Method and device for completing wire point cloud, electronic equipment, medium and product

Abstract

This application provides a method, apparatus, electronic device, medium, and product for completing traverse point clouds. The method includes: acquiring a reference scene point cloud and a scene point cloud to be converted; selecting a target traverse from multiple traverses and performing a first coordinate transformation on the second traverse point cloud corresponding to the target traverse to obtain a first aligned point cloud, wherein the fitted lines of the horizontal plane projections of the first aligned point cloud and the first traverse point cloud corresponding to the target traverse are the same straight line; performing a second coordinate transformation on the first converted point cloud based on a first extracted point cloud and a second extracted point cloud to obtain a second converted point cloud; the first converted point cloud is obtained by performing a first coordinate transformation on the traverse point cloud to be converted; and determining the complete point cloud corresponding to each traverse based on the second converted point cloud, wherein the coordinate system of the second converted point cloud and the reference traverse point cloud is the same, which can improve the accuracy of point cloud fusion when the overlap area of ​​the two sets of point cloud data is insufficient.

Description

Technical Field

[0001] This application relates to the field of three-dimensional reconstruction of point cloud data, and in particular to a method, apparatus, electronic device, medium and product for completing wire point clouds. Background Technology

[0002] In practical applications, complete point cloud data of transmission lines may not be available due to equipment limitations, environmental obstacles (such as vegetation and buildings), or terrain complexity. Currently, multiple sets of partial point cloud data corresponding to transmission lines can be collected, and then precise registration and point cloud fusion can be performed based on the overlapping areas between the multiple sets of data.

[0003] However, in the case of elevated bridges or other obstacles in the power transmission channel, or in the case of power poles erected between two mountains, there may not be enough overlapping area data in the multiple sets of partial point cloud data collected, resulting in the failure of point cloud data fusion. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, medium, and product for completing wire point clouds, which can achieve accurate fusion of point cloud data when the overlapping area is insufficient.

[0005] In a first aspect, embodiments of this application provide a method for completing a wire point cloud, including:

[0006] A reference scene point cloud and a scene point cloud to be converted are acquired. The reference scene point cloud and the scene point cloud to be converted are obtained by scanning the power transmission corridor from both sides of the same corridor using a scanning device. The reference scene point cloud includes a reference conductor point cloud and a point cloud of the first tower. The scene point cloud to be converted includes the point cloud of the conductor to be converted and the point cloud of the second tower. The reference conductor point cloud includes the first conductor point cloud corresponding to each conductor. The conductor point cloud to be converted includes the second conductor point cloud corresponding to each conductor.

[0007] The target traverse is selected from multiple traverses, and the second traverse point cloud corresponding to the target traverse is subjected to a first coordinate transformation to obtain a first aligned point cloud. The fitted lines of the horizontal plane projections corresponding to the first aligned point cloud and the first traverse point cloud corresponding to the target traverse are the same straight line.

[0008] Based on the first extracted point cloud and the second extracted point cloud, a second coordinate transformation is performed on the first transformed point cloud to obtain the second transformed point cloud; wherein, the first extracted point cloud is the set of points in the first conductor point cloud whose distance from the first tower is within a preset range, the second extracted point cloud is the set of points in the first aligned point cloud whose distance from the second tower is within the preset range, and the first transformed point cloud is obtained by performing the first coordinate transformation on the conductor point cloud to be transformed;

[0009] Based on the second transformed point cloud, the complete point cloud corresponding to each traverse is determined, wherein the coordinate system of the second transformed point cloud and the reference traverse point cloud is the same.

[0010] Optionally, a first coordinate transformation is performed on the second traverse point cloud corresponding to the traverse to obtain a first aligned point cloud, including:

[0011] The first traverse point cloud and the second traverse point cloud are projected onto the horizontal plane respectively to obtain the first projected point cloud and the second projected point cloud.

[0012] The first and second projected point clouds were fitted with straight lines using the least squares method to obtain the first and second line equations.

[0013] Based on the first straight line equation and the second straight line equation, determine the first angle of rotation of the second traverse point cloud around the z-axis and the first distance of translation along the y-axis, wherein the z-axis is in the vertical direction and the y-axis is in the horizontal direction and perpendicular to the distance direction between the two towers;

[0014] The second guide point cloud is rotated around the z-axis by the first angle and translated along the y-axis by the first distance to obtain the first aligned point cloud.

[0015] Optionally, based on the first extracted point cloud and the second extracted point cloud, a second coordinate transformation is performed on the first transformed point cloud to obtain the second transformed point cloud, including:

[0016] A straight line is fitted to the second extracted point cloud to obtain the second direction of the fitted line; a straight line is fitted to the first extracted point cloud to obtain the first direction of the fitted line.

[0017] Based on the first direction and the second direction, a second coordinate transformation is performed on the first transformed point cloud to obtain a second transformed point cloud.

[0018] Optionally, based on the first direction and the second direction, a second coordinate transformation is performed on the first transformed point cloud to obtain a second transformed point cloud, including:

[0019] Based on the first direction and the second direction, determine the second angle of rotation of the first conversion point cloud around the x-axis;

[0020] The first converted point cloud is rotated around the x-axis by the second angle to obtain the third converted point cloud, wherein the x-axis is along the distance between the two towers;

[0021] Based on the third transformed point cloud and the reference traverse point cloud, determine the second distance of translation along the x-axis and the third distance of translation along the z-axis of the second transformed point cloud, wherein the z-axis is along the vertical direction;

[0022] The second transformed point cloud is obtained by translating the second transformed point cloud along the x-axis by the second distance and along the z-axis by the third distance.

[0023] Optionally, determining a second angle of rotation of the first conversion point cloud around the x-axis based on the first direction and the second direction includes:

[0024] The coordinate range of the first bounding box is determined according to the first direction; the coordinate range of the second bounding box is determined according to the second direction; the first bounding box and the second bounding box are rectangular bodies of the same shape, the direction of the first side of the first bounding box is the same as the first direction, the direction of the second side of the second bounding box is the same as the second direction, and the lengths of the first side and the second side are the same.

[0025] The point cloud located within the coordinate range of the first bounding box is projected onto the first plane to obtain the third projected point cloud. The point cloud located within the coordinate range of the second bounding box is projected onto the second plane to obtain the fourth projected point cloud. The point cloud located within the coordinate range of the second bounding box is a part of the first transformed point cloud. The first plane is perpendicular to the first side, and the second plane is perpendicular to the second side.

[0026] Based on the third and fourth projected point clouds, determine the second angle of rotation of the first transformed point cloud around the x-axis.

[0027] Optionally, determining a second angle of rotation of the first transformed point cloud around the x-axis based on the third and fourth projected point clouds includes:

[0028] At least one first feature point is extracted from the third projected point cloud, and the descriptor corresponding to each first feature point is determined; at least one second feature point is extracted from the fourth projected point cloud, and the descriptor corresponding to each second feature point is determined.

[0029] Based on the descriptors corresponding to each first feature point and each second feature point, at least one first feature point and at least one second feature point are matched to obtain multiple matching point pairs.

[0030] Based on multiple matching point pairs, a homography matrix is ​​determined using the RANSAC algorithm, and based on the homography matrix, a second angle of rotation of the first conversion point cloud around the x-axis is determined; wherein, the x-axis is along the distance direction between the two towers.

[0031] Optionally, based on the third transformed point cloud and the reference traverse point cloud, determining a second distance of translation along the x-axis and a third distance of translation along the z-axis of the second transformed point cloud includes:

[0032] The third transformed point cloud is projected onto the xoz plane to obtain the fifth projected point cloud; the reference guide point cloud is projected onto the xoz plane to obtain the sixth projected point cloud.

[0033] Calculate Δx and Δz when the following objective function reaches its minimum value;

[0034]

[0035] Among them, (x i , z i (u) is any point in the sixth projected point cloud. j v j ) represents any point in the fifth projection point cloud, m represents the number of points included in the sixth projection point cloud, and n represents the number of points included in the fifth projection point cloud.

[0036] The second distance is determined to be equal to Δx, and the third distance is determined to be equal to Δz.

[0037] Optionally, based on the second transformed point cloud, the complete point cloud corresponding to each traverse is determined, including:

[0038] For each traverse, the corresponding projection point cloud is extracted from the sixth and seventh projection point clouds respectively, and the quadratic curve of the corresponding projection point cloud is fitted to obtain the quadratic curve equation of the traverse; wherein, the seventh projection point cloud is obtained by translating the fifth projection point cloud along the x-axis by the second distance and along the z-axis by the third distance;

[0039] The three-dimensional spatial equation corresponding to the traverse is determined based on the quadratic curve equation corresponding to the traverse and the fitted straight line equation of the horizontal plane projection of the first traverse point cloud corresponding to the traverse.

[0040] Based on the three-dimensional spatial equation, the complete point cloud corresponding to the traverse is determined.

[0041] Optionally, the preset range is 0 to a, where a is a fixed distance greater than zero.

[0042] Secondly, embodiments of this application provide a device for completing a wire point cloud, comprising:

[0043] The acquisition module is used to acquire a reference scene point cloud and a scene point cloud to be converted; wherein, the reference scene point cloud and the scene point cloud to be converted are obtained by scanning the power transmission corridor from both sides of the same power transmission corridor using a scanning device; the reference scene point cloud includes a reference conductor point cloud and a point cloud of the first tower; the scene point cloud to be converted includes a point cloud of the conductor to be converted and a point cloud of the second tower; the reference conductor point cloud includes a first conductor point cloud corresponding to each conductor; the conductor point cloud to be converted includes a second conductor point cloud corresponding to each conductor.

[0044] The first transformation module is used to filter out the target traverse from multiple traverses and perform a first coordinate transformation on the second traverse point cloud corresponding to the target traverse to obtain a first aligned point cloud. The fitted lines of the horizontal plane projections corresponding to the first aligned point cloud and the first traverse point cloud corresponding to the target traverse are the same straight line.

[0045] The second conversion module is used to perform a second coordinate transformation on the first converted point cloud based on the first extracted point cloud and the second extracted point cloud to obtain the second converted point cloud; wherein, the first extracted point cloud is a set of points in the first traverse point cloud whose distance from the first tower is within a preset range, the second extracted point cloud is a set of points in the first aligned point cloud whose distance from the second tower is within the preset range, and the first converted point cloud is obtained by performing the first coordinate transformation on the traverse point cloud to be converted;

[0046] The determining module is used to determine the complete point cloud corresponding to each traverse based on the second transformed point cloud, wherein the coordinate system of the second transformed point cloud and the reference traverse point cloud is the same.

[0047] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0048] The memory stores computer-executed instructions;

[0049] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0050] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0051] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0052] The conductor point cloud completion method, apparatus, electronic device, medium, and product provided in this application embodiment acquire a reference scene point cloud and a scene point cloud to be converted. The reference scene point cloud and the scene point cloud to be converted are obtained by scanning the same power transmission corridor from both sides using a scanning device. The reference scene point cloud includes a reference conductor point cloud and a point cloud of a first tower. The scene point cloud to be converted includes the point cloud of the conductor to be converted and the point cloud of the second tower. The reference conductor point cloud includes a first conductor point cloud corresponding to each conductor. The conductor point cloud to be converted includes a second conductor point cloud corresponding to each conductor. A target conductor is selected from multiple conductors, and a first coordinate transformation is performed on the second conductor point cloud corresponding to the target conductor to obtain a first aligned point cloud. The fitted straight lines of the horizontal plane projections corresponding to the first aligned point cloud and the first conductor point cloud corresponding to the target conductor are the same. A straight line is formed. Based on the first extracted point cloud and the second extracted point cloud, a second coordinate transformation is performed on the first transformed point cloud to obtain the second transformed point cloud. The first extracted point cloud is the set of points in the first traverse point cloud whose distance from the first tower is within a preset range, and the second extracted point cloud is the set of points in the first aligned point cloud whose distance from the second tower is within the preset range. The first transformed point cloud is obtained by performing the first coordinate transformation on the traverse point cloud to be transformed. Based on the second transformed point cloud, the complete point cloud corresponding to each traverse is determined. The coordinate system of the second transformed point cloud and the reference traverse point cloud is the same. Through two coordinate transformations (the first coordinate transformation and the second coordinate transformation), it is ensured that the scene point cloud to be transformed and the reference scene point cloud are aligned in the same coordinate system, which can improve the accuracy of point cloud fusion when the overlapping area of ​​the two sets of point cloud data is insufficient. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0054] Figure 1 An application scenario diagram provided for an embodiment of this application;

[0055] Figure 2 A flowchart illustrating a method for completing a wire point cloud, provided in an embodiment of this application;

[0056] Figure 3 This application provides a schematic diagram of a reference scene point cloud and a scene point cloud to be converted, which is an embodiment of the present application.

[0057] Figure 4 A schematic diagram of a reference guide point cloud and a guide point cloud to be converted, provided for embodiments of this application;

[0058] Figure 5A schematic diagram of a filtered reference conductor point cloud and a conductor point cloud to be converted, provided for an embodiment of this application;

[0059] Figure 6 A point cloud diagram of two scans of data belonging to the same conductor, provided for an embodiment of the application;

[0060] Figure 7 A schematic diagram of the structure of the wire point cloud completion device provided in this application;

[0061] Figure 8 A schematic diagram of the structure of the electronic device provided in this application.

[0062] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0063] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0064] In practical applications, complete point cloud data of transmission lines may not be available due to equipment limitations, environmental obstacles (such as vegetation and buildings), or terrain complexity. Currently, it is possible to collect multiple sets of partial point cloud data corresponding to the transmission line and then fuse these multiple sets of partial point clouds.

[0065] Fusing multiple partial point clouds relies primarily on two key aspects: first, accurate registration using the overlapping portions of two or more data sets; and second, data correction using high-precision positioning information such as RTK (Real-Time Kinematic) technology. However, in practical applications, terrain complexity and environmental factors (such as the challenge of scanning power poles on both sides due to altitude variations in mountainous areas, the need to erect power poles between mountains, and the presence of viaducts or other obstacles within power transmission channels) make it difficult to guarantee sufficient overlapping area data for each scan. Furthermore, RTK signals may become unstable due to environmental interference, leading to distorted positioning data. In addition, computational errors in SLAM (Simultaneous Localization and Mapping) algorithms during scanning further affect data quality. The combined effect of these factors can cause point cloud data fusion failure, thus impacting the effectiveness of subsequent analysis and applications.

[0066] In view of this, this application provides a method for completing conductor point clouds. A reference scene point cloud and a scene point cloud to be converted are obtained by scanning the same power transmission corridor from both sides using a scanning device. A server acquires the reference scene point cloud and the scene point cloud to be converted, filters out the target conductor from multiple conductors included in the power transmission corridor, and performs a first coordinate transformation on the second conductor point cloud corresponding to the target conductor to obtain a first aligned point cloud. The fitted lines of the horizontal plane projections of the first aligned point cloud and the first conductor point cloud corresponding to the target conductor are the same straight line. The first coordinate transformation is then performed on the conductor point cloud to be converted to obtain a first converted point cloud. Finally, the distance between the first conductor point cloud and the first tower is determined. The set of points within a preset range is the first extracted point cloud. The set of points in the first aligned point cloud whose distance from the second tower is within the preset range is the second extracted point cloud. Based on the first and second extracted point clouds, the first transformed point cloud is subjected to a second coordinate transformation to obtain the second transformed point cloud. Based on the second transformed point cloud, the complete point cloud corresponding to each conductor is determined. The coordinate system of the second transformed point cloud and the reference conductor point cloud are the same. Through two coordinate transformations (first coordinate transformation and second coordinate transformation), it is ensured that the point cloud of the scene to be transformed and the reference scene point cloud are aligned in the same coordinate system. This can improve the accuracy of point cloud fusion when the overlapping area of ​​the two sets of point cloud data is insufficient.

[0067] Figure 1 An application scenario diagram provided for an embodiment of this application, such as... Figure 1As shown, the scanning device scans the power transmission corridor from both sides to obtain a reference scene point cloud and a scene point cloud to be converted, and sends the reference scene point cloud and the scene point cloud to be converted to the server. The reference scene point cloud includes a reference conductor point cloud and a point cloud of the first tower; the scene point cloud to be converted includes a point cloud of the conductor to be converted and a point cloud of the second tower; the reference conductor point cloud includes the first conductor point cloud corresponding to each conductor; the conductor point cloud to be converted includes the second conductor point cloud corresponding to each conductor; after obtaining the reference scene point cloud and the scene point cloud to be converted, the server selects the target conductor from multiple conductors and performs a first coordinate transformation on the second conductor point cloud corresponding to the target conductor to obtain a first aligned point cloud. The first aligned point cloud and the first conductor point cloud corresponding to the target conductor are respectively projected onto the horizontal plane. The fitted straight line is the same straight line; based on the first extracted point cloud and the second extracted point cloud, the first transformed point cloud is subjected to a second coordinate transformation to obtain the second transformed point cloud; wherein, the first extracted point cloud is the set of points in the first conductor point cloud whose distance from the first tower is within a preset range, and the second extracted point cloud is the set of points in the first aligned point cloud whose distance from the second tower is within a preset range. The first transformed point cloud is obtained by performing a first coordinate transformation on the conductor point cloud to be transformed; based on the second transformed point cloud, the complete point cloud corresponding to each conductor is determined, wherein the coordinate system of the second transformed point cloud and the reference conductor point cloud is the same. The complete point cloud corresponding to each conductor is sent to the service terminal. The service terminal uses the complete point cloud data to assess changes in the environment around the transmission line, such as vegetation growth, building proximity, etc., and thus takes necessary preventive measures.

[0068] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0069] Figure 2 This is a flowchart illustrating a method for completing a wire point cloud according to an embodiment of this application. The execution entity in this embodiment can be any device with data processing capabilities. This application uses a server as the execution entity for specific description. Figure 2 As shown in the embodiment of this application, a method for completing a guideline point cloud may include:

[0070] Step 201: Obtain a reference scene point cloud and a scene point cloud to be converted; wherein, the reference scene point cloud and the scene point cloud to be converted are obtained by scanning the power transmission corridor on both sides of the same power transmission corridor using a scanning device; the reference scene point cloud includes a reference conductor point cloud and a point cloud of the first tower; the scene point cloud to be converted includes a point cloud of the conductor to be converted and a point cloud of the second tower; the reference conductor point cloud includes a first conductor point cloud corresponding to each conductor; the conductor point cloud to be converted includes a second conductor point cloud corresponding to each conductor.

[0071] Specifically, the scanning device scans the power transmission corridor from both sides to obtain a reference scene point cloud and a scene point cloud to be converted, and sends the reference scene point cloud and the scene point cloud to be converted to the server, which then obtains the reference scene point cloud and the scene point cloud to be converted.

[0072] Optionally, the scanning device scans the power transmission corridor from both sides to obtain an initial reference scene point cloud and an initial scene point cloud to be converted. Then, a statistical filtering algorithm is used to filter both the initial reference scene point cloud and the initial scene point cloud to be converted, removing outliers. The statistical filtering algorithm is based on the principle of statistical analysis, performing detailed statistical analysis on each point cloud data point to calculate its neighborhood statistical characteristics, such as mean and standard deviation. Based on these statistical characteristics, the statistical filtering algorithm can accurately determine which points are noise or outliers and effectively filter them out, finally obtaining a clean point cloud containing power conductor point cloud data and power pole tower data, i.e., the reference scene point cloud and the scene point cloud to be converted. These two point clouds are then sent to the server, which receives them.

[0073] Statistical filtering algorithms provide a high-quality data foundation for subsequent analysis and applications. The application of statistical filtering algorithms not only improves the accuracy and reliability of data but also enhances the efficiency and precision of data processing.

[0074] Step 202: Select the target traverse from multiple traverses, and perform a first coordinate transformation on the second traverse point cloud corresponding to the target traverse to obtain a first aligned point cloud. The fitted lines of the horizontal plane projections corresponding to the first aligned point cloud and the first traverse point cloud corresponding to the target traverse are the same straight line.

[0075] Specifically, the server selects the target wire from multiple wires, preferably the wire that is closest to the scanning device.

[0076] The server performs a first coordinate transformation on the second traverse point cloud corresponding to the target traverse to obtain a first aligned point cloud, wherein the fitted line of the first aligned point cloud projected onto the horizontal plane is the same as the fitted line of the first traverse point cloud projected onto the horizontal plane corresponding to the target traverse.

[0077] Optionally, a first coordinate transformation is performed on the second traverse point cloud corresponding to the traverse to obtain a first aligned point cloud, including:

[0078] The first traverse point cloud and the second traverse point cloud are projected onto the horizontal plane respectively to obtain the first projected point cloud and the second projected point cloud.

[0079] The first and second projected point clouds were fitted with straight lines using the least squares method to obtain the first and second line equations.

[0080] Based on the first straight line equation and the second straight line equation, determine the first angle of rotation of the second traverse point cloud around the z-axis and the first distance of translation along the y-axis, wherein the z-axis is in the vertical direction and the y-axis is in the horizontal direction and perpendicular to the distance direction between the two towers;

[0081] The second guide point cloud is rotated around the z-axis by the first angle and translated along the y-axis by the first distance to obtain the first aligned point cloud.

[0082] Specifically, the server projects the first traverse point cloud onto the horizontal plane to obtain the first projected point cloud. The first projected point cloud is then fitted with a straight line using the least squares method to obtain the first straight line. The equation corresponding to the first straight line is the equation of the first straight line, which, for example, is y = m1·x + b1.

[0083] The horizontal plane refers to the xoy plane.

[0084] Project the second traverse point cloud onto the horizontal plane to obtain the second projected point cloud. Use the least squares method to fit a straight line to the second projected point cloud to obtain the second straight line. The equation corresponding to the second straight line is the equation of the second straight line, which, for example, is y = m2·x + b2.

[0085] Based on the equations of the first and second lines, the angle between them can be calculated. This angle is the first angle of rotation of the second traverse point cloud around the z-axis. After rotating the second line around the z-axis by this first angle, the rotated second line is parallel to the first line. The distance between the two parallel lines is:

[0086]

[0087] The distance d is the first distance that the second traverse point cloud is translated along the y-axis. The second traverse point cloud is rotated around the z-axis by the first angle and translated along the y-axis by the first distance to obtain the first aligned point cloud.

[0088] Step 203: Based on the first extracted point cloud and the second extracted point cloud, perform a second coordinate transformation on the first transformed point cloud to obtain a second transformed point cloud; wherein, the first extracted point cloud is the set of points in the first conductor point cloud whose distance from the first tower is within a preset range, the second extracted point cloud is the set of points in the first aligned point cloud whose distance from the second tower is within the preset range, and the first transformed point cloud is obtained by performing the first coordinate transformation on the conductor point cloud to be transformed.

[0089] Specifically, the server extracts a first extracted point cloud from the first conductor point cloud, where the first extracted point cloud is a set of points whose distance from the first tower is within a preset range. It then extracts a second extracted point cloud from the first aligned point cloud, where the second extracted point cloud is a set of points whose distance from the second tower is within the preset range. The server performs a first coordinate transformation on the conductor point cloud to be transformed to obtain a first transformed point cloud. Finally, based on the first and second extracted point clouds, the server performs a second coordinate transformation on the first transformed point cloud to obtain a second transformed point cloud.

[0090] Optionally, the preset range is 0 to a, where a is a fixed distance greater than zero.

[0091] In this way, by using the first and second extracted point clouds corresponding to the preset range of 0 to a, the second coordinate transformation of the first transformed point cloud can be performed, which can improve the accuracy of the determined second coordinate transformation and improve the precision of point cloud alignment.

[0092] Optionally, based on the first extracted point cloud and the second extracted point cloud, a second coordinate transformation is performed on the first transformed point cloud to obtain the second transformed point cloud, including:

[0093] A straight line is fitted to the second extracted point cloud to obtain the second direction of the fitted line; a straight line is fitted to the first extracted point cloud to obtain the first direction of the fitted line.

[0094] Based on the first direction and the second direction, a second coordinate transformation is performed on the first transformed point cloud to obtain a second transformed point cloud.

[0095] Specifically, the server performs 3D spatial line fitting on the second extracted point cloud to obtain the second direction of the fitted line; an exemplary method for creating a 3D spatial line can use vectors. The process involves fitting a straight line in three-dimensional space to the first extracted point cloud to obtain the first direction of the fitted line; then, based on the first and second directions, performing a second coordinate transformation on the first transformed point cloud to obtain the second transformed point cloud.

[0096] In this way, by using the fitting direction for coordinate transformation, the accuracy and efficiency of point cloud data processing can be effectively improved, providing a more reliable foundation for subsequent analysis and applications.

[0097] Optionally, based on the first direction and the second direction, a second coordinate transformation is performed on the first transformed point cloud to obtain a second transformed point cloud, including:

[0098] Based on the first direction and the second direction, determine the second angle of rotation of the first conversion point cloud around the x-axis;

[0099] The first converted point cloud is rotated around the x-axis by the second angle to obtain the third converted point cloud, wherein the x-axis is along the distance between the two towers;

[0100] Based on the third transformed point cloud and the reference traverse point cloud, determine the second distance of translation along the x-axis and the third distance of translation along the z-axis of the second transformed point cloud, wherein the z-axis is along the vertical direction;

[0101] The second transformed point cloud is obtained by translating the second transformed point cloud along the x-axis by the second distance and along the z-axis by the third distance.

[0102] Specifically, the server determines a second angle of rotation of the first transformed point cloud around the x-axis based on the first and second directions; the first transformed point cloud is rotated around the x-axis by the second angle to obtain a third transformed point cloud; based on the third transformed point cloud and the reference traverse point cloud, a second distance of translation along the x-axis and a third distance of translation along the z-axis are determined; the second transformed point cloud is translated along the x-axis by the second distance and along the z-axis by the third distance to obtain a second transformed point cloud.

[0103] In this way, by rotating around the x-axis and translating along the x-axis and z-axis, the first transformed point cloud and the reference traverse point cloud can be aligned more accurately. This precise alignment is crucial for ensuring data consistency and accuracy.

[0104] Optionally, determining a second angle of rotation of the first conversion point cloud around the x-axis based on the first direction and the second direction includes:

[0105] The coordinate range of the first bounding box is determined according to the first direction; the coordinate range of the second bounding box is determined according to the second direction; the first bounding box and the second bounding box are rectangular bodies of the same shape, the direction of the first side of the first bounding box is the same as the first direction, the direction of the second side of the second bounding box is the same as the second direction, and the lengths of the first side and the second side are the same.

[0106] The point cloud located within the coordinate range of the first bounding box is projected onto the first plane to obtain the third projected point cloud. The point cloud located within the coordinate range of the second bounding box is projected onto the second plane to obtain the fourth projected point cloud. The point cloud located within the coordinate range of the second bounding box is a part of the first transformed point cloud. The first plane is perpendicular to the first side, and the second plane is perpendicular to the second side.

[0107] Based on the third and fourth projected point clouds, determine the second angle of rotation of the first transformed point cloud around the x-axis.

[0108] The bounding box is a rectangle with a defined width W, height H, and depth D. One side of the first bounding box is aligned with a first direction, while the plane formed by the other two sides is perpendicular to the first direction. One side of the second bounding box is aligned with a second direction, while the plane formed by the other two sides is perpendicular to the second direction.

[0109] For example, the specific steps for determining the coordinate range of the first bounding box based on the first direction are as follows:

[0110] 1. Determine the vector in the first direction:

[0111] 2. Finding the perpendicular vector: In order to find the perpendicular vector... For perpendicular vectors, the cross product can be used. First, choose a vector that is not perpendicular to the first vector. For parallel vectors, if c≠0, you can choose the vector. or The cross product yields the first perpendicular vector. If c = 0, then choose The calculation method is as follows:

[0112] choose but

[0113] If c = 0, choose but

[0114] 3. Normalizing a vector: Normalizing a vector Normalization, i.e. Make sure it is a unit vector.

[0115] 4. Find the second perpendicular vector: using and Performing the cross product again, we get... And normalize it.

[0116] 5. Calculate the vertices of the bounding box: Assume the center of the bounding box is C = (x... c y c , zc The bounding box has a length of L, a width of W, and a height of H along the first direction (these three values ​​are configured according to the specific situation). The eight vertices of the bounding box can be calculated by adding or subtracting the vector in the corresponding direction from the center point C. Specifically, the vertices can be calculated using the following formula:

[0117]

[0118] By following the steps above, the coordinates of all vertices of the bounding box can be calculated, thus completely defining the position and size of the bounding box.

[0119] Specifically, the server determines the coordinate range of the first bounding box based on the first direction and the steps described above; then, based on the second direction, it determines the coordinate range of the second bounding box based on similar steps described above. The point cloud located within the coordinate range of the first bounding box is projected onto a first plane, where the first plane is perpendicular to the first edge, resulting in a third projected point cloud. The point cloud located within the coordinate range of the second bounding box is projected onto a second plane, where the second plane is perpendicular to the second edge, resulting in a fourth projected point cloud. The point cloud located within the coordinate range of the first bounding box is part of the first transformed point cloud, the point cloud located within the coordinate range of the second bounding box is part of the first transformed point cloud, and the point cloud located within the coordinate range of the first bounding box is part of the reference traverse point cloud. Both the point clouds located within the coordinate ranges of the first and second bounding boxes include point clouds corresponding to multiple traverses, and are currently only a portion of each. Based on the third and fourth projected point clouds, a second angle of rotation around the x-axis is determined for the first transformed point cloud.

[0120] The specific steps for projecting the point cloud within the coordinate range of the first bounding box onto the first plane are as follows:

[0121] 1. Vector normalization: First, ensure that the vector in the first direction is normalized. It is a unit vector. If it is not a unit vector, it needs to be normalized.

[0122]

[0123] 2. Calculate the projection points: For each point P in the point cloud located within the coordinate range of the first bounding box... i =(x i y i , z i Its projection point P′ on the first plane i It can be calculated using the following formula: in It is point P i Vector to the origin and normal vector The dot product is calculated using the following formula:

[0124] Therefore, the projection point P′ i The coordinates are: P′ i =(x i ,y i , z i )-(x i a+y i b+z i c)(a,b,c).

[0125] The above steps can be used to obtain the projected coordinates of all points within the coordinate range of the first bounding box on the first plane.

[0126] The specific steps for projecting the point cloud within the coordinate range of the second bounding box onto the second plane are similar to those described above, and will not be repeated here.

[0127] In this way, by defining the bounding box using the first and second directions and projecting the point cloud onto the corresponding plane, the required rotation angle can be determined more accurately, thereby improving the overall accuracy of point cloud alignment.

[0128] Optionally, determining a second angle of rotation of the first transformed point cloud around the x-axis based on the third and fourth projected point clouds includes:

[0129] At least one first feature point is extracted from the third projected point cloud, and the descriptor corresponding to each first feature point is determined; at least one second feature point is extracted from the fourth projected point cloud, and the descriptor corresponding to each second feature point is determined.

[0130] Based on the descriptors corresponding to each first feature point and each second feature point, at least one first feature point and at least one second feature point are matched to obtain multiple matching point pairs.

[0131] Based on multiple matching point pairs, a homography matrix is ​​determined using the RANSAC algorithm, and based on the homography matrix, a second angle of rotation of the first conversion point cloud around the x-axis is determined; wherein, the x-axis is along the distance direction between the two towers.

[0132] Specifically, the server extracts at least one first feature point from the third projected point cloud based on a feature detection algorithm and determines the descriptor corresponding to each first feature point; it also extracts at least one second feature point from the fourth projected point cloud based on the same feature detection algorithm and determines the descriptor corresponding to each second feature point. Based on the descriptors corresponding to each first feature point and each second feature point, a matching algorithm is used to match at least one first feature point and at least one second feature point, and reliable matching point pairs are selected using a certain threshold or ratio test (such as Lowe's ratio test).

[0133] Next, the server uses RANSAC (Random Sample Consensus Algorithm) to estimate the homography matrix H from multiple matching point pairs. The homography matrix H describes the transformation relationship between two planes, including rotation, translation, and scaling. The homography matrix H is decomposed into a rotation matrix R, a translation vector T, and a scaling factor S. The specific decomposition method is as follows:

[0134]

[0135] Where R is a 2x2 rotation matrix, T is a translation vector, and S is a scaling factor.

[0136] Then, the rotation angle θ is extracted from the rotation matrix R. For a 2x2 rotation matrix:

[0137]

[0138] The rotation angle θ can be calculated using the following formula: θ = arctan2(r 21 r 11 ), where the determined rotation angle θ is the second angle of the first conversion point cloud rotating around the x-axis.

[0139] The feature detection algorithm can be any algorithm that can extract feature points. This application does not limit it. For example, the feature detection algorithm can be SIFT (Scale Invariant Feature Transform), SURF (Speed-Up Robust Feature Transform), ORB (Oriented Fast and Rotated BRIEF), etc.

[0140] A descriptor is a vector used to describe local information around a feature point.

[0141] The matching algorithm can be any algorithm capable of matching feature points, and this application does not limit it. For example, the matching algorithm can be nearest neighbor matching (such as the FLANN matcher) or other matching algorithms.

[0142] In this way, by extracting and matching feature points, the geometric relationship between the third and fourth projected point clouds can be accurately captured. Further optimization of the matching results using the RANSAC algorithm can effectively improve the accuracy of rotation angle calculations.

[0143] Optionally, based on the third transformed point cloud and the reference traverse point cloud, determining a second distance of translation along the x-axis and a third distance of translation along the z-axis of the second transformed point cloud includes:

[0144] The third transformed point cloud is projected onto the xoz plane to obtain the fifth projected point cloud; the reference guide point cloud is projected onto the xoz plane to obtain the sixth projected point cloud.

[0145] Calculate Δx and Δz when the following objective function reaches its minimum value;

[0146]

[0147] Among them, (x i , z i (u) is any point in the sixth projected point cloud. j v j ) represents any point in the fifth projection point cloud, m represents the number of points included in the sixth projection point cloud, and n represents the number of points included in the fifth projection point cloud.

[0148] The second distance is determined to be equal to Δx, and the third distance is determined to be equal to Δz.

[0149] Specifically, the server projects the third transformed point cloud onto the xoz plane to obtain the fifth projected point cloud. For example, the coordinates of the points in the fifth projected point cloud are {(u1,v1),(u2,v2),…,(u…}. n v n )}.

[0150] The server projects the reference guide point cloud onto the xoz plane to obtain the sixth projected point cloud. For example, the coordinates of points in the sixth projected point cloud are {(x1,z1),(x2,z2),……,(x...}. m ,z m )}.

[0151] By shifting the parameters Δx and Δz, {(u j v j )} translate to the new position {(u j +△x, v j +△z)}.

[0152] The objective function can be expressed as:

[0153]

[0154] Optimization algorithms (such as gradient descent, BFGS, etc.) are used to minimize the objective function E(Δx, Δz). The optimization algorithm iteratively adjusts Δx and Δz until it finds the parameter values ​​Δx and Δz that minimize E(Δx, Δz). A second distance is determined to be equal to Δx, and a third distance is determined to be equal to Δz.

[0155] In this way, by projecting and calculating on the xoz plane, the translation distance between point clouds can be determined more accurately, thereby improving the overall accuracy of point cloud alignment and ensuring the accuracy of subsequent analysis.

[0156] Step 204: Based on the second transformed point cloud, determine the complete point cloud corresponding to each traverse, wherein the coordinate system of the second transformed point cloud and the reference traverse point cloud is the same.

[0157] Specifically, the server determines the complete point cloud corresponding to each traverse based on the second converted point cloud.

[0158] Optionally, based on the second transformed point cloud, the complete point cloud corresponding to each traverse is determined, including:

[0159] For each traverse, the corresponding projection point cloud is extracted from the sixth and seventh projection point clouds respectively, and a quadratic curve is fitted to the corresponding projection point cloud to obtain the quadratic curve equation of the traverse; wherein, the seventh projection point cloud is obtained by translating the fifth projection point cloud along the x-axis by the second distance and along the z-axis by the third distance;

[0160] The three-dimensional spatial equation corresponding to the traverse is determined based on the quadratic curve equation corresponding to the traverse and the fitted straight line equation of the horizontal plane projection of the first traverse point cloud corresponding to the traverse.

[0161] Based on the three-dimensional spatial equation, the complete point cloud corresponding to the traverse is determined.

[0162] Specifically, the server translates the fifth projected point cloud by a second distance along the x-axis and a third distance along the z-axis to obtain the seventh projected point cloud.

[0163] For each traverse, the corresponding projection point cloud is extracted from the sixth and seventh projection point clouds respectively, and a quadratic curve is fitted to the corresponding projection point cloud based on the least squares method to obtain the quadratic curve equation of the traverse.

[0164] For the target traverse, the three-dimensional spatial equation corresponding to the target traverse is determined based on the quadratic curve equation and the first straight line equation. Based on the three-dimensional spatial equation and the distance between the two towers, the complete point cloud corresponding to the target traverse is determined.

[0165] For any non-target traverse, project the non-target traverse onto a horizontal plane, and perform straight line fitting on the point cloud projected onto the horizontal plane to obtain the fitted straight line equation corresponding to the non-target traverse. Based on the quadratic curve equation and the fitted straight line equation corresponding to the non-target traverse, determine the three-dimensional spatial equation corresponding to the non-target traverse. Based on the three-dimensional spatial equation and the distance between the two towers, determine the complete point cloud corresponding to the target traverse.

[0166] In this way, by combining the fitting results of the projected point cloud with the fitting straight line of the horizontal plane projection, the consistency between the traverse model generated in three-dimensional space and the actual situation is ensured, thus improving the accuracy of three-dimensional reconstruction.

[0167] The conductor point cloud completion method provided in this application can obtain a reference scene point cloud and a scene point cloud to be converted; wherein, the reference scene point cloud and the scene point cloud to be converted are obtained by scanning the same power transmission corridor on both sides of the corridor using a scanning device; the reference scene point cloud includes a reference conductor point cloud and a point cloud of the first tower; the scene point cloud to be converted includes a point cloud of the conductor to be converted and a point cloud of the second tower; the reference conductor point cloud includes a first conductor point cloud corresponding to each conductor; the conductor point cloud to be converted includes a second conductor point cloud corresponding to each conductor; a target conductor is selected from multiple conductors, and a first coordinate transformation is performed on the second conductor point cloud corresponding to the target conductor to obtain a first aligned point cloud, wherein the fitted lines of the horizontal plane projections of the first aligned point cloud and the first conductor point cloud corresponding to the target conductor are the same straight line; according to the first extraction... A first transformed point cloud is obtained by taking a point cloud and extracting a second point cloud, and then performing a second coordinate transformation on the first transformed point cloud. The first extracted point cloud is a set of points in the first traverse point cloud whose distance to the first tower is within a preset range. The second extracted point cloud is a set of points in the first aligned point cloud whose distance to the second tower is within the preset range. The first transformed point cloud is obtained by performing the first coordinate transformation on the traverse point cloud to be transformed. Based on the second transformed point cloud, the complete point cloud corresponding to each traverse is determined. The second transformed point cloud and the reference traverse point cloud share the same coordinate system. Through two coordinate transformations (first and second), it is ensured that the point cloud to be transformed and the reference point cloud are aligned in the same coordinate system, which improves the accuracy of point cloud fusion even when the overlap area between the two sets of point cloud data is insufficient.

[0168] The following is another method for completing point clouds with guide lines, provided in an embodiment of this application. The main steps are as follows:

[0169] 1. Acquire and segment spatial point cloud data of power conductors

[0170] To filter out the power conductor point cloud data required by the algorithm from the point cloud data and avoid the inclusion of other interfering data, we first extracted key information from the complete LiDAR-acquired LAS point cloud data, including the three-dimensional spatial coordinates (X, Y, Z) of each point, the acquisition timestamp, and radar reflectivity information. Then, we simultaneously loaded two sets of point cloud data and used CloudCompare to perform visual analysis on these data. Using the software's built-in segmentation tool, we extracted the power conductor data belonging to the same power transmission corridor from the point cloud data and stored them as two separate sets, A and B.

[0171] Figure 3 This application provides a schematic diagram of a reference scene point cloud and a scene point cloud to be converted. It is a power transmission corridor point cloud data of one span obtained by the scanning device after two scanning operations. The point cloud data of the middle part of the span cannot be obtained due to environmental conditions.

[0172] Figure 4 This application provides a schematic diagram of a reference conductor point cloud and a conductor point cloud to be converted, as shown in the embodiments of this application. Figure 4 It is aimed at Figure 3 Point cloud data obtained by cutting power lines from two sets of point cloud data are labeled as P. a P b The data clearly shows that the two sets of point cloud data are not in the same traverse direction, indicating that there is a deviation in the coordinate system used by these two sets of point cloud data.

[0173] 2. Statistical filtering algorithms are used for filtering out outliers.

[0174] To reduce the impact of noise and outlier point cloud coordinates on the results during data acquisition, we employed a highly efficient statistical filtering algorithm to remove outliers from the point cloud data. This algorithm, based on the principles of statistical analysis, performs detailed statistical analysis on each point cloud data point, calculating its neighborhood's statistical characteristics, such as mean and standard deviation. Based on these statistical characteristics, the algorithm can accurately identify which points are noise or outliers and effectively filter them out. This processing yields a clean set of point cloud data, containing only power conductor and power pole point cloud data, providing a high-quality data foundation for subsequent analysis and applications. The application of the statistical filtering algorithm not only improves the accuracy and reliability of the data but also enhances the efficiency and precision of data processing. Figure 5 This is a schematic diagram of a filtered reference conductor point cloud and a conductor point cloud to be converted, provided for an embodiment of this application.

[0175] 3. Extraction of the same strand of power conductor

[0176] The filtered point cloud data was loaded using a tool, and then segmented to obtain point cloud data belonging to the same power line, which were then stored separately. Due to the manual scanning angle, the point cloud data closer to the radar was denser and of higher quality. We selected the power line on the same side at the bottom as the target power line for segmentation. As shown in the figure below, we segmented the point cloud data belonging to the same power line and displayed them using different colors, named P1 and P2 from left to right. Figure 6 This is a point cloud diagram of two scans of data belonging to the same conductor, provided for an embodiment of the application.

[0177] This method allows for the clear identification and differentiation of different power conductors, ensuring the accuracy and reliability of subsequent processing.

[0178] 4. Calculation of the projection rotation angle of power conductors and generation of the three-dimensional transformation matrix

[0179] For a single-strand power conductor within a span, its projection onto the XOY plane should belong to the same linear equation of the projected cross section. Here, we use one set of collected data as the point cloud data (P2) of the target coordinate system, and transform the coordinate system of the remaining point cloud data (P1) to the target coordinate system. Simultaneously, we name the line segments projected onto the XOY plane L1, L2, and L2 from left to right.

[0180] Specifically, we need to use the three-dimensional spatial coordinate information of the single-strand power conductor point cloud cluster obtained in the previous step to solve the equation of its projected cross-section line in the XOY plane using the least squares method: y = m·x + b, where (m) is the slope of the line and (b) is the intercept. Further, using the slope m, we calculate the horizontal angle of the projected line segment data using the arctangent function: θ = arctan(m).

[0181] The Least Squares Method is a mathematical optimization technique that finds the best function fit for data by minimizing the sum of squared errors. This method is widely used in regression analysis, especially linear regression analysis.

[0182] The core idea of ​​the least squares method is to find a function (usually a linear function) such that the sum of the squares of the differences between this function and a given set of data points is minimized. Specifically, if there are n data points (x1, y1), (x2, y2), ..., (x... n ,y n We hope to find a function y = f(x, a) (where a is the parameter of the function) such that the sum of the squares of the differences between all data points and the function is minimized.

[0183] A major advantage of the least squares method is its ease of computation and its good results in many cases. However, it also has some limitations, such as its assumption that the errors are independent and identically distributed and follow a normal distribution. If these assumptions do not hold, then the least squares method may not be the optimal choice.

[0184] In summary, the least squares method is a powerful and commonly used mathematical tool for finding the best-fitting model from data.

[0185] Using the angle difference above, we can calculate the included angle θ1 between line segments L1 and L2. Assume the equations of the two lines L1 and L2 are y = m1·x + b1 and y = m2·x + b2, respectively. Then we can obtain the formula for the distance between the two parallel lines after line segment L1 is rotated to be parallel to line segment L2:

[0186]

[0187] At this point, the distance value d is the distance between the two rotated straight lines in the Y-axis direction.

[0188] 5. Orthographic projection, rotation, and translation transformation of power conductors

[0189] For the point cloud data P1, perform a rotation transformation based on the horizontal rotation angle θ from the previous step, with the Z-axis as the rotation axis. Then, perform a translation operation on the P1 point cloud data along the Y-axis based on the horizontal distance d obtained in the previous step, finally obtaining the new point cloud data P′1. Repeat the same operation for P′1. a Perform the same operation to obtain new point cloud data P′ a .

[0190] 6. Solving for the spatial bounding box used to extract the X-axis cross-sectional projection data of point cloud data.

[0191] First, the least squares method is used to solve the three-dimensional spatial linear equations of the point cloud data obtained in the previous step.

[0192] a. Representation of a straight line:

[0193] Suppose the line is formed by a point P0 = (x0, y0, z0) and a direction vector. By definition, any point P on the line can be represented by the following parametric equation: Where t is a real number parameter.

[0194] b. Definition of bounding box:

[0195] The bounding box is a rectangle aligned in a Cartesian coordinate system, with a defined width W, height H, and depth D. In this scenario, one edge of the bounding box aligns with the direction of the line, while the other two edges are perpendicular to this direction. To construct such a bounding box, we need to find two vectors aligned with the direction of the line. Perpendicular vectors and These two vectors will be used to define the other two dimensions of the bounding box.

[0196] c. Calculation steps:

[0197] 1. Determine the direction vector of the line: Given

[0198] 2. Finding the perpendicular vector: In order to find the perpendicular vector... For perpendicular vectors, we can use the cross product. First, choose one that is not... For parallel vectors, if c≠0, you can choose the vector. or The cross product yields the first perpendicular vector. If c = 0, then choose The calculation method is as follows:

[0199] choose but

[0200] If c = 0, choose but

[0201] 3. Normalizing a vector: Normalizing a vector Normalization, i.e. Make sure it is a unit vector.

[0202] 4. Find the second perpendicular vector: using and Performing the cross product again, we get... And normalize it.

[0203] 5. Calculate the vertices of the bounding box: Assume the center of the bounding box is C = (x... c y c , z c The bounding box has a length of L, a width of W, and a height of H along the straight line (these three values ​​are configured according to the specific situation). The eight vertices of the bounding box can be calculated by adding or subtracting the vector in the corresponding direction from the center point C. Specifically, the vertices can be calculated using the following formula:

[0204]

[0205] By following the steps above, the coordinates of all vertices of the bounding box can be calculated, thus completely defining the position and size of the bounding box.

[0206] 6. Segment and project power conductor point cloud data

[0207] For point cloud data P′ a The point cloud data is segmented using the bounding box parameters obtained in the previous step, and then the resulting point cloud data is projected onto their respective normal vectors. Projection data.

[0208] 1. Normal vector normalization: First, ensure that the normal vector... It is a unit vector. If it is not a unit vector, it needs to be normalized.

[0209]

[0210] 2. Calculate the projection points: For each point P i =(x i y i , z i ), which is in the normal vector Projection point P′ on i It can be calculated using the following formula: in It is point P i Vector to the origin and normal vector The dot product is calculated using the following formula:

[0211] Therefore, the projection point P′ i The coordinates are: P′ i =(x i ,y i , z i )-(x i a+y i b+z i c)(a,b,c).

[0212] This process allows us to obtain the projected two-dimensional coordinate data T. a T b .

[0213] 7. Calculate the X-axis rotation angle

[0214] The feature matching detection algorithm was used to analyze T respectively. a T b The rotation angle in the X-axis direction is calculated using the following steps.

[0215] a. Feature detection and description

[0216] 1. Select a feature detection algorithm: Commonly used feature detection algorithms include SIFT (Scale Invariant Feature Transform), SURF (Speed-Up Robust Feature Transform), and ORB (Oriented Fast and Rotated BRIEF).

[0217] 2. Feature point detection: Detect feature points in both images.

[0218] 3. Extract feature descriptors: Extract a descriptor for each feature point. A descriptor is a vector used to describe the local information around the feature point.

[0219] b. Feature matching

[0220] 1. Matching feature points: Use nearest neighbor matching (such as the FLANN matcher) or other matching algorithms to match feature points in two images.

[0221] 2. Filter matching points: Use certain thresholds or ratio tests (such as Lowe's ratio test) to filter out reliable matching point pairs.

[0222] c. Calculate the homography matrix

[0223] 1. Calculate the homography matrix: Estimate the homography matrix H from the matching point pairs using RANSAC (Random Sample Consensus Algorithm). The homography matrix H describes the transformation relationship between two planes, including rotation, translation, and scaling.

[0224] 2. Decompose the homography matrix: Extract the rotation part from the homography matrix H.

[0225] d. Extract rotation angle

[0226] 1. Decomposition of the homography matrix: The homography matrix H can be decomposed into a rotation matrix R, a translation vector T, and a scaling factor S. The specific decomposition method is as follows:

[0227] Where R is a 2x2 rotation matrix, T is a translation vector, and S is a scaling factor.

[0228] 2. Calculate the rotation angle: Extract the rotation angle θ from the rotation matrix R. For a 2x2 rotation matrix:

[0229]

[0230] The rotation angle θ can be calculated using the following formula: θ = arctan2(r 21 ,r 11 )

[0231] For P′ aThe point cloud data is transformed by rotating the horizontal rotation angle θ from the previous step around the X-axis to obtain new point cloud data P″. a .

[0232] 8. Calculate the offset values ​​of the power conductors on the X and Z axes.

[0233] Regarding the point cloud data P″ obtained in the previous step a P b XOZ plane projection operation is performed to obtain the projection curve data Q. a Q b Then, the projection curve data Q were processed separately. a Q b The following steps are used to calculate its coordinate system offset values ​​in the X and Z axis directions.

[0234] The method of obtaining translation parameters by calculating the sum of squared distances between two curves is based on the idea of ​​the least squares method. The least squares method is a commonly used optimization technique that aims to find the optimal parameters by minimizing the sum of squared errors. In the problem of aligning two curves, the goal is to find the translation parameters Δx and Δz that minimize the distance between the two curves.

[0235] 1. Objective function:

[0236] Define an objective function representing the sum of squared distances between two curves. Assume the data points for the two curves are {(x1, z1), (x2, z2), ..., (x...z1)}. m ,z m )} and {(u1,v1),(u2,v2),...,(u n ,v n )}.

[0237] The second curve {(u} is shifted by the translation parameters Δx and Δz. j v j )} translate to the new position {(u j +△x, v j +△z)}.

[0238] The objective function can be expressed as:

[0239]

[0240] 2. Optimization:

[0241] Use optimization algorithms (such as gradient descent, BFGS, etc.) to minimize the objective function E(△x,△z).

[0242] The optimization algorithm iteratively adjusts Δx and Δz until it finds the parameter values ​​that minimize E(Δx,Δz).

[0243] Then for P″ a The point cloud data is transformed by translation based on the calculated Δx and Δz values ​​to obtain new point cloud data P″. a1 .

[0244] At this point, we have obtained the rotational offset angles of the two original point cloud data on the X and Z axes, as well as the translational offset distances on the X, Y, and Z axes (the rotational offset angle error on the Y axis is small and can be ignored). By applying these transformation parameters, the coordinate systems of the two point cloud data can be unified.

[0245] 9. Solve for the equation of the cross section.

[0246] Iterate through the point cloud data P″ obtained in the previous step a1 and P b Based on the three-dimensional spatial coordinates of the point cloud cluster, the solution of the quadratic curve equation of the projected cross section of the XOZ plane is obtained using the least squares method.

[0247] 10. Fitting data for single-strand power conductors

[0248] Based on the coefficients of the quadratic curve equation of the projected cross-section in the XOZ plane obtained in the previous step and the coefficients of the straight line equation obtained in step 4, and combined with the distance values ​​between the two ends of the power conductor, we generate point coordinate data on the X-axis at intervals of 0.05 meters. Subsequently, using these coefficient values, we can fit and generate point cloud data of a single-strand power conductor using a polynomial evaluation method.

[0249] 11. Fit the remaining power conductor data

[0250] Generate the point cloud data results for the remaining power conductors.

[0251] 12. Save the generated results data

[0252] After successfully fitting and generating point cloud data using the power line completion algorithm, we store this data in LAS format files for subsequent processing and application. To clearly distinguish between the fitted point cloud data and the original acquired data, we added a special label when saving this data. This label, as a unique identifier, clearly identifies which data was generated by the completion algorithm and which data was originally acquired. This distinction not only ensures the accuracy and completeness of the data but also facilitates subsequent data processing and analysis, ensuring that all types of data are correctly applied and processed.

[0253] Corresponding to the above-described method for completing traverse point clouds, this application also provides a device for completing traverse point clouds. Figure 7A schematic diagram of the structure of the wire point cloud completion device provided in this application is shown below. Figure 7 As shown, the wire point cloud completion device provided in this embodiment includes:

[0254] The acquisition module 701 is used to acquire a reference scene point cloud and a scene point cloud to be converted; wherein, the reference scene point cloud and the scene point cloud to be converted are obtained by scanning the power transmission corridor on both sides of the same power transmission corridor using a scanning device; the reference scene point cloud includes a reference conductor point cloud and a point cloud of the first tower; the scene point cloud to be converted includes a point cloud of the conductor to be converted and a point cloud of the second tower; the reference conductor point cloud includes a first conductor point cloud corresponding to each conductor; the conductor point cloud to be converted includes a second conductor point cloud corresponding to each conductor.

[0255] The first conversion module 702 is used to filter out the target traverse from multiple traverses and perform a first coordinate transformation on the second traverse point cloud corresponding to the target traverse to obtain a first aligned point cloud. The fitted lines of the horizontal plane projections corresponding to the first aligned point cloud and the first traverse point cloud corresponding to the target traverse are the same straight line.

[0256] The second conversion module 703 is used to perform a second coordinate transformation on the first converted point cloud based on the first extracted point cloud and the second extracted point cloud to obtain a second converted point cloud; wherein, the first extracted point cloud is a set of points in the first conductor point cloud whose distance from the first tower is within a preset range, the second extracted point cloud is a set of points in the first aligned point cloud whose distance from the second tower is within the preset range, and the first converted point cloud is obtained by performing the first coordinate transformation on the conductor point cloud to be converted;

[0257] The determining module 704 is used to determine the complete point cloud corresponding to each traverse based on the second transformed point cloud, wherein the coordinate system of the second transformed point cloud and the reference traverse point cloud is the same.

[0258] Optionally, when the first conversion module 702 performs a first coordinate transformation on the second conductor point cloud corresponding to the conductor to obtain a first aligned point cloud, it is specifically used for:

[0259] The first traverse point cloud and the second traverse point cloud are projected onto the horizontal plane respectively to obtain the first projected point cloud and the second projected point cloud.

[0260] The first and second projected point clouds were fitted with straight lines using the least squares method to obtain the first and second line equations.

[0261] Based on the first straight line equation and the second straight line equation, determine the first angle of rotation of the second traverse point cloud around the z-axis and the first distance of translation along the y-axis, wherein the z-axis is in the vertical direction and the y-axis is in the horizontal direction and perpendicular to the distance direction between the two towers;

[0262] The second guide point cloud is rotated around the z-axis by the first angle and translated along the y-axis by the first distance to obtain the first aligned point cloud.

[0263] Optionally, the second conversion module 703 is specifically used for:

[0264] A straight line is fitted to the second extracted point cloud to obtain the second direction of the fitted line; a straight line is fitted to the first extracted point cloud to obtain the first direction of the fitted line.

[0265] Based on the first direction and the second direction, a second coordinate transformation is performed on the first transformed point cloud to obtain a second transformed point cloud.

[0266] Optionally, when the second transformation module 703 performs a second coordinate transformation on the first transformed point cloud according to the first direction and the second direction to obtain a second transformed point cloud, it is specifically used for:

[0267] Based on the first direction and the second direction, determine the second angle of rotation of the first conversion point cloud around the x-axis;

[0268] The first converted point cloud is rotated around the x-axis by the second angle to obtain the third converted point cloud, wherein the x-axis is along the distance between the two towers;

[0269] Based on the third transformed point cloud and the reference traverse point cloud, determine the second distance of translation along the x-axis and the third distance of translation along the z-axis of the second transformed point cloud, wherein the z-axis is along the vertical direction;

[0270] The second transformed point cloud is obtained by translating the second transformed point cloud along the x-axis by the second distance and along the z-axis by the third distance.

[0271] Optionally, when determining the second angle of rotation of the first conversion point cloud around the x-axis based on the first direction and the second direction, the second conversion module 703 is specifically used for:

[0272] The coordinate range of the first bounding box is determined according to the first direction; the coordinate range of the second bounding box is determined according to the second direction; the first bounding box and the second bounding box are rectangular bodies of the same shape, the direction of the first side of the first bounding box is the same as the first direction, the direction of the second side of the second bounding box is the same as the second direction, and the lengths of the first side and the second side are the same.

[0273] The point cloud located within the coordinate range of the first bounding box is projected onto the first plane to obtain the third projected point cloud. The point cloud located within the coordinate range of the second bounding box is projected onto the second plane to obtain the fourth projected point cloud. The point cloud located within the coordinate range of the second bounding box is a part of the first transformed point cloud. The first plane is perpendicular to the first side, and the second plane is perpendicular to the second side.

[0274] Based on the third and fourth projected point clouds, determine the second angle of rotation of the first transformed point cloud around the x-axis.

[0275] Optionally, when the second conversion module 703 determines the second angle of rotation of the first converted point cloud around the x-axis based on the third and fourth projected point clouds, it is specifically used for:

[0276] At least one first feature point is extracted from the third projected point cloud, and the descriptor corresponding to each first feature point is determined; at least one second feature point is extracted from the fourth projected point cloud, and the descriptor corresponding to each second feature point is determined.

[0277] Based on the descriptors corresponding to each first feature point and each second feature point, at least one first feature point and at least one second feature point are matched to obtain multiple matching point pairs.

[0278] Based on multiple matching point pairs, a homography matrix is ​​determined using the RANSAC algorithm, and based on the homography matrix, a second angle of rotation of the first conversion point cloud around the x-axis is determined; wherein, the x-axis is along the distance direction between the two towers.

[0279] Optionally, when the second conversion module 703 determines the second distance of translation along the x-axis and the third distance of translation along the z-axis of the second converted point cloud based on the third converted point cloud and the reference traverse point cloud, it is specifically used for:

[0280] The third transformed point cloud is projected onto the xoz plane to obtain the fifth projected point cloud; the reference guide point cloud is projected onto the xoz plane to obtain the sixth projected point cloud.

[0281] Calculate Δx and Δz when the following objective function reaches its minimum value;

[0282]

[0283] Among them, (x i , z i (u) is any point in the sixth projected point cloud. j v j) represents any point in the fifth projection point cloud, m represents the number of points included in the sixth projection point cloud, and n represents the number of points included in the fifth projection point cloud.

[0284] The second distance is determined to be equal to Δx, and the third distance is determined to be equal to Δz.

[0285] Optionally, module 704 is defined, specifically for:

[0286] For each traverse, the corresponding projection point cloud is extracted from the sixth and seventh projection point clouds respectively, and a quadratic curve is fitted to the corresponding projection point cloud to obtain the quadratic curve equation of the traverse; wherein, the seventh projection point cloud is obtained by translating the fifth projection point cloud along the x-axis by the second distance and along the z-axis by the third distance;

[0287] The three-dimensional spatial equation corresponding to the traverse is determined based on the quadratic curve equation corresponding to the traverse and the fitted straight line equation of the horizontal plane projection of the first traverse point cloud corresponding to the traverse.

[0288] Based on the three-dimensional spatial equation, the complete point cloud corresponding to the traverse is determined.

[0289] Optionally, the preset range is 0 to a, where a is a fixed distance greater than zero.

[0290] The wire point cloud completion device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0291] Figure 8 A schematic diagram of the structure of the electronic device provided in this application. Figure 8 As shown, the electronic device 80 provided in this embodiment includes at least one processor 801 and a memory 802. Optionally, the device 80 further includes a communication component 803. The processor 801, memory 802, and communication component 803 are connected via a bus 804.

[0292] In a specific implementation, at least one processor 801 executes computer execution instructions stored in memory 802, causing at least one processor 801 to perform the above-described method.

[0293] The specific implementation process of processor 801 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0294] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0295] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0296] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0297] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0298] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0299] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0300] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0301] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0302] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0303] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0304] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0305] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0306] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for completing point clouds of guide lines, characterized in that, include: A reference scene point cloud and a scene point cloud to be converted are acquired. The reference scene point cloud and the scene point cloud to be converted are obtained by scanning the power transmission corridor from both sides of the same corridor using a scanning device. The reference scene point cloud includes a reference conductor point cloud and a point cloud of the first tower. The scene point cloud to be converted includes the point cloud of the conductor to be converted and the point cloud of the second tower. The reference conductor point cloud includes the first conductor point cloud corresponding to each conductor. The conductor point cloud to be converted includes the second conductor point cloud corresponding to each conductor. The target traverse is selected from multiple traverses, and the second traverse point cloud corresponding to the target traverse is subjected to a first coordinate transformation to obtain a first aligned point cloud. The fitted lines of the horizontal plane projections corresponding to the first aligned point cloud and the first traverse point cloud corresponding to the target traverse are the same straight line. Based on the first extracted point cloud and the second extracted point cloud, a second coordinate transformation is performed on the first transformed point cloud to obtain the second transformed point cloud; wherein, the first extracted point cloud is the set of points in the first conductor point cloud whose distance from the first tower is within a preset range, the second extracted point cloud is the set of points in the first aligned point cloud whose distance from the second tower is within the preset range, and the first transformed point cloud is obtained by performing the first coordinate transformation on the conductor point cloud to be transformed; Based on the second transformed point cloud, the complete point cloud corresponding to each traverse is determined, wherein the coordinate system of the second transformed point cloud and the reference traverse point cloud is the same.

2. The method according to claim 1, characterized in that, Perform a first coordinate transformation on the point cloud of the second traverse corresponding to the traverse to obtain a first aligned point cloud, including: The first traverse point cloud and the second traverse point cloud are projected onto the horizontal plane respectively to obtain the first projected point cloud and the second projected point cloud. The first and second projected point clouds were fitted with straight lines using the least squares method to obtain the first and second line equations. Based on the first straight line equation and the second straight line equation, determine the first angle of rotation of the second traverse point cloud around the z-axis and the first distance of translation along the y-axis, wherein the z-axis is in the vertical direction and the y-axis is in the horizontal direction and perpendicular to the distance direction between the two towers; The second guide point cloud is rotated around the z-axis by the first angle and translated along the y-axis by the first distance to obtain the first aligned point cloud.

3. The method according to claim 1, characterized in that, Based on the first extracted point cloud and the second extracted point cloud, a second coordinate transformation is performed on the first transformed point cloud to obtain the second transformed point cloud, including: A straight line is fitted to the second extracted point cloud to obtain the second direction of the fitted line; a straight line is fitted to the first extracted point cloud to obtain the first direction of the fitted line. Based on the first direction and the second direction, a second coordinate transformation is performed on the first transformed point cloud to obtain a second transformed point cloud.

4. The method according to claim 3, characterized in that, Based on the first direction and the second direction, a second coordinate transformation is performed on the first transformed point cloud to obtain a second transformed point cloud, including: Based on the first direction and the second direction, determine the second angle of rotation of the first conversion point cloud around the x-axis; The first converted point cloud is rotated around the x-axis by the second angle to obtain the third converted point cloud, wherein the x-axis is along the distance between the two towers; Based on the third transformed point cloud and the reference traverse point cloud, determine the second distance of translation along the x-axis and the third distance of translation along the z-axis of the second transformed point cloud, wherein the z-axis is along the vertical direction; The second transformed point cloud is obtained by translating the second transformed point cloud along the x-axis by the second distance and by translating the third distance along the z-axis.

5. The method according to claim 4, characterized in that, Determining a second angle of rotation of the first conversion point cloud around the x-axis based on the first direction and the second direction includes: The coordinate range of the first bounding box is determined according to the first direction; the coordinate range of the second bounding box is determined according to the second direction; the first bounding box and the second bounding box are rectangular bodies of the same shape, the direction of the first side of the first bounding box is the same as the first direction, the direction of the second side of the second bounding box is the same as the second direction, and the lengths of the first side and the second side are the same. The point cloud located within the coordinate range of the first bounding box is projected onto the first plane to obtain the third projected point cloud. The point cloud located within the coordinate range of the second bounding box is projected onto the second plane to obtain the fourth projected point cloud. The point cloud located within the coordinate range of the second bounding box is a part of the first transformed point cloud. The first plane is perpendicular to the first side, and the second plane is perpendicular to the second side. Based on the third and fourth projected point clouds, determine the second angle of rotation of the first transformed point cloud around the x-axis.

6. The method according to claim 5, characterized in that, Based on the third and fourth projected point clouds, determining the second angle of rotation of the first transformed point cloud around the x-axis includes: At least one first feature point is extracted from the third projected point cloud, and the descriptor corresponding to each first feature point is determined; at least one second feature point is extracted from the fourth projected point cloud, and the descriptor corresponding to each second feature point is determined. Based on the descriptors corresponding to each first feature point and each second feature point, at least one first feature point and at least one second feature point are matched to obtain multiple matching point pairs. Based on multiple matching point pairs, a homography matrix is ​​determined using the RANSAC algorithm, and based on the homography matrix, a second angle of rotation of the first conversion point cloud around the x-axis is determined; wherein, the x-axis is along the distance direction between the two towers.

7. The method according to claim 4, characterized in that, Based on the third transformed point cloud and the reference traverse point cloud, determining the second distance of translation along the x-axis and the third distance of translation along the z-axis of the second transformed point cloud includes: The third transformed point cloud is projected onto the xoz plane to obtain the fifth projected point cloud; the reference guide point cloud is projected onto the xoz plane to obtain the sixth projected point cloud. Calculate Δx and Δz when the following objective function reaches its minimum value; Among them, (x i , z i (u) is any point in the sixth projected point cloud. j v j ) represents any point in the fifth projected point cloud, m represents the number of points included in the sixth projected point cloud, and n represents the number of points included in the fifth projected point cloud. The second distance is determined to be equal to Δx, and the third distance is determined to be equal to Δz.

8. The method according to claim 7, characterized in that, Based on the second transformed point cloud, determine the complete point cloud corresponding to each traverse, including: For each traverse, the corresponding projection point cloud is extracted from the sixth and seventh projection point clouds respectively, and the quadratic curve of the corresponding projection point cloud is fitted to obtain the quadratic curve equation of the traverse; wherein, the seventh projection point cloud is obtained by translating the fifth projection point cloud along the x-axis by the second distance and along the z-axis by the third distance; The three-dimensional spatial equation corresponding to the traverse is determined based on the quadratic curve equation corresponding to the traverse and the fitted straight line equation of the horizontal plane projection of the first traverse point cloud corresponding to the traverse. Based on the three-dimensional spatial equation, the complete point cloud corresponding to the traverse is determined.

9. The method according to any one of claims 1-8, characterized in that, The preset range is 0 to a, where a is a fixed distance greater than zero.

10. A device for completing a point cloud of a conductor, characterized in that, include: The acquisition module is used to acquire a reference scene point cloud and a scene point cloud to be converted; wherein, the reference scene point cloud and the scene point cloud to be converted are obtained by scanning the power transmission corridor from both sides of the same power transmission corridor using a scanning device; the reference scene point cloud includes a reference conductor point cloud and a point cloud of the first tower; the scene point cloud to be converted includes a point cloud of the conductor to be converted and a point cloud of the second tower; the reference conductor point cloud includes a first conductor point cloud corresponding to each conductor; the conductor point cloud to be converted includes a second conductor point cloud corresponding to each conductor. The first transformation module is used to filter out the target traverse from multiple traverses and perform a first coordinate transformation on the second traverse point cloud corresponding to the target traverse to obtain a first aligned point cloud. The fitted lines of the horizontal plane projections corresponding to the first aligned point cloud and the first traverse point cloud corresponding to the target traverse are the same straight line. The second conversion module is used to perform a second coordinate transformation on the first converted point cloud based on the first extracted point cloud and the second extracted point cloud to obtain the second converted point cloud; wherein, the first extracted point cloud is a set of points in the first traverse point cloud whose distance from the first tower is within a preset range, the second extracted point cloud is a set of points in the first aligned point cloud whose distance from the second tower is within the preset range, and the first converted point cloud is obtained by performing the first coordinate transformation on the traverse point cloud to be converted; The determining module is used to determine the complete point cloud corresponding to each traverse based on the second transformed point cloud, wherein the coordinate system of the second transformed point cloud and the reference traverse point cloud is the same.

11. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-9.

13. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-9.

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