Building surveying method, device, system and storage medium

Abstract

The present invention provides a building surveying and mapping method, device, system, and storage medium, belonging to the field of engineering surveying and mapping. The method comprises: filtering out facade wall point cloud coordinates from original three-dimensional point cloud coordinates; constructing a target grid map based on the facade wall point cloud coordinates; constructing target straight line segments from the target grid map; obtaining target point cloud coordinates from the target grid map based on the target straight line segments; calculating the intersection of the target point cloud coordinates to obtain straight line intersections; and performing image rendering using the target point cloud coordinates and straight line intersections to obtain the building surveying and mapping results. The present invention not only reduces certain computing power requirements and speeds up demodulation, but also significantly reduces the workload of manual drawing, requiring only simple manual verification and trimming to directly produce the map.

Description

Technical Field

[0001] The present invention mainly relates to the field of engineering surveying and mapping technology, and in particular to a building surveying and mapping method, device, system and storage medium. Background Art

[0002] The main process of real estate and land integration involves six key aspects: title surveying and mapping, title investigation, database development, real estate data integration, map compilation, and title confirmation and certification. Title surveying and mapping is the most critical, serving as the foundation for all subsequent processes. It is also the most labor-intensive, material-intensive, and financially intensive. In the past, title surveying and mapping typically used fixed-point angle measuring instruments such as total stations and RTK, utilizing a combination of analytical, door-to-door, simplified, and graphical methods to measure and map relevant land parcels. This method was time-consuming and labor-intensive both in the field and in the field, making it difficult to meet the needs of comprehensive and efficient land and resources mapping. In recent years, the continuous development of drone aerial surveying technology has led to continuous innovation and advancement in surveying and mapping technology for land and resources surveys and real estate and land integration. This technology has freed surveying and mapping practitioners from the arduous and demanding field work of intense heat and cold, significantly improving productivity in related surveying and mapping work. However, while field surveying has achieved revolutionary breakthroughs thanks to new hardware technologies like drone-based aerial surveying, office surveying has not kept pace with the development of compatible automated mapping methods. Currently, people still need to invest a significant amount of manpower and resources in tedious, complex, and repetitive mapping tasks, and this situation urgently needs to change.

[0003] New surveying and mapping methods using drone-mounted cameras for aerial photography or airborne LiDAR for ground scanning primarily utilize specialized oblique cameras or 3D laser scanners to acquire 3D information about features. Subsequent modeling and processing provide intuitive visualization of features' 3D position, appearance, shape, texture, and other information. This shifts a significant portion of fieldwork workload to office work, effectively addressing the shortcomings of traditional surveying methods, such as low efficiency, lack of visibility due to densely packed buildings, and limited data output. It also addresses the challenge of inaccessible home surveys in some residential areas. However, this model also increases the human and material resources required by practitioners, requiring more office labor and higher-performance computers. For example, commonly used 3D modeling software such as ContextCapture, PhotoScan, DJI Terra, and Reconstruction Master require computers with robust data processing and graphics capabilities. However, in the field of integrated real estate, hardware performance has only been utilized to the modeling and prototype stages, requiring significant personnel input for mapping and drawing. This not only wastes computing power but also fails to meet the growing demand for efficiency.

[0004] The traditional "aerial survey model + manual drawing" model requires 10 main steps: "① aerial photography → ② aerial triangulation solution → ③ irregular triangulation network construction → ④ texture mapping → ⑤ modeling → ⑥ manual interpretation → ⑦ point sampling on the model → ⑧ connecting points into lines → ⑨ connecting lines into surfaces → ⑩ drawing output". Among them, steps ②③④⑤ require strong computing performance and are relatively time-consuming, and steps ⑥⑦⑧⑨⑩ require a lot of manual participation. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a building surveying and mapping method, device, system and storage medium in response to the deficiencies of the existing technology.

[0006] The present invention solves the above technical problems with the following technical solutions: A building surveying and mapping method comprises the following steps:

[0007] Acquire multiple original three-dimensional point cloud coordinates from a preset tool, and filter out multiple facade wall point cloud coordinates from all the original three-dimensional point cloud coordinates;

[0008] Construct a target grid map based on all the vertical wall point cloud coordinates;

[0009] constructing a plurality of target straight line segments through the target grid map;

[0010] Acquire a plurality of target point cloud coordinates corresponding to each target straight line segment from the target grid map according to each target straight line segment;

[0011] Calculating the intersection points of the target point cloud coordinates to obtain a plurality of straight line intersection points;

[0012] The image is drawn by all the target point cloud coordinates and all the straight line intersections to obtain the building mapping result.

[0013] Another technical solution of the present invention to solve the above technical problems is as follows: A building surveying and mapping device, comprising:

[0014] A screening module, configured to obtain a plurality of original three-dimensional point cloud coordinates from a preset tool, and screen out a plurality of facade wall point cloud coordinates from all the original three-dimensional point cloud coordinates;

[0015] A grid map construction module, configured to construct a target grid map based on all the vertical wall point cloud coordinates;

[0016] A straight line segment construction module, configured to construct a plurality of target straight line segments using the target grid image;

[0017] a point cloud coordinate acquisition module, configured to acquire a plurality of target point cloud coordinates corresponding to each target straight line segment from the target grid image according to each target straight line segment;

[0018] A calculation module, configured to calculate the intersection points of the target point cloud coordinates to obtain a plurality of straight line intersection points;

[0019] The surveying and mapping result obtaining module is used to perform image drawing through all the target point cloud coordinates and all the straight line intersections to obtain the building surveying and mapping results.

[0020] Based on the above-mentioned building surveying and mapping method, the present invention also provides a building surveying and mapping system.

[0021] Another technical solution of the present invention to solve the above technical problem is as follows: A building surveying and mapping system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the building surveying and mapping method described above is implemented.

[0022] Based on the above-mentioned building surveying and mapping method, the present invention also provides a computer-readable storage medium.

[0023] Another technical solution of the present invention to solve the above technical problem is as follows: a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the building surveying and mapping method described above is implemented.

[0024] The beneficial effects of the present invention are as follows: by screening out the facade wall point cloud coordinates from the original three-dimensional point cloud coordinates, constructing a target grid map according to the facade wall point cloud coordinates, constructing a target straight line segment through the target grid map, obtaining the target point cloud coordinates from the target grid map according to the target straight line segment, calculating the intersection of the target point cloud coordinates to obtain the straight line intersection, and obtaining the building mapping result by drawing the image of the target point cloud coordinates and the straight line intersection. This not only reduces a certain amount of computing power requirements and speeds up the demolding speed, but also greatly reduces the workload of manual drawing. Only simple manual verification and trimming are required to directly output the map. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of a flow chart of a building surveying and mapping method provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of the separation principle of the vertical wall point cloud coordinates provided by an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of the separation effect of the vertical wall point cloud coordinates provided by an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of the point-line duality principle of the Hough transform provided by an embodiment of the present invention;

[0029] Figure 5A diagram showing the point-line duality result of the Hough transform provided by an embodiment of the present invention;

[0030] Figure 6 A schematic diagram of noise representation of the Hough transform in parameter space provided by an embodiment of the present invention;

[0031] Figure 7 A schematic diagram of noise representation in image space using the Hough transform provided by an embodiment of the present invention;

[0032] Figure 8 A schematic diagram of the noise reduction effect of the Hough transform in the image space provided by an embodiment of the present invention;

[0033] Figure 9 A schematic diagram of a straight line fitting result provided by an embodiment of the present invention;

[0034] Figure 10 A flow chart of a vertex faceting method provided in an embodiment of the present invention;

[0035] Figure 11 This is a module block diagram of a building surveying and mapping device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0037] Figure 1 A schematic flow chart of a building surveying and mapping method provided in an embodiment of the present invention.

[0038] like Figure 1 As shown, a building surveying and mapping method includes the following steps:

[0039] Acquire multiple original three-dimensional point cloud coordinates from a preset tool, and filter out multiple facade wall point cloud coordinates from all the original three-dimensional point cloud coordinates;

[0040] Construct a target grid map based on all the vertical wall point cloud coordinates;

[0041] constructing a plurality of target straight line segments through the target grid map;

[0042] Acquire a plurality of target point cloud coordinates corresponding to each target straight line segment from the target grid map according to each target straight line segment;

[0043] Calculating the intersection points of the target point cloud coordinates to obtain a plurality of straight line intersection points;

[0044] The image is drawn by all the target point cloud coordinates and all the straight line intersections to obtain the building mapping result.

[0045] It should be understood that the preset tool may be an airborne Lidar scanner or an airborne gimbal camera.

[0046] Specifically, an airborne Lidar scanner is used as a tool to directly scan and obtain the three-dimensional point cloud data of the building (i.e., the original three-dimensional point cloud coordinates); an airborne gimbal camera is used as a tool to obtain dense matching point cloud data (i.e., the original three-dimensional point cloud coordinates) after obtaining the visible light image of the building; and point cloud data (i.e., the original three-dimensional point cloud coordinates) is generated by resampling and reverse engineering on an existing three-dimensional grid model.

[0047] Specifically, there are currently three main operating modes for acquiring point cloud data of large-scale residential buildings. The first is to use visible light images to generate densely matched point clouds. This mode first uses an onboard RGB camera to obtain high-overlap photos of the survey area, then uses an aerial triangulation algorithm to obtain the coordinate pose (exterior orientation elements) of each photo. Finally, a dense matching algorithm is used to generate the point cloud data of the measured object. The second is to use an onboard lidar scanner to directly scan the survey area to obtain the point cloud data of the measured object. The third is to perform dense point resampling on the surface of an existing triangulated grid model to obtain the point cloud data of the measured object. All three modes can obtain the point cloud data of the measured object (i.e., the original 3D point cloud coordinates).

[0048] In the above embodiment, the facade wall point cloud coordinates are screened out from the original three-dimensional point cloud coordinates, a target grid map is constructed based on the facade wall point cloud coordinates, a target straight line segment is constructed through the target grid map, the target point cloud coordinates are obtained from the target grid map based on the target straight line segment, the intersection of the target point cloud coordinates is calculated to obtain the straight line intersection, and the building mapping result is obtained by drawing the image of the target point cloud coordinates and the straight line intersection. This not only reduces a certain amount of computing power requirements and speeds up the demodulation speed, but also greatly reduces the workload of manual drawing. Only simple manual verification and trimming are required to directly output the drawing.

[0049] Optionally, as an embodiment of the present invention, Figures 1 to 3 As shown, the process of selecting multiple facade wall point cloud coordinates from all the original three-dimensional point cloud coordinates includes:

[0050] Filtering out the minimum Z coordinate from all the original three-dimensional point cloud coordinates, and obtaining the minimum Z coordinate after filtering;

[0051] updating the Z coordinates of each of the original three-dimensional point cloud coordinates according to the minimum Z coordinate to obtain updated point cloud coordinates corresponding to each of the original three-dimensional point cloud coordinates;

[0052] Constructing a search area corresponding to each updated point cloud coordinate with each updated point cloud coordinate as a circle center and a preset search value as a radius;

[0053] Searching all the updated point cloud coordinates according to each of the search areas, and counting the number of updated point cloud coordinates searched in each of the search areas, to obtain the total number of adjacent points corresponding to each of the updated point cloud coordinates;

[0054] Verify whether the total number of adjacent points is greater than or equal to the preset adjacent point verification value. If the verification is successful, use the updated point cloud coordinates corresponding to the total number of adjacent points as the facade wall point cloud coordinates, thereby obtaining multiple facade wall point cloud coordinates.

[0055] It should be understood that the three-dimensional point cloud model of the residential building (i.e., the original three-dimensional point cloud coordinates) is projected and flattened to be converted into a two-dimensional plane point cloud model (i.e., the updated point cloud coordinates). The plane wall point cloud is screened out and the facade wall point cloud (i.e., the facade wall point cloud coordinates) is extracted by utilizing the principle of different densities of the flat facade wall point cloud after flattening.

[0056] It should be understood that the preset search value may be an input fixed value.

[0057] It should be understood that the minimum elevation value (ie, the minimum Z coordinate) of the current building point cloud is retrieved, and the elevation values ​​of all points in the current building point cloud are flattened to the minimum elevation value (ie, the minimum Z coordinate).

[0058] It should be understood that the number of adjacent points within a certain radius R of the i-th point (i.e., the total number of adjacent points) is retrieved one by one, a suitable threshold is set for the number of adjacent points (i.e., the preset adjacent point verification value), and the threshold is used to separate the facade wall point cloud (i.e., the facade wall point cloud coordinates).

[0059] Specifically, if Figure 2 and 3 As shown by Figure 3 Easy formula:

[0060]

[0061] It is easy to deduce from the above formula:

[0062] Among them, S vtc Refers to unit area, ρ vtc Point cloud surface density, ρ v Point cloud density, n vtc Refers to the number of points per unit area, H wall Refers to the height of the wall.

[0063] It is not difficult to see from the formula that the surface density of the point cloud after projection onto the plane is mainly related to the physical quantities of the original three-dimensional point cloud, such as the wall height and volume density. Therefore, according to the principle of controlled variables, it is necessary to maintain or unify the volume density uniformity of the entire building point cloud before using the surface density to extract the wall point cloud. For example, the point cloud scanned by the airborne radar needs to be resampled in the data preprocessing stage to unify the point cloud density. Figure 3 As shown, the present invention can basically completely and accurately separate the main facade wall point cloud of the residential building.

[0064] In the above embodiment, multiple facade wall point cloud coordinates are screened out from all original three-dimensional point cloud coordinates, which can filter out the plane wall point cloud and extract the facade wall point cloud. At the same time, the main facade wall point cloud of the residential building can also be completely and accurately separated.

[0065] Optionally, as an embodiment of the present invention, the process of constructing a target grid map based on all the vertical wall point cloud coordinates includes:

[0066] Filter out the maximum value of the X axis, the maximum value of the Y axis, the minimum value of the X axis, and the minimum value of the Y axis from all the point cloud coordinates of the facade wall, and obtain the maximum value of the X axis of the point cloud coordinate, the maximum value of the Y axis of the point cloud coordinate, the minimum value of the X axis of the point cloud coordinate, and the minimum value of the Y axis of the point cloud coordinate;

[0067] The total number of grids in the X-axis direction of the maximum value of the point cloud coordinate and the minimum value of the point cloud coordinate in the X-axis is calculated by the first formula to obtain the total number of grids in the X-axis direction. The first formula is:

[0068]

[0069] Among them, numX is the total number of grids in the X-axis direction, Xmax is the maximum value of the point cloud coordinate in the X-axis, Xmin is the minimum value of the point cloud coordinate in the X-axis, ΔL is the preset grid side length, and fix() is a rounding function towards 0;

[0070] The total number of grids in the Y-axis direction of the maximum value of the Y-axis of the point cloud coordinate and the minimum value of the Y-axis of the point cloud coordinate is calculated by the second formula, and the total number of grids in the Y-axis direction is obtained. The second formula is:

[0071]

[0072] Among them, numY is the total number of grids in the Y-axis direction, Ymax is the maximum value of the point cloud coordinate in the Y-axis, Ymin is the minimum value of the point cloud coordinate in the Y-axis, ΔL is the preset grid side length, and fix() is a rounding function towards 0;

[0073] The original grid map is constructed by the total number of grids in the X-axis direction and the total number of grids in the Y-axis direction;

[0074] Initializing the original grid image to obtain an initialized grid image, wherein the initialized grid image includes a plurality of initial grid coordinates;

[0075] The X-axis coordinates of the point grid corresponding to the point cloud coordinates of each facade wall are obtained by calculating the X-axis minimum value of the point cloud coordinates and the X-axis coordinates of the point grid of each facade wall point cloud coordinate using the third formula. The third formula is:

[0076]

[0077] Among them, X_1(i) is the grid X-axis coordinate corresponding to the i-th facade wall point cloud coordinate, X i is the X-axis coordinate of the point cloud of the i-th facade wall, Xmin is the minimum value of the X-axis of the point cloud coordinate, ΔL is the preset grid side length, and fix() is the rounding function towards 0;

[0078] The Y-axis coordinates of the point grid corresponding to the Y-axis point cloud coordinates of each of the facade wall points are calculated by the fourth formula, and the Y-axis coordinates of the point grid corresponding to each of the facade wall points are obtained. The fourth formula is:

[0079]

[0080] Among them, Y_1(i) is the grid Y-axis coordinate corresponding to the i-th facade wall point cloud coordinate, Y i is the Y-axis of the point cloud coordinate of the i-th facade wall, Ymin is the minimum value of the Y-axis of the point cloud coordinate, ΔL is the preset grid side length, and fix() is the rounding function towards 0;

[0081] Obtaining the point grid coordinates corresponding to the point cloud coordinates of each of the facade walls according to the point grid X-axis coordinates corresponding to the point cloud coordinates of each of the facade walls and the point grid Y-axis coordinates corresponding to the point cloud coordinates of each of the facade walls;

[0082] Determining whether each of the point grid coordinates is equal to any of the multiple initial grid coordinates, and if so, assigning a first set value to the binary grid data corresponding to each of the point grid coordinates; if not, assigning a second set value to the binary grid data corresponding to each of the point grid coordinates, and the first set value is not equal to the second set value;

[0083] A target grid map is constructed based on all the binary grid data.

[0084] Preferably, the first setting value may be 1, and the second setting value may be 0.

[0085] It should be understood that the facade wall point cloud data (ie, the facade wall point cloud coordinates) is converted into the binary raster data.

[0086] Specifically, to facilitate the use of the Hough transform method to detect straight line segments in the point cloud that meet certain conditions, we first need to binarize the separated facade wall point cloud data and convert it into binary raster data. The specific method is to detect the facade wall point cloud coordinate boundaries Xmin (i.e., the minimum X-axis value of the point cloud coordinate), Xmax (i.e., the maximum X-axis value of the point cloud coordinate), Ymin (i.e., the minimum Y-axis value of the point cloud coordinate), and Ymax (i.e., the maximum Y-axis value of the point cloud coordinate). Each small cell in the grid corresponding to the point cloud is predefined as a square with a side length equal to ΔL. The total number of vertical and horizontal grid cells in the grid, numX (i.e., the total number of grid cells in the X-axis direction) and numY (i.e., the total number of grid cells in the Y-axis direction), is calculated. Grid cells containing point clouds are assigned a value of 1, and grid cells without point clouds are assigned a value of 0. This results in the binary raster data corresponding to "grid coordinates (num_x, num_y) and point cloud coordinates (x, y)".

[0087] Specifically, the conversion formula between point cloud and (0,1) binary grid is as follows:

[0088]

[0089]

[0090] Img=zeros(numX, numY)

[0091]

[0092]

[0093] Img(X_1, Y_1)=1

[0094] Among them, numX refers to the number of grids in the grid image in the X direction (that is, the total number of grids in the X-axis direction), numY refers to the number of grids in the grid image in the Y direction (that is, the total number of grids in the Y-axis direction), Xmax refers to the maximum value of the point cloud x coordinate (that is, the maximum value of the point cloud coordinate X axis), Xmin refers to the minimum value of the point cloud x coordinate (that is, the minimum value of the point cloud coordinate X axis), Ymax refers to the maximum value of the point cloud y coordinate (that is, the maximum value of the point cloud coordinate Y axis), Ymin refers to the minimum value of the point cloud y coordinate (that is, the minimum value of the point cloud coordinate Y axis), fix is ​​a rounding function, ΔL refers to the preset grid side length, Img refers to the initialized grid variable, zeros is a grid matrix initialization function (which can generate a zero matrix with a specified number of rows and columns), X_1(i) refers to the coordinate X of the i-th point in the point cloud i The corresponding grid row coordinates (i.e., the grid X-axis coordinates) of the grid value 1, Y_1(i) refers to the Y coordinate of the i-th point in the point cloud iThe grid column coordinate (ie, the grid Y-axis coordinate) of the corresponding grid value is 1, and Img(X_1, Y_1) refers to the grid value corresponding to the X_1th row and Y_1th column.

[0095] In the above embodiment, a target grid map is constructed based on the point cloud coordinates of all facade walls, laying the foundation for subsequent data processing. This not only reduces certain computing power requirements and speeds up the demolding speed, but also greatly reduces the workload of manual drawing. Only simple manual verification and trimming are required to directly output the map.

[0096] Optionally, as an embodiment of the present invention, Figure 1 、 4 As shown in , 5, 6, 7 and 8, the process of constructing multiple target straight line segments through the target grid map includes:

[0097] Performing curve transformation on the target grid image using a Hough transform algorithm to obtain a plurality of grid image curves;

[0098] Calculate the intersection points of the grid graph curves using MATLAB to obtain the curve intersection points corresponding to the grid graph curves.

[0099] If the curve intersection is located on at least two of all the grid graph curves, then counting the total number of binary grid data on the at least two grid graph curves, thereby obtaining the total number of binary grid data corresponding to each of the curve intersections;

[0100] If the total amount of the binary raster data is greater than a preset raster data verification value, constructing a straight line through the binary raster data on at least two raster graph curves, thereby obtaining initial straight line segments corresponding to the intersection points of each of the curves;

[0101] Noise reduction processing is performed on each of the initial straight line segments to obtain multiple target straight line segments.

[0102] It should be understood that the binary raster data is subjected to Hough transform processing to obtain the curve corresponding to the point in the point cloud space in the parameter space (i.e., the raster graph curve), and the peak value of the intersection of the parameter space curve (i.e., the curve intersection) is searched under a certain threshold to obtain the corresponding straight line segment in the point cloud space (i.e., the initial straight line segment), and the short noise line segment in the straight line segment (i.e., the initial straight line segment) is denoised to obtain a regular and accurate straight line segment (i.e., the target straight line segment).

[0103] Specifically, the Hough transform (i.e., the Hough transform algorithm) is an effective detection method for line estimation proposed by Paul Hough in 1962. Its principle is to use the point-line duality between the image space and the Hough parameter space to transform the line detection problem in the image space into the intersection voting problem in the parameter space. Figure 4 As shown in , countless straight lines can be drawn through a point in the image space. For each straight line, there is a perpendicular line passing through the origin. By mapping the length ρ of each perpendicular line and its angle θ with the horizontal axis of the coordinate system to the (θ, ρ) parameter space, a curve can be obtained. Figure 5 As shown, it is not difficult to understand that when there are multiple points on the same straight line in the image space, the mapping to the parameter space is multiple curves intersecting at one point; similarly, the intersection point in this parameter space also corresponds to the same straight line where multiple points in the image space coexist. This is the point-line duality principle of the Hough transform.

[0104] However, the results of such an algorithm are subject to large noise, and the noise in the parameter space is as follows: Figure 6 As shown in the figure, “it appears as multiple groups of intersection points gathered in multiple places”, and the noise in the image space is as follows Figure 7 The image shows "many short, messy line segments surrounding the correct straight line segment"; therefore, noise reduction is required before accurate straight line fitting. Experimental results show that the accuracy of the line segment detected by the hough transform is proportional to its length. The longer the line segment, the higher the accuracy. The accuracy of the straight line segments voted by the main facade wall point cloud basically meets the requirements, while other noise line segments have a large angle difference with them. Therefore, an angle difference threshold Δ is introduced here. θ By controlling the threshold, most noise segments can be filtered out. The remaining noise has little impact on the subsequent accurate straight line fitting and can be ignored. The noise reduction principle here is described in mathematical language as follows:

[0105]

[0106] Figure 8 For noise reduction effect.

[0107] In the above embodiment, by constructing multiple target straight line segments through the target grid map, most of the noise segments can be filtered out, thereby improving the accuracy of image drawing. This not only reduces certain computing power requirements and speeds up the demodulation speed, but also greatly reduces the workload of manual drawing.

[0108] Optionally, as an embodiment of the present invention, the process of acquiring a plurality of target point cloud coordinates corresponding to each target straight line segment from the target grid image according to each target straight line segment includes:

[0109] Calculating the distance between each binary raster data and each target straight line segment to obtain a target distance corresponding to each binary raster data;

[0110] If the target distance is less than the preset verification distance, the facade wall point cloud coordinates corresponding to the target distance are used as target point cloud coordinates, thereby obtaining a plurality of target point cloud coordinates corresponding to each of the target straight line segments.

[0111] It should be understood that the distance between each binary grid data and each target straight line segment is calculated using a point-to-straight line distance formula, which is as follows:

[0112]

[0113] Where d is the distance from the point to the line, a, b, and c are the coefficients of the line equation, and x0 and y0 are the coordinates of the point on the line.

[0114] It should be understood that the line detection method using Hough transform is used to detect a set of points near the same straight line segment (ie, a plurality of target point cloud coordinates).

[0115] Specifically, taking the line segment (i.e., the target straight line segment) as the unit, the grid set near the denoised straight line segment (i.e., the target straight line segment) is retrieved, the original point cloud data corresponding to the grid set (i.e., the facade wall point cloud coordinates) is extracted, and the point set data near the same straight line segment (i.e., multiple target point cloud coordinates) is obtained.

[0116] It should be understood that after noise reduction, the point set around the correct straight line segment (ie, the plurality of target point cloud coordinates) can be reversely obtained through the conversion relationship between point cloud data and raster data.

[0117] In the above embodiment, multiple target point cloud coordinates corresponding to each target straight line segment are obtained from the target grid map according to each target straight line segment, which not only reduces a certain amount of computing power requirements and speeds up the demolding speed, but also greatly reduces the workload of manual drawing.

[0118] Optionally, as an embodiment of the present invention, Figure 1 and 8 As shown, the process of calculating the intersection points of the target point cloud coordinates to obtain the intersection points of the multiple straight lines includes:

[0119] Using a least squares algorithm to perform fitting processing on the coordinates of the multiple target point clouds corresponding to each of the target straight line segments, respectively, to obtain fitted straight lines corresponding to each of the target straight line segments;

[0120] The intersection points of the fitted straight lines are calculated respectively to obtain a plurality of straight line intersection points.

[0121] It should be understood that the straight line (i.e., the fitted straight line) is accurately fitted by a set of points near the same straight line segment (i.e., multiple target point cloud coordinates), and the intersection points of multiple different straight lines detected and fitted by the same residential building point cloud (i.e., the straight line intersection points) are calculated.

[0122] It should be understood that the least squares method is used to fit the i-th point set P i (i.e., multiple target point cloud coordinates) corresponding to the i-th straight line L i (i.e. the fitted straight line).

[0123] It should be understood that the intersection points of each pair of fitted straight lines are calculated using the method of finding intersection points using simultaneous equations to obtain multiple straight line intersection points.

[0124] It should be understood that the intersection points of the two intersecting straight lines can be obtained by using a method of finding intersection points using simultaneous equations.

[0125] It should be understood that the formula for finding the intersection point of the simultaneous straight line equations is as follows:

[0126]

[0127] Among them, a1, b1, c1 are the coefficient parameters of line equation 1, a2, b2, c2 are the coefficient parameters of line equation 2, and (x, y) is the coordinate value of the intersection point of the lines to be determined.

[0128] Specifically, although the hough transform can robustly detect straight lines in binary raster images, its detection method is based on the voting statistics principle of the number of points passing through the straight line, and its detection results are easily affected by the uniformity of the point cloud density. Therefore, its detection results are only the approximate range of the existence of the straight line in space. On the contrary, the least squares straight line fitting method can better overcome the influence of uneven point cloud density. In the fitting process, all points will be involved in the calculation to jointly find the optimal solution for straight line fitting; but its disadvantage is that it is not robust and is greatly affected by noise. When there are multiple straight lines in space at the same time, this method is difficult to use. Therefore, the two methods are combined here to learn from each other's strengths and weaknesses. First, the hough transform is used to determine the approximate point set near each straight line in space, and then the least squares are used to fit the optimal solution for each straight line one by one. There are currently two main algorithms for least squares straight line fitting:

[0129] One method is to find the optimal solution for straight line fitting by minimizing the error in the y value. This method is more commonly used and is also provided in textbooks. However, it assumes that the coefficient of y is not 0, that is, the straight line cannot be perpendicular to the x-axis. When the point set is distributed near the perpendicular line of the x-axis, the fitting result will be unstable. The other method is to find the optimal solution for straight line fitting by minimizing the error in the distance from the point to the straight line. This method better solves the above problem, making the fitting result no longer affected by the slope of the line. At the same time, this method has stronger physical significance and is more suitable for solving practical problems in the physical model of the present invention. Therefore, the present invention adopts the second method to find the optimal solution for straight line fitting.

[0130] The specific steps are as follows: Solve the average value of the data points and That is the following formula:

[0131]

[0132]

[0133] Where n represents the number of data points, x i and y i Represent the x and y coordinates of the i-th data point respectively.

[0134] Solve the covariance matrix S between the data points and the mean xy and S xx , which is the following formula:

[0135]

[0136]

[0137] Solve the slope a and intercept b of the straight line, which is the following formula:

[0138]

[0139]

[0140] According to the solved slope a and intercept b, the equation of the best fitting line can be obtained, which is the following formula:

[0141] y=ax+b

[0142] Among them, a represents the slope, b represents the intercept, and then the intersection point of the two straight lines can be found by using the method of solving the intersection point of simultaneous equations. Figure 11 In the figure, we can see the solution effect.

[0143] In the above embodiment, the intersection points of multiple target point cloud coordinates are calculated to obtain multiple straight line intersection points, which can accurately detect straight lines in binary raster images, better overcome the influence of uneven point cloud density, improve robustness, and be less affected by noise. It can also be applied to situations where multiple straight lines exist simultaneously in space.

[0144] Optionally, as an embodiment of the present invention, Figure 1 and 10 As shown, the process of performing image drawing by all the target point cloud coordinates and all the straight line intersections to obtain the building mapping result includes:

[0145] S61: Gathering all the target point cloud coordinates to construct a point cloud coordinate data set, and filtering out the minimum value of the X axis from all the straight line intersections to obtain at least one minimum value of the X axis of the straight line intersection;

[0146] S62: Filtering out the maximum value on the Y axis from the minimum value on the X axis of at least one of the straight line intersections to obtain filtered intersections, numbering the filtered intersections to obtain a first number, and using the filtered intersections as starting intersections;

[0147] S63: taking the starting intersection as the starting point and the positive direction of the X axis as the starting direction as the initial trajectory;

[0148] S64: rotating the initial trajectory clockwise according to a preset angle to obtain a rotated direction;

[0149] S65: Determine whether there is at least one straight line intersection in the rotated direction. If so, execute S66; if not, use the starting intersection as the starting point and the rotated direction as the starting direction as the initial trajectory, and return to S64;

[0150] S66: Calculate the distance between each of the straight line intersections and the starting intersection point to obtain a first intersection distance corresponding to each of the straight line intersections;

[0151] S67: Filter out the minimum value among all the first intersection distances, and use the straight line intersection corresponding to the minimum value among all the first intersection distances as the intersection to be determined;

[0152] S68: Determine whether the intersection point to be determined is the filtered intersection point. If so, execute S69; if not, execute S614;

[0153] S69: Connecting all the filtered intersection points and the starting intersection point corresponding to the first numbers in order to obtain an original drawn image;

[0154] S610: Determine whether the target point cloud coordinates exist in the point cloud coordinate data set. If yes, execute S611; if not, execute S612;

[0155] S611: Obtain a target drawing image according to all the original drawing images, and execute S618;

[0156] S612: Randomly select any target point cloud coordinate from the point cloud coordinate data set, and calculate the distance between each straight line intersection point and the selected target point cloud coordinate to obtain a second intersection point distance corresponding to each straight line intersection point;

[0157] S613: Filter out the minimum value among all the second intersection distances, and use the straight line intersection corresponding to the minimum value among all the second intersection distances as the starting point and the direction from the minimum value among all the second intersection distances to the selected target point cloud coordinates as the starting direction as the initial trajectory, and return to S64;

[0158] S614: Determine whether there is at least one target point cloud coordinate between the starting intersection point and the to-be-determined intersection point. If so, execute S615; if not, use the starting intersection point as the starting point and the rotated direction as the starting direction as the initial trajectory, and return to S64.

[0159] S615: Add the first number to a preset number to obtain a second number, and use the second number as the updated first number;

[0160] S616: deleting all target point cloud coordinates between the starting intersection point and the to-be-determined intersection point in the point cloud coordinate dataset, and obtaining an updated point cloud coordinate dataset after the deletion;

[0161] S617: Taking the intersection point to be determined as the starting point and the direction after rotation as the starting direction as the initial trajectory, and returning to S64;

[0162] S618: Using the target drawn image as a building surveying and mapping result.

[0163] It should be understood that, by using the vertex faceting method, each intersection point (ie, the straight line intersection points) is topologically connected to form a closed polygon (ie, the building surveying result).

[0164] Specifically, if Figure 10 As shown, a. Detect the upper left intersection JD of the free point set in the coordinate space i (ie the intersection point after screening); b. JD i (i.e. the intersection after screening) as the starting point, with the positive direction of the X axis as the starting direction; c. Rotate 90° each time, and detect the next intersection JD in clockwise direction i+1 (ie the intersection of the straight lines); d. Determine the intersection JDi (i.e. the intersection point after screening) and JD i+1 (i.e. the intersection of the straight lines) whether there is a point cloud (i.e. the point cloud coordinate data set) on the line, if yes, then JD i+1 (i.e. the intersection of the straight lines) is added to the point sequence set of this cycle. If there is no intersection, continue to rotate and search for the next intersection; e. Repeat steps cd until returning to the starting point; f. Check whether there are any point clouds (i.e. the point cloud coordinate data set) that the straight line has not passed through. If there are, select the point cloud (i.e. the point cloud coordinate data set) with the nearest intersection as the starting point JD i (i.e. the intersection point after screening), with the intersection point JD i The direction (i.e. the filtered intersection point) that does not pass through any point in the point cloud is the starting direction, and step cdef is repeated; if there is no such direction, the program ends and the drawing is completed.

[0165] In the above embodiment, the building surveying and mapping results are obtained by image drawing through all target point cloud coordinates and all straight line intersections. This not only reduces a certain amount of computing power requirements and speeds up the demodulation speed, but also greatly reduces the workload of manual drawing. Only simple manual verification and trimming are required to directly output the drawing.

[0166] Optionally, as another embodiment of the present invention, the present invention uses an airborne Lidar scanner or an airborne pan-tilt camera as a tool to obtain three-dimensional point cloud data of residential buildings. After individualizing the residential buildings, the point cloud data of the individual residential buildings are flattened and projected into the same horizontal plane. According to the different point cloud densities, the plane wall point clouds are screened out and the facade wall point clouds are extracted. The facade wall point cloud data is converted into binary raster data. The line detection method of Hough transform is used to detect the point set near the same straight line segment, and then the line is accurately fitted from the point set to obtain the intersection points of different straight lines (i.e., the vertices of the residential vector map), and then the vertex construction method is used to connect the vertices into closed polygons. The computer can automatically complete the vectorization of the residential buildings.

[0167] Optionally, as another embodiment of the present invention, the present invention only needs to go through 5 main steps of "① airborne Lidar scanning → ② point cloud demolding → ③ automatic drawing → ④ manual verification → ⑤ drawing output", which not only reduces a certain amount of computing power requirements and speeds up the demolding speed, but also greatly reduces the workload of manual drawing. After the computer automatically draws, only simple manual verification and trimming are required to directly output the drawing, realizing the computer's automatic recognition and drawing operations.

[0168] Optionally, as another embodiment of the present invention, the present invention converts the three-dimensional residential building point cloud into a two-dimensional point cloud by projection flattening, and then extracts the facade wall point cloud by using the principle of different densities; then uses the principle of Hough transform to detect and accurately fit the projection straight line of the facade wall in the two-dimensional space, and finally calculates the intersection points of each straight line and uses the vertex construction method to complete the drawing of the closed polygon.

[0169] Figure 11 This is a module block diagram of a building surveying and mapping device provided by an embodiment of the present invention.

[0170] Alternatively, as another embodiment of the present invention, Figure 11 As shown, a building surveying and mapping device includes:

[0171] A screening module, configured to obtain a plurality of original three-dimensional point cloud coordinates from a preset tool, and screen out a plurality of facade wall point cloud coordinates from all the original three-dimensional point cloud coordinates;

[0172] A grid map construction module, configured to construct a target grid map based on all the vertical wall point cloud coordinates;

[0173] A straight line segment construction module, configured to construct a plurality of target straight line segments using the target grid image;

[0174] a point cloud coordinate acquisition module, configured to acquire a plurality of target point cloud coordinates corresponding to each target straight line segment from the target grid image according to each target straight line segment;

[0175] A calculation module, configured to calculate the intersection points of the target point cloud coordinates to obtain a plurality of straight line intersection points;

[0176] The surveying and mapping result obtaining module is used to perform image drawing through all the target point cloud coordinates and all the straight line intersections to obtain the building surveying and mapping results.

[0177] Alternatively, another embodiment of the present invention provides a building surveying and mapping system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the building surveying and mapping method described above is implemented. The system may be a computer or other system.

[0178] Optionally, another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the building surveying and mapping method as described above is implemented.

[0179] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0180] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0181] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another system, or ignoring or not implementing certain features.

[0182] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present invention.

[0183] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0184] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0185] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A building surveying and mapping method, characterized in that: The steps include: Acquire multiple original three-dimensional point cloud coordinates from a preset tool, and filter out multiple facade wall point cloud coordinates from all the original three-dimensional point cloud coordinates; Construct a target grid map based on all the vertical wall point cloud coordinates; constructing a plurality of target straight line segments through the target grid map; Acquire a plurality of target point cloud coordinates corresponding to each target straight line segment from the target grid map according to each target straight line segment; Calculating the intersection points of the target point cloud coordinates to obtain a plurality of straight line intersection points; Perform image drawing using all the target point cloud coordinates and all the straight line intersections to obtain a building mapping result; The process of calculating the intersection points of the target point cloud coordinates to obtain the intersection points of the multiple straight lines includes: Using a least squares algorithm to perform fitting processing on the coordinates of the multiple target point clouds corresponding to each of the target straight line segments, respectively, to obtain fitted straight lines corresponding to each of the target straight line segments; The intersection points of the fitted straight lines are calculated respectively to obtain a plurality of straight line intersection points.

2. The building surveying and mapping method according to claim 1, wherein: The process of selecting a plurality of facade wall point cloud coordinates from all the original three-dimensional point cloud coordinates includes: Filtering out the minimum Z coordinate from all the original three-dimensional point cloud coordinates, and obtaining the minimum Z coordinate after filtering; updating the Z coordinates of each of the original three-dimensional point cloud coordinates according to the minimum Z coordinate to obtain updated point cloud coordinates corresponding to each of the original three-dimensional point cloud coordinates; Constructing a search area corresponding to each updated point cloud coordinate with each updated point cloud coordinate as a circle center and a preset search value as a radius; Searching all the updated point cloud coordinates according to each of the search areas, and counting the number of updated point cloud coordinates searched in each of the search areas, to obtain the total number of adjacent points corresponding to each of the updated point cloud coordinates; Verify whether the total number of adjacent points is greater than or equal to the preset adjacent point verification value. If the verification is successful, use the updated point cloud coordinates corresponding to the total number of adjacent points as the facade wall point cloud coordinates, thereby obtaining multiple facade wall point cloud coordinates.

3. The building surveying and mapping method according to claim 1, wherein: The process of constructing a target grid map based on all the vertical wall point cloud coordinates includes: Filter out the maximum value of the X axis, the maximum value of the Y axis, the minimum value of the X axis, and the minimum value of the Y axis from all the point cloud coordinates of the facade wall, and obtain the maximum value of the X axis of the point cloud coordinate, the maximum value of the Y axis of the point cloud coordinate, the minimum value of the X axis of the point cloud coordinate, and the minimum value of the Y axis of the point cloud coordinate; The total number of grids in the X-axis direction of the maximum value of the point cloud coordinate and the minimum value of the point cloud coordinate in the X-axis is calculated by the first formula to obtain the total number of grids in the X-axis direction. The first formula is: Among them, numX is the total number of grids in the X-axis direction, Xmax is the maximum value of the point cloud coordinate in the X-axis, Xmin is the minimum value of the point cloud coordinate in the X-axis, ΔL is the preset grid side length, and fix() is a rounding function towards 0; The total number of grids in the Y-axis direction of the maximum value of the Y-axis of the point cloud coordinate and the minimum value of the Y-axis of the point cloud coordinate is calculated by the second formula, and the total number of grids in the Y-axis direction is obtained. The second formula is: Among them, numY is the total number of grids in the Y-axis direction, Ymax is the maximum value of the point cloud coordinate in the Y-axis, Ymin is the minimum value of the point cloud coordinate in the Y-axis, ΔL is the preset grid side length, and fix() is a rounding function towards 0; The original grid map is constructed by the total number of grids in the X-axis direction and the total number of grids in the Y-axis direction; Initializing the original grid image to obtain an initialized grid image, wherein the initialized grid image includes a plurality of initial grid coordinates; The X-axis coordinates of the point grid corresponding to the point cloud coordinates of each facade wall are obtained by calculating the X-axis minimum value of the point cloud coordinates and the X-axis coordinates of the point grid of each facade wall point cloud coordinate using the third formula. The third formula is: Among them, X_1(i) is the grid X-axis coordinate corresponding to the i-th facade wall point cloud coordinate, X i is the X-axis of the point cloud coordinate of the i-th facade wall, Xmin is the minimum value of the X-axis of the point cloud coordinate, ΔL is the preset grid side length, and fix() is the rounding function towards 0; The Y-axis coordinates of the point grid corresponding to the Y-axis point cloud coordinates of each of the facade wall points are calculated by the fourth formula, and the Y-axis coordinates of the point grid corresponding to each of the facade wall points are obtained. The fourth formula is: Among them, Y_1(i) is the grid Y-axis coordinate corresponding to the i-th facade wall point cloud coordinate, Y i is the Y-axis of the point cloud coordinate of the i-th facade wall, Ymin is the minimum value of the Y-axis of the point cloud coordinate, ΔL is the preset grid side length, and fix() is the rounding function towards 0; Obtaining the point grid coordinates corresponding to the point cloud coordinates of each of the facade walls according to the point grid X-axis coordinates corresponding to the point cloud coordinates of each of the facade walls and the point grid Y-axis coordinates corresponding to the point cloud coordinates of each of the facade walls; Determining whether each of the point grid coordinates is equal to any of the multiple initial grid coordinates, and if so, assigning a first set value to the binary grid data corresponding to each of the point grid coordinates; if not, assigning a second set value to the binary grid data corresponding to each of the point grid coordinates, and the first set value is not equal to the second set value; A target grid map is constructed based on all the binary grid data.

4. The building surveying and mapping method according to claim 3, wherein: The process of constructing a plurality of target straight line segments using the target grid image includes: Performing curve transformation on the target grid image using a Hough transform algorithm to obtain a plurality of grid image curves; Calculate the intersection points of the grid graph curves using MATLAB to obtain the curve intersection points corresponding to the grid graph curves. If the curve intersection is located on at least two of all the grid graph curves, then counting the total number of binary grid data on the at least two grid graph curves, thereby obtaining the total number of binary grid data corresponding to each of the curve intersections; If the total amount of the binary raster data is greater than a preset raster data verification value, constructing a straight line using the binary raster data on at least two raster graph curves, thereby obtaining initial straight line segments corresponding to the intersection points of each of the curves; Noise reduction processing is performed on each of the initial straight line segments to obtain multiple target straight line segments.

5. The building surveying and mapping method according to claim 4, characterized in that: The process of acquiring a plurality of target point cloud coordinates corresponding to each target straight line segment from the target grid image according to each target straight line segment comprises: Calculating the distance between each binary raster data and each target straight line segment to obtain a target distance corresponding to each binary raster data; If the target distance is less than the preset verification distance, the facade wall point cloud coordinates corresponding to the target distance are used as target point cloud coordinates, thereby obtaining a plurality of target point cloud coordinates corresponding to each of the target straight line segments.

6. The building surveying and mapping method according to claim 1, wherein: The process of performing image drawing by using all the target point cloud coordinates and all the straight line intersections to obtain the building mapping result includes: S61: Gathering all the target point cloud coordinates to construct a point cloud coordinate data set, and filtering out the minimum value of the X axis from all the straight line intersections to obtain at least one minimum value of the X axis of the straight line intersection; S62: Filtering out the maximum value on the Y axis from the minimum value on the X axis of at least one of the straight line intersections to obtain filtered intersections, numbering the filtered intersections to obtain a first number, and using the filtered intersections as starting intersections; S63: taking the starting intersection as the starting point and the positive direction of the X axis as the starting direction as the initial trajectory; S64: rotating the initial trajectory clockwise according to a preset angle to obtain a rotated direction; S65: Determine whether there is at least one straight line intersection in the rotated direction. If so, execute S66; if not, use the starting intersection as the starting point and the rotated direction as the starting direction as the initial trajectory, and return to S64; S66: Calculate the distance between each of the straight line intersections and the starting intersection point to obtain a first intersection distance corresponding to each of the straight line intersections; S67: Filter out the minimum value among all the first intersection distances, and use the straight line intersection corresponding to the minimum value among all the first intersection distances as the intersection to be determined; S68: Determine whether the intersection point to be determined is the filtered intersection point. If so, execute S69; if not, execute S614; S69: Connecting all the filtered intersection points and the starting intersection point corresponding to the first numbers in order to obtain an original drawn image; S610: Determine whether the target point cloud coordinates exist in the point cloud coordinate data set. If yes, execute S611; if not, execute S612; S611: Obtain a target drawing image according to all the original drawing images, and execute S618; S612: Randomly select any target point cloud coordinate from the point cloud coordinate data set, and calculate the distance between each straight line intersection point and the selected target point cloud coordinate to obtain a second intersection point distance corresponding to each straight line intersection point; S613: Filter out the minimum value among all the second intersection distances, and use the straight line intersection corresponding to the minimum value among all the second intersection distances as the starting point and the direction from the minimum value among all the second intersection distances to the selected target point cloud coordinates as the starting direction as the initial trajectory, and return to S64; S614: Determine whether there is at least one target point cloud coordinate between the starting intersection point and the to-be-determined intersection point. If so, execute S615; if not, use the starting intersection point as the starting point and the rotated direction as the starting direction as the initial trajectory, and return to S64. S615: Add the first number to a preset number to obtain a second number, and use the second number as the updated first number; S616: deleting all target point cloud coordinates between the starting intersection point and the to-be-determined intersection point in the point cloud coordinate dataset, and obtaining an updated point cloud coordinate dataset after the deletion; S617: Taking the intersection point to be determined as the starting point and the direction after rotation as the starting direction as the initial trajectory, and returning to S64; S618: Using the target drawn image as a building surveying and mapping result.

7. A building surveying and mapping device, characterized in that: include: A screening module, configured to obtain a plurality of original three-dimensional point cloud coordinates from a preset tool, and screen out a plurality of facade wall point cloud coordinates from all the original three-dimensional point cloud coordinates; A grid map construction module, configured to construct a target grid map based on all the vertical wall point cloud coordinates; A straight line segment construction module, configured to construct a plurality of target straight line segments using the target grid image; a point cloud coordinate acquisition module, configured to acquire a plurality of target point cloud coordinates corresponding to each target straight line segment from the target grid image according to each target straight line segment; A calculation module, configured to calculate the intersection points of the target point cloud coordinates to obtain a plurality of straight line intersection points; A surveying and mapping result acquisition module is used to perform image drawing using all the target point cloud coordinates and all the straight line intersections to obtain a building surveying and mapping result; The calculation module is specifically used for: Using a least squares algorithm to perform fitting processing on the coordinates of the multiple target point clouds corresponding to each of the target straight line segments, respectively, to obtain fitted straight lines corresponding to each of the target straight line segments; The intersection points of the fitted straight lines are calculated respectively to obtain a plurality of straight line intersection points.

8. A building surveying and mapping system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the building surveying method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the building surveying method according to any one of claims 1 to 6 is implemented.

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