A method, device, equipment and medium for evaluating the completeness rate of three-dimensional measurement point cloud

Abstract

The present application discloses a method, device, equipment and medium for evaluating the completeness rate of a three-dimensional measurement point cloud, which relates to the field of three-dimensional measurement technology and aims to solve the technical problem that the prior art cannot accurately evaluate the quality of the measurement point cloud data. The method for evaluating the completeness rate of a three-dimensional measurement point cloud comprises the following steps: obtaining the measurement point cloud data of the target workpiece; obtaining the measurement point cloud area value based on the measurement point cloud data; obtaining the measurement point cloud completeness rate based on the ratio of the measurement point cloud area value to the standard point cloud area value; and evaluating the three-dimensional measurement point cloud completeness rate based on the measurement point cloud completeness rate. The evaluation method proposed in the embodiment of the present application obtains the measurement point cloud completeness rate value by calculating the ratio of the measurement point cloud area value to the standard point cloud area value to evaluate the point cloud quality, thereby solving the current technical problem of the lack of evaluation of point cloud data.

Description

Technical Field

[0001] The present application relates to the field of three-dimensional measurement technology, and in particular to a method, device, equipment and medium for evaluating the completeness rate of a three-dimensional measurement point cloud. Background Art

[0002] Surface is the basis for realizing part functions, and surface quality is the key factor affecting part performance. With the advancement of science and technology, in order to realize some complex or specific functions, the surface of parts has evolved from simple regular surfaces to complex regular surfaces and even free-form surfaces. Currently, various surfaces are increasingly used. On the other hand, with the improvement of performance requirements, the requirements for surface accuracy are also getting higher and higher. Aircraft parts have large curvature variations and complex features. Surface measurement has always been the main research direction of precision measurement technology because of the dual challenges of complexity and high precision. Traditional pallet and coordinate measurement are difficult to accurately describe the shape of parts due to the low point cloud density.

[0003] Scanning measurement and visual measurement are increasingly used in shape measurement due to their high single-shot data acquisition density and high efficiency. However, for 3D measurement, the actual measured point cloud may have holes, noise points, etc. The quality of the point cloud data, especially its completeness, directly determines the accuracy of the description of the component's shape contour, and plays an important role in the analysis and evaluation of 3D measurement data. At the same time, the quality of the measured point cloud directly affects the quality of subsequent model reconstruction and product design. Loss or outliers of data points during the measurement process will cause distortion of the reconstructed surface.

[0004] However, currently, there is no method to evaluate the quality of point cloud data, so it is impossible to accurately evaluate the quality of measured point cloud data. Summary of the invention

[0005] The main purpose of this application is to provide a method, device, equipment and medium for evaluating the completeness of a three-dimensional measurement point cloud, aiming to solve the technical problem that the prior art cannot accurately evaluate the quality of measurement point cloud data.

[0006] In order to solve the above technical problems, the present application embodiment proposes: a method for evaluating the completeness rate of a three-dimensional measurement point cloud, comprising the following steps:

[0007] Obtaining measurement point cloud data of the target workpiece;

[0008] Based on the measured point cloud data, obtaining a measured point cloud area value;

[0009] Based on the ratio of the measured point cloud area value to the standard point cloud area value, a measurement point cloud completeness rate is obtained; based on the measurement point cloud completeness rate, the three-dimensional measurement point cloud completeness rate is evaluated.

[0010] As some optional implementation manners of the present application, obtaining a measured point cloud area value based on the measured point cloud data includes:

[0011] Based on the measurement point cloud data, determining the measurement area type of the target workpiece; wherein the measurement area type includes a plane type and a curved surface type;

[0012] Based on the measurement area type of the target workpiece, filter and obtain valid point cloud data;

[0013] Based on the valid point cloud data, a measured point cloud area value is obtained.

[0014] As some optional implementations of the present application, the screening and obtaining of valid point cloud data based on the measurement area type of the target workpiece includes:

[0015] If the measurement area type of the target workpiece is a plane type;

[0016] Then, based on the measured point cloud data, valid point cloud data is obtained;

[0017] Based on the valid point cloud data, obtaining valid boundary point cloud data;

[0018] The effective boundary point cloud data are connected to obtain the measured point cloud area value.

[0019] As some optional implementations of the present application, obtaining valid point cloud data based on the measured point cloud data includes:

[0020] Based on the measured point cloud data, obtaining a distance value between a first point and a second point; wherein the first point and the second point are two adjacent point clouds;

[0021] When the distance between the first point and the second point is less than or equal to a preset minimum distance, the first point and the second point are considered to be valid points;

[0022] The distance between the first point and the second point satisfies the following relationship:

[0023]

[0024] Among them, d ij represents the distance between the first point and the second point, x i Indicates the horizontal coordinate value of the first point, y i Indicates the ordinate value of the first point, x j Indicates the horizontal coordinate value of the second point, y j Indicates the ordinate value of the second point.

[0025] As some optional implementations of the present application, obtaining valid point cloud data based on the measured point cloud data includes:

[0026] Acquire a first boundary point cloud set along a vertical direction and a second boundary point cloud set along a horizontal direction of the measured point cloud data;

[0027] Taking the central intersection of the first boundary point cloud set and the second boundary point cloud set as the coordinate origin, establishing a coordinate system, so as to divide the measured point cloud data into a first area, a second area, a third area and a fourth area through the coordinate system; wherein the first area refers to an area formed by the positive direction of the X-axis and the positive direction of the Y-axis, the second area refers to an area formed by the negative direction of the X-axis and the positive direction of the Y-axis, the third area refers to an area formed by the negative direction of the X-axis and the negative direction of the Y-axis, and the fourth area refers to an area formed by the positive direction of the X-axis and the negative direction of the Y-axis;

[0028] At the coordinate origin, a search is performed within a preset search range to obtain valid point cloud data.

[0029] As some optional implementations of the present application, the searching within a preset search range at the coordinate origin to obtain valid point cloud data includes:

[0030] In the first area, a search is performed along the positive direction of the X axis and along the positive direction of the Y axis with a preset search radius to obtain valid points and boundary points in the first area; wherein the valid points include core points and associated points, wherein the core point refers to a point where at least one other point has a distance value less than a preset minimum spacing value from the target search point, and the target search point is the core point; the associated point refers to a point where the distance value between the target search points is less than the preset minimum spacing value; wherein the boundary point refers to a point where the distance value of any point in the search direction is greater than or equal to the preset minimum spacing value, and the boundary point is an associated point of other core points;

[0031] In the second area, searching along the negative direction of the X axis and the positive direction of the Y axis with a preset search radius to obtain valid points and boundary points in the second area;

[0032] In the third area, searching is performed along the negative direction of the X axis and the negative direction of the Y axis with a preset search radius to obtain valid points and boundary points in the third area;

[0033] In the fourth area, a search is performed along the positive direction of the X axis and the negative direction of the Y axis with a preset search radius to obtain valid points and boundary points in the fourth area.

[0034] As some optional implementations of the present application, the screening and obtaining of valid point cloud data based on the measurement area type of the target workpiece includes:

[0035] If the measurement area type of the target workpiece is a curved surface type;

[0036] Based on the measured point cloud data, first valid point cloud data is obtained; the first valid point cloud data is traversed, and when the number of points with d as the radius of a certain point is less than a preset number, the point is deleted to obtain second valid point cloud data; wherein the preset number is ≥2;

[0037] After registering the second valid point cloud data with the theoretical numerical model, the surface type is divided according to a preset spacing value to obtain a plurality of sub-areas; the maximum curvature value and the minimum curvature value of the theoretical numerical model of each sub-area are calculated; based on the maximum curvature value and the minimum curvature value, a maximum sampling interval value and a minimum sampling interval value are obtained; based on the maximum sampling interval value and the minimum sampling interval value, a plurality of sampling points are obtained;

[0038] Based on each of the sampling points, adjacent points of each of the sampling points are obtained; adjacent points of the sampling points of each of the sub-areas are connected to obtain the area of ​​a triangular patch; and the areas of the triangular patches are summed to obtain an area value of a measured point cloud.

[0039] As some optional implementations of the present application, obtaining multiple sampling points based on the maximum sampling interval value and the minimum sampling interval value includes:

[0040] Based on the maximum sampling interval value and the minimum sampling interval value, obtaining an average sampling interval value;

[0041] Selecting sampling points based on the average sampling interval value to obtain multiple sampling points;

[0042] The average sampling interval value satisfies the following relationship:

[0043]

[0044] Among them, the C i represents the average sampling interval value, the C max Indicates the maximum sampling interval value, the C min Indicates the minimum sampling interval value, the q min represents the minimum curvature value, the q max represents the maximum curvature value, the q i Represents the curvature value of the i-th sub-region.

[0045] As some optional implementations of the present application, if the measurement area type is a plane type, the standard point cloud area value is obtained by the following steps: obtaining the side length of the measurement area and the angle between adjacent sides, and calculating to obtain the standard point cloud area value.

[0046] As some optional implementations of the present application, if the measurement area type is a surface type, the standard point cloud area value is obtained by the following steps:

[0047] Based on the measured point cloud data, first valid point cloud data is obtained; the first valid point cloud data is traversed, and when the number of points with d as the radius of a certain point is less than a preset number, the point is deleted to obtain second valid point cloud data; wherein the preset number is ≥2;

[0048] After registering the second valid point cloud data with the theoretical numerical model, the surface type is divided according to a preset spacing value to obtain a plurality of sub-areas;

[0049] The surface equation of each of the sub-areas is obtained, and based on the surface equation, a standard point cloud area value is obtained.

[0050] As some optional implementations of the present application, the surface equations of the sub-regions satisfy the following relationship:

[0051] z i =z(x,y),(x,y)∈D i

[0052] Among them, z i represents the surface equation of region i, z(x,y) represents the surface equation of the region; D i Represents the point cloud data within each of the sub-areas.

[0053] As some optional implementations of the present application, the standard point cloud area value satisfies the following relationship:

[0054]

[0055] Wherein, S2 represents the standard point cloud area value, S i Represents the standard point cloud area value of each sub-region;

[0056] The standard point cloud area value of each sub-region satisfies the following relationship:

[0057]

[0058] Among them, S i represents the standard point cloud area value of each sub-region; D i represents the point cloud data in each of the sub-areas; z represents the surface equation of the area; x represents the x-coordinate value in the surface equation; y represents the y-coordinate value in the surface equation.

[0059] In order to solve the above technical problems, the embodiment of the present application further proposes: a device for evaluating the completeness rate of a three-dimensional measurement point cloud, characterized in that it includes:

[0060] A data acquisition module is used to acquire the measurement point cloud data of the target workpiece;

[0061] A measurement area module, used for obtaining a measurement point cloud area value based on the measurement point cloud data;

[0062] The evaluation module is used to obtain the completeness rate of the measured point cloud based on the ratio of the measured point cloud area value to the standard point cloud area value; and evaluate the completeness rate of the three-dimensional measured point cloud based on the measured point cloud completeness rate.

[0063] To solve the above technical problems, an embodiment of the present application further proposes: an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method for evaluating the completeness rate of a three-dimensional measurement point cloud.

[0064] To solve the above technical problems, an embodiment of the present application further proposes: a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above-mentioned method for evaluating the completeness rate of a three-dimensional measurement point cloud.

[0065] Compared with the prior art, the embodiment of the present application proposes a method for evaluating the completeness rate of a three-dimensional measurement point cloud, comprising the following steps: obtaining the measurement point cloud data of the target workpiece; obtaining the measurement point cloud area value based on the measurement point cloud data; obtaining the measurement point cloud completeness rate based on the ratio of the measurement point cloud area value to the standard point cloud area value; and evaluating the three-dimensional measurement point cloud completeness rate based on the measurement point cloud completeness rate. It can be seen that the evaluation method proposed in the embodiment of the present application solves the current technical problem of the lack of evaluation of point cloud data by obtaining the measurement point cloud completeness rate value by calculating the ratio of the measurement point cloud area value to the standard point cloud area value to evaluate the point cloud quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0067] Figure 1 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application;

[0068] Figure 2 is a schematic diagram of a plane sample point cloud provided in an embodiment of the present application;

[0069] Figure 3It is a schematic diagram of a plane sample point cloud retrieval strategy provided in an embodiment of the present application;

[0070] Figure 4 is a schematic diagram of a point cloud of a curved surface sample provided in an embodiment of the present application;

[0071] Figure 5 It is a schematic diagram of the registration between the measured point cloud and the model of the curved surface sample provided in the embodiment of the present application;

[0072] Figure 6 It is a schematic diagram of curvature calculation of a curved surface sample provided in an embodiment of the present application;

[0073] Figure 7 It is a schematic diagram of a device for evaluating the completeness rate of a three-dimensional measurement point cloud provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0075] The main solution of the embodiment of the present application is: a method for evaluating the completeness rate of a three-dimensional measurement point cloud, comprising the following steps: obtaining measurement point cloud data of a target workpiece; obtaining a measurement point cloud area value based on the measurement point cloud data; obtaining a measurement point cloud completeness rate based on a ratio of the measurement point cloud area value to the standard point cloud area value; and evaluating the three-dimensional measurement point cloud completeness rate based on the measurement point cloud completeness rate.

[0076] Surface is the basis for realizing the function of parts, and surface quality is the key factor affecting the performance of parts. With the advancement of science and technology, in order to realize some complex or specific functions, the surface of the part surface has gone through the development from simple regular surface to complex regular surface and even free surface. At present, the use of various surfaces is becoming more and more common; on the other hand, with the improvement of performance requirements, the requirements for surface accuracy are also getting higher and higher. Aircraft parts have large curvature changes and complex features. Surface measurement has always been the main research direction of precision measurement technology due to the dual challenges of complexity and high precision. Traditional pallet and coordinate measurement are difficult to accurately describe the shape of parts due to low point cloud density. Scanning measurement and visual measurement are increasingly widely used in shape measurement due to their high single data acquisition density and high efficiency. However, for three-dimensional measurement, the actual measured point cloud may have holes, noise points, etc. The quality of point cloud data, especially its completeness, directly determines the accuracy of the description of the part shape contour, and plays an important role in the analysis and evaluation of three-dimensional measurement data. The quality of the measured point cloud directly affects the quality of subsequent model reconstruction and product design. The loss or outlier of data points during the measurement process will cause the reconstructed surface to be distorted.

[0077] Currently, most measurements focus on point cloud scanning accuracy, that is, the minimum spacing of point clouds. There is a lack of point cloud quality evaluation indicators and methods, and it is impossible to accurately evaluate the quality of measured point clouds.

[0078] In this regard, the technical solution provided in the embodiment of the present application obtains the measurement point cloud completeness value by calculating the ratio of the measurement point cloud area value to the standard point cloud area value to evaluate the point cloud quality, thereby solving the current technical problem of lack of evaluation of point cloud data.

[0079] Reference Figure 1 , Figure 1 A schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiment of the present application.

[0080] like Figure 1 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0081] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0082] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and an electronic program.

[0083] exist Figure 1In the electronic device shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the electronic device of the present application can be set in the electronic device, and the electronic device calls the drone task allocation device stored in the memory 1005 through the processor 1001, and executes the three-dimensional measurement point cloud completeness evaluation method provided in the embodiment of the present application.

[0084] The embodiment of the present application provides a method for evaluating the completeness rate of a three-dimensional measurement point cloud, which is characterized by comprising the following steps:

[0085] Step S10: Acquire measurement point cloud data of the target workpiece.

[0086] It should be noted that the target workpiece refers to a workpiece that needs to be measured on the surface, such as aircraft parts or automobile parts. The measured point cloud data refers to the measurement data obtained by scanning measurement, visual measurement or three-dimensional measurement. Since the obtained measurement data generally has holes, noise points, etc., the embodiment of the present application proposes a method for evaluating the point cloud data. In order to avoid the subsequent impact on the quality of model reconstruction and product design, the loss or outliers of data points during the measurement process will cause distortion of the reconstructed surface.

[0087] Step S20: Based on the measured point cloud data, obtain a measured point cloud area value (also referred to as an actual surface area S1).

[0088] It should be noted that, since the surface of the target workpiece may be a plane or a curved surface, the embodiment of the present application proposes different methods for calculating the area value of the measurement point cloud for target workpieces with different surfaces, namely:

[0089] First, based on the measurement point cloud data, the measurement area type of the target workpiece is determined; wherein the measurement area type includes a plane type and a curved surface type;

[0090] Then, based on the measurement area type of the target workpiece, valid point cloud data is screened and obtained; based on the valid point cloud data, a measurement point cloud area value is obtained.

[0091] Below, this application is described with the measurement area type being a plane type and a curved surface type respectively:

[0092] The first case, such as Figure 2 As shown, if the measurement area type of the target workpiece is a plane type; based on the measurement point cloud data, valid point cloud data is obtained; based on the valid point cloud data, valid boundary point cloud data is obtained; the valid boundary point cloud data are connected to obtain the measurement point cloud area value.

[0093] Specifically, the obtaining of valid point cloud data based on the measured point cloud data includes: obtaining a distance value between a first point and a second point based on the measured point cloud data; wherein the first point and the second point are two adjacent point clouds; when the distance value between the first point and the second point is less than or equal to a preset minimum spacing value, the first point and the second point are considered to be valid points; wherein the distance value between the first point and the second point satisfies the following relationship:

[0094]

[0095] Among them, d ij represents the distance between the first point and the second point, x i Indicates the horizontal coordinate value of the first point, y i Indicates the ordinate value of the first point, x j Indicates the horizontal coordinate value of the second point, y j Indicates the ordinate value of the second point.

[0096] Specifically, obtaining valid point cloud data based on the measured point cloud data includes: obtaining a first boundary point cloud set along the vertical direction and a second boundary point cloud set along the horizontal direction of the measured point cloud data; taking the central intersection of the first boundary point cloud set and the second boundary point cloud set as the coordinate origin, establishing a coordinate system, so as to divide the measured point cloud data into a first area, a second area, a third area and a fourth area through the coordinate system; wherein the first area refers to an area formed by the positive direction of the X-axis and the positive direction of the Y-axis, the second area refers to an area formed by the negative direction of the X-axis and the positive direction of the Y-axis, the third area refers to an area formed by the negative direction of the X-axis and the negative direction of the Y-axis, and the fourth area refers to an area formed by the positive direction of the X-axis and the negative direction of the Y-axis; at the coordinate origin, searching is performed with a preset search range to obtain valid point cloud data.

[0097] Specifically, Figure 3As shown, at the coordinate origin, searching is performed with a preset search range to obtain valid point cloud data, including: in the first area, searching is performed with a preset search radius along the positive direction of the X axis and along the positive direction of the Y axis to obtain valid points and boundary points in the first area; wherein, the valid points include core points and associated points, and the core point refers to a point where the distance between at least one other point and the target search point is less than a preset minimum spacing value, and the target search point is the core point; the associated point refers to a point where the distance between the target search points is less than the preset minimum spacing value; wherein, the boundary point is a point in the In the search direction, the distance value of any point is greater than or equal to the preset minimum spacing value, and the boundary point is an associated point of other core points; in the second area, with a preset search radius, the search is performed along the negative direction of the X-axis and the positive direction of the Y-axis to obtain valid points and boundary points in the second area; in the third area, with a preset search radius, the search is performed along the negative direction of the X-axis and the negative direction of the Y-axis to obtain valid points and boundary points in the third area; in the fourth area, with a preset search radius, the search is performed along the positive direction of the X-axis and the negative direction of the Y-axis to obtain valid points and boundary points in the fourth area.

[0098] It should be noted that when the point cloud boundary is retrieved, or there is a hole in the point cloud, there is no point with a spacing that meets the requirements along the search direction, and this point is a boundary point.

[0099] It should be noted that if the measurement area type is a plane type, the standard point cloud area value (also called the point cloud theoretical surface area S2) is obtained by the following steps: obtaining the side length of the measurement area and the angle between the adjacent sides, and calculating the standard point cloud area value. The side length of the polygonal sample and the angle between the adjacent sides can be measured by a measuring tool such as a vernier caliper.

[0100] That is to say, when the sample is a plane, the actual surface area S1 is calculated as follows:

[0101] T1, point cloud resampling, through resampling, screen the points that meet the sampling requirements. The sampling requirement point screening method is: set the minimum distance d of the point cloud, the adjacent points p in the collected point cloud i With p j The distance between points d ij :

[0102]

[0103] When the adjacent point p i With p j The distance between points d ij satisfy:

[0104] d ij ≤d

[0105] The point is considered to meet the point cloud density and spacing requirements and is regarded as a valid point.

[0106] Furthermore, in order to improve the calculation efficiency, the effective point acquisition method is as follows:

[0107] T1.1. Divide the collected plane point cloud area into 4 parts through the XY coordinate system in the vertical and horizontal directions;

[0108] T1.2. Further, the boundaries of the point cloud along the X-axis and Y-axis directions are obtained in the coordinate system respectively;

[0109] T1.3. Further, a coordinate system is established with the central intersection of the obtained boundary in the X-axis direction and the Y-axis direction as the coordinate origin, and the point cloud is divided into four regions. The region formed by the positive direction of the X-axis and the positive direction of the Y-axis is region 1, the region formed by the negative direction of the X-axis and the positive direction of the Y-axis is region 2, the region formed by the negative direction of the X-axis and the negative direction of the Y-axis is region 3, and the region formed by the positive direction of the X-axis and the negative direction of the Y-axis is region 4;

[0110] T1.4, with 2d as the diameter at the origin, search for point P within the range k (k=1,2,3,...), and then continue searching at this point. k (k=1,2,3,...) Different search strategies are adopted in different areas.

[0111] T1.5. Search the points and obtain valid points.

[0112] T1.5.1. For points in area 1, search in the direction passing through the point and in the positive direction of the X axis and the positive direction of the Y axis, with a search radius of 2d. If at least one point within the range is less than d from the point, the point is a core point; points less than d from the point are defined as associated points. If within the search range of the point, no point is less than d from the point, and the point is not an associated point of other core points, it is an invalid point and needs to be removed. If within the search range of the point, no point is less than d from the point, but the point is an associated point of other valid points, the point is a boundary point. Core points and associated points are valid points.

[0113] T1.5.2. For points in area 2, search in the direction passing through the point and in the negative direction of the X axis and the positive direction of the Y axis. The search radius is 2d. If at least one point within the range is less than d from the point, the point is a core point; points less than d from the point are defined as associated points. If within the search range of the point, there is no point whose distance from the point is less than d, and the point is not an associated point of other core points, it is an invalid point and needs to be removed. If within the search range of the point, there is no point whose distance from the point is less than d, but the point is an associated point of other valid points, the point is a boundary point. Core points and associated points are valid points.

[0114] T1.5.3. For points in area 3, search in the direction passing through the point and in the negative direction of the X axis and the negative direction of the Y axis, with a search radius of 2d. If at least one point within the range is less than d from the point, the point is a core point; points less than d from the point are defined as associated points. If within the search range of the point, no point is less than d from the point, and the point is not an associated point of other core points, it is an invalid point and needs to be removed. If within the search range of the point, no point is less than d from the point, but the point is an associated point of other valid points, the point is a boundary point. Core points and associated points are valid points.

[0115] T1.5.4. For points in area 4, search in the direction passing through the point and in the positive direction of the X axis and the negative direction of the Y axis, with a search radius of 2d. If at least one point within the range is less than d from the point, the point is a core point; points less than d from the point are defined as associated points. If within the search range of the point, no point is less than d from the point, and the point is not an associated point of other core points, it is an invalid point and needs to be removed. If within the search range of the point, no point is less than d from the point, but the point is an associated point of other valid points, the point is a boundary point. Core points and associated points are valid points.

[0116] T1.6. Repeat step T1.5 to retrieve the acquired point cloud and obtain all boundary points and core points.

[0117] T1.7. Connect all boundary points, and the area they contain is the actual surface area S1 of the point cloud.

[0118] The calculation method of the point cloud theoretical surface area S2 is as follows: T2 uses a measuring tool such as a vernier caliper to measure the angle between the side length of the polygonal sample and the adjacent sides, and calculates the point cloud theoretical surface area S2.

[0119] The second situation, such as Figure 4As shown, if the measurement area type of the target workpiece is a surface type; then based on the measured point cloud data, obtain the first valid point cloud data; traverse the first valid point cloud data, when the number of points with a radius of d at a certain point is less than the preset number, delete the point to obtain the second valid point cloud data; wherein the preset number is ≥ 2; after aligning the second valid point cloud data with the theoretical numerical model, divide the surface type according to the preset spacing value to obtain multiple sub-areas; calculate the maximum curvature value and the minimum curvature value of the theoretical numerical model of each sub-area; based on the maximum curvature value and the minimum curvature value, obtain the maximum sampling interval value and the minimum sampling interval value; based on the maximum sampling interval value and the minimum sampling interval value, obtain multiple sampling points; based on each sampling point, obtain the adjacent points of each sampling point; connect the adjacent points of the sampling points of each sub-area to obtain the area of ​​the triangle patch; add the areas of the triangle patches to obtain the area value of the measured point cloud. Wherein, the schematic diagram of aligning the second valid point cloud data with the theoretical numerical model is shown as follows. Figure 5 The curvature calculation diagram of the curved surface area is shown in Figure 6 shown.

[0120] Specifically, the obtaining of multiple sampling points based on the maximum sampling interval value and the minimum sampling interval value includes: obtaining an average sampling interval value based on the maximum sampling interval value and the minimum sampling interval value; and selecting sampling points based on the average sampling interval value to obtain multiple sampling points; wherein the average sampling interval value satisfies the following relationship:

[0121]

[0122] Among them, the C i represents the average sampling interval value, the C max Indicates the maximum sampling interval value, the C min Indicates the minimum sampling interval value, the q min represents the minimum curvature value, the q max represents the maximum curvature value, the q i Represents the curvature value of the i-th sub-region.

[0123] It should be noted that if the measurement area type is a surface type, the standard point cloud area value is obtained through the following steps: based on the measurement point cloud data, obtain the first valid point cloud data; traverse the first valid point cloud data, and when the number of points with a radius of d at a certain point is less than the preset number, delete the point to obtain the second valid point cloud data; wherein the preset number is ≥2; after aligning the second valid point cloud data with the theoretical numerical model, divide the surface type according to the preset spacing value to obtain multiple sub-areas; obtain the surface equation of each of the sub-areas, and obtain the standard point cloud area value based on the surface equation.

[0124] Specifically, the surface equation of each of the sub-areas satisfies the following relationship:

[0125] z i =z(x,y),(x,y)∈D i

[0126] Among them, z i represents the surface equation of region i, z(x,y) represents the surface equation of the region; D i Represents the point cloud data within each of the sub-areas.

[0127] The standard point cloud area value satisfies the following relationship:

[0128]

[0129] Wherein, S2 represents the standard point cloud area value, S i Represents the standard point cloud area value of each sub-region;

[0130] The standard point cloud area value of each sub-region satisfies the following relationship:

[0131]

[0132] Among them, S i represents the standard point cloud area value of each sub-region; D i represents the point cloud data in each of the sub-areas; z represents the surface equation of the area; x represents the x-coordinate value in the surface equation; y represents the y-coordinate value in the surface equation.

[0133] That is to say, when the sample is a curved surface, the actual surface area S1 is calculated as follows:

[0134] W1.1 Point cloud resampling, through resampling, screen the points that meet the sampling requirements. The sampling requirement point screening method is: set the minimum point cloud spacing d, in the collected point cloud, the adjacent points p i With p j The distance between points d ij :

[0135]

[0136] When the adjacent point p i With p j The distance between points d ij satisfy:

[0137] d ij ≤d

[0138] The point is considered to meet the point cloud density and spacing requirements and is regarded as a valid point.

[0139] Traverse all points, and when the number of points contained in a point with radius d is less than m, remove the point. The value of m is set according to actual needs. The larger the value, the higher the required point cloud density, and the smaller the value, the lower the required point cloud density. The minimum value of m is set to 2.

[0140] W1.2 aligns the measured point cloud data with the theoretical digital model and adjusts the point cloud posture to the model posture.

[0141] W1.3 Divide the surface into n regions with interval d, and calculate the curvature q of the theoretical numerical model of each region n (n=1,2,3,…), and calculate the maximum curvature q max and the minimum curvature q min .

[0142] W1.4 Set the number of sampling points a according to the curvature and the maximum sampling interval C according to the sampling point requirements max and the minimum sampling interval C min , so the other curvature C i The sampling interval is:

[0143]

[0144] W1.5 connects the adjacent points of the sampling points in each area to form triangles, and calculates the area of ​​all triangles, which is the current area S i .

[0145] W1.6 Calculate the actual surface area S1.

[0146]

[0147] The calculation method of the theoretical surface area S2 of the sample is as follows:.

[0148] W2.1 According to the above steps W1.3, divide the surface into n regions, each region is denoted as D i , and obtain the surface equation of each region:

[0149] z i =z(x,y),(x,y)∈D i

[0150] Theoretical area S i for:

[0151]

[0152] The total area of ​​W2.2 is:

[0153]

[0154] Step S30, obtaining a completeness rate of the measured point cloud based on a ratio of the measured point cloud area value to the standard point cloud area value; and evaluating the completeness rate of the three-dimensional measured point cloud based on the measured point cloud completeness rate.

[0155] It should be noted that the ratio of the measured point cloud area value to the standard point cloud area value is: Where S1 represents the measured point cloud area value, and S2 represents the standard point cloud area value.

[0156] Generally speaking, the larger the measurement data completeness rate λ is, the higher the point cloud quality is, and the higher the surface shape measurement accuracy is required, so the completeness rate threshold can be set to 95% to control the shape measurement accuracy.

[0157] It can be seen that the method described in the embodiment of the present application can realize the evaluation of point cloud quality and solve the current problem of lack of point cloud data evaluation. And the proposed resampling method of plane point cloud data has high computational efficiency and can quickly extract valid point clouds. The point cloud data boundary can be effectively obtained by using the resampling method of plane point cloud data proposed in the present application. The point cloud obtained by this method can more accurately evaluate the deviation of the actual measurement object. The surface point cloud evaluation method proposed in the present application can effectively obtain key evaluation points according to the change of surface curvature and more accurately evaluate the actual situation of surface measurement.

[0158] See also Figure 7 Based on the same inventive concept, an embodiment of the present application further provides a device for evaluating the completeness rate of a three-dimensional measurement point cloud, comprising:

[0159] A data acquisition module is used to acquire the measurement point cloud data of the target workpiece;

[0160] A measurement area module, used for obtaining a measurement point cloud area value based on the measurement point cloud data;

[0161] The evaluation module is used to obtain the completeness rate of the measured point cloud based on the ratio of the measured point cloud area value to the standard point cloud area value; and evaluate the completeness rate of the three-dimensional measured point cloud based on the measured point cloud completeness rate.

[0162] It should be noted that each module in the drone task allocation device in this embodiment corresponds one-to-one to each step in the drone task allocation method in the aforementioned embodiment. Therefore, the specific implementation method and technical effects achieved in this embodiment can refer to the implementation method of the aforementioned drone task allocation method, which will not be repeated here.

[0163] In addition, in one embodiment, the present application also provides an electronic device, which includes a processor, a memory, and an acquisition machine program stored in the memory, and the acquisition machine program implements the steps of the method in the aforementioned embodiment when executed by the processor.

[0164] In addition, in one embodiment, the present application also provides an acquisition machine storage medium, on which an acquisition machine program is stored, and when the acquisition machine program is executed by a processor, the steps of the method in the aforementioned embodiment are implemented.

[0165] In some embodiments, the acquisition machine readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories. The acquisition machine may be various acquisition devices including intelligent terminals and servers.

[0166] In some embodiments, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in an acquisition environment.

[0167] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0168] As an example, the executable instructions may be deployed to be executed on one acquisition device, or on multiple acquisition devices located at one site, or on multiple acquisition devices distributed at multiple sites and interconnected by a communication network.

[0169] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0170] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0171] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, and the acquisition machine software product is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, an optical disk), and includes a number of instructions for a multimedia terminal device (which can be a mobile phone, an acquisition machine, a television receiver, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0172] What is disclosed above is only a partial embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. A person skilled in the art can understand that the entire or partial process of implementing the above embodiment and the equivalent changes made according to the claims of the present application are still within the scope of the invention.

Claims

1. A method for evaluating the completeness of a three-dimensional measurement point cloud, characterized in that: The following steps are involved: Obtaining measurement point cloud data of the target workpiece; Based on the measured point cloud data, the type of the measurement area of ​​the target workpiece is determined; wherein the type of the measurement area includes a plane type and a curved surface type; based on the type of the measurement area of ​​the target workpiece, valid point cloud data is screened and obtained; based on the valid point cloud data, a measurement point cloud area value is obtained; the screening and obtaining of valid point cloud data based on the type of the measurement area of ​​the target workpiece includes: if the type of the measurement area of ​​the target workpiece is a plane type; then based on the measured point cloud data, valid point cloud data is obtained; based on the valid point cloud data, valid boundary point cloud data is obtained; the valid boundary point cloud data is connected to obtain a measurement point cloud area value; the screening and obtaining of valid point cloud data based on the type of the measurement area of ​​the target workpiece includes: if the type of the measurement area of ​​the target workpiece is a curved surface type; then based on the measured point cloud data, Acquire first valid point cloud data; traverse the first valid point cloud data, and when the number of points with radius d of a certain point is less than a preset number, delete the point to obtain second valid point cloud data; wherein the preset number is ≥ 2; after aligning the second valid point cloud data with the theoretical numerical model, divide the surface type according to the preset spacing value to obtain multiple sub-areas; calculate the maximum curvature value and the minimum curvature value of the theoretical numerical model of each sub-area; based on the maximum curvature value and the minimum curvature value, obtain the maximum sampling interval value and the minimum sampling interval value; based on the maximum sampling interval value and the minimum sampling interval value, obtain multiple sampling points; based on each sampling point, obtain the adjacent points of each sampling point; connect the adjacent points of the sampling points of each sub-area to obtain the area of ​​the triangle patch; sum the areas of the triangle patches to obtain the area value of the measured point cloud; Based on the ratio of the measured point cloud area value and the standard point cloud area value, the completeness rate of the measured point cloud is obtained; based on the measured point cloud completeness rate, the completeness rate of the three-dimensional measured point cloud is evaluated; if the measurement area type is a plane type, the standard point cloud area value is obtained by the following steps: obtaining the side length of the measurement area and the angle between adjacent sides, and calculating the standard point cloud area value; if the measurement area type is a surface type, the standard point cloud area value is obtained by the following steps: based on the measured point cloud data, first valid point cloud data is obtained; traversing the first valid point cloud data, when the number of points with d as the radius of a certain point is less than a preset number, the point is deleted to obtain second valid point cloud data; wherein the preset number is ≥2; after aligning the second valid point cloud data with the theoretical numerical model, the surface type is divided according to the preset spacing value to obtain multiple sub-areas; obtaining the surface equation of each of the sub-areas, and obtaining the standard point cloud area value based on the surface equation.

2. The method for evaluating the completeness rate of a three-dimensional measurement point cloud according to claim 1, characterized in that: The obtaining of valid point cloud data based on the measured point cloud data comprises: Based on the measured point cloud data, obtaining a distance value between a first point and a second point; wherein the first point and the second point are two adjacent point clouds; When the distance between the first point and the second point is less than or equal to a preset minimum distance, the first point and the second point are considered to be valid points; The distance between the first point and the second point satisfies the following relationship: Among them, d ij represents the distance between the first point and the second point, x i Indicates the horizontal coordinate value of the first point, y i Indicates the ordinate value of the first point, x j Indicates the horizontal coordinate value of the second point, y j Indicates the ordinate value of the second point.

3. The method for evaluating the completeness rate of a three-dimensional measurement point cloud according to claim 1, characterized in that: The obtaining of valid point cloud data based on the measured point cloud data comprises: Acquire a first boundary point cloud set along a vertical direction and a second boundary point cloud set along a horizontal direction of the measured point cloud data; Taking the central intersection of the first boundary point cloud set and the second boundary point cloud set as the coordinate origin, establishing a coordinate system, so as to divide the measured point cloud data into a first area, a second area, a third area and a fourth area through the coordinate system; wherein the first area refers to an area formed by the positive direction of the X-axis and the positive direction of the Y-axis, the second area refers to an area formed by the negative direction of the X-axis and the positive direction of the Y-axis, the third area refers to an area formed by the negative direction of the X-axis and the negative direction of the Y-axis, and the fourth area refers to an area formed by the positive direction of the X-axis and the negative direction of the Y-axis; At the coordinate origin, a search is performed within a preset search range to obtain valid point cloud data.

4. The method for evaluating the completeness rate of a three-dimensional measurement point cloud according to claim 3, characterized in that: The searching at the coordinate origin within a preset search range to obtain valid point cloud data includes: In the first area, a search is performed along the positive direction of the X axis and along the positive direction of the Y axis with a preset search radius to obtain valid points and boundary points in the first area; wherein the valid points include core points and associated points, wherein the core point refers to a point where at least one other point has a distance value less than a preset minimum spacing value from the target search point, and the target search point is the core point; the associated point refers to a point where the distance value between the target search points is less than the preset minimum spacing value; wherein the boundary point refers to a point where the distance value of any point in the search direction is greater than or equal to the preset minimum spacing value, and the boundary point is an associated point of other core points; In the second area, searching is performed along the negative direction of the X axis and the positive direction of the Y axis with a preset search radius to obtain valid points and boundary points in the second area; In the third area, searching is performed along the negative direction of the X axis and the negative direction of the Y axis with a preset search radius to obtain valid points and boundary points in the third area; In the fourth area, a search is performed along the positive direction of the X axis and the negative direction of the Y axis with a preset search radius to obtain valid points and boundary points in the fourth area.

5. The method for evaluating the completeness rate of a three-dimensional measurement point cloud according to claim 1, characterized in that: The obtaining of a plurality of sampling points based on the maximum sampling interval value and the minimum sampling interval value comprises: Based on the maximum sampling interval value and the minimum sampling interval value, obtaining an average sampling interval value; Selecting sampling points based on the average sampling interval value to obtain multiple sampling points; The average sampling interval value satisfies the following relationship: Among them, the C i represents the average sampling interval value, the C max Indicates the maximum sampling interval value, the C min Indicates the minimum sampling interval value, the q min represents the minimum curvature value, the q max represents the maximum curvature value, the q i Represents the curvature value of the i-th sub-region.

6. The method for evaluating the completeness rate of a three-dimensional measurement point cloud according to claim 1, characterized in that: The surface equations of the sub-regions satisfy the following relationship: z i =z(x,y),(x,y)∈D i Among them, z i represents the surface equation of region i, z(x,y) represents the surface equation of the region; D i Represents the point cloud data within each of the sub-areas.

7. The method for evaluating the completeness rate of a three-dimensional measurement point cloud according to claim 1, characterized in that: The standard point cloud area value satisfies the following relationship: Wherein, S2 represents the standard point cloud area value, S i represents the standard point cloud area value of each sub-region; n represents the number of sub-regions; The standard point cloud area value of each sub-region satisfies the following relationship: Among them, S i represents the standard point cloud area value of each sub-region; D i represents the point cloud data in each of the sub-areas; z represents the surface equation of the area; x represents the x-coordinate value in the surface equation; y represents the y-coordinate value in the surface equation.

8. A device for evaluating the completeness of a three-dimensional measurement point cloud, characterized in that: include: A data acquisition module is used to acquire the measurement point cloud data of the target workpiece; A measurement area module is used to determine the measurement area type of the target workpiece based on the measurement point cloud data; wherein the measurement area type includes a plane type and a curved surface type; based on the measurement area type of the target workpiece, filter and obtain valid point cloud data; based on the valid point cloud data, obtain a measurement point cloud area value; the filter and obtain valid point cloud data based on the measurement area type of the target workpiece includes: if the measurement area type of the target workpiece is a plane type; then based on the measurement point cloud data, obtain valid point cloud data; based on the valid point cloud data, obtain valid boundary point cloud data; connect the valid boundary point cloud data to obtain a measurement point cloud area value; the filter and obtain valid point cloud data based on the measurement area type of the target workpiece includes: if the measurement area type of the target workpiece is a curved surface type; then based on the measurement point cloud data, obtain first valid point cloud data; traverse the first valid point cloud data, when the number of points with d as radius of a certain point is less than the preset number, delete the point to obtain second valid point cloud data; wherein the preset number is ≥2; after aligning the second valid point cloud data with the theoretical numerical model, divide the surface type according to the preset spacing value to obtain multiple sub-areas; calculate the maximum curvature value and the minimum curvature value of the theoretical numerical model of each sub-area; based on the maximum curvature value and the minimum curvature value, obtain the maximum sampling interval value and the minimum sampling interval value; based on the maximum sampling interval value and the minimum sampling interval value, obtain multiple sampling points; based on each sampling point, obtain the adjacent points of each sampling point; connect the adjacent points of the sampling points of each sub-area to obtain the area of ​​the triangle piece; add up the areas of the triangle pieces to obtain the area value of the measured point cloud; An evaluation module is used to obtain a completeness rate of a measured point cloud based on a ratio of a measured point cloud area value to a standard point cloud area value; based on the measured point cloud completeness rate, the completeness rate of the three-dimensional measured point cloud is evaluated; if the measurement area type is a plane type, the standard point cloud area value is obtained by the following steps: obtaining the side length of the measurement area and the angle between adjacent sides, and calculating the standard point cloud area value; if the measurement area type is a surface type, the standard point cloud area value is obtained by the following steps: based on the measurement point cloud data, obtaining first valid point cloud data; traversing the first valid point cloud data, when the number of points with d as a radius of a certain point is less than a preset number, deleting the point to obtain second valid point cloud data; wherein the preset number is ≥2; after aligning the second valid point cloud data with a theoretical numerical model, dividing the surface type with a preset spacing value to obtain a plurality of sub-areas; obtaining a surface equation of each of the sub-areas, and obtaining a standard point cloud area value based on the surface equation.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for evaluating the completeness rate of a three-dimensional measurement point cloud as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the method for evaluating the completeness rate of a three-dimensional measurement point cloud as described in any one of claims 1 to 7.

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