Real estate stereo mapping method and platform driven by 3D laser scanning

Abstract

The present invention discloses a real estate stereo mapping method and platform driven by three-dimensional laser scanning, which belongs to the field of geographic information mapping, wherein the method includes: obtaining real estate engineering drawings, controlling equipment to collect image data and establish an initial data set; constructing a three-dimensional coordinate system based on the drawings, and calculating the mapping complexity; planning the acquisition path according to the acquisition results and complexity, configuring acquisition density identification and pre-control points; performing multi-angle scanning according to the path to establish point cloud data; aligning the point cloud data in the coordinate system and using pre-control points for rough registration; calculating the correlation between adjacent points between point clouds, matching feature points to complete precise registration and fusion, and obtaining three-dimensional mapping results. This application solves the technical problems of low efficiency of field data collection and insufficient accuracy of mapping data in existing real estate mapping, and achieves the technical effect of improving the efficiency of field data collection for real estate mapping and enhancing the accuracy of mapping data by adopting three-dimensional laser scanning.

Description

Technical Field

[0001] The present invention relates to the field of geographic information surveying and mapping, and in particular to a real estate stereoscopic surveying and mapping method and platform driven by three-dimensional laser scanning. Background Art

[0002] Real estate surveying and mapping is the basic work of natural resource management, providing spatial attribute information for real estate ownership registration, transaction flow, etc. Traditional real estate surveying and mapping mainly uses total stations, GPS and other equipment for field data collection, which has the problems of low operation efficiency and high labor intensity. In recent years, with the development of surveying and mapping technology, some new technologies such as oblique photogrammetry and mobile measurement systems have been applied, which have improved the efficiency of field data collection to a certain extent, but still cannot fully meet the growing surveying and mapping needs. In addition, traditional real estate surveying and mapping mainly collects two-dimensional vector data, which is difficult to fully and three-dimensionally reflect the spatial attribute characteristics of real estate. In order to meet the needs of real estate space management, it is urgent to obtain high-precision and high-density three-dimensional surveying and mapping data. In the existing technology, when using point cloud data for real estate three-dimensional modeling, there are problems such as difficulty in multi-view point cloud registration and low accuracy, resulting in insufficient accuracy of surveying and mapping data. Summary of the invention

[0003] This application aims to solve the technical problems of low efficiency of field data collection and insufficient accuracy of surveying and mapping data in existing real estate surveying and mapping by providing a real estate stereo mapping method and platform driven by three-dimensional laser scanning.

[0004] In view of the above problems, the present application provides a real estate stereo mapping method and platform driven by three-dimensional laser scanning.

[0005] In a first aspect disclosed in the present application, a real estate stereo mapping method driven by three-dimensional laser scanning is provided, the method comprising: obtaining engineering drawings of the real estate to be mapped, controlling an image acquisition device to perform image data acquisition of the real estate to be mapped based on the engineering drawings, and establishing an initial data set based on the image data acquisition results and the engineering drawings; constructing a three-dimensional coordinate system based on the engineering drawings, and using the initial data set to perform mapping complexity calculation on the real estate to be mapped, and establishing a mapping complexity calculation result; planning an acquisition path according to the image data acquisition results and the mapping complexity calculation results, wherein the acquisition points in the acquisition path are configured with acquisition density identifiers, and pre-control points are set synchronously; controlling the scanner to perform multi-angle scanning with the acquisition path and the acquisition density identifier to establish a point cloud data set; after aligning the point cloud data set in the three-dimensional coordinate system, using the pre-control points to perform coarse alignment of the point cloud data set to establish a coarse alignment result; using the coarse alignment result as a reference result, calculating the correlation between adjacent points between point clouds, matching point cloud feature points based on the adjacent point correlation calculation result, completing precise alignment point cloud fusion, and establishing a three-dimensional mapping result.

[0006] Another aspect disclosed in the present application provides a real estate stereo mapping platform driven by three-dimensional laser scanning, the platform comprising: a data acquisition module, used to obtain engineering drawings of the real estate to be mapped, and based on the engineering drawings, control the image acquisition device to perform image data acquisition of the real estate to be mapped, and establish an initial data set based on the image data acquisition results and the engineering drawings; a complexity analysis module, used to construct a three-dimensional coordinate system based on the engineering drawings, and use the initial data set to perform mapping complexity calculations on the real estate to be mapped, and establish mapping complexity calculation results; a path planning module, used to calculate the mapping complexity of the real estate to be mapped based on the image data acquisition results and the mapping complexity calculation results; The acquisition path is planned according to the results. The acquisition points in the acquisition path are configured with acquisition density identifiers, and pre-control points are set synchronously; the point cloud acquisition module is used to control the scanner to perform multi-angle scanning with the acquisition path and acquisition density identifier to establish a point cloud data set; the point cloud rough matching module is used to align the point cloud data set in the three-dimensional coordinate system, and then use the pre-control points to perform rough alignment of the point cloud data set to establish a rough alignment result; the point cloud fusion module is used to use the rough alignment result as a reference result, calculate the correlation between adjacent points between point clouds, match point cloud feature points based on the calculation result of the adjacent point correlation degree, complete the precise alignment point cloud fusion, and establish a three-dimensional mapping result.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] Because the engineering drawings of the real estate to be surveyed are obtained, the engineering drawings are used as the sampling basis, the image acquisition equipment is controlled to execute the image data acquisition of the real estate to be surveyed, and the initial data set is established based on the image data acquisition results and the engineering drawings. By guiding the data acquisition with the engineering drawings, the pertinence and completeness of the acquisition are improved; a three-dimensional coordinate system is constructed based on the engineering drawings, and the surveying and mapping complexity of the real estate to be surveyed is calculated using the initial data set to provide guidance for subsequent acquisition; according to the image acquisition results and the surveying and mapping complexity calculation results, the acquisition path of the three-dimensional laser scanning is planned, and the acquisition density and pre-control points are set on the path, which is helpful to fully obtain the real estate space information, and the pre-control points are used for alignment; the three-dimensional laser scanner is controlled to scan at multiple angles according to the acquisition path and density identification. For the real estate to be measured, obtain as complete and high-density point cloud data as possible to provide a data basis for accurate 3D reconstruction; unify the point cloud data set into the engineering drawing coordinate system, use pre-control points to perform coarse alignment of the point cloud data, and achieve preliminary stitching of point clouds from different perspectives to ensure the effectiveness of coarse alignment; based on the coarse alignment results, calculate the geometric, color, normal vector, density and other feature correlations of corresponding points between different point clouds, and achieve fine alignment through correlation matching to obtain a precisely fused 3D model. The technical solution to ensure the accuracy of fine alignment solves the technical problems of low efficiency in field data collection and insufficient accuracy of surveying and mapping data in existing real estate surveying and mapping, and achieves the technical effect of improving the efficiency of field data collection in real estate surveying and mapping and enhancing the accuracy of surveying and mapping data by adopting 3D laser scanning.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of a real estate stereo mapping method driven by three-dimensional laser scanning is provided for the embodiment of the present application;

[0011] Figure 2 A structural schematic diagram of a real estate stereo mapping platform driven by three-dimensional laser scanning is provided for the embodiment of the present application.

[0012] Explanation of the reference numerals: data acquisition module 11 , complexity analysis module 12 , path planning module 13 , point cloud acquisition module 14 , point cloud rough matching module 15 , point cloud fusion module 16 . DETAILED DESCRIPTION

[0013] The overall idea of ​​the technical solution provided by this application is as follows:

[0014] The embodiment of the present application provides a real estate stereo mapping method and platform driven by three-dimensional laser scanning. Based on the engineering drawings of the real estate, high-density point cloud data is obtained through three-dimensional laser scanning, and the geometry, color, normal vector, density and other features of the point cloud are comprehensively utilized to perform multi-view point cloud precision alignment and fusion, so as to achieve high-precision three-dimensional reconstruction of the real estate. Specifically, first, guide the image acquisition device to obtain the initial data set, and evaluate the complexity of real estate mapping based on this to guide the formulation of subsequent scanning plans. Then, plan the scanning path and density, and use the pre-arranged control points as a reference to scan the entire real estate from multiple angles to obtain comprehensive and high-density point cloud data. In the data processing stage, firstly, the point cloud is roughly aligned through coordinate transformation and pre-control points, and then the precise alignment is achieved based on the correlation measurement of the local features of the point cloud, so as to obtain a high-precision three-dimensional model in a unified coordinate system and obtain a three-dimensional mapping result.

[0015] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0016] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a real estate stereo mapping method driven by three-dimensional laser scanning, the method comprising:

[0017] S1: Acquire engineering drawings of the real estate to be surveyed and mapped, and control the image acquisition device to perform image data acquisition of the real estate to be surveyed and mapped based on the engineering drawings, and establish an initial data set based on the image data acquisition results and the engineering drawings.

[0018] Specifically, first, the engineering drawings of the real estate to be surveyed are obtained. The engineering drawings can be design drawings, construction drawings, completion drawings, etc. of the real estate to be surveyed, which contain spatial geometry information (such as plane layout, facade size) and structural layout information (such as wall material, door and window position) of the real estate to be surveyed, and provide reference for the subsequent surveying process. After obtaining the engineering drawings, they are used as the sampling basis to control the image acquisition device to perform image data acquisition on the real estate to be surveyed. Among them, the image acquisition device can be an imaging device such as an ordinary visible light camera, a multi-spectral camera, an infrared camera, etc., which is used to obtain visual information such as texture and color of the real estate surface. By controlling the shooting position, angle, resolution and other parameters of the image acquisition device, the required high-quality image data can be collected in a targeted manner. After completing the image data acquisition, the image data acquisition results are obtained, and the collected image data is associated with the engineering drawings obtained above to establish an initial data set. The initial data set not only contains information such as spatial geometry and structural layout of the real estate, but also contains rich surface texture and color features, providing necessary data support for subsequent surveying complexity calculation, surveying path planning, etc.

[0019] S200: constructing a three-dimensional coordinate system based on the engineering drawings, and using the initial data set to calculate the surveying complexity of the real estate to be surveyed, and establishing a surveying complexity calculation result.

[0020] Specifically, after obtaining the engineering drawings and initial data sets of the real estate to be surveyed, a three-dimensional coordinate system is constructed based on the obtained engineering drawings. The purpose of the three-dimensional coordinate system is to provide a unified spatial reference framework for subsequent spatial data processing, three-dimensional modeling, etc. For example, according to the orientation, size and other information marked on the engineering drawings, a three-dimensional rectangular coordinate system corresponding to the actual space of the real estate is established, and the directions of its origin, X-axis, Y-axis and Z-axis are set according to the content of the drawings and the surveying and mapping requirements. After the three-dimensional coordinate system is established, the initial data set obtained is used to carry out the surveying and mapping complexity calculation of the real estate to be surveyed, and the surveying and mapping complexity calculation results are established. Among them, the surveying and mapping complexity refers to the complexity of the spatial structure, surface characteristics and other aspects of the real estate, which directly affects the difficulty and workload of subsequent surveying and mapping. By analyzing the engineering drawings and image data in the initial data set, the surveying and mapping complexity of the real estate is quantitatively or qualitatively evaluated from multiple dimensions (such as structural complexity, texture complexity, occlusion, etc.). For example, the surveying and mapping complexity calculation comprehensively considers the geometric structure characteristics, material characteristics, environmental occlusion and other factors of the real estate, so as to obtain the surveying and mapping complexity evaluation results. The calculation results of the surveying and mapping complexity are used to guide the subsequent surveying and mapping parameter settings, manpower arrangements, etc., to improve the pertinence and efficiency of surveying and mapping.

[0021] S3: planning an acquisition path according to the image data acquisition results and the surveying and mapping complexity calculation results, wherein the acquisition points in the acquisition path are configured with acquisition density identifiers, and pre-control points are set synchronously.

[0022] Specifically, first, use the collected image data to preliminarily determine the position and angle of the scanner. By analyzing the structural layout and occlusion of the real estate in the image, select the appropriate scanning position to cover as much of the area to be surveyed as possible and reduce the scanning blind area. Secondly, give full consideration to the calculation results of the surveying complexity. In areas with high surveying complexity (such as areas with complex textures, severe occlusion, and poor accessibility), appropriately increase the scanning density to obtain more detailed point cloud data. For areas with lower complexity, the scanning density can be reduced accordingly to improve scanning efficiency.

[0023] After determining the scanner position and scanning density, a complete acquisition path for the scanner scan data can be generated. The acquisition path consists of a series of scanner position points (i.e., acquisition points), and each acquisition point has corresponding parameter settings such as scanning angle and resolution. At the same time, a specific acquisition density identifier is configured in the acquisition point of the acquisition path. Through this identifier, the scanning density of the acquisition point can be further controlled to achieve local density adjustment during the surveying and mapping process, so as to more flexibly deal with the local complex features of the real estate. At the same time, in order to facilitate the subsequent registration and splicing of point cloud data, some pre-control points are set synchronously when planning the acquisition path. The pre-control points are selected at locations where the real estate features are obvious and easy to identify (such as corners, edges of doors and windows, etc.) as common connection points between different scan data. By setting pre-control points, the registration accuracy and efficiency of point cloud data can be improved.

[0024] S4: Control the scanner to scan at multiple angles using the acquisition path and acquisition density identifier to establish a point cloud data set.

[0025] Specifically, after planning the acquisition path of the 3D laser scanning data, the actual data acquisition work is started using the scanner. Among them, the scanner can be a ground laser scanner, a handheld laser scanner, a mobile laser scanning system, etc. These scanners quickly acquire a large amount of 3D coordinate point data on the surface of the target object by emitting laser beams and receiving reflected laser signals. According to the planned acquisition path and acquisition points, the scanner is controlled to perform multi-angle and multi-position scanning. During the scanning process, the scanner completes 360-degree horizontal scanning and at least -30 degrees to +90 degrees vertical scanning at each acquisition point to obtain all-round point cloud data.

[0026] At the same time, strictly implement the set collection density mark. In areas with high surveying and mapping complexity, increase the scanned point cloud density (i.e. the number of points per unit area) accordingly to obtain more detailed and accurate 3D data. In areas with low complexity, reduce the point cloud density appropriately to balance surveying and mapping efficiency and data quality.

[0027] By precisely controlling the position, angle, resolution and other parameters of the scanner and dynamically adjusting the scanning settings according to the density mark of the collection points, highly targeted and high-quality original point cloud data can be obtained. After completing the scanning of all collection points, the massive point cloud data obtained is pre-processed to remove invalid data such as noise points and outliers, and a high-quality point cloud data set can be obtained, laying a data foundation for subsequent point cloud registration, 3D reconstruction and other processing.

[0028] S5: After aligning the coordinates of the point cloud dataset in a three-dimensional coordinate system, the pre-control points are used to perform a rough registration of the point cloud dataset to establish a rough registration result.

[0029] Specifically, after obtaining the point cloud data set, the point cloud data registration process begins. The purpose of registration is to spatially transform and combine the point cloud data obtained from multiple angles and multiple positions to form a complete and continuous point cloud model in the same three-dimensional coordinate system.

[0030] First, all point cloud data in the point cloud dataset are imported into a three-dimensional coordinate system to ensure the consistency of the point cloud data and engineering drawings in spatial position. Through a unified coordinate system, spatial transformation and alignment operations of point cloud data can be easily performed. Next, the point cloud data is roughly registered using the set pre-control points. Pre-control points are easily identifiable feature points in real estate, and they have corresponding points in scanned data at different angles and positions. By identifying the coordinates of pre-control points in different point cloud data and using them as a reference for coordinate transformation, the rough alignment of point cloud data in three-dimensional space can be quickly achieved. For example, three-dimensional transformation algorithms such as similarity transformation and affine transformation are used. After rough registration, the point cloud data has been basically aligned in space, but due to the influence of scanning errors, noise and other factors, there are still certain deviations and misalignments between different point cloud data. In order to further improve the registration accuracy, it is also necessary to perform fine registration optimization based on the rough registration results. The rough registration results serve as the initial value for subsequent fine registration, providing a good initial state for subsequent fine registration, reducing the amount of calculation and registration difficulty, and improving registration efficiency.

[0031] S6: Using the rough registration result as a reference result, calculate the correlation between adjacent points between point clouds, match the feature points of point clouds based on the calculation result of the correlation between adjacent points, complete the precise registration point cloud fusion, and establish the 3D mapping result.

[0032] Specifically, after obtaining the coarse registration result of the point cloud data set, the fine registration of the point cloud data and the final three-dimensional model generation are started.

[0033] First, based on the rough registration results, the point-to-point correspondence between different point cloud data is calculated, that is, to determine which points in each point cloud are closest to other point clouds in spatial position, so as to establish a fine correspondence between the point clouds. For example, the iterative closest point (ICP) algorithm or its variants are used to continuously optimize the transformation matrix between point clouds by minimizing the distance error between corresponding points until the preset accuracy threshold or number of iterations is reached. While establishing a fine correspondence between point clouds, the local feature information of the point clouds is considered, such as geometric features such as normal vectors and curvature, as well as attribute features such as color and reflection intensity. By comprehensively analyzing these features, the similarity and correspondence between point clouds can be more accurately judged, and the accuracy and robustness of the registration can be improved. After multiple iterative optimizations, the point cloud data has achieved high-precision registration and alignment in space.

[0034] At this point, all point cloud data are merged into a complete 3D mapping result, which not only contains the precise 3D coordinate information of the mapping object, but also integrates rich geometric and attribute features, truly reflecting the spatial structure and surface details of the real estate. When performing precise registration of point cloud data, problems such as missing point clouds in local areas and high noise may occur, resulting in poor local registration results. For these situations, methods such as local weighted registration and robust estimation are used to reduce the impact of abnormal point clouds and improve the overall registration accuracy.

[0035] Furthermore, the embodiment of the present application also includes:

[0036] The external coordinate information of the scanner corresponding to the point cloud dataset is obtained in the three-dimensional coordinate system, and the external coordinate information of the scanner is used as the basic alignment coordinate; the internal coordinate of the point cloud dataset in the scanner is obtained, and the coordinate transformation of the point cloud dataset is performed according to the internal coordinate and the basic alignment coordinate, and the global coordinate of the point cloud dataset is established to complete the coordinate alignment; the point cloud dataset is rotated and translated using the global coordinate and the pre-control point to establish a coarse alignment result.

[0037] In a preferred embodiment, when performing coordinate alignment and rough registration of point cloud data sets, first, the external coordinate information of the scanner corresponding to each point cloud data set is obtained in the three-dimensional coordinate system. The external coordinate information of the scanner refers to the spatial position and posture information of the scanner during the actual surveying and mapping process, that is, the position coordinates and rotation angle of the scanner itself in the three-dimensional coordinate system. These external coordinate information are directly obtained through the positioning devices such as GPS and INS built into the scanner, or indirectly obtained through manual measurement, calibration, etc. The obtained external coordinate information of the scanner is used as the basic alignment coordinate for the subsequent coordinate transformation and alignment of the point cloud data set. Then, the internal coordinates of each point cloud data set in its corresponding scanner's own coordinate system are obtained. The internal coordinates of the scanner refer to the relative position relationship of the point cloud data relative to the scanner, and usually a local coordinate system is established with the optical center of the scanner as the origin. According to the internal coordinates of the scanner and the basic alignment coordinates obtained, the internal coordinates of the point cloud data set are converted into global coordinates through coordinate transformation. Among them, the coordinate transformation methods include Euclidean transformation, similarity transformation, etc., and the point cloud data in different scanner coordinate systems are unified into the global coordinate system through operations such as rotation, translation, and scaling, so as to achieve preliminary alignment between point cloud data sets.

[0038] Afterwards, the global coordinates of the established point cloud dataset are used in combination with the set pre-control points to further rotate and translate the point cloud dataset to optimize the relative position relationship between the point clouds. By identifying the coordinates of the pre-control points in different point cloud datasets, the correspondence between the pre-control points is established, and this is used as a constraint to solve the optimal transformation matrix between the point cloud datasets. The transformation matrix usually contains a rotation matrix and a translation vector, which is used to describe the relative rotation and translation relationship between the point cloud datasets. By applying the transformation matrix to the global coordinates of the point cloud dataset, the point cloud position after coarse registration can be obtained, so that different point cloud datasets can achieve a better spatial alignment effect.

[0039] Through the coarse registration process, the coordinate alignment and coarse registration process of the point cloud dataset in the three-dimensional coordinate system are completed, which lays the foundation for the subsequent fine registration, helps to improve the efficiency and accuracy of the registration, and ultimately obtains high-quality three-dimensional surveying and mapping results.

[0040] Furthermore, the embodiment of the present application also includes:

[0041] Feature extraction is performed on each point cloud data set to establish a point cloud feature set, which includes a geometric feature set, a color feature set, a normal vector feature set, and a density feature set; a similarity search range is set based on the coarse registration result, and within the similarity search range, point cloud association calculations are performed using the point cloud feature set to establish point cloud association calculation results; weighted calculations are performed on the point cloud association calculation results to generate adjacent point association calculation results between point clouds.

[0042] In a preferred implementation, for the correlation between adjacent points between point clouds, first, feature extraction is performed on each point cloud data set to establish a point cloud feature set. Point cloud features refer to quantitative indicators that can reflect the local or global characteristics of point clouds. Among them, the extracted point cloud features include four aspects, namely geometric features, color features, normal vector features and density features. Among them, geometric features reflect the spatial distribution and shape characteristics of point clouds, such as curvature, roughness, etc.; color features reflect the spectral information of point clouds, such as RGB values, grayscale values, etc.; normal vector features reflect the directional information of the local surface of the point cloud, which can be used to judge the topological structure of the point cloud; density features reflect the point distribution of the local area of ​​the point cloud, which can be used to distinguish the boundary and internal area of ​​the object. By extracting and quantifying these four types of features, a feature set of each point cloud data set is established, providing a rich feature description for subsequent point cloud association calculations.

[0043] Then, based on the obtained rough registration results, the similarity search range between point clouds is set. The similarity search range refers to determining a local neighborhood for each point cloud point when performing point cloud association calculations, and searching for potential corresponding points in the neighborhood. The relative position relationship of different point cloud data sets in space can be roughly determined through the rough registration results. Setting a reasonable search range based on this can reduce unnecessary calculations and improve matching efficiency. Within the determined similarity search range, the established point cloud feature set is used to perform geometric feature association, color feature association, normal vector feature association, and density feature association calculations. The purpose of the association calculation is to measure the similarity between points in different point cloud data sets. By comparing the distance or similarity of point cloud points in each feature space, the initial correspondence between point clouds is established to obtain the point cloud association calculation results.

[0044] Afterwards, the obtained point cloud association calculation results are weightedly fused to generate the final correlation between adjacent points between point clouds. Since different types of point cloud features contribute differently to the association calculation, it is necessary to assign appropriate weight coefficients to each type of feature based on the distinguishing ability and stability of the features. Through weighted calculation, the association results of multiple features such as geometry, color, normal vector, density, etc. are comprehensively considered to obtain a more accurate and reliable adjacent point correlation, and generate the calculation results of the adjacent point correlation between point clouds. Point pairs with high correlation will serve as an important reference for subsequent fine registration, guiding the point cloud data set to perform precise registration and alignment in local areas.

[0045] By taking the rough registration result as a reference result, the correlation between adjacent points between point clouds is calculated, which fully utilizes the multivariate feature information of the point cloud and provides a robust and accurate point-to-point correspondence, laying an important foundation for achieving high-precision registration of point cloud datasets.

[0046] Furthermore, the embodiment of the present application also includes:

[0047] The geometric feature set is used to calculate the position distance association and the local surface shape consistency as follows:

[0048] ;

[0049] ;

[0050] in, Characterization point and Point The position distance correlation calculation results are as follows: and are points of different point clouds, and points and Point is the nearest neighbor point, Characterization point and Point The local surface shape consistency calculation results of Characterization point The curvature of Characterization point The curvature.

[0051] The color feature set, normal vector feature set, and density feature set are used to calculate color association, normal vector feature association, and density feature association, respectively, as follows:

[0052] ;

[0053] ;

[0054] ;

[0055] in, Characterizes color association, and Respectively characterize the points and Point The color vector, Characterizes the normal vector feature correlation, and Points and Point The normal vector of Characterizes the correlation of density features, and Points and Point The local neighborhood density of .

[0056] In a preferred embodiment, when performing point cloud association calculation using a point cloud feature set, first, position distance association calculation and local surface shape consistency calculation are performed using a point cloud geometric feature set. Position distance association calculation refers to comparing the Euclidean distance between two point cloud points to measure the proximity of the two point cloud points in space. Perform calculations, Indicate point and Point The position distance correlation is and The smaller the distance between the two nearest neighbor points, the closer the two points are in space and the higher the correlation. The local surface shape consistency calculation refers to comparing the curvature difference in the neighborhood of two point cloud points to measure the shape similarity of their local surfaces. Perform calculations, Indicate point and Point The local surface shape consistency of and Respectively indicate points and Point The smaller the curvature difference, the more similar the shapes of the local surfaces of the two points are, and the higher the consistency. Through the position distance association calculation and the local surface shape consistency calculation, the spatial position and local shape characteristics of the point cloud are comprehensively considered to provide geometric constraints for the subsequent point cloud registration.

[0057] Subsequently, the color feature set, normal feature set and density feature set of the point cloud are used to perform color association, normal feature association and density feature association calculations respectively. Among them, color association calculation refers to comparing the color attribute differences of two point cloud points to measure the similarity of their spectral characteristics. calculate, Indicate point and Point The color correlation of and Respectively indicate points and Point The color vector can be a common color representation such as RGB, HSV, etc. The smaller the color difference, the closer the color attributes of the two points are, and the higher the correlation. Normal vector feature association calculation refers to comparing the cosine value of the angle between the normal vectors of two point clouds to measure their consistency in the local surface direction, using calculate, Indicate point and Point The normal vector feature correlation of and Respectively indicate points and Point The closer the cosine value of the angle is to 1, the more consistent the normal vector directions of the two points are, and the higher the correlation is. Density feature correlation calculation refers to comparing the point density differences in the local neighborhood of two point clouds to measure the similarity of the local point distribution of the two point clouds. calculate, Indicate point and Point The density feature correlation of and Respectively indicate points and Point Point density in a local neighborhood. The smaller the density difference, the more similar the point distributions in the local area where the two points are located are, and the higher the correlation. Through color association, normal vector feature association, and density feature association calculation, the similarity between point clouds is evaluated from different angles, providing diversified feature constraints for point cloud registration.

[0058] By using the geometric features, color features, normal vector features and density features of point clouds, the point clouds are associated and calculated from multiple aspects such as spatial position, local shape, spectral properties, normal vector direction and point distribution, and comprehensive and rich point cloud association results are obtained. These association results will serve as important inputs for subsequent point cloud weighted fusion, laying a solid feature foundation for achieving high-precision point cloud registration.

[0059] Furthermore, the embodiment of the present application also includes:

[0060] Parse the initial data set and generate parsing results; use the parsing results to calculate surface texture and generate texture complexity calculation results; use the parsing results to calculate the number of occluders and the size of the occluded area, and establish the occlusion complexity calculation results; use the parsing results to calculate the scanning coverage of the scanner and establish the accessibility complexity calculation results; use the parsing results to calculate the surface curvature, and establish the geometric complexity calculation results based on the surface curvature calculation results; perform weighted analysis on the texture complexity calculation results, occlusion complexity calculation results, accessibility complexity calculation results, and geometric complexity calculation results to establish the mapping complexity calculation results.

[0061] In a preferred embodiment, when establishing the calculation result of the mapping complexity, first, the obtained initial data set is parsed to generate the parsing result. The initial data set includes engineering drawings and image data of the real estate, which contains rich information such as spatial structure, material properties, occlusion relationship, etc. By parsing the initial data set, these implicit information is extracted to provide necessary data support for the subsequent complexity calculation. The parsing process can use image processing, pattern recognition, machine learning and other technologies, and select corresponding algorithms and models according to different data types and analysis objectives. The parsing results are usually expressed in the form of feature vectors, attribute labels, relationship diagrams, etc., which are convenient for subsequent quantitative analysis and evaluation. Secondly, the obtained parsing results are used to calculate the surface texture of the real estate and generate the calculation result of texture complexity. Texture complexity reflects the richness and diversity of the real estate surface material, pattern, etc., and has an important impact on data collection and processing in the mapping process. By analyzing the texture features in the image data, such as grayscale co-occurrence matrix, local binary pattern, etc., the complexity of the real estate surface texture is quantitatively evaluated. Generally speaking, the richer and more varied the texture, the higher the corresponding texture complexity and the greater the difficulty of mapping. The calculation results of texture complexity can be expressed in the form of numerical values ​​or levels, providing a quantitative basis for subsequent comprehensive evaluation. Then, the obtained analytical results are used to calculate the number of occluders and the size of the occluded area in the real estate, and establish the calculation results of occlusion complexity. Occluders refer to objects or structures in real estate that block and hinder the scanning field of view, such as columns, beams, pipelines, etc. The existence of occluders will affect the data acquisition of the scanner, resulting in the appearance of scanning blind areas, and increasing the difficulty of surveying and mapping. By analyzing engineering drawings and image data, the occluders in the real estate are identified and located, and their number and the size of the occluded area are counted. The more the number and the larger the occluded area, the higher the occlusion complexity, and the surveying and mapping process requires more supplementary scanning and data processing. The calculation results of occlusion complexity can be expressed in the form of numerical values ​​or levels, providing a quantitative basis for subsequent comprehensive evaluation.

[0062] At the same time, the obtained analytical results are used to calculate the scanning coverage of the scanner and establish the calculation results of the accessibility complexity. The scanning coverage refers to the proportion of the area in the real estate where the scanner can effectively collect data, reflecting the accessibility and mapping difficulty of the real estate. By analyzing the spatial structure, layout characteristics and other factors of the real estate, the visible range and blind spots of the scanner at different positions are estimated, and then the scanning coverage is calculated. The lower the coverage, the worse the accessibility of the real estate, the more scanning positions and data splicing work are required in the mapping process, and the higher the corresponding accessibility complexity. The calculation results of the accessibility complexity can be expressed in the form of values ​​or levels, providing a quantitative basis for subsequent comprehensive evaluation. Subsequently, the obtained analytical results are used to calculate the curvature of the real estate surface, and the calculation results of the geometric complexity are established based on the curvature calculation results. Curvature is an important indicator reflecting the complexity of the real estate surface shape. The larger the curvature and the more drastic the change, the more complex the geometric structure of the surface, and the higher the corresponding mapping difficulty. By analyzing the three-dimensional point cloud data or image data, the curvature distribution of the real estate surface is estimated, and the geometric feature quantities such as the average curvature and Gaussian curvature are calculated. The size and changes of these characteristic quantities can be used to evaluate the geometric complexity of real estate. The higher the geometric complexity, the higher the sampling density and data processing accuracy required in the surveying and mapping process. The results of geometric complexity calculation can be expressed in the form of numerical values ​​or levels, providing a quantitative basis for subsequent comprehensive evaluation.

[0063] Afterwards, the calculation results of texture complexity, occlusion complexity, accessibility complexity, geometric complexity and other aspects are weighted analyzed to establish the final calculation results of surveying and mapping complexity. Weighted analysis is a method of comprehensive evaluation of multiple factors. By assigning weights to each complexity index and performing weighted summation or other mathematical operations, a comprehensive complexity score or grade is obtained. Among them, the allocation of weights needs to be determined according to the importance and influence of different indicators, and can be obtained by expert experience, statistical analysis and other methods. Through weighted analysis, the complexity of multiple aspects such as texture, occlusion, accessibility, geometry, etc. is organically combined to obtain a comprehensive and objective surveying and mapping complexity evaluation result, which provides an important basis for subsequent surveying and mapping and helps to improve surveying and mapping efficiency and quality.

[0064] Furthermore, the embodiment of the present application also includes:

[0065] Use the initial data set to identify the materials of the real estate to be surveyed and establish material identification; when the material identification triggers the preset threshold, activate the synchronous acquisition instruction; use the synchronous acquisition instruction to control the infrared imaging and thermal imaging equipment to perform local area synchronous data acquisition; merge the local area synchronous data acquisition results with the precise registration point cloud fusion results to update the 3D surveying and mapping results.

[0066] In a preferred embodiment, after completing the basic 3D laser scanning mapping process, material identification and synchronous data collection are introduced to further improve the information richness and accuracy of the mapping results.

[0067] First, the obtained initial data set is used to identify the material of the real estate to be surveyed and establish a material identification. Material identification refers to inferring the material type of the real estate surface, such as wood, concrete, metal, glass, etc., by analyzing the image, point cloud and other data in the initial data set. Among them, the material identification method includes classification based on texture features, classification based on spectral features, etc. Through material identification, a material identification is assigned to each real estate surface area to be surveyed to indicate its main material type. The material identification can be represented in the form of digital coding or text labels, which is convenient for subsequent processing and analysis. Then, it is determined whether the established material identification triggers the preset threshold. The preset threshold refers to a judgment condition set according to the characteristics of the material type and the surveying and mapping requirements. For example, for materials with obvious infrared characteristics (such as metal, glass, etc.), a higher threshold is set; and for materials with unclear infrared characteristics (such as wood, plastic, etc.), a lower threshold is set. When the material identification of a certain real estate surface area meets the preset threshold, the synchronous acquisition instruction is triggered, indicating that the area needs additional synchronous data acquisition.

[0068] Subsequently, according to the triggered synchronous acquisition instruction, the infrared imaging device and the thermal imaging device are controlled to synchronously collect data on the target area. The infrared imaging device can capture the infrared radiation information of the surface of the object, reflecting its temperature distribution and heat transfer characteristics; the thermal imaging device can capture the thermal radiation information of the surface of the object and generate an intuitive thermal image. Through the synchronous acquisition of infrared imaging and thermal imaging, important information such as infrared texture and temperature distribution of the target area can be obtained, which can be used as a beneficial supplement to the three-dimensional mapping results and provide a more comprehensive description of the surface characteristics of the real estate. After that, the obtained local area synchronous data acquisition results are fused with the generated precise registration point cloud fusion results to update the three-dimensional mapping results. Specifically, the infrared texture, thermal map and other data obtained by synchronous acquisition are first registered with the point cloud model to achieve precise alignment in the spatial coordinate system. Then, the registered synchronous acquisition data is used as an additional attribute of the point cloud model to give each point cloud point corresponding infrared texture coordinates, temperature values ​​and other information. Through data fusion, a comprehensive three-dimensional mapping result containing multivariate information such as geometry, color, infrared, and thermal is finally obtained, which fully reflects the spatial structure and surface characteristics of the real estate.

[0069] Based on 3D laser scanning surveying and mapping, the links of material identification and synchronous data collection are introduced, and infrared imaging and thermal imaging are supplemented for the surface area of ​​real estate of specific materials, and the collection results are integrated with the point cloud model, finally obtaining 3D surveying and mapping results with richer information and more comprehensive description. This surveying and mapping method makes full use of the complementary characteristics of different sensors and improves the accuracy of surveying and mapping results.

[0070] Furthermore, the embodiment of the present application also includes:

[0071] Identify abnormal areas in the three-dimensional surveying and mapping results and establish abnormal area concerns; reconstruct data collection based on abnormal area concerns, and use the reconstructed data collection results to authenticate regional hazards; and issue dynamic safety warnings based on the regional hazard authentication results.

[0072] In a preferred embodiment, first, the abnormal area of ​​the obtained three-dimensional mapping results is identified, and the abnormal area concern is established. The abnormal area refers to the area in the three-dimensional mapping results that is obviously deviated from the normal range or expected state, such as structural deformation, material abnormality, data missing, etc. By analyzing the geometric features and color attributes of the point cloud data, and combining the prior knowledge of the design specifications and construction standards of the real estate, the abnormal area in the three-dimensional mapping results is automatically detected and located. Then, for the established abnormal area concern, reconstruction data collection is performed, and regional hazard certification is performed based on the collection results. Reconstruction data collection refers to supplementary scanning and mapping near the abnormal area to obtain more accurate and detailed local three-dimensional data. This supplementary collection uses high-resolution scanning equipment, and optimizes scanning parameters and strategies according to the characteristics of the abnormal area, such as increasing the scanning density, adjusting the scanning angle, etc. Through reconstruction data collection, a fine three-dimensional model of the abnormal area is obtained, providing reliable data support for subsequent hazard analysis. After obtaining the reconstructed data, the structural characteristics, material properties, data quality and other factors of the abnormal area are comprehensively considered to evaluate and certify its hazard. By calculating the stress distribution, deformation degree and other mechanical indicators of the abnormal area, and referring to the relevant safety standards and specifications, it is possible to determine whether there are safety hazards in the abnormal area and give the corresponding danger level. After that, the obtained regional danger certification results will be used for dynamic safety early warning. Dynamic safety early warning is a real-time safety monitoring and warning mechanism that detects and responds to potential safety threats in a timely manner by continuously tracking the state changes of the abnormal area. Specifically, sensors, monitoring equipment, etc. are deployed near the abnormal area to collect dynamic data such as deformation and vibration of the area in real time, and compare and analyze it with the danger certification results. When the monitoring data exceeds the safety threshold or deviates significantly from the expected state, the early warning will be automatically triggered, and the relevant personnel will be warned through sound and light alarms, message push, etc. At the same time, according to the warning level and on-site conditions, corresponding emergency disposal plans and safety protection measures are provided, such as evacuating personnel, reinforcing structures, cutting off power and water, etc., to maximize the safety of people and property.

[0073] Based on 3D laser scanning surveying and mapping, a series of safety assurance measures such as abnormal area identification, reconstruction data collection, regional hazard certification and dynamic safety warning are introduced, making full use of the rich information contained in the surveying and mapping data. Through intelligent analysis and evaluation, safety hazards in real estate can be discovered and responded to in a timely manner, thus improving the practical value and reliability of surveying and mapping results. At the same time, the dynamic warning and response mechanism also provides strong support for the safety management of real estate, helping to reduce accidents and protect the safety of life and property.

[0074] In summary, the real estate stereo mapping method driven by three-dimensional laser scanning provided in the embodiments of the present application has the following technical effects:

[0075] Obtain the engineering drawings of the real estate to be surveyed and mapped, use the engineering drawings as the sampling basis, control the image acquisition equipment to perform image data acquisition of the real estate to be surveyed and mapped, establish an initial data set based on the image data acquisition results and engineering drawings, use engineering drawings to guide data acquisition, improve the pertinence and completeness of acquisition, and lay the foundation for subsequent processing. Construct a three-dimensional coordinate system based on the engineering drawings, and use the initial data set to calculate the surveying and mapping complexity of the real estate to be surveyed and mapped, establish the surveying and mapping complexity calculation results, evaluate the complexity of real estate surveying and mapping, and provide a basis for optimizing the data acquisition plan. Plan the acquisition path according to the image data acquisition results and the surveying and mapping complexity calculation results. The acquisition points in the acquisition path are configured with acquisition density identifiers, and pre-control points are set simultaneously. Plan the acquisition path, acquisition density, and control point layout of the three-dimensional laser scanning to comprehensively collect real estate spatial information and prepare for alignment. Use the acquisition path and acquisition density identifier to control the scanner to perform multi-angle scanning, establish a point cloud data set, and obtain high-density, all-round point cloud data by scanning the real estate from different angles and different densities, providing a basis for accurate three-dimensional reconstruction. After aligning the point cloud dataset in the three-dimensional coordinate system, the pre-control points are used to perform rough registration of the point cloud dataset, and the rough registration results are established to achieve the transformation of point cloud datasets from different perspectives to a unified coordinate system, and the preliminary splicing is completed with the help of pre-control points. The rough registration results are used as reference results to calculate the correlation between adjacent points between point clouds, and point cloud feature points are matched based on the calculation results of the adjacent point correlation, and the precise registration of point cloud fusion is completed to establish the three-dimensional surveying and mapping results. Fine registration is achieved through correlation matching to ensure the accuracy of the final three-dimensional model. By using three-dimensional laser scanning, the efficiency of field data collection for real estate surveying and mapping is improved, and the accuracy of surveying and mapping data is enhanced.

[0076] Embodiment 2, based on the same inventive concept as the real estate stereo mapping method driven by three-dimensional laser scanning in the above embodiment, Figure 2 As shown, the embodiment of the present application provides a real estate stereo mapping platform driven by three-dimensional laser scanning, the platform comprising:

[0077] The data acquisition module 11 is used to obtain engineering drawings of the real estate to be surveyed and mapped, and control the image acquisition device to perform image data acquisition of the real estate to be surveyed and mapped based on the engineering drawings, and establish an initial data set based on the image data acquisition results and the engineering drawings.

[0078] The complexity analysis module 12 is used to construct a three-dimensional coordinate system based on the engineering drawings, and use the initial data set to perform surveying and mapping complexity calculations on the real estate to be surveyed and mapped, and establish surveying and mapping complexity calculation results.

[0079] The path planning module 13 is used to plan the acquisition path according to the image data acquisition results and the mapping complexity calculation results. The acquisition points in the acquisition path are configured with acquisition density identifiers, and pre-control points are set synchronously.

[0080] The point cloud acquisition module 14 is used to control the scanner to perform multi-angle scanning based on the acquisition path and acquisition density identifier to establish a point cloud data set.

[0081] The point cloud rough matching module 15 is used to perform rough matching of the point cloud data set using pre-controlled points after aligning the coordinates of the point cloud data set in a three-dimensional coordinate system, and to establish a rough matching result.

[0082] The point cloud fusion module 16 is used to calculate the correlation between adjacent points between point clouds using the rough registration result as a reference result, match the feature points of the point clouds based on the calculation result of the correlation between adjacent points, complete the precise registration point cloud fusion, and establish a three-dimensional mapping result.

[0083] Furthermore, the point cloud rough matching module 15 includes the following execution steps:

[0084] The external coordinate information of the scanner corresponding to the point cloud dataset is obtained in the three-dimensional coordinate system, and the external coordinate information of the scanner is used as the basic alignment coordinate; the internal coordinate of the point cloud dataset in the scanner is obtained, and the coordinate transformation of the point cloud dataset is performed according to the internal coordinate and the basic alignment coordinate, and the global coordinate of the point cloud dataset is established to complete the coordinate alignment; the point cloud dataset is rotated and translated using the global coordinate and the pre-control point to establish a coarse alignment result.

[0085] Furthermore, the point cloud fusion module 16 includes the following execution steps:

[0086] Feature extraction is performed on each point cloud data set to establish a point cloud feature set, which includes a geometric feature set, a color feature set, a normal vector feature set, and a density feature set; a similarity search range is set based on the coarse registration result, and within the similarity search range, point cloud association calculations are performed using the point cloud feature set to establish point cloud association calculation results; weighted calculations are performed on the point cloud association calculation results to generate adjacent point association calculation results between point clouds.

[0087] Furthermore, the point cloud fusion module 16 further includes the following execution steps:

[0088] The geometric feature set is used to calculate the position distance association and the local surface shape consistency as follows:

[0089] ;

[0090] ;

[0091] in, Characterization point and Point The position distance correlation calculation results are as follows: and are points of different point clouds, and points and Point is the nearest neighbor point, Characterization point and Point The calculation results of the local surface shape consistency are as follows: Characterization point The curvature of Characterization point The curvature.

[0092] The color feature set, normal vector feature set, and density feature set are used to calculate color association, normal vector feature association, and density feature association, respectively, as follows:

[0093] ;

[0094] ;

[0095] ;

[0096] in, Characterizes color association, and Respectively characterize the points and Point The color vector, Characterizes the normal vector feature correlation, and Points and Point The normal vector of Characterizes the correlation of density features, and Points and Point The local neighborhood density of .

[0097] Furthermore, the complexity analysis module 12 includes the following execution steps:

[0098] Parse the initial data set and generate parsing results; use the parsing results to calculate surface texture and generate texture complexity calculation results; use the parsing results to calculate the number of occluders and the size of the occluded area, and establish the occlusion complexity calculation results; use the parsing results to calculate the scanning coverage of the scanner and establish the accessibility complexity calculation results; use the parsing results to calculate the surface curvature, and establish the geometric complexity calculation results based on the surface curvature calculation results; perform weighted analysis on the texture complexity calculation results, occlusion complexity calculation results, accessibility complexity calculation results, and geometric complexity calculation results to establish the mapping complexity calculation results.

[0099] Furthermore, the embodiment of the present application also includes a surveying and mapping result updating module, which includes the following execution steps:

[0100] Use the initial data set to identify the materials of the real estate to be surveyed and establish material identification; when the material identification triggers the preset threshold, activate the synchronous acquisition instruction; use the synchronous acquisition instruction to control the infrared imaging and thermal imaging equipment to perform local area synchronous data acquisition; merge the local area synchronous data acquisition results with the precise registration point cloud fusion results to update the 3D surveying and mapping results.

[0101] Furthermore, the embodiment of the present application also includes a dynamic security warning module, which includes the following execution steps:

[0102] Identify abnormal areas in the three-dimensional surveying and mapping results and establish abnormal area concerns; reconstruct data collection based on abnormal area concerns, and use the reconstructed data collection results to authenticate regional hazards; and issue dynamic safety warnings based on the regional hazard authentication results.

[0103] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0104] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A real estate stereo mapping method driven by three-dimensional laser scanning, characterized in that: The method comprises: Acquire engineering drawings of the real estate to be surveyed and mapped, control the image acquisition device to perform image data acquisition of the real estate to be surveyed and mapped based on the engineering drawings, and establish an initial data set based on the image data acquisition results and the engineering drawings; Construct a three-dimensional coordinate system based on the engineering drawings, and use the initial data set to calculate the surveying complexity of the real estate to be surveyed and mapped, and establish the surveying complexity calculation results; Planning an acquisition path according to the image data acquisition results and the surveying and mapping complexity calculation results, wherein the acquisition points in the acquisition path are configured with acquisition density identifiers, and pre-control points are set synchronously; The scanner is controlled to scan at multiple angles using the acquisition path and acquisition density identifiers to establish a point cloud data set; After aligning the coordinates of the point cloud data set in a three-dimensional coordinate system, coarse registration of the point cloud data set is performed using pre-control points to establish a coarse registration result; Using the rough registration result as a reference result, the correlation between adjacent points between point clouds is calculated, and point cloud feature points are matched based on the calculation result of the correlation between adjacent points. The precise registration point cloud fusion is completed to establish the 3D mapping result. The method of using the rough registration result as a reference result to calculate the correlation between adjacent points between point clouds also includes: Perform feature extraction on each point cloud data set to establish a point cloud feature set, wherein the point cloud feature set includes a geometric feature set, a color feature set, a normal vector feature set, and a density feature set; Based on the coarse registration result, a similar search range is set, and within the similar search range, point cloud association calculations are performed using the point cloud feature set to establish point cloud association calculation results; Perform weighted calculation on the point cloud association calculation results to generate the adjacent point association calculation results between the point clouds; The point cloud association calculation is performed using the point cloud feature set to establish the point cloud association calculation result, and further includes: The geometric feature set is used to calculate the position distance association and the local surface shape consistency as follows: ； ； in, Characterization point and Point The position distance correlation calculation results are as follows: and are points of different point clouds, and points and Point is the nearest neighbor point, Characterization point and Point The local surface shape consistency calculation results of Characterization point The curvature of Characterization point The curvature of The color feature set, normal vector feature set, and density feature set are used to calculate color association, normal vector feature association, and density feature association, respectively, as follows: ； ； ； in, Characterizes color association, and Respectively characterize the points and Point The color vector, Characterizes the normal vector feature correlation, and Points and Point The normal vector of Characterizes the correlation of density features, and Points and Point The local neighborhood density of .

2. The real estate stereo mapping method driven by three-dimensional laser scanning as claimed in claim 1, characterized in that: After aligning the point cloud data set in the three-dimensional coordinate system, using the pre-control points to perform rough registration of the point cloud data set to establish a rough registration result, the method further includes: Obtain the scanner external coordinate information corresponding to the point cloud data set in the three-dimensional coordinate system, and use the scanner external coordinate information as the basic alignment coordinate; Obtain the internal coordinates of the point cloud dataset in the scanner, perform coordinate transformation of the point cloud dataset according to the internal coordinates and basic alignment coordinates, and establish the global coordinates of the point cloud dataset to complete the coordinate alignment; The global coordinates and pre-control points are used to perform rotation and translation of the point cloud data set to establish a coarse registration result.

3. The real estate stereo mapping method driven by three-dimensional laser scanning as claimed in claim 1, characterized in that: The method of calculating the surveying complexity of the real estate to be surveyed and mapped by using the initial data set and establishing the surveying complexity calculation result also includes: Parse the initial data set and generate parsing results; Utilize the analytical results to calculate the surface texture and generate the texture complexity calculation results; The analytical results are used to calculate the number of occluders and the size of the occluded area, and the calculation results of the occlusion complexity are established; The analytical results are used to calculate the scanning coverage of the scanner and establish the calculation results of the reachability complexity; Calculate the surface curvature using the analytical results, and establish the geometric complexity calculation results based on the surface curvature calculation results; A weighted analysis is performed on the calculation results of texture complexity, occlusion complexity, accessibility complexity, and geometric complexity to establish the calculation results of surveying and mapping complexity.

4. The real estate stereo mapping method driven by three-dimensional laser scanning as claimed in claim 1, characterized in that: The method further comprises: Use the initial data set to identify materials of the real estate to be surveyed and establish material identification; When the material identification triggers the preset threshold, the synchronous acquisition instruction is activated; Use synchronous acquisition instructions to control infrared imaging and thermal imaging equipment to perform synchronous data acquisition in local areas; The local area synchronous data collection results are integrated with the precise registration point cloud fusion results to update the 3D surveying and mapping results.

5. The real estate stereo mapping method driven by three-dimensional laser scanning as claimed in claim 1, characterized in that: The method further comprises: Identify abnormal areas in 3D mapping results and establish abnormal area attention; Reconstruct data collection based on abnormal area attention, and use the reconstruction data collection results to perform regional hazard certification; The regional hazard certification results are issued in a dynamic safety early warning manner.

6. Real estate stereo mapping platform driven by 3D laser scanning, characterized by: The platform is used to implement the real estate stereo mapping method driven by three-dimensional laser scanning according to any one of claims 1 to 5, and comprises: A data acquisition module, the data acquisition module is used to obtain engineering drawings of the real estate to be surveyed and mapped, and control the image acquisition device to perform image data acquisition of the real estate to be surveyed and mapped based on the engineering drawings, and establish an initial data set based on the image data acquisition results and the engineering drawings; A complexity analysis module, the complexity analysis module is used to construct a three-dimensional coordinate system based on the engineering drawings, and use the initial data set to perform surveying and mapping complexity calculations on the real estate to be surveyed and mapped, and establish surveying and mapping complexity calculation results; Path planning module, the path planning module is used to plan the acquisition path according to the image data acquisition results and the mapping complexity calculation results. The acquisition points in the acquisition path are configured with acquisition density identifiers, and pre-control points are set synchronously: A point cloud acquisition module, wherein the point cloud acquisition module is used to control the scanner to perform multi-angle scanning with acquisition paths and acquisition density identifiers to establish a point cloud data set; A point cloud rough matching module, wherein the point cloud rough matching module is used to perform rough matching of the point cloud data set using pre-control points after aligning the coordinates of the point cloud data set in a three-dimensional coordinate system, and establish a rough matching result; The point cloud fusion module is used to use the rough registration result as a reference result, calculate the correlation between adjacent points between point clouds, match point cloud feature points based on the calculation result of the adjacent point correlation, complete the precise registration point cloud fusion, and establish a three-dimensional surveying and mapping result.

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