Indoor Scene Wireless Channel Modeling Method Based on LiDAR Perception Reconstruction

Abstract

This application relates to the cross-disciplinary field of 3D scene reconstruction and wireless channel modeling, and provides a method for wireless channel modeling of indoor scenes based on LiDAR perception reconstruction. The method includes: first, using LiDAR to collect point cloud data of the indoor scene; then, performing noise reduction and sampling processing on the point cloud data; next, extracting structural elements including walls, ceilings, and floors based on random sampling consistency and Hough transform algorithms; then, for internal elements, using an image semantic segmentation model to detect wall opening structures and reconstructing furniture based on template deformation; finally, using a surface subdivision algorithm to reconstruct 3D surfaces; and finally, matching electromagnetic parameters to different material surfaces and completing wireless channel modeling through ray tracing simulation. This method divides complex scenes into structural elements and internal elements, and designs corresponding algorithms for different elements to extract key geometric features, thereby improving the accuracy and robustness of the reconstruction.

Description

Technical Field

[0001] This application belongs to the technical field of cross-integration of 3D scene reconstruction and wireless channel modeling, and in particular relates to a method for wireless channel modeling of indoor scenes based on LiDAR perception reconstruction. Background Technology

[0002] With the in-depth research of 6G communication technology, achieving high-precision and high-efficiency scene reconstruction and wireless channel modeling has become a key technical challenge in the field of communication. Indoor environments, due to their enclosed and diverse nature, exhibit complex signal propagation characteristics, thus requiring accurate modeling of key reflectors and scatterers within the scene. LiDAR technology, by emitting laser pulses and precisely measuring their return time, enables accurate distance determination of target objects. Based on this, the resulting point cloud data provides detailed three-dimensional information about the indoor scene. This information encompasses the specific locations and shapes of key reflectors and scatterers, constructing an accurate three-dimensional environment model. The application of this technology is crucial for a deep understanding and effective modeling of the propagation characteristics of indoor wireless signals. It can more accurately assist channel simulators in calculating the electromagnetic propagation effects of reflection, refraction, and scattering of electromagnetic signals indoors, thereby further exploring the complex mapping relationship between channel characteristics and the environment.

[0003] However, in LiDAR-assisted communication scene reconstruction, the complexity of the scene presents significant challenges to the acquisition and processing of point cloud data. On the one hand, the sheer volume of point cloud data in large-scale scenes limits data transmission and processing speed; on the other hand, accurately extracting key geometric structures from massive point cloud data is a challenging task. This requires algorithms with efficient data filtering and noise reduction capabilities, as well as the ability to accurately identify and distinguish key features in the scene, such as edges, corners, and planes, to facilitate subsequent communication scene analysis and modeling. Furthermore, the potential for occlusion, reflection, and noise in point cloud data further increases the difficulty of extracting key geometric structures, placing higher demands on the robustness and accuracy of the algorithm.

[0004] Ray tracing simulation, as an effective method for predicting electromagnetic wave propagation, has significant application value in 6G communication systems. However, traditional ray tracing methods often require extensive manual measurements and meticulous hand-modeling to construct accurate 3D scene models when dealing with complex scenes. This process is not only time-consuming but also extremely labor-intensive. These tedious steps include accurately recording the size, material, and positional parameters of various objects in the scene, as well as meticulously verifying numerous details in the model. These tasks place extremely high demands on researchers and, to some extent, limit the efficiency of ray tracing technology in rapid response and large-scale scene applications. Therefore, lidar point cloud-assisted scene reconstruction provides an efficient, accurate, and cost-effective solution for indoor scene ray tracing simulation, which has significant practical application value for promoting the development of 6G communication systems. Summary of the Invention

[0005] This application provides a method for modeling indoor wireless channels based on LiDAR perception reconstruction, which can solve the problems of low robustness and accuracy in current communication scene reconstruction based on LiDAR perception assistance.

[0006] In a first aspect, embodiments of this application provide a method for modeling indoor wireless channels based on lidar perception reconstruction, comprising: step S1, collecting dense three-dimensional point cloud data of an indoor communication scene using lidar, wherein the three-dimensional point cloud data includes three-dimensional point cloud data of ceiling, floor, walls, columns, doors, windows, and furniture surfaces; step S2, performing region clipping, filtering, denoising, and point cloud downsampling preprocessing based on the three-dimensional point cloud data in step S1; step S3, projecting the processed three-dimensional point cloud data in step S2 onto a two-dimensional plane and performing grid segmentation to obtain a point cloud density map, and extracting key facade structure parameters from the point cloud density map using an algorithm combining random sampling consensus algorithm and Hough transform, wherein the key facade structure parameters include wall structure parameters and column structure parameters, and detecting and extracting the height parameters of the ceiling structure and the floor structure from the point cloud density map by calculating statistical histograms on the height axis; step S4 Step S4: Based on the key facade structure parameters obtained in step S3, project the key facade structure onto a two-dimensional plane to obtain a wall image. Extract the door and window structures on the wall image based on the idea of ​​image semantic segmentation. Remove the point cloud of the key facade structure, the ceiling structure, and the floor structure surface from step S3 to obtain the internal furniture surface point cloud data. Generate a set of furniture parameters for each piece of furniture in the internal furniture surface point cloud data by adjusting the shape and position parameters of the pre-created furniture template. Step S5: Based on the height parameters of the ceiling structure, the height parameters of the floor structure, the key facade structure parameters, and the furniture parameters extracted in steps S2, S3, and S4, construct a three-dimensional model of the indoor scene using a triangulation algorithm. After matching the electromagnetic parameters of the three-dimensional model, obtain an electromagnetic simulation model. Use a ray tracing algorithm to simulate the propagation process of electromagnetic signals in the indoor scene to simulate the channel characteristics of the receiving point.

[0007] In one possible implementation of the first aspect, step S2 specifically includes:

[0008] Step S201: The collected 3D point cloud data is cropped in 3D space using a bounding box to remove the point cloud data outside the room.

[0009] Step S202: Denoise the 3D point cloud data obtained in step S201 according to the outlier removal algorithm, and downsample the 3D point cloud data using a random sampling algorithm to compress the data size.

[0010] Optionally, in another possible implementation of the first aspect, step S3 specifically includes:

[0011] Step S301: The 3D point cloud data is dimensionality reduced and projected onto a 2D plane, and a mesh is generated based on a preset mesh size to obtain a height of N. h Width is N w Point cloud density map

[0012] Step S302: For the point cloud density map obtained in step S301, extract the key facade structure from the point cloud density map by combining Hough transform and random sampling consensus algorithm, and remove the point cloud data of the key facade structure surface to update the point cloud density map.

[0013] Step S303: For the key facade structures extracted in step S302, fit the best connection method between the key facade structures based on the residual point cloud of the connection area between the endpoints of the key facade structures, and reconstruct the topological relationship between the key facade structures.

[0014] Step S304: For the updated point cloud density map in step S302, filter out the height parameters of the ceiling structure and the floor structure based on the statistical histogram on the height axis.

[0015] Optionally, in another possible implementation of the first aspect, step S4 specifically includes:

[0016] Step S401: Project the key facade structure extracted in step S3 onto the vertical facade corresponding to the facade structure to construct the wall image;

[0017] Step S402: For the wall image in step S401, perform semantic segmentation based on the zero-shot image semantic segmentation model SegmentAnything 2, extract the door and window structures on the wall image, and obtain the opening structure of the wall corresponding to the door and window structures based on the door and window structures on the wall image.

[0018] Step S403: For the ceiling, floor, and key facade structures extracted in step S3, remove the point cloud data of the corresponding structural surfaces from the point cloud data. For common furniture in indoor scenes, pre-construct furniture templates defined by a set of shape parameters and position parameters. By adjusting the shape parameters and position parameters, fit the furniture from the remaining point cloud data to achieve 3D reconstruction of the furniture.

[0019] Optionally, in another possible implementation of the first aspect, step S5 specifically includes:

[0020] Step S501: When performing 3D modeling of the key facade structure, perform sequential 3D modeling according to the direction of wall connection based on the key facade structure parameters extracted in step S3, and further subdivide each wall segment based on the left and right boundaries and upper and lower boundaries of the opening structure of the wall extracted in step S4 to complete the subdivision of the door and window area.

[0021] Step S502: Based on the polygons of the key facade structure extracted in step S3 projected onto the two-dimensional plane, as well as the height parameters of the ceiling structure and the floor structure, the polygons are divided into triangular face elements based on the ear-scissor algorithm to complete the three-dimensional modeling of the ceiling structure and the floor structure.

[0022] Step S503: Determine the surface elements to which different structures belong, and perform electromagnetic parameter matching on different surface elements based on the electromagnetic material parameters recommended by ITU in order to construct an electromagnetic simulation model.

[0023] Step S504: For the electromagnetic simulation model obtained in step S503, import it into the ray tracing channel simulation software Wireless Insite, configure the transmit and receive antenna parameters, the frequency and bandwidth of the transmitted signal to ensure that the simulation conditions match the actual communication environment, and calculate the propagation path of electromagnetic waves in the indoor communication scenario through the ray tracing algorithm, and record the electromagnetic propagation phenomena of reflection and diffraction between electromagnetic waves and the surrounding environment in the propagation path.

[0024] Optionally, in another possible implementation of the first aspect, the algorithm described above, which combines the random sampling consensus algorithm with the Hough transform, extracts key facade structural parameters from the point cloud density map, including:

[0025] The key facade structure is modeled as a straight line on a plane projection, defined by a set of parameters Ψ. vplane ={θ,rho} describes the density-weighted parameter subspace for calculating the density map of a point cloud based on the Hough transform.

[0026]

[0027] in For parameter Ψ vplane The set of density map points S through which the line described by {θ, rho} passes. θ,rho The corresponding grid cluster density uses the DBSCAN clustering algorithm.

[0028] Based on density-weighted parameter subspace Select the optimal sampling set S optimal :

[0029]

[0030] Based on the random sample consensus algorithm, from the optimal sample set S optimal The wall structure parameters are extracted from the random sample consensus algorithm, where the sampling threshold ε is determined by the optimal sample set S. optimal The width w obtained from the pre-clustering is calculated as follows:

[0031]

[0032] Where k1.k2 is used to describe the influence of different wall widths on the threshold selection, ε max ,ε min These are the maximum and minimum values ​​selected by the random sampling consensus algorithm threshold.

[0033] This application provides a method for indoor scene wireless channel modeling based on LiDAR perception reconstruction. First, LiDAR is used to collect dense 3D point cloud data of the indoor communication scene. The 3D point cloud data includes 3D point cloud data of the ceiling, floor, walls, columns, doors, windows, and furniture surfaces. Then, based on the 3D point cloud data from step S1, region clipping, filtering, noise reduction, and point cloud downsampling preprocessing are performed. Next, the processed 3D point cloud data from step S2 is projected onto a 2D plane and meshed to obtain a point cloud density map. An algorithm combining random sampling consensus and Hough transform is used to extract key facade structural parameters from the point cloud density map. These key facade structural parameters include wall structural parameters and column structural parameters. Additionally, the height parameters of the ceiling structure and floor structure are detected and extracted from the point cloud density map by calculating statistical histograms on the height axis. Then, based on… In step S3, the key facade structure parameters are projected onto a two-dimensional plane to obtain a wall image. Based on image semantic segmentation, door and window structures are extracted from the wall image. Point clouds of the key facade, ceiling, and floor structures from step S3 are removed to obtain point cloud data of the interior furniture surfaces. By adjusting the shape and position parameters of pre-created furniture templates, a set of furniture parameters is generated for each piece of furniture in the internal furniture surface point cloud data. Finally, based on the height parameters of the ceiling structure, floor structure, key facade structure parameters, and furniture parameters extracted in steps S2, S3, and S4, a three-dimensional model of the interior scene is constructed using a triangulation algorithm. After electromagnetic parameter matching of the three-dimensional model, an electromagnetic simulation model is obtained. A ray tracing algorithm is used to simulate the propagation process of electromagnetic signals in the interior scene to simulate the channel characteristics of the receiving point. The indoor communication scene reconstruction method proposed in this invention divides complex scenes into structural elements and internal elements, and designs corresponding algorithms for different elements to extract key geometric features, thereby improving reconstruction accuracy and robustness. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the indoor communication scene reconstruction and channel modeling method based on lidar perception assistance provided in one embodiment of this application;

[0036] Figure 2 A schematic diagram of a wall structure extracted based on the RANSAC algorithm and the Hough transform algorithm, provided in an embodiment of this application;

[0037] Figure 3 This is a schematic diagram illustrating the effect of discrete wall connection in one embodiment of this application;

[0038] Figure 4 This is a schematic diagram of point cloud semantic segmentation provided in an embodiment of this application;

[0039] Figure 5 This is a schematic diagram of an L-shaped workstation furniture template provided in one embodiment of this application;

[0040] Figure 6 A schematic diagram of furniture geometric model reconstruction based on a furniture template provided in an embodiment of this application;

[0041] Figure 7 A schematic diagram of door and window opening structure extraction based on semantic segmentation algorithm provided in an embodiment of this application;

[0042] Figure 8 This is a schematic diagram of a ray tracing channel simulation provided in an embodiment of this application;

[0043] Figure 9 The simulation results of path loss for ray tracing channel simulation provided in an embodiment of this application. Detailed Implementation

[0044] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0045] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0046] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0047] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0048] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0049] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0050] Example 1.

[0051] See Figures 1-9 ,like Figure 1 As shown, the indoor scene wireless channel modeling method based on lidar perception reconstruction includes the following steps:

[0052] Step S1: Use lidar to collect dense 3D point cloud data of the indoor communication scene. The 3D point cloud data includes 3D point cloud data of the ceiling, floor, walls, columns, doors, windows and furniture surfaces.

[0053] Step S2: Based on the 3D point cloud data in Step S1, perform region cropping, filtering, noise reduction, and point cloud downsampling preprocessing.

[0054] Step S3: Project the 3D point cloud data processed in step S2 onto a 2D plane and perform grid segmentation to obtain a point cloud density map. Use an algorithm combining random sampling consensus algorithm and Hough transform to extract key facade structural parameters from the point cloud density map. Key facade structural parameters include wall structural parameters and column structural parameters. Also, detect and extract the height parameters of the ceiling structure and the floor structure from the point cloud density map by calculating the statistical histogram on the height axis.

[0055] Step S4: Based on the key facade structure parameters in Step S3, project the key facade structure onto a two-dimensional plane to obtain a wall image. Extract the door and window structures on the wall image based on the idea of ​​image semantic segmentation. Remove the point cloud of the key facade structure, ceiling structure, and floor structure surface in Step S3 to obtain the internal furniture surface point cloud data. Generate a set of furniture parameters for each piece of furniture in the internal furniture surface point cloud data by adjusting the shape and position parameters of the pre-created furniture template.

[0056] Step S5: Based on the height parameters of the ceiling structure, floor structure, key facade structure, and furniture extracted in steps S2, S3, and S4, a three-dimensional model of the indoor scene is constructed using a triangulation algorithm. After matching the electromagnetic parameters of the three-dimensional model, an electromagnetic simulation model is obtained. The ray tracing algorithm is then used to simulate the propagation process of electromagnetic signals in the indoor scene to simulate the channel characteristics of the receiving point.

[0057] Furthermore, in one possible implementation of this application embodiment, step S2 above may include:

[0058] Step S201: The collected 3D point cloud data is cropped in 3D space using a bounding box to remove the point cloud data outside the room.

[0059] Step S202: Denoise the 3D point cloud data obtained in step S201 according to the outlier removal algorithm, and downsample the 3D point cloud data using a random sampling algorithm to compress the data size.

[0060] Furthermore, in one possible implementation of this application embodiment, step S3 above may include:

[0061] Step S301: The 3D point cloud data is dimensionality reduced and projected onto a 2D plane, and a mesh is generated based on a preset mesh size to obtain a height of N. h Width is N w Point cloud density map

[0062] Step S302: For the point cloud density map obtained in step S301, extract the key facade structure from the point cloud density map by combining Hough transform and random sampling consensus algorithm, and remove the point cloud data of the key facade structure surface to update the point cloud density map.

[0063] Step S303: For the key facade structures extracted in step S302, fit the best connection method between the key facade structures based on the residual point cloud of the connection area between the endpoints of the key facade structures, and reconstruct the topological relationship between the key facade structures.

[0064] Step S304: For the updated point cloud density map in step S302, filter out the height parameters of the ceiling structure and the floor structure based on the statistical histogram on the height axis.

[0065] Furthermore, in one possible implementation of this application embodiment, step S4 above may include:

[0066] Step S401: Project the key facade structure extracted in step S3 onto the vertical facade corresponding to the facade structure to construct the wall image;

[0067] Step S402: For the wall image in step S401, perform semantic segmentation based on the zero-shot image semantic segmentation model SegmentAnything 2, extract the door and window structures on the wall image, and obtain the opening structure of the wall corresponding to the door and window structures based on the door and window structures on the wall image.

[0068] Step S403: For the ceiling, floor, and key facade structures extracted in step S3, remove the point cloud data of the corresponding structural surfaces from the point cloud data. For common furniture in interior scenes, pre-construct furniture templates defined by a set of shape and position parameters. By adjusting the shape and position parameters, fit the furniture from the remaining point cloud data to achieve 3D reconstruction of the furniture. For example... Figure 5 As shown, the template created in this embodiment is described by ten shape parameters, including the length of the left desktop l. A Right desktop length l B Left desktop width w A Right desktop width w B Length of left partition l DA Left partition width w DA Length of right partition l DB Right partition width w DB Desktop height h and partition height h D .

[0069] Furthermore, in one possible implementation of this application embodiment, step S5 may include:

[0070] Step S501: When performing 3D modeling of the key facade structure, perform sequential 3D modeling according to the direction of wall connection based on the key facade structure parameters extracted in step S3, and further subdivide each wall segment based on the left and right boundaries and upper and lower boundaries of the opening structure of the wall extracted in step S4 to complete the subdivision of the door and window area.

[0071] Step S502: Based on the polygons of the key facade structure extracted in step S3 projected onto the two-dimensional plane, as well as the height parameters of the ceiling structure and the floor structure, the polygons are divided into triangular face elements based on the ear-scissor algorithm to complete the three-dimensional modeling of the ceiling structure and the floor structure.

[0072] Step S503: Determine the surface elements to which different structures belong, and perform electromagnetic parameter matching on different surface elements based on the electromagnetic material parameters recommended by ITU in order to construct an electromagnetic simulation model.

[0073] Step S504: For the electromagnetic simulation model obtained in step S503, import it into the ray tracing channel simulation software Wireless Insite, configure the transmit and receive antenna parameters, the frequency and bandwidth of the transmitted signal to ensure that the simulation conditions match the actual communication environment, and calculate the propagation path of electromagnetic waves in the indoor communication scenario through the ray tracing algorithm, and record the electromagnetic propagation phenomena of reflection and diffraction between electromagnetic waves and the surrounding environment in the propagation path.

[0074] In this embodiment of the application, the algorithm combining the random sampling consensus algorithm and the Hough transform is used to extract key facade structure parameters from the point cloud density map, specifically including:

[0075] The key facade structure is modeled as a straight line on a plane projection, defined by a set of parameters Ψ. vplane ={θ,rho} describes the density-weighted parameter subspace for calculating the density map of a point cloud based on the Hough transform.

[0076]

[0077] in For parameter Ψ vplane The set of density map points S through which the line described by {θ, rho} passes. θ,tho The corresponding grid cluster density uses the DBSCAN clustering algorithm.

[0078] Based on density-weighted parameter subspace Select the optimal sampling set S optimal :

[0079]

[0080] Based on the random sample consensus algorithm, from the optimal sample set S optimal The wall structure parameters are extracted from the random sample consensus algorithm, where the sampling threshold ε is determined by the optimal sample set S. optimal The width w obtained from the pre-clustering is calculated as follows:

[0081]

[0082] Where k1.k2 is used to describe the influence of different wall widths on the threshold selection, ε max ,ε min These are the maximum and minimum values ​​selected by the random sampling consensus algorithm threshold.

[0083] The wireless channel modeling method for indoor scenes based on LiDAR perception reconstruction provided in this application is a method for extracting the geometric features of key objects in indoor scenes by utilizing the high-precision perception characteristics of LiDAR. It uses the random sampling consensus algorithm and the Hough transform algorithm to extract key wall structures, and then uses furniture template fitting and image semantic segmentation to achieve high-precision furniture reconstruction and door and window structure extraction. Finally, it uses ray tracing simulation software to simulate the channel characteristics of indoor scenes and is applicable to the simulation of high-precision channel characteristics in indoor scenes. The complex scene is divided into structural elements and internal elements, and corresponding algorithms are designed for different elements to extract key geometric features, thereby improving the reconstruction accuracy and robustness.

[0084] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0085] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 this application, and should all be included within the protection scope of this application.

Claims

1. A method for modeling indoor scene wireless channels based on lidar perception reconstruction, characterized in that, The method includes: Step S1: Use lidar to collect dense three-dimensional point cloud data of indoor communication scene, the three-dimensional point cloud data includes three-dimensional point cloud data of ceiling, floor, wall, column, door, window and furniture surface; Step S2: Based on the 3D point cloud data described in Step S1, perform region cropping, filtering, noise reduction, and point cloud downsampling preprocessing. Step S3: Project the three-dimensional point cloud data processed in step S2 onto a two-dimensional plane and perform grid segmentation to obtain a point cloud density map. Use an algorithm combining random sampling consensus algorithm and Hough transform to extract key facade structure parameters from the point cloud density map. The key facade structure parameters include wall structure parameters and column structure parameters. Also, detect and extract the height parameters of the ceiling structure and the floor structure from the point cloud density map by calculating the statistical histogram on the height axis. Step S4: Based on the key facade structure parameters in Step S3, project the key facade structure onto a two-dimensional plane to obtain a wall image. Extract the door and window structures on the wall image based on the idea of ​​image semantic segmentation. Remove the point cloud of the key facade structure, the ceiling structure, and the floor structure surface in Step S3 to obtain the internal furniture surface point cloud data. Generate a set of furniture parameters for each piece of furniture in the internal furniture surface point cloud data by adjusting the shape and position parameters of the pre-created furniture template. Step S5: Based on the height parameters of the ceiling structure, the floor structure, the key facade structure, and the furniture parameters extracted in steps S2, S3, and S4, a three-dimensional model of the indoor scene is constructed using a triangulation algorithm. After matching the electromagnetic parameters of the three-dimensional model, an electromagnetic simulation model is obtained. A ray tracing algorithm is then used to simulate the propagation process of electromagnetic signals in the indoor scene to simulate the channel characteristics of the receiving point.

2. The method according to claim 1, characterized in that, Step S2 specifically includes: Step S201: The collected three-dimensional point cloud data is cropped in three-dimensional space using a bounding box to remove the point cloud data outside the room; Step S202: Denoise the 3D point cloud data obtained in step S201 according to the outlier removal algorithm, and downsample the 3D point cloud data using a random sampling algorithm to compress the data size.

3. The method according to claim 2, characterized in that, Step S3 specifically includes: Step S301: The 3D point cloud data is dimensionality-reduced and projected onto a 2D plane, and a mesh is generated based on a preset mesh size to obtain a height of N. h Width is N w The point cloud density map Step S302: For the point cloud density map obtained in step S301, extract the key facade structure from the point cloud density map by combining Hough transform and random sampling consensus algorithm, and remove the point cloud data of the surface of the key facade structure to update the point cloud density map. Step S303: For the key facade structures extracted in step S302, fit the optimal connection mode between the key facade structures based on the residual point cloud of the connection area between the endpoints of the key facade structures, and reconstruct the topological relationship between the key facade structures. Step S304: For the point cloud density map updated in step S302, the height parameters of the ceiling structure and the floor structure are filtered out based on the statistical histogram on the height axis.

4. The method according to claim 3, characterized in that, Step S4 specifically includes: Step S401: Project the key facade structure extracted in step S3 onto the vertical facade corresponding to the facade structure to construct the wall image; Step S402: For the wall image in step S401, perform semantic segmentation based on the zero-shot image semantic segmentation model SegmentAnything 2, extract the door and window structures on the wall image, and obtain the opening structure of the wall corresponding to the door and window structures based on the door and window structures on the wall image. Step S403: For the ceiling, floor and key facade structures extracted in step S3, remove the point cloud data of the corresponding structural surfaces from the point cloud data. For common furniture in the indoor scene, pre-construct furniture templates defined by a set of shape parameters and position parameters. By adjusting the shape parameters and position parameters, fit the furniture from the remaining point cloud data to achieve the three-dimensional reconstruction of the furniture.

5. The method according to claim 4, characterized in that, Step S5 specifically includes: Step S501: When performing three-dimensional modeling of the key facade structure, the key facade structure parameters extracted in step S3 are used to perform sequential three-dimensional modeling according to the direction of wall connection. Based on the left and right boundaries and upper and lower boundaries of the opening structure of the wall extracted in step S4, each wall segment is further subdivided to complete the subdivision of the door and window area. Step S502: Based on the polygons of the key facade structure extracted in step S3 projected onto the two-dimensional plane, as well as the height parameters of the ceiling structure and the floor structure, the polygons are divided into triangular face elements according to the ear-scissor algorithm to complete the three-dimensional modeling of the ceiling structure and the floor structure. Step S503: Determine the surface elements to which different structures belong, and perform electromagnetic parameter matching on different surface elements based on the electromagnetic material parameters recommended by ITU in order to construct an electromagnetic simulation model. Step S504: For the electromagnetic simulation model obtained in step S503, import it into the ray tracing channel simulation software Wireless Insite, configure the transmit and receive antenna parameters, the frequency and bandwidth of the transmitted signal to ensure that the simulation conditions match the actual communication environment, and calculate the propagation path of electromagnetic waves in the indoor communication scenario through the ray tracing algorithm, and record the electromagnetic propagation phenomena of reflection and diffraction between electromagnetic waves and the surrounding environment in the propagation path.

6. The method according to claim 3, characterized in that, The algorithm, which combines random sampling consensus and Hough transform, extracts key facade structure parameters from the point cloud density map, including: The key facade structure is modeled as a straight line on a plane projection, defined by a set of parameters Ψ. vplane ={θ, rho} describes the density-weighted parameter subspace of the point cloud density map calculated based on the Hough transform. in For parameter Ψ vplane The set of density map points S through which the line described by {θ, rho} passes. θ,rho The corresponding grid cluster density uses the DBSCAN clustering algorithm. Based on density-weighted parameter subspace Select the optimal sampling set S optimal : According to the random sampling consensus algorithm, from the optimal sample set S optimal The wall structure parameters are extracted from the sample consensus algorithm, where the sampling threshold ε corresponding to the random sample consensus algorithm is determined by the optimal sample set S. optimal The width w obtained from the pre-clustering is calculated as follows: Where k1.k2 is used to describe the influence of different wall widths on the threshold selection, ε max , ε min These are the maximum and minimum thresholds selected by the random sampling consensus algorithm.

Images