A method, device and storage medium for correcting lidar point cloud data

Abstract

The application discloses a kind of laser radar point cloud data's correction method, device and storage medium, for adjusting the measurement data of laser radar installed on measurement area, first point cloud data under the pan-tilt coordinate system containing reference body is obtained by laser radar;According to the installation position of laser radar, first point cloud data is converted into third point cloud data located on the first rough world coordinate system;Third point cloud data is converted into two-dimensional image located on three coordinate planes respectively, and each coordinate axis deflection angle is obtained from each two-dimensional image, finally the obtained each axis deflection angle is combined as the correction matrix of this laser radar with first transformation matrix, and the point cloud data under the accurate world coordinate system of measurement area is obtained by correction matrix. The method obtains the angle deviation between pan-tilt coordinate system and world coordinate system by the attitude correction of virtual camera for converting three-dimensional data into two-dimensional data, to realize the correction of pan-tilt installation error.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional detection technology, and in particular to a method, apparatus and storage medium for correcting lidar point cloud data. Background Technology

[0002] Laser measurement technology is a rapidly developing and widely applied surveying and mapping technology in recent years. A typical product of this technology, lidar, is widely used in industrial fields such as aerial surveying and autonomous driving. Compared to traditional surveying and mapping technologies, lidar offers advantages such as large data volume, ease of use, and high production efficiency; compared to image photogrammetry, it boasts high resolution and high precision, making it highly irreplaceable. Currently, most lidar scanning gimbals employ a scheme combining a two-dimensional lidar with a one-dimensional scanning gimbal. The calibration of the pose relationship between the lidar body and the turntable is now quite accurate, and the current turntable angle can be obtained by reading the encoder. Therefore, when the turntable is at a known angle, the three-dimensional points measured by the lidar can be transformed into the turntable base coordinate system. However, under current conditions, when each gimbal is installed independently at the measurement site, only a certain range of installation accuracy in the gimbal's position and angle can be guaranteed. In complex real-world production environments, it is virtually impossible to guarantee ultra-high precision in the gimbal's installation position and angle, thus making it impossible to accurately obtain the ultra-high precision mutual position and angle relationship between the gimbal coordinate system and the required world coordinate system. Summary of the Invention

[0003] This invention addresses the shortcomings of existing technologies by providing a method for correcting lidar point cloud data. This method adjusts the measurement data of a lidar installed in a measurement area and includes the following steps:

[0004] S1, acquire first point cloud data in a gimbal coordinate system containing at least a first reference body and a second reference body through a lidar, wherein the first reference body and the second reference body are placed sequentially along the central axis of the region within the measurement area of ​​the lidar;

[0005] S2, obtain the first transformation matrix of the lidar according to the installation position of the lidar, and convert the first point cloud data into third point cloud data located on the first coarse world coordinate system through the first transformation matrix;

[0006] S3, convert the third point cloud data into two-dimensional images located on three coordinate planes respectively, and obtain the angle between the first reference body and / or the second reference body on each two-dimensional image and the image edge, as the deflection angle around the coordinate axis perpendicular to the image;

[0007] S4. The obtained deflection angles of each axis are combined with the first transformation matrix to form the correction matrix of the lidar. The point cloud data of the measured object acquired by the lidar is transformed using the correction matrix to obtain the point cloud data of the measurement area in the precise world coordinate system.

[0008] Preferably, step S3 specifically includes:

[0009] The third point cloud data is converted into a first two-dimensional image located on the XY coordinate plane. The Z-axis data of each point is used as the gray value corresponding to each point on the first two-dimensional image. The angle between the line connecting the sides of the first and second reference bodies on the first two-dimensional image and the edge of the image is identified and obtained as the Z-axis deflection angle.

[0010] The third point cloud data is converted into a second two-dimensional image located on the XZ coordinate plane. The Y-axis data of each point is used as the gray value corresponding to each point on the second two-dimensional image. The angle between the bottom edge of the first or second reference body on the second two-dimensional image and the edge of the image is identified and obtained as the Y-axis deflection angle.

[0011] The third point cloud data is converted into a third two-dimensional image located on the YZ coordinate plane. The X-axis data of each point is used as the gray value corresponding to each point on the third two-dimensional image. The angle between the bottom edge of the first or second reference body on the third two-dimensional image and the edge of the image is identified and obtained as the X-axis deflection angle.

[0012] Preferably, step S3 further includes:

[0013] The third point cloud data is converted into a first two-dimensional image located on the XY coordinate plane. The Z-axis data of each point is used as the gray value corresponding to each point on the first two-dimensional image. The angle between the line connecting the sides of the first and second reference bodies on the first two-dimensional image and the edge of the image is obtained as the Z-axis deflection angle.

[0014] The third point cloud data is converted into a second two-dimensional image located on the XZ coordinate plane. The Y-axis data of each point is used as the gray value corresponding to each point on the second two-dimensional image. The angle between the ground image and the edge of the image on the second two-dimensional image is obtained as the Y-axis deflection angle.

[0015] The third point cloud data is converted into a third two-dimensional image located on the YZ coordinate plane. The X-axis data of each point is used as the gray value corresponding to each point on the third two-dimensional image. The angle between the ground image and the edge of the image on the third two-dimensional image is obtained as the X-axis deflection angle.

[0016] Preferably, at least two lidars with intersecting areas are installed in the measurement area, and step S1 further includes:

[0017] A first point cloud data containing first, second, and third reference bodies is acquired using a first lidar, and a fourth point cloud data containing second, third, and fourth reference bodies is acquired using a second lidar. The first to fourth reference bodies are placed sequentially along the central axis of the region within the measurement areas of the two lidars. The second and third reference bodies are located within the measurement intersection area of ​​the two lidars, with one side of the second reference body and the other side of the third reference body relative to the second reference body located on the central axis of the region. The rear side of the second reference body and the front side of the third reference body are located on vertical planes perpendicular to the central axis of the region. The first and fourth reference bodies are located within the non-intersecting measurement areas of the first and second lidars, respectively, with the same side of the first and fourth reference bodies located on the central axis of the region.

[0018] Preferably, step S3 includes:

[0019] The third point cloud data is converted into a first two-dimensional image located on the XY coordinate plane, and the Z-axis data of each point is used as the gray value corresponding to each point on the first two-dimensional image.

[0020] Identify and obtain the line connecting the first reference body near the central axis of the region in the first two-dimensional image to the intersection of the second and third reference bodies, and calculate the angle between the line and the edge of the image as the Z-axis deflection angle.

[0021] The present invention also discloses a correction device for lidar point cloud data, used to adjust the measurement data of lidar installed on a measurement area, including:

[0022] The first point cloud acquisition module is used to acquire first point cloud data in a gimbal coordinate system containing at least a first reference body and a second reference body through a lidar, wherein the first reference body and the second reference body are placed sequentially along the central axis of the region within the measurement area of ​​the lidar.

[0023] The first transformation module is used to obtain the first transformation matrix of the lidar according to the installation position of the lidar, and to convert the first point cloud data into third point cloud data located on the first coarse world coordinate system through the first transformation matrix.

[0024] The deflection angle detection module is used to convert the third point cloud data into two-dimensional images located on three coordinate planes, and to obtain the angle between the first reference body and / or the second reference body on each two-dimensional image and the image edge, as the deflection angle around the coordinate axis perpendicular to the image.

[0025] The correction module is used to combine the obtained deflection angles of each axis with the first transformation matrix as the correction matrix of the lidar. The correction matrix is ​​used to transform the point cloud data of the measured object acquired by the lidar to obtain the point cloud data of the measurement area in the precise world coordinate system.

[0026] Preferably, the deflection angle detection module specifically includes:

[0027] The first image acquisition module is used to convert the third point cloud data into a first two-dimensional image located on the XY coordinate plane, use the Z-axis data of each point as the gray value corresponding to each point on the first two-dimensional image, and identify and obtain the angle between the line connecting the sides of the first and second reference bodies on the first two-dimensional image and the edge of the image as the Z-axis deflection angle.

[0028] The second image acquisition module is used to convert the third point cloud data into a second two-dimensional image located on the XZ coordinate plane, use the Y-axis data of each point as the gray value corresponding to each point on the second two-dimensional image, and identify and obtain the angle between the bottom edge of the first reference body or the second reference body on the second two-dimensional image and the edge of the image as the Y-axis deflection angle.

[0029] The third image acquisition module is used to convert the third point cloud data into a third two-dimensional image located on the YZ coordinate plane, use the X-axis data of each point as the gray value corresponding to each point on the third two-dimensional image, and identify and obtain the angle between the bottom edge of the first or second reference body on the third two-dimensional image and the edge of the image as the X-axis deflection angle.

[0030] Preferably, at least two lidars with intersecting areas are installed in the measurement area. The first point cloud acquisition module is further used to acquire first point cloud data containing first, second, and third reference bodies through the first lidar, and to acquire fourth point cloud data containing second, third, and fourth reference bodies through the second lidar. The first to fourth reference bodies are placed sequentially along the central axis of the region within the measurement areas of the two lidars. The second and third reference bodies are located within the intersecting measurement areas of the two lidars, and one side of the second reference body and the other side of the third reference body relative to the second reference body are respectively located on the central axis of the region. The rear side of the second reference body and the front side of the third reference body are respectively located on vertical planes perpendicular to the central axis of the region. The first and fourth reference bodies are located within the non-intersecting measurement areas of the first and second lidars, and the same side of the first and fourth reference bodies is located on the central axis of the region.

[0031] The present invention also discloses a correction device for lidar point cloud data, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0032] The present invention also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the methods described above.

[0033] This invention discloses a method, apparatus, and storage medium for correcting lidar point cloud data. It is used to adjust the measurement data of a lidar installed in a measurement area. The method involves acquiring first point cloud data in a gimbal coordinate system containing a reference body using the lidar; obtaining a first transformation matrix of the lidar based on its installation position and converting the first point cloud data into third point cloud data in a first coarse world coordinate system; converting the third point cloud data into two-dimensional images on three coordinate planes and obtaining the angles between the first and / or second reference bodies relative to the image edges in each two-dimensional image, which are used as deflection angles around coordinate axes perpendicular to the image; finally, combining the obtained deflection angles with the first transformation matrix to obtain a correction matrix for the lidar. This correction matrix is ​​then used to convert the point cloud data of the measured object acquired by the lidar to obtain precise point cloud data in the world coordinate system of the measurement area. This method corrects gimbal installation errors by correcting the pose of a virtual camera that converts three-dimensional data into two-dimensional data, thereby obtaining the angular deviation between the gimbal coordinate system and the world coordinate system.

[0034] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0036] Figure 1 This is a flowchart illustrating a method for correcting lidar point cloud data according to an embodiment of the present invention.

[0037] Figure 2 and 6 This is a schematic diagram of the reference arrangement of the measurement area disclosed in an embodiment of the present invention.

[0038] Figure 3-5 This is a schematic diagram of the first point cloud data and the second point cloud data disclosed in an embodiment of the present invention.

[0039] Figure 7 This is a schematic diagram of the first point cloud data disclosed in an embodiment of the present invention.

[0040] Figure 8 This is a schematic diagram of a first two-dimensional image disclosed in an embodiment of the present invention.

[0041] Figure 9 This is a schematic diagram of a second two-dimensional image disclosed in an embodiment of the present invention.

[0042] Figure 10This is a schematic diagram of a third two-dimensional image disclosed in an embodiment of the present invention.

[0043] Figure 11 This is a schematic diagram of the point cloud after splicing and combining, as disclosed in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0045] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0046] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0047] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in the specification and claims of this patent application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a limitation of quantity, but rather indicate the presence of at least one.

[0048] Example 1

[0049] A method for correcting lidar point cloud data is provided for adjusting measurement data from lidar installed in a measurement area, as shown in the attached figure. Figure 1 As shown, the correction method may include the following steps:

[0050] Step S1: Acquire first point cloud data in a gimbal coordinate system containing at least a first reference body and a second reference body using a lidar, wherein the first reference body and the second reference body are placed sequentially along the central axis of the region within the measurement area of ​​the lidar.

[0051] In this embodiment, the measurement of the three-dimensional dimensions of the vehicle compartment to be loaded, issued by the loading machine, is used as an example. The set measurement area of ​​the lidar is the parking area issued by the moving loading area of ​​the loading machine. Therefore, the central axis of the equipment can be set as the central axis of the loading machine above, which is the central axis of its moving area. The reference line of the central axis of the equipment on the ground of the measurement area can be obtained by placing a laser ruler, etc. Then, the reference body is placed according to the reference line. The first reference body and the second reference body are placed sequentially along the central axis of the area in the measurement area of ​​the lidar.

[0052] A point cloud is a dataset where each point represents a set of X, Y, and Z geometric coordinates, representing a 3D shape or object in space. The gimbal is the mechanism that generates the point cloud using a rotating radar. First, let's introduce the world coordinate system w. The world coordinate system has a definite number of translational relationships with the known equipment coordinate system, where the horizontal ground is the xy-plane. The x-axis direction is along the equipment's central axis towards the equipment's running tail end; the y-axis direction is the direction of rotating the x-axis 90° clockwise while looking down at the ground; and the z-axis direction is perpendicular to the xy-plane and downwards. The origin position can be set and adjusted as needed.

[0053] Step S2: Obtain the first transformation matrix of the lidar based on its installation location, and convert the first point cloud data into third point cloud data located in the first coarse world coordinate system using the first transformation matrix.

[0054] Step S3: Convert the third point cloud data into two-dimensional images located on three coordinate planes, and obtain the angle between the first reference body and / or the second reference body on each two-dimensional image and the image edge, as the deflection angle around the coordinate axis perpendicular to the image.

[0055] Step S3 can specifically include:

[0056] The third point cloud data is converted into a first two-dimensional image located on the XY coordinate plane. The Z-axis data of each point is used as the gray value corresponding to each point on the first two-dimensional image. The angle between the line connecting the sides of the first and second reference bodies on the first two-dimensional image and the edge of the image is identified and obtained as the Z-axis deflection angle.

[0057] Specifically, the projection of each point in the point cloud onto the xoy plane constitutes the position of each pixel in the two-dimensional image. The grayscale value of a pixel can be the x, y, or z value of that point, or a proportional value. For example, the projection of a point in a 3D point cloud... n (x n ,y n ,z n Convert the image to a 2D image, specifying z. n The grayscale value of the image is the grayscale value of the 2D image at x=x. n y = y n The position grayscale value is z n A point image.

[0058] The third point cloud data is converted into a second two-dimensional image located on the XZ coordinate plane. The Y-axis data of each point is used as the gray value corresponding to each point on the second two-dimensional image. The angle between the bottom edge of the first or second reference body on the second two-dimensional image and the edge of the image is identified and obtained as the Y-axis deflection angle.

[0059] The third point cloud data is converted into a third two-dimensional image located on the YZ coordinate plane. The X-axis data of each point is used as the gray value corresponding to each point on the third two-dimensional image. The angle between the bottom edge of the first or second reference body on the third two-dimensional image and the edge of the image is identified and obtained as the X-axis deflection angle.

[0060] In addition, step S3 may specifically include:

[0061] The third point cloud data is converted into a first two-dimensional image located on the XY coordinate plane. The Z-axis data of each point is used as the gray value corresponding to each point on the first two-dimensional image. The angle between the line connecting the sides of the first and second reference bodies on the first two-dimensional image and the edge of the image is obtained as the Z-axis deflection angle.

[0062] The third point cloud data is converted into a second two-dimensional image located on the XZ coordinate plane. The Y-axis data of each point is used as the gray value corresponding to each point on the second two-dimensional image. The angle between the ground image and the edge of the image on the second two-dimensional image is obtained as the Y-axis deflection angle.

[0063] The third point cloud data is converted into a third two-dimensional image located on the YZ coordinate plane. The X-axis data of each point is used as the gray value corresponding to each point on the third two-dimensional image. The angle between the ground image and the edge of the image on the third two-dimensional image is obtained as the X-axis deflection angle.

[0064] Step S4: Combine the obtained deflection angles of each axis with the first transformation matrix as the correction matrix of the lidar. Use the correction matrix to transform the point cloud data of the measured object acquired by the lidar to obtain the point cloud data of the measurement area in the precise world coordinate system.

[0065] The above embodiment acquires first point cloud data in a gimbal coordinate system containing a reference body using a lidar; it then obtains a first transformation matrix for the lidar based on its installation position and converts the first point cloud data into third point cloud data in a first coarse world coordinate system; the third point cloud data is converted into two-dimensional images on three coordinate planes, and the angles between the first and / or second reference bodies on each two-dimensional image and the image edges are obtained as deflection angles around coordinate axes perpendicular to the image; finally, the obtained deflection angles are combined with the first transformation matrix to form a correction matrix for the lidar. This correction matrix is ​​used to transform the point cloud data of the measured object acquired by the lidar to obtain precise point cloud data in the world coordinate system of the measurement area. This method corrects gimbal installation errors by correcting the pose of a virtual camera that converts three-dimensional data into two-dimensional data, thereby obtaining the angular deviation between the gimbal coordinate system and the world coordinate system.

[0066] Example 2

[0067] In this embodiment, at least two lidars with intersecting areas are installed in the measurement area to acquire at least two stitchable point cloud data sets. However, the three-dimensional coordinate relationship between the two independently installed lidars can only have an approximate relative position size during installation, and it is even more difficult to ensure consistency in angles. Furthermore, the ground point clouds mostly represent different scenes, making calibration more challenging. In this embodiment, taking the cement slab loading machine's working site as an example, three-dimensional dimension detection is performed on the long trucks waiting to be loaded below the loading machine to acquire point cloud data of the vehicles, especially the cargo compartments. Ultimately, the dimensions of the cargo compartments are obtained. Two or more lidar-equipped gimbals can be installed one after the other above the loading area where the vehicles are parked. In this embodiment, the lidar point cloud data method specifically includes the following:

[0068] Step S101: Obtain first point cloud data containing a reference body in the first gimbal coordinate system using the first lidar, and obtain second point cloud data containing a reference body in the second gimbal coordinate system using the second lidar.

[0069] As attached Figure 2 and 3 As shown, in this embodiment, the zero point of the x-axis can be the position where the forward and backward travel distance of the device is equal to zero, the zero point of the y-axis is the position where the central axis of the device moves 3 meters in the negative y direction, and the zero point of the z-axis is the position where the xy plane moves 6 meters in the negative z direction.

[0070] The coordinate system direction of the first gimbal coordinate system c1 is already determined by the gimbal composite point cloud, as shown in the attached figure based on the current installation orientation. Figure 4As shown, the x-axis direction is roughly the same as the y-axis direction of the world coordinate system, but there is an installation deviation; the y-axis direction is roughly the same as the x-axis direction of the world coordinate system, but there is an installation deviation; the z-axis direction is roughly the same as the z-axis direction of the world coordinate system, but there is an installation deviation; and the origin position is the actual installation position of the first gimbal.

[0071] The coordinate system direction of the second gimbal coordinate system c2 is determined by the gimbal composite point cloud, as shown in the attached figure based on the current installation orientation. Figure 5 As shown, the x-axis direction is roughly the same as the y-axis direction of the world coordinate system, but there is an installation deviation; the y-axis direction is roughly the same as the x-axis direction of the world coordinate system, but there is an installation deviation; the z-axis direction is roughly the same as the z-axis direction of the world coordinate system, but there is an installation deviation; and the origin position is the actual installation position of the first gimbal.

[0072] In this embodiment, step S1 may further include the following:

[0073] Step S1011: Obtain first point cloud data containing first, second, and third reference bodies in the gimbal coordinate system using the first lidar; the first to fourth reference bodies are placed sequentially along the central axis of the region within the measurement area, wherein the second and third reference bodies are located within the measurement intersection area of ​​the two lidars, and one side of the second reference body and the other side of the third reference body relative to the second reference body are respectively located on the central axis of the region, the rear side of the second reference body and the front side of the third reference body are respectively located on a vertical plane perpendicular to the central axis of the region, and the first and fourth reference bodies are respectively located within the measurement areas of the first and second lidars that do not intersect, and the same side of the first and fourth reference bodies is located on the central axis of the region.

[0074] Step S1012: Obtain the second point cloud data containing the second, third, and fourth reference bodies in the gimbal coordinate system using the second lidar.

[0075] Specifically, this embodiment assists in coordinate system transformation by placing multiple reference bodies within the measurement area, that is, rotating and translating multiple point cloud data into a unified coordinate system so that they can form a complete environmental point cloud data. (See attached image.) Figure 2 and 6As shown, in this embodiment, four reference bodies are precisely placed in the measurement area. These reference bodies are preferably cuboids, but can also be other shapes or quantities; the processing is similar. In this embodiment, the central axis of the equipment is the central axis of the loading machine above, which is also the central axis of its moving area. A reference line for the central axis of the equipment on the ground in the measurement area can be obtained by placing a laser ruler, etc., and then the reference bodies are placed according to this reference line. Specifically, the first reference body 1, the second reference body 2, the third reference body 3, and the fourth reference body 4 are arranged sequentially from the first gimbal to the second gimbal along the central axis of the region. The first and fourth reference bodies are located in the non-intersecting measurement areas of the first and second laser radars, respectively, with their same side placed on the central axis. The second and third reference bodies are located in the measurement intersection area of ​​the two laser radars, and are positioned on opposite sides of the central axis. One side of the second reference body and the side opposite the third reference body are placed on the central axis. The rear side of the second reference body and the front side of the third reference body are located on vertical planes perpendicular to the central axis. At the same time, the rear edge of the first reference body can be placed at the position where the front and rear travel distance of the device is equal to zero, that is, the zero point of the x-axis of the world coordinate system W.

[0076] Step S102: Obtain the first transformation matrix according to the installation position of the first lidar, and convert the first point cloud data into third point cloud data in the first coarse world coordinate system using the first transformation matrix; obtain the second transformation matrix according to the installation position of the second lidar, and convert the second point cloud data into fourth point cloud data in the second coarse world coordinate system using the second transformation matrix.

[0077] The first transformation matrix is ​​obtained based on the installation location of the first lidar. Based on the previously mentioned installation location of the first gimbal where the first lidar is installed, the coordinate system of the first gimbal can be transformed into c1 using the first transformation matrix. After transformation, a first coarse world coordinate system w' is obtained. The x, y, and z axes of the first coarse world coordinate system are basically in the same direction as the first precise world coordinate system, but there will still be a small angle due to installation error. That is, the first point cloud data is rotated 90 degrees around the z-axis, moved 6m in the positive x-axis direction, and moved 2m in the positive y-axis direction, thus transforming the first point cloud data into the third point cloud data.

[0078] However, if we want to transform the first gimbal coordinate system c1 into a first precise world coordinate system that is highly orthogonal to the world coordinate system, then c1 needs to be transformed through... The transformation yields a coordinate system w” that is orthogonal to w with high precision, where α, β, and γ are the angles between the x, y, and z axes of the first coarse world coordinate system and the first precise world coordinate system.

[0079] Similarly, the second transformation matrix is ​​obtained based on the installation location of the second lidar, and the second point cloud data is converted into the fourth point cloud data in the second coarse world coordinate system through the second transformation matrix. This will not be repeated here.

[0080] Step S103 involves converting the third point cloud data into two-dimensional images located in the three coordinate planes, obtaining a first angle offset matrix based on the offset angle of the reference body in the two-dimensional image, and combining the first angle offset matrix with the first transformation matrix to form a third transformation matrix; similarly, converting the fourth point cloud data into two-dimensional images located in the three coordinate planes, obtaining a second angle offset matrix based on the offset angle of the reference body in the two-dimensional image, and combining the second angle offset matrix with the second transformation matrix to form a fourth transformation matrix. In this embodiment, step S103 may specifically include the following:

[0081] The third point cloud data is converted into a first two-dimensional image located on the XY coordinate plane. The Z-axis data of each point is used as the gray value corresponding to each point on the first two-dimensional image. The line connecting the first reference body in the first two-dimensional image to the side of the central axis near the region and the intersection of the second and third reference bodies is identified. The angle between the line and the edge of the image is calculated as the Z-axis deflection angle of the third point cloud data.

[0082] As described in step S102 above, based on the installation position of the first gimbal, a rough transformation relationship between the first gimbal coordinate system c1 and the world coordinate system w can be obtained. front (6,2,0,0,0,90) is the first transformation matrix. The first gimbal coordinate system c1 is transformed by the first transformation matrix. After the transformation, we obtain a first coarse world coordinate system w'. The x, y, and z axes of the first coarse world coordinate system w' and the world coordinate system w are basically in the same direction, but there is still a small angle between them. The following transformation can be performed to calculate the angle between the x, y, and z axes of the two coordinate systems w' and w respectively:

[0083] After Pose over The ground plane calculated by the transformation (6, 2, 0, 0, 0, 90) is the rotation angle around the z-axis of the w coordinate system.

[0084] After Pose side The ground plane calculated by the transformation (6, 0, 3, 270, 0, 90) is the rotation angle around the y-axis of the w coordinate system.

[0085] After Pose back The ground plane calculated by the transformation (6, 0, 10, 270, 0, 180) is the rotation angle around the x-axis of the w coordinate system.

[0086] First, calculate the angle between w′ and the x-axis of w. The point cloud of c1 passes through... Changes. (Appendix) Figure 9 It is C1 after Pose over Point cloud in pose (6, 2, 0, 0, 0, 90), with appendix Figure 10 It is a 2D image converted from a 3D point cloud. The horizontal line with arrows is the line connecting the side of the first reference object near the central axis of the region to the intersection of the second and third reference objects. Using the 2D image algorithm, the angle between the horizontal line with arrows and the horizontal axis in the above image can be calculated, that is, the angle between w' and the z-axis rotation of w is λ'. The horizontal axis is in the same direction as the edge of the image, so the angle between the connecting line and the edge of the image, i.e., λ', can also be obtained directly.

[0087] The third point cloud data is converted into a second two-dimensional image located on the XZ coordinate plane. The Y-axis data of each point is used as the gray value corresponding to each point on the second two-dimensional image. The angle between the bottom edge of the first reference body, the bottom edge of the second reference body, the bottom edge of the third reference body, or the ground and the edge of the image on the second two-dimensional image is identified and obtained as the Y-axis deflection angle of the third point cloud data.

[0088] Specifically, obtaining c1 via Pose side Point clouds in pose (6, 0, 3, 270, 0, 90), with appendix Figure 11 This involves converting the 3D point cloud into a 2D image, from which the attachments can be calculated using various existing conventional 2D image algorithms. Figure 11 The angle between the line connecting the middle arrow and the horizontal axis can be the line connecting the bottom edge of the first reference body and the bottom edges of the second and third reference bodies, or it can be the ground marking line in the figure. Thus, the rotation angle between w' and the y-axis of w is β'.

[0089] The third point cloud data is converted into a third two-dimensional image located on the YZ coordinate plane. The X-axis data of each point is used as the gray value corresponding to each point on the third two-dimensional image. The angle between the bottom edge of the first reference body, the bottom edge of the second reference body, the bottom edge of the third reference body, or the ground and the edge of the image on the third two-dimensional image is identified and obtained as the X-axis deflection angle of the third point cloud data.

[0090] Specifically, obtaining c1 via Pose back Point cloud in pose (6, 0, 10, 270, 0, 180), with appendix Figure 11 This is a 2D image converted from the 3D point cloud. The attachments can be calculated using 2D image algorithms. Figure 11 The angle between the arrowed horizontal line and the horizontal axis can be either the line connecting the bottom edge of the reference body or the ground marking line in the figure, thus obtaining the rotation angle α' between w' and the x-axis of w.

[0091] The first angle offset matrix is ​​formed by the deflection angles of each axis of the obtained third point cloud data, and the first angle offset matrix is ​​combined with the first transformation matrix to form the third transformation matrix.

[0092] The above calculations show that the xyz axis angular deviations between the first coarse world coordinate system w and the world coordinate system w or the first precise world coordinate system are α', β', and λ', respectively. This means that the first point cloud data c1, after undergoing the third transformation matrix... The transformation yields a coordinate system w' that is in the same direction as w with high precision. Additionally, based on the position of the device's zero point in the w coordinate system, i.e., ... Figure 7 As shown in the figure, the translation distances from the zero point of the device to the origin w' are x', y'+3, and z'+6, from which the attitude of c1 transformed to w can be obtained.

[0093] Specifically, according to the above steps, in this embodiment, the first point cloud data C1 attitude is converted into the first precise world coordinate system w”, which may include the following transformation process.

[0094] Posture description:

[0095] Pose over (TransX,TransY,TransZ,RotX,RotY,RotZ)

[0096] Pose over : Indicates the pose. The subscript is a description or note of the 2D image after the pose transformation. Over indicates a top-down pose, side indicates a side-down pose, and back indicates a back-down pose.

[0097] TransX: Translation along the x-axis, positive values ​​for point cloud moving in the positive x-direction and negative values ​​for moving in the negative x-direction;

[0098] TransY: Translation along the y-axis, positive values ​​for point cloud moving in the positive y-direction and negative values ​​for moving in the negative direction;

[0099] TransZ: Translation along the z-axis, positive values ​​for point cloud moving in the positive z-direction and negative values ​​for moving in the negative z-direction;

[0100] RotX: Rotation around the x-axis. When the x-axis extends outward from the observer, clockwise rotation is positive. The unit is °.

[0101] RotY: Rotation around the y-axis. When the y-axis extends outward from the observer, clockwise rotation is positive. The unit is °.

[0102] RotZ: Rotation around the z-axis. When the z-axis extends outward from the observer, clockwise rotation is positive. The unit is °.

[0103] Attitude transition calculation formula:

[0104] point Around Pose(x) t ,y t,z t The formulas for calculating attitude transformations (α, β, γ) are as follows:

[0105]

[0106] From the above formula, we get

[0107]

[0108]

[0109]

[0110] Each point in c1 n = 0, 1, 2, 3Λn around Pose(x) t ,y t ,z t The pose (α, β, γ) can be transformed using the above formulas, thus changing the point cloud coordinate system. c1 is then transformed... After the transformation, we get w', which is basically in the same direction as the x, y, and z axes of w, but there is still a small included angle due to installation error.

[0111] Method for measuring the angle and position deviation between c1 and w: As described above, the coordinate systems are specified. Based on the installation position of the first gimbal, a rough transformation relationship between the first gimbal coordinate system c1 and the world coordinate system w can be obtained. front (6,2,0,0,0,90), c1 passes through After the transformation, we obtain w', which is basically in the same direction as w along its x, y, and z axes, but there is still a small angle between them. To correct the angle of w' to obtain w''", we pass c1 through... The transformation yields a coordinate system w that is highly orthogonal to w.

[0112] In this embodiment, step S103 further includes the following: correcting the installation error angle of the fourth point cloud data obtained by the second lidar on the second gimbal:

[0113] The fourth point cloud data is converted into a fourth two-dimensional image located on the XY coordinate plane. The Z-axis data of each point is used as the gray value corresponding to each point on the fourth two-dimensional image. The line connecting the intersection of the second and third reference bodies in the fourth two-dimensional image and the side of the first reference body near the central axis of the region is identified and obtained. The angle between the line and the edge of the image is calculated and obtained as the Z-axis deflection angle of the fourth point cloud data.

[0114] The fourth point cloud data is converted into a fifth two-dimensional image located on the XZ coordinate plane. The Y-axis data of each point is used as the gray value corresponding to each point on the fifth two-dimensional image. The bottom edge of the second reference body, the bottom edge of the third reference body, the bottom edge of the fourth reference body, or the angle between the ground and the edge of the image on the fifth two-dimensional image are identified and obtained as the Y-axis deflection angle of the fourth point cloud data.

[0115] The fourth point cloud data is converted into a sixth two-dimensional image located on the YZ coordinate plane. The X-axis data of each point is used as the gray value corresponding to each point on the sixth two-dimensional image. The bottom edge of the second reference body, the bottom edge of the third reference body, the bottom edge of the fourth reference body, or the angle between the ground and the edge of the image on the sixth two-dimensional image are identified and obtained as the X-axis deflection angle of the fourth point cloud data.

[0116] The deflection angles of each axis of the obtained fourth point cloud data are used to form the second angle offset matrix. The second angle offset matrix is ​​combined with the second transformation matrix to form the fourth transformation matrix.

[0117] The above steps involve processing and transforming the point cloud data obtained by the second lidar on the second gimbal. The content is basically the same as the aforementioned processing and transformation of the point cloud data obtained by the first lidar on the first gimbal, so it will not be described again. For specific steps and effects, please refer to the aforementioned steps.

[0118] Step S104: Convert the third point cloud data into the fifth point cloud data in the first precise world coordinate system using the third transformation matrix; convert the fourth point cloud data into the sixth point cloud data in the second precise world coordinate system using the fourth transformation matrix; obtain the offset matrix based on the position of the same reference body in the fifth and sixth point cloud data.

[0119] As attached Figure 6 As shown in the figure, region ① is the ground of c1, region ② is the ground of c2, and region ③ is the intersection of the two. Four black rectangular reference volumes in the figure penetrate regions 1 and 2, perfectly connecting the x-axis of the two regions, ensuring the consistency of the x-axis direction of w and w2. Due to the horizontality of the ground, the consistency of the y-axis direction is also ensured. With the x and y axes unified, the z-axis is naturally unified as well. Then, by converting the 3D point cloud into a 2D image again, and using conventional methods such as 2D image processing algorithms, the coordinates of the interaction point o′ in w can be measured as x″2.

[0120] In another embodiment, step S104 may further include: obtaining the position offset of the second or third reference body in the fifth point cloud data and the sixth point cloud data, and obtaining the offset matrix based on the offset of each axis, as follows:

[0121] Step S1041: The sixth point cloud data is transformed using the fifth transformation matrix to obtain the seventh point cloud data. The third reference data in the seventh point cloud data is the same as the first reference data in the fifth point cloud data.

[0122] Step S1042: Obtain the offset distance between the first reference body and the second reference body on the X-axis, and add the X-axis offset distance to the fifth transformation matrix to form the offset matrix.

[0123] In this embodiment of step S104, the coordinate system is corrected by the position of the reference body. With the first reference body and the third reference body as references, the sixth point cloud data located in the second precise coordinate system is translated into point cloud data located in the first precise coordinate system. Then, the seventh point cloud data can be offset and supplemented by the offset distance x″2 between the first reference body and the second reference body on the X axis, so as to obtain two point cloud data in the same coordinate system.

[0124] Step S105: The point cloud data of the object under test acquired by the first lidar is transformed by the third transformation matrix and then superimposed with the point cloud data of the object under test acquired by the second lidar after being transformed by the fourth transformation matrix and the offset matrix to obtain the complete point cloud data of the object under test.

[0125] Ultimately, both the first and second point cloud data can be transformed to the same world coordinate system w, allowing for stitching, combination, and calibration. After various pose transformations and the conversion from 3D to 2D images, the measured angles and displacements are compensated through complex coordinate transformations and formula calculations. and

[0126] The pose of the first point cloud data c1 transformed into the world coordinate system w:

[0127]

[0128] The pose of the second point cloud data c2 transformed into the world coordinate system w:

[0129]

[0130] The first point cloud data c1 and the second point cloud data c2 are respectively processed... and The conversion completes the stitching and calibration of the point cloud from the dual gimbals. In this embodiment, the point cloud is converted to the w coordinate system corresponding to the actual physical coordinates.

[0131] Within the measurement area where the first and second gimbals are installed as disclosed in the above embodiments, the third transformation matrix can be: The combination of the fourth transformation matrix and the offset matrix is: When scanning and detecting the cargo compartment of the vehicle to be loaded within the measurement area, the point cloud data c1 obtained by the first lidar on the first pan-tilt unit is processed... After conversion to w, the point cloud data c2 obtained by the second lidar on the second gimbal is processed... Transform to w, after passing through the original point cloud c1 and c2 and After transforming to the same world coordinate system w, and then superimposing the results, the attached image can be obtained. Figure 11 The restored point cloud vehicle shown is exactly the same size as the actual vehicle.

[0132] By stitching together local point cloud data of the front and rear portions of a large-span object acquired by at least two LiDARs within the measurement area, coordinate system transformation and correction are performed using a reference body set within the measurement area. The angular deviation between the gimbal coordinate system and the world coordinate system is obtained through attitude correction of a virtual camera converted from 3D to 2D. Using a common object reference plane, the relationship between the two gimbal coordinate systems is determined. Finally, the coordinate systems of both gimbals are transformed to the same device coordinate system. The point clouds acquired separately by the two gimbals are then stitched together to form ultra-large-scale point cloud data. This allows for the acquisition of point cloud data for ultra-large-scale objects by stitching together point cloud data from two separate gimbals, even with only a small overlap between the two LiDARs.

[0133] Example 3

[0134] In another embodiment, a correction device for lidar point cloud data is also disclosed, used to adjust the measurement data of a lidar installed on a measurement area, comprising: a first point cloud acquisition module, used to acquire first point cloud data in a gimbal coordinate system containing at least a first reference body and a second reference body, wherein the first reference body and the second reference body are placed sequentially along the central axis of the area within the measurement area of ​​the lidar; a first transformation module, used to acquire a first transformation matrix of the lidar according to the installation position of the lidar, and convert the first point cloud data into third point cloud data located in a first coarse world coordinate system through the first transformation matrix; a deflection angle detection module, used to convert the third point cloud data into two-dimensional images located on three coordinate planes respectively, and acquire the angle between the first reference body and / or the second reference body on each two-dimensional image and the image edge, as the deflection angle around the coordinate axis perpendicular to the image; and a correction module, used to combine the acquired deflection angles of each axis with the first transformation matrix as the correction matrix of the lidar, and convert the point cloud data of the measured object acquired by the lidar to obtain point cloud data in the precise world coordinate system of the measurement area through the correction matrix.

[0135] In this embodiment, the deflection angle detection module specifically includes: a first image acquisition module, used to convert the third point cloud data into a first two-dimensional image located on the XY coordinate plane, using the Z-axis data of each point as the gray value corresponding to each point on the first two-dimensional image, and identifying the angle between the line connecting the sides of the first and second reference bodies on the first two-dimensional image and the edge of the image as the Z-axis deflection angle; a second image acquisition module, used to convert the third point cloud data into a second two-dimensional image located on the XZ coordinate plane, using the Y-axis data of each point as the gray value corresponding to each point on the second two-dimensional image, and identifying the angle between the bottom edge of the first or second reference body on the second two-dimensional image and the edge of the image as the Y-axis deflection angle; and a third image acquisition module, used to convert the third point cloud data into a third two-dimensional image located on the YZ coordinate plane, using the X-axis data of each point as the gray value corresponding to each point on the third two-dimensional image, and identifying the angle between the bottom edge of the first or second reference body on the third two-dimensional image and the edge of the image as the X-axis deflection angle.

[0136] In this embodiment, at least two lidars with intersecting areas are installed on the measurement area. The first point cloud acquisition module is further used to acquire first point cloud data containing first, second, and third reference bodies through the first lidar, and to acquire fourth point cloud data containing second, third, and fourth reference bodies through the second lidar. The first to fourth reference bodies are placed sequentially along the central axis of the region within the measurement areas of the two lidars. The second and third reference bodies are located within the intersecting measurement areas of the two lidars, and one side of the second reference body and the other side of the third reference body relative to the second reference body are respectively located on the central axis of the region. The rear side of the second reference body and the front side of the third reference body are respectively located on a vertical plane perpendicular to the central axis of the region. The first and fourth reference bodies are located within the non-intersecting measurement areas of the first and second lidars, and the same side of the first and fourth reference bodies is located on the central axis of the region.

[0137] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. As the lidar point cloud data correction device disclosed in the embodiments corresponds to the lidar point cloud data correction method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to the method section.

[0138] The present invention also discloses a correction device for lidar point cloud data, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the lidar point cloud data correction method steps as described in any of the foregoing embodiments.

[0139] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the server.

[0140] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the server device, connecting various parts of the server device via various interfaces and lines.

[0141] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the server device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc. In addition, the memory may include high-speed random access memory and non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0142] If the cargo stacking control method of the robotic arm 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, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0144] In summary, the above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should be covered by the present invention.

Claims

1. A method for correcting lidar point cloud data, used to adjust the measurement data of a lidar installed in a measurement area, characterized in that, Includes the following steps: S1, acquire first point cloud data in a gimbal coordinate system containing at least a first reference body and a second reference body through a lidar, wherein the first reference body and the second reference body are placed sequentially along the central axis of the region within the measurement area of ​​the lidar; S2, obtain the first transformation matrix of the lidar according to the installation position of the lidar, and convert the first point cloud data into third point cloud data located on the first coarse world coordinate system through the first transformation matrix; S3, convert the third point cloud data into two-dimensional images located on three coordinate planes, and obtain the angles between the first reference body and / or the second reference body on each two-dimensional image and the image edge, as the deflection angles around the coordinate axes perpendicular to the image; specifically including: The third point cloud data is converted into a first two-dimensional image located on the XY coordinate plane. The Z-axis data of each point is used as the gray value corresponding to each point on the first two-dimensional image. The angle between the line connecting the sides of the first and second reference bodies on the first two-dimensional image and the edge of the image is identified and obtained as the Z-axis deflection angle. The third point cloud data is converted into a second two-dimensional image located on the XZ coordinate plane. The Y-axis data of each point is used as the gray value corresponding to each point on the second two-dimensional image. The angle between the bottom edge of the first or second reference body on the second two-dimensional image and the edge of the image is identified and obtained as the Y-axis deflection angle. The third point cloud data is converted into a third two-dimensional image located on the YZ coordinate plane. The X-axis data of each point is used as the gray value corresponding to each point on the third two-dimensional image. The angle between the bottom edge of the first or second reference body on the third two-dimensional image and the edge of the image is identified and obtained as the X-axis deflection angle. S4. The obtained deflection angles of each axis are combined with the first transformation matrix to form the correction matrix of the lidar. The point cloud data of the measured object acquired by the lidar is transformed using the correction matrix to obtain the point cloud data of the measurement area in the precise world coordinate system.

2. The method for correcting lidar point cloud data according to claim 1, characterized in that, Step S3 specifically includes: The third point cloud data is converted into a first two-dimensional image located on the XY coordinate plane. The Z-axis data of each point is used as the gray value corresponding to each point on the first two-dimensional image. The angle between the line connecting the sides of the first and second reference bodies on the first two-dimensional image and the edge of the image is obtained as the Z-axis deflection angle. The third point cloud data is converted into a second two-dimensional image located on the XZ coordinate plane. The Y-axis data of each point is used as the gray value corresponding to each point on the second two-dimensional image. The angle between the ground image and the edge of the image on the second two-dimensional image is obtained as the Y-axis deflection angle. The third point cloud data is converted into a third two-dimensional image located on the YZ coordinate plane. The X-axis data of each point is used as the gray value corresponding to each point on the third two-dimensional image. The angle between the ground image and the edge of the image on the third two-dimensional image is obtained as the X-axis deflection angle.

3. The method for correcting lidar point cloud data according to claim 1 or 2, characterized in that, At least two lidar sensors with intersecting areas are installed in the measurement area, and step S1 further includes: A first point cloud data containing first, second, and third reference bodies is acquired using a first lidar, and a fourth point cloud data containing second, third, and fourth reference bodies is acquired using a second lidar. The first to fourth reference bodies are placed sequentially along the central axis of the region within the measurement areas of the two lidars. The second and third reference bodies are located within the measurement intersection area of ​​the two lidars, with one side of the second reference body and the other side of the third reference body relative to the second reference body located on the central axis of the region. The rear side of the second reference body and the front side of the third reference body are located on vertical planes perpendicular to the central axis of the region. The first and fourth reference bodies are located within the non-intersecting measurement areas of the first and second lidars, respectively, with the same side of the first and fourth reference bodies located on the central axis of the region.

4. The method for correcting lidar point cloud data according to claim 3, characterized in that, Step S3 includes: The third point cloud data is converted into a first two-dimensional image located on the XY coordinate plane, and the Z-axis data of each point is used as the gray value corresponding to each point on the first two-dimensional image. Identify and obtain the line connecting the first reference body near the central axis of the region in the first two-dimensional image to the intersection of the second and third reference bodies, and calculate the angle between the line and the edge of the image as the Z-axis deflection angle.

5. A calibration device for lidar point cloud data, used to adjust the measurement data of a lidar installed in a measurement area, characterized in that, include: The first point cloud acquisition module is used to acquire first point cloud data in a gimbal coordinate system containing at least a first reference body and a second reference body through a lidar, wherein the first reference body and the second reference body are placed sequentially along the central axis of the region within the measurement area of ​​the lidar. The first transformation module is used to obtain the first transformation matrix of the lidar according to the installation position of the lidar, and to convert the first point cloud data into third point cloud data located on the first coarse world coordinate system through the first transformation matrix. The deflection angle detection module is used to convert the third point cloud data into two-dimensional images located on three coordinate planes, and to obtain the angle between the first reference body and / or the second reference body on each two-dimensional image and the image edge, as the deflection angle around the coordinate axis perpendicular to the image. The deflection angle detection module specifically includes: a first image acquisition module, used to convert the third point cloud data into a first two-dimensional image located on the XY coordinate plane, using the Z-axis data of each point as the gray value corresponding to each point on the first two-dimensional image, and identifying the angle between the line connecting the sides of the first and second reference bodies on the first two-dimensional image and the edge of the image as the Z-axis deflection angle; a second image acquisition module, used to convert the third point cloud data into a second two-dimensional image located on the XZ coordinate plane, using the Y-axis data of each point as the gray value corresponding to each point on the second two-dimensional image, and identifying the angle between the bottom edge of the first or second reference body on the second two-dimensional image and the edge of the image as the Y-axis deflection angle; and a third image acquisition module, used to convert the third point cloud data into a third two-dimensional image located on the YZ coordinate plane, using the X-axis data of each point as the gray value corresponding to each point on the third two-dimensional image, and identifying the angle between the bottom edge of the first or second reference body on the third two-dimensional image and the edge of the image as the X-axis deflection angle; The correction module is used to combine the obtained deflection angles of each axis with the first transformation matrix as the correction matrix of the lidar. The correction matrix is ​​used to transform the point cloud data of the measured object acquired by the lidar to obtain the point cloud data of the measurement area in the precise world coordinate system.

6. The lidar point cloud data correction device according to claim 5, characterized in that, At least two lidars with intersecting areas are installed in the measurement area. The first point cloud acquisition module is also used to acquire first point cloud data containing first, second, and third reference bodies through the first lidar, and to acquire fourth point cloud data containing second, third, and fourth reference bodies through the second lidar. The first to fourth reference bodies are placed sequentially along the central axis of the region within the measurement areas of the two lidars. The second and third reference bodies are located within the intersecting measurement areas of the two lidars, and one side of the second reference body and the other side of the third reference body relative to the second reference body are respectively located on the central axis of the region. The rear side of the second reference body and the front side of the third reference body are respectively located on vertical planes perpendicular to the central axis of the region. The first and fourth reference bodies are located within the non-intersecting measurement areas of the first and second lidars, and the same side of the first and fourth reference bodies is located on the central axis of the region.

7. A device for correcting lidar point cloud data, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-4.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-4.

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