A method for calculating sediment removal volume based on point cloud feature library removal

Abstract

The present invention discloses a method for calculating the amount of sediment removed based on the removal of impurities from a point cloud feature library, comprising: obtaining underwater point cloud data of a water body, the underwater point cloud data including the three-dimensional coordinates of each point in the point cloud; obtaining engineering design data within the dredging range, the engineering design data including the three-dimensional coordinates of each key point on the section at the start, change, and / or end of the dredging; performing noise reduction on the underwater point cloud data; removing impurities from the noise-reduced underwater point cloud data; and calculating the amount of sediment removed from the water body. The calculation method of the present invention has a high degree of automation and can be completed automatically by a computer. Human intervention only requires object feature determination to improve the accuracy of the calculation results. The calculation results are highly accurate and effective. The feature determination of underwater objects is completed simultaneously with the calculation, and the positioning of underwater pipelines, aquatic plants and animals can be achieved before construction, thereby facilitating pipeline and ecological protection during construction.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering measurement, and in particular to a method for calculating the amount of sediment removed based on point cloud feature library impurities removal. Background Art

[0002] In various river and lake dredging and water ecological management projects, it is necessary to measure and calculate the amount of water body bottom sediment removed for the purpose of preparing construction plans, measuring and calculating engineering quantities, etc.

[0003] Existing methods for measuring and calculating sediment removal volume are divided into two categories based on the implementation scenario: ① underwater measurement; ② pumping measurement. The former, performed underwater, is widely applicable to various engineering scenarios, including engineering survey and design, pre-construction investigations, and pumping / suction desilting. The latter, performed non-underwater, can be used in limited situations, such as pre-construction re-measurement of sediment removal volume.

[0004] The existing methods for measuring and calculating sediment removal volume can be roughly classified into the following three categories based on technical principles: ③ traditional measurement; ④ three-dimensional reconstruction measurement.

[0005] The implementation methods of the three types of methods are as follows: ③ Traditional measurement: Use a sounding rod and a depth sounder to collect data such as the elevation, plane coordinates, and bottom mud thickness (depth) of multiple single-point positions underwater, and use the typical section method and the grid method to calculate the amount of bottom mud dredging. ④ Three-dimensional reconstruction measurement: Use ship-borne sonar and other devices to quickly collect underwater terrain data, invert the sonar readings, process them to obtain a point cloud of the underwater terrain, and then reconstruct a model in three dimensions. Thereafter, according to the engineering design parameters, the model is cut and measured to calculate the volume, and the amount of bottom mud dredging is obtained. (In this type of method, an underwater camera can also be used instead of sonar, and the images obtained by the underwater camera are matched to extract a point cloud of the underwater terrain. However, the underwater camera method is greatly affected by factors such as light and water turbidity, and is less used, so it is not discussed in this invention.)

[0006] Comparing the above two methods, their respective shortcomings are:

[0007] ③ Traditional measurement: The calculation time is relatively short.

[0008] ④ Three-dimensional reconstruction and measurement: The measurement is short and simple, the calculation results are highly accurate, only a small amount of manual measurement is required, and the degree of visualization is high. Summary of the Invention

[0009] In order to solve or partially solve the problems existing in the related art, the present invention provides a method for calculating the amount of sediment desilting based on the removal of impurities in a point cloud feature library. The method can automatically calculate the desilting amount of a water body, and the calculation results are highly accurate and effective. The method can also complete the feature discrimination of underwater objects during the calculation, and can realize the positioning of underwater pipelines, aquatic plants and animals before construction, thereby facilitating the protection of pipelines and ecology during construction.

[0010] The present invention provides a method for calculating the amount of sediment removed based on point cloud feature library impurities removal, comprising:

[0011] Acquiring underwater point cloud data of a water body, wherein the underwater point cloud data includes three-dimensional coordinates of each point in the point cloud;

[0012] Acquiring engineering design data within the desilting range, the engineering design data including the three-dimensional coordinates of key points on the desilting starting section, the changing section, and / or the ending section;

[0013] performing noise reduction on the underwater point cloud data;

[0014] De-noising the underwater point cloud data;

[0015] The desilting volume of the water body is calculated based on the engineering design data and the cleaned underwater point cloud data.

[0016] Optionally, the method further includes dividing the underwater point cloud data into several subsets according to actual needs, wherein the actual needs include construction segmentation requirements and / or computer hardware performance requirements.

[0017] Optionally, the engineering design data is extracted manually or automatically based on design drawings.

[0018] Optionally, the three-dimensional coordinates of each point in the point cloud are (x 1…n ,y 1…n ,z 1…n ), where x 1…n and y 1…n are the plane coordinates of each point, and z is the depth of each point; the denoising of the underwater point cloud data specifically includes:

[0019] sorting the points in the point cloud;

[0020] The radius filtering method is used to perform noise reduction on each sorted point layer by layer.

[0021] Optionally, sorting the points in the point cloud according to the value of the z coordinate; and performing layer-by-layer noise reduction on the sorted points using the radius filtering method specifically includes:

[0022] S301, determining each point where z=0 in the point cloud;

[0023] S302, performing radius filtering on all points in the layer;

[0024] If the radius filter passes, proceed directly to step S303;

[0025] If the radius filter fails, the corresponding noise data is deleted and then step S303 is performed;

[0026] S303 , determining each point corresponding to the z value of the next layer, and performing step S302 until all points in the point cloud have completed radius filtering.

[0027] Optionally, the radius of the filter is an adjustable control constant.

[0028] Optionally, removing impurities from the denoised underwater point cloud data specifically includes:

[0029] Matching the denoised underwater point cloud data with object features in a point cloud feature library category by category;

[0030] The identified object point cloud that affects the calculation result of the dredging amount is deleted from the underwater point cloud data after noise reduction.

[0031] Optionally, the point cloud feature library is a collection of multiple groups of parameterized point clouds; the point cloud feature library includes: basic classes, biological classes and / or structural classes, wherein the basic classes include bottom mud, sediment and / or rock, the biological classes include fish, various benthic organisms and / or wood, and the structural classes include oil pipelines, water pipes, communication pipelines and / or bridge substructures.

[0032] Optionally, matching the noise-reduced underwater point cloud data with object features in a point cloud feature library category by category specifically includes:

[0033] S401, enter the three-dimensional coordinates (x, y, z) of each point where z=0 into the list Lb or Lb 1…no ;

[0034] S402, performing summation of variance calculation on the plane coordinates (x, y) of each point corresponding to the z value of the next layer and the (x', y') of each point in the top layer of the object point cloud feature to be compared in the feature library;

[0035] S403: When the sum of the variances of each point is less than the allowable error, the judgment is passed and the point cloud in this layer is recorded in Lb or Lb 1…no , proceed to S405;

[0036] S404: When the sum of the variances of all points is greater than the allowable error, the determination is failed, and the process returns to S402;

[0037] S405, perform the summation of the feature variance of the object point cloud in the xy plane layer by layer along the z axis downward until the summation of the plane variance of the point cloud of the lowest layer of the object in the feature library is completed; when the pass rate of the number of layers of variance judgment exceeds the pass rate of the judgment condition, it is determined that the feature object exists in the point cloud data and the point cloud of the object has been recorded in Lb or Lb 1…no ;

[0038] S406: Repeat steps S401 to S405 until all object features to be compared are completed.

[0039] Optionally, the calculating of the desilting amount of the water body based on the engineering design data and the cleaned underwater point cloud data specifically includes:

[0040] Based on the engineering design data and the cleaned underwater point cloud data, the actual total volume and the designed total volume are calculated;

[0041] The desilting volume is calculated based on the actual total volume and the designed total volume.

[0042] The technical solution provided by the present invention can have the following beneficial effects:

[0043] 1. High degree of automation. This method can be completed entirely by computer. Human intervention is only required to determine the object's features to improve the accuracy of the calculation results.

[0044] 2. Highly accurate calculation results and effective results. Compared with commonly used traditional calculation methods such as the typical cross-section method and the grid method, this method provides highly accurate results. Simultaneously identifying the characteristics of underwater objects during calculation allows for the location of underwater pipelines, aquatic plants and animals before construction, thereby facilitating pipeline and ecological protection during construction.

[0045] 3. Wide range of applications. This method can be widely used to calculate the amount of sediment removed from various lakes and rivers.

[0046] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 This is a flow chart of a method for calculating sediment removal volume based on point cloud feature library removal provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0049] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the accompanying drawings illustrate embodiments of the present invention, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided to make the present invention more thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0050] The terms used in this invention are for the purpose of describing specific embodiments only and are not intended to limit the invention. The singular forms "a," "the," and "the" used in this invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0051] It should be understood that although the terms "first", "second", "third", etc. may be used to describe various information in the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0052] The technical solution of this embodiment will be described in detail below with reference to the accompanying drawings.

[0053] See also Figure 1 This embodiment provides a method for calculating the amount of sediment removed based on point cloud feature library impurities removal, and the method specifically includes the following steps:

[0054] S1. Obtain underwater point cloud data of a water body.

[0055] Obtain underwater point cloud data of rivers, lakes, and other water bodies to be calculated (hereinafter referred to as the original list Lp, the file format of the original list Lp is not limited); the original list Lp is the underwater point cloud data obtained based on sonar reading processing.

[0056] The original list Lp must contain basic point coordinate data (x 1…n ,y 1…n ,z 1…n ), where n is the number of points in the point cloud, x and y are the plane coordinates of each point, z is the depth, and z=0 is the actual water surface elevation.

[0057] S2. Obtain engineering design data within the dredging scope.

[0058] According to the design drawings, the engineering design information within the dredging scope (hereinafter referred to as the design list Ld) is input; the original list Ld can be obtained through various methods, including but not limited to manual extraction and automatic extraction from engineering CAD software.

[0059] Taking manual extraction as an example, technicians extract the three-dimensional coordinate data of each key point in the design list Ld based on engineering plan drawings, cross-sectional drawings and other drawings.

[0060] The design list Ld should at least include the three-dimensional coordinates of each key point on the desilting starting section, the changing section, and the end section, and the coordinates are in the same coordinate system as the points in the point cloud. The design list Ld must include basic point coordinate data (x d1…m ,y d1…m ,z d1…m ), where m is the number of key points, x d ,y d is the plane coordinate of each key point, z d is the design depth in the original list Lp coordinate system.

[0061] S3, denoising the underwater point cloud data, wherein the underwater point cloud data refers to the original list Lp or several subsets Lp of the original list 1…no .

[0062] For the original list Lp or several subsets Lp of the original list 1…no The points in the image are arranged in ascending order according to the value of the z coordinate, and then the radius filtering method is used to perform noise reduction layer by layer.

[0063] The purpose of noise reduction is to remove overly discrete isolated points in the point cloud. These isolated points can be caused by a variety of factors, such as fish activity, surrounding noise disturbances, instrument system errors, and sonar reading processing errors.

[0064] The specific steps of noise reduction include:

[0065] S301, in the sorted Lp or Lp 1…no In the example, the radius filtering method is applied to the plane coordinates (x, y) of the corresponding point when z = 0. The radius of the filter is based on a control constant that can be adjusted as needed;

[0066] S302: When the radius filtering method passes, it means that all points at this height are continuous points, and the process goes to step S304;

[0067] S303: If the radius filtering method fails, it means that there is a noise point at this height; the noise data is converted from Lp or Lp 1…no If the value is eliminated, the process goes to step S304;

[0068] S304, performing radius filtering on the plane coordinates (x, y) of each point corresponding to the z value of the next layer;

[0069] S305, perform corresponding operations according to the result of the radius filtering method, see S303 or S304, and then proceed to step S306;

[0070] S306, repeat steps S304 and S305 to perform noise reduction until all points are noise-reduced. The new list after noise reduction is hereinafter referred to as the noise-reduced list La or the segmented noise-reduced list La. 1…no .

[0071] S4. De-noising the underwater point cloud data.

[0072] The noise reduction list La or the segmented noise reduction list (La 1…no ) is matched with the object features in the point cloud feature library Lc. After the matching is completed, the identified object point cloud is recorded, classified and marked. According to the actual needs of the project, the method of manual judgment is used to select the identified object point cloud from La or La that may significantly affect the calculation result of the desilting amount. 1…no Eliminate in.

[0073] The point cloud feature library Lc is a collection of multiple sets of parameterized point clouds. Each point cloud in the Lc library reflects the geometric features of a common object used in sediment removal calculations, including but not limited to: basic objects (such as sediment and rock); biological objects (such as fish, benthic organisms, and wood); and structural objects (such as oil pipelines, water pipelines, communication lines, and bridge substructures).

[0074] Among them, the specific steps of removing impurities include:

[0075] S401, La or La 1…no Arrange the points in ascending order according to the value of the z coordinate; enter the three-dimensional coordinates (x, y, z) of each point when z = 0 into the list Lb or Lb 1…no ;

[0076] S402, performing summation of variance calculation on the plane coordinates (x, y) of each point corresponding to the z value of the next layer and the (x', y') of each point in the top layer of the object point cloud feature to be compared in the feature library Lc;

[0077] S403, when the sum of the variances of each point is less than the allowable error, the judgment is passed, and there is a high possibility that a feature object point cloud exists. The La or La 1…no Point cloud input Lb or Lb 1…no , proceed to S405;

[0078] S404: When the sum of the variances of all points is greater than the allowable error, the determination is failed, and the process returns to S402;

[0079] S405, perform the summation of the feature variance of the object point cloud in the xy plane layer by layer along the z axis until the summation of the plane variance of the point cloud of the lowest layer of the object in the feature library is completed. When the pass rate of the number of layers of variance judgment exceeds the pass rate of the judgment condition, it is determined that La or La 1…no The feature object exists in the , and the point cloud of the object has been recorded in Lb or Lb 1…no ;

[0080] S406, repeat S401 to S405 until all object features to be compared are completed;

[0081] S407, according to the actual needs of the project, manually identify Lb or Lb 1…no Whether the object point clouds in each group need to be eliminated. 1…no Remove from the jar to complete the impurity removal.

[0082] S5. Calculate the desilting amount of the water body based on the engineering design data and the cleaned underwater point cloud data.

[0083] Based on the z value in La, the points in the design list Ld are encrypted using linear interpolation. When the point cloud is segmented, first separate Ld into Lda according to the coordinates of the segmented truncation. 1…no , and then La 1…no The z value in is the basis, and linear interpolation is used to calculate Lda 1…no Point encryption in .

[0084] Based on the design value, starting from z=0, first calculate La or La 1…no Each point in the middle, Lda or Lda 1…no The area of ​​each point in the xy plane is summed. Then the volume is calculated, the area is the area of ​​the current z value, and the height is the difference between the z values ​​of the two adjacent layers. Repeat until all z values ​​are calculated. Calculate La or La respectively 1…no , Lda or Lda 1…no The total volume of La or La 1…no The corresponding volume is the actual total volume, recorded as Va, Lda or Lda 1…no The corresponding volume is the designed total volume, denoted as Vd.

[0085] The total desilting volume is V = Vd - Va. If V is a negative number, it means that the entire project section needs to be filled. At this time, a corresponding filling signal is sent to the technicians or the computer equipment used to control the filling.

[0086] In this embodiment, the method further includes:

[0087] The underwater point cloud data is divided into several subsets.

[0088] According to actual needs, the original list Lp is divided into several subsets Lp 1…no (no is the number of subsets), thereby reducing computer processing load and improving overall efficiency.

[0089] Among them, actual needs refer to the following situations: construction segmentation needs, computer hardware performance, etc.

[0090] It should be pointed out that for small-scale projects, this step can be skipped; for larger-scale projects, segmentation is necessary due to the large number of point clouds.

[0091] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and scope of the present invention are included in the scope of protection of the present invention.

Claims

1. A method for calculating sediment removal volume based on point cloud feature library impurities removal, characterized in that: include: Step S1, obtaining underwater point cloud data of a water body; Step S2: obtaining engineering design data within the desilting range; Step S3, denoising the underwater point cloud data; Step S4, removing impurities from the denoised underwater point cloud data; Step S5: calculating the desilting amount of the water body based on the engineering design data and the cleaned underwater point cloud data; The step S4 comprises: Matching the denoised underwater point cloud data with object features in a point cloud feature library category by category; Deleting the identified object point cloud that affects the calculation result of the dredging amount from the underwater point cloud data after noise reduction; The step of matching the noise-reduced underwater point cloud data with the object features in the point cloud feature library in a category-by-category manner specifically includes: S401, enter the three-dimensional coordinates (x, y, z) of each point where z=0 into the list Lb or Lb 1…no ; S402, performing summation of variance calculation on the plane coordinates (x, y) values ​​of each point corresponding to the z value of the next layer and the (x', y') values ​​of each point in the top layer of the point cloud feature of the object to be compared in the point cloud feature library; S403: When the sum of the variances of each point is less than the allowable error, the judgment is passed and the point cloud in this layer is recorded in Lb or Lb 1…no , proceed to S405; S404: When the sum of the variances of all points is greater than the allowable error, the determination is failed, and the process returns to S402; S405, summing the feature variance of the object point cloud in the xy plane layer by layer along the z axis downward until the plane variance summation of the point cloud at the bottom layer of the object in the point cloud feature library is completed; when the pass rate of the number of layers of variance judgment exceeds the pass rate of the judgment condition, it is determined that the feature object exists in the point cloud data and the point cloud of the object has been recorded in Lb or Lb 1…no ; S406, repeating steps S401 to S405 until all object features to be compared are completed; Among them, list Lb represents the noise reduction list, list Lb 1…no A denoised list representing the segments; The step S5 comprises: Calculating the actual total volume and the designed total volume based on the engineering design data and the cleaned underwater point cloud data; The actual total volume is subtracted from the designed total volume to obtain the desilting volume.

2. The method for calculating the amount of sediment removed based on point cloud feature library impurities removal according to claim 1, characterized in that: Also includes: The underwater point cloud data is divided into several subsets according to actual needs, wherein the actual needs include construction segmentation requirements and / or computer hardware performance requirements.

3. The method for calculating the amount of sediment removed based on point cloud feature library impurities removal according to claim 1, characterized in that: The engineering design data is extracted manually or automatically based on the design drawings.

4. The method for calculating the amount of sediment removed based on point cloud feature library impurities removal according to claim 1 or 2, characterized in that: The step S3 comprises: The underwater point cloud data includes the three-dimensional coordinates of each point in the point cloud; The three-dimensional coordinates of each point in the point cloud are (x 1…n , y 1…n , z 1…n ), where x 1…n and y 1…n are the plane coordinates of each point, and z is the depth of each point; the denoising of the underwater point cloud data specifically includes: sorting the points in the point cloud; The radius filtering method is used to perform noise reduction on each sorted point layer by layer.

5. The method for calculating the amount of sediment removed based on point cloud feature library removal according to claim 4, characterized in that: Sorting each point in the point cloud according to the value of the z coordinate; and performing layer-by-layer noise reduction on each sorted point using the radius filtering method specifically includes: S301, determining each point where z=0 in the point cloud; S302, performing radius filtering on all points in the current layer; If the radius filter passes, proceed directly to step S303; If the radius filter fails, the corresponding noise data is deleted and then step S303 is performed; S303 , determining each point corresponding to the z value of the next layer, and performing step S302 until all points in the point cloud have completed radius filtering.

6. The method for calculating the amount of sediment removed based on point cloud feature library impurities removal according to claim 5, characterized in that: The radius of the filter is an adjustable control constant.

7. The method for calculating the amount of sediment removed based on point cloud feature library removal according to claim 6, characterized in that: The point cloud feature library is a collection of multiple groups of parameterized point clouds; the point cloud feature library includes: basic classes, biological classes and / or structural classes, wherein the basic classes include bottom mud, sediment and / or rock, the biological classes include fish, various benthic organisms and / or wood, and the structural classes include oil pipelines, water pipelines, communication pipelines and / or bridge substructures.

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