Method, device, equipment, medium and program product for determining camera posture of vehicle

Abstract

The present application relates to a method, device, equipment, medium and program product for determining the camera posture of a vehicle. The method comprises: obtaining a preset camera posture, the preset camera posture is a set value; obtaining an expected calibration image, the expected calibration image includes a checkerboard pattern, and the intersection of the checkerboard in the checkerboard pattern is a calibration feature point; performing image detection on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image; according to the preset camera posture and the image coordinates of each calibration feature point in the expected calibration image, mapping each calibration feature point in the expected calibration image to a world coordinate system to obtain the world coordinates corresponding to each calibration feature point, the world coordinates corresponding to each calibration feature point are used to determine a calibration plate pattern, and the calibration plate obtained according to the calibration plate pattern is used to calibrate the camera posture of the vehicle, which can effectively improve the calibration accuracy without increasing the complexity of the operation.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining a camera posture of a vehicle. Background Art

[0002] Camera pose refers to the position and orientation of the camera in the world coordinate system. Camera pose is one of the key parameters to ensure the accuracy and reliability of the system, and an important part of the vehicle's autonomous driving and other tasks. Camera pose not only determines the visual perception effect of the system, but also directly affects the distance estimation of targets such as motor vehicles, non-motor vehicles and pedestrians on the road. With the help of the positional relationship between the vehicle's camera pose (such as the camera pose of the vehicle's electronic rearview mirror) and the target, the system can accurately calculate the lateral and longitudinal distances of the target, thereby providing reliable distance measurement results for the vehicle's driving assistance and safety protection.

[0003] At present, the determination of the vehicle's camera pose relies on the accurate detection of feature points on the calibration plate during the camera calibration process. However, the vehicle's camera may be set at a special position on the vehicle, and the camera takes a calibration image of the calibration plate at a special viewing angle. The pattern in the calibration image will be severely deformed at this viewing angle and become difficult to recognize, thus affecting the calibration accuracy of the vehicle's camera pose. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for determining the camera posture of a vehicle in response to the above-mentioned technical problems, which can effectively improve the calibration accuracy under the above-mentioned special viewing angle without increasing the complexity of the operation.

[0005] In a first aspect, the present application provides a method for determining a camera pose of a vehicle, comprising:

[0006] Obtain a preset camera pose, where the preset camera pose is a set value;

[0007] Acquire an expected calibration image, wherein the expected calibration image includes a checkerboard pattern, and the intersection points of checkerboards in the checkerboard pattern are calibration feature points;

[0008] Performing image detection on the expected calibration image to obtain image coordinates of each calibration feature point in the expected calibration image;

[0009] According to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, each calibration feature point in the expected calibration image is mapped to a world coordinate system to obtain the world coordinates corresponding to each calibration feature point. The world coordinates corresponding to each calibration feature point are used to determine a calibration plate pattern. The calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle.

[0010] In one of the embodiments, obtaining the expected calibration image includes: obtaining an array size of the expected calibration image; and drawing the expected calibration image with a rectangular checkerboard according to the array size, wherein the rectangular checkerboard in the expected calibration image has the array size.

[0011] In one embodiment, performing image detection on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image includes:

[0012] A feature point detection model is obtained, and the feature point detection model is called to perform feature point detection on the expected calibration image, and the image coordinates of each calibration feature point in the expected calibration image are identified.

[0013] In one of the embodiments, the method further includes: generating a calibration plate image according to the world coordinates corresponding to each calibration feature point, wherein the calibration plate pattern in the calibration plate image is determined according to the world coordinates corresponding to each calibration feature point; and outputting a calibration plate production instruction according to the calibration plate image, wherein the calibration plate production instruction is used to instruct the generation of a calibration plate.

[0014] In one of the embodiments, the method further includes: acquiring a target calibration image, wherein the target calibration image is obtained by the vehicle's camera performing image acquisition on the calibration plate, wherein the calibration plate is placed at a set position in a world coordinate system during image acquisition, and the set position is within the visible range of the vehicle's camera; performing image detection on the target calibration image to obtain image coordinates of each calibration feature point in the target calibration image; and calculating the camera pose of the vehicle according to the image coordinates of each calibration feature point in the target calibration image and the corresponding world coordinates.

[0015] In one of the embodiments, performing image detection on the target calibration image to obtain the image coordinates of each calibration feature point in the target calibration image includes: acquiring a feature point detection model, calling the feature point detection model to perform feature point detection on the target calibration image, and identifying the image coordinates of the calibration feature points in the target calibration image.

[0016] In one of the embodiments, after obtaining the image coordinates of each calibration feature point in the target calibration image, the method further includes: clustering the calibration feature points according to the image coordinates of the calibration feature points in the target calibration image to obtain multiple cluster centers and calibration feature points belonging to each cluster center, and eliminating calibration feature points whose distance to the corresponding cluster center is greater than a preset threshold to obtain the remaining calibration feature points;

[0017] Calculating the camera pose of the vehicle according to the image coordinates and the corresponding world coordinates of each calibration feature point in the target calibration image includes: calculating the camera pose of the vehicle according to the image coordinates and the corresponding world coordinates of the remaining calibration feature points.

[0018] In a second aspect, the present application also provides a device for determining a camera position and posture of a vehicle, comprising:

[0019] A posture acquisition module, used to acquire a preset camera posture, wherein the preset camera posture is a set value;

[0020] An image acquisition module, used to acquire a desired calibration image, wherein the desired calibration image includes a checkerboard pattern, and the intersection points of the checkerboards in the checkerboard pattern are calibration feature points;

[0021] An image detection module, used to perform image detection on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image;

[0022] A pattern determination module is used to map each calibration feature point in the expected calibration image to a world coordinate system according to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, so as to obtain the world coordinates corresponding to each calibration feature point, wherein the world coordinates corresponding to each calibration feature point are used to determine a calibration plate pattern, and the calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle.

[0023] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0024] Obtain a preset camera pose, where the preset camera pose is a set value;

[0025] Acquire an expected calibration image, wherein the expected calibration image includes a checkerboard pattern, and the intersection points of checkerboards in the checkerboard pattern are calibration feature points;

[0026] Performing image detection on the expected calibration image to obtain image coordinates of each calibration feature point in the expected calibration image;

[0027] According to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, each calibration feature point in the expected calibration image is mapped to a world coordinate system to obtain the world coordinates corresponding to each calibration feature point. The world coordinates corresponding to each calibration feature point are used to determine a calibration plate pattern. The calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle.

[0028] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0029] Obtain a preset camera pose, where the preset camera pose is a set value;

[0030] Acquire an expected calibration image, wherein the expected calibration image includes a checkerboard pattern, and the intersection points of checkerboards in the checkerboard pattern are calibration feature points;

[0031] Performing image detection on the expected calibration image to obtain image coordinates of each calibration feature point in the expected calibration image;

[0032] According to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, each calibration feature point in the expected calibration image is mapped to a world coordinate system to obtain the world coordinates corresponding to each calibration feature point. The world coordinates corresponding to each calibration feature point are used to determine a calibration plate pattern. The calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle.

[0033] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0034] Obtain a preset camera pose, where the preset camera pose is a set value;

[0035] Acquire an expected calibration image, wherein the expected calibration image includes a checkerboard pattern, and the intersection points of checkerboards in the checkerboard pattern are calibration feature points;

[0036] Performing image detection on the expected calibration image to obtain image coordinates of each calibration feature point in the expected calibration image;

[0037] According to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, each calibration feature point in the expected calibration image is mapped to a world coordinate system to obtain the world coordinates corresponding to each calibration feature point. The world coordinates corresponding to each calibration feature point are used to determine a calibration plate pattern. The calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle.

[0038] The camera pose determination method, device, computer equipment, computer-readable storage medium and computer program product of the above-mentioned vehicle obtain a preset camera pose, the preset camera pose is a set value; obtain an expected calibration image, the expected calibration image includes a checkerboard pattern, and the intersection of the checkerboards in the checkerboard pattern is a calibration feature point; perform image detection on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image; according to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, map each calibration feature point in the expected calibration image to a world coordinate system to obtain the world coordinates corresponding to each calibration feature point, and the world coordinates corresponding to each calibration feature point are used to determine the calibration plate pattern, and the calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle.

[0039] By setting an expected calibration image, since the expected calibration image includes a checkerboard pattern, the intersection of the checkerboards in the checkerboard pattern is the calibration feature point. The expected calibration image is an image that is expected to be collected under a special viewing angle without severe deformation. By presetting the camera pose and the image coordinates of each calibration feature point in the expected calibration image, the world coordinates corresponding to each calibration feature point in the expected calibration image in the world coordinate system are inferred. Based on this world coordinate, a more ideal calibration plate pattern can be determined, that is, the image collected by the vehicle's camera for the calibration plate pattern under a special viewing angle will not be severely deformed. Then, the vehicle's camera pose is calibrated based on the calibration plate obtained according to the calibration plate pattern, which can improve the calibration accuracy of the vehicle's camera pose. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 is a schematic diagram of a calibration plate provided in some embodiments;

[0042] Figure 2 is a schematic diagram of a calibration image collected by an electronic rearview mirror of a vehicle in one embodiment;

[0043] Figure 3 A diagram showing an application environment of a method for determining a camera pose of a vehicle in one embodiment;

[0044] Figure 4 is a schematic flow chart of a method for determining a camera pose of a vehicle in one embodiment;

[0045] Figure 5 is a schematic diagram of an image that is expected to be calibrated in one embodiment;

[0046] Figure 6 is a schematic diagram of a calibration plate pattern obtained in one embodiment;

[0047] Figure 7 is a schematic diagram of a target calibration image obtained in one embodiment;

[0048] Figure 8 is a structural block diagram of a camera position determination device for a vehicle in one embodiment;

[0049] Fig. 9 is an internal structure diagram of a computer device in one embodiment;

[0050] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in another embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with 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.

[0052] At present, the cameras (or cameras) installed on vehicles have many practical functions and uses. For example, the electronic rearview mirror is an indirect vision device that obtains a specified field of view through a system composed of a camera and a monitor. It includes electronic equipment such as a high-definition camera, a digital visual processing system, a safety system, and a liquid crystal display. It is a new type of rearview mirror that can replace the traditional optical rearview mirror. For another example, a roof camera is usually installed on the top of a car or other means of transportation to provide a field of view of the vehicle's surroundings.

[0053] In some cases, in order to establish a geometric model of camera imaging and obtain accurate field of view information, so as to accurately judge objects and distances, it is necessary to calibrate some cameras on the vehicle, that is, to determine the camera pose of the vehicle. The determination of the camera pose of the vehicle depends on the accurate detection of feature points on the calibration plate during the camera calibration process. Figure 1 FIG. 2 is a schematic diagram of a calibration plate provided in some embodiments. Figure 1 The calibration plate is a checkerboard calibration plate having a checkerboard pattern. In the checkerboard calibration plate, the calibration feature points used to implement camera calibration are the intersection points of the checkerboard.

[0054] In the related art, due to the special viewing angle of the camera at the vehicle's installation position, the calibration plate will be severely deformed in the calibration image obtained by image acquisition, becoming difficult to identify, resulting in inaccurate detection results of calibration feature points in the calibration image, thereby affecting the calibration accuracy of the vehicle's camera posture. Figure 2 FIG. 1 is a schematic diagram of a calibration image collected by an electronic rearview mirror of a vehicle in one embodiment. Figure 2 ,From the perspective of the electronic rearview mirror, the chessboard in the collected ,calibration image will be deformed.

[0055] The method for determining the camera pose of a vehicle provided in an embodiment of the present application sets an expected calibration image. Since the expected calibration image includes a checkerboard pattern, the intersection points of the checkerboards in the checkerboard pattern are calibration feature points. The expected calibration image is an image that is expected to be captured at a special viewing angle and is not severely deformed. By presetting the camera pose and the image coordinates of each calibration feature point in the expected calibration image, the world coordinates corresponding to each calibration feature point in the expected calibration image in the world coordinate system are inferred. Based on this world coordinate, a more ideal calibration plate pattern can be determined, that is, the image captured by the vehicle's camera of the calibration plate pattern at a special viewing angle will not be severely deformed. Therefore, the camera pose of the vehicle is calibrated based on the calibration plate obtained according to the calibration plate pattern, which can improve the calibration accuracy of the camera pose of the vehicle.

[0056] The vehicle camera posture determination method provided in the embodiment of the present application can be applied to Figure 3 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0057] In some embodiments, taking the server 104 executing the method provided in the embodiment of the present application as an example, the server 104 can obtain a preset camera pose, the preset camera pose is a set value, the server 104 obtains the expected calibration image, the expected calibration image includes a checkerboard pattern, and the intersection of the checkerboard in the checkerboard pattern is a calibration feature point; the server 104 can perform image detection on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image; according to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, each calibration feature point in the expected calibration image is mapped to the world coordinate system to obtain the world coordinates corresponding to each calibration feature point, and the world coordinates corresponding to each calibration feature point are used to determine the calibration plate pattern. In some embodiments, a calibration plate can be made manually according to the world coordinates corresponding to each calibration feature point. In some embodiments, the server 104 can generate a calibration plate image according to the world coordinates corresponding to each calibration feature point, the calibration plate pattern in the calibration plate image is determined according to the world coordinates corresponding to each calibration feature point, and the calibration plate production instructions are output according to the calibration plate image, and the calibration plate production instructions are used to instruct the generation of the calibration plate.

[0058] In some implementations, the prepared calibration plate can be placed at a set position in the world coordinate system, and the set position belongs to the visible range of the vehicle's camera. The vehicle's camera collects images of the calibration plate placed at the set position to obtain a target calibration image. In some implementations, the terminal 102 can obtain the target calibration image, perform image detection on the target calibration image, and obtain the image coordinates of each calibration feature point in the target calibration image; according to the image coordinates of each calibration feature point in the target calibration image and the corresponding world coordinates, the camera pose of the vehicle is calculated. In some implementations, the server 104 can obtain the target calibration image, perform image detection on the target calibration image, and obtain the image coordinates of each calibration feature point in the target calibration image; according to the image coordinates of each calibration feature point in the target calibration image and the corresponding world coordinates, the camera pose of the vehicle is calculated. In some implementations, the terminal 102 can call a feature point detection model from the server 104, perform image detection on the target calibration image, and obtain the image coordinates of the calibration feature points in the calibration image. Of course, in some implementations, the terminal 102 may send the calibration image to the server 104 , and the server 104 may perform image detection on the target calibration image to obtain the image coordinates of the calibration feature points in the calibration image.

[0059] In an exemplary embodiment, Figure 4 As shown, a method for determining the camera position of a vehicle is provided, and the method is applied to Figure 3 The computer device (terminal 102 or server 104) in the example is used for description, and the steps include the following steps 402 to 408. Among them:

[0060] Step 402, obtaining a preset camera posture, where the preset camera posture is a set value.

[0061] In order to prevent the image captured by the vehicle's camera from being seriously deformed under a special viewing angle, so as to improve the accuracy of the calibration feature point detection and thus improve the calibration accuracy of the vehicle's camera pose, in an embodiment of the present application, the computer device may first set a camera pose, which is a set value, i.e., a preset camera pose. The value of the preset camera pose may be set based on experience, and the embodiment of the present application does not limit this. It can be understood that the preset camera pose is used to obtain a calibration plate through subsequent steps, and is not the pose obtained by calibrating the vehicle's camera. The camera pose, i.e., the rotation matrix and displacement vector of the camera, can be used to link the world coordinate system and the camera coordinate system.

[0062] Step 404 , obtaining a desired calibration image, wherein the desired calibration image includes a checkerboard pattern, and the intersection points of the checkerboards in the checkerboard pattern are calibration feature points.

[0063] In order to ensure that the image captured by the vehicle's camera on the calibration plate at a special viewing angle does not undergo serious deformation, so as to improve the accuracy of calibration feature point detection and thus improve the calibration accuracy of the vehicle's camera posture, in an embodiment of the present application, the computer device can set a pattern, which is a pattern that the vehicle's camera expects to capture at a special viewing angle without undergoing serious deformation, and the image with the pattern is the expected calibration image. In theory, the pattern in the expected calibration image is convenient for image detection to obtain more accurate image coordinates, and at the same time, the world coordinates corresponding to each calibration feature point finally obtained based on the expected calibration image are convenient for making the calibration plate. In an embodiment of the present application, the expected calibration image includes a checkerboard pattern, and the intersection of the checkerboards in the checkerboard pattern is the calibration feature point.

[0064] In some embodiments, obtaining a desired calibration image includes: obtaining an array size of the desired calibration image; and drawing the desired calibration image with a rectangular checkerboard according to the array size, wherein the rectangular checkerboard in the desired calibration image has the array size.

[0065] For example, if the array size of the desired calibration image is m×n, the computer device may draw a rectangular checkerboard pattern including m×n checkerboards, and draw the desired calibration image with rectangular checkerboards according to the pattern. Figure 5 , which is a schematic diagram of a desired calibration image in some embodiments, in which a 5×4 rectangular checkerboard pattern is included, and the desired calibration image includes the rectangular checkerboard pattern.

[0066] Step 406: perform image detection on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image.

[0067] Among them, image detection is the process of detecting the image coordinates of the calibrated feature points in the image. It can be understood that the world coordinates represent the real coordinates of the object in the three-dimensional world coordinate system. The world coordinates are represented by (X, Y, Z). The image coordinates refer to the image pixel coordinates. The origin of the image pixel coordinate system is in the upper left corner, and the unit is pixel. The image coordinates can be recorded as (u, v), where u represents the horizontal coordinate and v represents the vertical coordinate. The range of image coordinates is usually from (0, 0) to (1,1), with the upper left corner being (0,0) and the lower right corner being (1,1).

[0068] In some embodiments, image detection is performed on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image, including: acquiring a feature point detection model, calling the feature point detection model to perform feature point detection on the expected calibration image, and identifying the image coordinates of each calibration feature point in the expected calibration image.

[0069] The feature point detection model is a computer vision model based on a convolutional neural network. The computer device calls the feature point detection model to perform feature point detection on the expected calibration image, and identifies the position information of each calibration feature point in the expected calibration image, that is, detects the image coordinates of the calibration feature point.

[0070] Step 408, according to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, each calibration feature point in the expected calibration image is mapped to the world coordinate system to obtain the world coordinates corresponding to each calibration feature point, and the world coordinates corresponding to each calibration feature point are used to determine the calibration plate pattern. The calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle.

[0071] In some embodiments, the computer device can use the PnP (Perspective-n-Point) algorithm to solve the corresponding world coordinates according to the corresponding image coordinates of each calibration feature point in the desired calibration image and the above-mentioned preset camera pose, and obtain the world coordinates corresponding to each calibration feature point, that is, the world coordinates of each calibration feature point in the world coordinate system. The world coordinates corresponding to each calibration feature point are used to determine the calibration plate pattern, and the calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle.

[0072] like Figure 6 FIG. 1 is a schematic diagram of a calibration plate pattern obtained in one embodiment. Figure 6 ,It can be seen that the obtained calibration plate pattern is different from the calibration plate pattern of the conventional calibration plate, Figure 6 The calibration pattern shown in is a diagonal checkerboard, while Figure 1 The calibration pattern in the conventional calibration plate is an upright checkerboard.

[0073] In the above-mentioned method for determining the camera pose of the vehicle, an expected calibration image is set. Since the expected calibration image includes a checkerboard pattern, the intersection of the checkerboards in the checkerboard pattern is a calibration feature point. The expected calibration image is an image that is expected to be collected under a special viewing angle without severe deformation. By presetting the camera pose and the image coordinates of each calibration feature point in the expected calibration image, the world coordinates corresponding to each calibration feature point in the expected calibration image in the world coordinate system are inferred. According to this world coordinate, a more ideal calibration plate pattern can be determined, that is, the image collected by the vehicle's camera under a special viewing angle with respect to the calibration plate pattern will not be severely deformed. Therefore, the camera pose of the vehicle is calibrated based on the calibration plate obtained according to the calibration plate pattern, which can improve the calibration accuracy of the camera pose of the vehicle.

[0074] In an exemplary embodiment, the method also includes: generating a calibration plate image according to the world coordinates corresponding to each calibration feature point, wherein the calibration plate pattern in the calibration plate image is determined according to the world coordinates corresponding to each calibration feature point; and outputting a calibration plate production instruction according to the calibration plate image, wherein the calibration plate production instruction is used to instruct the generation of the calibration plate.

[0075] Optionally, after obtaining the world coordinates corresponding to each calibration feature point, the computer device can generate a calibration plate image according to the world coordinates corresponding to each calibration feature point. The computer device can also send the calibration plate image generated according to the world coordinates corresponding to each calibration feature point to other devices, and the other devices generate the calibration plate image according to the world coordinates corresponding to each calibration feature point. In this embodiment, the computer device can output a calibration plate production instruction according to the calibration plate image, and the calibration plate production instruction is used to instruct the generation of the calibration plate, so as to obtain a calibration plate for calibrating the camera of the vehicle, and in the calibration image collected by the calibration plate, the checkerboard will not be severely deformed.

[0076] In an exemplary embodiment, the method also includes: acquiring a target calibration image, the target calibration image is obtained by the vehicle's camera performing image acquisition on a calibration plate, the calibration plate being placed at a set position in a world coordinate system during image acquisition, and the set position being within the visible range of the vehicle's camera; performing image detection on the target calibration image to obtain image coordinates of each calibration feature point in the target calibration image; and calculating the vehicle's camera pose based on the image coordinates of each calibration feature point in the target calibration image and the corresponding world coordinates.

[0077] In this embodiment, after obtaining the calibration plate, the vehicle's camera can be calibrated using the calibration plate. First, the vehicle's camera can capture images of the calibration plate to obtain a target calibration image. Based on the above derivation, the target calibration image will theoretically be similar to the above-mentioned expected calibration image, that is, the checkerboard pattern therein is a clear rectangle without serious deformation. Figure 7, which is a schematic diagram of a target calibration image obtained in some embodiments. Next, the target calibration image is subjected to image detection to obtain the image coordinates of each calibration feature point in the target calibration image, and then the camera pose of the vehicle is calculated based on the image coordinates of each calibration feature point in the target calibration image and the corresponding world coordinates. Since the checkerboard in the target calibration image does not undergo serious deformation, subsequent image detection is accurate, which can improve the accuracy of determining the camera pose of the vehicle.

[0078] In an exemplary embodiment, image detection is performed on a target calibration image to obtain image coordinates of each calibration feature point in the target calibration image, including: acquiring a feature point detection model, calling the feature point detection model to perform feature point detection on the target calibration image, and identifying the image coordinates of the calibration feature points in the target calibration image.

[0079] In an exemplary embodiment, after obtaining the image coordinates of each calibrated feature point in the target calibration image, the method also includes: clustering the calibrated feature points according to the image coordinates of the calibrated feature points in the target calibration image, obtaining multiple cluster centers and calibrated feature points belonging to each cluster center, eliminating the calibrated feature points whose distance to the corresponding cluster center is greater than a preset threshold, and obtaining the remaining calibrated feature points; calculating the camera pose of the vehicle according to the image coordinates and the corresponding world coordinates of each calibrated feature point in the target calibration image, including: calculating the camera pose of the vehicle according to the image coordinates and the corresponding world coordinates of the remaining calibrated feature points.

[0080] The computer device can cluster the calibration feature points according to the image coordinates of each calibration feature point, obtain multiple cluster centers and calibration feature points belonging to each cluster center, and then remove the calibration feature points whose distance to the corresponding cluster center is greater than a preset threshold according to the image coordinates of the calibration feature points and the image coordinates of the cluster centers to which they belong, to obtain the remaining calibration feature points. In this way, feature points that are obviously not satisfied with the position constraint relationship or distance constraint relationship of the feature points on the calibration plate can be removed. The image coordinates of these feature points are obviously offset, and removal can help further improve the accuracy of feature point detection. Among them, the embodiment of the present application does not limit the clustering method. For example, a K-mean clustering algorithm, a density-based non-parametric clustering algorithm, and the like can be used.

[0081] In some embodiments, the calibration feature points are clustered according to the image coordinates of each calibration feature point to obtain multiple cluster centers and calibration feature points belonging to each cluster center, and the calibration feature points whose distance to the corresponding cluster center is greater than a preset threshold are eliminated to obtain the remaining calibration feature points, including: acquiring the array size of the calibration plate and obtaining a first number according to the array size; according to the first number, the calibration feature points are clustered according to the image coordinates of each calibration feature point to obtain a first number of cluster clusters, wherein each cluster cluster includes a cluster center with image coordinates and calibration feature points belonging to each cluster cluster; for each cluster cluster, the distance from the calibration feature point to the cluster center is calculated according to the image coordinates; the calibration feature points whose distance is greater than the preset threshold are regarded as abnormal feature points, and the abnormal feature points are eliminated to obtain the remaining calibration feature points.

[0082] For example, assuming that the array size is m×n, the number of cluster centers is m×n. The calibrated feature points are clustered according to the image coordinates of each calibrated feature point according to the m×n number to obtain m×n clusters. The specific clustering process is to first initialize the coordinates of the m×n cluster centers, calculate the distance from the corresponding image coordinates to each cluster center for each calibrated feature point, and divide the calibrated feature points into the clusters with the nearest cluster center. After all the calibrated feature points are divided, for each cluster, the mean is calculated according to the image coordinates of all the calibrated feature points included, and the mean is used as the new cluster center of the cluster to update the cluster center. Then, continue a new round of iterations, and for each calibrated feature point, calculate the distance from the corresponding image coordinates to each updated cluster center, and divide the calibrated feature points into the clusters with the nearest cluster center. After all the calibrated feature points are divided, for each cluster, calculate the mean according to the image coordinates of all the calibrated feature points included, and use the mean as the new cluster center of the cluster. Repeat this process until the number of iterations reaches the set value, or until the difference between the currently calculated mean and the mean calculated in the previous round for each cluster is less than the set threshold, the clustering stops, and m×n clusters can be obtained, each with a cluster center. Then, for each cluster, calculate the distance from the calibrated feature point to the cluster center according to the image coordinates, and use the calibrated feature points with a distance greater than the preset threshold as abnormal feature points. Remove the abnormal feature points to obtain the remaining calibrated feature points.

[0083] In a specific embodiment, a method for determining a camera pose of a vehicle is provided, which is executed by a computer device and includes the following steps:

[0084] 1. Get the preset camera pose, which is the set value;

[0085] 2. Obtain the array size, and draw a desired calibration image with a rectangular checkerboard according to the array size. The rectangular checkerboard in the desired calibration image has the array size, the desired calibration image includes a checkerboard pattern, and the intersection of the checkerboard in the checkerboard pattern is the calibration feature point;

[0086] 3. Obtain a feature point detection model, call the feature point detection model to perform feature point detection on the expected calibration image, and identify the image coordinates of each calibration feature point in the expected calibration image;

[0087] 4. According to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, each calibration feature point in the expected calibration image is mapped to the world coordinate system to obtain the world coordinates corresponding to each calibration feature point;

[0088] 5. Generate a calibration plate image according to the world coordinates corresponding to each calibration feature point, and the calibration plate pattern in the calibration plate image is determined according to the world coordinates corresponding to each calibration feature point;

[0089] 6. Output calibration plate making instructions according to the calibration plate image, the calibration plate making instructions are used to instruct the generation of the calibration plate;

[0090] 7. Obtain the target calibration image. The target calibration image is obtained by the vehicle's camera acquiring the image of the calibration plate. When acquiring the image, the calibration plate is placed at a set position in the world coordinate system, and the set position is within the visible range of the vehicle's camera.

[0091] 8. Obtain a feature point detection model, call the feature point detection model to perform feature point detection on the target calibration image, and identify the image coordinates of the calibration feature points in the target calibration image;

[0092] 9. Cluster the calibration feature points according to the image coordinates of the calibration feature points in the target calibration image to obtain multiple cluster centers and calibration feature points belonging to each cluster center, and remove the calibration feature points whose distance to the corresponding cluster center is greater than a preset threshold to obtain the remaining calibration feature points;

[0093] 10. Calculate the camera pose of the vehicle based on the image coordinates and corresponding world coordinates of the remaining calibrated feature points.

[0094] In this embodiment, for special viewing angle scenarios of vehicle cameras, such as electronic rearview mirror business scenarios, the problem of unstable and inconsistent image detection caused by deformation of images collected by conventional calibration plates is solved, effectively improving the calibration feature point detection accuracy and camera pose calibration accuracy.

[0095] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0096] Based on the same inventive concept, the embodiment of the present application also provides a vehicle camera pose determination device for implementing the above-mentioned vehicle camera pose determination method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the vehicle camera pose determination device provided below can refer to the limitations of the vehicle camera pose determination method above, and will not be repeated here.

[0097] In an exemplary embodiment, Figure 8 As shown, a device 800 for determining a camera posture of a vehicle is provided, comprising: a posture acquisition module 802, an image acquisition module 804, an image detection module 806 and a pattern determination module 808, wherein:

[0098] A posture acquisition module 802 is used to acquire a preset camera posture, where the preset camera posture is a set value;

[0099] An image acquisition module 804 is used to acquire a desired calibration image, wherein the desired calibration image includes a checkerboard pattern, and the intersection points of the checkerboards in the checkerboard pattern are calibration feature points;

[0100] An image detection module 806 is used to perform image detection on the desired calibration image to obtain the image coordinates of each calibration feature point in the desired calibration image;

[0101] The pattern determination module 808 is used to map each calibration feature point in the expected calibration image to the world coordinate system according to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, and obtain the world coordinates corresponding to each calibration feature point. The world coordinates corresponding to each calibration feature point are used to determine the calibration plate pattern. The calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle.

[0102] In an exemplary embodiment, the image acquisition module 804 is further used to acquire the array size of the desired calibration image; and draw the desired calibration image with a rectangular checkerboard according to the array size, wherein the rectangular checkerboard in the desired calibration image has the array size.

[0103] In an exemplary embodiment, the image detection module 806 is further used to obtain a feature point detection model, call the feature point detection model to perform feature point detection on the expected calibration image, and identify the image coordinates of each calibration feature point in the expected calibration image.

[0104] In an exemplary embodiment, the vehicle camera pose determination device 800 further includes:

[0105] The calibration plate generation module is used to generate a calibration plate image according to the world coordinates corresponding to each calibration feature point, and the calibration plate pattern in the calibration plate image is determined according to the world coordinates corresponding to each calibration feature point; the calibration plate production instruction is output according to the calibration plate image, and the calibration plate production instruction is used to instruct the generation of the calibration plate.

[0106] In an exemplary embodiment, the vehicle camera pose determination device 800 further includes:

[0107] The posture calibration module is used to obtain a target calibration image. The target calibration image is obtained by the vehicle's camera performing image acquisition on the calibration plate. When performing image acquisition, the calibration plate is placed at a set position in the world coordinate system, and the set position is within the visible range of the vehicle's camera; image detection is performed on the target calibration image to obtain the image coordinates of each calibration feature point in the target calibration image; the vehicle's camera posture is calculated based on the image coordinates of each calibration feature point in the target calibration image and the corresponding world coordinates.

[0108] In an exemplary embodiment, the image detection module 806 is further used to obtain a feature point detection model, call the feature point detection model to perform feature point detection on the target calibration image, and identify the image coordinates of the calibration feature points in the target calibration image.

[0109] In an exemplary embodiment, the vehicle camera pose determination device 800 further includes:

[0110] The feature point filtering module is used to cluster the calibration feature points according to the image coordinates of the calibration feature points in the target calibration image, obtain multiple cluster centers and the calibration feature points belonging to each cluster center, and remove the calibration feature points whose distance to the corresponding cluster center is greater than a preset threshold to obtain the remaining calibration feature points;

[0111] The pose calibration module is used to calculate the camera pose of the vehicle based on the image coordinates and corresponding world coordinates of the remaining calibration feature points.

[0112] The camera pose determination device 800 of the above-mentioned vehicle obtains a preset camera pose, which is a set value; obtains an expected calibration image, which includes a checkerboard pattern, and the intersection of the checkerboards in the checkerboard pattern is a calibration feature point; performs image detection on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image; maps each calibration feature point in the expected calibration image to a world coordinate system according to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, and obtains the world coordinates corresponding to each calibration feature point, and the world coordinates corresponding to each calibration feature point are used to determine the calibration plate pattern, and the calibration plate obtained according to the calibration plate pattern is used to calibrate the camera pose of the vehicle. By setting an expected calibration image, since the expected calibration image includes a checkerboard pattern, the intersection of the checkerboards in the checkerboard pattern is the calibration feature point. The expected calibration image is an image that is expected to be collected under a special viewing angle without severe deformation. By presetting the camera pose and the image coordinates of each calibration feature point in the expected calibration image, the world coordinates corresponding to each calibration feature point in the expected calibration image in the world coordinate system are inferred. Based on this world coordinate, a more ideal calibration plate pattern can be determined, that is, the image collected by the vehicle's camera for the calibration plate pattern under a special viewing angle will not be severely deformed. Then, the vehicle's camera pose is calibrated based on the calibration plate obtained according to the calibration plate pattern, which can improve the calibration accuracy of the vehicle's camera pose.

[0113] Each module in the above-mentioned vehicle camera pose determination device 800 can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0114] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the camera posture of a vehicle is implemented.

[0115] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a method for determining the camera posture of a vehicle is implemented.

[0116] Those skilled in the art will understand that Fig. 9 , Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0117] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method for determining the camera posture of a vehicle provided in an embodiment of the present application are implemented.

[0118] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining the camera pose of a vehicle provided in an embodiment of the present application are implemented.

[0119] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the method for determining the camera pose of a vehicle provided in an embodiment of the present application.

[0120] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0121] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0122] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0123] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a camera position of a vehicle, characterized in that: The method comprises: Obtaining a preset camera pose, where the preset camera pose is a set value, and the preset camera pose is used to obtain a calibration plate, and the preset camera pose is not obtained by calibrating the vehicle's camera; Acquire an expected calibration image, wherein the expected calibration image includes a set checkerboard pattern, wherein the intersection of the checkerboards in the checkerboard pattern is a calibration feature point, wherein the checkerboard pattern is a pattern that is not severely deformed and is captured by a camera of the expected vehicle at a special viewing angle, wherein the checkerboard pattern facilitates image detection to obtain accurate image coordinates, and wherein the checkerboard pattern is a rectangular checkerboard pattern; Performing image detection on the expected calibration image to obtain image coordinates of each calibration feature point in the expected calibration image; According to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, each calibration feature point in the expected calibration image is mapped to a world coordinate system to obtain the world coordinates corresponding to each calibration feature point; Generate a calibration plate image according to the world coordinates corresponding to each calibration feature point, wherein a calibration plate pattern in the calibration plate image is determined according to the world coordinates corresponding to each calibration feature point, and the calibration plate pattern is an oblique chessboard; Outputting a calibration plate making instruction according to the calibration plate image, wherein the calibration plate making instruction is used to instruct the generation of a calibration plate; When the generated calibration plate is placed at a set position within the visible range of the camera of the vehicle in the world coordinate system, the camera of the vehicle acquires an image of the calibration plate to obtain a target calibration image; Performing image detection on the target calibration image to obtain image coordinates of each calibration feature point in the target calibration image; The camera pose of the vehicle is calculated according to the image coordinates and the corresponding world coordinates of each calibration feature point in the target calibration image.

2. The method according to claim 1, characterized in that The obtaining of the desired calibration image comprises: Get the array size of the desired calibration image; According to the array size, a desired calibration image with a rectangular checkerboard is drawn, where the rectangular checkerboard in the desired calibration image has the array size.

3. The method according to claim 1, characterized in that The performing image detection on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image includes: A feature point detection model is obtained, and the feature point detection model is called to perform feature point detection on the expected calibration image, and the image coordinates of each calibration feature point in the expected calibration image are identified.

4. The method according to claim 1, characterized in that: The performing image detection on the target calibration image to obtain the image coordinates of each calibration feature point in the target calibration image includes: A feature point detection model is obtained, and the feature point detection model is called to perform feature point detection on the target calibration image to identify image coordinates of calibration feature points in the target calibration image.

5. The method according to claim 1, characterized in that After obtaining the image coordinates of each calibration feature point in the target calibration image, the method further includes: Clustering the calibration feature points in the target calibration image according to their image coordinates to obtain a plurality of cluster centers and calibration feature points belonging to each cluster center, removing calibration feature points whose distance to the corresponding cluster center is greater than a preset threshold, and obtaining the remaining calibration feature points; The step of calculating the camera pose of the vehicle according to the image coordinates and the corresponding world coordinates of each calibration feature point in the target calibration image comprises: The camera pose of the vehicle is calculated according to the image coordinates and corresponding world coordinates of the remaining calibrated feature points.

6. The method according to claim 5, characterized in that The step of clustering the calibration feature points according to the image coordinates of the calibration feature points in the target calibration image to obtain a plurality of cluster centers and calibration feature points belonging to each cluster center, and removing calibration feature points whose distance to the corresponding cluster center is greater than a preset threshold to obtain the remaining calibration feature points includes: Acquire the generated array size of the calibration plate, and obtain a first quantity according to the array size; According to the first number, the calibration feature points are clustered according to the image coordinates of the calibration feature points in the target calibration image to obtain a first number of clusters, wherein each cluster includes a cluster center having image coordinates and calibration feature points belonging to each cluster; For each cluster, the distance from the calibrated feature point to the cluster center is calculated according to the image coordinates; The calibrated feature points whose distance is greater than a preset threshold are regarded as abnormal feature points, and the abnormal feature points are removed to obtain the remaining calibrated feature points.

7. A camera posture determination device for a vehicle, characterized in that: The device comprises: A posture acquisition module, used to acquire a preset camera posture, wherein the preset camera posture is a set value, and the preset camera posture is used to obtain a calibration plate, and the preset camera posture is not obtained by calibrating the vehicle's camera; An image acquisition module is used to acquire an expected calibration image, wherein the expected calibration image includes a set checkerboard pattern, wherein the intersection of the checkerboards in the checkerboard pattern is a calibration feature point, wherein the checkerboard pattern is a pattern that is not severely deformed and is captured by the camera of the expected vehicle at a special viewing angle, wherein the checkerboard pattern facilitates image detection to obtain accurate image coordinates, and wherein the checkerboard pattern is a rectangular checkerboard pattern; An image detection module, used to perform image detection on the expected calibration image to obtain the image coordinates of each calibration feature point in the expected calibration image; A pattern determination module, configured to map each calibration feature point in the expected calibration image to a world coordinate system according to the preset camera pose and the image coordinates of each calibration feature point in the expected calibration image, so as to obtain the world coordinates corresponding to each calibration feature point; A calibration plate generation module, used to generate a calibration plate image according to the world coordinates corresponding to each calibration feature point, wherein the calibration plate pattern in the calibration plate image is determined according to the world coordinates corresponding to each calibration feature point, and the calibration plate pattern is an oblique chessboard; output a calibration plate production instruction according to the calibration plate image, wherein the calibration plate production instruction is used to instruct the generation of a calibration plate; The pose calibration module is used to, when the generated calibration plate is placed at a set position within the visible range of the camera of the vehicle in the world coordinate system, acquire an image of the calibration plate through the camera of the vehicle to obtain a target calibration image, perform image detection on the target calibration image to obtain the image coordinates of each calibration feature point in the target calibration image; and calculate the camera pose of the vehicle according to the image coordinates of each calibration feature point in the target calibration image and the corresponding world coordinates.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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