Dynamic environment positioning method based on laser radar and IMU

Abstract

The invention provides a dynamic environment positioning method based on a laser radar and an IMU (Inertial Measurement Unit). The method comprises the following steps: step 1, obtaining a current environment point cloud through a robot front-end odometer; 2, matching the current environment point cloud with an off-line map, and determining the difference between the current environment point cloud and the off-line map; 3, judging whether the difference value exceeds a preset threshold value or not; if the difference degree does not exceed a preset threshold value, it is considered that the current scene is basically consistent with the environment, no large change occurs, and the environment point cloud is transferred to a robot coordinate system and stored in a real-time local map based on incremental voxels; if the difference degree exceeds a preset threshold value, it is considered that the current scene and the environment change greatly; the environment point cloud is matched with a real-time local map, and positioning information at the moment is obtained on the basis of an initial pose obtained through IMU pre-integration. According to the method provided by the invention, accurate positioning in a dynamic environment can be realized, the positioning stability of the autonomous platform is improved, and the occurrence of a positioning jump condition is prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic positioning methods, and in particular to a dynamic environment positioning method based on laser radar and IMU. Background Art

[0002] Positioning methods based on Lidar and Inertial Measurement Unit (IMU) play an important role in many fields, especially in applications such as autonomous navigation, robotics, autonomous driving and geographic information system (GIS); Lidar and IMU each have different advantages and disadvantages. Lidar provides high-precision environmental geometry information, but is easily affected by environmental changes and occlusions; IMU provides continuous acceleration and angular velocity measurements, but is prone to accumulated errors. Combining the data of these two sensors can complement their respective shortcomings and improve the overall positioning accuracy.

[0003] The existing positioning methods based on LiDAR and IMU mainly rely on offline maps established a priori. If the current environment and some areas of the offline map have significant changes, the existing positioning methods will likely cause positioning jumps. In order to effectively position the autonomous platform based on LiDAR and IMU positioning functions, the present invention proposes a hybrid positioning method based on the fusion of the current environment and the a priori map, which can effectively reduce the probability of positioning jumps. Summary of the invention

[0004] In view of the deficiencies in the prior art, the object of the present invention is to provide a dynamic environment positioning method based on laser radar and IMU, which can solve the existing problems.

[0005] In order to achieve the above object, the technical solution of the present invention is as follows:

[0006] The present invention is implemented by the following technical solution: a dynamic environment positioning method based on laser radar and IMU, comprising the following steps:

[0007] Step 1: Obtain the current environment point cloud through the robot front-end odometer;

[0008] Step 2: Match the current environment point cloud with the offline map to determine the difference between the two;

[0009] Step 3: Determine whether the difference value exceeds a preset threshold;

[0010] If the difference does not exceed the preset threshold, it is considered that the current scene is basically consistent with the environment and no major changes have occurred. The environmental point cloud is transferred to the robot coordinate system and saved in a real-time local map based on incremental voxels;

[0011] If the difference exceeds the preset threshold, it is considered that the current scene and environment have changed significantly; the environmental point cloud is matched with the real-time local map, and the positioning information at this time is obtained based on the initial pose obtained by IMU pre-integration.

[0012] Furthermore, matching the current environment point cloud with the offline map includes:

[0013] The laser environment point cloud of the new frame is dedistorted, and the environment point cloud is divided into corner points and plane points for feature extraction. Then, the processed laser environment point cloud of the new frame is scanned and matched with the offline map.

[0014] Furthermore, the environment point cloud is divided into corner points and plane points based on the curvature of the points. Points with larger curvature are considered as corner points, while points with smaller curvature are considered as plane points. The curvature calculation formula is as follows:

[0015]

[0016] In formula (1) is the curvature of the i-th point of the j-th line, is the corresponding detection distance, n is the search range, yes is the detection distance of its neighboring points.

[0017] Furthermore, the calculation formula of the difference is:

[0018]

[0019] In formula (2), m s and n s is the number of matches that do not satisfy the point-to-line and point-to-plane distance constraints; m is the total number of points in the current frame that meet the point-to-line match, and n is the total number of points in the current frame that meet the point-to-plane match.

[0020] Furthermore, the process of IMU pre-integration and bias optimization includes:

[0021] First, the world coordinate system and the robot body coordinate system are set to W and B respectively, where the IMU coordinate system coincides with the robot body coordinate system; the state of the robot in the W system at the i-th laser radar frame can be described as: i = {R i , p i , v i , b i}, where R i ∈SO3 is the rotation matrix, p i is the translation vector, v i is the speed, b i is the bias of the IMU;

[0022] The initial position of the scan match is pre-integrated by the IMU. After obtaining the accurate pose matching result, it will also be used to optimize the IMU bias. The IMU measures the angular velocity and acceleration in the B system through the current accelerometer and gyroscope. The measurement formula is:

[0023]

[0024] In formula (3), and is the raw angular velocity and acceleration measured by the IMU in frame B at time t, subject to a slowly changing bias and white noise with mean 0 , g is the stable gravity vector in the W system, is the rotation matrix from W to B, ω t is the true angular velocity, a t is the real acceleration;

[0025] The median integration method is used to calculate the angular velocity and acceleration between two consecutive IMU measurements. The calculation formula is:

[0026]

[0027] In formula (4) is the original angular velocity at time i, is the original acceleration at time i, is the bias at time i, is the original angular velocity at time i+1, is the bias at time i+1, is the rotation matrix from W system to B system at time i, is the rotation matrix from W system to B system at time i+1;

[0028] From the median integral, we can get that if the timestamps of two adjacent laser frames are l t and l t+1 , IMU pre-integration can get the corresponding translation integral Speed ​​integral And the rotation integral It can be obtained by frame-by-frame iteration. Assuming that the time interval between two adjacent IMU frames is Δt, the calculation formula is:

[0029]

[0030] In formula (5) It is the pre-integrated translation from the IMU moment of one frame in the two-frame lidar to the initial lidar moment. It is the pre-integrated velocity from the IMU moment of one frame in the two-frame laser radar to the initial laser radar moment. It is the pre-integrated rotation from the IMU moment of one frame in the two laser radar frames to the initial laser radar moment;

[0031] Finally, the IMU pre-integration state can be expressed as:

[0032]

[0033] In formula (6), is the pre-integrated translation of the two-frame lidar, is the pre-integrated velocity of the two-frame lidar, is the pre-integrated rotation of the two-frame lidar, They are the angular velocity bias and acceleration bias at the second laser radar frame moment;

[0034] Let q be the quaternion corresponding to R, [.] xyz It is to take the imaginary part corresponding to the quaternion, and the corresponding residual can be expressed as:

[0035]

[0036] Furthermore, the positioning information calculation process at this time is:

[0037] First, the laser environment point cloud is transferred to the world coordinate system according to the pose obtained by IMU pre-integration. For each point, the five nearest points in the offline map are found. The corner points perform point-to-line matching. The straight line is fitted by five adjacent corner points. The distance from the point to the line is required to be within 1m. The plane points perform point-to-plane matching. The surface is fitted by five adjacent plane points. The distance from the point to the surface is required to be within 1m. Set the corner points The corresponding fitting line has two points The plane point The corresponding fitting plane has three points Their residuals can be calculated separately:

[0038]

[0039] Finally, the Gauss-Newton method is used to optimize the posture, that is, the positioning information, as follows:

[0040]

[0041] A vision-assisted millimeter-wave wireless communication beam alignment device for an Internet of Vehicles, comprising a processor and a memory; the memory is used to store a program; the processor executes the program to implement any of the methods described above.

[0042] A computer-readable storage medium stores a program, wherein the program is executed by a processor to implement any of the methods described above.

[0043] Compared with the prior art, the beneficial effects of the present invention include:

[0044] The present invention uses a front-end odometer to match the current environment point cloud with an offline map to obtain the degree of matching between the current positioning result and the point cloud, and uses an incremental voxel structure to update a real-time local map based on the current posture; the map change is judged according to the degree of point cloud matching, and if the degree of change is large, the odometer result is not trusted, and the matching result of the current environment point cloud and the real-time local map is used as a positioning information source; through the method of the present invention, accurate positioning in a dynamic environment can be achieved, and the positioning stability of the autonomous platform can be improved; the degree of environmental change is judged according to the point cloud matching result of the front-end odometer to prevent the occurrence of positioning jumps; the local map based on the current posture is maintained in real time to ensure efficient and accurate positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The disclosure of the present invention is described with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In the drawings, the same reference numerals are used to refer to the same components. Among them:

[0046] Figure 1 A schematic diagram of a dynamic environment positioning method based on laser radar and IMU according to the present invention;

[0047] Figure 2 Detailed flow chart of the dynamic environment positioning method of the present invention is shown in FIG. DETAILED DESCRIPTION

[0048] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific implementation modes and drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction to the technical solution of the present invention.

[0049] The present invention is described by taking the application of laser radar and IMU to robot positioning as an example, but is not limited to this. The present invention provides a dynamic environment positioning method based on laser radar and IMU, and its process is as follows: Figure 1 As shown, the following steps are included:

[0050] Step 1: Obtain the current environment point cloud through the robot front-end odometer;

[0051] Step 2: Match the current environment point cloud with the offline map to determine the difference between the two;

[0052] Taking the laser radar odometer as an example, when matching the current environment point cloud with the offline map, the laser environment point cloud of the new frame is firstly dedistorted, and the environment point cloud is divided into corner points and plane points for feature extraction, and then the processed laser environment point cloud of the new frame is scanned and matched with the offline local map in the offline map; Figure 2 As shown;

[0053] For example, when a new frame of laser environment point cloud comes, after the de-distortion operation is completed, the point cloud needs to be divided into corner points and plane points based on the curvature of the point. Points with larger curvature are considered corner points, while points with smaller curvature are considered plane points. The curvature calculation formula is as follows:

[0054]

[0055] In formula (1) is the curvature of the i-th point of the j-th line, is the corresponding detection distance, n is the search range, is the detection distance of its neighboring points.

[0056] The calculation formula of the difference is:

[0057]

[0058] In formula (2), m s and n s is the number of matches that do not satisfy the point-to-line and point-to-plane distance constraints; m is the total number of points in the current frame that meet the point-to-line match, and n is the total number of points in the current frame that meet the point-to-plane match.

[0059] Calculate the difference between the current frame environment point cloud and the offline map match, use this as the difference in the map update degree, and choose whether to use the real-time local map for supplementary matching.

[0060] Step 3: Determine whether the difference value exceeds a preset threshold;

[0061] 3.1. If the difference does not exceed the preset threshold, it is considered that the current scene is basically consistent with the environment and no major changes have occurred. The environmental point cloud is transferred to the robot coordinate system and saved in a real-time local map based on incremental voxels. Only a certain number of environmental point clouds are saved in the map, and the excess will be cleared according to the first-in-first-out principle of the queue.

[0062] Among them, Incremental Voxelization (iVox) is a commonly used technology in real-time point cloud processing and 3D reconstruction. It efficiently processes and stores point cloud data by dynamically updating the voxel grid, and is used in application scenarios that require real-time updating and maintenance of environmental models; it mainly includes voxelization and incremental update, where voxelization is the process of dividing the three-dimensional space into regular cubic units (voxels). Each voxel can contain one or more point cloud data points. The main purpose of voxelization is to discretize the continuous three-dimensional space to facilitate efficient storage and processing of point cloud data. The core idea of ​​incremental update is that when new point cloud data is received, only the changed parts are updated instead of rebuilding the entire voxel grid. This greatly improves processing efficiency, especially in real-time applications.

[0063] The incremental voxel workflow includes:

[0064] Initialize voxel grid: Define the size (resolution) of voxels and initialize an empty voxel grid. Usually choose a suitable voxel size to balance storage efficiency and detail preservation.

[0065] Receive new point cloud data: Get new point cloud data from sensors (such as LiDAR).

[0066] Point cloud to voxel mapping: Map the new point cloud data into a voxel grid. Each point is assigned to a corresponding voxel according to its coordinates.

[0067] Incremental update: For each new point, check if the voxel it is in already exists. If the voxel already exists, update the information of the voxel (such as average color, normal direction, etc.). If the voxel does not exist, create a new voxel and add it to the grid.

[0068] Removing redundant points: In some cases, there may be multiple points in the same voxel. These points can be merged through some strategy (such as nearest neighbor, average, etc.) to reduce redundancy.

[0069] 3.2. If the difference exceeds the preset threshold, it is considered that the current scene and environment have changed significantly. The environmental point cloud is matched with the real-time local map, and the positioning information at this time is obtained based on the initial pose obtained by IMU pre-integration.

[0070] The process of IMU pre-integration and bias optimization includes:

[0071] First, the world coordinate system and the robot body coordinate system are set as W and B respectively, where the IMU coordinate system coincides with the robot body coordinate system. The state of the robot in the W system at the i-th laser radar frame can be described as: i = {R i , pi , v i , b i}, where R i ∈SO3 is the rotation matrix, p i is the translation vector, v i is the speed, b i is the bias of the IMU.

[0072] The initial position guess of the scan matching is pre-integrated by the IMU. After obtaining the accurate pose matching result, it will also be used to optimize the IMU bias. The IMU measures the angular velocity and acceleration in the B frame through the current accelerometer and gyroscope. The measurement method can be described as:

[0073]

[0074] In formula (3), and are the raw angular velocity and acceleration measured by the IMU in frame B at time t, which are subject to a slowly changing bias and white noise with mean 0 , g is the stable gravity vector in the W system, is the rotation matrix from W to B, ω t is the true angular velocity, a t is the real acceleration.

[0075] The median integral method is used to calculate the angular velocity and acceleration between two consecutive IMU measurements, which can be described as:

[0076]

[0077]

[0078] In formula (4) is the original angular velocity at time i, is the original acceleration at time i, is the bias at time i, is the original angular velocity at time i+1, is the bias at time i+1, is the rotation matrix from W system to B system at time i, is the rotation matrix from W system to B system at time i+1;

[0079] From the median integral, we can get that if the timestamps of two adjacent laser frames are l t and l t+1 , IMU pre-integration can get the corresponding translation integral Speed ​​integral And the rotation integral It can be obtained by frame-by-frame iteration, assuming that the time interval between two adjacent IMU frames is Δt:

[0080]

[0081] In formula (5) It is the pre-integrated translation from the IMU moment of one frame in the two-frame lidar to the initial lidar moment. It is the pre-integrated velocity from the IMU moment of one frame in the two-frame laser radar to the initial laser radar moment. It is the pre-integrated rotation from the IMU moment of one frame in the two laser radar frames to the initial laser radar moment.

[0082] Finally, the IMU pre-integration state can be expressed as:

[0083]

[0084] In formula (6), is the pre-integrated translation of the two-frame lidar, is the pre-integrated velocity of the two-frame lidar, is the pre-integrated rotation of the two-frame lidar, They are the angular velocity bias and acceleration bias at the second frame of LiDAR.

[0085] Let q be the quaternion corresponding to R, [.] xyz It is to take the imaginary part corresponding to the quaternion, and the corresponding residual can be expressed as:

[0086]

[0087] In formula (7), Yes, p t+1 Yes, p t Yes, v t Yes, Δ t Yes, yes, Yes, v t+1 yes, yes, yes, yes, yes, yes, yes, yes.

[0088] The above is the process of IMU pre-integration and its bias optimization, which is the factor graph optimization part of the overall process.

[0089] The positioning information calculation process at this time is:

[0090] First, the laser point cloud is transferred to the W system according to the pose obtained by IMU pre-integration. For each point, the five nearest points in the offline local map are found. The corner points perform point-to-line matching. The straight line is fitted by five adjacent corner points. The distance between the point and the line is required to be within 1m. The plane points perform point-to-plane matching. The surface is fitted by five adjacent plane points. The distance between the point and the surface is required to be within 1m. The corresponding fitting straight line has two points The plane point The corresponding fitting plane has three points Their residuals can be calculated separately:

[0091]

[0092] Finally, the Gauss-Newton method is used to optimize the posture, that is, the positioning information, as follows:

[0093]

[0094] The present invention uses a front-end odometer to match the current environment point cloud with an offline map to obtain the degree of matching between the current positioning result and the point cloud, and uses an incremental voxel structure to update a real-time local map based on the current posture; the map change is judged according to the degree of point cloud matching, and if the degree of change is large, the odometer result is not trusted, and the matching result of the current environment point cloud and the real-time local map is used as a positioning information source; through the method of the present invention, accurate positioning in a dynamic environment can be achieved, and the positioning stability of the autonomous platform can be improved; the degree of environmental change is judged according to the point cloud matching result of the front-end odometer to prevent the occurrence of positioning jumps; the local map based on the current posture is maintained in real time to ensure efficient and accurate positioning.

[0095] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this article.

[0096] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0097] In the several embodiments provided herein, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this article.

[0099] In addition, each functional unit in each embodiment of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional unit.

[0100] If the integrated 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, the technical solution of this article is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of this article. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0101] Specific embodiments are used in this article to illustrate the principles and implementation methods of this article. The description of the above embodiments is only used to help understand the methods and core ideas of this article. At the same time, for general technicians in this field, according to the ideas of this article, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on this article.

Claims

1. A dynamic environment positioning method based on LiDAR and IMU, Features: The following steps are involved: Step 1: Obtain the current environment point cloud through the robot front-end odometer; Step 2: Match the current environment point cloud with the offline map to determine the difference between the two; Step 3: Determine whether the difference value exceeds a preset threshold; If the difference does not exceed the preset threshold, it is considered that the current scene is basically consistent with the environment and no major changes have occurred. The environmental point cloud is transferred to the robot coordinate system and saved in a real-time local map based on incremental voxels; If the difference exceeds the preset threshold, it is considered that the current scene and environment have changed significantly; the environmental point cloud is matched with the real-time local map, and the positioning information at this time is obtained based on the initial pose obtained by IMU pre-integration.

2. A dynamic environment positioning method based on laser radar and IMU according to claim 1, Features: The matching of the current environment point cloud with the offline map includes: The laser environment point cloud of the new frame is dedistorted, and the environment point cloud is divided into corner points and plane points for feature extraction. Then, the processed laser environment point cloud of the new frame is scanned and matched with the offline map.

3. A dynamic environment positioning method based on laser radar and IMU according to claim 2, Features: The environmental point cloud is divided into corner points and plane points based on the curvature of the points. Points with larger curvature are considered as corner points, while points with smaller curvature are considered as plane points. The calculation formula of curvature is as follows: In formula (1) is the curvature of the i-th point of the j-th line, is the corresponding detection distance, n is the search range, is the detection distance of its neighboring points.

4. The dynamic environment positioning method based on laser radar and IMU according to claim 1, Features: The calculation formula of the difference is: In formula (2), m s and n s is the number of matches that do not satisfy the point-to-line and point-to-plane distance constraints; m is the total number of points in the current frame that meet the point-to-line match, and n is the total number of points in the current frame that meet the point-to-plane match.

5. The dynamic environment positioning method based on laser radar and IMU according to claim 1, Features: The process of IMU pre-integration and bias optimization includes: First, the world coordinate system and the robot body coordinate system are set to W and B respectively, where the IMU coordinate system coincides with the robot body coordinate system; the state of the robot in the W system at the i-th laser radar frame can be described as: i = {R i , p i , v i , b i }, where R i ∈SO3 is the rotation matrix, p i is the translation vector, v i is the speed, b i is the bias of the IMU; The initial position of the scan matching is pre-integrated by the IMU, and after obtaining the accurate pose matching result, it is used to optimize the IMU bias; the IMU measures the angular velocity and acceleration in the B system through the current accelerometer and gyroscope, and its measurement formula is: In formula (3), and is the raw angular velocity and acceleration measured by the IMU in frame B at time t, subject to a slowly changing bias and white noise with mean 0 , g is the stable gravity vector in the W system, is the rotation matrix from W to B, ω t is the true angular velocity, a t is the real acceleration; The median integration method is used to calculate the angular velocity and acceleration between two consecutive IMU measurements. The calculation formula is: In formula (4) is the original angular velocity at time i, is the original acceleration at time i, is the bias at time i, is the original angular velocity at time i+1, is the bias at time i+1, is the rotation matrix from W system to B system at time i, is the rotation matrix from W system to B system at time i+1; From the median integral, we can get that if the timestamps of two adjacent laser frames are l t and l t+1 , IMU pre-integration can get the corresponding translation integral Speed ​​integral And the rotation integral It can be obtained by frame-by-frame iteration. Assuming that the time interval between two adjacent IMU frames is Δt, the calculation formula is: In formula (5) It is the pre-integrated translation from the IMU moment of one frame in the two-frame lidar to the initial lidar moment. It is the pre-integrated velocity from the IMU moment of one frame in the two-frame laser radar to the initial laser radar moment. It is the pre-integrated rotation from the IMU moment of one frame in the two laser radar frames to the initial laser radar moment; Finally, the IMU pre-integration state can be expressed as: In formula (6), is the pre-integrated translation of the two-frame lidar, is the pre-integrated velocity of the two-frame lidar, is the pre-integrated rotation of the two-frame lidar, They are the angular velocity bias and acceleration bias at the second laser radar frame moment; Let q be the quaternion corresponding to R, [·] xyz It is to take the imaginary part corresponding to the quaternion, and the corresponding residual can be expressed as:

6. The dynamic environment positioning method based on laser radar and IMU according to claim 1, Features: The positioning information calculation process at this time is: First, the laser environment point cloud is transferred to the world coordinate system according to the pose obtained by IMU pre-integration. For each point, the five nearest points in the offline map are found. The corner points perform point-to-line matching. The straight line is fitted by five adjacent corner points. The distance from the point to the line is required to be within 1m. The plane points perform point-to-plane matching. The surface is fitted by five adjacent plane points. The distance from the point to the surface is required to be within 1m. Set the corner points The corresponding fitting line has two points The plane point The corresponding fitting plane has three points Their residuals can be calculated separately: Finally, the Gauss-Newton method is used to optimize the posture, that is, the positioning information, as follows:

7. A vision-assisted millimeter-wave wireless communication beam alignment device for Internet of Vehicles, Features: It comprises a processor and a memory; the memory is used to store a program; the processor executes the program to implement the method as described in any one of claims 1-6.

8. A computer-readable storage medium, Features: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 6.

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