CN114049460A - Point cloud data filtering method and device for coal inventory of coal yard - Google Patents
Point cloud data filtering method and device for coal inventory of coal yard Download PDFInfo
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Abstract
The disclosure provides a point cloud data filtering method and device for coal inventory of a coal yard. The method comprises the following steps: acquiring three-dimensional point cloud data of a coal pile; performing triangular meshing on the projection of the three-dimensional point cloud data of the coal pile on a horizontal plane; calculating the actual height overall variance of three vertex vectors in each triangular mesh; calculating the actual slope absolute value between three vertex vectors in each triangular mesh; determining whether abnormal point cloud data exists in the triangular meshes or not according to the actual height overall variance and the actual slope absolute value of each triangular mesh; carrying out correction filtering processing on the triangular mesh with abnormal point cloud data; and reconstructing a three-dimensional model of the coal pile according to the triangular meshes after the correction filtering processing and the corresponding height information of each point cloud data. The method disclosed by the invention can reduce coal inventory data errors caused by abnormal point cloud data in the coal yard laser coal inventory process, effectively retain on-site fuel pile coal details, improve data accuracy and improve coal yard coal inventory management.
Description
Technical Field
The disclosure belongs to the technical field of coal inventory in coal yards, and particularly relates to a point cloud data filtering method and device for coal inventory in coal yards.
Background
The fuel management of coal-fired power plants is always an important problem, 60-70% of the operation cost of the coal-fired power plants is paid to coal, and the improvement of the management level of coal yards can reduce the operation cost of the coal-fired power plants to a great extent. The existing coal yard fuel management relies on manual establishment of coal yard coal storage graphs to a great extent according to coal yard inlet and outlet information, the method is poor in precision and low in efficiency, the coal yard coal storage graphs can be roughly established only according to experience and visual observation of field conditions, and the information graphs have great influence on stacking and taking of subsequent fuels, for example, different coal quality test data of different coal types have great influence on boiler combustion. Therefore, how to more accurately complete the informatization and digitization of the data of coal type distribution in a coal yard by means of automation means becomes a main research direction at present.
In the process of laser coal inventory, the operation of a coal pusher or a bucket wheel machine stacker-reclaimer arm above a coal yard can often occur on the coal yard, so that the laser scanner can scan operation equipment in the scanning process, abnormal data occurs, and the accuracy of stock yard inventory data is influenced.
The existing algorithms for filtering point cloud data comprise a voxel filtering algorithm, a statistical filtering algorithm, a radius filtering algorithm and the like, which are all general point cloud filtering algorithms, and can eliminate partial noise data to a certain extent, but can eliminate partial details of the whole model, so that the model becomes smooth and fuzzy, and the error of coal inventory data calculated based on the data is increased.
Disclosure of Invention
The present disclosure is directed to at least one of the technical problems in the prior art, and provides a method and an apparatus for filtering point cloud data of coal inventory in a coal yard.
In one aspect of the present disclosure, a point cloud data filtering method for coal inventory of a coal yard is provided, the method comprising:
acquiring three-dimensional point cloud data of a coal pile;
performing triangular meshing on the projection of the three-dimensional point cloud data of the coal pile on a horizontal plane to obtain a plurality of triangular meshes;
calculating the actual height overall variance of three vertex vectors in each triangular mesh;
calculating the actual slope absolute value between three vertex vectors in each triangular mesh;
determining whether abnormal point cloud data exists in each triangular mesh according to the actual height overall variance and the actual slope absolute value of each triangular mesh;
carrying out correction filtering processing on each triangular mesh with the abnormal point cloud data;
and reconstructing a three-dimensional model of the coal pile according to the triangular meshes after the correction filtering processing and the corresponding height information of each point cloud data.
In some embodiments, the determining whether abnormal point cloud data exists in the triangular meshes according to the actual height overall variance and the actual slope absolute value of each triangular mesh comprises:
comparing the actual height overall variance of the triangular mesh with a preset height overall variance threshold, and comparing the actual slope absolute value of each vertex vector of the triangular mesh with a preset slope absolute threshold;
and if the actual height overall variance of the triangular mesh is greater than the height overall variance threshold value and the actual slope absolute value of each vertex vector of the triangular mesh is greater than the slope absolute threshold value, judging that abnormal point cloud data exists in the triangular mesh.
In some embodiments, the determining that the triangular mesh has abnormal point cloud data includes:
and sequencing all vertexes of the triangular mesh with the abnormal point cloud data according to the height, wherein the vertex corresponding to the maximum value is the abnormal point cloud data.
In some embodiments, the performing a rectification filtering process on each of the triangular meshes in which the abnormal point cloud data exists includes:
and correcting and filtering the abnormal point cloud data in the triangular network by using the other two normal point cloud data in the triangular network.
In some embodiments, the performing, by using the other two normal point cloud data in the triangular network, a correction filtering process on the abnormal point cloud data in the triangular network includes:
and carrying out correction filtering processing on the abnormal point cloud data according to the following relational expression:
NoisePoint′=(xn,yn,(zi+zj)/2)
wherein NoisePoint' is abnormal point cloud data after correction, xnIs the x-axis coordinate, y, of abnormal point cloud data before correctionnIs the y-axis coordinate, z, of the abnormal point cloud data before rectificationiZ-axis coordinate of one normal point cloud data in triangular mesh in which abnormal point cloud data is locatedjThe coordinate of the z axis of the other normal point cloud data in the triangular mesh where the abnormal point cloud data is located.
In another aspect of the present disclosure, there is provided a point cloud data filtering apparatus for coal inventory of a coal yard, the apparatus including:
the acquisition module is used for acquiring three-dimensional point cloud data of the coal pile;
the meshing module is used for triangulating the projection of the three-dimensional point cloud data of the coal pile on a horizontal plane to obtain a plurality of triangular meshes;
the calculation module is used for calculating the actual height overall variance of three vertex vectors in each triangular mesh; and the number of the first and second groups,
the calculation module is further configured to calculate an actual slope absolute value between three vertex vectors in each triangular mesh;
the processing module is used for determining whether abnormal point cloud data exists in the triangular meshes according to the actual height overall variance and the actual slope absolute value of each triangular mesh;
the correction module is used for correcting and filtering each triangular mesh with the abnormal point cloud data;
and the establishing module is used for reconstructing a three-dimensional model of the coal pile according to the triangular grids after the correction filtering processing and the height information of each corresponding point cloud data.
In some embodiments, the processing module is further specifically configured to:
comparing the actual height overall variance of the triangular mesh with a preset height overall variance threshold, and comparing the actual slope absolute value of each vertex vector of the triangular mesh with a preset slope absolute threshold;
and if the actual height overall variance of the triangular mesh is greater than the height overall variance threshold value and the actual slope absolute value of each vertex vector of the triangular mesh is greater than the slope absolute threshold value, judging that abnormal point cloud data exists in the triangular mesh.
In some embodiments, the processing module is further specifically configured to:
and sequencing all vertexes of the triangular mesh with the abnormal point cloud data according to the height, wherein the vertex corresponding to the maximum value is the abnormal point cloud data.
In some embodiments, the orthotic module is further specifically configured to:
and correcting and filtering the abnormal point cloud data in the triangular network by using the other two normal point cloud data in the triangular network.
In some embodiments, the correction module performs correction filtering processing on the abnormal point cloud data according to the following relation:
NoisePoint′=(xn,yn,(zi+zj)/2)
wherein NoisePoint' is abnormal point cloud data after correction, xnIs the x-axis coordinate, y, of abnormal point cloud data before correctionnIs the y-axis coordinate, z, of the abnormal point cloud data before rectificationiZ-axis coordinate of one normal point cloud data in triangular mesh in which abnormal point cloud data is locatedjThe coordinate of the z axis of the other normal point cloud data in the triangular mesh where the abnormal point cloud data is located.
According to the point cloud data filtering method and device for coal inventory in the coal yard, errors of coal inventory data caused by abnormal point cloud data in the process of laser coal inventory in the coal yard can be reduced to a large extent, abnormal point cloud data are filtered out on the basis that details of on-site fuel pile are reserved to the maximum extent, data accuracy is improved, and coal inventory management in the coal yard is improved.
Drawings
FIG. 1 is a schematic diagram of a three-dimensional model obtained by reconstructing a coal pile in the prior art;
FIG. 2 is a schematic diagram of meshing coal pile three-dimensional point cloud data according to an embodiment of the disclosure;
fig. 3 is a schematic diagram of three-dimensional gridding of abnormal point cloud data included in point cloud data according to another embodiment of the disclosure;
FIG. 4 is a flow chart of a point cloud data filtering method for coal inventory of a coal yard according to another embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a three-dimensional model obtained by reconstructing a coal pile according to another embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a point cloud data filtering apparatus for coal inventory in a coal yard according to another embodiment of the present disclosure.
Detailed Description
For a better understanding of the technical aspects of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
The background to the present disclosure is explained below.
The coal yard coal pile coal type is relatively gentle, and the reconstructed three-dimensional model is relatively gentle. When a coal pusher or a bucket wheel machine material piling and taking arm works above a coal yard, the scanned point cloud data can show a plurality of discrete outliers, and after a three-dimensional model is reconstructed, a plurality of burrs can be shown, so that the situation of fuel distribution on the site is seriously not met. As shown in fig. 1.
The point cloud data is a set of vectors in a three-dimensional coordinate system, and includes spatial coordinate information (x, y, z) of each point, and the projection of the point cloud data of the coal yard on the horizontal plane is triangulated through a triangularization grid algorithm, as shown in fig. 2.
After the projection of the point cloud data on the horizontal plane is triangulated, a three-dimensional model can be reconstructed according to the gridding information and the height information of each point. The noise data included in the point cloud data when three-dimensionally gridded is represented as shown in fig. 3.
As can be seen from fig. 3, the NoisePoint represents abnormal point cloud data that is often collected when data is collected by field laser scanning, the points p1, p2, p3 and p4 are normal point cloud data (generally normal fuel distribution data), the abnormal point cloud data features are represented as protrusions and outliers, and are represented as protrusion burrs in the reconstructed three-dimensional model.
As shown in fig. 4, an embodiment of the present disclosure relates to a point cloud data filtering method S100 for coal inventory of a coal yard, where the method S100 includes:
and S110, acquiring three-dimensional point cloud data of the coal pile.
Specifically, in this step, three-dimensional point cloud data of the entire coal pile may be acquired by using a laser scanning device, and as shown in fig. 2, spatial position coordinates of all points can be obtained during laser scanning, and p1(x1, y1, z1), p2(x2, y2, z2), p3(x3, y3, z3), p4(x4, y4, z4), NoisePoint = (x 3624, y4, z4) can be setn,yn,zn)。
And S120, performing triangular meshing on the projection of the three-dimensional point cloud data of the coal pile on a horizontal plane to obtain a plurality of triangular meshes.
Specifically, in this step, as shown in fig. 2 and 3, the combinations of the top lines of the triangular faces can be known by the triangular meshing algorithm, as shown in fig. 3, (p1, p2, NoisePoint), (p3, p2, NoisePoint), (p3, p4, NoisePoint), (p2, p4, NoisePoint).
And S130, calculating the actual height overall variance of three vertex vectors in each triangular mesh.
Specifically, in this step, the actual height overall variance of the three vertex vectors in each triangular mesh is calculated as follows:
heightAvg=(z1+z2+z3)/3
height2=((z1-heightAvg)2+(z2-heightAvg)2+(z3-heightAvg)2)/3
and S140, calculating the actual slope absolute value between three vertex vectors in each triangular mesh.
Specifically, in this step, the actual absolute value of the slope between the three vertex vectors in each of the triangular meshes is calculated according to the following relation:
s150, determining whether abnormal point cloud data exists in the triangular meshes according to the actual height overall variance and the actual slope absolute value of each triangular mesh.
Specifically, in this step, the actual height overall variance of the triangular mesh may be compared with a preset height overall variance threshold, and the actual slope absolute value of each vertex vector of the triangular mesh may be compared with a preset slope absolute threshold.
And if the actual height overall variance of the triangular mesh is greater than the height overall variance threshold value and the actual slope absolute value of each vertex vector of the triangular mesh is greater than the slope absolute threshold value, judging that abnormal point cloud data exists in the triangular mesh.
In the step, the height overall variance threshold and the slope absolute threshold can be calculated by analyzing the equipment parameters of abnormal point cloud data caused by the laser disc coal occurrence place.
More specifically, after determining that abnormal point cloud data exists in a certain triangular mesh, it is necessary to determine which vertex in the triangular mesh is the abnormal point cloud data. As an example, the vertices of the triangular mesh where the abnormal point cloud data exists may be sorted according to height, and the vertex corresponding to the maximum value is the abnormal point cloud data.
And S160, carrying out correction filtering processing on each triangular mesh with the abnormal point cloud data.
Specifically, in this step, since the density of the point cloud data is high in the process of laser coal inventory, the area of the triangular surface reconstructed by triangular meshing basically does not exceed 1m3At the actual coal pile site, 1m3The fuel distribution in the area is uniform, and the model is smooth, so that the raised abnormal point cloud data can be corrected through other two normal point cloud data.
Specifically, the abnormal point cloud data is subjected to correction filtering processing according to the following relational expression:
NoisePoint′=(xn,yn,(zi+zj)/2)
wherein NoisePoint' is abnormal point cloud data after correction, xnIs the x-axis coordinate, y, of abnormal point cloud data before correctionnIs the y-axis coordinate, z, of the abnormal point cloud data before rectificationiOne normal point cloud number in the triangular mesh in which the abnormal point cloud data is positionedAccording to z-axis coordinate, zjThe coordinate of the z axis of the other normal point cloud data in the triangular mesh where the abnormal point cloud data is located.
S170, reconstructing a three-dimensional model of the coal pile according to the triangular meshes after rectification filtering processing and the corresponding height information of each point cloud data.
Specifically, in this step, a three-dimensional model of the coal pile is reconstructed according to each triangular mesh after rectification filtering processing and corresponding height information of each point cloud data, and the three-dimensional model is as shown in fig. 5. As can be seen from FIG. 5, the burrs of the reconstructed three-dimensional model disappear, and the details of the field fuel pile can be retained to the maximum extent.
The point cloud data filtering method for coal inventory in the coal yard can greatly reduce coal inventory data errors caused by abnormal point cloud data in the coal yard laser coal inventory process, filter the abnormal point cloud data on the basis of retaining on-site fuel pile coal details to the maximum extent, improve data accuracy and improve coal inventory management in the coal yard.
In another aspect of the present disclosure, as shown in fig. 6, a point cloud data filtering apparatus 100 for coal inventory in a coal yard is provided, where the apparatus 100 may be adapted to the method described above, and it may refer to the related description, which is not repeated herein. The apparatus 100 comprises:
the acquiring module 110 is configured to acquire three-dimensional point cloud data of the coal pile.
And the meshing module 120 is configured to triangulate the projection of the three-dimensional point cloud data of the coal pile on a horizontal plane to obtain a plurality of triangular meshes.
A calculating module 130, configured to calculate an actual height overall variance of three vertex vectors in each triangular mesh. The calculating module 130 is further configured to calculate an actual absolute slope value between three vertex vectors in each of the triangular meshes.
And the processing module 140 is configured to determine whether abnormal point cloud data exists in each triangular mesh according to the actual height overall variance and the actual slope absolute value of each triangular mesh.
And the correcting module 150 is configured to perform correction filtering processing on each triangular mesh in which the abnormal point cloud data exists.
The establishing module 160 is configured to reconstruct a three-dimensional model of the coal pile according to each triangular mesh after the rectification filtering processing and the height information of each corresponding point cloud data.
The point cloud data filtering device for coal inventory in the coal yard can greatly reduce coal inventory data errors caused by abnormal point cloud data in the coal yard laser coal inventory process, and filter the abnormal point cloud data on the basis of retaining on-site fuel pile coal details to the maximum extent, so that the data precision is improved, and the coal inventory management of the coal yard is improved.
In some embodiments, the processing module 140 is further specifically configured to:
and comparing the actual height overall variance of the triangular mesh with a preset height overall variance threshold, and comparing the actual slope absolute value of each vertex vector of the triangular mesh with a preset slope absolute threshold.
And if the actual height overall variance of the triangular mesh is greater than the height overall variance threshold value and the actual slope absolute value of each vertex vector of the triangular mesh is greater than the slope absolute threshold value, judging that abnormal point cloud data exists in the triangular mesh.
In some embodiments, the processing module 140 is further specifically configured to:
and sequencing all vertexes of the triangular mesh with the abnormal point cloud data according to the height, wherein the vertex corresponding to the maximum value is the abnormal point cloud data.
In some embodiments, the orthotic module 150 is further specifically configured to:
and correcting and filtering the abnormal point cloud data in the triangular network by using the other two normal point cloud data in the triangular network.
In some embodiments, the rectification module 150 performs rectification filtering on the abnormal point cloud data according to the following relationship:
NoisePoint′=(xn,yn,(zi+zj)/2)
wherein NoisePoint' is abnormal point cloud data after correction, xnIs the x-axis coordinate, y, of abnormal point cloud data before correctionnIs the y-axis coordinate, z, of the abnormal point cloud data before rectificationiZ-axis coordinate of one normal point cloud data in triangular mesh in which abnormal point cloud data is locatedjThe coordinate of the z axis of the other normal point cloud data in the triangular mesh where the abnormal point cloud data is located.
It is to be understood that the above embodiments are merely exemplary embodiments that are employed to illustrate the principles of the present disclosure, and that the present disclosure is not limited thereto. It will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the disclosure, and these are to be considered as the scope of the disclosure.
Claims (10)
1. A point cloud data filtering method for coal inventory of a coal yard is characterized by comprising the following steps:
acquiring three-dimensional point cloud data of a coal pile;
performing triangular meshing on the projection of the three-dimensional point cloud data of the coal pile on a horizontal plane to obtain a plurality of triangular meshes;
calculating the actual height overall variance of three vertex vectors in each triangular mesh;
calculating the actual slope absolute value between three vertex vectors in each triangular mesh;
determining whether abnormal point cloud data exists in each triangular mesh according to the actual height overall variance and the actual slope absolute value of each triangular mesh;
carrying out correction filtering processing on each triangular mesh with the abnormal point cloud data;
and reconstructing a three-dimensional model of the coal pile according to the triangular meshes after the correction filtering processing and the corresponding height information of each point cloud data.
2. The method of claim 1, wherein the determining whether abnormal point cloud data exists in the triangular meshes according to the actual height overall variance and the actual slope absolute value of each triangular mesh comprises:
comparing the actual height overall variance of the triangular mesh with a preset height overall variance threshold, and comparing the actual slope absolute value of each vertex vector of the triangular mesh with a preset slope absolute threshold;
and if the actual height overall variance of the triangular mesh is greater than the height overall variance threshold value and the actual slope absolute value of each vertex vector of the triangular mesh is greater than the slope absolute threshold value, judging that abnormal point cloud data exists in the triangular mesh.
3. The method of claim 2, wherein said determining that abnormal point cloud data exists in the triangular mesh comprises:
and sequencing all vertexes of the triangular mesh with the abnormal point cloud data according to the height, wherein the vertex corresponding to the maximum value is the abnormal point cloud data.
4. The method according to any one of claims 1 to 3, wherein the performing of the rectification filtering process on each of the triangular meshes in which the abnormal point cloud data exists comprises:
and correcting and filtering the abnormal point cloud data in the triangular network by using the other two normal point cloud data in the triangular network.
5. The method according to claim 4, wherein the performing a correction filtering process on the abnormal point cloud data in the triangular network by using the other two normal point cloud data in the triangular network comprises:
and carrying out correction filtering processing on the abnormal point cloud data according to the following relational expression:
NoisePoint′＝(xn,yn,(zi+zj)/2)
wherein NoisePoint' is abnormal point cloud data after correction, xnIs the x-axis coordinate, y, of abnormal point cloud data before correctionnIs the y-axis coordinate, z, of the abnormal point cloud data before rectificationiZ-axis coordinate of one normal point cloud data in triangular mesh in which abnormal point cloud data is locatedjThe coordinate of the z axis of the other normal point cloud data in the triangular mesh where the abnormal point cloud data is located.
6. A point cloud data filtering device for coal inventory of a coal yard, the device comprising:
the acquisition module is used for acquiring three-dimensional point cloud data of the coal pile;
the meshing module is used for triangulating the projection of the three-dimensional point cloud data of the coal pile on a horizontal plane to obtain a plurality of triangular meshes;
the calculation module is used for calculating the actual height overall variance of three vertex vectors in each triangular mesh; and the number of the first and second groups,
the calculation module is further configured to calculate an actual slope absolute value between three vertex vectors in each triangular mesh;
the processing module is used for determining whether abnormal point cloud data exists in the triangular meshes according to the actual height overall variance and the actual slope absolute value of each triangular mesh;
the correction module is used for correcting and filtering each triangular mesh with the abnormal point cloud data;
and the establishing module is used for reconstructing a three-dimensional model of the coal pile according to the triangular grids after the correction filtering processing and the height information of each corresponding point cloud data.
7. The apparatus of claim 6, wherein the processing module is further specifically configured to:
comparing the actual height overall variance of the triangular mesh with a preset height overall variance threshold, and comparing the actual slope absolute value of each vertex vector of the triangular mesh with a preset slope absolute threshold;
and if the actual height overall variance of the triangular mesh is greater than the height overall variance threshold value and the actual slope absolute value of each vertex vector of the triangular mesh is greater than the slope absolute threshold value, judging that abnormal point cloud data exists in the triangular mesh.
8. The apparatus of claim 7, wherein the processing module is further specifically configured to:
and sequencing all vertexes of the triangular mesh with the abnormal point cloud data according to the height, wherein the vertex corresponding to the maximum value is the abnormal point cloud data.
9. The device according to any one of claims 6 to 8, wherein the corrective module is further configured to:
and correcting and filtering the abnormal point cloud data in the triangular network by using the other two normal point cloud data in the triangular network.
10. The apparatus according to claim 9, wherein the correction module performs correction filtering processing on the abnormal point cloud data according to the following relation:
NoisePoint′＝(xn,yn,(zi+zj)/2)
wherein NoisePoint' is abnormal point cloud data after correction, xnIs the x-axis coordinate, y, of abnormal point cloud data before correctionnIs the y-axis coordinate, z, of the abnormal point cloud data before rectificationiZ-axis coordinate of one normal point cloud data in triangular mesh in which abnormal point cloud data is locatedjThe coordinate of the z axis of the other normal point cloud data in the triangular mesh where the abnormal point cloud data is located.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116109692A (en) * | 2023-02-22 | 2023-05-12 | 中钢集团马鞍山矿山研究总院股份有限公司 | Method for calculating volume and surface deformation volume of tailing dam based on three-dimensional point cloud |
| CN117011309A (en) * | 2023-09-28 | 2023-11-07 | 济宁港航梁山港有限公司 | Automatic coal-coiling system based on artificial intelligence and depth data |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116109692A (en) * | 2023-02-22 | 2023-05-12 | 中钢集团马鞍山矿山研究总院股份有限公司 | Method for calculating volume and surface deformation volume of tailing dam based on three-dimensional point cloud |
| CN116109692B (en) * | 2023-02-22 | 2023-09-26 | 中钢集团马鞍山矿山研究总院股份有限公司 | Method for calculating volume and surface deformation volume of tailing dam based on three-dimensional point cloud |
| CN117011309A (en) * | 2023-09-28 | 2023-11-07 | 济宁港航梁山港有限公司 | Automatic coal-coiling system based on artificial intelligence and depth data |
| CN117011309B (en) * | 2023-09-28 | 2023-12-26 | 济宁港航梁山港有限公司 | Automatic coal-coiling system based on artificial intelligence and depth data |
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