Automobile four-wheel positioning method and device, electronic equipment and readable storage medium

Abstract

The invention belongs to the technical field of automobiles, and mainly provides an automobile four-wheel positioning method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a first target image and a second target image, carrying out the image recognition of the first target image and the second target image, and obtaining a first group of feature point coordinates and a second group of feature point coordinates; calculating a rotation translation matrix between the first group of feature point coordinates and the second group of feature point coordinates, and calculating a rotation angle between the first group of feature point coordinates and the second group of feature point coordinates based on the rotation translation matrix; the direction vector corresponding to the rolling axis of the wheel is calculated based on the rotation angle, the four-wheel positioning parameters of the automobile are calculated based on the direction vector, the target image shot by the camera is recognized and calculated through an algorithm, the four-wheel positioning parameters of the automobile are obtained, the operation process of four-wheel positioning of the automobile is simplified, and the positioning accuracy of the automobile is improved. The efficiency of positioning the four wheels of the automobile is improved, and the cost of positioning the four wheels of the automobile is reduced.

Description

Technical Field

[0001] This application belongs to the technical field of automobiles, and particularly relates to an automobile four-wheel alignment method, device, electronic device, and readable storage medium. Background Art

[0002] Automobile four-wheel alignment is mainly used to adjust the position and angle of vehicle tires to ensure the correctness of their contact with the ground. The parameters of automobile four-wheel alignment mainly include the camber angle, toe angle, caster angle, and kingpin inclination angle of the front wheels. Achieving precise four-wheel alignment for an automobile is of great significance for maintaining vehicle stability, avoiding vehicle deviation, improving driving safety, reducing uneven tire wear, and extending tire life. In addition, correct four-wheel alignment can also improve handling performance, make steering more precise, braking more effective, and reduce the wear of suspension system components. Therefore, correct automobile four-wheel alignment is of great significance for optimizing automobile performance.

[0003] Currently, for automobile four-wheel alignment, it is usually necessary to use special four-wheel alignment equipment to measure the four wheels separately. The operation process is complex, the measurement speed is slow, and it requires high manpower and material resources, making it impossible to achieve efficient and convenient four-wheel alignment of automobiles. Summary of the Invention

[0004] This application provides an automobile four-wheel alignment method, device, electronic device, and readable storage medium, which can solve the technical problem that the existing automobile four-wheel alignment method requires high manpower and material resources.

[0005] In the first aspect of the embodiments of this application, an automobile four-wheel alignment method is provided. The automobile four-wheel alignment method includes:

[0006] Obtain a first target image and a second target image; the first target image and the second target image are obtained by a camera photographing a target installed on a wheel when the wheel is at different rolling angles;

[0007] Perform image recognition on the first target image and the second target image to obtain a first set of feature point coordinates and a second set of feature point coordinates; the first set of feature point coordinates is the coordinates of the feature points in the first target image in the camera coordinate system, and the second set of feature point coordinates is the coordinates of the feature points in the second target image in the camera coordinate system;

[0008] Calculate the rotation and translation matrix between the first set of feature point coordinates and the second set of feature point coordinates, and calculate the rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation and translation matrix;

[0009] Calculate a direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the coordinates of the first set of feature points and the coordinates of the second set of feature points, and calculate four-wheel alignment parameters of the vehicle based on the direction vector corresponding to the rolling axis of the wheel.

[0010] The second aspect of the embodiments of the present application further provides a vehicle four-wheel alignment device, which includes:

[0011] An acquisition unit, configured to acquire a first target image and a second target image; the first target image and the second target image are obtained by a camera photographing a target installed on the wheel when the wheel is at different rolling angles;

[0012] An identification unit, configured to perform image recognition on the first target image and the second target image to obtain coordinates of a first set of feature points and coordinates of a second set of feature points; the coordinates of the first set of feature points are the coordinates of the feature points in the first target image in the camera coordinate system, and the coordinates of the second set of feature points are the coordinates of the feature points in the second target image in the camera coordinate system;

[0013] A first calculation unit, configured to calculate a rotation and translation matrix between the coordinates of the first set of feature points and the coordinates of the second set of feature points, and calculate a rotation angle between the coordinates of the first set of feature points and the coordinates of the second set of feature points based on the rotation and translation matrix;

[0014] A second calculation unit, configured to calculate a direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the coordinates of the first set of feature points and the coordinates of the second set of feature points, and calculate four-wheel alignment parameters of the vehicle based on the direction vector corresponding to the rolling axis of the wheel.

[0015] The third aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the computer program is executed by the processor, the steps of the vehicle four-wheel alignment method described in the first aspect are implemented.

[0016] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the vehicle four-wheel alignment method described in the first aspect are implemented.

[0017] The embodiments of the present application further provide a computer program product, which includes a computer program, and when the computer program runs on a processor, the steps of the vehicle four-wheel alignment method described in the first aspect are implemented.

[0018] In the embodiments of the present application, a first target image and a second target image obtained by a camera photographing a target installed on a wheel are acquired; then, image recognition is performed on the first target image and the second target image to obtain a first set of feature point coordinates and a second set of feature point coordinates; then, a rotation and translation matrix between the first set of feature point coordinates and the second set of feature point coordinates is calculated, and a rotation angle between the first set of feature point coordinates and the second set of feature point coordinates is calculated based on the rotation and translation matrix; finally, a direction vector corresponding to the rolling axis of the wheel is calculated based on the rotation angle and the first set of feature point coordinates and the second set of feature point coordinates, and four-wheel alignment parameters of the vehicle are calculated based on the direction vector corresponding to the rolling axis of the wheel. By recognizing and calculating the photographed target images through an algorithm to obtain the four-wheel alignment parameters of the vehicle, compared with measuring using a dedicated four-wheel alignment device, the operation process of vehicle four-wheel alignment is simplified, the efficiency of vehicle four-wheel alignment is improved, the four-wheel alignment of the vehicle is made more efficient and convenient, the cost of vehicle four-wheel alignment is reduced, and the technical problem that the existing vehicle four-wheel alignment method requires high labor and material resources is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic flowchart of the implementation process of the vehicle four-wheel alignment method provided by the embodiments of the present application.

[0020] Figure 2 is a schematic diagram of four wheel target patterns provided by the embodiments of the present application.

[0021] Figure 3 is a schematic diagram of the target image provided by the embodiments of the present application.

[0022] Figure 4 is a schematic diagram of the direction vector of the rolling axis of the wheel provided by the embodiments of the present application.

[0023] Figure 5 is a schematic flowchart of determining the center coordinates of the ordinary circle and the concentric circles in the image coordinate system in the target target image provided by the embodiments of the present application.

[0024] Figure 6 is a schematic diagram of the target target image with relatively low clarity provided by the embodiments of the present application.

[0025] Figure 7 is a schematic diagram of performing perspective transformation on the target target image provided by the embodiments of the present application.

[0026] Figure 8a is a schematic diagram of the target target image with unrecognizable concentric circles provided by the embodiments of the present application.

[0027] Figure 8bSchematic diagram for predicting the position of an unrecognizable concentric circle provided by an embodiment of the present application.

[0028] Figure 9 Schematic structural diagram of an automobile four-wheel alignment device provided by an embodiment of the present application.

[0029] Figure 10 Schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0030] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0031] It should be understood that in the description of the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0032] In addition, in the description of the specification of the present application and the appended claims, the terms "first", "second", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0033] The reference to "one embodiment" or "some embodiments" etc. in the description of the specification of the present application means that a specific feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. appearing in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The technical solutions claimed in the present application will be described in detail below.

[0034] As Figure 1 shown, it is a schematic implementation flow diagram of an automobile four-wheel alignment method provided by an embodiment of the present application. The automobile four-wheel alignment method can be implemented by the following steps 101 to 104.

[0035] Step 101, obtain a first target image and a second target image.

[0036] In the embodiment of the present application, the first target image and the second target image are obtained by a camera photographing a target installed on a wheel when the wheel is at different rolling angles.

[0037] For example, during the rolling process of the wheel, by using a camera to capture the target installed on the wheel, at least two target images captured by the camera can be obtained when the wheel is at different rolling angles. By grouping the target images in pairs, one or more groups of target images can be obtained, and each group of target images includes a first target image and a second target image. In this application, a group of target images is also referred to as a group of first target images and second target images. That is, a group of first target images and second target images includes a first target image and a second target image, and the rolling angles of the wheels corresponding to the first target image and the second target image are different.

[0038] In an embodiment of this application, the above step 101 of obtaining the first target image and the second target image may include obtaining a group of first target images and second target images, or obtaining multiple groups of first target images and second target images. When multiple groups of first target images and second target images are obtained, the following steps 102 to 104 may be respectively executed for each group of first target images and second target images to obtain multiple groups of four-wheel alignment parameters of the vehicle. By calculating the average value of the multiple groups of four-wheel alignment parameters and using this average value as the target four-wheel alignment parameter of the vehicle, the accuracy of the four-wheel alignment of the vehicle can be improved.

[0039] In an embodiment of this application, the target pattern of the target installed on the wheel may be provided with a common circle and concentric circles located in the vertex area of the polygon.

[0040] As Figure 2 shown, there are four wheel target patterns provided by the embodiment of this application. These four wheel target patterns include ArUco markers located in the center of the target pattern to distinguish each target serial number and 12 circular patterns located on the four sides of the quadrilateral. Among them, the circular patterns located at the four vertices of the quadrilateral are concentric circles, and the remaining circular patterns are common circles.

[0041] In an embodiment of this application, the common circle is a white circle, and the concentric circle is composed of a large white circle and a small black circle. Since black and white are a pair of colors with strong contrast, setting the common circle as a white circle and the concentric circle as including a large white circle and a small black circle is beneficial to reducing the difficulty of identifying the target image (for example, the first target image and the second target image), and thus improving the accuracy of identifying the target image.

[0042] It should be noted that Figure 2The wheel target pattern is only used as an example. In other embodiments of the present application, the pattern of the target can also be other patterns. For example, the ArUco marker used to distinguish each target serial number in the middle can be other ArUco markers, the number of ordinary circles can be more or less, and the concentric circles can be replaced with ordinary circles. The present application does not limit this.

[0043] For the convenience of description, in the following, the target pattern is Figure 2 the wheel target pattern shown as an example to illustrate the technical solution of the present application.

[0044] Among them, Figure 2 the targets corresponding to the four wheel target patterns from left to right in can be sequentially installed on the right rear wheel, left rear wheel, right front wheel, and left front wheel of the vehicle respectively.

[0045] In step 101 above, during the rolling of the wheel, the targets on the wheel are photographed by the cameras located on both sides of the wheel, and the target images corresponding to the right rear wheel, the left rear wheel, the right front wheel, and the left front wheel as shown in Figure 3 can be obtained.

[0046] Step 102: Perform image recognition on the first target image and the second target image to obtain the first set of feature point coordinates and the second set of feature point coordinates.

[0047] In the embodiment of the present application, the first set of feature point coordinates is the coordinates of the feature points in the first target image in the camera coordinate system, and the second set of feature point coordinates is the coordinates of the feature points in the second target image in the camera coordinate system.

[0048] The feature points in the first target image and the feature points in the second target image are corresponding feature points one by one. That is, the feature points in the second target image are the points obtained by rotating and translating the feature points in the first target image.

[0049] When the target pattern of the target installed on the wheel is the target pattern with ordinary circles and concentric circles located in the vertex area of the polygon as shown in Figure 2 , the feature points in the first target image can be the centers of the ordinary circles and concentric circles in the first target image; the feature points in the second target image can be the centers of the ordinary circles and concentric circles in the second target image.

[0050] Step 103: Calculate the rotation and translation matrix between the first set of feature point coordinates and the second set of feature point coordinates, and calculate the rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation and translation matrix.

[0051] In homogeneous coordinates, a rotation matrix and a translation matrix can be combined into a 4×4 rotation-translation matrix. In an embodiment of the present application, during the process of calculating the rotation-translation matrix between the first set of feature point coordinates and the second set of feature point coordinates, the rotation-translation matrix can be constructed. After calculating the values of each element in the rotation-translation matrix RT, the rotation angle between the first set of feature point coordinates and the second set of feature point coordinates can be calculated based on the rotation-translation matrix.

[0052] In an embodiment of the present application, during the process of calculating the values of each element in the rotation-translation matrix RT, when the first set of feature point coordinates is P and the second set of feature point coordinates is Q, since P = RT * Q, therefore, when the first set of feature point coordinates P and the second set of feature point coordinates Q are known, the values of each element in the rotation-translation matrix RT can be calculated based on the formula P = RT * Q.

[0053] In an embodiment of the present application, during the process of calculating the values of each element in the rotation-translation matrix RT, the values of each element in the rotation-translation matrix RT can be obtained by least squares fitting based on the first set of feature point coordinates and the second set of feature point coordinates, so as to obtain the rotation-translation matrix.

[0054] Specifically, when the first set of feature point coordinates is P and the second set of feature point coordinates is Q, since P = RT * Q, the values of each element in RT (i.e., the values of rt00, rt11, rt22,... rt33) can be obtained by least squares fitting based on the formula P = RT * Q.

[0055] In the embodiment of the present application, by using the least squares fitting method to solve the values of each element in the rotation-translation matrix RT, the obtained rotation-translation matrix RT can be made as close as possible to the true value, thus improving the accuracy of the rotation angle.

[0056] In an embodiment of the present application, during the process of calculating the rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation-translation matrix, it can be calculated based on the element values on the diagonal of the rotation-translation matrix.

[0057] In an embodiment, the calculation formula for the rotation angle can be:

[0058] s = arccos((rt00 + rt11 + rt22 - 1) / 2). After obtaining the element values on the diagonal of the rotation and translation matrix RT, that is, the values of rt00, rt11, and rt22, substitute the values of rt00, rt11, and rt22 into the rotation angle calculation formula s = arccos((rt00 + rt11 + rt22 - 1) / 2), and the rotation angle between the first set of feature point coordinates and the second set of feature point coordinates can be calculated.

[0059] Step 104: Calculate the direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the first set of feature point coordinates and the second set of feature point coordinates, and calculate the four-wheel alignment parameters of the vehicle based on the direction vector corresponding to the rolling axis of the wheel.

[0060] In the embodiment of the present application, the direction vector corresponding to the rolling axis of the wheel can be the plane normal vector corresponding to the plane where the walking trajectory of the point on the wheel or the point on the target corresponds during the rolling of the wheel. The direction vector corresponding to the rolling axis of the wheel can be expressed as (nx, ny, nz).

[0061] For example, as Figure 4 shown, F is the plane where the walking trajectory of the point on the wheel or the point on the target corresponds during the rolling of the wheel. The direction vector corresponding to the rolling axis of the wheel where nx represents the coordinate (abscissa) on the horizontal axis, ny represents the coordinate (ordinate) on the vertical axis, and nz represents the coordinate (ordinate) on the vertical axis.

[0062] In the embodiment of the present application, in the process of calculating the direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the first set of feature point coordinates and the second set of feature point coordinates, it can be obtained based on the following steps A01 to A03.

[0063] Step A01: Obtain the coordinate transformation relationship between the preset first set of feature point coordinates and the second set of feature point coordinates and the radius of the wheel.

[0064] Step A02: Based on the rotation angle, the radius of the wheel, the first set of feature point coordinates, and the second set of feature point coordinates, use the coordinate transformation relationship to calculate multiple element values in the coordinate transformation matrix between the first set of feature point coordinates and the second set of feature point coordinates.

[0065] Step A03: Calculate the direction vector corresponding to the rolling axis of the wheel based on multiple element values in the coordinate transformation matrix.

[0066] In this embodiment, the rolling center n of the wheel can be obtained as (cx, cy, cz) first, and the rolling axis When it is (nx, ny, nz), the coordinate transformation matrix M for the feature point to rotate by the rotation angle s around the rolling center n and the rolling axis direction vector and the coordinate transformation formula.

[0067] Specifically, in homogeneous coordinates, the coordinate transformation matrix for the feature point to rotate by the rotation angle s around the rolling center n and the rolling axis can be a 4×4 coordinate transformation matrix where

[0068] m00 = nx 2 *(1 - cos(s)) + cos(s);

[0069] m01 = nx * ny * (1 - cos(s)) - nz * sin(s);

[0070] m02 = nx * nz * (1 - cos(s)) - ny * sin(s);

[0071] m03 = cx * (1 - nx 2 ) - nx * (cy * ny + cz * nz)) * (1 - cos(s)) + (cy * nz - cz * ny) * sin(s);

[0072] m10 = ny * nx * (1 - cos(s)) + nz * sin(s);

[0073] m11 = ny 2 *(1 - cos(s)) + cos(s);

[0074] m12 = ny * nz * (1 - cos(s)) - nx * sin(s);

[0075] m13 = (cy * (1 - ny 2 ) - ny * (cx * nx + cz * nz)) * (1 - cos(s)) + (cz * nx - cx * nz) * sin(s);

[0076] m20 = nx * nz * (1 - cos(s)) - ny * sin(s);

[0077] m21 = ny * nz * (1 - cos(s)) + nx * sin(s);

[0078] m22 = nz 2 *(1 - cos(s)) + cos(s);

[0079] m23 = (cz * (1 - nz 2) - nz * (cx * nx + cy * ny)) * (1 - cos(s)) + (cx * ny - cy * nx) * sin(s);

[0080] m30 = 0; m31 = 0; m32 = 0; m33 = 1.

[0081] The coordinate transformation formula may include:

[0082] qx = m00 * px + m01 * py + m02 * pz + r * s * ax;

[0083] qy = m10 * px + m11 * py + m12 * pz + r * s * ay;

[0084] qz = m20 * px + m21 * py + m22 * pz + r * s * az; where r is the wheel radius, which can be measured in advance, and (qx, qy, qz) represents the coordinates of the second set of feature points, and (px, py, pz) represents the coordinates of the first set of feature points.

[0085] Next, the coordinates of the first set of feature points obtained in step 102, the coordinates of the second set of feature points, as well as the values of the rotation angle s and the radius r of the wheel can be substituted into the coordinate transformation formula to calculate the values of multiple elements in the coordinate transformation matrix M.

[0086] For example, substituting the coordinate points {p1(p1x, p1y, p1z), p2(p2x, p2y, p2z)... pn(pnx, pny, pnz)} of the first set of feature points P and the coordinate points {q1(q1x, q1y, q1z), q2(q2x, q2y, q2z)... qn(qnx, qny, qnz)} of the second set of feature points Q into the above coordinate transformation formula, the values of m00, m01, m02, m10, m11, m12, m20, m21, m22 in the coordinate transformation formula can be obtained, that is, the values of the elements m00, m01, m02, m10, m11, m12, m20, m21, m22 in M, where n is greater than or equal to 3.

[0087] Then, substituting the values of multiple elements in the coordinate transformation matrix M into the calculation formulas corresponding to multiple elements in the coordinate transformation matrix M, the coordinates (nx, ny, nz) of the rolling axis direction vector included in the calculation formula can be calculated. of the value.

[0088] Specifically, after obtaining the values of the elements m00, m01, m02, m10, m11, m12, m20, m21, m22 in M, substitute the values of m00, m01, m02, m10, m11, m12, m20, m21, m22 and the rotation angle s into the following calculation formulas respectively:

[0089] m00 = nx 2 *(1 - cos(s)) + cos(s);

[0090] m01 = nx * ny * (1 - cos(s)) - nz * sin(s);

[0091] m02 = nx * nz * (1 - cos(s)) - ny * sin(s);

[0092] m10 = ny * nx * (1 - cos(s)) + nz * sin(s);

[0093] m11 = ny 2 *(1 - cos(s)) + cos(s);

[0094] m12 = ny * nz * (1 - cos(s)) - nx * sin(s);

[0095] m20 = nx * nz * (1 - cos(s)) - ny * sin(s);

[0096] m21 = ny * nz * (1 - cos(s)) + nx * sin(s);

[0097] m22 = nz 2 *(1 - cos(s)) + cos(s); The direction vector of the wheel rolling axis can be calculated of the coordinates (nx, ny, nz).

[0098] In the embodiments of the present application, by obtaining the first target image and the second target image captured by the camera for the target installed on the wheel; then, performing image recognition on the first target image and the second target image to obtain the first set of feature point coordinates and the second set of feature point coordinates; then, calculating the rotation and translation matrix between the first set of feature point coordinates and the second set of feature point coordinates, and calculating the rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation and translation matrix; finally, calculating the direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the first set of feature point coordinates and the second set of feature point coordinates, and calculating the four-wheel alignment parameters of the vehicle based on the direction vector corresponding to the rolling axis of the wheel, it realizes the recognition and calculation of the captured target image through the algorithm to obtain the four-wheel alignment parameters of the vehicle. Compared with using a dedicated four-wheel alignment device for measurement, it can improve the efficiency of vehicle four-wheel alignment, simplify the operation process of vehicle four-wheel alignment, make the four-wheel alignment of the vehicle more efficient and convenient, reduce the cost of vehicle four-wheel alignment, and solve the technical problem that the existing vehicle four-wheel alignment methods require high labor and material resources.

[0099] In one embodiment of the present application, the four-wheel alignment parameters may include the wheel camber angle and the wheel toe angle.

[0100] Among them, the wheel camber angle refers to the angle between the plane where the wheel is located and the longitudinal vertical plane after the wheel is installed, with the end face of the wheel tilting outward. When the tires are spread in a "V" shape, it is called negative camber, and when they are spread in an "A" shape, it is called positive camber. The wheel toe angle refers to the angle between the horizontal diameter of the wheel and the longitudinal vertical plane of the vehicle.

[0101] After calculating the direction vector corresponding to the rolling axis of the wheel, in the process of calculating the four-wheel alignment parameters of the vehicle based on the direction vector corresponding to the rolling axis of the wheel in step 104 above, the wheel camber angle can be calculated based on the first coordinate and the second coordinate in the direction vector corresponding to the rolling axis of the wheel; the wheel toe angle can be calculated based on the second coordinate and the third coordinate in the direction vector corresponding to the rolling axis of the wheel.

[0102] For example, based on the values of the vertical coordinate nz (the first coordinate) and the horizontal coordinate nx (the second coordinate) in the direction vector corresponding to the rolling axis of the wheel, the wheel camber angle can be calculated as arctan(nz / nx); based on the values of the vertical coordinate ny (the third coordinate) and the horizontal coordinate nx (the second coordinate) in the direction vector corresponding to the rolling axis of the wheel, the wheel toe angle can be calculated as arctan(ny / nx), where arctan(·) represents the arctangent function.

[0103] In one embodiment of the present application, after obtaining the four-wheel alignment parameters of the vehicle in step 104, steps 101 to 104 may be returned and executed again to obtain multiple sets of four-wheel alignment parameters, and the average value of the multiple sets of four-wheel alignment parameters may be calculated, and this average value may be used as the final four-wheel alignment parameters of the vehicle to improve the four-wheel alignment accuracy of the vehicle.

[0104] For example, after obtaining 4 sets of wheel camber angles and wheel toe angles, the average value of the wheel camber angles and the average value of the wheel toe angles in the 4 sets of wheel camber angles and wheel toe angles can be calculated, and the average value of the wheel camber angles and the average value of the wheel toe angles can be used as the final wheel camber angle and wheel toe angle of the vehicle.

[0105] In one embodiment of the present application, in step 102 above, in the process of performing image recognition on the first target image and the second target image to obtain the first set of feature point coordinates and the second set of feature point coordinates, the following steps B01 to B03 can be adopted to implement.

[0106] Step B01: Identify the ordinary circles and concentric circles in the target target image, and obtain the center coordinates of the ordinary circles and concentric circles in the target target image in the image coordinate system.

[0107] In the embodiments of the present application, the target target image includes a first target image and a second target image. When identifying the ordinary circles and concentric circles in the first target image and obtaining the center coordinates of the ordinary circles and concentric circles in the first target image in the image coordinate system, the target target image is the first target image; when identifying the ordinary circles and concentric circles in the second target image and obtaining the center coordinates of the ordinary circles and concentric circles in the second target image in the image coordinate system, the target target image is the second target image.

[0108] Step B02: Based on the center coordinates in the image coordinate system, calculate the center coordinates of the ordinary circles and concentric circles in the target target image in the camera coordinate system.

[0109] That is, based on the center coordinates of the ordinary circles and concentric circles in the first target image in the image coordinate system, calculate the center coordinates of the ordinary circles and concentric circles in the first target image in the camera coordinate system; and based on the center coordinates of the ordinary circles and concentric circles in the second target image in the image coordinate system, calculate the center coordinates of the ordinary circles and concentric circles in the second target image in the camera coordinate system.

[0110] Step B03: Use the center coordinates of the ordinary circles and concentric circles in the first target image in the camera coordinate system as the first set of feature point coordinates, and use the center coordinates of the ordinary circles and concentric circles in the second target image in the camera coordinate system as the second set of feature point coordinates.

[0111] Among them, in the above step B01, if the image recognition technology is used to directly recognize the obtained target target image (the first target image and the second target image) to obtain the center coordinates of the ordinary circles and concentric circles in the target target image in the image coordinate system, it may be affected by shooting factors such as the camera shooting angle, resulting in a problem of low accuracy of the recognized center coordinates.

[0112] Therefore, in an embodiment of the present application, in order to improve the accuracy of the center coordinates of the recognized ordinary circles and concentric circles in the image coordinate system, when the target pattern of the target is provided with concentric circles in the vertex region of the polygon and ordinary circles in the non-vertex region of the polygon, for example, when the target pattern of the target is as Figure 2 shown in the target pattern, the above step B01 can be implemented in the manner of steps 401 to 404 as Figure 5 shown.

[0113] Step 401: Perform image recognition on the target target image to obtain the first center coordinates of the concentric circles in the target target image.

[0114] The above-mentioned step 401 refers to directly performing image recognition on the target target image to obtain the first center coordinates of the concentric circles in the target target image. It includes performing image recognition on the first target image to obtain the first center coordinates of the concentric circles in the first target image, and performing image recognition on the second target image to obtain the first center coordinates of the concentric circles in the second target image.

[0115] Among them, when the above-mentioned step 401 performs image recognition on the target target image to obtain the first center coordinates of the concentric circles in the target target image, it can be implemented based on the following methods from step C01 to step C02.

[0116] Step C01, identify the ellipses in the target target image, and filter out the ellipses located on the target based on the average values of the lengths of the major axis and minor axis of the ellipses.

[0117] Specifically, when performing image recognition on the target target image, all the ellipses in the target target image can be identified first, and the image coordinates of each ellipse are recorded. Then, the ellipses located on the target are filtered out based on the average values of the lengths of the major axis and minor axis of the ellipses.

[0118] For example, the average value of the lengths of the major axis and minor axis corresponding to each ellipse can be obtained by adding the lengths of the major axis and minor axis of each ellipse and then dividing by 2. Then, the ellipses with similar average values (for example, the difference is less than or equal to 4 pixels) are divided into the same group, and the ellipses in this group are determined as the ellipses located on the target to filter out the interfering ellipses outside the target.

[0119] Step C02, calculate the pixel average value corresponding to the pixels in the center area of each ellipse located on the target, determine the concentric circles in the target target image based on the pixel average value, and obtain the first center coordinates of the concentric circles in the target target image.

[0120] For example, when the wheel target pattern is the target pattern as shown in Figure 2 That is, when the ordinary circles on the wheel target pattern are white circles and the concentric circles are composed of a large white circle and a small black circle, step C02 can calculate the pixel average value corresponding to the pixels in the 3*3 pixel area where the center of each ellipse located on the target is located. After sorting the pixel average values from large to small, calculate the pixel difference between two adjacent pixel average values. The ellipse corresponding to the subtrahend with the largest pixel difference and the pixel average value corresponding to the ellipse after the subtrahend are determined as the concentric circles in the target target image. The minuend corresponding to the largest pixel difference and the ellipse corresponding to the pixel average value before the minuend are the ordinary circles in the target target image. And after identifying the concentric circles and ordinary circles in the target target image, perform circle fitting on the identified concentric circles in the target target image, and the first center coordinates of the concentric circles in the target target image can be obtained.

[0121] In the embodiments of the present application, by calculating the pixel mean value corresponding to the pixels in the elliptical center region of each target on the target, and determining the concentric circles in the target target image based on the pixel mean value, when the clarity of the target target image is low (for example, as shown in the target target image Figure 6 ), the concentric circles and ordinary circles in the target target image can still be accurately distinguished.

[0122] In an embodiment of the present application, when the clarity of the target target image is high, in addition to calculating the first center coordinates of the concentric circles in the target target image in the manner of step C02 described above, two circles with the distance between the centers in the ellipse on the target less than a preset distance threshold can also be used as concentric circles, and the first center coordinates of the concentric circles are calculated based on the center coordinates of the two circles.

[0123] For example, the coordinate mean value of the center coordinates of the two circles is used as the first center coordinates of the concentric circles.

[0124] In the present application, by using two circles with the distance between the centers in the ellipse on the target less than a preset distance threshold as concentric circles, the calculation amount of concentric circle recognition can be reduced. Therefore, the recognition efficiency of the target target image can be improved, and further the efficiency of four-wheel alignment of the vehicle can be improved.

[0125] Step 402: Based on the first center coordinates, perform perspective transformation on the target target image to obtain the target image after perspective transformation.

[0126] In the target target image, since the concentric circles are located in the polygon vertex region, therefore, based on the first center coordinates of the concentric circles in the target target image, the target target image can be converted into a target image after perspective transformation without distortion through the perspective transformation formula according to the set ratio.

[0127] For example, as shown in Figure 6 , after calculating the center coordinates of the 4 concentric circles in the target image (i.e., the target target image) corresponding to the left front wheel shown in Figure 3 , the center coordinate with the smallest y value among the center coordinates of the 4 concentric circles can be used as the first coordinate, and the remaining three center coordinates are sorted in a clockwise direction. The target target image is converted into a target image after perspective transformation without distortion shown on the right side in Figure 6 according to the set ratio through perspective transformation.

[0128] Step 403: Perform circle fitting on the concentric circles and ordinary circles in the target image after perspective transformation to obtain the second center coordinates of the concentric circles and ordinary circles in the target image after perspective transformation.

[0129] In the embodiments of the present application, algorithms such as the least squares method, the minimum zone method, the maximum inscribed circle, and the minimum circumscribed circle can be used to perform circle fitting on the concentric circles and ordinary circles in the target image after perspective transformation, so as to obtain the second center coordinates of the concentric circles and ordinary circles in the target image after perspective transformation. The specific manner of circle fitting in the present application is not limited.

[0130] Step 404: Map the second center coordinates back to the target target image to obtain the center coordinates of the ordinary circles and concentric circles in the target target image in the image coordinate system.

[0131] In the embodiments of the present application, the process of mapping the second center coordinates of the concentric circles and ordinary circles in the target image after perspective transformation back to the target target image is the reverse process of step 402. The center coordinates of the ordinary circles and concentric circles in the target target image obtained in step 404 are the center coordinates after eccentricity compensation, that is, the center coordinates after correcting the first center coordinates of the ordinary circles and concentric circles directly obtained by image recognition in step 401.

[0132] In the embodiments of the present application, by performing circle fitting on the concentric circles and ordinary circles in the target image after perspective transformation, the second center coordinates of the concentric circles and ordinary circles in the target image after perspective transformation are obtained. Then, the second center coordinates of the concentric circles and ordinary circles in the target image after perspective transformation are mapped back to the target target image (that is, the first target image or the second target image collected in step 101), and the center coordinates of the ordinary circles and concentric circles in the target target image obtained, that is, the center coordinates of the concentric circles and ordinary circles in the first target image after eccentricity compensation are obtained, which is equivalent to performing an eccentricity compensation on the first center coordinates in step 401. Therefore, when using the center coordinates after eccentricity compensation as the center coordinates of the ordinary circles and concentric circles in the target target image in the image coordinate system, compared with directly using the first center coordinates obtained by image recognition as the center coordinates of the ordinary circles and concentric circles in the image coordinate system, the recognition accuracy of the target target image can be improved, and thus it is beneficial to improve the accuracy of four-wheel alignment of the vehicle.

[0133] It should be noted that the target target image in steps 401 to 404 may include the first target image and the second target image, that is, steps 401 to 404 are the processes of identifying the ordinary circles and concentric circles in the first target image and the second target image, and obtaining the center coordinates of the ordinary circles and concentric circles in the first target image and the second target image in the image coordinate system.

[0134] In an embodiment of the present application, after obtaining the center coordinates of the ordinary circles and concentric circles in the first target image and the second target image in the image coordinate system, in the above step B02, in the process of calculating the center coordinates of the ordinary circles and concentric circles in the target target image in the camera coordinate system based on the center coordinates in the image coordinate system, it is necessary to obtain the conversion relationship between the camera coordinate system and the world coordinate system of the camera based on the center coordinates in the image coordinate system, the internal parameters of the camera, and the three-dimensional coordinates of the centers of the ordinary circles and concentric circles in the world coordinate system in the target target image, and obtain the center coordinates of the centers of the ordinary circles and concentric circles in the target target image in the image coordinate system based on the conversion relationship between the camera coordinate system and the world coordinate system of the camera.

[0135] In an embodiment of the present application, the center coordinates of the ordinary circles and concentric circles in the first target image in the image coordinate system, the internal parameters of the camera, and the three-dimensional coordinates of the centers of the ordinary circles and concentric circles in the first target image in the world coordinate system can be input into the solvePnP algorithm to obtain the conversion relationship between the camera coordinate system and the world coordinate system of the camera.

[0136] Among them, the solvePnP algorithm is a function in the OpenCV library. By inputting the three-dimensional point coordinates in the world coordinate system, the two-dimensional coordinates of these points on the image, and the internal parameters of the camera, the rotation vector and translation vector of the camera can be calculated, that is, the conversion relationship between the camera coordinate system and the world coordinate system of the camera.

[0137] After obtaining the conversion relationship between the camera coordinate system and the world coordinate system of the camera, using this conversion relationship, the three-dimensional coordinates of the centers of the ordinary circles and concentric circles in the first target image and the second target image in the world coordinate system can be converted to obtain the coordinates of the centers of the ordinary circles and concentric circles in the first target image and the second target image in the camera coordinate system. Furthermore, the coordinates of the centers of the ordinary circles and concentric circles in the first target image in the camera coordinate system can be used as the first set of feature point coordinates, and the coordinates of the centers of the ordinary circles and concentric circles in the second target image in the camera coordinate system can be used as the second set of feature point coordinates, that is, the first set of feature point coordinates and the second set of feature point coordinates in the above step 102 are obtained.

[0138] It should be noted that the conversion relationship between the camera coordinate system and the world coordinate system of the camera can be obtained by inputting the center coordinates of the ordinary circles and concentric circles in the first target image in the image coordinate system and the three-dimensional coordinates of the centers of the ordinary circles and concentric circles in the first target image in the world coordinate system into the solvePnP algorithm, or by inputting the center coordinates of the ordinary circles and concentric circles in the second target image in the image coordinate system and the three-dimensional coordinates of the centers of the ordinary circles and concentric circles in the second target image in the world coordinate system into the solvePnP algorithm. The present application does not limit this.

[0139] In practical applications, affected by factors such as ambient light, for example Figure 8a As shown, in the above step 401, when performing image recognition on the target target image to obtain the first center coordinates of the concentric circles in the target target image, there may be unrecognized concentric circles among the concentric circles located in the polygon vertex region in the target target image. At this time, the number of recognized concentric circles located in the polygon vertex region will be less than the preset number threshold. Based on this, in an embodiment of the present application, if the number of recognized concentric circles located in the polygon vertex region is less than the preset number threshold, then the center coordinates of the unrecognized concentric circles in the polygon vertex region can be predicted based on the center coordinates of the already recognized concentric circles. Correspondingly, in step 402, the target target image can be perspectively transformed based on the predicted first center coordinates of the concentric circles to obtain the perspectively transformed target image.

[0140] Specifically, for example Figure 8a and Figure 8b As shown, when the polygon is a parallelogram (for example, a square or a rectangle) and the number of unrecognized concentric circles in the target target image is one, a triangle can be constructed with the first center coordinates of the three already recognized concentric circles as vertices, the largest interior angle among the three interior angles of the triangle can be determined, and the opposite side of the largest interior angle; the coordinates of the symmetric point of the vertex where the largest interior angle is located with respect to the opposite side are used as the first center coordinates corresponding to the unrecognized concentric circle.

[0141] In specific implementation, after constructing the triangle, the sizes of the three interior angles of the triangle can be calculated, and the three centers can be sorted in ascending order according to the sizes of the three interior angles. Based on the sorted three centers, assuming that the fourth center forms a parallelogram with these three centers, and the fourth center is symmetric about the straight line of the opposite side of the vertex where the largest interior angle in the triangle is located, the position of the fourth center can be inferred.

[0142] In this embodiment, the above preset number threshold can be equal to the number of polygon vertex regions. For example, when the polygon is a parallelogram, the number of polygon vertex regions is 4, and at this time the preset number threshold can also be 4.

[0143] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence. In some embodiments of the present application, certain steps can be performed in other sequences.

[0144] The embodiment of the present application also provides an automobile four-wheel alignment device. As Figure 9 shown, the automobile four-wheel alignment device 80 may include:

[0145] An acquisition unit 81 for acquiring a first target image and a second target image; the first target image and the second target image are obtained by a camera photographing a target mounted on a wheel when the wheel is at different rolling angles;

[0146] An identification unit 82 for performing image identification on the first target image and the second target image to obtain a first set of feature point coordinates and a second set of feature point coordinates; the first set of feature point coordinates are the coordinates of the feature points in the first target image in the camera coordinate system, and the second set of feature point coordinates are the coordinates of the feature points in the second target image in the camera coordinate system;

[0147] A first calculation unit 83 for calculating a rotation and translation matrix between the first set of feature point coordinates and the second set of feature point coordinates, and calculating a rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation and translation matrix;

[0148] A second calculation unit 84 for calculating a direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the first set of feature point coordinates and the second set of feature point coordinates, and calculating four-wheel alignment parameters of the vehicle based on the direction vector corresponding to the rolling axis of the wheel.

[0149] It should be noted that for the convenience and brevity of description, the specific working process of the above-described vehicle four-wheel alignment device 80 can refer to the corresponding process of the method described above Figures 1 to 8b and will not be elaborated here.

[0150] As Figure 10 shown, an embodiment of the present application also provides an electronic device. As Figure 10 shown, the electronic device 9 may include: a processor 90, a memory 91, and a computer program 92 stored in the memory 91 and executable on the processor 90. When the processor 90 executes the computer program 92, the steps in the above-described various embodiments of the vehicle four-wheel alignment method are implemented, for example, Figure 1 the steps 101 to 104 shown.

[0151] The so-called processor 90 may be a Central Processing Unit (CPU), or may also be 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. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0152] The memory 91 may be an internal storage unit of the electronic device, for example, a hard disk or a memory. The memory 91 may also be an external storage device for the electronic device, for example, a plug-in hard disk equipped on the electronic device, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 91 may also include both an internal storage unit of the electronic device and an external storage device. The memory 91 is used to store the above-mentioned computer program and other programs and data required by the electronic device.

[0153] The above-mentioned computer program may be divided into one or more units, and the above-mentioned one or more units are stored in the above-mentioned memory 91 and executed by the above-mentioned processor 90 to complete the present application. The above-mentioned one or more units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the process of the above-mentioned computer program executing the above-mentioned four-wheel alignment method of an automobile in the electronic device.

[0154] For example, the above-mentioned computer program may be divided into: an acquisition unit, an identification unit, a first calculation unit, and a second calculation unit, and the specific functions are as follows:

[0155] The acquisition unit is used to acquire a first target image and a second target image; the first target image and the second target image are obtained by the camera photographing the target installed on the wheel when the wheel is at different rolling angles;

[0156] The identification unit is used to perform image recognition on the first target image and the second target image to obtain a first set of feature point coordinates and a second set of feature point coordinates; the first set of feature point coordinates are the coordinates of the feature points in the first target image in the camera coordinate system, and the second set of feature point coordinates are the coordinates of the feature points in the second target image in the camera coordinate system;

[0157] A first calculation unit, configured to calculate a rotation and translation matrix between the first set of feature point coordinates and the second set of feature point coordinates, and calculate a rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation and translation matrix;

[0158] A second calculation unit, configured to calculate a direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the first set of feature point coordinates and the second set of feature point coordinates, and calculate four-wheel alignment parameters of the vehicle based on the direction vector corresponding to the rolling axis of the wheel.

[0159] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In practical applications, the above-mentioned functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0160] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the four-wheel alignment method of the vehicle in the above-mentioned various embodiments are implemented.

[0161] The embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program runs on a processor, the steps of the four-wheel alignment method of the vehicle in the above-mentioned various embodiments are implemented.

[0162] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0163] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0164] In the embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the electronic device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, systems or units, and can be electrical, mechanical or other forms.

[0165] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0167] When the integrated module / unit is implemented in the form of 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, to implement all or part of the processes in the above-described embodiment methods of this application, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or recording medium capable of carrying the computer program code, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0168] The above embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An automobile four-wheel alignment method, characterized in that, The described four-wheel alignment method for an automobile includes: Obtaining a first target image and a second target image; the first target image and the second target image are obtained by a camera photographing a target installed on a wheel when the wheel is at different rolling angles; Performing image recognition on the first target image and the second target image to obtain a first set of feature point coordinates and a second set of feature point coordinates; the first set of feature point coordinates are the coordinates of the feature points in the first target image in the camera coordinate system, and the second set of feature point coordinates are the coordinates of the feature points in the second target image in the camera coordinate system; Calculating a rotation and translation matrix between the first set of feature point coordinates and the second set of feature point coordinates, and calculating a rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation and translation matrix; Calculating a direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the first set of feature point coordinates and the second set of feature point coordinates, and calculating four-wheel alignment parameters of the automobile based on the direction vector corresponding to the rolling axis of the wheel.

2. The four-wheel alignment method of an automobile according to claim 1, characterized in that, After calculating the four-wheel alignment parameters of the automobile based on the direction vector corresponding to the rolling axis of the wheel, the four-wheel alignment method for the automobile further includes: Returning to execute the steps of obtaining multiple sets of first target images and second target images and subsequent steps to obtain multiple sets of four-wheel alignment parameters of the automobile; Calculating an average value of the multiple sets of four-wheel alignment parameters and using the average value as the target four-wheel alignment parameters of the automobile.

3. The four-wheel alignment method of an automobile according to claim 1, characterized in that, The calculating the rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation and translation matrix includes: Calculating the rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the element values on the diagonal of the rotation and translation matrix.

4. The four-wheel alignment method of an automobile according to claim 1, wherein The calculating the direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the first set of feature point coordinates and the second set of feature point coordinates includes: Obtaining a preset coordinate transformation relationship between the first set of feature point coordinates and the second set of feature point coordinates and the radius of the wheel; Calculating multiple element values in the coordinate transformation matrix between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation angle, the radius of the wheel, the first set of feature point coordinates, and the second set of feature point coordinates by using the coordinate transformation relationship; Calculating the direction vector corresponding to the rolling axis of the wheel based on the multiple element values in the coordinate transformation matrix.

5. The four-wheel alignment method of an automobile according to claim 1, wherein, The four-wheel alignment parameters include wheel camber angle and wheel toe angle. The calculating the four-wheel alignment parameters of the automobile based on the direction vector corresponding to the rolling axis of the wheel includes: Calculating the wheel camber angle based on the first coordinate and the second coordinate of the direction vector corresponding to the rolling axis of the wheel; Calculating the wheel toe angle based on the second coordinate and the third coordinate of the direction vector corresponding to the rolling axis of the wheel.

6. The four-wheel alignment method of an automobile according to any one of claims 1-5, characterized in that, In the target pattern of the target, there are concentric circles located in the vertex area of the polygon and ordinary circles located in the non-vertex area of the polygon; performing image recognition on the first target image and the second target image to obtain the first set of feature point coordinates and the second set of feature point coordinates, including: Identifying the ordinary circles and concentric circles in the target target image to obtain the center coordinates of the ordinary circles and concentric circles in the target target image in the image coordinate system; the target target image includes the first target image and the second target image; Based on the center coordinates in the image coordinate system, calculating the center coordinates of the ordinary circles and concentric circles in the target target image in the camera coordinate system; Taking the center coordinates of the ordinary circles and concentric circles in the first target image in the camera coordinate system as the first set of feature point coordinates, and taking the center coordinates of the ordinary circles and concentric circles in the second target image in the camera coordinate system as the second set of feature point coordinates.

7. The four-wheel alignment method of an automobile according to claim 6, characterized in that, The identifying the ordinary circles and concentric circles in the target target image to obtain the center coordinates of the ordinary circles and concentric circles in the target target image in the image coordinate system includes: Performing image recognition on the target target image to obtain the first center coordinates of the concentric circles in the target target image; Based on the first center coordinates, performing perspective transformation on the target target image to obtain the target target image after perspective transformation; Performing circle fitting on the concentric circles and ordinary circles in the target target image after perspective transformation to obtain the second center coordinates of the concentric circles and ordinary circles in the target target image after perspective transformation; Mapping the second center coordinates back to the target target image to obtain the center coordinates of the ordinary circles and concentric circles in the target target image in the image coordinate system.

8. An automobile four-wheel alignment device, characterized in that, The vehicle four-wheel alignment device includes: An acquisition unit for acquiring a first target image and a second target image; the first target image and the second target image are obtained by a camera photographing a target installed on a wheel when the wheel is at different rolling angles; An identification unit for performing image recognition on the first target image and the second target image to obtain a first set of feature point coordinates and a second set of feature point coordinates; the first set of feature point coordinates are the coordinates of the feature points in the first target image in the camera coordinate system, and the second set of feature point coordinates are the coordinates of the feature points in the second target image in the camera coordinate system; A first calculation unit for calculating the rotation and translation matrix between the first set of feature point coordinates and the second set of feature point coordinates, and calculating the rotation angle between the first set of feature point coordinates and the second set of feature point coordinates based on the rotation and translation matrix; A second calculation unit for calculating the direction vector corresponding to the rolling axis of the wheel based on the rotation angle and the first set of feature point coordinates and the second set of feature point coordinates, and calculating the four-wheel alignment parameters of the vehicle based on the direction vector corresponding to the rolling axis of the wheel.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by a processor, it implements the steps of the vehicle four-wheel alignment method according to any one of claims 1-7.

10. 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 four-wheel alignment method for an automobile according to any one of claims 1-7.

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