Wall verticality and flatness detection method based on 3D scanning technology

Abstract

The present application provides a wall verticality and flatness detection method, device, equipment and storage medium based on three-dimensional scanning technology. The method divides the point cloud data, which not only reduces the amount of point cloud data calculation, reduces the load demand of point cloud computing, speeds up the point cloud data calculation efficiency, but also ensures that there is enough point cloud data to fit the three-dimensional point cloud wall, thereby ensuring the accuracy of wall fitting, while improving the accuracy of wall data fitting, it also improves the low efficiency of wall flatness and verticality measurement.

Description

Technical Field

[0001] The present application relates to the technical field of wall detection, and in particular to a method for detecting the verticality and flatness of a wall based on three-dimensional scanning technology. Background Art

[0002] In civil engineering construction, surveying is a basic technical work throughout all stages of the construction process. The content of construction surveying and the accuracy of its completion play a vital role in whether the project can be smoothly constructed and its quality level.

[0003] In the prior art, the method of detecting the flatness and verticality of a construction building using three-dimensional scanning technology usually uses three-dimensional scanning technology to collect building wall data and perform wall fitting. However, the walls of a building are numerous and large in area, and the amount of point cloud data collected is large, which places high demands on the computer computing load. Although the amount of data can be reduced by thinning and other methods in the prior art, the amount of data reduced is mainly duplicate data and interference data. For the point cloud data of the entire wall, the amount of calculation is still large. And if the point cloud data after thinning is too small, it will affect the detection accuracy of the flatness and verticality of the wall.

[0004] Therefore, how to solve the current low efficiency of wall flatness and verticality measurement has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a method for detecting the verticality and flatness of a wall based on three-dimensional scanning technology, aiming to improve the inefficiency of measuring the flatness and verticality of a wall.

[0006] In a first aspect, the present application provides a method for detecting the verticality and flatness of a wall based on three-dimensional scanning technology, the method comprising:

[0007] Based on the three-dimensional scanning technology, point cloud data of at least one wall of the building is collected to obtain at least one three-dimensional point cloud wall;

[0008] Based on a preset scale, each of the three-dimensional point cloud walls is cut into at least one sub-wall area;

[0009] Based on the least squares method, the flatness of the point cloud data corresponding to each of the sub-wall surface areas is calculated to obtain the regional flatness corresponding to each of the sub-wall surface areas;

[0010] Calculating the wall flatness of the three-dimensional point cloud wall based on the area flatness corresponding to each of the sub-wall areas;

[0011] Fitting the wall plane of the three-dimensional point cloud wall based on the wall flatness of the three-dimensional point cloud wall;

[0012] Based on the relative posture between the wall plane and a preset standard vertical line, the verticality of the three-dimensional point cloud wall is calculated.

[0013] In a second aspect, the present application further provides a wall verticality and flatness detection device based on three-dimensional scanning technology, the wall verticality and flatness detection device based on three-dimensional scanning technology comprising:

[0014] A point cloud data acquisition module, used to acquire point cloud data of at least one wall of a building based on three-dimensional scanning technology to obtain at least one three-dimensional point cloud wall;

[0015] A point cloud wall partitioning module, used for cutting each of the three-dimensional point cloud walls into at least one sub-wall area based on a preset scale;

[0016] A regional flatness calculation module, used to calculate the flatness of the point cloud data corresponding to each of the sub-wall regions based on the least squares method, to obtain the regional flatness corresponding to each of the sub-wall regions;

[0017] A wall surface flatness calculation module, configured to calculate the wall surface flatness of the three-dimensional point cloud wall surface based on the area flatness corresponding to each of the sub-wall surface areas;

[0018] A wall plane fitting module, used for fitting the wall plane of the three-dimensional point cloud wall based on the wall flatness of the three-dimensional point cloud wall;

[0019] The verticality calculation module is used to calculate the verticality of the three-dimensional point cloud wall based on the relative posture between the wall plane and a preset standard vertical line.

[0020] In a third aspect, the present application also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the wall verticality and flatness detection method based on three-dimensional scanning technology as described above are implemented.

[0021] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the wall verticality and flatness detection method based on three-dimensional scanning technology as described above are implemented.

[0022] The present application provides a wall verticality and flatness detection method, device, equipment and storage medium based on three-dimensional scanning technology. The method of the present application includes: based on three-dimensional scanning technology, collecting point cloud data of at least one wall of a building to obtain at least one three-dimensional point cloud wall; based on a preset ratio, cutting each of the three-dimensional point cloud walls into at least one sub-wall area; based on the least squares method, performing flatness calculation on the point cloud data corresponding to each of the sub-wall areas to obtain the regional flatness corresponding to each of the sub-wall areas; based on the regional flatness corresponding to each of the sub-wall areas, calculating the wall flatness of the three-dimensional point cloud wall; based on the wall flatness of the three-dimensional point cloud wall, fitting the wall plane of the three-dimensional point cloud wall; based on the relative posture of the wall plane and a preset standard vertical line, calculating the verticality of the three-dimensional point cloud wall. Through the above method, the present application divides the point cloud data, which not only reduces the calculation amount of the point cloud data, reduces the load demand of the point cloud computing, and speeds up the calculation efficiency of the point cloud data, but also ensures that there is enough point cloud data to fit the three-dimensional point cloud wall, thereby ensuring the accuracy of the wall fitting. While improving the accuracy of the wall data fitting, it also improves the efficiency of the wall flatness and verticality measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 A schematic flow chart of a first embodiment of a wall verticality and flatness detection method based on three-dimensional scanning technology provided by the present application;

[0025] Figure 2 It is a structural schematic diagram of a first embodiment of a wall verticality and flatness detection device based on three-dimensional scanning technology provided by the present application;

[0026] Figure 3 It is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application.

[0027] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0030] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0031] Please refer to Figure 1 , Figure 1 A schematic flow chart of a first embodiment of a wall verticality and flatness detection method based on three-dimensional scanning technology provided in this application. The wall verticality and flatness detection method based on three-dimensional scanning technology can be used in a server. The server includes an independent server or a server cluster.

[0032] like Figure 1 As shown, the wall verticality and flatness detection method based on three-dimensional scanning technology includes steps S101 to S106.

[0033] S101, collecting point cloud data of at least one wall of a building based on three-dimensional scanning technology to obtain at least one three-dimensional point cloud wall;

[0034] In one embodiment, the point data set of the product appearance surface obtained by measuring instruments in reverse engineering is also called a point cloud. The point cloud is a data set of points in a certain coordinate system, and the points in the data set include three-dimensional coordinates X, Y, Z axis, color, classification value, intensity value, time and other data.

[0035] In one embodiment, point cloud data can be acquired by collecting data through a three-dimensional scanning device such as a three-dimensional laser scanner; three-dimensional reconstruction can be performed through two-dimensional images to obtain point cloud data during the reconstruction process; point clouds can also be calculated through a three-dimensional model.

[0036] In one embodiment, three-dimensional scanning refers to a high-tech technology that integrates optical, mechanical, electrical and computer technologies, and is mainly used to scan the spatial shape, structure and color of an object to obtain the spatial coordinates of the object's surface.

[0037] In one embodiment, the three-dimensional scanning technology may be to collect data through a three-dimensional laser scanner or a multi-camera acquisition device.

[0038] Generally speaking, the number of points obtained using three-dimensional coordinate measuring equipment (multi-eye camera, etc.) is relatively small, and the distance between points is relatively large, which is called a sparse point cloud; while the point cloud obtained using a three-dimensional laser scanner or a photographic scanner has a relatively large number of points and is relatively dense, which is called a dense point cloud.

[0039] In one embodiment, 3D laser scanning technology can quickly acquire massive point cloud data of the measurement target in a short time. Massive point cloud data can describe the characteristics of the object in detail, but the large amount of data also increases the load requirements for data processing. After the point cloud data of different measuring stations are aligned, there are often a large number of repeated point cloud data in the overlapping area. These repeated point cloud data will lead to inefficient computer operation, slow data processing, and low computing efficiency. Therefore, the data needs to be de-redundant and simplified before use.

[0040] Generally speaking, data reduction is to reduce the amount of point cloud data and extract effective information while maintaining accuracy. This can generally include: removing redundancy and simplification.

[0041] Among them, redundant data refers to the data in the repeated area after data alignment. This part of data is large and mostly useless data, which has a great impact on the speed and quality of modeling. This part of data must be removed.

[0042] Among them, thinning and simplification means that the scanned data density is too high and the quantity is too large, and part of the data is not very useful for later modeling. Therefore, under the premise of meeting a certain accuracy and maintaining the geometric characteristics of the measured object, the data is simplified to improve the data operation speed, modeling efficiency and model accuracy.

[0043] In one embodiment, during the acquisition process of point cloud data, it is usually affected by lighting, scanning equipment, suspended objects in the air, etc., and the scanned data will be contaminated by noise, making the acquired data unable to be directly used, so it is necessary to remove the noise. There are still a large number of isolated point clouds around the point cloud data of the research object after thinning, so they need to be removed.

[0044] In a specific embodiment, the data denoising method can be divided into different processing methods according to different situations, which can include a denoising method based on ordered point cloud data using a smoothing filter and a denoising method based on scattered point cloud data.

[0045] Exemplarily, the smoothing filter denoising method based on the ordered point cloud data may include Gaussian filtering, mean filtering and median filtering.

[0046] Among them, Gaussian filtering is a linear smoothing filter, which is a weighted average of the data in a specified area and can remove high-frequency information. Its advantage is that it can retain the characteristic information of point cloud data while ensuring the quality of denoising.

[0047] Mean filtering is also called average filtering. It is also a typical linear filtering. Its principle is to select points within a certain range to calculate their average value to replace the original data points. The advantage is that the algorithm is simple and easy to implement. The disadvantage is that the denoising effect is relatively average. And it cannot retain the characteristic details of the point cloud well.

[0048] Median filtering is a nonlinear smoothing filter. Its principle is to find the median of three or more data adjacent to a certain point, and the result replaces the original value. Its advantage is that it is very effective in removing burr noise and can also well protect the edge feature information of the data.

[0049] Exemplarily, the denoising method based on scattered point cloud data may include Laplace denoising, mean curvature flow method, and bilateral filtering algorithm.

[0050] Among them, although the Laplace algorithm can well guarantee the detailed characteristics of the model, there will still be residual noise points.

[0051] Although the bilateral filtering algorithm can remove noise points well, it cannot preserve the detailed features of the model well.

[0052] The mean curvature relies on curvature estimation. It has a better denoising effect on data with simple models and fewer noise points. However, for complex data with many noise points, its calculation speed is slow and the denoising effect is poor.

[0053] S102, cutting each of the three-dimensional point cloud walls into at least one sub-wall area based on a preset scale;

[0054] In one embodiment, different walls have different sizes and areas. Therefore, the three-dimensional point cloud wall data can be segmented according to a preset proportional size, and the point cloud data can be proportionally segmented into several equally divided sub-wall areas. Data calculation is performed on the point cloud data of each sub-wall area respectively, so as to improve the point cloud data processing efficiency.

[0055] In one embodiment, the wall is divided into equal proportions according to its length and width. For example, if the wall size is 5m×3m, it can be cut into 15 sub-wall areas of 1m×1m, or it can be divided into smaller areas, such as 100 sub-wall areas of 0.5m×0.3m.

[0056] S103, performing flatness calculation on the point cloud data corresponding to each of the sub-wall surface areas based on the least squares method to obtain the regional flatness corresponding to each of the sub-wall surface areas;

[0057] In one embodiment, when calculating the flatness, it is first necessary to fit a plane according to the point cloud data, that is, to fit the sub-wall plane according to the point cloud data corresponding to the sub-wall area.

[0058] In one embodiment, the main idea of ​​estimating the point cloud normal vector is to perform plane fitting on the N points of the K-nearest neighbors (the plane passes through the center of gravity of the N points), and the plane normal vector is what is required. The least squares fitting can be converted into finding the eigenvector corresponding to the minimum eigenvalue of the covariance matrix; this solution is highly robust to data noise, and the key point is to decentralize the data and move the coordinate origin to the center of gravity of the data.

[0059] Furthermore, based on the least squares method, plane fitting is performed on the point cloud data to obtain fitting sub-planes corresponding to each sub-wall area; based on the vertical distance between each point corresponding to each sub-wall area and the corresponding fitting sub-plane, the flatness of the area corresponding to each sub-wall area is calculated.

[0060] In one embodiment, the spatial plane equation can be expressed as:

[0061] f(x,y,z)=Ax+By+Cz+D=0

[0062] Among them, A, B, C, and D are constants. Dividing both sides of the above equation by D, we have

[0063] f(x,y,z)=ax+by+cz+1=0

[0064] That is, by solving the values ​​of the three parameters a, b, and c, the fitting plane can be determined.

[0065] In a specific embodiment, if there are n point cloud data in a sub-wall area, recorded as (x i ,y i ,z i )(i=1,2,…,n) According to the least squares principle, this method of plane fitting must meet the following conditions:

[0066]

[0067] Then we have:

[0068]

[0069] That is, it is necessary to make:

[0070]

[0071] From this, we can get:

[0072]

[0073] Converted into matrix form, we have:

[0074]

[0075] Among them, so that:

[0076]

[0077] Then there is AX T =L, so the sub-wall area plane equation parameter X = A -1 L.

[0078] In one embodiment, every point (x i ,y i ,z i ) to the distance d from the fitting plane i , indicating the ups and downs of the point, and its overall shape can represent the flatness of the wall.

[0079] in,

[0080]

[0081] In one embodiment, the flatness of the sub-wall surface can be expressed using the mean error m:

[0082]

[0083] It can be understood that the mean error is very sensitive to the particularly large or effective errors in a set of measurements, and can well reflect the magnitude of the fluctuation in the measurement results. Therefore, the mean error can be used to represent the flatness of the sub-wall surface.

[0084] Furthermore, when the vertical distance between a point cloud data point and the fitting sub-plane is greater than a preset distance threshold, the point cloud data point is marked as a defect point; when the number of defect points corresponding to the sub-wall area is greater than a preset defect point number threshold, the sub-wall area is determined to be a defect area.

[0085] In one embodiment, after fitting the sub-plane corresponding to the sub-wall area, the vertical distance between each point cloud data corresponding to the sub-wall area and the fitting sub-plane, that is, the length of the vertical line between the point and the fitting sub-plane, can be calculated. When the vertical distance is greater than a preset distance threshold, the point cloud data point is marked as a defect point.

[0086] In one embodiment, the defective area is an area, wherein the determination of the defective area may be interfered by point cloud data points such as dust, and therefore, interference elimination can be performed based on multiple vertical distances of point cloud data points in a certain area. If the vertical distances corresponding to the point cloud data points in a certain area are all greater than a preset distance threshold, it can be considered that there is a defect in the area; conversely, if only the vertical distances corresponding to individual point cloud data points are greater than the preset distance threshold, it can be determined that the individual point cloud data points are interference points and can be eliminated.

[0087] For example, assuming that the preset defect point number threshold is 30% and the preset distance threshold is 0.2 mm, if more than 30% of the vertical distances corresponding to the point cloud data points in a certain area are greater than 0.2 mm, it is determined that there are defects in the concentrated area, that is, it is marked as a defective area.

[0088] S104, calculating the wall flatness of the three-dimensional point cloud wall based on the area flatness corresponding to each of the sub-wall areas;

[0089] In one embodiment, there are differences in the regional flatness corresponding to the sub-wall areas in different areas, especially in the sub-wall areas where there are defects (such as convex hulls, concave hulls, convex points, scratches, etc.), the regional flatness is relatively low. Therefore, when calculating the wall flatness of the three-dimensional point cloud wall, it is necessary to identify the sub-wall area where the defective points are located.

[0090] In one embodiment, the average value of the regional flatness corresponding to each sub-wall region may be calculated as the wall flatness of the three-dimensional point cloud wall.

[0091] In one embodiment, the wall flatness of the three-dimensional point cloud wall may also be calculated according to the regional flatness of each sub-wall area by means of distribution statistics.

[0092] Furthermore, based on a distribution statistics method, the data distribution of the regional flatness corresponding to each of the sub-wall regions is counted; based on the data distribution, the wall flatness of the three-dimensional point cloud wall is determined.

[0093] Generally speaking, distribution statistics study the distribution characteristics and distribution types of data. Data distribution describes the frequency of occurrence of each value. The most common method to represent distribution is the histogram, which is used to show the frequency or probability of occurrence of each value. Frequency refers to the number of times a value appears in a data set. Probability is the frequency divided by the number of samples n. Frequency divided by n can convert frequency into probability, which is called normalization. The normalized histogram is called PMF (Probability Mass Function), which is a mapping of values ​​to their probabilities.

[0094] Exemplarily, according to the distribution statistics method, the distribution of regional flatness data of each sub-wall area is counted, and the regional flatness with the largest number of occurrences is used as the wall flatness of the three-dimensional point cloud wall.

[0095] For example, the number of occurrences of the flatness of each region may be sorted according to the distribution of the regional flatness data of the sub-wall region, and the flatness of multiple regions with a large number of occurrences may be averaged. For example, the flatness of the top three regions with the highest number of occurrences may be averaged, and the average value may be used as the wall flatness of the three-dimensional point cloud wall.

[0096] Furthermore, the flatness difference between the regional flatness corresponding to each of the sub-wall regions and the wall flatness is calculated; and based on a preset flatness difference threshold and the flatness difference, the defective region in the three-dimensional point cloud wall is determined.

[0097] In one embodiment, after obtaining the wall flatness in the three-dimensional point cloud wall, the flatness difference between the regional flatness of each sub-wall area and the wall flatness is calculated, and a preset flatness difference threshold and the flatness difference are compared.

[0098] In one embodiment, when the flatness difference is less than the flatness difference threshold, the sub-wall area corresponding to the flatness difference is determined to be a normal area, that is, a flat wall area.

[0099] In one embodiment, when the flatness difference is greater than the flatness difference threshold, the sub-wall area corresponding to the flatness difference is determined to be a defect area, that is, the sub-wall area has defects, such as convex hulls, concave hulls, convex points, scratches, etc.

[0100] S105, fitting the wall plane of the three-dimensional point cloud wall based on the wall flatness of the three-dimensional point cloud wall;

[0101] In one embodiment, according to the wall flatness of the three-dimensional point cloud wall, several points in the point cloud data that meet the wall flatness requirements are screened, and based on the data of these points, the wall plane of the three-dimensional point cloud wall is fitted by the least squares method and other methods.

[0102] In one embodiment, a sub-wall area whose regional flatness is the same as the wall flatness or whose difference is less than a preset threshold may be screened out, and a plane obtained by fitting the sub-wall area is used as the wall plane of the three-dimensional point cloud wall.

[0103] Furthermore, based on the three-dimensional scanning technology, point cloud data of a standard vertical object is collected; based on the least squares method, the point cloud data of the standard vertical object is fitted to obtain the standard vertical line.

[0104] In one embodiment, a standard vertical object or vertical measuring device (such as a vertical ruler) may be arranged in the point cloud acquisition area. The standard vertical object may be a device such as a hanging hammer, and the standard vertical object may be placed vertically naturally, or the vertical measuring device may be placed vertically or horizontally, and the point cloud data of the standard vertical object or the vertical measuring device may be scanned by three-dimensional scanning technology, and then a three-dimensional model of the standard vertical object may be fitted according to the point cloud data.

[0105] S106. Calculate the verticality of the three-dimensional point cloud wall based on the relative posture between the wall plane and a preset standard vertical line.

[0106] In one embodiment, in the three-dimensional modeling software, a standard vertical line can be added to the page where the wall plane is located, and then the angle between the projection of the standard vertical line on the wall plane and the standard vertical line is calculated, and the angle is used as the verticality of the three-dimensional point cloud wall in the vertical direction.

[0107] In one embodiment, in the 3D model software, the edge line of the wall plane can also be extracted. For example, the bottom edge line of the wall plane is extracted, and the angle between the bottom edge line and the standard vertical line is calculated as the verticality of the 3D point cloud wall in the horizontal direction. The side edge line of the wall plane is extracted, and the angle between the side edge line and the standard vertical line is calculated as the verticality of the 3D point cloud wall in the vertical direction.

[0108] Furthermore, based on the relative posture of at least two of the wall planes, the verticality of the three-dimensional point cloud wall is calculated.

[0109] In one embodiment, the fitting planes of multiple walls of the same building can also be added to the same page to calculate the angles between the walls. For example, the verticality of the wall in the vertical direction can be measured based on the angle between the ground plane and the wall plane; and the angle between the wall planes can be used to measure the verticality between the walls.

[0110] The present embodiment provides a method for detecting the verticality and flatness of a wall based on three-dimensional scanning technology. The method of the present application reduces the amount of calculation of point cloud data, reduces the load demand for point cloud computing, speeds up the efficiency of point cloud data calculation, and ensures that there is enough point cloud data to fit the three-dimensional point cloud wall, thereby ensuring the accuracy of wall fitting. While improving the accuracy of wall data fitting, it also improves the efficiency of measuring the flatness and verticality of the wall.

[0111] like Figure 2As shown, the wall verticality and flatness detection device 200 based on three-dimensional scanning technology includes: a point cloud data acquisition module 201, a point cloud wall partitioning module 202, a regional flatness calculation module 203, a wall flatness calculation module 204, a wall plane fitting module 205 and a verticality calculation module 206.

[0112] The point cloud data acquisition module 201 is used to acquire point cloud data of at least one wall of a building based on three-dimensional scanning technology to obtain at least one three-dimensional point cloud wall;

[0113] The point cloud wall partitioning module 202 is used to cut each of the three-dimensional point cloud walls into at least one sub-wall area based on a preset scale;

[0114] A regional flatness calculation module 203 is used to perform flatness calculation on the point cloud data corresponding to each of the sub-wall regions based on the least square method to obtain the regional flatness corresponding to each of the sub-wall regions;

[0115] A wall surface flatness calculation module 204, configured to calculate the wall surface flatness of the three-dimensional point cloud wall surface based on the area flatness corresponding to each of the sub-wall surface areas;

[0116] A wall plane fitting module 205 is used to fit the wall plane of the three-dimensional point cloud wall based on the wall flatness of the three-dimensional point cloud wall;

[0117] The verticality calculation module 206 is used to calculate the verticality of the three-dimensional point cloud wall based on the relative posture between the wall plane and a preset standard vertical line.

[0118] In one embodiment, the regional flatness calculation module 203 includes:

[0119] A sub-plane fitting unit, used for performing plane fitting on the point cloud data based on the least squares method to obtain a fitting sub-plane corresponding to each sub-wall area;

[0120] The regional flatness calculation unit is used to calculate the regional flatness corresponding to each of the sub-wall surface areas based on the vertical distance between each point corresponding to each of the sub-wall surface areas and the corresponding fitting sub-plane.

[0121] In one embodiment, the regional flatness calculation unit includes:

[0122] A defect point marking subunit, used for marking the point cloud data point as a defect point when the vertical distance between the point cloud data point and the fitting sub-plane is greater than a preset distance threshold;

[0123] The defect area detection subunit is used to determine that the sub-wall area is a defect area when the number of defect points corresponding to the sub-wall area is greater than a preset defect point number threshold.

[0124] In one embodiment, the wall flatness calculation module 204 includes:

[0125] A data distribution statistics unit, used for counting the data distribution of the flatness of the area corresponding to each of the sub-wall areas based on a distribution statistics method;

[0126] A wall flatness determination unit is used to determine the wall flatness of the three-dimensional point cloud wall based on the data distribution.

[0127] In one embodiment, the wall flatness calculation module 204 further includes:

[0128] A flatness difference calculation unit, used for calculating the flatness difference between the flatness of the area corresponding to each of the sub-wall areas and the flatness of the wall surface;

[0129] The defect area detection unit is used to determine the defect area in the three-dimensional point cloud wall based on a preset flatness difference threshold and the flatness difference.

[0130] In one embodiment, the wall verticality and flatness detection device 200 based on three-dimensional scanning technology further includes a standard vertical line acquisition module, including:

[0131] A standard vertical object point cloud acquisition unit, used to acquire point cloud data of a standard vertical object based on the three-dimensional scanning technology;

[0132] The standard vertical line fitting unit is used to fit the point cloud data of the standard vertical object based on the least squares method to obtain the standard vertical line.

[0133] In one embodiment, the verticality calculation module 206 further includes:

[0134] A verticality calculation unit is used to calculate the verticality of the three-dimensional point cloud wall based on the relative posture of at least two wall planes.

[0135] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned embodiment of the wall verticality and flatness detection method based on three-dimensional scanning technology, and will not be repeated here.

[0136] The apparatus provided in the above embodiment may be implemented in the form of a computer program. The computer program may be Figure 3 Runs on the computer device shown.

[0137] See also Figure 3 , Figure 3 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0138] See also Figure 3 The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0139] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any wall verticality and flatness detection method based on three-dimensional scanning technology.

[0140] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0141] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any wall verticality and flatness detection method based on three-dimensional scanning technology.

[0142] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0143] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0144] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0145] Based on the three-dimensional scanning technology, point cloud data of at least one wall of the building is collected to obtain at least one three-dimensional point cloud wall;

[0146] Based on a preset scale, each of the three-dimensional point cloud walls is cut into at least one sub-wall area;

[0147] Based on the least squares method, the flatness of the point cloud data corresponding to each of the sub-wall surface areas is calculated to obtain the regional flatness corresponding to each of the sub-wall surface areas;

[0148] Calculating the wall flatness of the three-dimensional point cloud wall based on the area flatness corresponding to each of the sub-wall areas;

[0149] Fitting the wall plane of the three-dimensional point cloud wall based on the wall flatness of the three-dimensional point cloud wall;

[0150] Based on the relative posture between the wall plane and a preset standard vertical line, the verticality of the three-dimensional point cloud wall is calculated.

[0151] In one embodiment, when the processor performs the flatness calculation on the point cloud data corresponding to each of the sub-wall surface areas based on the least squares method to obtain the regional flatness corresponding to each of the sub-wall surface areas, it is used to implement:

[0152] Based on the least squares method, plane fitting is performed on the point cloud data to obtain fitting sub-planes corresponding to each sub-wall area;

[0153] Based on the vertical distance between each point corresponding to each sub-wall surface area and the corresponding fitting sub-plane, the flatness of the area corresponding to each sub-wall surface area is calculated.

[0154] In one embodiment, when the processor calculates the flatness of the area corresponding to each of the sub-wall surface areas based on the vertical distance between each point corresponding to each of the sub-wall surface areas and the corresponding fitting sub-plane, it is configured to implement:

[0155] When the vertical distance between a point cloud data point and the fitting sub-plane is greater than a preset distance threshold, marking the point cloud data point as a defect point;

[0156] When the number of defect points corresponding to the sub-wall area is greater than a preset defect point number threshold, the sub-wall area is determined to be a defect area.

[0157] In one embodiment, when the processor calculates the wall flatness of the three-dimensional point cloud wall based on the area flatness corresponding to each of the sub-wall areas, it is used to implement:

[0158] Based on a distribution statistics method, statistics are collected on the data distribution of the flatness of the area corresponding to each of the sub-wall areas;

[0159] Based on the data distribution, the wall flatness of the three-dimensional point cloud wall is determined.

[0160] In one embodiment, after determining the wall flatness of the three-dimensional point cloud wall based on the data distribution, the processor is further configured to implement:

[0161] Calculating the flatness difference between the flatness of the area corresponding to each of the sub-wall areas and the flatness of the wall surface;

[0162] Based on a preset flatness difference threshold and the flatness difference, a defective area in the three-dimensional point cloud wall is determined.

[0163] In one embodiment, before calculating the verticality of the three-dimensional point cloud wall based on the relative posture of the wall plane and a preset standard vertical line, the processor is further configured to implement:

[0164] Based on the three-dimensional scanning technology, point cloud data of standard vertical objects are collected;

[0165] Based on the least squares method, the point cloud data of the standard vertical object is fitted to obtain the standard vertical line.

[0166] In one embodiment, after implementing the wall flatness based on the three-dimensional point cloud wall and fitting the wall plane of the three-dimensional point cloud wall, the processor is further used to implement:

[0167] Based on the relative posture of at least two of the wall planes, the verticality of the three-dimensional point cloud wall is calculated.

[0168] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement any one of the wall verticality and flatness detection methods based on three-dimensional scanning technology provided in the embodiments of the present application.

[0169] The computer-readable storage medium may be an internal storage unit of the computer device described in the above embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the computer device.

[0170] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A wall verticality and flatness detection method based on three-dimensional scanning technology, characterized in that: The method comprises: Based on the three-dimensional scanning technology, point cloud data of at least one wall of the building is collected to obtain at least one three-dimensional point cloud wall; Based on a preset scale, each of the three-dimensional point cloud walls is cut into at least one sub-wall area; Based on the least squares method, the flatness of the point cloud data corresponding to each of the sub-wall surface areas is calculated to obtain the regional flatness corresponding to each of the sub-wall surface areas; Calculating the wall flatness of the three-dimensional point cloud wall based on the area flatness corresponding to each of the sub-wall areas; Fitting the wall plane of the three-dimensional point cloud wall based on the wall flatness of the three-dimensional point cloud wall; Calculating the verticality of the three-dimensional point cloud wall based on the relative posture between the wall plane and a preset standard vertical line; The method of calculating the flatness of the point cloud data corresponding to each sub-wall surface area based on the least squares method to obtain the regional flatness corresponding to each sub-wall surface area includes: Based on the least squares method, plane fitting is performed on the point cloud data to obtain fitting sub-planes corresponding to each sub-wall area; Based on the vertical distance between each point corresponding to each sub-wall surface area and the corresponding fitting sub-plane, the flatness of the area corresponding to each sub-wall surface area is calculated.

2. The wall verticality and flatness detection method based on three-dimensional scanning technology according to claim 1 is characterized in that: The calculating the regional flatness corresponding to each of the sub-wall surface regions based on the vertical distance between each point corresponding to each of the sub-wall surface regions and the corresponding fitting sub-plane comprises: When the vertical distance between a point cloud data point and the fitting sub-plane is greater than a preset distance threshold, marking the point cloud data point as a defect point; When the number of defect points corresponding to the sub-wall area is greater than a preset defect point number threshold, the sub-wall area is determined to be a defect area.

3. The wall verticality and flatness detection method based on three-dimensional scanning technology according to claim 1 is characterized in that: The calculating the wall flatness of the three-dimensional point cloud wall based on the area flatness corresponding to each of the sub-wall areas includes: Based on a distribution statistics method, statistics are collected on the data distribution of the flatness of the area corresponding to each of the sub-wall areas; Based on the data distribution, the wall flatness of the three-dimensional point cloud wall is determined.

4. The wall verticality and flatness detection method based on three-dimensional scanning technology according to claim 3 is characterized in that: After determining the wall flatness of the three-dimensional point cloud wall based on the data distribution, the method further includes: Calculating the flatness difference between the flatness of the area corresponding to each of the sub-wall areas and the flatness of the wall surface; Based on a preset flatness difference threshold and the flatness difference, a defective area in the three-dimensional point cloud wall is determined.

5. The wall verticality and flatness detection method based on three-dimensional scanning technology according to claim 1 is characterized in that: Before calculating the verticality of the three-dimensional point cloud wall based on the relative posture of the wall plane and the preset standard vertical line, the method further includes: Based on the three-dimensional scanning technology, point cloud data of standard vertical objects are collected; Based on the least squares method, the point cloud data of the standard vertical object is fitted to obtain the standard vertical line.

6. The wall verticality and flatness detection method based on three-dimensional scanning technology according to claim 1 is characterized in that: After fitting the wall plane of the three-dimensional point cloud wall based on the wall flatness of the three-dimensional point cloud wall, the method further includes: Based on the relative posture of at least two of the wall planes, the verticality of the three-dimensional point cloud wall is calculated.

7. A wall verticality and flatness detection device based on three-dimensional scanning technology, characterized in that: The wall verticality and flatness detection device based on three-dimensional scanning technology includes: A point cloud data acquisition module, used to acquire point cloud data of at least one wall of a building based on three-dimensional scanning technology to obtain at least one three-dimensional point cloud wall; A point cloud wall partitioning module, used for cutting each of the three-dimensional point cloud walls into at least one sub-wall area based on a preset scale; A regional flatness calculation module, used to calculate the flatness of the point cloud data corresponding to each of the sub-wall regions based on the least squares method, to obtain the regional flatness corresponding to each of the sub-wall regions; A wall surface flatness calculation module, configured to calculate the wall surface flatness of the three-dimensional point cloud wall surface based on the area flatness corresponding to each of the sub-wall surface areas; A wall plane fitting module, used for fitting the wall plane of the three-dimensional point cloud wall based on the wall flatness of the three-dimensional point cloud wall; A verticality calculation module, used to calculate the verticality of the three-dimensional point cloud wall based on the relative posture between the wall plane and a preset standard vertical line; Among them, the regional flatness calculation module is also used to perform plane fitting on the point cloud data based on the least squares method to obtain the fitting sub-planes corresponding to each of the sub-wall areas; based on the vertical distance between each point corresponding to each of the sub-wall areas and the corresponding fitting sub-plane, calculate the regional flatness corresponding to each of the sub-wall areas.

8. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the wall verticality and flatness detection method based on three-dimensional scanning technology as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the wall verticality and flatness detection method based on three-dimensional scanning technology as described in any one of claims 1 to 6 are implemented.

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