adopting a point cloud denoising and filtering method based on density analysis according to the three-dimensional point cloud data of the pipeline to obtain the three-dimensional point cloud data of the pipeline after filtering and denoising;
step three: performing circle fitting on the point cloud data of each section of the pipeline by adopting a histogram-based statistical method and a spatial linear fitting method according to the three-dimensional point cloud data of the filtered and denoised pipeline to obtain the radius of a fitting circle and a linearly-changed central point;
step four: performing three-dimensional reconstruction on the pipeline by adopting an alpha Shape algorithm according to the radius of the fitting circle and the center point of the linear change to obtain a three-dimensional image of the pipeline;
the first step comprises the following steps:
classifying and extracting image pixel points by adopting a threshold value method and a canny edge detection method according to an underwater sonar image obtained by a multi-beam sounding sonar detection pipeline; the underwater sonar image obtained by the multi-beam sounding sonar detection pipeline does not use methods such as bottom detection and the like, but directly extracts pixel points; the threshold value method carries out binarization processing on the pixel points to obtain two types of pixel points with different setting values; the canny edge detection method carries out edge detection processing on all pixel points and extracts the boundary points of the pipeline in the sonar image; converting the boundary points of the pipeline into space coordinates to obtain three-dimensional point cloud data of the pipeline;
among the space coordinates, the positive direction of the X axis is the multi-beam sonar advancing direction:
x＝t*v
wherein, x is the abscissa of the point cloud, v is the sonar moving speed, and t represents the interval time of acquiring an image by the multi-beam sonar;
the Y-axis and Z-axis coordinates of the point cloud space rectangular coordinate system correspond to the horizontal and vertical coordinates of a single sonar image, the Y-axis represents the vertical direction of a sonar track, and the Z-axis represents the normal vector direction of the ground;
the second step comprises the following steps:
setting different initial radiuses R and minimum neighborhood numbers k by adopting a point cloud denoising and filtering method based on density analysis according to the three-dimensional point cloud data of the pipeline; the density analysis method comprises the steps of defining a spherical area with the radius of R by taking a certain point in point clouds as the center of a sphere, inquiring the number of point clouds contained in the spherical area, defining a point cloud neighbor number k, and deleting the point clouds with the number of point clouds in the spherical area smaller than k as noise, otherwise, keeping the point clouds; the point cloud denoising and filtering method is carried out twice, the initial radius R and the minimum neighborhood number k are adjusted, and a noise cluster far away from a pipeline and a sparse noise point close to the pipeline are filtered respectively;
the third step comprises the following steps:
performing circle fitting on the point cloud data of each section of the pipeline by adopting a histogram-based statistical method and a spatial linear fitting method according to the three-dimensional point cloud data of the filtered and denoised pipeline; wherein, the form of each section of the pipeline is prior knowledge; the method comprises the steps of solving the average value of the radius of a fitting circle obtained after fitting a point cloud data circle of each section based on a histogram statistical method to obtain the average radius of the fitting circle; the space linear fitting method is used for fitting a circle center point set of a fitting circle obtained after the point cloud data circle of each section is fitted to obtain a linearly-changed circle center point and the direction information of the pipeline;
the invention has the beneficial effects that:
1. for a pipeline with a smaller radius, the number of point clouds on the surface of the pipeline obtained by the multi-beam sounding system is extremely small, and the detection and three-dimensional reconstruction of the pipeline cannot be met. Compared with the depth measurement point, the method has the advantages that the pipeline pixel points are directly extracted from the sonar image to be processed and the point cloud data are extracted, so that more point cloud data can be obtained, and sufficient data bases are provided for next accurate fitting of the pipeline;
2. by classifying noise into two categories: the method comprises the following steps of firstly carrying out filtering with different scales twice on all point clouds of a marine pipe, wherein the large-scale filtering mainly removes outlier noise, the small-scale filtering mainly removes noise on the surface of the near pipe, and then carrying out circle fitting on the point clouds corresponding to each section of the pipe, so that the influence of the noise on the pipe fitting can be effectively reduced, the pipe can be more accurately subjected to the circle fitting, and more accurate pipe fitting radius and circle center positions can be obtained;
3. the mean value of the fitting radius is obtained through a histogram statistical method, the radius value with low occurrence frequency is ignored, the influence of the radius extreme value on the mean radius is effectively avoided, and the submarine pipeline is generally not easy to bend, so that the center point set is fitted by linear fitting, the fitting result meeting the practical situation can be achieved, and the pipeline three-dimensional reconstruction in the next step is facilitated.
Drawings
FIG. 1 is an overall flow chart of a submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud;
FIG. 2 is a schematic diagram of the multi-beam sonar for submarine pipeline detection according to the present invention;
FIG. 3 is a pipeline point cloud image after denoising an original point cloud according to the present invention;
FIG. 4 is a pipeline point cloud image after the invention performs circle fitting on the denoised point cloud;
FIG. 5 is a schematic diagram of the present invention for linearly fitting the center of a fitting circle;
FIG. 6 is a cloud point view of the pipeline resulting from the final processing of the present invention;
FIG. 7(a) is a side view of the three-dimensional reconstruction of the pipeline according to the present invention;
FIG. 7(b) is a cross-sectional view of the result of three-dimensional reconstruction of the pipeline according to the present invention;
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, the present invention is further described with reference to the accompanying drawings:
fig. 1 is an overall flow chart of a submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud.
The technical scheme of the invention is realized as follows:
step (1): obtain underwater sonar image through multi-beam sonar collection processing, adopt the threshold value method to carry out binarization processing to the image, adopt canny edge detection method to extract the boundary point of pipeline in the sonar image, turn into space coordinate with the boundary point, obtain the three-dimensional point cloud data of pipeline, in the space rectangular coordinate system that the normal vector that uses ground is the Z axle, X axle positive direction is multi-beam sonar advancing direction:
x＝t*v
wherein x is the abscissa of the point cloud, v is the sonar moving speed, and t represents the interval time of acquiring an image by the multi-beam sonar;
the Y-axis and Z-axis coordinates of the point cloud space rectangular coordinate system correspond to horizontal and vertical coordinates of a sonar image, the Y-axis represents the vertical direction of a sonar track, and the Z-axis represents the normal vector direction of the ground;
step (2): filtering the pipeline point cloud data by adopting a density analysis method, defining a spherical area with the radius of R by taking a certain point in the point cloud as a sphere center, inquiring the number of the point clouds contained in the spherical area, defining a point cloud neighbor number k, if the number of the point clouds contained in the spherical area is less than k, regarding the point cloud as noise and deleting the point cloud, otherwise, keeping the point cloud, and performing the operation on each point in the point cloud to finish primary filtering processing. The method adopts a twice filtering mode, adjusts the initial radius and the minimum neighbor number, and respectively filters a noise cluster far away from a pipeline and a sparse noise point close to the pipeline, and FIG. 3 is point cloud data after filtering processing;
and (3): and (3) performing circle fitting on the point cloud data of each section by taking the circular section of the pipeline as a prior condition, and calculating the radius and the center of a circle of the fitting circle, wherein the point cloud of the pipeline obtained after fitting is shown in FIG. 4, and the radius of each fitting circle is different from the radius of each fitting circle seen from the diagram, and the center of the circle is not on one axis, which is not in accordance with the actual existence condition of the submarine pipeline, so that the processing of the step (4) needs to be performed on the point cloud data.
And (4): calculating the average radius of the fitting circle by using a histogram statistical method, fitting the circle center point set by using a spatial linear fitting method to obtain linearly changing circle center points, wherein the figure 5 is a fitting result of the circle center, and the figure 6 is a result graph of the pipeline point cloud obtained after processing, wherein the fitting result can meet the actual fitting result by using linear fitting because the submarine pipeline is generally not easy to bend.
And (5): referring to fig. 7, according to the average radius and the circle center obtained by calculation, the pipeline is three-dimensionally reconstructed by using the alphaShape algorithm to the point cloud, so as to obtain a three-dimensional image of the submarine pipeline. Fig. 7 is a result of three-dimensional reconstruction of a pipeline, in which fig. 7(a) is a side view of the pipeline and fig. 7(b) is a cross-sectional view of the pipeline. From the results, the treatment method of the invention can obtain good pipeline reconstruction effect.
Claims (2)
1. A submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud is characterized by comprising the following steps:
the method comprises the following steps: classifying and extracting image pixel points by adopting a threshold value method and a canny edge detection method according to an underwater sonar image obtained by detecting a pipeline by using a multi-beam sounding sonar to obtain three-dimensional point cloud data of the pipeline;
step two: setting different initial radiuses R and minimum neighborhood number k by adopting a point cloud denoising and filtering method based on density analysis according to the three-dimensional point cloud data of the pipeline to obtain the three-dimensional point cloud data of the pipeline after filtering and denoising;
the point cloud denoising and filtering method based on density analysis specifically comprises the following steps:
defining a spherical area with the radius of R by taking a certain point in the point clouds as the center of sphere, inquiring the number of the point clouds in the spherical area, defining a point cloud neighbor number k, and deleting the point clouds of which the number is less than k in the spherical area as noise, otherwise, keeping the point clouds; the point cloud denoising and filtering method based on density analysis is carried out twice, the initial radius R and the minimum neighborhood number k are adjusted, and a noise cluster far away from a pipeline and a sparse noise point close to the pipeline are filtered respectively;
step three: performing circle fitting on the point cloud data of each section of the pipeline by adopting a histogram-based statistical method and a spatial linear fitting method according to the three-dimensional point cloud data of the filtered and denoised pipeline to obtain the radius of a fitting circle and a linearly-changed central point;
the form of each section of the pipeline is priori knowledge; the method comprises the steps of solving the average value of the radius of a fitting circle obtained after fitting a point cloud data circle of each section based on a histogram statistical method to obtain the average radius of the fitting circle; the space linear fitting method is used for fitting a circle center point set of a fitting circle obtained after the point cloud data circle of each section is fitted to obtain a linearly-changed circle center point and the direction information of the pipeline;
step four: and performing three-dimensional reconstruction on the pipeline by adopting an alphaShape algorithm according to the radius of the fitting circle and the linearly changed central point to obtain a three-dimensional image of the pipeline.
2. The method for submarine pipeline detection and three-dimensional reconstruction based on multi-beam point cloud according to claim 1, wherein the first step comprises:
classifying and extracting image pixel points by adopting a threshold value method and a canny edge detection method according to an underwater sonar image obtained by a multi-beam sounding sonar detection pipeline; the underwater sonar image obtained by the multi-beam sounding sonar detection pipeline does not use a through-bottom detection method but directly extracts pixel points; the threshold value method carries out binarization processing on the pixel points to obtain two types of pixel points with different setting values; the canny edge detection method carries out edge detection processing on all pixel points and extracts the boundary points of the pipeline in the sonar image; converting the boundary points of the pipeline into space coordinates to obtain three-dimensional point cloud data of the pipeline;
among the space coordinates, the positive direction of the X axis is the multi-beam sonar advancing direction:
x＝t*v
wherein, x is the abscissa of the point cloud, v is the sonar moving speed, and t represents the interval time of acquiring an image by the multi-beam sonar;
the Y-axis and Z-axis coordinates of the point cloud space rectangular coordinate system correspond to the horizontal and vertical coordinates of a single sonar image, the Y-axis represents the vertical direction of a sonar track, and the Z-axis represents the normal vector direction of the ground.
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