Method and system for building surveying and mapping

Abstract

The invention relates to the technical field of measurement, in particular to a method and system for building surveying and mapping, and the method comprises the steps: collecting the point cloud data of a target building at each preset observation station position; obtaining a normal consistency coefficient, a distance characteristic value and a distance significance coefficient of each point cloud data, and carrying out denoising processing on the collected point cloud data; presetting each overlapping region, and obtaining a building discrete coefficient of each overlapping region; obtaining each point cloud set of all point cloud data in each overlapping region, comprehensively analyzing the curvature of all point cloud data in each point cloud set of each overlapping region and the distribution of distance significance coefficients, and obtaining the distribution coefficient of each overlapping region in combination with the building discrete coefficient; and registering the point cloud data in combination with a point cloud registration algorithm to obtain complete point cloud data of the target building and obtain a building surveying and mapping model of the target building. According to the method, the accuracy of building surveying and mapping is improved by removing noise points and improving the registration precision.

Description

Technical Field

[0001] The present application relates to the field of measurement technology, and in particular to a method and system for architectural surveying and mapping. Background Art

[0002] Building surveying and mapping refers to the use of surveying and mapping instruments and technical means to accurately measure and record the shape, size, location and structure of buildings. 3D laser surveying and mapping technology plays an increasingly important role in building surveying and mapping with its high precision, high efficiency and non-contact measurement advantages.

[0003] The process of architectural surveying and mapping usually includes data collection, data preprocessing and model building. Among them, the data preprocessing link is crucial to the efficiency and accuracy of the surveying and mapping results. Due to the influence of the accuracy of the acquisition equipment, the interference of the external environment and the reflective characteristics of the building surface, there is noise in the collected point cloud data. In addition, the richness of details and complex structural characteristics of the building surface will also have a significant impact on the processing of point cloud data. However, the existing technology only uses conventional methods when processing point cloud data, and does not fully analyze the above-mentioned interference factors, which makes it difficult to obtain high-precision point cloud data, which is not conducive to subsequent model construction. Summary of the invention

[0004] In view of the above, it is necessary to provide a method and system for building surveying and mapping, which can improve the accuracy of building surveying and mapping by removing noise points and improving the registration accuracy compared with the traditional building surveying and mapping method: In a first aspect, an embodiment of the present application provides a method for building surveying and mapping, the method comprising the following steps: Collect point cloud data of the target building at each preset measuring station location; Obtain each neighboring point cloud data of each point cloud data collected at each of the preset measuring station positions, and perform preliminary denoising on each point cloud data by analyzing the distribution density of each point cloud data and all of its neighboring point cloud data; obtain the normal consistency coefficient of each point cloud data by analyzing the direction difference of the normal vector between each point cloud data and its neighboring point cloud data; obtain the distance characteristic value of each point cloud data by analyzing the spatial distribution of each point cloud data and all of its neighboring point cloud data, and obtain the distance significance coefficient of each point cloud data by combining the distance characteristic value; perform denoising on the point cloud data collected at each of the preset measuring station positions respectively by using the distance significance coefficient; Preset each overlapping area, and obtain the building discrete coefficient of each overlapping area by analyzing the curvature of each location on the surface of the target building corresponding to each overlapping area and the discrete degree of the distance significant coefficient of all point cloud data in each overlapping area; obtain each point cloud set of all point cloud data in each overlapping area by using a point cloud segmentation algorithm, and obtain the distribution coefficient of each overlapping area by comprehensively analyzing the distribution of the curvature and distance significant coefficient of all point cloud data in each point cloud set of each overlapping area, combined with the building discrete coefficient; through the distribution coefficient, in combination with a point cloud registration algorithm, register the point cloud data collected at all the preset measuring station positions to obtain the complete point cloud data of the target building; An architectural surveying and mapping model of the target building is obtained through the complete point cloud data.

[0005] In one embodiment, the process of obtaining the normal consistency coefficient is: Obtaining the fitting plane of all neighboring point cloud data of each point cloud data, and using the normal vector of the fitting plane as the normal vector of each point cloud data; The mean of the cosine similarity of the normal vectors between each point cloud data and all its neighboring point cloud data is taken as the normal consistency coefficient of each point cloud data.

[0006] In one embodiment, the process of obtaining the distance feature value is as follows: The distance between each point cloud data and the fitting plane is recorded as a first distance, and the distance between each neighboring point cloud data of each point cloud data and the fitting plane is recorded as a second distance; The ratio of the first distance to the second distance is calculated, and the sum of all the ratios corresponding to each point cloud data is used as the distance feature value of each point cloud data.

[0007] In one embodiment, the process of obtaining the distance significance coefficient is as follows: Mapping the normal consistency coefficient to a positive number; The distance significance coefficient is the ratio of the distance characteristic value to the positive number.

[0008] In one embodiment, the method of performing denoising processing on the point cloud data collected at each of the preset measuring station positions is: All point cloud data collected at any of the preset measuring station positions are arranged in descending order according to the distance significance coefficient; and point cloud data of a preset proportion are taken as noise points and removed from the point cloud data.

[0009] In one embodiment, the process of obtaining the building dispersion coefficient is: The degree of discreteness of the curvature of all point cloud data in each overlapping area is recorded as the first discreteness, the degree of discreteness of the distance significance coefficient of all point cloud data in each overlapping area is recorded as the second discreteness, and the sum of the first discreteness and the second discreteness is taken as the building discrete coefficient of each overlapping area.

[0010] In one embodiment, the process of obtaining the distribution coefficient is: The curvature and distance significance coefficient of each point cloud data in each overlapping area are used to form a two-dimensional feature data point of each point cloud data; By analyzing the dispersion degree of all two-dimensional feature data points of the point cloud data in each point cloud set, the dispersion coefficient of each point cloud set is obtained; The fusion result of the dispersion coefficient mean of all point cloud sets in each overlapping area and the building dispersion coefficient is used as the distribution coefficient of each overlapping area.

[0011] In one embodiment, the process of obtaining the dispersion coefficient is: The average value of the curvature of all the point cloud data in each point cloud set is recorded as the first average value, and the average value of the distance significance coefficient of all the point cloud data in each point cloud set is recorded as the second average value; The first average value and the second average value are used to form a two-dimensional central data point, and the dispersion of the distance between the two-dimensional feature data points of all point cloud data in each point cloud set and the two-dimensional central data point is used as the dispersion coefficient of each point cloud set.

[0012] In one of the embodiments, during the process of aligning the point cloud data collected at all the preset measuring station positions, the overlapping area under any two adjacent measuring station positions is recorded as the overlapping area to be analyzed, and the product of the normalized value of the distribution coefficient of the overlapping area to be analyzed and the preset value is used as the number of sampling points when the point cloud matching algorithm aligns the point cloud data collected at any two adjacent measuring station positions.

[0013] In a second aspect, an embodiment of the present application further provides a system for building surveying, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned methods for building surveying are implemented.

[0014] This application has at least the following beneficial effects: This application analyzes the characteristics of noise points generated during the acquisition of point cloud data due to the accuracy limitations of the three-dimensional laser scanner itself, the interference of the surrounding environment of the target building, and the influence of the surface material of the target building on the reflection characteristics of the laser scanning. According to the difference between the normal vector directions of the point cloud data and the distribution of the point cloud data in space, the distance significance coefficient is obtained to reflect the possibility that the point cloud data is a noise point. The application analyzes from multiple angles to improve the denoising accuracy of the point cloud data, which is conducive to improving the accuracy and reliability of feature point extraction in the subsequent point cloud registration process. Furthermore, when registering point cloud data, the influence of the number of sampling points of the point cloud registration algorithm on the registration efficiency and accuracy is taken into account. By analyzing the curvature of the target building surface corresponding to the overlapping area and the distribution of the distance significance coefficient of the point cloud data in the overlapping area, the detail richness and structural complexity of the target building surface corresponding to the overlapping area are evaluated, and then the appropriate number of sampling points is selected according to the actual situation, which can improve the registration accuracy while ensuring the registration efficiency; the point cloud data that has been denoised and registered is modeled to obtain a building surveying and mapping model, which improves the accuracy of building surveying and mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of the steps of a method for building surveying and mapping provided in one embodiment of the present application; Figure 2 Schematic diagram of the process of obtaining the distribution coefficient; Figure 3 Schematic diagram of the process of obtaining the number of sampling points. DETAILED DESCRIPTION

[0017] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. It should be understood that, unless otherwise specified, " / " means or.

[0019] It should also be noted that the terms "first" and "second" in the present application are used to distinguish similar objects rather than to describe a specific order or sequence.

[0020] The specific scheme of the method and system for building surveying and mapping provided by the present application is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flowchart of a method for building surveying and mapping provided by an embodiment of the present application, the method comprising the following steps: Step 1: Collect point cloud data of the target building at each preset measuring station location.

[0022] In this embodiment, a teaching building is used as the target building for architectural mapping. The technicians use a total station and a digital camera to conduct on-site surveys to understand the relevant information of the target building and its surrounding areas, so as to understand the shape of the target building and possible shielding conditions around it. The number of measuring stations required in the 3D scanning process and the specific locations of the measuring stations are further determined.

[0023] In this embodiment, the number of measuring stations is 15, and the number and specific locations of the measuring stations need to be determined according to actual conditions.

[0024] The 3D laser scanner is set up at the measuring station. First, the scanning mode is set to panoramic scanning with a corresponding resolution of 12 mm. The purpose is to determine the range and orientation of the target building. Then the resolution is adjusted to 2 mm for fine scanning to obtain the point cloud data of the target building.

[0025] In this embodiment, a Riegl VZ-1000 3D laser scanner is used to collect point cloud data. The Riegl VZ-1000 3D laser scanner has a transmission frequency of up to 300,000 points per second, a maximum scanning range of up to 1,400 meters, and supports both vertical scanning and horizontal scanning working modes, which helps to obtain more accurate point cloud data.

[0026] After a round of scanning, the 3D laser scanner will perform a quality check on the collected point cloud data. If the point cloud data in a local area is missing or obviously abnormal, additional measurements will be made in the corresponding area to improve the integrity and availability of the point cloud data. Scanning will continue at the next station until data is collected at all stations. Point cloud data is collected for part of the building at each station. The building areas collected at adjacent stations need to overlap in order to facilitate the subsequent registration of the point cloud data.

[0027] In this embodiment, the size of the overlapping area between two adjacent measuring station positions is set to: 30% of the minimum value of the scanning area area between the two adjacent measuring station positions, where 30% is only an embodiment of the present application, and the implementer can set the specific value by himself.

[0028] Step 2, obtaining each neighboring point cloud data of each point cloud data collected at each of the preset measuring station positions, and performing preliminary denoising on each point cloud data by analyzing the distribution density of each point cloud data and all its neighboring point cloud data; obtaining the normal consistency coefficient of each point cloud data by analyzing the direction difference of the normal vector between each point cloud data and its neighboring point cloud data; obtaining the distance characteristic value of each point cloud data by analyzing the spatial distribution of each point cloud data and all its neighboring point cloud data, and obtaining the distance significance coefficient of each point cloud data by combining the distance characteristic value; and performing denoising on the point cloud data collected at each of the preset measuring station positions by using the distance significance coefficient.

[0029] After the point cloud data is collected at all measuring station locations, a series of processing steps are required to improve the integrity and accuracy of the point cloud data and ensure the integrity and reliability of the subsequently constructed model.

[0030] In the process of collecting point cloud data, due to the accuracy limitation of the 3D laser scanner itself, the interference of the surrounding environment of the target building, and the influence of the surface material of the target building on the reflection characteristics of the laser scanning, noise is inevitably present in the collected point cloud data, which affects the accuracy of the surveying and mapping results. Therefore, it is necessary to denoise the collected point cloud data. Take the point cloud data collected at the f-th measuring station as an example.

[0031] Point cloud noise caused by insufficient precision of the 3D laser scanner itself or interference from water vapor and floating objects in the air appears as sparsely distributed, scattered isolated points, which are obviously far away from the main body of the target building in space, and the surrounding point cloud density is low. Therefore, this embodiment uses a denoising algorithm based on spatial distribution (Statistical Outlier Removal, SOR) to perform preliminary denoising on the point cloud data, where the SOR algorithm is a well-known technology and will not be described in detail in this application.

[0032] In addition, the point cloud noise generated by the influence of the surface material of the target building on the reflection characteristics of the laser scanning usually forms a relatively dense and randomly distributed noise cluster near the surface of the target building. Depending on the degree of diffuse reflection interference, the noise cluster may overlap with the point cloud data of the target building to a certain extent. However, the distribution of such noise points is more random and irregular than that of the target building point cloud data. Since the distribution of the target building point cloud data is more regular, the direction of the normal vector of the target building point cloud data is also more uniform, while the direction of the normal vector of the noise point is random. Therefore, there is a large difference between the normal vector of the noise point and the normal vector of the target building point cloud data, and there is a certain distance between the noise point and the surface of the target building.

[0033] Based on the above analysis, the K-nearest neighbor algorithm is used to obtain the neighboring point cloud data of any point cloud data, obtain the fitting plane of all the neighboring point cloud data of the any point cloud data, use the normal vector of the fitting plane as the normal vector of the any point cloud data, and obtain the normal vector of each neighboring point cloud data of the any point cloud data in the same way as the normal vector of the any point cloud data. The cosine similarity of the normal vector between the any point cloud data and its neighboring point cloud data is calculated respectively, and the average of all the cosine similarities obtained is used as the normal consistency coefficient of the any point cloud data. The smaller the value of the normal consistency coefficient, the greater the directional difference between the normal vectors of the any point cloud data and the remaining neighboring point cloud data. Among them, the K-nearest neighbor algorithm and the calculation of the normal vector of the fitting plane are both well-known technologies, and will not be repeated in this application.

[0034] In this embodiment, when the number of neighboring point cloud data is too small, the estimation of the normal vector is easily affected by noise, resulting in inaccurate normal vector estimation. When the number of neighboring point cloud data is too large, the neighboring point cloud data of any point cloud data may span different geometric feature areas, resulting in the fitting plane being affected by multiple different geometric features, thereby causing inaccurate normal vector estimation. Therefore, the number of neighboring point cloud data is 20. On the basis of satisfying the value range of the number of neighboring point cloud data being [20,30], the implementer can set the value of the number of neighboring point cloud data by themselves.

[0035] In this embodiment, the least square method is used to obtain the fitting plane.

[0036] Furthermore, since there is a certain distance between the noise points and the surface of the target building, and the distribution of the point cloud data of the target building is more dense and numerous, the distance between the point cloud of the target building and the fitting plane is smaller than the distance between the noise points and the fitting plane. The distance between any point cloud data and the fitting plane is recorded as the first distance, and the distance between each neighboring point cloud data of the any point cloud data and the fitting plane is recorded as the second distance. The ratio of the first distance to the second distance is calculated, and the sum of all the ratios is obtained as the distance characteristic value of any point cloud data. The larger the distance characteristic value, the farther the distance between any point cloud data and the surface of the target building is.

[0037] In this embodiment, the distance between the point cloud data and the fitting plane is a vertical distance.

[0038] Furthermore, the distance significance coefficient of any point cloud data is obtained by combining the normal consistency coefficient and the distance eigenvalue of any point cloud data, and the expression is: ; In the formula, Represents the distance significance coefficient of the i-th point cloud data; Represents the distance feature value of the i-th point cloud data; Represents the normal consistency coefficient of the i-th point cloud data; exp( ) represents an exponential function with a natural constant as the base, which is used to avoid the denominator being 0.

[0039] It should be noted that: the farther the distance between the i-th point cloud data and the surface of the target building is, and the greater the direction difference between the normal vectors of the i-th point cloud data and its neighboring point cloud data is, the more likely the i-th point cloud data is to be a noise point.

[0040] All point cloud data collected at the f-th measuring station are arranged in descending order according to the distance significance coefficient, and the point cloud data of the preset proportion are taken as noise points and removed from all point cloud data collected at the f-th measuring station.

[0041] In this embodiment, the value of the preset ratio is 1%, and the value of the preset ratio is preset by humans and can be set by the implementer. This application does not impose any special restrictions. For example, when the number of point clouds collected at the fth measuring station position is 10,000, the first 100 point cloud data are used as noise points.

[0042] According to the method of eliminating noise points in all point cloud data collected at the f-th measuring station position, noise points in all point cloud data collected at the remaining measuring station positions are eliminated.

[0043] Step 3, preset each overlapping area, and obtain the building discrete coefficient of each overlapping area by analyzing the curvature of each location on the surface of the target building corresponding to each overlapping area and the discrete degree of the distance significance coefficient of all point cloud data in each overlapping area; obtain each point cloud set of all point cloud data in each overlapping area by using the point cloud segmentation algorithm, and obtain the distribution coefficient of each overlapping area by comprehensively analyzing the distribution of the curvature and distance significance coefficient of all point cloud data in each point cloud set of each overlapping area, combined with the building discrete coefficient; through the distribution coefficient, combined with the point cloud registration algorithm, register the point cloud data collected at all the preset measuring station positions to obtain the complete point cloud data of the target building.

[0044] Noise points in point cloud data can interfere with the accuracy of point cloud registration, especially when the point cloud density is uneven or the noise points are similar to the point cloud density of the target building, which can easily lead to incorrect feature matching, thus affecting the alignment effect between point clouds. By removing noise points, the accuracy and reliability of feature point extraction during point cloud registration can be improved, so that the features of the target building surface can be accurately identified and utilized, thereby improving the accuracy of point cloud registration.

[0045] Since the point cloud data collected at each measuring station only contains part of the entire target building, it is necessary to obtain complete point cloud data through registration, while removing some redundant data. The point cloud data of the overlapping area of ​​any two adjacent measuring station positions are processed and analyzed. This application uses the 4-Point Congruent Sets (4PCS) algorithm for point cloud registration, in which the number of sampling points of the 4PCS algorithm has a significant impact on the efficiency and accuracy of the registration results. The fewer the number of sampling points, the higher the registration efficiency, but there is a defect of insufficient accuracy. Conversely, the more sampling points, the higher the registration accuracy, but the registration efficiency will drop significantly.

[0046] Taking the overlapping area under the fth and f+1th measuring station positions as an example, the overlapping area under the fth and f+1th measuring station positions is recorded as the overlapping area to be analyzed. By analyzing the richness of the detailed features of the target building surface corresponding to the overlapping area to be analyzed and the complex features of the target building structure, the number of sampling points of the 4PCS algorithm is optimized and adjusted. In building surveying and mapping, the curvature of the building surface and the difference characteristics of the normal vector direction at each point on the surface can reflect the richness of the detailed features contained in the building surface.

[0047] Based on the above analysis, by analyzing the degree of change of the curvature and distance significance coefficient of all point cloud data in the overlapping area, the richness of the surface detail features of the target building is obtained, wherein the curvature size is used to reflect the curvature degree of the target building surface. In the area with more detail features, the normal vector directions of the point cloud data are more different, and the distance between the point cloud data and the fitting plane is more unstable, then the distance significance coefficient is more unstable. The degree of discreteness of the curvature of all point cloud data in the overlapping area to be analyzed is recorded as the first discreteness, and the degree of discreteness of the distance significance coefficient of all point cloud data in the overlapping area to be analyzed is recorded as the second discreteness. The sum of the first discreteness and the second discreteness is taken as the building discreteness coefficient of the overlapping area to be analyzed. The larger the building discreteness coefficient is, the more detailed information on the surface of the target building corresponding to the overlapping area to be analyzed is. The calculation of curvature is a well-known technology and will not be repeated in this application.

[0048] In this embodiment, the method for calculating the degree of discreteness of the curvature of all point cloud data in the overlapping area is: randomly arrange the curvatures of all point cloud data in the overlapping area to form a curvature sequence, use the Higuchi algorithm to process the curvature sequence, and use the obtained fractal dimension as the degree of discreteness of the curvature of all point cloud data in the overlapping area, and use the same calculation method as the degree of discreteness of the curvature of all point cloud data in the overlapping area to calculate the degree of discreteness of the distance significance coefficient of all point cloud data in the overlapping area, wherein the Higuchi algorithm is a well-known technology and will not be described in detail in this application; as other implementation methods, on the basis of being able to measure the degree of uneven distribution of the curvature of all point cloud data in the overlapping area and the degree of uneven distribution of the distance significance coefficient of all point cloud data in the overlapping area, the implementer may use other existing technologies for measurement, such as standard deviation, variance, coefficient of variation, etc., and this application does not impose any special restrictions.

[0049] Furthermore, compared with directly analyzing the spatial position of point cloud data and the distance relationship between point cloud data, the complex characteristics of the target building structure are obtained by deeply considering the curvature of the target building surface, the change in the direction of the normal vector of the point cloud data, and the distance relationship between the point cloud data and the fitting plane. Through the difference in curvature and the difference in distance significance coefficient, the complex characteristics of the target building structure can be more accurately reflected.

[0050] Furthermore, the point cloud segmentation algorithm is used to segment the point cloud data of the overlapping area to be analyzed to obtain each point cloud set. The structural morphology of the target building presents diverse characteristics, some structures are relatively simple, and some structures are highly complex. By analyzing the structural morphological characteristics of the target building surface corresponding to different point cloud sets, the complexity of the overlapping area to be analyzed is obtained.

[0051] In this embodiment, a point cloud segmentation algorithm based on region growing is used to segment the point cloud data of the overlapping area. The point cloud segmentation algorithm based on region growing is a well-known technology and will not be described in detail in this application. The implementer can select other feasible algorithms to segment the point cloud data of the overlapping area.

[0052] The curvature and distance significance coefficient of each point cloud data are used to form the two-dimensional feature data points of each point cloud data. In the target building structure with a relatively simple appearance, the distribution of the two-dimensional feature data points of the point cloud data is relatively concentrated, while in the target building structure with a relatively complex appearance, the distribution of the two-dimensional feature data points of the point cloud data is relatively discrete as a whole.

[0053] Based on the above analysis, taking any point cloud set as an example, the average value of the curvature of all point cloud data in the any point cloud set is recorded as the first average value, and the average value of the distance significance coefficient of all point cloud data in the any point cloud set is recorded as the second average value. The first average value and the second average value are combined to form a two-dimensional central data point, and the distance between the two-dimensional feature data point and the two-dimensional central data point of each point cloud data in the any point cloud set is calculated respectively. The discreteness of the distance between the two-dimensional feature data point and the two-dimensional central data point of all point cloud data in the any point cloud set is used as the dispersion coefficient of the point cloud set. The larger the dispersion coefficient, the more complex the shape of the target building structure corresponding to the any point cloud set.

[0054] In this embodiment, the distance between the two-dimensional feature data point and the two-dimensional center data point is the Euclidean distance.

[0055] In this embodiment, the discreteness is the variance. As other implementation methods, when it is possible to measure the uneven distribution of the distances between the two-dimensional feature data points and the two-dimensional center data points of all point cloud data in any point cloud set, the implementer may use other existing technologies for measurement, such as standard deviation, coefficient of variation, etc., and this application does not impose any special restrictions.

[0056] Furthermore, the mean of the dispersion coefficients of all point cloud sets in the overlapping area to be analyzed is recorded as the complex mean, and the fusion result of the complex mean and the building dispersion coefficient is used as the distribution coefficient of the overlapping area to be analyzed. The larger the distribution coefficient, the more complex the shape of the target building corresponding to the overlapping area to be analyzed. The schematic diagram of the distribution coefficient acquisition process is shown in the figure. Figure 2 shown.

[0057] It should be understood that fusion refers to combining multiple independent variables in a way that enhances the overall effect, such as additive relationship, multiplicative relationship, etc., and implementers can limit it according to actual conditions.

[0058] In this embodiment, the cumulative value of the complex mean and the building dispersion coefficient is used as the distribution coefficient of the overlapping area to be analyzed.

[0059] In another embodiment, the product of the complex mean and the building dispersion coefficient is used as the distribution coefficient of the overlapping area to be analyzed.

[0060] If the shape of the target building is more complex, more sampling points need to be set during the processing of the 4PCS algorithm to improve the registration accuracy. Conversely, if the structure of the target building is simpler, fewer sampling points can be set to improve the registration efficiency.

[0061] Based on the above analysis, the product of the normalized value of the distribution coefficient of the overlapping area to be analyzed and the preset value is used as the number of sampling points when the 4PCS algorithm is used to align the point cloud data collected at the f-th and f+1-th measuring station positions. In order to avoid too few sampling points, which results in the sampling points being unable to capture the overall structure of the point cloud, a threshold needs to be set. When the product is less than the preset threshold, the preset threshold is used as the number of sampling points when the 4PCS algorithm is used to align the point cloud data collected at the f-th and f+1-th measuring station positions. The schematic diagram of the process of obtaining the number of sampling points is shown in the figure. Figure 3 shown.

[0062] In this embodiment, in order to avoid too many sampling points, which will cause the 4PCS algorithm to run too long, the preset value is 1000, and the preset threshold is 100. The values ​​of the preset value and the preset threshold are both preset by humans and can be set by the implementer. This application does not impose any special restrictions.

[0063] A method for obtaining the number of sampling points when the point cloud data collected at the f-th and f+1-th measuring station positions are aligned using the 4PCS algorithm, and the number of sampling points when the point cloud data collected at any two adjacent measuring station positions are aligned using the 4PCS algorithm.

[0064] Based on the number of sampling points of the 4PCS algorithm, the 4PCS algorithm is used for point cloud registration to obtain complete point cloud data of the target building. The 4PCS algorithm is a well-known technology, and the specific process will not be repeated in this application.

[0065] Step 4: Obtain a building surveying and mapping model of the target building through the complete point cloud data.

[0066] In architectural surveying and mapping, the larger the building area of ​​the building and the higher the scanning resolution, the more point cloud data is obtained. Although the point cloud registration method can effectively improve the integrity of the point cloud data, it may also cause data redundancy problems, thereby affecting the speed and efficiency of subsequent modeling. In order to solve the problem of data redundancy, it is necessary to streamline the point cloud data. This embodiment uses the "uniform sampling" function of Geomagic Studio software to adjust the point cloud data by uniform thinning, specifically including: randomly removing some points from the overly dense point cloud, and readjusting the point cloud spacing to ensure the uniformity of the point cloud distribution, thereby achieving the purpose of data simplification.

[0067] A three-dimensional building model is constructed based on the point cloud data after denoising, registration and streamlining. This embodiment uses the "Package" function of Geomagic Studio software to convert the point cloud data into a polygonal mesh model, and restores the CAD solid model through spatial triangle approximation. Due to the geometric topological relationship or occlusion of the building itself, there may be a situation where the point cloud data of part of the surface of the target building cannot be collected. Therefore, the constructed polygonal mesh model may have holes. The holes are filled by the "Fill Holes" command of Geomagic Studio software to obtain a complete three-dimensional building model.

[0068] Furthermore, a building elevation is generated based on the 3D building model, and the building stereogram shows the appearance and external structure information of the target building. Since the amount of point cloud data is very large, it takes a long time for the drawing software to process the data. Therefore, in order to improve work efficiency, the 3D building model needs to be segmented. This embodiment uses the segmentation tool provided by Geomagic Studio software for segmentation, and uses AutoCAD software to draw building elevations for each local 3D building model obtained by segmentation, and then all the building elevations are spliced ​​to obtain a building surveying and mapping model.

[0069] Based on the same inventive concept as the above method, an embodiment of the present application also provides a system for building surveying, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above methods for building surveying are implemented.

[0070] In summary, the present application analyzes the characteristics of noise points generated during the acquisition of point cloud data due to the accuracy limitations of the three-dimensional laser scanner itself, the interference of the surrounding environment of the target building, and the influence of the surface material of the target building on the reflection characteristics of the laser scanning. According to the difference between the normal vector directions of the point cloud data and the distribution of the point cloud data in space, the distance significance coefficient is obtained to reflect the possibility that the point cloud data is a noise point. The application analyzes from multiple angles to improve the denoising accuracy of the point cloud data, which is conducive to improving the accuracy and reliability of feature point extraction in the subsequent point cloud registration process. Furthermore, when registering point cloud data, the influence of the number of sampling points of the point cloud registration algorithm on the registration efficiency and accuracy is taken into account. By analyzing the curvature of the target building surface corresponding to the overlapping area and the distribution of the distance significance coefficient of the point cloud data in the overlapping area, the detail richness and structural complexity of the target building surface corresponding to the overlapping area are evaluated, and then the appropriate number of sampling points is selected according to the actual situation, which can improve the registration accuracy while ensuring the registration efficiency; the point cloud data that has been denoised and registered is modeled to obtain a building surveying and mapping model, which improves the accuracy of building surveying and mapping.

[0071] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0072] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. A method for architectural surveying, characterized in that The method comprises the following steps: Collect point cloud data of the target building at each preset measuring station location; Obtain each neighboring point cloud data of each point cloud data collected at each of the preset measuring station positions, and perform preliminary denoising on each point cloud data by analyzing the distribution density of each point cloud data and all of its neighboring point cloud data; obtain the normal consistency coefficient of each point cloud data by analyzing the direction difference of the normal vector between each point cloud data and its neighboring point cloud data; obtain the distance characteristic value of each point cloud data by analyzing the spatial distribution of each point cloud data and all of its neighboring point cloud data, and obtain the distance significance coefficient of each point cloud data by combining the distance characteristic value; perform denoising on the point cloud data collected at each of the preset measuring station positions respectively by using the distance significance coefficient; Preset each overlapping area, and obtain the building discrete coefficient of each overlapping area by analyzing the curvature of each location on the surface of the target building corresponding to each overlapping area and the discrete degree of the distance significant coefficient of all point cloud data in each overlapping area; obtain each point cloud set of all point cloud data in each overlapping area by using a point cloud segmentation algorithm, and obtain the distribution coefficient of each overlapping area by comprehensively analyzing the distribution of the curvature and distance significant coefficient of all point cloud data in each point cloud set of each overlapping area, combined with the building discrete coefficient; through the distribution coefficient, in combination with a point cloud registration algorithm, register the point cloud data collected at all the preset measuring station positions to obtain the complete point cloud data of the target building; An architectural surveying and mapping model of the target building is obtained through the complete point cloud data.

2. The method for building surveying according to claim 1, characterized in that: The process of obtaining the normal consistency coefficient is as follows: Obtaining the fitting plane of all neighboring point cloud data of each point cloud data, and using the normal vector of the fitting plane as the normal vector of each point cloud data; The mean of the cosine similarity of the normal vectors between each point cloud data and all its neighboring point cloud data is taken as the normal consistency coefficient of each point cloud data.

3. The method for architectural surveying as claimed in claim 2, characterized in that: The process of obtaining the distance feature value is as follows: The distance between each point cloud data and the fitting plane is recorded as a first distance, and the distance between each neighboring point cloud data of each point cloud data and the fitting plane is recorded as a second distance; The ratio of the first distance to the second distance is calculated, and the sum of all the ratios corresponding to each point cloud data is used as the distance feature value of each point cloud data.

4. The method for building surveying according to claim 1, characterized in that: The process of obtaining the distance significant coefficient is as follows: Mapping the normal consistency coefficient to a positive number; The distance significance coefficient is the ratio of the distance characteristic value to the positive number.

5. The method for building surveying according to claim 1, characterized in that: The method for performing denoising processing on the point cloud data collected at each of the preset measuring station positions is: All point cloud data collected at any of the preset measuring station positions are arranged in descending order according to the distance significance coefficient; and point cloud data of a preset proportion are taken as noise points and removed from the point cloud data.

6. The method for building surveying according to claim 1, characterized in that: The process of obtaining the building dispersion coefficient is as follows: The degree of discreteness of the curvature of all point cloud data in each overlapping area is recorded as the first discreteness, the degree of discreteness of the distance significance coefficient of all point cloud data in each overlapping area is recorded as the second discreteness, and the sum of the first discreteness and the second discreteness is taken as the building discrete coefficient of each overlapping area.

7. The method for building surveying according to claim 1, characterized in that: The process of obtaining the distribution coefficient is as follows: The curvature and distance significance coefficient of each point cloud data in each overlapping area are used to form a two-dimensional feature data point of each point cloud data; By analyzing the dispersion degree of all two-dimensional feature data points of the point cloud data in each point cloud set, the dispersion coefficient of each point cloud set is obtained; The fusion result of the dispersion coefficient mean of all point cloud sets in each overlapping area and the building dispersion coefficient is used as the distribution coefficient of each overlapping area.

8. The method for architectural surveying according to claim 7, characterized in that: The process of obtaining the dispersion coefficient is as follows: The average value of the curvature of all the point cloud data in each point cloud set is recorded as the first average value, and the average value of the distance significance coefficient of all the point cloud data in each point cloud set is recorded as the second average value; The first average value and the second average value are used to form a two-dimensional central data point, and the dispersion of the distance between the two-dimensional feature data points of all point cloud data in each point cloud set and the two-dimensional central data point is used as the dispersion coefficient of each point cloud set.

9. The method for building surveying according to claim 1, characterized in that: In the process of registering the point cloud data collected at all the preset measuring station positions, the overlapping area under any two adjacent measuring station positions is recorded as the overlapping area to be analyzed, and the product of the normalized value of the distribution coefficient of the overlapping area to be analyzed and the preset value is used as the number of sampling points when the point cloud matching algorithm registers the point cloud data collected at any two adjacent measuring station positions.

10. A system for architectural surveying, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for building surveying according to any one of claims 1 to 9 are implemented.

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