Point cloud data processing method, device, equipment and storage medium

Abstract

The present application provides a point cloud data processing method, apparatus, device, and storage medium, relating to the field of surveying, mapping, and remote sensing. The method comprises: acquiring collected point cloud data; distinguishing the collected point clouds according to multiple flight strips to obtain point cloud data corresponding to each flight strip; setting multiple connecting lines of preset lengths in the point cloud data of the overlapping area; searching for cross-sectional connecting lines to obtain N groups of connecting lines in the overlapping area, wherein the connecting lines include: two connecting lines between two adjacent flight strips; cross-sectional connecting lines are: connecting lines on a vertical plane perpendicular to the horizontal plane in the overlapping area; N is an integer greater than or equal to 2; calculating an angle correction value based on the state parameters of the connecting lines; and correcting the angle of the point cloud data based on the angle correction value to obtain a point cloud rendering. This method reduces the amount of computation required for point cloud data processing, saves time and hardware costs, and also ensures the accuracy of point cloud splicing.

Description

Technical Field

[0001] The present invention relates to the field of surveying, mapping and remote sensing, and in particular to a point cloud data processing method, device, equipment and storage medium. Background Art

[0002] LiDAR (Light Detection and Ranging) measurement technology is a new type of remote sensing equipment that integrates GPS, IMU, laser scanners, digital cameras, and other technologies. It features high measurement accuracy, minimal fieldwork, minimal exposure to weather images, a high degree of automation, and a short mapping cycle. It is widely used in basic surveying and mapping, urban 3D modeling, forestry applications, railways, and power generation. Due to limited scanning angles and ranging distances, it is impossible to acquire point cloud data for the entire survey area all at once. Therefore, the actual scanning process involves collecting data with a certain degree of overlap, resulting in strips of data. However, during flight, strips inevitably overlap. In these overlapping areas, the elevations calculated for the same location will differ between different strips. This can lead to systematic and random errors when the two strips are spliced ​​together, resulting in layered locations, which directly affects subsequent production applications.

[0003] In related technologies, ICP (Iterative Closest Point) registration is used to calculate vectors and features of point cloud data to solve the problem of point cloud splicing and stratification between flight strips.

[0004] However, in related technologies, the amount of point cloud data collected is often very large, and the ICP matching criterion requires vector and feature calculations on a large amount of point cloud data, which results in relatively high time and hardware costs, and thus cannot achieve the goal of efficiently solving the problem of point cloud splicing and stratification between flight strips. Summary of the Invention

[0005] The purpose of the present invention is to provide a point cloud data processing method, device, equipment and storage medium to address the deficiencies of the above-mentioned prior art, so as to quickly solve the problem of insufficient accuracy of point cloud splicing between flight strips when different flight strips are spliced ​​due to GPS positioning errors, reduce the amount of calculation of point cloud data, and also ensure that the quality and accuracy of point clouds after splicing between different flight strips are improved, thereby saving time and hardware costs.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for processing point cloud data, the method comprising:

[0008] Obtain point cloud data collected by airborne lidar;

[0009] Differentiating the collected point cloud data according to a plurality of flight strips to obtain point cloud data corresponding to each flight strip;

[0010] Setting a plurality of connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips;

[0011] Searching for connecting lines of a cross-section type along directions of different flight strips in the overlapping region, to obtain N groups of connecting lines in the overlapping region, each group of connecting lines including two connecting lines of the two adjacent flight strips; wherein the connecting lines of the cross-section type are connecting lines on a vertical plane perpendicular to a horizontal plane in the overlapping region; and N is an integer greater than or equal to 2;

[0012] Calculating an angle correction value for each group of connecting lines according to the state parameters of each group of connecting lines;

[0013] According to the angle correction value of each group of connecting lines, the angle of the point cloud data is corrected to obtain a point cloud rendering of the feature with the same name.

[0014] Optionally, the distinguishing the point cloud data according to a plurality of flight strips to obtain point cloud data corresponding to each flight strip includes:

[0015] The position trajectory file of the airborne laser radar and the position time information in the collected point cloud data are used to distribute the collected point cloud data to each of the flight strips to obtain the point cloud data corresponding to each of the flight strips.

[0016] Optionally, before setting a plurality of connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips, the method further includes:

[0017] The point cloud data of each flight strip is divided into ground point cloud data and point cloud data of the same-named features;

[0018] The step of setting a plurality of connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips comprises:

[0019] A plurality of connection lines of the preset length are respectively set in the ground point cloud data in the overlapping area and the point cloud data of the feature with the same name.

[0020] Optionally, calculating the angle correction value of each group of connecting lines according to the state parameters of each group of connecting lines includes:

[0021] Calculating a roll angle correction value of each group of connecting lines according to the height difference between each group of connecting lines and the preset length;

[0022] Calculating a pitch angle correction value of each group of connecting lines according to the horizontal difference between each group of connecting lines and the average flight altitude of the two adjacent flight strips;

[0023] The heading angle correction value of each group of connecting lines is calculated according to the horizontal difference between each group of connecting lines and the vertical distance between the flight tracks corresponding to each group of connecting lines.

[0024] Optionally, the angle of the point cloud data is corrected according to the angle correction value of each group of connecting lines to obtain a point cloud rendering of the feature with the same name, including:

[0025] Determining a target angle correction value for each group of connecting lines according to the angle correction values ​​of the N groups of connecting lines;

[0026] The angle of the point cloud data is corrected according to the target angle correction value to obtain a point cloud rendering of the feature with the same name.

[0027] Optionally, the angle correction value includes: a correction value of at least one angle; and determining a target angle correction value of each group of connecting lines according to the angle correction values ​​of the N groups of connecting lines includes:

[0028] Performing curve fitting on the correction value of each angle of the N groups of connecting lines and the serial number of the corresponding connecting line to obtain a fitting curve of the correction value of each angle and the serial number of the connecting line;

[0029] A target correction value for each angle of each group of connecting lines is determined according to the fitting curve.

[0030] Optionally, determining, from the angle correction values ​​of the N groups of connecting lines according to the fitting curve, an angle correction value having a minimum error with each angle correction value of each group of connecting lines as the target angle correction value includes:

[0031] Using the extreme value method, under the condition of minimizing the sum of squared errors, calculate m groups of weight parameters of the fitting equation corresponding to the fitting curve; m is an integer greater than or equal to 1;

[0032] According to the m groups of weight parameters and the sequence number of each group of connecting lines, using the fitting equation, respectively calculating m fitting angle correction values ​​for each group of connecting lines;

[0033] The fitting angle correction value having the smallest error value with respect to each angle correction value of each group of connecting lines among the m fitting angle correction values ​​is determined as the target angle correction value.

[0034] In a second aspect, an embodiment of the present application further provides a point cloud data processing device, the device comprising:

[0035] Acquisition module, used to obtain point cloud data collected by airborne lidar;

[0036] a division module, configured to distinguish the collected point cloud data according to a plurality of flight strips, and obtain point cloud data corresponding to each flight strip;

[0037] A setting module, used for setting a plurality of connection lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips;

[0038] a search module configured to search for connecting lines of a cross-section type along directions of different flight strips in the overlapping region along the two adjacent flight strips, to obtain N groups of connecting lines in the overlapping region, each group of connecting lines comprising: two connecting lines of the two adjacent flight strips; wherein the connecting lines of the cross-section type are connecting lines on a vertical plane perpendicular to a horizontal plane in the overlapping region; and N is an integer greater than or equal to 2;

[0039] a calculation module, configured to calculate an angle correction value of each group of connecting lines according to a state parameter of each group of connecting lines;

[0040] The correction module is used to correct the angle of the point cloud data according to the angle correction value of each group of connecting lines to obtain a point cloud rendering of the same-name feature.

[0041] In a third aspect, an embodiment of the present application further provides a computer device comprising: a memory and a processor, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, the point cloud data processing method described in the first aspect is implemented.

[0042] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the point cloud data processing method described in the first aspect is executed.

[0043] The beneficial effects of the present invention are:

[0044] The embodiments of the present application provide a point cloud data processing method, apparatus, device and storage medium. The method obtains collected point cloud data, distinguishes the collected point cloud data according to multiple flight strips, obtains the number of point clouds corresponding to each flight strip, sets multiple connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips, searches for connecting lines of cross-section types in the overlapping area, obtains N groups of connecting lines in the overlapping area, calculates the angle correction value of each group of connecting lines according to the state parameters of each group of connecting lines, and corrects the angle of the point cloud data according to the angle correction value of each group of connecting lines to obtain a point cloud rendering of the same-named feature, thereby ensuring that the computational amount of point cloud data processing is reduced when processing the point cloud data, saving time and hardware costs while also ensuring the stitching accuracy of the point cloud. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a point cloud data processing method provided in an embodiment of the present application;

[0046] Figure 2 A flowchart of setting connection lines in another point cloud data processing method provided in an embodiment of the present application;

[0047] Figure 3 A flowchart of obtaining angle correction values ​​for features with the same name in a point cloud data processing method provided in an embodiment of the present application;

[0048] Figure 4 A flowchart of calculating a point cloud rendering of a feature with the same name in a point cloud data processing method provided in an embodiment of the present application;

[0049] Figure 5 A flowchart of calculating a target correction value for each angle in a point cloud data processing method provided in an embodiment of the present application;

[0050] Figure 6 A flowchart of calculating a target correction value for each angle in another point cloud data processing method provided in an embodiment of the present application;

[0051] Figure 7 A schematic diagram of a point cloud data processing device provided in an embodiment of the present application;

[0052] Figure 8 A block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0054] Figure 1A flowchart of a point cloud data processing method provided in an embodiment of the present application. The execution order of the various steps of the point cloud data processing method is not limited by the order disclosed in this embodiment. The point cloud data processing method can be implemented by a computer device, which can be, for example, a laptop computer, a desktop computer, a tablet computer, or any other device with computing and processing capabilities. The computer device used in this application is a device having a central processing unit (CPU) and a graphics processing unit (GPU). The following point cloud data processing method can be implemented by the cooperation of the central processing unit and the graphics processing unit in the computer device. Figure 1 As shown, the point cloud data processing method includes:

[0055] S100: Obtain point cloud data collected by an airborne laser radar.

[0056] When collecting point cloud data, airborne lidar follows a specific pattern. It can collect point cloud data for a specific area through multiple round trips. The horizontal distance between each round trip is equal. Once the airborne lidar has acquired all point cloud data for the area, it completes the final round trip using a route that is perpendicular to and bisects the parallel round trip routes. It should be noted that all round trip routes of the airborne lidar are located in a plane at a certain height, parallel to the ground surface of the point cloud data collection area.

[0057] S200 , differentiating the collected point cloud data according to a plurality of flight strips to obtain point cloud data corresponding to each flight strip.

[0058] When airborne laser radar collects point cloud data according to the route, there will be repeated collection of point cloud data between two adjacent routes. The point cloud data collected within the route area of ​​the same route belongs to the same flight strip, and there are overlapping areas between the flight strips. When airborne laser radar collects point data, it will perform flight collection in a specific order. The collected point cloud data can be divided into different flight strips according to the position and time characteristics of different routes to obtain the point cloud data corresponding to each flight strip.

[0059] S300: Setting a plurality of connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips.

[0060] According to a preset rule, point cloud data that meets the rule is screened in the overlapping region of two adjacent flight strips. Multiple connecting lines are set in the point cloud data that meets the preset rule, where the length of each connecting line is a preset length w, which can be, for example, 10 meters. The multiple connecting lines set in the overlapping region belong to two different flight strips.

[0061] In the overlapping area, connecting lines of different section types are searched along the directions of two adjacent flight strips to obtain N groups of connecting lines in the overlapping area.

[0062] Each group of connecting lines includes: two connecting lines of two adjacent flight strips; wherein, the cross-sectional type connecting lines are: connecting lines on the vertical plane perpendicular to the horizontal plane in the overlapping area; N is an integer greater than or equal to 2.

[0063] In the overlapping region, there are at least two groups of connecting lines of the cross-section type, that is, N is greater than or equal to 2. The two connecting lines in each group belong to the two adjacent flight strips. The direction of the flight strip can be, for example, parallel to the flight route corresponding to the flight strip, or perpendicular to the flight route.

[0064] When N groups of connection lines are obtained in the overlapping area through searching, the N groups of connection lines may be sorted according to the search order of the connection lines. The sorted N groups of connection lines TLi may be represented as: TL1, TL2, TL3, ..., TLN.

[0065] When the N groups of connecting lines are searched and obtained, each group of connecting lines can also be displayed based on the air strip to which each group of connecting lines belongs, so that the display of each group of connecting lines is associated with the air strip attributes to which it belongs, such as the air strip number, and the display color of each group of connecting lines is the air strip color corresponding to the air strip to which each group of connecting lines belongs.

[0066] S400: Calculate the angle correction value of each group of connecting lines according to the state parameters of each group of connecting lines.

[0067] After searching for N groups of connecting lines, the state parameters of each group of connecting lines can be obtained, such as the positional relationship between each group of connecting lines, the positional relationship between the connecting lines and the route, etc., and the angle correction value of each group of connecting lines can be calculated based on the state parameters of each group of connecting lines.

[0068] S500: Correct the angle of the point cloud data according to the angle correction value of each set of connecting lines to obtain a point cloud rendering of the feature with the same name.

[0069] When processing point cloud renderings of features with the same name, computer equipment can directly correct the angle of the point cloud data based on the calculated angle correction value of each set of connecting lines, or it can first process the calculated angle correction value of each set of connecting lines, and based on the angle correction value of each set of connecting lines, select the angle correction value that best matches the features with the same name as the target angle correction value, and then correct the angle of the point cloud data based on the target angle correction value.

[0070] An embodiment of the present application provides a point cloud data processing method, which classifies the acquired point cloud data, presets connecting lines for the classified point cloud data in the overlapping area of ​​the flight strip, obtains the positional relationship between each group of connecting lines of the cross-section type and the positional relationship with the flight route, calculates the angle correction value, determines the angle correction value with the smallest error, and applies the correction value to correct the angle of the point cloud data to obtain a point cloud rendering of the same-name feature. By only calculating the connecting lines, the amount of calculation in point cloud data processing is reduced, saving time and hardware costs. At the same time, by applying the angle correction value with the smallest error, the stitching accuracy of the point cloud is guaranteed.

[0071] Optional, in the above Figure 1 On the basis of the method shown, the embodiment of the present application also provides another possible implementation case of setting connecting lines in the point cloud data processing method, which is explained below with reference to the accompanying drawings. Figure 2 This is a flow chart of setting connection lines in another point cloud data processing method provided in an embodiment of the present application. Figure 2 As shown, the point cloud data is distinguished according to multiple flight strips, and the point cloud data corresponding to each flight strip is obtained, including:

[0072] S310 , using the position trajectory file of the airborne laser radar and the position time information in the collected point cloud data, the collected point cloud data is allocated to each flight strip to obtain the point cloud data corresponding to each flight strip.

[0073] Airborne laser radar collects point cloud data. Airborne laser radar has POS trajectory files. By matching the POS trajectory files with the GPS time in the point cloud, the point cloud data is divided so that the point cloud data corresponds to different flight strips, and the flight strip number L attribute is added to the corresponding point cloud to distinguish them.

[0074] Before setting a plurality of connection lines of a preset length in the point cloud data of the overlapping area of ​​two adjacent flight strips, the method further includes:

[0075] S320 , dividing the point cloud data of each flight strip into ground point cloud data and point cloud data of features with the same name.

[0076] For example, low points and aerial points can be removed from the point cloud data of each flight strip. Low points are points below the ground, and aerial points are points above the target object. After removing the noise points, an iterative algorithm can be used to extract ground points from the point cloud data of each flight strip after noise removal, based on the parameter thresholds of the largest feature size, iteration angle, and iteration distance. This is the ground point cloud data.

[0077] After obtaining the ground points, the ground points can be used as a reference to classify points by elevation, where the distance from the ground points is greater than a preset distance threshold. A minimum feature size is then set for the classified points to extract the point cloud data for the features with the same name. For example, the features with the same name could be houses. When extracting the features with the same name, the point cloud data extracted would be the roof points of the houses, i.e., the roof point cloud data.

[0078] Optionally, in the above method, setting a plurality of connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips may include:

[0079] S330 , setting a plurality of connection lines of preset lengths in the ground point cloud data and the point cloud data of the same-named feature in the overlapping area.

[0080] For example, the preset length can be 10m, and connecting lines are set for ground point cloud data according to the preset length. Connecting lines are also set for point cloud data of features with the same name based on the preset length. Whether it is ground point cloud data or point cloud data of features with the same name, in the process of setting connecting lines, each point cloud data can be traversed in sequence according to the preset setting order, and connecting lines are set for the traversed point cloud data that meets the connecting line requirements until all point cloud data are traversed.

[0081] An embodiment of the present application provides a method for setting connecting lines in a point cloud data processing method, which classifies the collected point cloud data according to location and time information so that it corresponds to different flight strips, processes the classified point cloud data, removes noise, determines ground point cloud data and point cloud data of features with the same name, determines the point cloud data connecting lines according to preset rules, and selects valid point cloud data for processing, thereby ensuring that the present application will not be interfered with by unnecessary point cloud data and improving the reliability of the present application. Setting connecting lines for the point cloud data also ensures that the point cloud data will not be scattered. By directly processing the connecting lines, the efficiency of the present application is improved and the high efficiency of the present application is guaranteed.

[0082] Optional, in the above Figure 1 Based on the method shown, the embodiment of the present application also provides another possible implementation case of obtaining the angle correction value of the same-name feature in the point cloud data processing method, which is explained below with reference to the accompanying drawings. Figure 3This is a flowchart of obtaining angle correction values ​​of features with the same name in another point cloud data processing method provided in an embodiment of the present application. Figure 3 As shown, according to the status parameters of each set of connecting lines, the angle correction value of each set of connecting lines is calculated, including:

[0083] S410: Calculate a roll angle correction value for each set of connecting lines according to a height difference and a preset length between each set of connecting lines.

[0084] For example, the roll angle correction value of each set of connecting lines can be calculated using the following formula (1) based on the height difference and preset length between each set of connecting lines:

[0085] Formula (1)

[0086] Wherein, Roll is the roll angle of each set of connecting lines, h is the height difference between each set of connecting lines, and w is the preset length of the connecting lines.

[0087] S420. Calculate the pitch angle correction value of each group of connecting lines according to the horizontal difference between each group of connecting lines and the average flight altitude of two adjacent flight strips.

[0088] For example, the pitch angle correction value of each set of connecting lines can be calculated based on the horizontal difference between each set of connecting lines and the average flight altitude of two adjacent flight strips using the following formula (2):

[0089] Formula (2)

[0090] Among them, Picth is the pitch angle of each group of connecting lines, d is the horizontal difference between each group of connecting lines, and H is the average flight altitude of two adjacent flight strips.

[0091] S430: Calculate a heading angle correction value for each set of connecting lines based on the horizontal difference between each set of connecting lines and the vertical distance between the flight tracks corresponding to each set of connecting lines.

[0092] For example, the heading angle correction value of each group of connecting lines can be calculated based on the horizontal difference between each group of connecting lines and the vertical distance between each connecting line in each adjacent group of connecting lines and the flight track of the corresponding flight strip using the following formula (3):

[0093] Formula (3)

[0094] Where Heading is the heading angle, d is the horizontal difference between each set of connecting lines, and D is the vertical distance between each connecting line in each set of connecting lines and the flight track of the corresponding flight strip.

[0095] The point cloud data processing method provided in the embodiment of the present application obtains the angle correction value of the same-name features, and determines the angle correction value of the connecting line through the positional relationship between multiple groups of connecting lines and the positional relationship between the connecting line and the flight path. By obtaining the angle correction values ​​of multiple groups of connecting lines, it is ensured that the different states of point cloud data collection of the airborne laser radar can be obtained. The diverse angle correction values ​​also ensure the diversity of the data, providing data support and comparison for the technical solution of the present application. By selecting the most appropriate angle correction value, the splicing accuracy of the point cloud of the present application is guaranteed.

[0096] Optional, in the above Figure 1 Based on the method shown, this application also provides another possible implementation case of calculating the point cloud rendering of the same-named feature in another point cloud data processing method. Figure 4 This is a flowchart of obtaining a point cloud rendering of a feature with the same name in a point cloud data processing method provided in an embodiment of the present application, such as Figure 4 As shown, according to the angle correction value of each set of connecting lines, the angle of the point cloud data is corrected to obtain the point cloud rendering of the same-name feature, including:

[0097] S510 : Determine a target angle correction value for each group of connecting lines based on the angle correction values ​​of the N groups of connecting lines.

[0098] The target angle correction value for each set of connecting lines can be determined by comparing the angle correction values ​​of the N sets of connecting lines with the calculated angle value, and selecting the angle correction value with the smallest error from the calculated angle value as the target angle correction value.

[0099] S520: Correct the angle of the point cloud data according to the target angle correction value to obtain a point cloud rendering of the feature with the same name.

[0100] According to the target angle correction value, the angle of the point cloud data is made consistent with the target angle correction value. According to the modified angle correction value, calculation and rendering are performed through computer equipment to obtain a point cloud rendering of the same-name feature.

[0101] An embodiment of the present application provides a method for calculating point cloud renderings of features with the same name in a point cloud data processing method. By calculating N groups of connecting lines, the angle correction value of each group of connecting lines is determined, and the angle of the point cloud data is corrected according to the calculated angle correction value to obtain a point cloud rendering of features with the same name. This ensures that the correction of each group of connecting lines is sufficiently accurate, so that the computer device can more realistically render the point cloud renderings of features with the same name when performing calculations and rendering. By selecting the angle correction value of the error value, it is ensured that the rendered point cloud renderings are more in line with the actual situation, thereby improving the accuracy of the present application.

[0102] Optional, in the above Figure 1Based on the method shown, this application also provides a possible implementation case of calculating the target correction value for each angle in a point cloud data processing method. Figure 5 This is a flow chart of calculating the target correction value for each angle in a point cloud data processing method provided in an embodiment of the present application, such as Figure 5 As shown, optionally, the angle correction value includes: a correction value of at least one angle, for example, a correction value of at least one angle among a roll angle correction value, a pitch angle correction value, and a heading angle correction value.

[0103] S511 , performing curve fitting on the correction value of each angle of the N groups of connecting lines and the serial number of the corresponding connecting line to obtain a fitting curve of the correction value of each angle and the serial number of the connecting line.

[0104] According to the following formula (4), the least squares method is used to perform polynomial fitting:

[0105] Formula (4)

[0106] Among them, a0, a1, a2 are preset fitting coefficients, and xi is the serial number of each connecting line.

[0107] The curve is fitted using the following formula (5) to minimize the sum of square errors:

[0108] Formula (5)

[0109] Where Yi is the angle correction value of each set of connecting lines, a0, a1, and a2 are preset fitting coefficients, xi is the serial number of each connecting line, F(xi) is the value of the fitting curve of the correction value of each angle and the serial number of the connecting line, and (f(xi)-yi) also known as ei is the target angle correction value.

[0110] S512. Determine a target correction value for each angle of each set of connecting lines based on the fitting curve.

[0111] According to the above formula (5), the angle correction value of the connecting line with the minimum error value of the fitting curve is determined as the target correction value.

[0112] An embodiment of the present application provides a method for calculating a target correction value for each angle in a point cloud data processing method. By performing curve fitting on the angle correction value and the serial number, the value with the smallest sum of the squares of the error between the angle correction value and the fitting curve is selected as the target angle correction value. This reduces the amount of calculation during point cloud data processing, saves time and hardware costs, and improves the efficiency of the present application.

[0113] Optional, in the above Figure 1Based on the method shown, this application also provides another possible implementation case of calculating the target correction value for each angle in another point cloud data processing method. Figure 6 This is a flow chart of calculating the target correction value for each angle in a point cloud data processing method provided in an embodiment of the present application, such as Figure 6 As shown, optionally, as shown above, determining, from each angle correction value of the N groups of connecting lines according to the fitting curve, an angle correction value having the smallest error value with each angle correction value of each group of connecting lines as a target angle correction value may include:

[0114] S513. Using the extreme value method, under the condition of minimizing the sum of squared errors, calculate m groups of weight parameters of the fitting equation corresponding to the fitting curve.

[0115] m is an integer greater than or equal to 1. The extreme value method is used to calculate the m groups of weight parameters of the fitting equation corresponding to the fitting curve, which can be obtained by the following formula (6):

[0116] Formula (6)

[0117] Where m is the number of correction values ​​required on a single flight strip, x is the connecting line number, and y is the correction value corresponding to the connecting line.

[0118] S514 , using a fitting equation to calculate m fitting angle correction values ​​for each group of connecting lines according to the m groups of weight parameters and the serial number of each group of connecting lines.

[0119] By using the above formula (6), each set of connecting lines is calculated to obtain the required fitting angle correction values ​​for all flight strips.

[0120] S515 , determining, among the m fitting angle correction values, the fitting angle correction value having the smallest error with each angle correction value of each group of connecting lines as the target angle correction value.

[0121] Compare all the fitted angle correction values ​​obtained on the flight strip with each angle correction value of each group of connecting lines, and take the fitted angle correction value with the smallest error as the target angle correction value.

[0122] An embodiment of the present application provides a method for calculating a target correction value for each angle in a point cloud data processing method. Multiple fitting angle correction values ​​are calculated through a fitting equation using weight parameters of multiple groups of correction values ​​and the serial number of each group of connecting lines. The fitting angle correction value with the smallest error value is then selected as the target angle correction value to correct the angle correction value of the point cloud data. This reduces the amount of calculation during point cloud data processing while ensuring the accuracy of point cloud splicing and saving time and hardware costs.

[0123] The following describes an apparatus, device, and storage medium for executing a volume adjustment method provided in an embodiment of the present application. The specific implementation process and technical effects are described above and will not be repeated below.

[0124] Figure 7 A schematic diagram of a point cloud data processing device provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, the point cloud data processing device may include:

[0125] An acquisition module 11 is used to acquire point cloud data collected by an airborne laser radar;

[0126] A division module 12 is used to distinguish the collected point cloud data according to multiple flight strips to obtain point cloud data corresponding to each flight strip;

[0127] A setting module 13 is used to set a plurality of connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips;

[0128] A search module 14 is configured to search for connecting lines of different cross-section types along two adjacent flight strips in the overlapping region, obtaining N groups of connecting lines in the overlapping region, each group of connecting lines comprising two connecting lines of two adjacent flight strips; wherein a connecting line of a cross-section type is a connecting line on a vertical plane perpendicular to a horizontal plane in the overlapping region; and N is an integer greater than or equal to 2.

[0129] A calculation module 15 is used to calculate the angle correction value of each group of connecting lines according to the state parameters of each group of connecting lines;

[0130] The correction module 16 is used to correct the angle of the point cloud data according to the angle correction value of each group of connecting lines to obtain a point cloud rendering of the same-named feature.

[0131] Optionally, the division module 12 is specifically used to use the position trajectory file of the airborne laser radar and the position time information in the collected point cloud data to distribute the collected point cloud data to each flight strip to obtain the point cloud data corresponding to each flight strip.

[0132] Optionally, the division module 12 is further specifically configured to divide the point cloud data of each flight strip into ground point cloud data and point cloud data of features with the same name.

[0133] Optionally, the setting module 13 is specifically configured to set a plurality of connection lines of preset lengths in the ground point cloud data of the overlapping area and the point cloud data of the feature with the same name.

[0134] Optionally, the calculation module 15 is specifically used to calculate the roll angle correction value of each group of connecting lines based on the height difference and preset length between each group of connecting lines; calculate the pitch angle correction value of each group of connecting lines based on the horizontal difference between each group of connecting lines and the average flight altitude of two adjacent flight strips; calculate the heading angle correction value of each group of connecting lines based on the horizontal difference between each group of connecting lines and the vertical distance of the flight track corresponding to each group of connecting lines.

[0135] Optionally, the correction module 16 is specifically used to determine the target angle correction value of each group of connecting lines based on the angle correction values ​​of N groups of connecting lines; according to the target angle correction value, the angle of the point cloud data is corrected to obtain a point cloud rendering of the same-named feature.

[0136] Optionally, the correction module 16 is further specifically used to perform curve fitting on the correction value of each angle of the N groups of connecting lines and the serial number of the corresponding connecting lines to obtain a fitting curve of the correction value of each angle and the serial number of the connecting line; and determine the target correction value of each angle of each group of connecting lines based on the fitting curve.

[0137] Optionally, the calculation module 15 is also specifically used to use the extreme value method to calculate m groups of weight parameters of the fitting equation corresponding to the fitting curve under the condition that the sum of squared errors is minimized; m is an integer greater than or equal to 1; based on the m groups of weight parameters and the serial number of each group of connecting lines, the fitting equation is used to calculate the m fitting angle correction values ​​of each group of connecting lines.

[0138] Optionally, the correction module 16 is further configured to determine, among the m fitting angle correction values, a fitting angle correction value having the smallest error with each angle correction value of each group of connecting lines as a target angle correction value.

[0139] The above-mentioned device is used to execute the method provided in the above-mentioned embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0140] The above modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital singular processors (DSPs), or one or more field programmable gate arrays (FPGAs). For example, when a module is implemented by scheduling program code through a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0141] Figure 8 Schematic diagram of a computer device provided in an embodiment of the present application. The computer device 1000 includes a memory 1001 and a processor 1002. The memory 1001 and the processor 1002 are connected via a bus.

[0142] The memory 1001 is used to store programs, and the processor 1002 calls the programs stored in the memory 1001 to execute the above method embodiment. The specific implementation method and technical effects are similar and will not be repeated here.

[0143] Optionally, the present invention further provides a program product, such as a computer-readable storage medium, comprising a program, which is used to perform the above method embodiment when executed by a processor.

[0144] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional units.

[0147] The aforementioned integrated unit implemented as a software functional unit can be stored in a computer-readable storage medium. The software functional unit, stored in a storage medium, includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) or a processor to execute portions of the method steps described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a removable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0148] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited to them. Any changes or substitutions that can be easily conceived by any person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A point cloud data processing method, characterized in that: The method comprises: Obtain point cloud data collected by airborne lidar; Differentiating the collected point cloud data according to a plurality of flight strips to obtain point cloud data corresponding to each flight strip; Setting a plurality of connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips; Searching for connecting lines of a cross-section type along directions of different flight strips in the overlapping region, to obtain N groups of connecting lines in the overlapping region, each group of connecting lines including two connecting lines of the two adjacent flight strips; wherein the connecting lines of the cross-section type are connecting lines on a vertical plane perpendicular to a horizontal plane in the overlapping region; and N is an integer greater than or equal to 2; Calculating the angle correction value of each group of connecting lines according to the state parameters of each group of connecting lines; wherein the state parameters of each group of connecting lines include: the positional relationship between the connecting lines in each group, and the positional relationship between the connecting lines and the flight path; Determining a target angle correction value for each group of connecting lines according to the angle correction values ​​of the N groups of connecting lines; According to the target angle correction value, the angle of the point cloud data is corrected to obtain a point cloud rendering of the feature with the same name.

2. The method according to claim 1, characterized in that The step of distinguishing the point cloud data according to the plurality of flight strips to obtain point cloud data corresponding to each flight strip includes: The position trajectory file of the airborne laser radar and the position time information in the collected point cloud data are used to distribute the collected point cloud data to each of the flight strips to obtain the point cloud data corresponding to each of the flight strips.

3. The method according to claim 1, characterized in that Before setting a plurality of connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips, the method further includes: The point cloud data of each flight strip is divided into ground point cloud data and point cloud data of the same-named features; The step of setting a plurality of connecting lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips comprises: A plurality of connection lines of the preset length are respectively set in the ground point cloud data in the overlapping area and the point cloud data of the feature with the same name.

4. The method according to claim 1, wherein Calculating the angle correction value of each group of connecting lines according to the state parameters of each group of connecting lines includes: Calculating a roll angle correction value of each group of connecting lines according to the height difference between each group of connecting lines and the preset length; Calculating a pitch angle correction value of each group of connecting lines according to the horizontal difference between each group of connecting lines and the average flight altitude of the two adjacent flight strips; The heading angle correction value of each group of connecting lines is calculated according to the horizontal difference between each group of connecting lines and the vertical distance between the flight tracks corresponding to each group of connecting lines.

5. The method according to claim 1, characterized in that The angle correction value includes: a correction value of at least one angle; and determining a target angle correction value of each group of connecting lines based on the angle correction values ​​of the N groups of connecting lines includes: Performing curve fitting on the correction value of each angle of the N groups of connecting lines and the serial number of the corresponding connecting line to obtain a fitting curve of the correction value of each angle and the serial number of the connecting line; A target correction value for each angle of each group of connecting lines is determined according to the fitting curve.

6. The method according to claim 5, characterized in that Determining, from each angle correction value of the N groups of connecting lines according to the fitting curve, an angle correction value having the smallest error with each angle correction value of each group of connecting lines as the target angle correction value includes: Using the extreme value method, under the condition of minimizing the sum of squared errors, calculate m groups of weight parameters of the fitting equation corresponding to the fitting curve; m is an integer greater than or equal to 1; According to the m groups of weight parameters and the sequence number of each group of connecting lines, using the fitting equation, respectively calculating m fitting angle correction values ​​for each group of connecting lines; The fitting angle correction value having the smallest error value with respect to each angle correction value of each group of connecting lines among the m fitting angle correction values ​​is determined as the target angle correction value.

7. A point cloud data processing device, characterized in that: The device comprises: Acquisition module, used to obtain point cloud data collected by airborne lidar; a division module, configured to distinguish the collected point cloud data according to a plurality of flight strips, and obtain point cloud data corresponding to each flight strip; A setting module, used for setting a plurality of connection lines of preset lengths in the point cloud data of the overlapping area of ​​two adjacent flight strips; a search module configured to search for connecting lines of a cross-section type along directions of different flight strips in the overlapping region along the two adjacent flight strips, to obtain N groups of connecting lines in the overlapping region, each group of connecting lines comprising: two connecting lines of the two adjacent flight strips; wherein the connecting lines of the cross-section type are connecting lines on a vertical plane perpendicular to a horizontal plane in the overlapping region; and N is an integer greater than or equal to 2; a calculation module, configured to calculate an angle correction value of each group of connecting lines according to a state parameter of each group of connecting lines; wherein the state parameter of each group of connecting lines includes: a positional relationship between the connecting lines in each group, and a positional relationship between the connecting lines and the flight path; a correction module, configured to determine a target angle correction value for each group of connecting lines according to the angle correction values ​​of the N groups of connecting lines; According to the target angle correction value, the angle of the point cloud data is corrected to obtain a point cloud rendering of the feature with the same name.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, the point cloud data processing method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the point cloud data processing method according to any one of claims 1 to 6 is executed.

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