Point cloud-based control point visibility detection method, device and equipment and storage medium

Abstract

The present application relates to the field of engineering surveying, and in particular to a control point visibility detection method and device based on point cloud, equipment and storage medium. The method comprises: obtaining the spatial coordinate information of two control points, the erection height of the total station at the survey station, the erection height of the prism at the target control point, and the point cloud data of the survey area; simulating a target point cloud representing the spatial position of the prism in the point cloud data and fusing it with the original point cloud; establishing a survey station coordinate system with the survey station control point as the reference, and converting the fused point cloud to the survey station perspective; constructing a simulation imaging plane based on the survey station perspective point cloud and generating a simulation image of the survey station perspective; and determining whether the two control points satisfy the visibility condition according to whether the target feature representing the prism can be identified in the simulation image. The present application realizes intuitive determination of the visibility of control points without the need for on-site investigation, thereby effectively reducing the workload of field work and improving the efficiency and reliability of visibility detection in engineering surveying.

Description

Technical Field

[0001] This invention relates to the field of engineering surveying technology, and in particular to a method, apparatus, equipment, and storage medium for detecting the visibility of control points based on point clouds. Background Technology

[0002] In engineering surveying, a total station is typically set up at one control point to observe another. To ensure observation accuracy and feasibility, the two control points must be mutually visible. Currently, confirming visual visibility between control points usually involves on-site investigation. This is done through manual visual inspection, temporary instrument setup, or multiple trial measurements to determine if any obstructions exist, thus confirming whether the visibility requirement for total station observation is met. This method relies heavily on the experience of field personnel and is significantly affected by factors such as terrain, vegetation, and the construction environment, often requiring substantial manpower and time investment.

[0003] With the development of UAV lidar technology, more and more engineering projects are conducting UAV lidar aerial surveys of the survey area and its surrounding areas during the construction or surveying phases to obtain 3D point cloud data with wide coverage and high accuracy. This type of point cloud data can realistically reflect the spatial distribution of the ground surface and obstacles, and theoretically possesses the basic conditions for use in visibility analysis. However, existing technologies lack an effective means to directly simulate the total station observation perspective and determine the visibility of control points once point cloud data has been acquired, resulting in the underutilization of the application value of point cloud data in control point visibility detection.

[0004] Therefore, the existing technology still has the following problems: even when the point cloud data of the survey area and the spatial location information of the control points are available, it is still necessary to rely on on-site surveys or repeated field operations to determine whether the control points are visible to each other. It is difficult to complete the visibility test and scheme evaluation in the office stage. The visibility judgment is inefficient and costly, and it is not conducive to the advance planning and optimization of the control point layout scheme in engineering surveying. Summary of the Invention

[0005] This invention aims to at least solve the problem in existing technologies where, even with acquired point cloud data of the survey area, on-site surveys are still required to determine the visibility of control points. This invention provides a method, apparatus, device, and storage medium for detecting the visibility of control points based on point clouds. It can detect whether the visibility conditions between control points are met by total station observation without the need for on-site surveys, thus obtaining the control point visibility determination result.

[0006] In the specific implementation process, the point cloud can be simulated from the perspective of the station based on the spatial location information of the control point, the installation height of the instrument and prism, and the point cloud data of the survey area, and a simulation image from the perspective of the station can be generated. By identifying the target features representing the prism in the simulation image, the visibility of the control point can be judged, thereby completing the visibility detection and evaluation in the office stage, achieving the technical effect of reducing the workload of the field and improving the efficiency and reliability of visibility judgment.

[0007] In a first aspect, embodiments of the present invention provide a control point visibility detection method based on point clouds, the method comprising:

[0008] Acquire the spatial coordinate information of the control point to be detected and the target control point, the installation height of the total station at the station, the installation height of the prism at the target control point, and the point cloud data covering the area where the control point is located; Target point cloud data for characterizing the spatial position of the prism is generated from the point cloud data, and the target point cloud data is fused with the point cloud data. A station coordinate system is established based on the station control point, and the fused point cloud data is converted to the station coordinate system to obtain point cloud data from the station's perspective. Based on the point cloud data from the station's perspective, a simulated imaging plane of the station's perspective is constructed, and a simulated image of the station's perspective is generated according to the distribution relationship of the point cloud on the simulated imaging plane. Based on whether the target features representing the prism can be identified in the simulated image, it is determined whether the two control points meet the line-of-sight condition.

[0009] Optionally, the step of simulating and generating target point cloud data to characterize the spatial position of the prism in the point cloud data may include: generating multiple point cloud points in the vertical direction at preset intervals based on the spatial coordinates of the target control point and the erection height of the prism, and setting the point cloud points as a vertical point cloud structure with uniform color characteristics.

[0010] Optionally, establishing a station coordinate system based on the station control point may include: using the station control point as the origin of the coordinate system and using the spatial direction from the station control point to the target control point as the forward direction of the station coordinate system, thereby determining the spatial coordinate relationship from the station's perspective.

[0011] Optionally, after converting the fused point cloud data to the station coordinate system, the converted point cloud data is filtered by direction. Point cloud points are determined one by one to determine whether they are located in the forward direction of the station in the station coordinate system, and point cloud points located in the reverse region of the forward direction of the station are removed.

[0012] Optionally, the simulation imaging plane for constructing the station's perspective includes: selecting the X-axis direction and the Z-axis direction to form an imaging plane in the station coordinate system, and dividing the imaging plane into regular grid units according to a preset spatial resolution.

[0013] Optionally, after mapping the point cloud points to the corresponding grid cells, when a grid cell contains multiple point cloud points, the point cloud point closest to the station in the forward direction of the station is selected, and the color information of the grid cell is determined based on the point cloud point.

[0014] Secondly, embodiments of the present invention provide a control point visibility detection device based on point clouds, the device comprising: The first acquisition module is used to acquire the spatial coordinate information of the station control point to be detected and the target control point, the installation height of the total station at the station, the installation height of the prism at the target control point, and the point cloud data covering the area where the control point is located. The first calculation module is used to simulate and generate target point cloud data in the point cloud data to characterize the spatial position of the prism, and to fuse the target point cloud data with the point cloud data. The second calculation module is used to establish a station coordinate system based on the station control point, and to convert the fused point cloud data into the station coordinate system to obtain point cloud data from the station's perspective. The third calculation module is used to construct a simulated imaging plane of the station's perspective based on the point cloud data from the station's perspective, and to generate a simulated image of the station's perspective based on the distribution relationship of the point cloud on the simulated imaging plane. The first determination module is used to determine whether the two control points meet the line-of-sight condition based on whether the target features representing the prism can be identified in the simulation image.

[0015] Thirdly, embodiments of the present invention provide a computer device, the computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the methods described above.

[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions for performing the point cloud-based control point visibility detection method as described in the first aspect embodiment above. Since the computer-readable storage medium employs all the technical solutions of the point cloud-based control point visibility detection method of the above embodiments, it possesses at least all the beneficial effects brought about by the technical solutions of the above embodiments.

[0017] The control point visibility detection method, apparatus, device, and storage medium based on point cloud according to the present invention have at least the following beneficial effects: The point cloud-based control point visibility detection method of the present invention first acquires the spatial coordinate information of the control point to be detected and the target control point, the total station's setup height at the station, the prism's setup height at the target control point, and point cloud data covering the area where the control point is located. Based on this, target point cloud data representing the spatial position of the prism is simulated and generated from the point cloud data. This target point cloud data is then fused with the original point cloud data, thereby introducing identifiable target features corresponding to the actual observed target into the point cloud scene. Subsequently, a station coordinate system is established based on the station control point, and the fused point cloud data is uniformly converted to this coordinate system, ensuring consistency between the point cloud data and the actual total station observation perspective. Afterwards, a simulated imaging plane of the station perspective is constructed based on the point cloud data from the station perspective, and a simulated image of the station perspective is generated according to the spatial distribution relationship of the point cloud on the simulated imaging plane, allowing the point cloud occlusion relationship to be intuitively reflected in the simulated image. Finally, by judging whether the target features representing the prism can be identified in the simulated image, it is determined whether the visibility condition between the two control points is met. In this way, by converting the three-dimensional point cloud data into a simulated image from the perspective of the station and making a judgment based on the target features, the visibility of control points can be accurately and intuitively detected without the need for on-site surveys. This effectively reduces the workload of fieldwork, lowers the manpower and time costs of visibility judgment, and improves the efficiency and reliability of control point layout and observation scheme evaluation in engineering surveying.

[0018] The point cloud-based control point visibility detection device of the present invention, through the coordinated cooperation between various computing modules, realizes the device-based execution of the above-mentioned method steps, and can automatically complete prism simulation, point cloud coordinate transformation, station viewpoint simulation imaging, and visibility determination; the corresponding equipment and storage medium execute the stored program instructions, enabling the method to run stably and efficiently on the computing device, thereby also having the beneficial effects of reducing the need for on-site surveys and improving the efficiency and accuracy of visibility detection.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0020] Figure 1 This is a flowchart of the control point visibility detection method based on point cloud according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the station coordinate system in Embodiment 2 of the present invention; Figure 3This is a schematic diagram of the pixel grid division of the X–Z section in Embodiment 2 of the present invention; Figure 4 The flowchart is shown below for the control point visibility detection method based on point cloud in Embodiment 3 of the present invention. Figure 5 This is a point cloud simulation diagram of the station's perspective under the condition that the prism is not visible in Embodiment 4 of the present invention. Figure 6 This is a point cloud simulation diagram of the station's perspective under the condition that the prism is visible in Embodiment 4 of the present invention. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments. All technologies implemented based on the content of the present invention fall within the scope of protection of the present invention.

[0022] Unless otherwise specified, the use of terms such as "upper," "lower," "left," "right," "center," "inner," "outer," and "side" to indicate orientation or positional relationships in the description of specific embodiments of the present invention is based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationship in which the product / equipment / device is typically placed during use. These terms are merely for the purpose of facilitating the description of the present invention or simplifying the description in specific embodiments, enabling those skilled in the art to quickly understand the solution, and do not indicate or imply that a particular device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship. Therefore, they should not be construed as limitations on the present invention.

[0023] In the description of the embodiments of this invention, technical terms such as "first" and "second" only distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary or secondary relationship of the indicated technical features. In the description of the embodiments of this invention, "multiple" means two or more, unless otherwise explicitly defined.

[0024] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0025] Example 1 During their research, the inventors discovered that when using a total station to observe control points, if it is necessary to determine whether two control points meet the line-of-sight requirement before construction or during the surveying plan development stage, confirmation is usually achieved through on-site surveys, repeated instrument setup, or manual visual inspection. This process is cumbersome, inefficient, and highly susceptible to changes in terrain, vegetation, and the site environment, and can only be completed when field conditions permit. Especially in engineering scenarios where point cloud data for the survey area has already been acquired, repeated on-site surveys are still required, resulting in the ineffective utilization of existing data resources.

[0026] In solving practical engineering surveying problems, to achieve the technical objectives of determining the visibility of control points in advance during the office work phase, reducing fieldwork workload, and improving the efficiency of scheme evaluation, existing technologies cannot achieve intuitive and reliable determination of visibility solely based on point cloud data and control point location information. Therefore, after in-depth research on the above-mentioned problems, the applicant proposed a point cloud-based control point visibility detection method. Addressing the technical problem of difficulty in determining control point visibility under existing point cloud data, this method introduces a prism simulation, constructs station perspectives, and generates simulated images. This enables the visual detection and determination of control point visibility, achieving the technical effect of completing visibility determination without on-site surveys, thus improving the efficiency and reliability of engineering surveying.

[0027] Please refer to Figure 1 , Figure 1 This is a schematic diagram illustrating the steps of a point cloud-based control point visibility detection method provided in an embodiment of the present invention. The method may include: Acquire the spatial coordinate information of the control point to be detected and the target control point, the installation height of the total station at the station, the installation height of the prism at the target control point, and the point cloud data covering the area where the control point is located; Target point cloud data for characterizing the spatial position of the prism is generated from the point cloud data, and the target point cloud data is fused with the point cloud data. A station coordinate system is established based on the station control point, and the fused point cloud data is converted to the station coordinate system to obtain point cloud data from the station's perspective. Based on the point cloud data from the station's perspective, a simulated imaging plane of the station's perspective is constructed, and a simulated image of the station's perspective is generated according to the distribution relationship of the point cloud on the simulated imaging plane. Based on whether the target features representing the prism can be identified in the simulated image, it is determined whether the two control points meet the line-of-sight condition.

[0028] In a specific implementation, this method may include: First, the spatial coordinates of the control point to be detected and the target control point, the installation height of the total station at the station, the installation height of the prism at the target control point, and point cloud data covering the area where the control point is located are acquired. Based on the spatial coordinates of the target control point and the installation height of the prism, multiple point cloud points are generated vertically at preset intervals to form a vertical target point cloud structure representing the prism. The target point cloud data is then fused with the point cloud data to obtain fused point cloud data containing the prism target.

[0029] Subsequently, a station coordinate system is established based on the station control point, with the station control point as the origin and the spatial direction pointing from the station control point to the target control point as the forward direction of the station coordinate system. Under this station coordinate system, the fused point cloud data is uniformly converted to the station coordinate system to obtain point cloud data from the station's perspective. After the conversion, the point cloud data from the station's perspective is subjected to directional filtering. Points are checked point by point to determine whether they are located in the forward direction of the station, and point points located in the opposite region of the forward direction are removed to retain valid point cloud data for imaging analysis.

[0030] Based on this, a simulation imaging plane is formed by selecting the X-axis and Z-axis directions in the station coordinate system, and regular grid cells are divided within the imaging plane according to a preset spatial resolution. Each point cloud point in the effective point cloud data is mapped to its corresponding grid cell. In the station coordinate system, the station control point is the origin, the Y-axis is the forward direction from the station to the target control point, the X-axis is the direction facing to the right of the Y-axis and perpendicular to the Y-axis, and the Z-axis is the vertical direction perpendicular to the XY plane and upwards. When a grid cell contains multiple point cloud points, the point cloud point closest to the station in the X-axis direction is selected, and the color information of the grid cell is determined based on this point cloud point. After determining the color information of the grid cell, the color information is attenuated and corrected according to the distance relationship between the corresponding point cloud point of the grid cell and the station.

[0031] After determining the color of each grid cell, a simulation image of the station's viewpoint is generated based on the corrected color information. Finally, based on whether the target features representing the prism can be identified in the simulation image, it is determined whether the line-of-sight condition is met between the two control points.

[0032] This embodiment introduces a target point cloud to characterize the spatial position of a prism based on existing point cloud data, and constructs a station coordinate system with the station control point as the reference. The fused point cloud data is then converted to simulated imaging from the station's perspective. This allows for a direct reflection of the occlusion relationship between the station and the target control point without relying on on-site surveys. By analyzing the distribution of the point cloud in the simulated imaging plane and identifying the target features characterizing the prism, the visibility of the control point is effectively determined. This enables the visibility detection of the control point to be completed in the office stage, thereby reducing the workload in the field, lowering the manpower and time costs of visibility judgment, and improving the efficiency and reliability of control point layout and observation scheme evaluation in engineering surveying.

[0033] The point cloud-based control point visibility detection method provided in this embodiment can be applied to many technical fields, such as engineering surveying, engineering exploration, smart construction sites, urban mapping, and transportation and municipal engineering construction. These include, but are not limited to, control network layout and optimization, construction surveying scheme demonstration, existing engineering renovation and expansion surveying, and feasibility assessment of surveying under complex terrain conditions. In the above implementation, when determining the visibility relationship between control points, the spatial occlusion situation from the station's perspective can be simulated and analyzed based on the acquired point cloud data. By introducing target features representing the prism position into the point cloud, an intuitive judgment of the visibility of the observed target can be achieved. This allows for visibility detection without repeated on-site surveys, reducing fieldwork workload, improving the efficiency of surveying scheme evaluation, and enhancing the reliability of engineering implementation.

[0034] Example 2 In Embodiment 1 above, the overall process and functional hierarchy of the control point visibility detection method based on point clouds of the present invention were generally described. The focus was on the overall technical approach and implementation effects of the present invention in control point visibility detection. Some processing steps were described abstractly in natural language to facilitate understanding of the overall technical solution of the present invention. To further illustrate the specific implementation of the present invention in practical engineering applications and to verify the feasibility and effectiveness of the method under specific parameter conditions, the technical solution of the present invention will be described in more detail below, combined with specific calculation processes.

[0035] Therefore, without changing the overall technical concept and processing flow described in Example 1, Example 2 further provides the implementation method of the control point visibility detection method based on point cloud in the specific implementation process, and specifically explains the calculation relationship of key steps such as point cloud simulation generation, coordinate transformation, imaging processing and visibility determination, so that those skilled in the art can more clearly understand the technical details of the present invention and implement or reproduce the technical solution of the present invention accordingly.

[0036] There are two control points A and B. The coordinates of A are (XA, YA, HA), and the coordinates of B are (XB, YB, HB). Point cloud data is already available for this survey area. The total station at point A is set up at an altitude of JA, and the prism at point B is set up at an altitude of JB.

[0037] 1. Simulate and generate prism point cloud data The prism is set as a red vertical line, and L points are simulated, each with three-dimensional coordinates and RGB color values:

[0038] In the formula, i represents the point number, and the point number ranges from 0 to L. This indicates the resolution of the simulated station-view image; , , These are the X-axis plane coordinates, Y-axis plane coordinates, and Z-axis elevation coordinates of the i-th simulated prism point cloud in the original survey area point cloud coordinate system, respectively. , , Let L represent the red, green, and blue channel component values ​​in the RGB color model of the i-th simulated prism point cloud. L is calculated using the following formula:

[0039] In the formula, Indicates taking the integer part.

[0040] Once generated, add this set of point cloud data to the existing point cloud data in the survey area.

[0041] 2. Coordinate Transformation Please refer to Figure 2 , Figure 2 This is a schematic diagram of the coordinate system for the survey station. Establishment is as follows: Figure 2 The new coordinate system shown has A1 as the origin, the Y-axis pointing towards B1; the X-axis extending horizontally to the right (clockwise) and perpendicular to the Y-axis; and the Z-axis pointing upwards perpendicular to the XY plane.

[0042] The coordinates of A1 are (XA1, YA1, HA1), and the coordinates of B1 are (XB1, YB1, HB1). They are calculated using the following formulas: XA1=XA, YA1=YA, HA1=HA+JA, XB1=XB, YB1=YB, HB1=HB+JB.

[0043] Following the seven-parameter coordinate transformation method, based on the spatial relationship between the station control point and the target control point, the spatial transformation relationship between the original point cloud coordinate system and the station coordinate system is determined. A comprehensive coordinate transformation is then performed on each point in the point cloud, ensuring that the transformed point cloud data is uniformly expressed in a station coordinate system with the station control point as the origin and the forward direction as the reference. The transformed point cloud data is as follows: , where n is the number of points.

[0044] Iterate through all points in the point cloud. For any i-th point, if If so, then that point is removed.

[0045] 3. Generate simulated images from the station's perspective. Please refer to Figure 3 , Figure 3 This is a schematic diagram of pixel grid division along the X–Z section. The section is divided into several square grids of the same size, with a side length of f. Each square represents one pixel.

[0046] Following the above division method, the point cloud is placed into the corresponding squares. For any point ( The method to determine which square it is located in is:

[0047] In the formula, m represents the row number and n represents the column number. This represents the maximum value of the Z-coordinate in the point cloud. This represents the minimum value of the X-coordinate in the point cloud. This indicates rounding up to the nearest integer.

[0048] At this point, each square may be empty or contain several points. Iterating through each square, for any square F(m, n), if the square is not empty, its RGB color value is:

[0049] In the formula, , , This represents the RGB value of the point with the smallest Y-coordinate among all the points in the grid.

[0050] If the square is empty, set it to sky blue:

[0051] To make the visual effect more realistic, the farther away from the measuring station, the lower the RGB value of the color. For a non-empty square F(m, n), let the minimum y-coordinate be found among all points in the square. The RGB values ​​are then corrected using the following formula:

[0052] In the formula, , , This indicates the corrected RGB value of the square. This indicates the maximum visible distance when collecting point cloud data.

[0053] Based on the final determined RGB values ​​of each grid cell, a simulated image of the station's perspective is generated according to the spatial arrangement of the grid cells in the simulated imaging plane. In this simulated image, target features representing the prism are identified. When an image region with preset color features and continuously distributed vertically can be observed in the simulated image, it is determined that the station can observe the prism, thus confirming that the corresponding two control points meet the line-of-sight condition. When the target features cannot be observed in the simulated image, it is determined that the station cannot observe the prism, and the corresponding control points do not meet the line-of-sight condition.

[0054] By using the above method, the imaging results from the perspective of the station are generated by simulating the acquired point cloud data, which realizes the intuitive detection and judgment of the visibility of control points. This allows the visibility judgment to be completed without the need for on-site investigation, thereby improving the efficiency and reliability of the visibility detection of control points.

[0055] Example 3 In Embodiment 2 above, the implementation of the control point visibility detection method based on point clouds of the present invention is described in detail, combining specific parameter relationships and calculation processes. The focus is on the implementation logic of key steps such as point cloud simulation generation, coordinate transformation, imaging processing, and visibility determination at the specific calculation level. To further summarize and generalize the technical solution of the present invention from the perspective of algorithm implementation, enabling those skilled in the art to understand the overall calculation flow and data processing sequence of the present invention without relying on specific formula derivations, the algorithm implementation process of the present invention is given below. Embodiment 3, without changing the technical ideas and processing results described in Embodiments 1 and 2, abstracts the above specific implementation process into an algorithm flow, explaining the data dependencies and execution order between each processing step, so as to facilitate the programmatic implementation and engineering application of the present invention.

[0056] like Figure 4 As shown, Figure 4 This is a flowchart of the algorithm for a point cloud-based control point visibility detection method. This embodiment describes the method of the present invention from the perspective of algorithm implementation without changing the technical ideas described in Embodiments 1 and 2 above. Specifically, it includes: First, control point data is acquired, and prism simulation point cloud data is generated based on the control point data to characterize the prism position at the target control point. At the same time, the original point cloud data covering the survey area is acquired, and the prism simulation point cloud data is merged with the original point cloud data to obtain merged point cloud data.

[0057] Based on this, coordinate transformation is performed on the merged point cloud data using the control point data, uniformly transforming the merged point cloud data into a station coordinate system based on the station control points, resulting in transformed point cloud data. Subsequently, the transformed point cloud data is filtered by direction, checking whether the Y-coordinate of each point in the station coordinate system is greater than 0; if the Y-coordinate of a point is not greater than 0, the point is discarded; if the Y-coordinate of a point is greater than 0, the point is retained for subsequent processing.

[0058] Then, based on the retained point cloud data, a regular grid is created on the X–Z section in the station coordinate system, and each point cloud point is placed into its corresponding grid cell within the regular grid. For each grid cell, it is determined whether the grid cell is empty; if the grid cell is not empty, the point cloud point with the smallest Y coordinate within the grid cell is selected, and the RGB value of that point cloud point is used as the RGB value of the grid cell; after determining the RGB value, the RGB value is corrected. After determining the RGB value of each grid cell, a simulation image of the station's perspective is created based on the RGB values ​​of each grid cell in the regular grid.

[0059] Finally, it is determined whether the red vertical line feature representing the prism can be seen in the simulation image; when the red vertical line feature can be seen, it is determined that the line-of-sight condition between the station control point and the target control point is met; when the red vertical line feature cannot be seen, it is determined that the line-of-sight condition between the station control point and the target control point is not met.

[0060] The algorithm described in this embodiment can be implemented through software programs. These programs can run on general-purpose computing devices, dedicated measurement terminals, or engineering measurement-related computing platforms, or they can be implemented using existing point cloud processing software frameworks. In specific implementation, each step of the algorithm can be completed by the processor executing program instructions stored in the storage medium. The point cloud data acquisition, coordinate transformation, mesh construction, image generation, and judgment processing involved can all be implemented using different programming languages, data structures, or computing libraries according to actual application requirements; this invention does not limit this.

[0061] The algorithm described above can simulate spatial occlusion relationships from the perspective of a total station, given the acquired point cloud data of the survey area. Based on the simulation results, the visibility between control points can be determined, transforming visibility detection from a traditional method relying on on-site investigation to a processing method primarily based on office calculations. This algorithm is characterized by its flexible implementation and strong applicability, effectively improving the efficiency and reliability of control point visibility assessment, reducing fieldwork workload, and is suitable for various engineering surveying and exploration application scenarios.

[0062] Example 4 In Embodiment 2 above, the control point visibility detection method based on point cloud of the present invention was described from the perspective of specific calculation implementation. To further verify the feasibility and application effect of the present invention in actual engineering scenarios, the application of the method of the present invention under real survey area conditions is described below with reference to specific engineering examples.

[0063] Two control points are set up within a survey area at a construction site, with a horizontal distance of approximately 250m between them. The spatial coordinates and elevation information of each control point are known. The elevation of the target control point B is lower than that of the survey station control point A, and the survey station's view is from above. Point cloud data is collected from the survey area using a UAV lidar system. The maximum visible distance of the obtained point cloud data is approximately 10km. In this embodiment, the spatial resolution of the simulated imaging is set to 0.25m.

[0064] In this engineering scenario, the total station height at the survey station and the prism height at the target control point are both set to 2m. Following the point cloud-based control point visibility detection method described in the previous embodiment, the point cloud data of the survey area is processed to generate a point cloud simulation image from the station's perspective. The results are as follows: Figure 5 As shown. By Figure 5 It can be seen that the red vertical line feature representing the prism was not identified in the simulation image, indicating that under the current conditions, station control point A cannot observe the prism at target control point B.

[0065] While keeping the total station height at the survey station constant, gradually increase the prism height at the target control point. When the prism height reaches 16m, generate the point cloud simulation image from the survey station's perspective again using the method described above. The result is as follows. Figure 6 As shown. By Figure 6 The red vertical line representing the prism can be clearly observed, indicating that under this condition, station control point A can observe the prism at target control point B.

[0066] The above engineering application examples demonstrate that the control point visibility detection method based on point cloud provided by this invention can intuitively reflect the visibility relationship between the station and the target control point in specific engineering scenarios, and the visibility can be evaluated by adjusting the instrument or prism setup conditions, thereby providing an effective reference for the layout of control points and the formulation of observation schemes in engineering surveying.

[0067] Example 5 In Embodiments 1 to 4 above, the control point visibility detection technology solution based on point cloud of the present invention was described from the perspectives of method flow, specific calculation implementation, algorithm execution process, and engineering application examples. To further illustrate the implementation of the technical solution of the present invention at the device level, and to verify that the method can be stably implemented with a modular structure, the embodiments of the present invention are described below in conjunction with the device, equipment, and storage medium.

[0068] This embodiment provides a control point visibility detection device based on point clouds. The device includes a first acquisition module, a first calculation module, a second calculation module, a third calculation module, and a first determination module. The first acquisition module acquires the spatial coordinates of the control point to be detected and the target control point, the total station's installation height at the station, the prism's installation height at the target control point, and point cloud data covering the area where the control point is located, thus providing necessary basic data for subsequent processing. The first calculation module simulates and generates target point cloud data characterizing the prism's spatial position from the point cloud data, and fuses the target point cloud data with the point cloud data, so that the point cloud data contains target feature information for visibility determination.

[0069] The second calculation module is used to establish a station coordinate system based on the station control points, and to convert the fused point cloud data into the station coordinate system to obtain point cloud data from the station's perspective, thus ensuring that the spatial representation of the point cloud data is consistent with the actual total station observation perspective. The third calculation module is used to construct a simulated imaging plane of the station's perspective based on the point cloud data from the station's perspective, and to generate a simulated image of the station's perspective based on the distribution relationship of the point cloud on the simulated imaging plane, so that the point cloud occlusion relationship is presented intuitively in image form. The first determination module is used to determine whether the two control points meet the visibility condition based on whether the target features representing the prism can be identified in the simulated image, thereby outputting the visibility detection result.

[0070] Through the collaborative work of the above modules, the device in this embodiment can implement the control point visibility detection method based on point cloud in a device form, so that the visibility detection process has good module division and execution stability, which is convenient for integration and deployment in engineering measurement systems or related software platforms.

[0071] It should be understood that the various modules of the control point visibility detection device based on point cloud provided in the above embodiments are only illustrated by the division of each functional module in the above description when performing visibility detection. In practical applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0072] The functional modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of the present invention.

[0073] Based on the same application concept, embodiments of the present invention also provide a computer device, which may include a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the control point visibility detection method based on point clouds as described above. Based on the same application concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the control point visibility detection method based on point clouds as described above. Thus, under different hardware environments and application scenarios, the technical effects of reducing fieldwork workload and improving visibility judgment efficiency and reliability can be achieved.

[0074] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0075] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A control point visibility detection method based on point clouds, characterized in that, include: Acquire the spatial coordinate information of the control point to be detected and the target control point, the installation height of the total station at the station, the installation height of the prism at the target control point, and the point cloud data covering the area where the control point is located; Target point cloud data for characterizing the spatial position of the prism is generated from the point cloud data, and the target point cloud data is fused with the point cloud data. A station coordinate system is established based on the station control point, and the fused point cloud data is converted to the station coordinate system to obtain point cloud data from the station's perspective. Based on the point cloud data from the station's perspective, a simulated imaging plane of the station's perspective is constructed, and a simulated image of the station's perspective is generated according to the distribution relationship of the point cloud on the simulated imaging plane. Based on whether the target features representing the prism can be identified in the simulated image, it is determined whether the line-of-sight condition is met between the station control point and the target control point.

2. The control point visibility detection method based on point cloud according to claim 1, characterized in that, The step of simulating and generating target point cloud data to characterize the spatial position of the prism in the point cloud data includes: generating multiple point cloud points in the vertical direction at preset intervals based on the spatial coordinates of the target control point and the erection height of the prism, and setting the point cloud points as a vertical point cloud structure with uniform color characteristics.

3. The control point visibility detection method based on point cloud as described in claim 1, characterized in that, The establishment of the station coordinate system based on the station control point includes: taking the station control point as the origin of the coordinate system and taking the spatial direction from the station control point to the target control point as the forward direction of the station coordinate system, thereby determining the spatial coordinate relationship from the perspective of the station.

4. The control point visibility detection method based on point cloud according to claim 3, characterized in that, After converting the fused point cloud data to the station coordinate system, the converted point cloud data is filtered by direction. Point cloud points are determined one by one to see if they are located in the forward direction of the station in the station coordinate system, and point cloud points located in the reverse region of the forward direction of the station are removed.

5. The control point visibility detection method based on point cloud according to claim 4, characterized in that, The simulation imaging plane for constructing the station's perspective includes: selecting the X-axis and Z-axis directions to form an imaging plane in the station coordinate system, and dividing the imaging plane into regular grid units according to a preset spatial resolution.

6. The control point visibility detection method based on point cloud according to claim 5, characterized in that, After mapping the point cloud points to the corresponding grid cells, when a grid cell contains multiple point cloud points, select the point cloud point that is closest to the station in the forward direction of the station, and determine the color information of the grid cell based on the point cloud point.

7. The control point visibility detection method based on point cloud according to claim 6, characterized in that, After determining the color information of the grid cell, the color information is attenuated and corrected according to the distance relationship between the point cloud point corresponding to the grid cell and the station. Based on the corrected color information, a simulation image of the station's perspective is generated for use in visibility determination.

8. A control point visibility detection device based on point clouds, characterized in that, include: The first acquisition module is used to acquire the spatial coordinate information of the station control point to be detected and the target control point, the installation height of the total station at the station, the installation height of the prism at the target control point, and the point cloud data covering the area where the control point is located. The first calculation module is used to simulate and generate target point cloud data in the point cloud data to characterize the spatial position of the prism, and to fuse the target point cloud data with the point cloud data. The second calculation module is used to establish a station coordinate system based on the station control point, and to convert the fused point cloud data into the station coordinate system to obtain point cloud data from the station's perspective. The third calculation module is used to construct a simulated imaging plane of the station's perspective based on the point cloud data from the station's perspective, and to generate a simulated image of the station's perspective based on the distribution relationship of the point cloud on the simulated imaging plane. The first determination module is used to determine whether the line-of-sight condition is met between the station control point and the target control point based on whether the target features representing the prism can be identified in the simulation image.

9. A computer device, characterized in that, The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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