A data processing method, device and storage medium

Abstract

This invention discloses a data processing method, apparatus, and storage medium. The method includes: acquiring a frame of laser point cloud data using a lidar, and correcting the laser point cloud data to obtain corrected laser point cloud data; performing line detection on the laser point clouds included in the corrected laser point cloud data to obtain the line corresponding to each laser point cloud in the corrected laser point cloud data; setting a corresponding weight for each laser point cloud in the corrected laser point cloud data based on its distance to the corresponding line; and using a point-to-line nearest iteration algorithm, based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data, to determine the lidar's positioning information and the actual laser point cloud data. This technical solution improves the accuracy of data processing.

Description

Technical Field

[0001] This application relates to the field of robotics, and more particularly to a data processing method, apparatus, and storage medium. Background Technology

[0002] In recent years, with the rapid development of the robotics industry, the demand for mobile robots has become increasingly widespread across various sectors. Therefore, mobile robots need to be deployed in various industry applications, and those that can adapt to changing environments, are cost-effective, and can operate normally within those environments are highly sought after. Two-dimensional LiDAR, as a low-cost environmental perception device, is widely used in these mobile robots.

[0003] However, due to the low frequency of point cloud data acquisition by 2D LiDAR, the mobile robot may experience jitter during movement. Alternatively, the presence of long-distance or small objects in the current scene may cause the acquired point cloud data to contain points with offset errors, resulting in low accuracy. Furthermore, this low-accuracy point cloud data will affect the mapping accuracy and positioning accuracy of the mobile robot. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a data processing method, apparatus, and storage medium that improves the accuracy of data processing.

[0005] The technical solution of this invention is implemented as follows:

[0006] This invention provides a data processing method, the method comprising:

[0007] A frame of laser point cloud data is acquired using a lidar, and the laser point cloud data is corrected to obtain corrected laser point cloud data.

[0008] Line detection is performed on the laser point cloud included in the corrected laser point cloud data to obtain the line corresponding to each laser point cloud included in the corrected laser point cloud data;

[0009] For each laser point cloud included in the corrected laser point cloud data, a corresponding weight is set according to the distance to the corresponding straight line;

[0010] Using the point-to-line nearest iteration algorithm, based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data, the positioning information of the lidar and the actual laser point cloud data are determined.

[0011] In the above method, the step of correcting the laser point cloud data to obtain corrected laser point cloud data includes:

[0012] The pose change information of the lidar during the process of acquiring the lidar point cloud data;

[0013] Based on the pose change information, the laser point cloud data is corrected to obtain the corrected laser point cloud data.

[0014] In the above method, the pose change information of the lidar during the process of acquiring the lidar point cloud data includes:

[0015] Acquire inertial measurement unit data, and / or lidar odometry data;

[0016] The pose change information is determined using a nonlinear Kalman filter method based on the inertial measurement unit data and / or the lidar odometry data.

[0017] In the above method, the pose change information includes: the initial pose information when acquiring the first laser point cloud and the final pose information when acquiring the last laser point cloud during the process of the lidar acquiring the laser point cloud data; the step of correcting the laser point cloud data based on the pose change information to obtain corrected laser point cloud data includes:

[0018] The acquisition time of the laser point cloud data by the lidar is divided into multiple sub-time periods;

[0019] For each of the multiple sub-time periods, the corresponding pose information is estimated by linear interpolation based on the initial pose information and the final pose information.

[0020] From the laser point cloud data, obtain the laser point cloud collected in each of the multiple sub-time periods, and obtain multiple sets of laser point clouds that correspond one-to-one with the multiple sub-time periods;

[0021] For each of the multiple sets of laser point clouds, the pose information corresponding to the same sub-time period is determined as the pose information corresponding to each laser point cloud in the group;

[0022] For each laser point cloud included in the laser point cloud data, the coordinate system where the first laser point cloud is located is used as the reference coordinate system. The corresponding pose information is used to determine the corresponding laser point cloud data in the reference coordinate system, and the corrected laser point cloud data is obtained.

[0023] In the above method, the step of performing line detection on the laser point cloud included in the corrected laser point cloud data to obtain the line corresponding to each laser point cloud included in the corrected laser point cloud data includes:

[0024] Select a first laser point cloud from the laser point clouds included in the corrected laser point cloud data, and determine the first laser point cloud as the first laser point cloud;

[0025] Using the first laser point cloud as a reference, laser point clouds are sequentially selected from the corrected laser point cloud data according to a preset direction until the kth laser point cloud is selected, and the linear residual determined based on the first laser point cloud to the kth laser point cloud is greater than a preset residual threshold; where k is a natural number greater than 1.

[0026] The straight line determined based on the first laser point cloud to the (k-1)th laser point cloud is defined as the straight line corresponding to each laser point cloud in the first laser point cloud to the (k-1)th laser point cloud.

[0027] Using the k-th laser point cloud as a reference, continue to perform straight line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction until the straight line corresponding to each laser point cloud included in the corrected laser point cloud data is obtained.

[0028] In the above method, the step of selecting laser point clouds sequentially from the corrected laser point cloud data according to a preset direction, based on the first laser point cloud, until the kth laser point cloud is selected, and ensuring that the linear residual determined from the first laser point cloud to the kth laser point cloud is greater than a preset residual threshold, includes:

[0029] Using the first laser point cloud as a reference, laser point clouds are sequentially selected from the corrected laser point cloud data according to the preset direction until the nth laser point cloud is selected. The straight line parameters determined based on the first laser point cloud to the (n-1)th laser point cloud are greater than the straight line parameters determined based on the first laser point cloud to the nth laser point cloud, respectively, and are greater than a preset parameter threshold. Here, n is a natural number greater than 1 and less than or equal to k.

[0030] Remove the nth laser point cloud from the corrected laser point cloud data, and re-determine the nth laser point cloud as the next selected laser point cloud;

[0031] Continue to select laser point clouds sequentially from the corrected laser point cloud data according to the preset direction until the kth laser point cloud is selected, and the linear residual determined based on the 1st laser point cloud to the kth laser point cloud is greater than the preset residual threshold.

[0032] In the above method, the step of using the k-th laser point cloud as a reference and continuing to perform straight-line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction includes:

[0033] If the linear residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, and the linear residual determined based on the m-th laser point cloud to the last laser point cloud and the 1st laser point cloud is greater than the preset residual threshold, then the linear residual determined based on the m-th laser point cloud to the last laser point cloud is determined as the linear residual corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud; where m is a natural number greater than k.

[0034] In the above method, the step of using the k-th laser point cloud as a reference and continuing to perform straight-line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction includes:

[0035] If the linear residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, and the linear residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, then the linear residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is determined as the linear residual corresponding to each laser point cloud in the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud; where m is a natural number greater than k.

[0036] In the above method, the step of using the k-th laser point cloud as a reference and continuing to perform straight-line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction includes:

[0037] If the linear residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, and the linear residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is greater than the preset residual threshold, then the linear residual determined based on the m-th laser point cloud to the last laser point cloud is determined as the linear residual corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud; where m is a natural number greater than k.

[0038] This invention provides a data processing apparatus, comprising:

[0039] The acquisition module is used to acquire a frame of laser point cloud data using a lidar and to correct the laser point cloud data to obtain corrected laser point cloud data.

[0040] The detection module is used to perform straight line detection on the laser point cloud included in the corrected laser point cloud data to obtain the straight line corresponding to each laser point cloud included in the corrected laser point cloud data.

[0041] The setting module is used to set a corresponding weight for each laser point cloud included in the corrected laser point cloud data, based on the distance to the corresponding straight line.

[0042] The determination module is used to determine the positioning information of the lidar and the actual lidar point cloud data based on the corrected lidar point cloud data and the weight of each lidar point cloud included in the corrected lidar point cloud data, using the point-to-line nearest iteration algorithm.

[0043] This invention provides a data processing apparatus, comprising: a processor, a memory, and a communication bus;

[0044] The communication bus is used to realize the communication connection between the processor and the memory;

[0045] The processor is used to execute the computer program stored in the memory to implement the above-described data processing method.

[0046] The present invention provides a computer-readable storage medium storing one or more computer programs, which can be executed by one or more processors to implement the above-described data processing method.

[0047] This invention provides a data processing method, apparatus, and storage medium. The method includes: acquiring a frame of laser point cloud data using a lidar, and correcting the laser point cloud data to obtain corrected laser point cloud data; performing line detection on the laser point clouds included in the corrected laser point cloud data to obtain a line corresponding to each laser point cloud in the corrected laser point cloud data; setting a corresponding weight for each laser point cloud in the corrected laser point cloud data based on its distance to the corresponding line; and using a point-to-line nearest iteration algorithm, determining the lidar positioning information and the actual laser point cloud data based on the corrected laser point cloud data and the weights of each laser point cloud included in the corrected laser point cloud data. The technical solution provided by this invention, before the positioning and scene construction process based on the laser point cloud data acquired by lidar, not only corrects the motion distortion present in the laser point cloud data, but also removes point cloud data with large offset errors by line detection; furthermore, it sets corresponding weights for each laser point cloud, so that the positioning information and the actual laser point cloud data determined based on the more accurate laser point cloud data are more accurate, thereby improving the accuracy of data processing. Attached Figure Description

[0048] Figure 1A flowchart illustrating a data processing method provided in an embodiment of the present invention;

[0049] Figure 2 A schematic diagram illustrating an exemplary laser point cloud correction process provided in an embodiment of the present invention;

[0050] Figure 3 A schematic flowchart of an exemplary line detection method provided in an embodiment of the present invention;

[0051] Figure 4 A schematic diagram illustrating an exemplary laser point cloud data processing flow provided for an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of the structure of a data processing device provided in an embodiment of the present invention. Figure 1 ;

[0053] Figure 6 A schematic diagram of the structure of a data processing device provided in an embodiment of the present invention. Figure 2 . Detailed Implementation

[0054] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings of the embodiments of the present invention. It is to be understood that the specific embodiments described herein are merely for explaining the relevant application and are not intended to limit the application. Furthermore, it should be noted that, for ease of description, only the parts relevant to the relevant application are shown in the accompanying drawings.

[0055] This invention provides a data processing method, implemented by a data processing device. Figure 1 This is a flowchart illustrating a data processing method provided in an embodiment of the present invention. Figure 1 As shown, the main steps include:

[0056] S101. Use a lidar to collect a frame of lidar point cloud data, and correct the lidar point cloud data to obtain corrected lidar point cloud data.

[0057] In an embodiment of the present invention, the data processing device uses a lidar to collect a frame of lidar point cloud data and corrects the lidar point cloud data to obtain corrected lidar point cloud data.

[0058] It should be noted that, in the embodiments of the present invention, a lidar may be installed on the data processing device, so that the data processing device can use the lidar to acquire a frame of lidar point cloud data. A frame of lidar point cloud data is the lidar point cloud acquired by rotating 360 degrees, and this lidar point cloud data includes the two-dimensional coordinates of all acquired lidar point clouds.

[0059] It should be noted that, in the embodiments of the present invention, during the process of the data processing device acquiring a frame of laser point cloud data using the lidar, the data processing device may move, which may cause distortion in the laser point cloud data acquired by the lidar installed on the data processing device. That is, as the lidar moves, the two-dimensional coordinates of each laser point cloud included in the acquired laser point cloud data are not in the same coordinate system, and therefore it is necessary to correct the laser point cloud data to obtain corrected laser point cloud data.

[0060] Specifically, in an embodiment of the present invention, the data processing device corrects the laser point cloud data to obtain corrected laser point cloud data, including: acquiring the pose change information of the lidar during the process of the lidar acquiring laser point cloud data; and correcting the laser point cloud data based on the pose change information to obtain corrected laser point cloud data.

[0061] It should be noted that, in the embodiments of the present invention, during the process of correcting the laser point cloud data, the data processing device can obtain the pose change information of the laser radar during the process of acquiring the laser point cloud data, and then correct the laser point cloud data based on the pose change information to obtain corrected laser point cloud data.

[0062] Specifically, in the embodiments of the present invention, the data processing device acquires the pose change information of the lidar during the process of acquiring lidar point cloud data, including: acquiring inertial measurement unit data and / or lidar odometry data; and using a nonlinear Kalman filter method to determine the pose change information based on the inertial measurement unit data and / or lidar odometry data.

[0063] It should be noted that, in the embodiments of the present invention, the data processing device can acquire inertial measurement unit (IMU) data and / or lidar odometer data. For example, an IMU and / or a sensor for measuring lidar odometer data can be installed on the data processing device to acquire IMU data and / or lidar odometer data. The IMU data includes the lidar's three-axis attitude angles or angular rates, and acceleration; the lidar odometer data includes the lidar's forward velocity and angular velocity.

[0064] It should be noted that, in the embodiments of the present invention, after the data processing device acquires the inertial measurement unit data and / or the lidar odometry data, it can use a nonlinear Kalman filter method to determine the pose change information of the lidar based on the inertial measurement unit data and / or the lidar odometry data.

[0065] Specifically, in the embodiments of the present invention, the pose change information includes: the initial pose information when acquiring the first laser point cloud and the final pose information when acquiring the last laser point cloud during the process of lidar acquiring laser point cloud data; the data processing device corrects the laser point cloud data based on the pose change information to obtain corrected laser point cloud data, including: dividing the acquisition time of lidar acquiring laser point cloud data into multiple sub-time periods; for each of the multiple sub-time periods, estimating the corresponding pose information using linear interpolation based on the initial pose information and the final pose information; obtaining the laser point clouds acquired within each of the multiple sub-time periods from the laser point cloud data to obtain multiple sets of laser point clouds that correspond one-to-one with the multiple sub-time periods; for each of the multiple sets of laser point clouds, determining the pose information corresponding to the same sub-time period as the pose information corresponding to each laser point cloud in the group; for each laser point cloud included in the laser point cloud data, using the coordinate system where the first laser point cloud is located as the reference coordinate system, using the corresponding pose information to determine the corresponding laser point cloud data in the reference coordinate system to obtain corrected laser point cloud data.

[0066] It should be noted that, in the embodiments of the present invention, the pose change information can be the initial pose information when acquiring the first laser point cloud and the final pose information when acquiring the last laser point cloud during the process of lidar acquiring laser point cloud data. Then, after the data processing device knows the acquisition time of the lidar acquiring one frame of laser point cloud data, it divides the acquisition time into multiple sub-times. Then, using linear interpolation, the pose information of each sub-time is determined based on the initial pose information and the final pose information, and this pose information is determined as the pose information of the laser point clouds acquired within the same sub-time. Finally, for each laser point cloud included in the laser point cloud data, using the coordinate system where the first laser point cloud is located as the reference coordinate system, the corresponding laser point cloud data in the reference coordinate system is determined using the corresponding pose information, thus obtaining the corrected laser point cloud data. That is, based on the pose information corresponding to each laser point cloud data, the laser point cloud is mapped to the reference coordinate system where the first laser point cloud is located to obtain the corrected laser point cloud data.

[0067] It should be noted that, in the embodiments of the present invention, the pose change information can also be multiple pose information that the data processing device can acquire during the acquisition of laser point cloud data. For example, it takes 10-20ms to acquire one frame of laser point cloud data, while the inertial measurement unit data and / or lidar odometry data may be acquired once every 5ms. Thus, during the acquisition of laser point cloud data, inertial measurement unit data and / or lidar odometry data at multiple time points may be acquired. At this time, the data processing device can determine the pose information of the lidar at multiple time points based on the inertial measurement unit data and / or lidar odometry data at multiple time points, and then use linear interpolation to determine the pose information of each sub-time period based on the pose information of the lidar at multiple time points.

[0068] Figure 2 This is a schematic diagram illustrating an exemplary laser point cloud correction process provided in an embodiment of the present invention. Figure 2 As shown, the exemplary data processing device implements laser point cloud correction as follows: S201, acquire inertial measurement unit data and / or lidar odometry data, and use a nonlinear Kalman filter method to determine pose change information based on the inertial measurement unit data and / or lidar odometry data; S202, divide the acquisition time of the lidar acquiring laser point cloud data into multiple sub-time periods, and estimate the corresponding pose information for each of the multiple sub-time periods based on the pose change information; S203, from the laser point cloud... In the data, laser point clouds collected within each of multiple sub-time periods are obtained, resulting in multiple sets of laser point clouds that correspond one-to-one with the multiple sub-time periods. For each set of laser point clouds, the pose information corresponding to the same sub-time period is determined as the pose information corresponding to each laser point cloud in the set. S204, for each laser point cloud included in the laser point cloud data, the coordinate system where the first laser point cloud is located is used as the reference coordinate system. Using the corresponding pose information, the corresponding laser point cloud data in the reference coordinate system is determined, thus obtaining the corrected laser point cloud data.

[0069] S102. Perform line detection on the laser point cloud included in the corrected laser point cloud data to obtain the line corresponding to each laser point cloud included in the corrected laser point cloud data.

[0070] In an embodiment of the present invention, the data processing device performs line detection on the laser point cloud included in the corrected laser point cloud data to obtain the line corresponding to each laser point cloud included in the corrected laser point cloud data.

[0071] It should be noted that, in the embodiments of the present invention, during the process of the data processing device acquiring a frame of laser point cloud data using lidar, due to the limitations of the data processing device itself, there may be jitter, or the accuracy of the data reflected back by objects that are long distances or small in size may be low, which may result in many outliers in the acquired laser point cloud data. Therefore, the data processing device needs to further perform line detection on the laser point cloud included in the corrected laser point cloud data in order to remove the outliers in the laser point cloud data.

[0072] Specifically, in an embodiment of the present invention, the data processing device performs line detection on the laser point clouds included in the corrected laser point cloud data to obtain the line corresponding to each laser point cloud included in the corrected laser point cloud data. This includes: selecting a first laser point cloud from the laser point clouds included in the corrected laser point cloud data and defining the first laser point cloud as the first laser point cloud; using the first laser point cloud as a reference, sequentially selecting laser point clouds from the corrected laser point cloud data according to a preset direction until the kth laser point cloud is selected, and the residual of the line determined based on the first laser point cloud to the kth laser point cloud is greater than a preset residual threshold; where k is a natural number greater than 1; defining the line determined based on the first laser point cloud to the (k-1)th laser point cloud as the line corresponding to each laser point cloud from the first laser point cloud to the (k-1)th laser point cloud; using the kth laser point cloud as a reference, continuing to perform line detection on the laser point clouds included in the corrected laser point cloud data according to a preset direction until the line corresponding to each laser point cloud included in the corrected laser point cloud data is obtained.

[0073] It should be noted that, in the embodiments of the present invention, the data processing device can randomly select a first laser point cloud from the corrected laser point cloud data and determine this selected laser point cloud as the first laser point cloud. Then, based on the first laser point cloud, laser point clouds are sequentially selected from the corrected laser point cloud data according to a preset direction until the kth laser point cloud is selected, and the linear residual determined based on the first laser point cloud to the kth laser point cloud is greater than a preset residual threshold. The preset direction can be counterclockwise or clockwise, and the specific preset direction can be set according to actual conditions and application requirements; the present invention does not limit this.

[0074] It should be noted that, in the embodiments of the present invention, when the data processing device selects each laser point cloud, it will use the least squares method to calculate the laser point cloud and the straight line formed by all laser point clouds from the laser point cloud to its reference laser point cloud. An exemplary straight line is shown in formula (1):

[0075] y = a i x+b i (1)

[0076] Among them, a i b i The parameters are linear parameters, which can be obtained using the least squares method, see formula (2):

[0077]

[0078] Among them, (x k ,y k (x) represents the coordinates of the reference laser point cloud. q ,y q ) represents the coordinates of the currently selected laser point cloud, i∈[k,q].

[0079] Furthermore, the data processing device will determine the linear residuals from the first laser point cloud to the kth laser point cloud. An exemplary residual calculation formula can be found in formula (3):

[0080]

[0081] Among them, e i The residuals are linear.

[0082] It should be noted that, in the embodiments of the present invention, if the linear residual determined by the data processing device based on the first laser point cloud to the kth laser point cloud is greater than the preset residual threshold, where k is a natural number greater than 1, then it means that the linear residual formed by the kth laser point cloud and the previous k-1 laser point clouds is large, that is, it is determined that the kth laser point cloud and the previous k-1 laser point clouds do not belong to the same straight line.

[0083] Figure 3 This is a schematic flowchart illustrating an exemplary line detection method provided in an embodiment of the present invention. Figure 3 As shown, the data processing device can first cache the traversed laser point clouds into a point cloud cluster. An exemplary method for implementing line detection can be: randomly selecting a laser point cloud from the corrected laser point cloud data and adding the selected laser point cloud to a point cloud cluster; using this laser point cloud as a reference, traversing the laser point clouds included in the corrected laser point cloud data in a counterclockwise direction, adding the traversed laser point clouds to the point cloud cluster, and calculating the line equation and line residual of the current point cloud cluster; if the calculated line residual is less than or equal to a preset residual threshold, continuing to traverse the laser point clouds included in the corrected laser point cloud data; if the calculated line residual is greater than the preset residual threshold, then adding the newly added laser point cloud to a new point cloud cluster.

[0084] Specifically, in an embodiment of the present invention, the data processing device uses the first laser point cloud as a reference and sequentially selects laser point clouds from the corrected laser point cloud data according to a preset direction until the kth laser point cloud is selected, and the linear residual determined based on the first laser point cloud to the kth laser point cloud is greater than a preset residual threshold. This includes: using the first laser point cloud as a reference, sequentially selecting laser point clouds from the corrected laser point cloud data according to a preset direction until the nth laser point cloud is selected, and determining the linear residual based on the first laser point cloud to the (n-1)th laser point cloud. The straight line parameters obtained are greater than the straight line parameters determined based on the first to the nth laser point clouds than a preset parameter threshold; where n is a natural number greater than 1 and less than or equal to k; the nth laser point cloud is removed from the corrected laser point cloud data, and the next selected laser point cloud is re-determined as the nth laser point cloud; continue to select laser point clouds sequentially from the corrected laser point cloud data according to the preset direction until the kth laser point cloud is selected, and the straight line residual determined based on the first to the kth laser point clouds is greater than the preset residual threshold.

[0085] It should be noted that, in the embodiments of the present invention, when the data processing device selects each laser point cloud, it uses the least squares method to calculate the straight line formed by the laser point cloud and all laser point clouds from the laser point cloud to its reference laser point cloud. If the straight line parameters determined based on the 1st to (n-1st)th laser point cloud are greater than the straight line parameters determined based on the 1st to nth laser point cloud, it indicates that the newly added nth laser point cloud has a large deviation, i.e., it is an outlier. At this time, the data processing device needs to remove the nth laser point cloud and determine the next selected laser point cloud as the nth laser point cloud. Then, it continues to select laser point clouds sequentially from the corrected laser point cloud data according to the preset direction until the kth laser point cloud is selected, and the straight line residual determined based on the 1st to kth laser point cloud is greater than the preset residual threshold. In this way, the data processing device removes all outliers during the straight line detection process of the laser point clouds included in the corrected laser point cloud data, which can improve the accuracy of data processing.

[0086] It should be noted that, in the embodiments of the present invention, when the linear residual determined based on the first laser point cloud to the kth laser point cloud is greater than the preset residual threshold, the data processing device determines the linear residual determined based on the first laser point cloud to the (k-1)th laser point cloud as the linear residual corresponding to each laser point cloud in the first laser point cloud to the (k-1)th laser point cloud. Then, taking the kth laser point cloud as the reference, the device continues to perform linear detection on the laser point clouds included in the corrected laser point cloud data in a preset direction until the linear residual corresponding to each laser point cloud included in the corrected laser point cloud data is obtained.

[0087] Specifically, in an embodiment of the present invention, the data processing device uses the k-th laser point cloud as a reference and continues to perform straight line detection on the laser point clouds included in the corrected laser point cloud data according to a preset direction. This includes: if the straight line residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to a preset residual threshold, and the straight line residual determined based on the m-th laser point cloud to the last laser point cloud and the 1st laser point cloud is greater than the preset residual threshold, then the straight line determined based on the m-th laser point cloud to the last laser point cloud is determined as the straight line corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud; where m is a natural number greater than k.

[0088] It should be noted that, in the embodiments of the present invention, if the linear residual determined by the data processing device based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to a preset residual threshold, and the linear residual determined based on the m-th laser point cloud to the last laser point cloud is greater than the preset residual threshold, it indicates that the m-th laser point cloud to the last laser point cloud exactly forms a straight line. At this time, the straight line determined based on the m-th laser point cloud to the last laser point cloud can be directly determined as the straight line corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud, so as to obtain the straight line corresponding to each laser point cloud included in the corrected laser point cloud data.

[0089] Specifically, in an embodiment of the present invention, the data processing device uses the k-th laser point cloud as a reference and continues to perform straight line detection on the laser point clouds included in the corrected laser point cloud data according to a preset direction. This includes: when the straight line residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to a preset residual threshold, and the straight line residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, the straight line determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is determined as the straight line corresponding to each laser point cloud in the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud; where m is a natural number greater than k.

[0090] It should be noted that, in the embodiments of the present invention, if the linear residual determined by the data processing device based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to a preset residual threshold, and the linear residual determined based on the 1st laser point cloud to the kth laser point cloud and the m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, it indicates that the linear lines determined by the 1st laser point cloud to the kth laser point cloud and the m-th laser point cloud to the last laser point cloud can be merged. At this time, the data processing device can determine the linear lines determined based on the 1st laser point cloud to the kth laser point cloud and the m-th laser point cloud to the last laser point cloud as the linear lines corresponding to each laser point cloud in the 1st laser point cloud to the kth laser point cloud and the m-th laser point cloud to the last laser point cloud.

[0091] Specifically, in an embodiment of the present invention, the data processing device uses the k-th laser point cloud as a reference and continues to perform straight line detection on the laser point clouds included in the corrected laser point cloud data according to a preset direction. This includes: when the straight line residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to a preset residual threshold, and the straight line residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is greater than the preset residual threshold, the straight line determined based on the m-th laser point cloud to the last laser point cloud is determined as the straight line corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud; where m is a natural number greater than k.

[0092] It should be noted that, in the embodiments of the present invention, if the linear residual determined by the data processing device based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to a preset residual threshold, and the linear residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is greater than the preset residual threshold, it indicates that the linear lines determined by the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud cannot be merged. In this case, the data processing device directly determines the linear line determined based on the m-th laser point cloud to the last laser point cloud as the linear line corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud.

[0093] S103. For each laser point cloud included in the corrected laser point cloud data, set the corresponding weight according to the distance to the corresponding straight line.

[0094] In an embodiment of the present invention, the data processing device sets a corresponding weight for each laser point cloud included in the corrected laser point cloud data based on its distance from the corresponding straight line.

[0095] It should be noted that, in the embodiments of the present invention, when the data processing device obtains each laser point cloud included in the corrected laser point cloud data, it sets a corresponding weight based on the corresponding straight-line distance, that is, it weights the offset of each laser point cloud. In this way, it can better characterize the importance of each laser point cloud data for positioning and improve the accuracy of data processing.

[0096] S104. Using the point-to-line nearest iteration algorithm, based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data, determine the positioning information of the lidar and the actual laser point cloud data.

[0097] In an embodiment of the present invention, the data processing device uses a point-to-line nearest iteration algorithm to determine the positioning information of the lidar and the actual lidar point cloud data based on the corrected lidar point cloud data and the weight of each lidar point cloud included in the corrected lidar point cloud data.

[0098] It should be noted that, in the embodiments of the present invention, the data processing device can input the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data into the point-to-line iterative closest point (PL-ICP) algorithm to obtain the positioning information of the lidar and the actual laser point cloud data. The PL-ICP algorithm can further adjust the corrected laser point cloud data to better reflect the current actual scene, thus obtaining the actual laser point cloud data.

[0099] Figure 4 This is a schematic diagram illustrating an exemplary laser point cloud data processing flow according to an embodiment of the present invention. Figure 4 As shown, the data processing device first acquires laser point cloud data, as well as inertial measurement unit (IMU) data and lidar odometry data. Then, it corrects the laser point cloud data based on the IMU and lidar odometry data, i.e., distortion elimination, to obtain corrected laser point cloud data. Next, it performs line detection on the laser point clouds included in the corrected laser point cloud data, i.e., removing outliers with large offsets from the corrected laser point cloud data, and assigns a corresponding weight to each laser point cloud in the outlier-removed laser point cloud data. Finally, based on the outlier-removed laser point cloud data and the weights of each laser point cloud, it performs positioning and adjustment to obtain actual laser point cloud data that conforms to the actual scene.

[0100] This invention provides a data processing method, comprising: acquiring a frame of laser point cloud data using a lidar, and correcting the laser point cloud data to obtain corrected laser point cloud data; performing line detection on the laser point clouds included in the corrected laser point cloud data to obtain a line corresponding to each laser point cloud included in the corrected laser point cloud data; setting a corresponding weight for each laser point cloud included in the corrected laser point cloud data based on its distance to the corresponding line; and using a point-to-line nearest iteration algorithm, determining the lidar positioning information and the actual laser point cloud data based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data. The data processing method provided by this invention, before the positioning and scene construction process based on the laser point cloud data acquired by the lidar, not only corrects the motion distortion existing in the laser point cloud data, but also removes point cloud data with large offset errors by line detection; furthermore, it sets a corresponding weight for each laser point cloud, so that the positioning information and the actual laser point cloud data determined based on the more accurate laser point cloud data are more accurate, thereby improving the accuracy of data processing.

[0101] This invention provides a data processing device. Figure 5 A schematic diagram of the structure of a data processing device provided in an embodiment of the present invention. Figure 1 .like Figure 5 As shown, it includes:

[0102] The acquisition module 501 is used to acquire a frame of laser point cloud data using a lidar and to correct the laser point cloud data to obtain corrected laser point cloud data.

[0103] The detection module 502 is used to perform straight line detection on the laser point cloud included in the corrected laser point cloud data to obtain the straight line corresponding to each laser point cloud included in the corrected laser point cloud data.

[0104] The setting module 503 is used to set a corresponding weight for each laser point cloud included in the corrected laser point cloud data according to the distance to the corresponding straight line.

[0105] The determination module 504 is used to determine the positioning information of the lidar and the actual lidar point cloud data based on the corrected lidar point cloud data and the weight of each lidar point cloud included in the corrected lidar point cloud data, using the point-to-line nearest iteration algorithm.

[0106] In one embodiment of the present invention, the acquisition module 501 is further configured to acquire the pose change information of the lidar during the process of the lidar acquiring the lidar point cloud data; and to correct the lidar point cloud data based on the pose change information to obtain the corrected lidar point cloud data.

[0107] In one embodiment of the present invention, the acquisition module 501 is further configured to acquire inertial measurement unit data and / or lidar odometer data; and determine the pose change information based on the inertial measurement unit data and / or lidar odometer data using a nonlinear Kalman filter method.

[0108] In one embodiment of the present invention, the pose change information includes: initial pose information when acquiring the first laser point cloud and final pose information when acquiring the last laser point cloud during the process of the lidar acquiring the laser point cloud data. The acquisition module 501 is further configured to divide the acquisition time of the lidar acquiring the laser point cloud data into multiple sub-time periods; for each of the multiple sub-time periods, estimate the corresponding pose information using linear interpolation based on the initial pose information and the final pose information; acquire the laser point clouds acquired within each of the multiple sub-time periods from the laser point cloud data to obtain multiple sets of laser point clouds that correspond one-to-one with the multiple sub-time periods; for each of the multiple sets of laser point clouds, determine the pose information corresponding to the same sub-time period as the pose information corresponding to each laser point cloud in the group; for each laser point cloud included in the laser point cloud data, using the coordinate system where the first laser point cloud is located as the reference coordinate system, determine the corresponding laser point cloud data in the reference coordinate system using the corresponding pose information to obtain the corrected laser point cloud data.

[0109] In one embodiment of the present invention, the detection module 502 is further configured to select a first laser point cloud from the laser point clouds included in the corrected laser point cloud data, and determine the first laser point cloud as the first laser point cloud; using the first laser point cloud as a reference, sequentially select laser point clouds from the corrected laser point cloud data according to a preset direction until the kth laser point cloud is selected, and the linear residual determined based on the first laser point cloud to the kth laser point cloud is greater than a preset residual threshold; where k is a natural number greater than 1; determine the linear line determined based on the first laser point cloud to the (k-1)th laser point cloud as the linear line corresponding to each laser point cloud from the first laser point cloud to the (k-1)th laser point cloud; using the kth laser point cloud as a reference, continue to perform linear detection on the laser point clouds included in the corrected laser point cloud data according to the preset direction until the linear line corresponding to each laser point cloud included in the corrected laser point cloud data is obtained.

[0110] In one embodiment of the present invention, the detection module 502 is further configured to, based on the first laser point cloud, sequentially select laser point clouds from the corrected laser point cloud data according to the preset direction until the nth laser point cloud is selected, and the straight line parameters determined based on the first laser point cloud to the (n-1)th laser point cloud are greater than the straight line parameters determined based on the first laser point cloud to the nth laser point cloud; wherein, n is a natural number greater than 1 and less than or equal to k; remove the nth laser point cloud from the corrected laser point cloud data, and re-determine the next selected laser point cloud as the nth laser point cloud; continue to sequentially select laser point clouds from the corrected laser point cloud data according to the preset direction until the kth laser point cloud is selected, and the straight line residual determined based on the first laser point cloud to the kth laser point cloud is greater than the preset residual threshold.

[0111] In one embodiment of the present invention, the detection module 502 is further configured to determine the straight line determined based on the m-th laser point cloud to the last laser point cloud as the straight line corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud when the straight line residual determined based on the m-th laser point cloud to the last laser point cloud and the 1st laser point cloud is less than or equal to the preset residual threshold, and the straight line residual determined based on the m-th laser point cloud to the last laser point cloud and the 1st laser point cloud is greater than the preset residual threshold; wherein, m is a natural number greater than k.

[0112] In one embodiment of the present invention, the detection module 502 is further configured to, when the linear residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, and the linear residual determined based on the first laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, determine the linear line determined based on the first laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud as the linear line corresponding to each laser point cloud in the first laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud; wherein m is a natural number greater than k.

[0113] In one embodiment of the present invention, the detection module 502 is further configured to determine the straight line determined based on the m-th laser point cloud to the last laser point cloud as the straight line corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud when the linear residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, and the linear residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is greater than the preset residual threshold; wherein, m is a natural number greater than k.

[0114] This invention provides a data processing device. Figure 6 A schematic diagram of the structure of a data processing device provided in an embodiment of the present invention. Figure 2 .like Figure 6 As shown, the data processing device includes: a processor 601, a memory 602, and a communication bus 603;

[0115] The communication bus 603 is used to realize the communication connection between the processor 601 and the memory 602;

[0116] The processor 601 is used to execute the computer program stored in the memory 602 to implement the above-described data processing method.

[0117] This invention provides a data processing device that uses a lidar to acquire a frame of lidar point cloud data and corrects the lidar point cloud data to obtain corrected lidar point cloud data. Line detection is performed on the lidar point clouds included in the corrected lidar point cloud data to obtain the line corresponding to each lidar point cloud in the corrected lidar point cloud data. For each lidar point cloud included in the corrected lidar point cloud data, a corresponding weight is set according to its distance to the corresponding line. Using a point-to-line nearest iteration algorithm, based on the corrected lidar point cloud data and the weights of each lidar point cloud included in the corrected lidar point cloud data, the lidar positioning information and the actual lidar point cloud data are determined. Before the positioning and scene construction process based on the lidar-acquired lidar point cloud data, the data processing device provided by this invention not only corrects motion distortion in the lidar point cloud data but also removes point cloud data with large offset errors by using line detection. Furthermore, it sets corresponding weights for each lidar point cloud to make the positioning information and the actual lidar point cloud data determined based on the more accurate lidar point cloud data more accurate, thereby improving the accuracy of data processing.

[0118] This invention provides a computer-readable storage medium storing one or more computer programs, which can be executed by one or more processors to implement the aforementioned data processing method. The computer-readable storage medium can be volatile memory, such as random-access memory (RAM); or non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or it can be a device comprising one or any combination of the above-mentioned memories, such as a mobile phone, computer, tablet device, personal digital assistant, etc.

[0119] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0120] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0123] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A data processing method, characterized in that, The method includes: A frame of laser point cloud data is acquired using a lidar, and the laser point cloud data is corrected to obtain corrected laser point cloud data. Line detection is performed on the laser point cloud included in the corrected laser point cloud data to obtain the line corresponding to each laser point cloud included in the corrected laser point cloud data; For each laser point cloud included in the corrected laser point cloud data, a corresponding weight is set according to the distance to the corresponding straight line; Using the point-to-line nearest iteration algorithm, based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data, the positioning information of the lidar and the actual laser point cloud data are determined.

2. The method according to claim 1, characterized in that, The step of correcting the laser point cloud data to obtain corrected laser point cloud data includes: The pose change information of the lidar during the process of acquiring the lidar point cloud data; Based on the pose change information, the laser point cloud data is corrected to obtain the corrected laser point cloud data.

3. The method according to claim 2, characterized in that, The pose change information of the lidar during the process of acquiring the lidar point cloud data includes: Acquire inertial measurement unit data, and / or lidar odometry data; The pose change information is determined using a nonlinear Kalman filter method based on the inertial measurement unit data and / or the lidar odometry data.

4. The method according to claim 2, characterized in that, The pose change information includes: the initial pose information when acquiring the first laser point cloud and the final pose information when acquiring the last laser point cloud during the process of the lidar acquiring the laser point cloud data; the step of correcting the laser point cloud data based on the pose change information to obtain corrected laser point cloud data includes: The acquisition time of the laser point cloud data by the lidar is divided into multiple sub-time periods; For each of the multiple sub-time periods, the corresponding pose information is estimated by linear interpolation based on the initial pose information and the final pose information. From the laser point cloud data, obtain the laser point cloud collected in each of the multiple sub-time periods, and obtain multiple sets of laser point clouds that correspond one-to-one with the multiple sub-time periods; For each of the multiple sets of laser point clouds, the pose information corresponding to the same sub-time period is determined as the pose information corresponding to each laser point cloud in the group; For each laser point cloud included in the laser point cloud data, the coordinate system where the first laser point cloud is located is used as the reference coordinate system. The corresponding pose information is used to determine the corresponding laser point cloud data in the reference coordinate system, and the corrected laser point cloud data is obtained.

5. The method according to claim 1, characterized in that, The step of performing line detection on the laser point clouds included in the corrected laser point cloud data to obtain the line corresponding to each laser point cloud in the corrected laser point cloud data includes: Select a first laser point cloud from the laser point clouds included in the corrected laser point cloud data, and determine the first laser point cloud as the first laser point cloud; Using the first laser point cloud as a reference, laser point clouds are sequentially selected from the corrected laser point cloud data according to a preset direction until the kth laser point cloud is selected, and the linear residual determined based on the first laser point cloud to the kth laser point cloud is greater than a preset residual threshold; where k is a natural number greater than 1. The straight line determined based on the first laser point cloud to the (k-1)th laser point cloud is defined as the straight line corresponding to each laser point cloud in the first laser point cloud to the (k-1)th laser point cloud. Using the k-th laser point cloud as a reference, continue to perform straight line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction until the straight line corresponding to each laser point cloud included in the corrected laser point cloud data is obtained.

6. The method according to claim 5, characterized in that, The step of selecting laser point clouds sequentially from the corrected laser point cloud data according to a preset direction, based on the first laser point cloud, until the kth laser point cloud is selected, and ensuring that the linear residual determined from the first laser point cloud to the kth laser point cloud is greater than a preset residual threshold, includes: Using the first laser point cloud as a reference, laser point clouds are sequentially selected from the corrected laser point cloud data according to the preset direction until the nth laser point cloud is selected. The straight line parameters determined based on the first laser point cloud to the (n-1)th laser point cloud are greater than the straight line parameters determined based on the first laser point cloud to the nth laser point cloud, respectively, and are greater than a preset parameter threshold. Here, n is a natural number greater than 1 and less than or equal to k. Remove the nth laser point cloud from the corrected laser point cloud data, and re-determine the nth laser point cloud as the next selected laser point cloud; Continue to select laser point clouds sequentially from the corrected laser point cloud data according to the preset direction until the kth laser point cloud is selected, and the linear residual determined based on the 1st laser point cloud to the kth laser point cloud is greater than the preset residual threshold.

7. The method according to claim 5, characterized in that, The step of using the k-th laser point cloud as a reference and continuing to perform straight-line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction includes: If the linear residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, and the linear residual determined based on the m-th laser point cloud to the last laser point cloud and the 1st laser point cloud is greater than the preset residual threshold, then the linear residual determined based on the m-th laser point cloud to the last laser point cloud is determined as the linear residual corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud; where m is a natural number greater than k.

8. The method according to claim 5, characterized in that, The step of using the k-th laser point cloud as a reference and continuing to perform straight-line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction includes: If the linear residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, and the linear residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, then the linear residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is determined as the linear residual corresponding to each laser point cloud in the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud; where m is a natural number greater than k.

9. The method according to claim 5, characterized in that, The step of using the k-th laser point cloud as a reference and continuing to perform straight-line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction includes: If the linear residual determined based on the selected m-th laser point cloud to the last laser point cloud is less than or equal to the preset residual threshold, and the linear residual determined based on the 1st laser point cloud to the k-th laser point cloud and the m-th laser point cloud to the last laser point cloud is greater than the preset residual threshold, then the linear residual determined based on the m-th laser point cloud to the last laser point cloud is determined as the linear residual corresponding to each laser point cloud in the m-th laser point cloud to the last laser point cloud; where m is a natural number greater than k.

10. A data processing apparatus, characterized in that, include: The acquisition module is used to acquire a frame of laser point cloud data using a lidar and to correct the laser point cloud data to obtain corrected laser point cloud data. The detection module is used to perform straight line detection on the laser point cloud included in the corrected laser point cloud data to obtain the straight line corresponding to each laser point cloud included in the corrected laser point cloud data. The setting module is used to set a corresponding weight for each laser point cloud included in the corrected laser point cloud data, based on the distance to the corresponding straight line. The determination module is used to determine the positioning information of the lidar and the actual lidar point cloud data based on the corrected lidar point cloud data and the weight of each lidar point cloud included in the corrected lidar point cloud data, using the point-to-line nearest iteration algorithm.

11. A data processing apparatus, characterized in that, include: Processor, memory, and communication bus; The communication bus is used to realize the communication connection between the processor and the memory; The processor is configured to execute a computer program stored in the memory to implement the data processing method according to any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more computer programs, which can be executed by one or more processors to implement the data processing method according to any one of claims 1-9.

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