3D laser line scanning system based on point cloud positioning algorithm and control method

Abstract

The invention belongs to the technical field of three-dimensional measurement, and discloses a 3D laser line scanning system based on a point cloud positioning algorithm and a control method. The method comprises the following steps: acquiring environment initial point cloud data and scanning target information; carrying out feature extraction, constructing an environment feature description model, carrying out three-dimensional space partitioning processing, carrying out regional reflectivity feature analysis, and constructing a space reflection feature model; adaptive optimization configuration is carried out on the laser scanning parameters, and a scanning strategy is generated; scanning path dynamic planning is carried out on the scanning target information, scanning density distribution calculation is carried out, and a scanning path is generated; performing laser line scanning control by using the scanning path and the scanning strategy, and performing scanning data quality real-time monitoring to obtain scanning quality evaluation data; scanning parameter iterative optimization is carried out on the scanning quality evaluation data, point cloud data real-time fusion reconstruction is carried out, a high-precision three-dimensional model is generated, the modeling precision is improved, and meanwhile the system resource utilization efficiency is optimized.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional measurement technology, and more specifically, to a 3D laser line scanning system and a control method based on a point cloud positioning algorithm. Background Art

[0002] 3D laser line scanning technology, as an efficient three-dimensional data acquisition method, has been widely used in fields such as industrial inspection, architectural surveying and mapping, and cultural relic preservation. This technology rapidly acquires three-dimensional spatial information of target objects by emitting a laser beam and receiving the reflected signal. However, existing 3D laser line scanning control technology faces numerous challenges in practical application. In complex industrial environments, the lack of effective analysis of material properties and environmental factors often leads to incomplete data acquisition and unstable scanning accuracy. This is particularly true when working with highly reflective materials such as metal and glass, which can lead to severe data distortion and missed scans. In architectural surveying and mapping, traditional scanning methods are unable to automatically adjust scanning parameters based on the complexity of the building structure, resulting in poor data quality in detailed areas and compromising the final modeling results. In precision operations such as cultural relic scanning, the lack of real-time quality monitoring and feedback mechanisms means that scan anomalies often require re-capture, significantly reducing efficiency and increasing equipment wear. Furthermore, existing technologies often require frequent manual intervention and parameter adjustments when dealing with complex scenarios such as occlusion and blind spots, increasing operational complexity and making it difficult to ensure consistent scanning results. In large-scale engineering projects, these issues can lead to extended construction periods and increased costs, severely impacting project progress and quality control. Furthermore, existing technologies lack effective quality optimization methods in data post-processing, making it difficult to meet the requirements of high-precision 3D reconstruction.

[0003] In view of this, the present invention proposes a 3D laser line scanning system and control method based on a point cloud positioning algorithm to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a 3D laser line scanning control method based on a point cloud positioning algorithm, comprising the following steps: Step S1: Obtaining initial point cloud data of the environment and scanning target information; extracting features from the initial point cloud data of the environment and constructing an environment feature description model; Step S2: performing three-dimensional spatial partitioning processing on the environmental feature description model, and performing regional reflectivity characteristic analysis to construct a spatial reflectance characteristic model; Step S3: Adaptively optimize and configure the laser scanning parameters based on the spatial reflection characteristic model, and generate a parameterized scanning strategy; Step S4: dynamically planning the scanning path for the scanning target information and calculating the scanning density distribution to generate an adaptive scanning path; Step S5: using the adaptive scanning path and parameterized scanning strategy to perform real-time laser line scanning control, and perform real-time monitoring of the scanning data quality to obtain scanning quality evaluation data; Step S6: Perform iterative optimization of scanning parameters on the scanning quality assessment data, and perform real-time fusion reconstruction of point cloud data to generate a high-precision three-dimensional model.

[0005] Furthermore, the specific steps of step S1 are: Obtain initial point cloud data of the environment and scanning target information; Performing geometric feature segmentation on the initial point cloud data of the environment to generate an environment geometric feature set; Perform key structure identification on the environment geometric feature set to obtain key structure data of the environment; Based on the key structure data of the environment, the features of the initial point cloud data of the environment are extracted to build an environment feature description model.

[0006] Furthermore, the feature extraction of the initial point cloud data of the environment based on the key structure data of the environment and the construction of the environment feature description model include: Extract edge features from the initial point cloud data of the environment based on the key structure data of the environment to obtain edge features of the environment; Perform curvature analysis on the edge features of the environment to obtain a surface curvature distribution map; Performing geometric complexity evaluation on the surface curvature distribution map to obtain environmental geometric complexity data; Analyze the material properties of the initial point cloud data of the environment to obtain the surface material type; Identify the reflection characteristics of the surface material type to obtain material reflection characteristic data; An environmental feature description model is constructed based on environmental geometric complexity data and material reflection characteristic data.

[0007] Furthermore, the specific steps of step S2 are: Adaptively divide the scan space into voxels according to the environmental feature description model to obtain multi-resolution spatial voxels; Calculating surface normal vectors of multi-resolution space voxels to obtain voxel normal vector data; Perform incident angle analysis on voxel normal vector data to generate laser incident angle distribution; Analyze the reflectivity characteristics of multi-resolution spatial voxels based on the laser incident angle distribution to generate a spatial reflectivity prediction model; The multi-resolution spatial voxels are partitioned into three dimensions according to the spatial reflectivity prediction model to construct a spatial reflectivity characteristic model.

[0008] Furthermore, the specific steps of step S3 are: Map the scanning target information to the spatial reflection characteristic model and perform scanning energy demand analysis to obtain the regional energy demand distribution; Optimize laser power parameters based on regional energy demand distribution and generate adaptive laser power configuration; Perform scanning accuracy requirement analysis on the spatial reflection characteristic model to generate regional accuracy requirement distribution; The scanning speed and density parameters are optimized for the regional accuracy requirement distribution to generate a parametric scanning strategy.

[0009] Furthermore, the laser power parameter optimization of the regional energy demand distribution to generate an adaptive laser power configuration includes: Perform energy threshold analysis on regional energy demand distribution and generate regional minimum energy threshold; Calculate the initial laser power configuration based on the regional minimum energy threshold; Performing material adaptability adjustment on the initial configuration of laser power to generate material adapted laser power; Performing reflectivity gradient analysis on the spatial reflectivity characteristic model to generate reflectivity change gradient; Perform power smooth transition processing on the material to adapt to the laser power according to the reflectivity change gradient to generate a transition smooth power configuration; The energy utilization efficiency of the transition smooth power configuration is optimized to generate an adaptive laser power configuration.

[0010] Furthermore, the specific steps of step S4 are: Perform scanning visibility analysis on the scanning target information to obtain a target visibility map; Performing occlusion risk calculation on the target visibility map to generate an occlusion risk distribution; Perform multi-view scanning planning based on occlusion risk distribution to obtain the initial scanning path; Conduct geometric complexity evaluation on the environmental feature description model and obtain a complexity distribution map; The scanning density distribution of the initial scanning path is calculated according to the complexity distribution map, thereby generating an adaptive scanning path.

[0011] Furthermore, the specific steps of step S5 are: Use adaptive scanning path and parametric scanning strategy to perform real-time laser line scanning control to obtain original scanning point cloud data; Perform real-time signal-to-noise ratio analysis on raw scan point cloud data to generate a data quality distribution map; Conduct coverage evaluation on the data quality distribution map to extract scanning blind area information; Based on the scanning blind area information, the original scanning point cloud data is analyzed for point cloud integrity to obtain scanning quality assessment data.

[0012] Furthermore, the specific steps of step S6 are: Performing regional quality scoring on the scan quality assessment data to generate a regional scan quality index; Incrementally optimize the parametric scanning strategy based on the regional scanning quality index to generate optimized scanning parameters; Perform outlier detection and filtering on the original scanned point cloud data to generate cleaned point cloud data; The cleaned point cloud data is then subjected to multi-view point cloud registration and fusion to generate a high-precision three-dimensional model.

[0013] A 3D laser line scanning system based on a point cloud positioning algorithm, which is used to implement the 3D laser line scanning control method based on a point cloud positioning algorithm, comprises: The preliminary fitting module is used to obtain the initial point cloud data of the environment and the scanning target information; extract the features of the initial point cloud data of the environment and build the environment feature description model; Partitioning module, used to perform three-dimensional spatial partitioning processing on the environmental feature description model, analyze regional reflectivity characteristics, and construct a spatial reflectivity characteristic model; Scan parameterization module, which adaptively optimizes the laser scanning parameters based on the spatial reflection characteristic model and generates a parameterized scanning strategy; The scanning path fitting module is used to dynamically plan the scanning path based on the scanning target information and calculate the scanning density distribution to generate an adaptive scanning path; The scanning evaluation module is used to perform real-time laser line scanning control using an adaptive scanning path and a parametric scanning strategy, and to monitor the quality of the scanning data in real time to obtain scanning quality evaluation data; The model reconstruction module is used to iteratively optimize the scanning parameters of the scanning quality assessment data and perform real-time fusion reconstruction of the point cloud data to generate a high-precision three-dimensional model. The various modules are connected by wired and / or wireless means.

[0014] The technical effects and advantages of the 3D laser line scanning system and control method based on the point cloud positioning algorithm of the present invention are as follows: The present invention improves the accuracy, completeness and reliability of scanned data, significantly reduces noise and errors in the data acquisition process, and achieves a significant improvement in scanning quality. Through intelligent control strategies, the efficiency of scanning operations is greatly improved, the need for manual intervention is reduced, and the difficulty of operation and labor costs are reduced. This method has strong environmental adaptability and robustness, and can maintain stable performance in complex and changing application scenarios, significantly improving the practicality and reliability of the scanning system. At the same time, the adaptive optimization mechanism ensures the continuity and stability of the scanning process, effectively solving the various technical difficulties faced by traditional methods in practical applications. In addition, while improving modeling accuracy, it also optimizes the efficiency of system resource utilization, reduces equipment loss, and extends the service life of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the 3D laser line scanning control method based on the point cloud positioning algorithm of the present invention; Figure 2 Schematic diagram of the 3D laser line scanning system based on the point cloud positioning algorithm of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] This application provides a method and system. The execution entities of the method and system include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that are equipped with the system, which can be regarded as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of a 3D laser scanner, a point cloud processing system, and a cloud data management system.

[0018] See also Figure 1 The present invention provides a 3D laser line scanning control method based on a point cloud positioning algorithm. The 3D laser line scanning control method based on a point cloud positioning algorithm includes the following steps: Step S1: Obtaining initial point cloud data of the environment and scanning target information; extracting features from the initial point cloud data of the environment and constructing an environment feature description model; Step S2: performing three-dimensional spatial partitioning processing on the environmental feature description model, and performing regional reflectivity characteristic analysis to construct a spatial reflectance characteristic model; Step S3: Adaptively optimize and configure the laser scanning parameters based on the spatial reflection characteristic model, and generate a parameterized scanning strategy; Step S4: dynamically planning the scanning path for the scanning target information and calculating the scanning density distribution to generate an adaptive scanning path; Step S5: using the adaptive scanning path and parameterized scanning strategy to perform real-time laser line scanning control, and perform real-time monitoring of the scanning data quality to obtain scanning quality evaluation data; Step S6: Perform iterative optimization of scanning parameters on the scanning quality assessment data, and perform real-time fusion reconstruction of point cloud data to generate a high-precision three-dimensional model.

[0019] The present invention extracts features from the initial point cloud data of the environment, constructs an environmental feature description model, provides basic data support for subsequent positioning and scanning strategies, performs three-dimensional spatial partitioning processing and regional reflectivity characteristic analysis on the environmental feature description model, constructs a spatial reflection characteristic model, and realizes accurate characterization of complex environments. Based on the spatial reflection characteristic model, the laser scanning parameters are adaptively optimized and configured to generate a parametric scanning strategy, thereby improving the accuracy and efficiency of laser scanning. The scanning path is dynamically planned and the scanning density distribution is calculated for the scanning target information to generate an adaptive scanning path, thereby ensuring comprehensive coverage and quality of the scanning. The adaptive scanning path and the parametric scanning strategy are used to perform real-time laser line scanning control and real-time monitoring of the scanning data quality to obtain scanning quality assessment data, thereby realizing real-time monitoring and quality assurance of the scanning process. The scanning quality assessment data is subjected to iterative scanning parameter optimization and real-time fusion and reconstruction of point cloud data to generate a high-precision three-dimensional model, thereby improving the accuracy and integrity of the final three-dimensional model.

[0020] In this embodiment of the present invention, the steps of the 3D laser line scanning control method based on the point cloud positioning algorithm include: Step S1: Obtaining initial point cloud data of the environment and scanning target information; extracting features from the initial point cloud data of the environment and constructing an environment feature description model; In this embodiment, initial point cloud data of the environment is first acquired using a 3D laser scanner or other point cloud acquisition device. This data includes information about the environment's geometry, surface reflectivity, and color. Information about the scanned target, such as its position, size, shape, and material, is also acquired. This heterogeneous data is then integrated into a comprehensive environment and target dataset. A feature extraction algorithm is then used to analyze and process the initial point cloud data, extracting key geometric features, surface characteristics, and spatial distribution characteristics of the environment. Based on these extracted features, a complete environmental feature description model is constructed. This model comprehensively characterizes the environment's geometric and physical characteristics, providing fundamental support for subsequent scanning strategy planning.

[0021] Step S2: performing three-dimensional spatial partitioning processing on the environmental feature description model, and performing regional reflectivity characteristic analysis to construct a spatial reflectance characteristic model; In this embodiment, based on the environmental feature description model constructed in step S1, an adaptive spatial partitioning algorithm is used to divide the three-dimensional space into several areas with different characteristics. For each divided area, the reflection characteristics of its surface material and geometric shape to the laser are analyzed, and the reflectivity, scattering characteristics and other parameters of the area are calculated. Taking into account the reflection characteristics and spatial position relationship of each area, a complete spatial reflection characteristic model is constructed. This model can predict the reflection behavior of different areas to the laser and provide a basis for the subsequent laser scanning parameter configuration.

[0022] Step S3: Adaptively optimize and configure the laser scanning parameters based on the spatial reflection characteristic model, and generate a parameterized scanning strategy; In this embodiment, the spatial reflection characteristic model constructed in step S2 is used to analyze the requirements of different areas for laser scanning. The laser scanning parameters are adaptively configured for areas with different reflection characteristics, including: laser power, scanning speed, scanning density, scanning angle, etc. Through the comprehensive optimization of multiple parameters, a complete parameterized scanning strategy is generated. This strategy can provide the best scanning parameter configuration for the characteristics of different areas, ensuring scanning quality and efficiency.

[0023] Step S4: dynamically planning the scanning path for the scanning target information and calculating the scanning density distribution to generate an adaptive scanning path; In this embodiment, a dynamic programming algorithm is used to plan the optimal scanning path based on the geometric shape, position and importance of the scanning target. This path can ensure full coverage of the target and reduce scanning blind spots and redundant scanning. The scanning density required for different areas is calculated based on the complexity and importance of the target surface. The scanning density is increased for important or complex areas, and reduced for simple areas. Based on the planned path and the calculated scanning density, an adaptive scanning path is generated. This path can improve scanning efficiency while ensuring scanning quality.

[0024] Step S5: using the adaptive scanning path and parameterized scanning strategy to perform real-time laser line scanning control, and perform real-time monitoring of the scanning data quality to obtain scanning quality evaluation data; In this embodiment, the parametric scanning strategy generated in step S3 and the adaptive scanning path generated in step S4 are applied to the 3D laser scanning system to perform real-time laser line scanning control. During the scanning process, the quality of the scanning data is monitored in real time, including indicators such as point cloud density, point cloud noise level, and point cloud coverage. The monitored data quality indicators are comprehensively analyzed and evaluated to generate scanning quality evaluation data. These evaluation data reflect the quality level of the current scanning state and provide a basis for subsequent parameter optimization.

[0025] Step S6: Perform iterative optimization of scanning parameters on the scanning quality assessment data, and perform real-time fusion reconstruction of point cloud data to generate a high-precision three-dimensional model.

[0026] In this embodiment, based on the scanning quality assessment data obtained in step S5, the deficiencies of the current scanning parameters are analyzed, and an iterative optimization algorithm is used to adjust and optimize the scanning parameters, such as laser power, scanning speed, scanning density, etc. The optimized parameters are fed back to the scanning system to achieve continuous improvement in scanning quality. The acquired point cloud data is processed and fused in real time, including operations such as noise filtering, point cloud registration, and point cloud fusion. Through an efficient point cloud processing and reconstruction algorithm, the discrete point cloud data is reconstructed into a high-precision three-dimensional model. The model has a high degree of accuracy, integrity, and authenticity, and meets the needs of various application scenarios.

[0027] In this embodiment, the detailed implementation steps of step S1 include: Obtain initial point cloud data of the environment and scanning target information; Performing geometric feature segmentation on the initial point cloud data of the environment to generate an environment geometric feature set; Perform key structure identification on the environment geometric feature set to obtain key structure data of the environment; Based on the key structure data of the environment, the features of the initial point cloud data of the environment are extracted to build an environment feature description model.

[0028] In this embodiment, the initial point cloud data of the environment is obtained by a 3D laser scanner, a depth camera or other point cloud acquisition device. These data include spatial coordinates (x, y, z), reflection intensity, color and other information. At the same time, detailed information of the scanned target is obtained, including key parameters such as the target's position, size, shape characteristics, and material. For the obtained initial point cloud data of the environment, a geometric segmentation method such as a region growing algorithm and a RANSAC algorithm is used to segment the point cloud data into different geometric regions, such as planes, curved surfaces, edges, etc., and the geometric features of each region, such as normal vectors, curvatures, and principal curvature directions, are extracted to generate a feature set containing the geometric features of the environment, namely, the environmental geometric feature set. The environmental geometric feature set uses a model matching-based method to identify key structures in the environment, such as walls, floors, ceilings, doors, windows, columns, etc., calculate the parameters of these key structures, such as size, position, orientation, etc., and generate environmental key structure data. Based on the identified environmental key structure data, targeted feature extraction is performed on the original point cloud data. The extracted features include: geometric features (such as normal vectors, curvature), texture features (such as roughness, color distribution), spatial relationship features (such as distance, angle), etc. These extracted features are integrated into a complete environmental feature description model, which can comprehensively characterize all aspects of the environment and provide a basis for the subsequent scanning strategy formulation.

[0029] In this embodiment, the specific steps of extracting features from the initial point cloud data of the environment based on the key structure data of the environment and constructing the environment feature description model are as follows: Extract edge features from the initial point cloud data of the environment based on the key structure data of the environment to obtain edge features of the environment; Perform curvature analysis on the edge features of the environment to obtain a surface curvature distribution map; Performing geometric complexity evaluation on the surface curvature distribution map to obtain environmental geometric complexity data; Analyze the material properties of the initial point cloud data of the environment to obtain the surface material type; Identify the reflection characteristics of the surface material type to obtain material reflection characteristic data; An environmental feature description model is constructed based on environmental geometric complexity data and material reflection characteristic data.

[0030] In this embodiment, edge features are identified and extracted from the initial point cloud data of the environment based on the key structure data of the environment. The 3D Canny algorithm or the edge detection method based on the change of the normal vector is used to extract the edge points in the point cloud data. The edge clarity, continuity and other characteristics are calculated to generate an edge feature set of the environment. The surface curvature characteristics (local surface curvature) are calculated for the extracted edge features of the environment. The local surface curvature is calculated using the following formula: ; where p represents a point in the point cloud, is the local surface curvature of p, 、 and are the three eigenvalues ​​of the neighborhood covariance matrix of point p. By calculating the local surface curvature of each point in the environment, a surface curvature distribution map is generated. The surface curvature distribution map shows the curvature change of the environment surface. Based on the surface curvature distribution map, the geometric complexity of the environment is evaluated. The geometric complexity C1 is calculated by the following formula: Where k_i is the average curvature of the i-th region, w_i is the weight coefficient of the i-th region, indicating the importance of the region. By calculating the geometric complexity of different regions, the geometric complexity data of the environment is obtained. For the initial point cloud data of the environment, the material properties of the surface are analyzed based on information such as reflection intensity and color. The material classification algorithm based on reflectivity and texture features is used to classify the environment surface into different material types, such as metal, wood, plastic, glass, etc. For the identified surface material type, its reflection characteristics to the laser are analyzed, that is, the reflection coefficient of the material is expressed by the following formula: ;in, is the laser incident angle, is the laser wavelength, and is a coefficient related to the material (using lasers of different wavelengths and incident angles to perform reflection tests on the material, record the reflection intensity, and fit the data to obtain and ), calculate the reflection characteristics of different materials at different incident angles and wavelengths, generate material reflection characteristic data, integrate the environmental geometric complexity data and material reflection characteristic data, and build a complete environmental feature description model. This model includes the geometric and physical characteristics of the environment, providing comprehensive information support for the subsequent scanning strategy formulation.

[0031] In this embodiment, the detailed implementation steps of step S2 include: Adaptively divide the scan space into voxels according to the environmental feature description model to obtain multi-resolution spatial voxels; Calculating surface normal vectors of multi-resolution space voxels to obtain voxel normal vector data; Perform incident angle analysis on voxel normal vector data to generate laser incident angle distribution; Analyze the reflectivity characteristics of multi-resolution spatial voxels based on the laser incident angle distribution to generate a spatial reflectivity prediction model; The multi-resolution spatial voxels are partitioned into three dimensions according to the spatial reflectivity prediction model to construct a spatial reflectivity characteristic model.

[0032] In this embodiment, the scanning space is adaptively divided into voxels based on the geometric complexity data in the environmental feature description model. A smaller voxel size is used in areas with high geometric complexity, and a larger voxel size is used in areas with low geometric complexity. The voxel size v is determined by the following formula: ;in, is the preset minimum voxel size, is the preset maximum voxel size, C1 is the geometric complexity of the region, is a control coefficient, a parameter used to control the relationship between voxel size and geometric complexity, and is usually selected between 0.5 and 2. Through adaptive voxel partitioning, a multi-resolution spatial voxel partitioning result is obtained. For the multi-resolution spatial voxels obtained by partitioning, the normal vector of the inner surface of each voxel is calculated. The normal vector n is calculated by principal component analysis (PCA) of the point set within the voxel. By calculating the normal vector of each voxel, voxel normal vector data is generated. According to the voxel normal vector data and the position of the laser scanner, the laser incident angle θ=arccos(|n·d|) between the laser beam and the voxel surface is calculated; where n is the normal vector of the voxel surface, d is the direction vector of the laser beam, and |n·d| represents the absolute value of the dot product of the two vectors. By calculating the incident angles of different voxels, a laser incident angle distribution map is generated. Combining the laser incident angle distribution and the material reflection characteristic data, the reflectivity characteristics (reflectivity) of different voxels are analyzed. The reflectivity is predicted by the following formula: ; where m is the material type, The scattering coefficient of a material describes its ability to scatter incident light and is related to properties such as its surface roughness and microstructure. By predicting the reflectivity of different voxels at different incident angles, a spatial reflectivity prediction model is generated. Based on this spatial reflectivity prediction model, multi-resolution spatial voxels are further spatially partitioned, grouping voxels with similar reflective properties into the same region. This partitioning process constructs a complete spatial reflectivity model, which can predict the reflection behavior of lasers in different areas of an environment and provide guidance for subsequent scanning parameter configuration.

[0033] In this embodiment, the detailed implementation steps of step S3 include: Map the scanning target information to the spatial reflection characteristic model and perform scanning energy demand analysis to obtain the regional energy demand distribution; Optimize laser power parameters based on regional energy demand distribution and generate adaptive laser power configuration; Perform scanning accuracy requirement analysis on the spatial reflection characteristic model to generate regional accuracy requirement distribution; The scanning speed and density parameters are optimized for the regional accuracy requirement distribution to generate a parametric scanning strategy.

[0034] In this embodiment, acquired scanning target information (such as target location, geometric features, and required accuracy) is mapped onto a spatial reflection characteristic model to identify the specific areas to be scanned. Combined with the reflectivity data in the spatial reflection characteristic model, the laser energy required to successfully obtain valid echo signals in different regions is analyzed. Low-reflectivity regions require higher laser energy, while high-reflectivity regions require lower laser energy. A regional energy demand distribution is generated to characterize the laser energy requirements of different regions. Based on the regional energy demand distribution, laser power parameter settings are optimized. The required laser power for each region is calculated based on the energy requirements and laser transmission characteristics. The power setting is ensured to be within a reasonable range, taking into account the performance limitations and safety factors of the laser. An adaptive laser power configuration scheme is generated to match the laser energy requirements of each region, ensuring signal quality while avoiding energy waste and overexposure. The scanning accuracy requirements of different regions are analyzed based on the accuracy requirements in the scanning target information and the geometric complexity data in the spatial reflection characteristic model. Regions with high geometric complexity or high accuracy requirements require higher scanning accuracy, while regions with simple geometry or low accuracy requirements can be appropriately reduced. The regional accuracy demand distribution is generated to guide subsequent scanning parameter settings. Optimize scanning speed and density parameters based on the distribution of regional accuracy requirements. For areas with high accuracy requirements, reduce the scanning speed and increase the sampling density. For areas with low accuracy requirements, increase the scanning speed and reduce the sampling density. Develop a balanced parameter setting plan by comprehensively considering time efficiency and accuracy requirements. Generate a complete parametric scanning strategy, including parameters such as power settings, scanning speed, and sampling density for each area, to provide comprehensive guidance for the actual scanning process.

[0035] In this embodiment, the specific steps of optimizing the laser power parameters based on the regional energy demand distribution and generating the adaptive laser power configuration are as follows: Perform energy threshold analysis on regional energy demand distribution and generate regional minimum energy threshold; Calculate the initial laser power configuration based on the regional minimum energy threshold; Performing material adaptability adjustment on the initial configuration of laser power to generate material adapted laser power; Performing reflectivity gradient analysis on the spatial reflectivity characteristic model to generate reflectivity change gradient; Perform power smooth transition processing on the material to adapt to the laser power according to the reflectivity change gradient to generate a transition smooth power configuration; The energy utilization efficiency of the transition smooth power configuration is optimized to generate an adaptive laser power configuration.

[0036] In this embodiment, regional energy demand distribution data is analyzed to calculate the minimum energy threshold required to obtain a valid echo signal in each region. The minimum energy threshold depends on factors such as the region's reflectivity, distance, and ambient noise. A signal-to-noise ratio (SNR) model is used to determine the minimum energy level required to meet signal detection requirements. A regional minimum energy threshold distribution map is generated as the basis for power configuration. Based on the regional minimum energy threshold and the laser transmission attenuation model, the initial laser power required to meet the minimum threshold requirement when reaching the target surface is calculated. Factors such as laser loss during transmission, such as atmospheric attenuation and optical system transmittance, are considered. A preliminary laser power configuration scheme is generated to ensure that each region receives sufficient laser energy. The initial laser power configuration scheme is material-adaptive adjusted. Based on the material information in the spatial reflection characteristic model, the power setting is adjusted according to the characteristics of different materials. For highly reflective materials (such as metal and mirrors), the power is appropriately reduced to avoid overexposure and glare. For low-reflective materials (such as black plastic and fabric), the power is appropriately increased to ensure signal quality. A material-adaptive laser power configuration scheme is generated to ensure that the power setting better meets actual scanning requirements. Analyze the reflectivity changes between adjacent regions in the spatial reflection characteristic model and calculate the spatial reflectivity gradient. Identify boundary regions with drastic reflectivity changes, which require special power configuration. Generate a reflectivity gradient to characterize the spatial reflectivity variation trend. Based on the reflectivity gradient, smoothly adapt the material to the laser power. Design a gradual power transition strategy in boundary regions with drastic reflectivity changes to avoid sudden power changes between adjacent regions. Use spatial interpolation algorithms (such as bilinear interpolation and spline interpolation) to achieve smooth power transition. Generate a power configuration scheme with a smooth transition to ensure the continuity and stability of power changes during scanning. Optimize the energy utilization efficiency of the power configuration scheme after the smooth transition. Evaluate the match between the energy demand and the configured power of each region, and identify areas with excess or insufficient energy. While meeting the minimum energy demand, appropriately reduce the power in areas with excess energy to improve energy utilization efficiency. Comprehensively consider factors such as signal quality, energy efficiency, and system load to generate the final adaptive laser power configuration scheme to achieve precise control and efficient utilization of laser energy.

[0037] In this embodiment, the specific steps of step S4 are: Perform scanning visibility analysis on the scanning target information to obtain a target visibility map; Performing occlusion risk calculation on the target visibility map to generate an occlusion risk distribution; Perform multi-view scanning planning based on occlusion risk distribution to obtain the initial scanning path; Conduct geometric complexity evaluation on the environmental feature description model and obtain a complexity distribution map; The scanning density distribution of the initial scanning path is calculated according to the complexity distribution map, thereby generating an adaptive scanning path.

[0038] In this embodiment, the scanning target information and the environmental feature description model are analyzed to evaluate the visibility of the target area from different angles; a visibility calculation algorithm, such as the ray tracing method, is used to simulate the interaction between laser beams emitted from different positions and the target surface; the visibility index of each part of the target surface is calculated. Specifically, the target surface is divided into small areas or points, and a series of potential scanning positions are defined in the scanning space. Virtual rays are emitted from each potential scanning position to each point on the target surface to check whether the rays are blocked by obstructions. For each point on the target surface, the number of scanning positions that can "see" it is calculated, and the number of scanning positions is normalized to obtain a visibility index; indicating how many potential scanning positions can observe the part; a target visibility map is generated to intuitively display the visibility of each part of the target surface. The target visibility map is analyzed to identify areas that may be obscured; the risk index of each area being obscured, i.e., the occlusion risk index, is calculated. This index is affected by factors such as the visibility of the area and the complexity of the surrounding environment; Specifically, the calculated visibility index is used to define the initial occlusion risk = 1-visibility index; introduce the environmental complexity factor C(p) to represent the complexity of the environment around point p; C(p) can be calculated based on local curvature, edge density, etc. Calculate the deviation θ(p) between the optimal scanning angle and the actual possible scanning angle. The corresponding occlusion risk index OR(p) is expressed as: ;in , , is the corresponding weight coefficient, is the influence function of the angular deviation (can be an exponential function).

[0039] For areas with low visibility, the occlusion risk is usually higher; an occlusion risk distribution map is generated to characterize the probability of occlusion of various parts of the target surface. Based on the occlusion risk distribution, multiple scanning perspectives are planned to ensure that all high occlusion risk areas can be effectively observed from at least one perspective; a perspective optimization algorithm, such as a greedy algorithm or a genetic algorithm, is used to select the best combination of scanning perspectives to achieve full coverage of the target; a scanning path is planned for each selected perspective, taking into account scanning efficiency and coverage completeness; the scanning paths of multiple perspectives are integrated to form an initial scanning path plan. The geometric information in the environmental feature description model is analyzed to evaluate the geometric complexity of various parts of the target surface; geometric complexity can be measured by indicators such as curvature change, surface gradient, and edge density; a geometric complexity distribution map is generated to characterize the complexity and detail richness of various parts of the target surface. According to the geometric complexity distribution map, the scanning density of the initial scanning path is adjusted; for areas with high geometric complexity, the scanning density is increased to capture detailed features; for areas with low geometric complexity, the scanning density is appropriately reduced to improve scanning efficiency; the optimal scanning density for each area is calculated to balance the requirements of accuracy and efficiency; based on the scanning density calculation results, an adaptive scanning path is generated to ensure that high-complexity areas are scanned more densely, while low-complexity areas adopt a more sparse scanning strategy to achieve rational allocation and utilization of resources.

[0040] In this embodiment, the specific steps of step S5 are: Use adaptive scanning path and parametric scanning strategy to perform real-time laser line scanning control to obtain original scanning point cloud data; Perform real-time signal-to-noise ratio analysis on raw scan point cloud data to generate a data quality distribution map; Conduct coverage evaluation on the data quality distribution map to extract scanning blind area information; Based on the scanning blind area information, the original scanning point cloud data is analyzed for point cloud integrity to obtain scanning quality assessment data.

[0041] In this embodiment, an adaptive scanning path and a parametric scanning strategy are applied to control the laser scanner to scan according to the planned path and parameters. During the scanning process, the operating parameters of the laser (such as power, scanning speed, etc.) and the motion trajectory of the scanning head are adjusted in real time. The laser line scanning process is performed, and the laser beam scans the target according to the planned path and density. The echo signal reflected by the laser is collected and converted into three-dimensional point cloud data through signal processing to obtain the original scanning point cloud data. The acquired raw point cloud data is subjected to real-time signal-to-noise ratio analysis. The ratio between the signal intensity and background noise of each point is calculated to evaluate the reliability of the data. For points with low signal-to-noise ratio, their position accuracy and reliability are generally poor. Based on the signal-to-noise ratio analysis results, a quality score is assigned to each point to characterize the accuracy and reliability of the data. A data quality distribution diagram is generated to intuitively display the spatial quality distribution of the scanning results. Analyze the data quality distribution map to assess the scanning coverage of the target surface; calculate the area ratio C (coverage rate) of effective point cloud coverage to determine the completeness of the scan; identify areas with low quality or missing data, which may be blind spots in the scanning process; the coverage rate C is calculated by dividing the area of ​​the scanned area by the total area of ​​the target. Through coverage analysis, identify the scanning blind spots, that is, areas that have not been effectively scanned, record the location and range of these blind spots, and form scanning blind spot information to provide a basis for subsequent supplementary scans. Combined with the scanning blind spot information and the quality distribution of the point cloud data, analyze the integrity of the point cloud data. The integrity score Q is calculated using the following formula: ; Where C is coverage, SNR is average signal-to-noise ratio, and B is blind area ratio. 、 and are the weighting coefficients for each item. It's important to explain that the blind area ratio refers to the proportion of areas that were not effectively scanned or whose data quality falls below a certain threshold to the total target area. This integrity analysis generates scan quality assessment data that comprehensively reflects the quality of the current scan and provides a basis for subsequent parameter optimization.

[0042] In this embodiment, the specific steps of step S6 are: Performing regional quality scoring on the scan quality assessment data to generate a regional scan quality index; Incrementally optimize the parametric scanning strategy based on the regional scanning quality index to generate optimized scanning parameters; Perform outlier detection and filtering on the original scanned point cloud data to generate cleaned point cloud data; The cleaned point cloud data is then subjected to multi-view point cloud registration and fusion to generate a high-precision three-dimensional model.

[0043] In this embodiment, the scan quality assessment data is divided into regions, and a quality score (regional quality score) is calculated for each region to quantify the quality of the regional scan. The regional quality score is calculated using the following formula: ; where SNR(r) is the average signal-to-noise ratio of region r, D(r) is the point cloud density of region r, C(r) is the coverage of region r, and E(r) is the edge error of region r (quantified by comparing the deviation between the scanned edge points and the ideal edge, which can be obtained by calculating the average distance between the edge points and the fitted curve). 、 、 and is the weighting coefficient for the corresponding terms above. By calculating the quality scores for different regions, a regional scan quality index is generated. This regional scan quality index (which maps the standardized scores to an easily understood index range) provides a visual representation of the quality differences in different regions of the scan results. Based on the regional scan quality index, the parametric scanning strategy is optimized. For regions with low quality indices, the causes of poor quality are analyzed (e.g., insufficient power, low scanning density, etc.). Based on the analysis results, the corresponding scanning parameters are adjusted, such as increasing power or scanning density. Simulations are performed to verify these parameter adjustments and predict their improved scan quality. Optimized scanning parameters are generated to improve the scan quality of problem areas. Outlier detection and filtering are performed on the raw scan point cloud data. Outliers are identified using statistical outlier detection algorithms (e.g., local outlier factors and random sample consistency algorithms). The spatial distribution characteristics of the point cloud are analyzed to identify outliers that do not conform to the distribution pattern. These identified outliers are filtered out to improve the accuracy and consistency of the point cloud data. This results in cleaned, high-quality point cloud data, providing a reliable foundation for subsequent 3D reconstruction. Perform registration and fusion on the cleaned point cloud data from different perspectives; use the iterative closest point (ICP) algorithm or its improved version to achieve precise alignment of point clouds from different perspectives; calculate the geometric transformation relationship between point clouds and convert them into a unified coordinate system; fuse the point clouds in overlapping areas to eliminate redundant points and retain high-quality data; apply point cloud reconstruction algorithms (such as Poisson surface reconstruction, moving least squares method, etc.) to convert point cloud data into a continuous surface model; generate a high-precision three-dimensional model to achieve accurate digital expression of the target and provide high-quality three-dimensional data support for subsequent analysis, design and application.

[0044] It should be noted that the above formulas are all dimensionless and numerical calculations. The formula is a formula for the latest real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0045] The above describes the 3D laser line scanning control method based on the point cloud positioning algorithm in the embodiment of the present application. The following describes the 3D laser line scanning system based on the point cloud positioning algorithm in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a 3D laser line scanning system based on a point cloud positioning algorithm includes: The preliminary fitting module is used to obtain the initial point cloud data of the environment and the scanning target information; extract the features of the initial point cloud data of the environment and build the environment feature description model; Partitioning module, used to perform three-dimensional spatial partitioning processing on the environmental feature description model, analyze regional reflectivity characteristics, and construct a spatial reflectivity characteristic model; Scan parameterization module, which adaptively optimizes the laser scanning parameters based on the spatial reflection characteristic model and generates a parameterized scanning strategy; The scanning path fitting module is used to dynamically plan the scanning path based on the scanning target information and calculate the scanning density distribution to generate an adaptive scanning path; The scanning evaluation module is used to perform real-time laser line scanning control using an adaptive scanning path and a parametric scanning strategy, and to monitor the quality of the scanning data in real time to obtain scanning quality evaluation data; The model reconstruction module is used to iteratively optimize the scanning parameters of the scanning quality assessment data and perform real-time fusion and reconstruction of point cloud data to generate a high-precision three-dimensional model. The modules are connected via wired and / or wireless means to achieve data transmission between modules.

[0046] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0047] The present invention can adaptively optimize scanning parameters and paths based on environmental characteristics and scanning target information, significantly improving scanning efficiency and accuracy. Through precise extraction of environmental features and spatial partitioning, accurate grasp of the characteristics of different regions is achieved, providing a scientific basis for parameter optimization. Adaptive power configuration and path planning make the allocation of scanning resources more reasonable, ensuring both fine scanning of highly complex areas and avoiding waste of resources in simple areas. Real-time quality monitoring and iterative parameter optimization ensure dynamic adjustment and continuous improvement of the scanning process. The resulting high-precision three-dimensional model has higher accuracy and completeness, meeting the needs of various application scenarios.

[0048] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A 3D laser line scanning control method based on point cloud positioning algorithm, characterized in that: The following steps are involved: Step S1: Obtaining initial point cloud data of the environment and scanning target information; extracting features from the initial point cloud data of the environment and constructing an environment feature description model; Step S2: performing three-dimensional spatial partitioning processing on the environmental feature description model, and performing regional reflectivity characteristic analysis to construct a spatial reflectance characteristic model; Step S3: Adaptively optimize and configure the laser scanning parameters based on the spatial reflection characteristic model, and generate a parameterized scanning strategy; Step S4: dynamically planning the scanning path for the scanning target information and calculating the scanning density distribution to generate an adaptive scanning path; Step S5: using the adaptive scanning path and parameterized scanning strategy to perform real-time laser line scanning control, and perform real-time monitoring of the scanning data quality to obtain scanning quality evaluation data; Step S6: Perform iterative optimization of scanning parameters on the scanning quality assessment data, and perform real-time fusion reconstruction of point cloud data to generate a high-precision three-dimensional model.

2. The 3D laser line scanning control method based on point cloud positioning algorithm according to claim 1, characterized in that: The specific steps of step S1 are: Obtain initial point cloud data of the environment and scanning target information; Performing geometric feature segmentation on the initial point cloud data of the environment to generate an environment geometric feature set; Perform key structure identification on the environment geometric feature set to obtain key structure data of the environment; Based on the key structure data of the environment, the features of the initial point cloud data of the environment are extracted to build an environment feature description model.

3. The 3D laser line scanning control method based on point cloud positioning algorithm according to claim 2, characterized in that: The feature extraction of the initial point cloud data of the environment based on the key structure data of the environment and the construction of the environment feature description model include: Extract edge features from the initial point cloud data of the environment based on the key structure data of the environment to obtain edge features of the environment; Perform curvature analysis on the edge features of the environment to obtain a surface curvature distribution map; Performing geometric complexity evaluation on the surface curvature distribution map to obtain environmental geometric complexity data; Analyze the material properties of the initial point cloud data of the environment to obtain the surface material type; Identify the reflection characteristics of the surface material type to obtain material reflection characteristic data; An environmental feature description model is constructed based on environmental geometric complexity data and material reflection characteristic data.

4. The 3D laser line scanning control method based on point cloud positioning algorithm according to claim 3 is characterized in that: The specific steps of step S2 are: Adaptively divide the scan space into voxels according to the environmental feature description model to obtain multi-resolution spatial voxels; Calculating surface normal vectors of multi-resolution space voxels to obtain voxel normal vector data; Perform incident angle analysis on voxel normal vector data to generate laser incident angle distribution; Analyze the reflectivity characteristics of multi-resolution spatial voxels based on the laser incident angle distribution to generate a spatial reflectivity prediction model; The multi-resolution spatial voxels are partitioned into three dimensions according to the spatial reflectivity prediction model to construct a spatial reflectivity characteristic model.

5. The 3D laser line scanning control method based on point cloud positioning algorithm according to claim 4, characterized in that: The specific steps of step S3 are: Map the scanning target information to the spatial reflection characteristic model and perform scanning energy demand analysis to obtain the regional energy demand distribution; Optimize laser power parameters based on regional energy demand distribution and generate adaptive laser power configuration; Perform scanning accuracy requirement analysis on the spatial reflection characteristic model to generate regional accuracy requirement distribution; The scanning speed and density parameters are optimized for the regional accuracy requirement distribution to generate a parametric scanning strategy.

6. The 3D laser line scanning control method based on point cloud positioning algorithm according to claim 5, characterized in that: Optimizing the laser power parameters based on the regional energy demand distribution to generate an adaptive laser power configuration includes: Perform energy threshold analysis on regional energy demand distribution and generate regional minimum energy threshold; Calculate the initial laser power configuration based on the regional minimum energy threshold; Performing material adaptability adjustment on the initial configuration of laser power to generate material adapted laser power; Performing reflectivity gradient analysis on the spatial reflectivity characteristic model to generate reflectivity change gradient; Perform power smooth transition processing on the material to adapt to the laser power according to the reflectivity change gradient to generate a transition smooth power configuration; The energy utilization efficiency of the transition smooth power configuration is optimized to generate an adaptive laser power configuration.

7. The 3D laser line scanning control method based on point cloud positioning algorithm according to claim 6, characterized in that: The specific steps of step S4 are: Perform scanning visibility analysis on the scanning target information to obtain a target visibility map; Performing occlusion risk calculation on the target visibility map to generate an occlusion risk distribution; Perform multi-view scanning planning based on occlusion risk distribution to obtain the initial scanning path; Conduct geometric complexity evaluation on the environmental feature description model and obtain a complexity distribution map; The scanning density distribution of the initial scanning path is calculated according to the complexity distribution map, thereby generating an adaptive scanning path.

8. The 3D laser line scanning control method based on point cloud positioning algorithm according to claim 7, characterized in that: The specific steps of step S5 are: Use adaptive scanning path and parametric scanning strategy to perform real-time laser line scanning control to obtain original scanning point cloud data; Perform real-time signal-to-noise ratio analysis on raw scan point cloud data to generate a data quality distribution map; Conduct coverage evaluation on the data quality distribution map to extract scanning blind area information; Based on the scanning blind area information, the original scanning point cloud data is analyzed for point cloud integrity to obtain scanning quality assessment data.

9. The 3D laser line scanning control method based on point cloud positioning algorithm according to claim 8, characterized in that: The specific steps of step S6 are: Performing regional quality scoring on the scan quality assessment data to generate a regional scan quality index; Incrementally optimize the parametric scanning strategy based on the regional scanning quality index to generate optimized scanning parameters; Perform outlier detection and filtering on the original scanned point cloud data to generate cleaned point cloud data; The cleaned point cloud data is then subjected to multi-view point cloud registration and fusion to generate a high-precision three-dimensional model.

10. A 3D laser line scanning system based on a point cloud positioning algorithm, which is used to implement the 3D laser line scanning control method based on a point cloud positioning algorithm according to any one of claims 1 to 9, characterized in that: include: The preliminary fitting module is used to obtain the initial point cloud data of the environment and the scanning target information; Extract features from the initial point cloud data of the environment and build an environment feature description model; Partitioning module, used to perform three-dimensional spatial partitioning processing on the environmental feature description model, analyze regional reflectivity characteristics, and construct a spatial reflectivity characteristic model; Scan parameterization module, which adaptively optimizes the laser scanning parameters based on the spatial reflection characteristic model and generates a parameterized scanning strategy; The scanning path fitting module is used to dynamically plan the scanning path based on the scanning target information and calculate the scanning density distribution to generate an adaptive scanning path; The scanning evaluation module is used to perform real-time laser line scanning control using an adaptive scanning path and a parametric scanning strategy, and to monitor the quality of the scanning data in real time to obtain scanning quality evaluation data; The model reconstruction module is used to iteratively optimize the scanning parameters of the scanning quality assessment data and perform real-time fusion reconstruction of the point cloud data to generate a high-precision three-dimensional model. The various modules are connected by wired and / or wireless means.

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