Modeling Method, Device, Readable Storage Medium and Program Product for Roadway Model

Abstract

The present invention relates to the field of modeling technology, and provides a modeling method, device, readable storage medium and program product for a roadway model. The modeling method for the roadway model includes: obtaining first point cloud data of the roadway, performing direction correction processing on the first point cloud data to obtain second point cloud data; performing data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations; performing data interpolation processing on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations; and performing data integration on the plurality of second point cloud combinations to obtain a roadway model, which greatly simplifies the modeling steps of the roadway model, improves the modeling efficiency of the roadway model, and simultaneously improves the model accuracy of the roadway model.

Description

Technical Field

[0001] The present invention relates to the technical field of modeling, and in particular, to a method, device, readable storage medium, and program product for modeling a roadway model. Background Art

[0002] In the tunneling operation of a roadway, it is necessary to create a three-dimensional model of the roadway to determine whether the forming quality of the roadway meets the operation requirements and correct the roadway according to the three-dimensional model. However, the existing modeling methods have technical problems such as cumbersome modeling steps and low model accuracy. Summary of the Invention

[0003] The present invention aims to at least solve the technical problems of cumbersome modeling steps and low model accuracy existing in the prior art or related technologies.

[0004] To this end, the first aspect of the present invention is to propose a method for modeling a roadway model.

[0005] The second aspect of the present invention is to propose a device for modeling a roadway model.

[0006] The third aspect of the present invention is to propose another device for modeling a roadway model.

[0007] The fourth aspect of the present invention is to propose a readable storage medium.

[0008] The fifth aspect of the present invention is to propose a computer program product.

[0009] In view of this, according to the first aspect of the present invention, a method for modeling a roadway model is proposed. The roadway model is used to display the structural information of the roadway. The method for modeling the roadway model includes: obtaining the first point cloud data of the roadway, performing direction correction processing on the first point cloud data to obtain the second point cloud data; performing data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations; performing data interpolation processing on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations; and performing data integration on the plurality of second point cloud combinations to obtain the roadway model.

[0010] The method for modeling the roadway model in this technical solution performs direction correction processing on the first point cloud data of the roadway to obtain the second point cloud data, divides the second point cloud data into a plurality of first point cloud combinations, performs data dense reconstruction on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations, and then performs data recombination on the plurality of second point cloud combinations to obtain the roadway model, greatly simplifying the modeling steps of the roadway model, improving the modeling efficiency of the roadway model, and at the same time improving the model accuracy of the roadway model.

[0011] According to a second aspect of the present invention, a modeling device for a roadway model is proposed. The roadway model is used to display the structural information of the roadway. The modeling device for the roadway model includes: a processing module, configured to obtain the first point cloud data of the roadway and perform direction correction processing on the first point cloud data to obtain second point cloud data; the processing module is further configured to perform data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations; the processing module is further configured to perform data interpolation processing on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations; the processing module is further configured to perform data integration on the plurality of second point cloud combinations to obtain a three-dimensional model of the roadway.

[0012] In the modeling device for the roadway model of this technical solution, the first point cloud data of the roadway is subjected to direction correction processing to obtain second point cloud data, the second point cloud data is segmented into a plurality of first point cloud combinations, data dense reconstruction is performed on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations, and then data recombination is performed on the plurality of second point cloud combinations to obtain the roadway model, which greatly simplifies the modeling steps of the roadway model, improves the modeling efficiency of the roadway model, and at the same time improves the model accuracy of the roadway model.

[0013] According to a third aspect of the present invention, a modeling device for a roadway model is proposed, including a processor and a memory. A program or instruction is stored in the memory. When the program or instruction is executed by the processor, the steps of the modeling method for the roadway model in any of the above technical solutions are implemented. Therefore, the modeling device for the roadway model has all the beneficial effects of the modeling method for the roadway model in any of the above technical solutions, which will not be elaborated here.

[0014] According to a fourth aspect of the present invention, a readable storage medium is proposed, on which a program or instruction is stored. When the program or instruction is executed by the processor, the modeling method for the roadway model in any of the above technical solutions is implemented. Therefore, the readable storage medium has all the beneficial effects of the modeling method for the roadway model in any of the above technical solutions, which will not be elaborated here.

[0015] According to a fifth aspect of the present invention, a computer program product is proposed, including computer instructions. When the computer instructions are executed by the processor, the modeling method for the roadway model in any of the above technical solutions is implemented. Therefore, the computer program product has all the beneficial effects of the modeling method for the roadway model in any of the above technical solutions, which will not be elaborated here.

[0016] The additional aspects and advantages of the present invention will become apparent in the following description section or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the description of the embodiments in conjunction with the following drawings, where:

[0018] Figure 1 One of the schematic flowcharts of the modeling method of the roadway model in an embodiment provided by the present invention is shown;

[0019] Figure 2 Another of the schematic flowcharts of the modeling method of the roadway model in an embodiment provided by the present invention is shown;

[0020] Figure 3 Another of the schematic flowcharts of the modeling method of the roadway model in an embodiment provided by the present invention is shown;

[0021] Figure 4 Another of the schematic flowcharts of the modeling method of the roadway model in an embodiment provided by the present invention is shown;

[0022] Figure 5 Another of the schematic flowcharts of the modeling method of the roadway model in an embodiment provided by the present invention is shown;

[0023] Figure 6 Another of the schematic flowcharts of the modeling method of the roadway model in an embodiment provided by the present invention is shown;

[0024] Figure 7 Another of the schematic flowcharts of the modeling method of the roadway model in an embodiment provided by the present invention is shown;

[0025] Figure 8 Another of the schematic flowcharts of the modeling method of the roadway model in an embodiment provided by the present invention is shown;

[0026] Figure 9 Another of the schematic flowcharts of the modeling method of the roadway model in an embodiment provided by the present invention is shown;

[0027] Figure 10 One of the block diagrams of the structure of the modeling device of the roadway model in an embodiment provided by the present invention is shown;

[0028] Figure 11 Another of the block diagrams of the structure of the modeling device of the roadway model in an embodiment provided by the present invention is shown. Detailed implementation manners

[0029] In order to be able to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0030] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the limitations of the specific embodiments disclosed below.

[0031] The following combines Figures 1 to 11 , and through specific embodiments and their application scenarios, the modeling method, device, readable storage medium, and program product of the roadway model provided by the embodiments of the present application are described in detail.

[0032] The execution subject of the technical solution of the modeling method of the roadway model provided by the present invention may be a modeling device, and can also be determined according to actual usage requirements, which is not specifically limited herein. In order to more clearly describe the modeling method of the roadway model provided by the present invention, the following description is made with the modeling device as the execution subject.

[0033] As Figure 1 shown, in the embodiment of the present invention, a modeling method of a roadway model is provided. The modeling method of the roadway model includes:

[0034] Step 102: Obtain the first point cloud data of the roadway, and perform direction correction processing on the first point cloud data to obtain second point cloud data;

[0035] Step 104: Perform data segmentation on the second point cloud data to obtain multiple first point cloud combinations;

[0036] Step 106: Perform data interpolation processing on the multiple first point cloud combinations to obtain multiple second point cloud combinations;

[0037] Step 108: Perform data integration on the multiple second point cloud combinations to obtain a roadway model.

[0038] In this embodiment, a modeling method of a roadway model is provided. The modeling device obtains the first point cloud data of the roadway, and performs direction correction processing on the first point cloud data to obtain second point cloud data. Among them, the roadway model is a model for displaying the structural information of the roadway, the first point cloud data is the point cloud data of the roadway, and the second point cloud data is the updated point cloud data based on the first point cloud data.

[0039] Exemplarily, the first point cloud data may be point cloud data collected by a multi-line lidar.

[0040] Exemplarily, the second point cloud data is the point cloud data after correcting the direction.

[0041] The modeling device divides the second point cloud data into multiple first point cloud combinations, where the first point cloud combination is a combination of point cloud data.

[0042] Exemplarily, data segmentation is performed along the axis of the second point cloud data to obtain a plurality of first point cloud combinations.

[0043] The modeling device performs data dense reconstruction on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations, where the second point cloud combination is a point cloud data combination after data interpolation.

[0044] Exemplarily, the point cloud density of the second point cloud combination is greater than that of the first point cloud combination.

[0045] The modeling device performs data recombination on the plurality of second point cloud combinations to obtain a roadway model.

[0046] Exemplarily, the roadway model can specifically be a three-dimensional model of the roadway.

[0047] The modeling method of the roadway model in this embodiment performs direction correction processing on the first point cloud data of the roadway to obtain second point cloud data, divides the second point cloud data into a plurality of first point cloud combinations, performs data dense reconstruction on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations, and then performs data recombination on the plurality of second point cloud combinations to obtain a roadway model, greatly simplifying the modeling steps of the roadway model, improving the modeling efficiency of the roadway model, and at the same time improving the model accuracy of the roadway model.

[0048] In some embodiments, optionally, as Figure 2 shown, a modeling method of a roadway model is proposed. The modeling method of the roadway model includes:

[0049] Step 202: Obtain the first point cloud data of the roadway, perform data calibration on the first point cloud data, and determine the first data direction of the first point cloud data;

[0050] Step 204: Perform data processing on the first point cloud data to determine the target normal vector and the target direction vector of the first point cloud data;

[0051] Step 206: Perform a cross product operation on the target normal vector and the target direction vector to obtain a second data direction;

[0052] Step 208: Update the first point cloud data to second point cloud data so that the data direction of the second point cloud data is updated from the first data direction to the second data direction;

[0053] Step 210: Perform data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations;

[0054] Step 212: Perform data interpolation processing on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations;

[0055] Step 214, perform data integration on multiple second point cloud combinations to obtain a roadway model.

[0056] In this embodiment, the modeling device performs data calibration on the first point cloud data to determine the first data direction of the first point cloud data, where the first data direction is the data direction of the first point cloud data.

[0057] Exemplarily, the first data direction may specifically be the initial data direction of the first point cloud data.

[0058] Perform data processing on the first point cloud data to determine the target normal vector and the target direction vector of the first point cloud data, where the target normal vector is the normal vector corresponding to the first point cloud data, and the target direction vector is the direction vector corresponding to the first point cloud data.

[0059] Exemplarily, according to the three-dimensional coordinates of the first point cloud data terminal, determine the target normal vector and the target direction vector of the first point cloud data.

[0060] The modeling device performs a cross product operation on the target normal vector and the target direction vector to obtain a second data direction, where the second data direction is the calibrated target direction.

[0061] Exemplarily, the second data direction may specifically be the data direction in the XY coordinate system.

[0062] The modeling device updates the first point cloud data to the second point cloud data so that the data direction of the second point cloud data is updated from the first data direction to the second data direction.

[0063] Exemplarily, the modeling device updates the first data direction of the first point cloud data to the second data direction to obtain the second point cloud data.

[0064] The modeling method of the roadway model in this embodiment determines the first data direction of the first point cloud data by performing data calibration on the first point cloud data, performs data processing on the first point cloud data to determine the target normal vector and the target direction vector of the first point cloud data, performs a cross product operation on the target normal vector and the target direction vector to obtain a second data direction, and updates the first point cloud data to the second point cloud data so that the data direction of the second point cloud data is updated from the first data direction to the second data direction, ensuring the data accuracy of the second point cloud data and thus ensuring the model accuracy of the roadway model.

[0065] In some embodiments, optionally, as Figure 3 shown, a modeling method of a roadway model is proposed. The modeling method of the roadway model includes:

[0066] Step 302, obtain the first point cloud data of the roadway, perform data calibration on the first point cloud data, and determine the first data direction of the first point cloud data;

[0067] Step 304, perform data calibration on the first point cloud data to determine the central point cloud data in the first point cloud data;

[0068] Step 306, based on the central point cloud data, perform dimensional transformation processing on the first point cloud data to obtain the first data direction of the first point cloud data;

[0069] Step 308, perform a cross product operation on the target normal vector and the target direction vector to obtain the second data direction;

[0070] Step 310, update the first point cloud data to the second point cloud data so that the data direction of the second point cloud data is updated from the first data direction to the second data direction;

[0071] Step 312, perform data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations;

[0072] Step 314, perform data interpolation processing on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations;

[0073] Step 316, perform data integration on the plurality of second point cloud combinations to obtain a roadway model.

[0074] In this embodiment, the modeling device performs data calibration on the first point cloud data to determine the central point cloud data in the first point cloud data, where the central point cloud data is the central point data of the first point cloud data.

[0075] Exemplarily, the central point cloud data may specifically be the origin of the first point cloud data.

[0076] The modeling device converts the first point cloud data from three-dimensional to two-dimensional according to the central point cloud data to obtain the first data direction of the first point cloud data.

[0077] Exemplarily, the first data direction may specifically be the initial direction of the first point cloud data.

[0078] The modeling method of the roadway model in this embodiment ensures the accuracy of the first data direction and thus the data accuracy of the first point cloud data by performing data calibration on the first point cloud data to determine the central point cloud data in the first point cloud data and converting the first point cloud data from three-dimensional to two-dimensional according to the central point cloud data to obtain the first data direction of the first point cloud data.

[0079] In some embodiments, optionally, as Figure 4 shown, a modeling method of a roadway model is proposed, and the modeling method of the roadway model includes:

[0080] Step 402: Obtain the first point cloud data of the roadway, perform data calibration on the first point cloud data, and determine the first data direction of the first point cloud data;

[0081] Step 404: Divide the first point cloud data into first-side point cloud data, second-side point cloud data, and third-side point cloud data;

[0082] Step 406: Perform data processing on the first-side point cloud data to obtain the first normal vector of the first-side point cloud data, and perform data processing on the second-side point cloud data to obtain the second normal vector of the second-side point cloud data;

[0083] Step 408: Determine the target normal vector according to the first normal vector and the second normal vector;

[0084] Step 410: Perform a cross product operation on the target normal vector and the target direction vector to obtain the second data direction;

[0085] Step 412: Update the first point cloud data to the second point cloud data so that the data direction of the second point cloud data is updated from the first data direction to the second data direction;

[0086] Step 414: Perform data segmentation on the second point cloud data to obtain multiple first point cloud combinations;

[0087] Step 416: Perform data interpolation processing on the multiple first point cloud combinations to obtain multiple second point cloud combinations;

[0088] Step 418: Perform data integration on the multiple second point cloud combinations to obtain a roadway model.

[0089] In this embodiment, the modeling device divides the first point cloud data into first-side point cloud data, second-side point cloud data, and third-side point cloud data, where the tangent planes corresponding to the first-side point cloud data and the second-side point cloud data are parallel, and the tangent plane corresponding to the third-side point cloud data is perpendicular to the tangent planes corresponding to the first-side point cloud data and the second-side point cloud data.

[0090] Exemplarily, the first-side point cloud data and the second-side point cloud data can be the point cloud data on both sides of the first point cloud data.

[0091] Exemplarily, the third-side point cloud data can be the point cloud data at the top of the first point cloud data.

[0092] The modeling device performs data processing on the first-side point cloud data to obtain the first normal vector of the first-side point cloud data, and then performs data processing on the second-side point cloud data to obtain the second normal vector of the second-side point cloud data, where the first normal vector is the normal vector of the first-side point cloud data and the second normal vector is the normal vector of the second-side point cloud data.

[0093] Exemplarily, the first normal vector can be the normal vector of the point cloud data on the left side of the first point cloud data.

[0094] Exemplarily, the second normal vector can be the normal vector of the point cloud data on the right side of the first point cloud data.

[0095] The modeling device processes the first normal vector and the second normal vector to determine the target normal vector, and processes the third-side point cloud data to determine the target direction vector.

[0096] Exemplarily, the target direction vector can be the direction vector of the point cloud data on the top of the first point cloud data.

[0097] Exemplarily, the target direction vector can be the elevation central axis direction vector.

[0098] Exemplarily, the target normal vector can be the normal vector of the left and right sidewall plane regions.

[0099] In the modeling method of the roadway model in this embodiment, by dividing the first point cloud data into the first-side point cloud data, the second-side point cloud data, and the third-side point cloud data, processing the first-side point cloud data to obtain the first normal vector of the first-side point cloud data, then processing the second-side point cloud data to obtain the second normal vector of the second-side point cloud data, processing the first normal vector and the second normal vector to determine the target normal vector, and processing the third-side point cloud data to determine the target direction vector, the accuracy of the target normal vector and the target direction vector is ensured, and further the direction accuracy of the second data direction is ensured.

[0100] In some embodiments, optionally, as Figure 5 shown, a modeling method of a roadway model is proposed, and the modeling method of the roadway model includes:

[0101] Step 502: Obtain the first point cloud data of the roadway, and perform direction correction processing on the first point cloud data to obtain the second point cloud data;

[0102] Step 504: Based on the spatial coordinate system corresponding to the second point cloud data, obtain the target coordinate axis in the spatial coordinate system;

[0103] Step 506: Perform data segmentation on the second point cloud data along the direction of the target coordinate axis to obtain a plurality of first point cloud combinations;

[0104] Step 508: Perform data interpolation processing on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations;

[0105] Step 510: Perform data integration on the plurality of second point cloud combinations to obtain the roadway model.

[0106] In this embodiment, the modeling device obtains the target coordinate axis in the spatial coordinate system according to the spatial coordinate system corresponding to the second point cloud data, where the spatial coordinate system is the coordinate axis corresponding to the second point cloud data, and the target coordinate axis is the X-axis, Y-axis, or Z-axis in the spatial coordinate system.

[0107] Exemplarily, the target coordinate axis can specifically be the X-axis in the spatial coordinate system.

[0108] Exemplarily, the target coordinate axis can specifically be the Y-axis in the spatial coordinate system.

[0109] Exemplarily, the target coordinate axis can specifically be the Z-axis in the spatial coordinate system.

[0110] The modeling device performs data segmentation on the second point cloud data along the direction of the target coordinate axis to obtain a plurality of first point cloud combinations.

[0111] Exemplarily, along the Y-axis in the spatial coordinate system, the second point cloud data is segmented into a plurality of first point cloud combinations.

[0112] The modeling method of the roadway model in this embodiment obtains the target coordinate axis in the spatial coordinate system according to the spatial coordinate system corresponding to the second point cloud data, performs data segmentation on the second point cloud data along the direction of the target coordinate axis to obtain a plurality of first point cloud combinations, ensures the data accuracy of the plurality of first point cloud combinations, and further ensures the model accuracy of the roadway model.

[0113] In some embodiments, optionally, as Figure 6 shown, a modeling method of a roadway model is proposed. The modeling method of the roadway model includes:

[0114] Step 602: Obtain the first point cloud data of the roadway, and perform direction correction processing on the first point cloud data to obtain the second point cloud data;

[0115] Step 604: Perform data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations;

[0116] Step 606: Obtain the target density, and perform data interpolation on the plurality of first point cloud combinations until the point cloud density of the plurality of first point cloud combinations reaches the target density to obtain a plurality of second point cloud combinations;

[0117] Step 608: Perform data integration on the plurality of second point cloud combinations to obtain the roadway model.

[0118] In this embodiment, the modeling device obtains the target density, and performs data interpolation on the plurality of first point cloud combinations until the point cloud density of the plurality of first point cloud combinations reaches the target density to obtain a plurality of second point cloud combinations, where the target density is the density index corresponding to the plurality of first point cloud combinations.

[0119] Exemplarily, the first point cloud combination is interpolated using MLS (Moving Least Squares) to obtain a second point cloud combination.

[0120] In the modeling method of the roadway model in this embodiment, data interpolation is performed on multiple first point cloud combinations until the point cloud density of the multiple first point cloud combinations reaches the target density, obtaining multiple second point cloud combinations, ensuring the data accuracy of the multiple second point cloud combinations, and further ensuring the model accuracy of the roadway model.

[0121] In some embodiments, optionally, as Figure 7 shown, a modeling method of a roadway model is proposed, and the modeling method of the roadway model includes:

[0122] Step 702, obtain the first point cloud data of the roadway, and perform direction correction processing on the first point cloud data to obtain second point cloud data;

[0123] Step 704, perform data segmentation on the second point cloud data to obtain multiple first point cloud combinations;

[0124] Step 706, perform data interpolation processing on the multiple first point cloud combinations to obtain multiple second point cloud combinations;

[0125] Step 708, obtain the target direction corresponding to the multiple second point cloud combinations, and perform data combination on the multiple second point cloud combinations along the target direction to obtain the roadway model.

[0126] In this embodiment, the modeling device obtains the target direction corresponding to the multiple second point cloud combinations, and performs data combination on the multiple second point cloud combinations along the target direction to obtain the roadway model, where the target direction is the data combination direction of the multiple second point clouds.

[0127] Exemplarily, the target direction may specifically be the direction of the X-axis in the space coordinate system.

[0128] Exemplarily, the target direction may specifically be the direction of the Y-axis in the space coordinate system.

[0129] Exemplarily, the target direction may specifically be the direction of the Z-axis in the space coordinate system.

[0130] In the modeling method of the roadway model in this embodiment, by obtaining the target direction corresponding to the multiple second point cloud combinations and performing data combination on the multiple second point cloud combinations along the target direction to obtain the roadway model, the modeling steps of the roadway model are simplified, and at the same time, the model accuracy of the roadway model is ensured.

[0131] In some embodiments, optionally, as Figure 8 shown, a modeling method of a roadway model is proposed, and the modeling method of the roadway model includes:

[0132] Step 802: Obtain the first point cloud data of the roadway, and perform direction correction processing on the first point cloud data to obtain the second point cloud data;

[0133] Step 804: Perform data segmentation on the second point cloud data to obtain multiple first point cloud combinations;

[0134] Step 806: Perform data interpolation processing on multiple first point cloud combinations to obtain multiple second point cloud combinations;

[0135] Step 808: Obtain the preset roadway shape of the roadway, and divide the preset roadway shape into multiple roadway segment shapes;

[0136] Step 810: Obtain the multiple point cloud shapes corresponding to the multiple second point cloud combinations, compare the multiple roadway segment shapes with the corresponding multiple point cloud shapes, and obtain multiple shape errors;

[0137] Step 812: Perform data integration on the multiple shape errors to obtain the overall shape error of the roadway;

[0138] Step 814: Perform data integration on the multiple second point cloud combinations to obtain a roadway model.

[0139] In this embodiment, the modeling device obtains the preset roadway shape of the roadway and divides the preset roadway shape into multiple roadway segment shapes. Here, the preset roadway shape is the preset shape of the roadway, and the roadway segment shape is the shape after the preset roadway shape is segmented.

[0140] Exemplarily, the preset roadway shape can be a pre-set drawing shape.

[0141] The modeling device obtains the multiple point cloud shapes corresponding to the multiple second point cloud combinations, compares the multiple roadway segment shapes with the corresponding multiple point cloud shapes, and obtains multiple shape errors. Here, the shape error is the error between the point cloud shape and the roadway segment shape.

[0142] Exemplarily, the shape error can specifically be the over-excavation and under-excavation evaluation index of the segmented roadway.

[0143] The modeling device performs data integration on the multiple shape errors to obtain the overall shape error of the roadway. Here, the overall shape error is the overall error of the roadway.

[0144] Exemplarily, the overall shape error can specifically be the overall over-excavation and under-excavation index of the roadway.

[0145] The modeling method of the roadway model in this embodiment obtains the preset roadway shape of the roadway, divides the preset roadway shape into multiple roadway segment shapes, then obtains the multiple point cloud shapes corresponding to the multiple second point cloud combinations, compares the multiple roadway segment shapes with the corresponding multiple point cloud shapes to obtain multiple shape errors, and then integrates the multiple shape errors to obtain the overall shape error of the roadway, ensuring the accuracy of the overall shape error and thus ensuring the model accuracy of the roadway model.

[0146] In some embodiments, optionally, as Figure 9 shown, a modeling method of a roadway model is proposed. The modeling method of the roadway model includes:

[0147] Step 902, using the PCA method to calculate the initial direction of the roadway;

[0148] Step 904, calculating the plane equations and normal vectors of the left and right sidewall plane regions, and calculating the elevation central axis direction vector;

[0149] Step 906, determining the accurate direction of the roadway coordinate system according to the plane equations and normal vectors of the left and right sidewall plane regions;

[0150] Step 908, slicing the point cloud along the Y-axis direction, and interpolating the point cloud in each slice through a preset shape and the MLS algorithm;

[0151] Step 910, integrating the point cloud in each slice, performing uniform sampling, and forming the final uniformly distributed dense roadway point cloud.

[0152] In this embodiment, the first step is to correct the direction of the roadway point cloud to ensure that the roadway direction is parallel to the Y-axis for subsequent slicing operations of the roadway point cloud along the Y-axis. The specific steps are as follows:

[0153] (1) Calculate the initial direction of the roadway: Extract the center point of the point cloud, perform PCA (dimensional transformation algorithm) processing on the point cloud, initially calculate the initial direction of the roadway point cloud, and perform a rotation transformation on the point cloud to make the initial direction of the roadway point cloud parallel to the X, Y, and Z axis directions of the lidar coordinate system.

[0154] (2) Sidewall plane estimation: Divide the channel point cloud into blocks along the X, Y, and Z axis directions, into the left point cloud, the right point cloud, and the upper point cloud; Calculate the spatial plane equation and normal vector of the left sidewall plane region by combining the left point cloud with the ransac (data sampling algorithm) method, and similarly calculate the spatial plane equation and normal vector of the right sidewall plane region. Obtain the normal vector of the roadway symmetry plane according to the normal vectors of the two sidewall planes.

[0155] (3)Calculation of the elevation central axis: Extract the point cloud with the same distance from the left and right side walls on the upper part of the roadway, which is the elevation central axis point cloud, and calculate the straight-line equation and direction vector of the elevation central axis through the least squares method.

[0156] (4)Recalculation of the roadway direction: Define the normal vector of the symmetric plane in “(2) Side wall plane estimation” and the direction vector of the elevation central axis in “(3) Calculation of the elevation central axis” as the two direction vectors of the roadway point cloud, and determine the third direction vector of the roadway point cloud through the cross product of the above two direction vectors; perform a rotation transformation on the point cloud to make the recalculated direction of the roadway point cloud parallel to the X, Y, and Z axis directions of the lidar coordinate system, and the next step is to perform the slicing operation (the slicing operation is in the second step).

[0157] The second step is the dense reconstruction of the roadway, and the specific steps are as follows:

[0158] (1)Slice fitting: Slice the roadway point cloud along the Y-axis direction, and use MLS (Moving Least Squares) to interpolate the sliced point cloud according to the preset shape of the roadway to construct a dense point cloud of the roadway contour under this slice.

[0159] (2)Dense reconstruction of the roadway point cloud: Traverse each slice and integrate the dense point clouds under all slices; through uniform downsampling of the point cloud, make the roadway point cloud reach the required dense degree (the dense degree can be customized according to actual needs), and at the same time ensure the uniform distribution of the point cloud.

[0160] The third step is the roadway shaping analysis, and the specific steps are as follows:

[0161] (1)Shaping analysis of each slice: Compare the sliced point cloud in S2 with the preset shape, and calculate the deviation percentage along the X-axis and Z-axis directions; take the maximum value of the deviation percentages in each direction as the overexcavation and under-excavation evaluation index under this slice.

[0162] (2)Overall shaping analysis of the roadway: Integrate the overexcavation and under-excavation indicators of each slice, and calculate their average value as the overall overexcavation and under-excavation indicator of the roadway.

[0163] As Figure 10 shown, in the embodiment of the present invention, a modeling device 1000 for a roadway model is provided. The modeling device 1000 for a roadway model includes:

[0164] A processing module 1002, configured to obtain first point cloud data of a roadway, and perform direction correction processing on the first point cloud data to obtain second point cloud data;

[0165] The processing module 1002 is further configured to perform data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations;

[0166] The processing module 1002 is further configured to perform data interpolation processing on a plurality of first point cloud combinations to obtain a plurality of second point cloud combinations;

[0167] The processing module 1002 is further configured to perform data integration on a plurality of second point cloud combinations to obtain a three-dimensional model of the roadway.

[0168] In this embodiment, a modeling device 100 for a roadway model is provided. The processing module 1002 acquires first point cloud data of the roadway and performs direction correction processing on the first point cloud data to obtain second point cloud data. Here, the roadway model is a model for displaying the structural information of the roadway, the first point cloud data is the point cloud data of the roadway, and the second point cloud data is the updated point cloud data based on the first point cloud data.

[0169] Exemplarily, the first point cloud data may be point cloud data collected by a multi-line lidar.

[0170] Exemplarily, the second point cloud data is the point cloud data after the direction is corrected.

[0171] The processing module 1002 divides the second point cloud data into a plurality of first point cloud combinations, where the first point cloud combination is a combination of point cloud data.

[0172] Exemplarily, data segmentation is performed along the axis of the second point cloud data to obtain a plurality of first point cloud combinations.

[0173] The processing module 1002 performs data dense reconstruction on a plurality of first point cloud combinations to obtain a plurality of second point cloud combinations, where the second point cloud combination is a combination of point cloud data after data interpolation.

[0174] Exemplarily, the point cloud density of the second point cloud combination is greater than that of the first point cloud combination.

[0175] The processing module 1002 performs data recombination on a plurality of second point cloud combinations to obtain a roadway model.

[0176] Exemplarily, the roadway model may specifically be a three-dimensional model of the roadway.

[0177] The modeling device 1000 for the roadway model in this embodiment simplifies the modeling steps of the roadway model, improves the modeling efficiency of the roadway model, and simultaneously improves the model accuracy of the roadway model by performing direction correction processing on the first point cloud data of the roadway to obtain second point cloud data, dividing the second point cloud data into a plurality of first point cloud combinations, performing data dense reconstruction on a plurality of first point cloud combinations to obtain a plurality of second point cloud combinations, and then performing data recombination on a plurality of second point cloud combinations to obtain a roadway model.

[0178] In some embodiments, optionally, the modeling device 1000 for the roadway model further includes:

[0179] A processing module 1002, further configured to perform data calibration on the first point cloud data to determine the first data direction of the first point cloud data;

[0180] A processing module 1002, further configured to perform data processing on the first point cloud data to determine the target normal vector and the target direction vector of the first point cloud data;

[0181] A processing module 1002, further configured to perform a cross product operation on the target normal vector and the target direction vector to obtain a second data direction;

[0182] A processing module 1002, further configured to update the first point cloud data to a second point cloud data, so that the data direction of the second point cloud data is updated from the first data direction to the second data direction.

[0183] In the modeling device 1000 for the roadway model in this embodiment, by performing data calibration on the first point cloud data to determine the first data direction of the first point cloud data, performing data processing on the first point cloud data to determine the target normal vector and the target direction vector of the first point cloud data, performing a cross product operation on the target normal vector and the target direction vector to obtain a second data direction, and updating the first point cloud data to a second point cloud data, so that the data direction of the second point cloud data is updated from the first data direction to the second data direction, the data accuracy of the second point cloud data is ensured, and thus the model accuracy of the roadway model is ensured.

[0184] In some embodiments, optionally, the modeling device 1000 for the roadway model further includes:

[0185] A processing module 1002, further configured to perform data calibration on the first point cloud data to determine the center point cloud data in the first point cloud data;

[0186] A processing module 1002, further configured to perform dimension conversion processing on the first point cloud data based on the center point cloud data to obtain the first data direction of the first point cloud data.

[0187] In the modeling device 1000 for the roadway model in this embodiment, by performing data calibration on the first point cloud data to determine the center point cloud data in the first point cloud data, and converting the first point cloud data from three-dimensional to two-dimensional according to the center point cloud data to obtain the first data direction of the first point cloud data, the accuracy of the first data direction is ensured, and thus the data accuracy of the first point cloud data is ensured.

[0188] In some embodiments, optionally, the modeling device 1000 for the roadway model further includes:

[0189] The processing module 1002 is further configured to divide the first point cloud data into first-side point cloud data, second-side point cloud data, and third-side point cloud data. The cutting plane corresponding to the first-side point cloud data is parallel to the cutting plane corresponding to the second-side point cloud data, and the cutting plane corresponding to the third-side point cloud data is perpendicular to the cutting plane corresponding to the first-side point cloud data and the cutting plane corresponding to the second-side point cloud data;

[0190] The processing module 1002 is further configured to perform data processing on the first-side point cloud data to obtain a first normal vector of the first-side point cloud data, and perform data processing on the second-side point cloud data to obtain a second normal vector of the second-side point cloud data;

[0191] The processing module 1002 is further configured to determine a target normal vector according to the first normal vector and the second normal vector;

[0192] The processing module 1002 is further configured to perform data processing on the third-side point cloud data to obtain a target direction vector.

[0193] By dividing the first point cloud data into first-side point cloud data, second-side point cloud data, and third-side point cloud data, performing data processing on the first-side point cloud data to obtain a first normal vector of the first-side point cloud data, then performing data processing on the second-side point cloud data to obtain a second normal vector of the second-side point cloud data, performing data processing on the first normal vector and the second normal vector to determine a target normal vector, and performing data processing on the third-side point cloud data to determine a target direction vector, the modeling device 1000 of the roadway model in this embodiment ensures the accuracy of the target normal vector and the target direction vector, and further ensures the direction accuracy of the second data direction.

[0194] In some embodiments, optionally, the modeling device 1000 of the roadway model further includes:

[0195] The processing module 1002 is further configured to obtain a target coordinate axis in the spatial coordinate system based on the spatial coordinate system corresponding to the second point cloud data, and the target coordinate axis is the X-axis, Y-axis, or Z-axis in the spatial coordinate system;

[0196] The processing module 1002 is further configured to perform data division on the second point cloud data along the direction of the target coordinate axis to obtain a plurality of first point cloud combinations.

[0197] By obtaining a target coordinate axis in the spatial coordinate system according to the spatial coordinate system corresponding to the second point cloud data of the modeling device 1000 of the roadway model in this embodiment, performing data division on the second point cloud data along the direction of the target coordinate axis, and obtaining a plurality of first point cloud combinations, the data accuracy of the plurality of first point cloud combinations is ensured, and further the model accuracy of the roadway model is ensured.

[0198] In some embodiments, optionally, the modeling device 1000 of the roadway model further includes:

[0199] The processing module 1002 is further configured to obtain the target density, and perform data interpolation on multiple first point cloud combinations until the point cloud density of the multiple first point cloud combinations reaches the target density, so as to obtain multiple second point cloud combinations.

[0200] The modeling device 1000 of the roadway model in this embodiment performs data interpolation on multiple first point cloud combinations until the point cloud density of the multiple first point cloud combinations reaches the target density, and obtains multiple second point cloud combinations, ensuring the data accuracy of the multiple second point cloud combinations, and further ensuring the model accuracy of the roadway model.

[0201] In some embodiments, optionally, the modeling device 1000 of the roadway model further includes:

[0202] The processing module 1002 is further configured to obtain the target direction corresponding to the multiple second point clouds, and perform data combination on the multiple second point clouds along the target direction, so as to obtain the roadway model.

[0203] The modeling device 1000 of the roadway model in this embodiment obtains the target direction corresponding to the multiple second point clouds, and performs data combination on the multiple second point clouds along the target direction to obtain the roadway model, simplifying the modeling steps of the roadway model and at the same time ensuring the model accuracy of the roadway model.

[0204] In some embodiments, optionally, the modeling device 1000 of the roadway model further includes:

[0205] The processing module 1002 is further configured to obtain the preset roadway shape of the roadway, and divide the preset roadway shape into multiple roadway segment shapes;

[0206] The processing module 1002 is further configured to obtain the multiple point cloud shapes corresponding to the multiple second point cloud combinations, and compare the multiple roadway segment shapes with the corresponding multiple point cloud shapes to obtain multiple shape errors;

[0207] The processing module 1002 is further configured to perform data integration on the multiple shape errors to obtain the overall shape error of the roadway.

[0208] The modeling device 1000 of the roadway model in this embodiment obtains the preset roadway shape of the roadway, divides the preset roadway shape into multiple roadway segment shapes, then obtains the multiple point cloud shapes corresponding to the multiple second point cloud combinations, compares the multiple roadway segment shapes with the corresponding multiple point cloud shapes to obtain multiple shape errors, and further performs data integration on the multiple shape errors to obtain the overall shape error of the roadway, ensuring the accuracy of the overall shape error and further ensuring the model accuracy of the roadway model.

[0209] In some embodiments, optionally, such asFigure 11 As shown, a modeling device 1100 for a roadway model is proposed, including a processor 1102 and a memory 1104. Programs or instructions are stored in the memory 1104, and when the programs or instructions are executed by the processor 1102, the steps of the modeling method for the roadway model in any of the above technical solutions are implemented. Therefore, the modeling device 1100 for the roadway model has all the beneficial effects of the modeling method for the roadway model in any of the above technical solutions, which will not be elaborated here.

[0210] In some embodiments, optionally, a readable storage medium is provided, on which programs or instructions are stored. When the programs or instructions are executed by a processor, the modeling method for the roadway model in any of the above embodiments is implemented, and thus has all the beneficial technical effects of the modeling method for the roadway model in any of the above embodiments.

[0211] Among them, the readable storage medium includes, for example, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc, etc.

[0212] In some embodiments, optionally, a computer program product is proposed, including computer instructions. When the computer instructions are executed by a processor, the modeling method for the roadway model in any of the above embodiments is implemented, and thus has all the beneficial technical effects of the modeling method for the roadway model in any of the above embodiments.

[0213] It should be clear that in the claims, the specification, and the drawings of the present invention, the term "a plurality" refers to two or more, unless otherwise clearly defined. The orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the drawings, and is only for more conveniently describing the present invention and making the description process simpler, rather than indicating or implying that the device or element referred to must have the specific orientation, be constructed and operated in the specific orientation. Therefore, these descriptions should not be construed as limitations on the present invention; terms such as "connection", "installation", and "fixation" should all be understood in a broad sense. For example, "connection" can be a fixed connection between multiple objects, a detachable connection between multiple objects, or an integral connection; it can be a direct connection between multiple objects, or an indirect connection between multiple objects through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances of the above data.

[0214] In the claims, specification, and specification drawings of the present invention, the descriptions of terms such as "one embodiment", "some embodiments", "specific embodiments", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In the claims, specification, and specification drawings of the present invention, the schematic representations of the above terms do not necessarily refer to the same embodiment or instance. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0215] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for modeling a roadway model, characterized in that, the roadway model is used to display the structural information of the roadway, and the method for modeling the roadway model includes: acquiring the first point cloud data of the roadway, and performing direction correction processing on the first point cloud data to obtain second point cloud data; performing data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations; performing data interpolation processing on the plurality of first point cloud combinations to obtain a plurality of second point cloud combinations; performing data integration on the plurality of second point cloud combinations to obtain the roadway model; the performing direction correction processing on the first point cloud data to obtain second point cloud data includes: performing data calibration on the first point cloud data to determine the first data direction of the first point cloud data; performing data processing on the first point cloud data to determine the target normal vector and the target direction vector of the first point cloud data; performing a cross product operation on the target normal vector and the target direction vector to obtain a second data direction; updating the first point cloud data to the second point cloud data so that the data direction of the second point cloud data is updated from the first data direction to the second data direction; the performing data processing on the first point cloud data to determine the target normal vector and the target direction vector of the first point cloud data includes: dividing the first point cloud data into first side point cloud data, second side point cloud data and third side point cloud data, the tangent planes corresponding to the first side point cloud data and the second side point cloud data are parallel, and the tangent plane corresponding to the third side point cloud data is perpendicular to the tangent planes corresponding to the first side point cloud data and the second side point cloud data; performing data processing on the first side point cloud data to obtain the first normal vector of the first side point cloud data, and performing data processing on the second side point cloud data to obtain the second normal vector of the second side point cloud data; determining the target normal vector according to the first normal vector and the second normal vector; performing data processing on the third side point cloud data to obtain the target direction vector; the performing data processing on the third side point cloud data to obtain the target direction vector specifically includes: extracting the point cloud with the same distance from the left and right side wall planes in the upper part of the roadway, which is the elevation central axis point cloud, and calculating the direction vector of the elevation central axis by the least square method, and taking the direction vector of the elevation central axis as the target direction vector.

2. The method for modeling a roadway model according to claim 1, characterized in that, the performing data calibration on the first point cloud data to determine the first data direction of the first point cloud data includes: performing data calibration on the first point cloud data to determine the central point cloud data in the first point cloud data; based on the central point cloud data, performing dimension conversion processing on the first point cloud data to obtain the first data direction of the first point cloud data.

3. The method for modeling a roadway model according to claim 1, characterized in that, the performing data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations includes: Based on the spatial coordinate system corresponding to the second point cloud data, obtain the target coordinate axis in the spatial coordinate system, where the target coordinate axis is the X-axis, Y-axis, or Z-axis in the spatial coordinate system; Perform data segmentation on the second point cloud data along the direction of the target coordinate axis to obtain a plurality of the first point cloud combinations.

4. The method for modeling a roadway model according to claim 1, wherein, The step of performing data interpolation processing on a plurality of the first point cloud combinations to obtain a plurality of second point cloud combinations includes: Obtain the target density, and perform data interpolation on a plurality of the first point cloud combinations until the point cloud density of the plurality of the first point cloud combinations reaches the target density, so as to obtain a plurality of the second point cloud combinations.

5. The method for modeling a roadway model according to claim 1, wherein, The step of performing data integration on a plurality of the second point cloud combinations to obtain the roadway model includes: Obtain the target direction corresponding to a plurality of the second point cloud combinations, and perform data combination on a plurality of the second point cloud combinations along the target direction to obtain the roadway model.

6. The method for modeling a roadway model according to any one of claims 1 to 5, wherein, After performing data interpolation processing on a plurality of the first point cloud combinations to obtain a plurality of second point cloud combinations, it further includes: Obtain the preset roadway shape of the roadway, and divide the preset roadway shape into a plurality of roadway segment shapes; Obtain a plurality of point cloud shapes corresponding to a plurality of the second point cloud combinations, compare the plurality of roadway segment shapes with the corresponding plurality of point cloud shapes, and obtain a plurality of shape errors; Perform data integration on a plurality of the shape errors to obtain the overall shape error of the roadway.

7. A device for modeling a roadway model, wherein, The roadway model is used to display the structural information of the roadway, and the device for modeling the roadway model includes: A processing module, configured to obtain the first point cloud data of the roadway, and perform direction correction processing on the first point cloud data to obtain second point cloud data; The processing module is further configured to perform data segmentation on the second point cloud data to obtain a plurality of first point cloud combinations; The processing module is further configured to perform data interpolation processing on a plurality of the first point cloud combinations to obtain a plurality of second point cloud combinations; The processing module is further configured to perform data integration on a plurality of the second point cloud combinations to obtain the roadway model; wherein, the step of performing direction correction processing on the first point cloud data to obtain second point cloud data includes: Perform data calibration on the first point cloud data to determine the first data direction of the first point cloud data; Perform data processing on the first point cloud data to determine the target normal vector and the target direction vector of the first point cloud data; Perform a cross product operation on the target normal vector and the target direction vector to obtain a second data direction; Update the first point cloud data to the second point cloud data, so that the data direction of the second point cloud data is updated from the first data direction to the second data direction; Performing data processing on the first point cloud data to determine the target normal vector and the target direction vector of the first point cloud data includes: Dividing the first point cloud data into first-side point cloud data, second-side point cloud data, and third-side point cloud data, wherein the tangent planes corresponding to the first-side point cloud data and the second-side point cloud data are parallel, and the tangent plane corresponding to the third-side point cloud data is perpendicular to the tangent planes corresponding to the first-side point cloud data and the second-side point cloud data; Performing data processing on the first-side point cloud data to obtain the first normal vector of the first-side point cloud data, and performing data processing on the second-side point cloud data to obtain the second normal vector of the second-side point cloud data; Determining the target normal vector according to the first normal vector and the second normal vector; Performing data processing on the third-side point cloud data to obtain the target direction vector; Performing data processing on the third-side point cloud data to obtain the target direction vector, specifically including: Extracting the point cloud with the same distance from the left and right sidewall planes in the upper part of the roadway, which is the elevation central axis point cloud, and calculating the direction vector of the elevation central axis by the least square method, and using the direction vector of the elevation central axis as the target direction vector.

8. A modeling device for a roadway model Characterized in that It includes: A processor; A memory, in which a program or instruction is stored, and when the processor executes the program or instruction in the memory, the steps of the modeling method of the roadway model according to any one of claims 1 to 6 are implemented.

9. A readable storage medium Characterized in that A program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the modeling method of the roadway model according to any one of claims 1 to 6 are implemented.

10. A computer program product Characterized in that It includes computer instructions, and when the computer instructions are executed by a processor, the steps of the modeling method of the roadway model according to any one of claims 1 to 6 are implemented.

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