A data processing method and device

Abstract

The present disclosure provides a data processing method and device, comprising: acquiring positioning data collected by a positioning device and point cloud data collected by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle; based on the positioning data and the point cloud data, determining the position data of the radar device when the positioning device collects the positioning data; based on the point cloud data, determining ground parameter information representing the ground, and, based on the position data, determining initial posture data of the radar device; based on the ground parameter information and the initial posture data, adjusting external parameter data representing the relative position relationship between the positioning device and the radar device.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing technology, and in particular to a data processing method and device. Background Art

[0002] With the development of information technology, unmanned driving technology has gradually been widely used. At present, unmanned driving technology mainly collects radar point cloud data through laser radar, and then determines the position information of the laser radar based on the positioning device (such as the Global Positioning System (GPS), combined inertial navigation, etc.), and then fuses the point cloud data with the position information to determine the position relationship between the unmanned driving device and the obstacle, thereby achieving obstacle avoidance. When determining the position information of the laser radar based on the positioning device, it is generally first determined by determining the position information of the positioning device, and then converted to the position information of the laser radar through external reference data, wherein the external reference data is used to represent the relative position relationship between the positioning device and the laser radar.

[0003] In the related art, when determining the external parameter data, it is necessary to calibrate the position of the positioning device and the laser radar with the help of a series of equipment. However, during the use of the unmanned driving device, the position of the positioning device and the laser radar may change, so the external parameter data needs to be updated every preset time. During each update, it is necessary to manually recalibrate the position of the positioning device and the laser radar with the help of measuring equipment, and then re-determine the external parameter data. This method is inefficient. Summary of the invention

[0004] The embodiments of the present disclosure at least provide a data processing method and device.

[0005] In a first aspect, an embodiment of the present disclosure provides a data processing method, including:

[0006] Acquire positioning data collected by a positioning device and point cloud data collected by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle;

[0007] Based on the positioning data and the point cloud data, determining the position data of the radar device when the positioning device collects the positioning data;

[0008] Based on the point cloud data, determining ground parameter information characterizing the ground, and based on the position data, determining initial position and posture data of the radar device;

[0009] Based on the ground parameter information and the initial posture data, external parameter data characterizing the relative position relationship between the positioning device and the radar device is adjusted.

[0010] Through the above implementation, the ground parameter information representing the ground and the initial posture data of the radar device can be determined according to the positioning data collected by the positioning device and the radar data collected by the radar device, and then the external parameter data can be adjusted based on the ground parameter information and the initial posture data of the radar device. Since the ground parameter information and the initial posture data of the radar device can be obtained through data collection or calculation, during the adjustment of the external parameter data, there is no need to calibrate the positions of the positioning device and the radar device by manpower with the help of other equipment, which improves the adjustment efficiency of the external parameter data and saves labor costs.

[0011] In a possible implementation manner, the point cloud data includes coordinate information of a plurality of radar scanning points;

[0012] The determining, based on the point cloud data, ground parameter information characterizing the ground includes:

[0013] Performing plane fitting based on the point cloud data to obtain fitting plane information;

[0014] Determine, based on the coordinate information of the radar scanning points in the point cloud data, the distance between each radar scanning point and the plane indicated by the fitting plane information;

[0015] Based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information, point cloud data is screened, and based on the screened point cloud data, the step of plane fitting based on the point cloud data is returned to until a preset iteration condition is reached, and the ground parameter information is determined based on the last screened point cloud data.

[0016] In the above implementation, the point cloud data is screened multiple times by iterative fitting, and then the ground parameter information is determined based on the finally screened point cloud data, thereby improving the accuracy of the ground parameter information.

[0017] In a possible implementation, before performing plane fitting based on the point cloud data, at least one of the following data screening processes is performed:

[0018] Downsampling the point cloud data so that the distribution density of radar scanning points in the point cloud data meets a preset condition;

[0019] According to the preset installation height of the radar device, the target coordinate range is determined, and the radar scanning points whose corresponding coordinate information is within the target coordinate range are screened out.

[0020] Before performing plane fitting based on point cloud data, the point cloud data should be screened first. On the one hand, this can improve the calculation accuracy of ground parameter information, and on the other hand, it can reduce the amount of calculation in the plane fitting process and improve the calculation efficiency.

[0021] In a possible implementation manner, the position data is height data;

[0022] The method further comprises:

[0023] Based on the height data in the position data and the corresponding relationship between the position data and the point cloud data, the point cloud data is divided into a plurality of collection intervals.

[0024] In a possible implementation manner, dividing the point cloud data into a plurality of collection intervals based on the height data in the position data and the correspondence between the position data and the point cloud data includes:

[0025] Filtering the height data to determine at least one extreme point in the height data retained after filtering;

[0026] The point cloud data is divided into multiple collection intervals using the collection time points corresponding to the extreme value points as the dividing points of the collection intervals.

[0027] In a possible implementation manner, determining ground parameter information characterizing the ground based on the point cloud data, and determining initial position and posture data of the radar device based on the position data, includes:

[0028] For each acquisition interval, determining ground parameter information corresponding to the acquisition interval based on the point cloud data in the acquisition interval, and determining initial posture data of the radar device corresponding to the acquisition interval based on the position data of the radar device when the point cloud data in the acquisition interval is acquired;

[0029] The adjusting, based on the ground parameter information and the initial posture data, the external parameter data characterizing the relative position relationship between the positioning device and the radar device comprises:

[0030] Based on the ground parameter information and the initial posture data corresponding to each acquisition interval, the external parameter data characterizing the relative position relationship between the positioning device and the radar device is adjusted.

[0031] In the above implementation, consideration is added to the ground height information by dividing the point cloud data into multiple acquisition intervals and determining the ground parameter information corresponding to different acquisition intervals. Then, based on the ground parameter information corresponding to different acquisition intervals and the initial posture data in the acquisition interval, when the external parameter data is adjusted, the adjusted external parameter is more accurate.

[0032] In a possible implementation, adjusting the external parameter data characterizing the relative position relationship between the positioning device and the radar device based on the ground parameter information and the initial posture data corresponding to each acquisition interval includes:

[0033] Determine the optimized posture data corresponding to each acquisition interval based on the ground parameter information and the initial posture data corresponding to each acquisition interval;

[0034] The external parameter data is adjusted based on the initial posture data corresponding to each acquisition interval and the optimized posture data.

[0035] In a possible implementation, determining the optimized posture data corresponding to each acquisition interval based on the ground parameter information and the initial posture data corresponding to each acquisition interval includes:

[0036] For any acquisition interval, based on the ground parameter information corresponding to the acquisition interval and the initial pose data, determine the pose data that minimizes the value of the objective function, and use the pose data that minimizes the value of the objective function as the optimized pose data corresponding to the acquisition interval, wherein the objective function is the sum of the absolute value of the difference between the ground parameter information before and after the pose data optimization and the absolute value of the difference between the pose data before and after the pose data optimization.

[0037] In a possible implementation, adjusting the external parameter data based on the initial posture data corresponding to each acquisition interval and the optimized posture data includes:

[0038] Based on the initial posture data corresponding to each acquisition interval and the optimized posture data, determining a first average posture change amount within the acquisition interval;

[0039] Determine, based on the first average posture change amount in each acquisition interval, a second average posture change amount corresponding to the multiple acquisition intervals;

[0040] Based on the second average posture change, the external parameter data is adjusted.

[0041] Due to the difference in ground parameter information in different acquisition intervals, the optimized posture data corresponding to the initial posture data determined according to the ground parameter information in different acquisition intervals is more accurate. Therefore, when calculating the first average posture change and the second average posture change, and adjusting the external parameter data based on the second average posture change, the adjusted external parameter data is more accurate.

[0042] In a possible implementation manner, adjusting the extrinsic parameter data based on the second average posture change includes:

[0043] The product of the second average posture change and the external parameter data before adjustment is determined as the adjusted external parameter data.

[0044] In a possible implementation manner, the positioning data includes a plurality of first collection time points and position data collected at each first collection time point, and the point cloud data includes a plurality of second collection time points and point cloud data collected at each second collection time point;

[0045] The determining, based on the positioning data and the point cloud data, the position data of the radar device when the positioning device collects the positioning data comprises:

[0046] For each second acquisition time point, two first acquisition time points with the shortest time interval from the second acquisition time point are respectively determined, wherein the two first acquisition time points include a first acquisition time point before the second acquisition time point and a first acquisition time point after the second acquisition time point;

[0047] Based on the position data respectively corresponding to the two first collection time points, the position data of the radar device when collecting the point cloud data at the second collection time point is determined.

[0048] Through the above implementation, the position data of the radar device when collecting point cloud data at each second collection time point can be determined, thereby avoiding the interference caused by the different data collection frequencies of the radar device and the positioning device.

[0049] In a second aspect, an embodiment of the present disclosure further provides a data processing device, including:

[0050] An acquisition module, used to acquire positioning data collected by a positioning device and point cloud data collected by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle;

[0051] A first determination module, configured to determine, based on the positioning data and the point cloud data, position data of the radar device when the positioning device collects the positioning data;

[0052] A second determination module is used to determine ground parameter information representing the ground based on the point cloud data, and to determine initial position and posture data of the radar device based on the position data;

[0053] An adjustment module is used to adjust the external parameter data characterizing the relative position relationship between the positioning device and the radar device based on the ground parameter information and the initial posture data.

[0054] In a third aspect, an embodiment of the present disclosure further provides a computer device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned first aspect, or any possible implementation of the first aspect are performed.

[0055] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned first aspect or any possible implementation of the first aspect are executed.

[0056] For a description of the effects of the above-mentioned data processing apparatus, computer device, and computer-readable storage medium, please refer to the description of the above-mentioned data processing method, which will not be repeated here.

[0057] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following is a brief introduction to the drawings required for use in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can also be obtained based on these drawings without creative work.

[0059] Figure 1 A schematic diagram showing a flow chart of a data processing method provided by an embodiment of the present disclosure;

[0060] Figure 2 A schematic diagram showing a comparison between a first acquisition time point and a second acquisition time point provided by an embodiment of the present disclosure is shown;

[0061] Figure 3 Another schematic diagram of comparison between a first acquisition time point and a second acquisition time point provided by an embodiment of the present disclosure is shown;

[0062] Figure 4 A schematic flow chart of a method for determining ground parameter information representing the ground based on point cloud data provided by an embodiment of the present disclosure is shown;

[0063] Figure 5A schematic diagram of a method for determining extreme value points of height data provided by an embodiment of the present disclosure is shown;

[0064] Figure 6 A schematic diagram of a flow chart of an external parameter data adjustment method provided by an embodiment of the present disclosure is shown;

[0065] Figure 7 A schematic diagram of the architecture of a data processing device provided by an embodiment of the present disclosure is shown;

[0066] Figure 8 A schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0068] Generally, before an autonomous driving vehicle leaves the factory, the position of the positioning device and radar device on the autonomous driving vehicle will be calibrated with the help of a series of equipment to determine the external parameter data. However, since the relative position between the positioning device and the radar device will change during the use of the autonomous driving vehicle, the external parameter data needs to be updated regularly.

[0069] In the related art, when updating the external parameter data, the external parameter data is re-determined using the method for determining the external parameter data when the autonomous driving vehicle leaves the factory. This process is complicated and inefficient.

[0070] In another method, the external parameter data can also be estimated through the hand-eye calibration algorithm. Specifically, the autonomous driving vehicle can be controlled to run a trajectory, and the positioning data collected by the positioning device on the autonomous driving vehicle and the point cloud data collected by the radar device during the operation can be recorded. Then, the position and posture trajectories of the positioning device and the radar device can be calculated respectively, and then the external parameters can be solved based on the hand-eye calibration algorithm.

[0071] However, in this method, the measurement accuracy of the positioning device and the radar device is relatively high, and it can only provide changing parameters in three degrees of freedom. However, since the external parameter data is parameters in six degrees of freedom, the external parameter error determined by this method is relatively large.

[0072] Based on this, the present disclosure provides a data processing method, which can determine ground parameter information characterizing the ground and the initial posture data of the radar device according to the positioning data collected by the positioning device and the radar data collected by the radar device, and then adjust the external parameter data based on the ground parameter information and the initial posture data of the radar device. Since the ground parameter information and the initial posture data of the radar device can be obtained through data collection or calculation, during the adjustment of the external parameter data, there is no need to calibrate the positions of the positioning device and the radar device by manpower with the help of other equipment, which improves the adjustment efficiency of the external parameter data and saves labor costs. In addition, in this scheme, the external parameter data is adjusted based on the posture change amount. Since the posture data is data on six degrees of freedom, when adjusting the external parameter data, the six quantities of the external parameter data can be adjusted, thereby improving the adjustment accuracy of the external parameter data.

[0073] The defects existing in the above solutions are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the present disclosure for the above problems below should be the contributions made by the inventor to the present disclosure during the disclosure process.

[0074] The technical solutions in the present disclosure will be clearly and completely described below in conjunction with the drawings in the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure claimed for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0075] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0076] To facilitate understanding of this embodiment, a data processing method disclosed in the embodiment of the present disclosure is first introduced in detail. The execution subject of the data processing method provided in the embodiment of the present disclosure is generally a computer device with certain computing capabilities, and the computer device includes, for example: a terminal device or a server or other processing device, and the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), etc. In some possible implementations, the data processing method can be processed by a processor installed on an autonomous driving device.

[0077] The data processing method provided by the embodiment of the present disclosure is described below by taking the execution subject as a processor installed on an automatic driving device as an example. It should be noted that the positioning device and the radar device are respectively connected to the processor, and the connection methods include but are not limited to wired connection and wireless connection, wherein the wireless connection method may be, for example, a Bluetooth connection, a wireless LAN connection, etc.

[0078] See also Figure 1 FIG. 1 is a flow chart of a data processing method provided by an embodiment of the present disclosure, which includes the following steps:

[0079] Step 101: Acquire positioning data collected by a positioning device and point cloud data collected by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle.

[0080] Step 102: Based on the positioning data and the point cloud data, determine the position data of the radar device when the positioning device collects the positioning data.

[0081] Step 103: Determine ground parameter information characterizing the ground based on the point cloud data, and determine initial position data of the radar device based on the position data.

[0082] Step 104: Based on the ground parameter information and the initial posture data, adjust the external parameter data characterizing the relative position relationship between the positioning device and the radar device.

[0083] Through the above implementation method, ground parameter information characterizing the ground and initial posture data of the radar device can be determined according to the positioning data collected by the positioning device and the radar data collected by the radar device, and then the external parameter data can be adjusted based on the ground parameter information and the initial posture data of the radar device. Since the ground parameter information and the initial posture data of the radar device can be obtained through data collection or calculation, during the adjustment of the external parameter data, there is no need to calibrate the positions of the positioning device and the radar device by manpower with the help of other equipment, which improves the adjustment efficiency of the external parameter data and saves labor costs.

[0084] The following is a detailed description of steps 101 to 104 .

[0085] For step 101:

[0086] The positioning device and the radar device can be installed on the same vehicle, and the positioning device and the radar device are installed at different locations. The positioning device can be, for example, a global positioning system (GPS) or a combined inertial navigation system, which is a system combining GPS and inertial sensors.

[0087] For step 102,

[0088] In actual applications, the data collection frequencies of radar devices and positioning devices are different in the same time period. When collecting location data, the positioning device can collect location data according to the collection frequency set by the GPS satellite within the data collection time; when collecting point cloud data, the radar device can emit a radio beam at a set frequency to collect point cloud data at the set frequency. In actual applications, the collection frequency set by the GPS satellite is higher than the corresponding set frequency of the radar device.

[0089] The positioning data collected by the positioning device includes multiple first collection time points and position data collected at each first collection time point, wherein the position data collected at each first collection time point includes longitude and latitude data, and altitude data, and the altitude data is the altitude relative to the sea level; the point cloud data includes multiple second collection time points and point cloud data collected at each second collection time point, wherein the point cloud data collected at each second collection time point includes coordinate information of multiple radar scanning points.

[0090] It should be noted that the positioning data output by the positioning device is positioning data adjusted based on external parameter data characterizing the relative position relationship between the positioning device and the radar device, that is, the positioning data output by the positioning device is positioning data of the radar device.

[0091] Since the frequency at which the radar device collects radar data is different from the frequency at which the positioning device collects positioning data, it is necessary to determine the position data of the radar device when the positioning device collects positioning data based on the positioning data and the radar data, that is, to determine the corresponding position data of the radar device when the positioning device collects point cloud data at each second collection time point.

[0092] Specifically, for each second collection time point, it is possible to first detect whether there is a first collection time point corresponding to the second collection time point; if the second collection time point has a corresponding first collection time point, the position data of the first collection time point corresponding to the second collection time point is determined as the position data of the radar device when collecting the point cloud data at the second collection time point; if the second collection time point does not have a corresponding first collection time point, the position data of the radar device when collecting the point cloud data at the second collection time point is calculated based on the time interval between the first collection time point and the second collection time point.

[0093] In a possible implementation, when calculating the position data of the radar device when collecting point cloud data at the second collection time point, for each second collection time point, two first collection time points with the shortest time interval between the second collection time point can be respectively determined, the two first collection time points including a first collection time point before the second collection time point and a first collection time point after the second collection time point, and then based on the position data corresponding to the two first collection time points, the position data of the radar device when collecting point cloud data at the second collection time point is determined.

[0094] Specifically, when determining the position data of the radar device when collecting point cloud data at the second acquisition time point based on the data positions corresponding to the two first acquisition time points, if the first acquisition time point and the second acquisition time point are evenly distributed, the average of the position data of the two first acquisition time points with the shortest interval between the second time point can be determined as the position data of the radar device when collecting point cloud data at the second acquisition time point.

[0095] For example, Figure 2 As shown, Figure 2The points a1, b1, c1, d1, e1, f1, and g1 above the horizontal line all represent the first acquisition time point, and each first acquisition time point has corresponding position data. The points a2, b2, c2, d2, and e2 below the horizontal line all represent the second acquisition time point, and each second acquisition time point has corresponding point cloud data. For the first acquisition time point a1, d1, and g1, there is a corresponding second acquisition time point. The specific corresponding relationship is a1→a2, d1→c2, and g1→e2. The position data corresponding to a1 is determined as the position data of the radar device when a2 collects point cloud data. The position data corresponding to d1 is determined as the position data of the radar device when collecting point cloud data at c2, and the position data corresponding to g1 is determined as the position data of the radar device when collecting point cloud data at e2; and for the second collection time point that has no corresponding first collection time point, such as b2, the first collection time points b1 and c1 that are closest to b2 are selected, and then the average of the position data corresponding to b1 and c1, respectively, is determined as the position data of the radar device when collecting point cloud data at b2, and so on, the position data of the radar device when collecting point cloud data at d2 is calculated.

[0096] Among them, when calculating the mean of the location data of the two first collection time points, each item of data contained in the location data can be averaged. For example, if the location data includes longitude, latitude, and altitude, the mean of the longitude, the mean of the latitude, and the mean of the altitude can be calculated respectively, and then the mean of the longitude, the mean of the latitude, and the mean of the altitude can be used as the location data after averaging.

[0097] When determining the position data of the radar device when collecting point cloud data at the second acquisition time point based on the data positions corresponding to the two first acquisition time points respectively, if the first acquisition time point and the second acquisition time point are not evenly distributed, then when calculating the position data of the radar device when collecting point cloud data at the second acquisition time point without the corresponding first acquisition time, it can be determined based on the distance between the two first time points and the second acquisition time point.

[0098] For example, Figure 3 As shown, Figure 3 In the figure, point a and point b are the first acquisition time points, and point c is the second acquisition time point. When determining the position data of the radar device when collecting point cloud data at point c, the weight corresponding to the position data of point a and the weight corresponding to the position data of point b can be determined according to the distance between point a and point c and the distance between point b and point c, respectively. Then, the position data of point a and the position data of point b are weightedly summed according to their respective corresponding weights to obtain the position data of the radar device when collecting point cloud data at point c.

[0099] Through the above implementation, the position data of the radar device when collecting point cloud data at each second collection time point can be determined, thereby avoiding the interference caused by the different data collection frequencies of the radar device and the positioning device.

[0100] Regarding step 103:

[0101] The ground parameter information representing the ground includes the normal vector and the intercept. Specifically, when determining the ground parameter information representing the ground based on the point cloud data, you can refer to Figure 4 The method shown includes the following steps:

[0102] Step 401: Perform plane fitting based on the point cloud data to obtain fitting plane information.

[0103] When plane fitting is performed based on point cloud data, the plane fitting can be performed based on a random sample consensus algorithm (RANSAC). Specifically, point cloud data of a preset proportion can be randomly selected from the point cloud data, and plane fitting can be performed based on the randomly selected point cloud data.

[0104] Step 402: Based on the coordinate information of the radar scanning points in the point cloud data, determine the distance between each radar scanning point and the plane indicated by the fitting plane information.

[0105] Step 403: perform point cloud data screening based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information.

[0106] When determining ground parameter information, it is necessary to fit the ground based on the point cloud data. Therefore, when filtering the point cloud data, the radar scanning points that are not on the ground are filtered out. Specifically, when filtering the point cloud data based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information, the radar scanning points whose distance to the plane indicated by the fitting information exceeds a preset distance range can be filtered out.

[0107] Step 404: Determine whether a preset iteration condition is met.

[0108] If the preset iteration condition is met, execute step 405;

[0109] If the preset iteration condition is not satisfied, the process returns to step 401 based on the filtered point cloud data.

[0110] The preset iteration condition may be that a preset number of iterations is reached, or when point cloud data is screened in step 403, the distances between all radar scanning points and the plane indicated by the fitting plane information are within a preset distance range.

[0111] Step 405: Determine ground parameter information based on the finally filtered point cloud data.

[0112] In the above method, the point cloud data is screened multiple times by iterative fitting, and then the ground parameter information is determined based on the finally screened point cloud data, thereby improving the accuracy of the ground parameter information.

[0113] When determining the ground parameter information based on the last filtered point cloud data, plane fitting may be performed again based on the last filtered point cloud data, and then the ground parameter information of the fitted plane is determined, wherein the ground parameter information includes a normal vector and an intercept.

[0114] In a possible implementation, in order to improve the efficiency of data screening, before performing plane fitting based on point cloud data, at least one of the following data screening processes may be performed:

[0115] 1. Downsample the point cloud data so that the distribution density of the radar scanning points in the point cloud data meets the preset conditions;

[0116] Among them, since the density of point cloud data will affect the efficiency of plane fitting, the point cloud data can be downsampled to improve the efficiency of point cloud data screening, thereby improving the efficiency of determining ground parameter information. Exemplarily, the point cloud data can be downsampled by voxel filtering.

[0117] 2. According to the preset installation height of the radar device, determine the target coordinate range, and filter out the radar scanning points whose corresponding coordinate information is within the target coordinate range.

[0118] The preset installation height of the radar device is the height of the radar device from the ground. When establishing the world coordinate system, the location of the radar device can be used as the starting point of the Z axis, and then the target coordinate range can be determined based on the preset installation height of the radar device. The target coordinate range can be used to represent the height range of the radar scanning point on the ground on the Z axis. By screening out the radar scanning points whose corresponding coordinate information is within the target coordinate range, the point cloud data can be roughly screened, thereby improving the efficiency of point cloud data screening and thereby improving the efficiency of determining ground parameter information.

[0119] The pose data of the radar device includes not only the coordinates of the radar device in the established world coordinate system, but also at least one of the pitch angle, the yaw angle, and the roll angle. When determining the initial pose data of the radar device based on the position data of the radar device when collecting point cloud data at each second collection time point, since the position data includes longitude, latitude and altitude, the position of the radar device in the three-dimensional space is determined. Therefore, after the world coordinate system is established for the three-dimensional space, the pose data of the radar device in the world coordinate system is also determined.

[0120] Taking into account that during the data collection process, the autonomous driving device may not be traveling on the same ground plane, and therefore the ground parameter information between different ground planes may not be the same. Therefore, in another embodiment of the present application, the point cloud data can be divided into multiple collection intervals based on the height data in the position data and the correspondence between the position data and the point cloud data. Each collection interval includes multiple second collection time points, and within each collection interval, the vehicle can be regarded as traveling on the same ground plane.

[0121] In a possible implementation, when dividing the point cloud data into multiple acquisition intervals according to the height data in the position data, the height data can be filtered based on the height value of the height data at each second acquisition time point, and then at least one extreme point in the height data retained after the filtering process is determined, and then the acquisition time points corresponding to the respective extreme points are used as the dividing points of the acquisition intervals to divide the point cloud data into multiple acquisition intervals.

[0122] Considering that the height data collected by the automatic driving device during driving may be affected by ground obstacles, such as bumps caused by obstacles, the height data collected by the automatic driving device may be filtered first. Specifically, the height values ​​with a fluctuation frequency higher than a set frequency or a fluctuation amplitude less than a set amplitude may be filtered out.

[0123] For example, Figure 5 As shown, Figure 5 In the coordinate graph in , the horizontal axis is the second acquisition time point, the vertical axis is the height data in the position data corresponding to each second acquisition time point, the points in the line graph are extreme points, and the second acquisition time point between every two extreme points is an acquisition interval. Each acquisition interval includes multiple second acquisition time points, and similarly also includes multiple frames of point cloud images.

[0124] After dividing the point cloud data into multiple acquisition intervals, for each acquisition interval, the ground parameter information corresponding to the acquisition interval can be determined based on the point cloud data in the acquisition interval, and the initial posture data of the radar device corresponding to the acquisition interval can be determined based on the position data of the radar device when collecting the point cloud data in the acquisition interval.

[0125] After determining the ground parameter information of each acquisition interval and the initial posture data of the radar device corresponding to the acquisition interval, the external parameter data characterizing the relative position relationship between the positioning device and the radar device can be adjusted based on the ground parameter information and the initial posture data corresponding to each of the acquisition intervals.

[0126] In a possible implementation manner, when adjusting the external parameter data based on the ground parameter information and the initial posture data corresponding to each acquisition interval, it is possible to refer to Figure 6 The external parameter data adjustment method shown includes the following steps:

[0127] Step 601: Determine the optimized posture data corresponding to each acquisition interval based on the ground parameter information and the initial posture data corresponding to each acquisition interval.

[0128] Specifically, when determining the optimized pose data corresponding to each acquisition interval based on the ground parameter information and the initial pose data corresponding to each acquisition interval, for any acquisition interval, the optimized pose data that minimizes the value of the objective function can be determined based on the ground parameter information corresponding to the acquisition interval and the initial pose data, and the pose data that minimizes the value of the objective function is used as the optimized pose data corresponding to the acquisition interval, wherein the objective function is the sum of the absolute value of the difference between the ground parameter information before and after the pose data optimization and the absolute value of the difference between the pose data before and after the pose data optimization.

[0129] For example, the objective function may be as shown in the following formula:

[0130]

[0131] in, Represents the initial pose data of the i-th frame point cloud image, represents the optimized pose data to be calculated corresponding to the initial pose data of the i-th frame point cloud image, Indicates the ground parameter information before calculation optimization, that is, the ground parameter information determined based on the point cloud data within the acquisition interval. represents the optimized ground parameter information, and N represents the number of point cloud images contained in the point cloud data within the acquisition interval, that is, the number of second acquisition time points within the acquisition interval.

[0132] For any acquisition interval, a group of point cloud data will be collected at each second acquisition time point in the acquisition interval. This group of point cloud data can constitute a frame of point cloud image. In the data acquisition process, multiple second acquisition time points are included, corresponding to multiple groups of point cloud data, each group of point cloud data corresponds to a frame of point cloud image, for each group of point cloud data, the initial pose data corresponding to the group of point cloud data is calculated and substituted into the above formula. By solving the formula by the least squares method, the optimized pose data corresponding to the initial pose data of each frame of point cloud image can be obtained.

[0133] In addition, by solving the above equations, optimized ground parameter information can also be obtained, but the optimized ground parameter information has nothing to do with the adjustment of external parameter data.

[0134] For example, if N=3, the above formula can be expanded as follows:

[0135]

[0136] Through the least squares method, the optimized pose data corresponding to the first frame point cloud image can be calculated respectively The optimized pose data corresponding to the second frame point cloud image The optimized pose data corresponding to the third frame point cloud image And the optimized ground parameter information

[0137] It should be noted that and It can be a six-dimensional vector, in which each element represents the coordinates x, y, z, pitch angle, yaw angle, and roll angle of the radar device in the world coordinate system. and It can be a four-dimensional vector. Each element in the vector represents the value and intercept of the normal vector. The normal vector is a three-dimensional vector.

[0138] Step 602: Adjust the external parameter data based on the initial posture data corresponding to each acquisition interval and the optimized posture data.

[0139] Specifically, when adjusting the external parameter data based on the initial posture data corresponding to each acquisition interval and the optimized posture data, the first average posture change within the acquisition interval can be determined based on the initial posture data corresponding to each acquisition interval and the optimized posture data; then, based on the first average posture change within each acquisition interval, the second average posture change corresponding to multiple acquisition intervals can be determined; and then, based on the second average posture change, the external parameter data can be adjusted.

[0140] In one possible implementation, when determining the first average pose change in any acquisition interval, for each frame of point cloud image corresponding to the point cloud data in the acquisition interval, the pose change between the initial pose data and the optimized pose data corresponding to the frame of point cloud image can be determined, and then the first average pose change can be calculated based on the pose change of multiple frames of point cloud images corresponding to the point cloud data.

[0141] For any frame of point cloud image, when calculating the pose change between the initial pose data corresponding to the frame of point cloud image and the optimized pose data, the change corresponding to the longitude, latitude and altitude in the initial pose data and the longitude, latitude and altitude in the optimized pose data can be calculated respectively.

[0142] In a possible implementation, when the external parameter data is adjusted based on the second average posture change, the product of the second average posture change and the external parameter data before the adjustment may be determined as the adjusted external parameter data.

[0143] Through the above method, the ground parameter information and the initial posture data of the radar device can be determined according to the positioning data collected by the positioning device and the radar data collected by the radar device, and then the external parameter data can be adjusted based on the ground parameter information and the initial posture data of the radar device. In the process of adjusting the external parameter data, there is no need to calibrate the relative positions of the positioning device and the radar device with the help of other equipment, which improves the accuracy and adjustment efficiency of the external parameter data, thereby achieving higher efficiency and accuracy in determining the posture data of the radar device.

[0144] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0145] Based on the same inventive concept, a data processing device corresponding to the data processing method is also provided in the embodiment of the present disclosure. Since the principle of solving the problem by the device in the embodiment of the present disclosure is similar to the above-mentioned data processing method in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0146] Reference Figure 7 , which is a schematic diagram of the architecture of a data processing device provided by an embodiment of the present disclosure, the device includes: an acquisition module 701, a first determination module 702, a second determination module 703, and an adjustment module 704; wherein,

[0147] An acquisition module 701 is used to acquire positioning data collected by a positioning device and point cloud data collected by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle;

[0148] A first determination module 702 is used to determine, based on the positioning data and the point cloud data, the position data of the radar device when the positioning device collects the positioning data;

[0149] A second determination module 703 is used to determine ground parameter information representing the ground based on the point cloud data, and to determine initial position and posture data of the radar device based on the position data;

[0150] The adjustment module 704 is used to adjust the external parameter data representing the relative position relationship between the positioning device and the radar device based on the ground parameter information and the initial posture data.

[0151] In a possible implementation, the point cloud data includes coordinate information of multiple radar scanning points;

[0152] The second determination module 703, when determining ground parameter information representing the ground based on the point cloud data, is used to:

[0153] Performing plane fitting based on the point cloud data to obtain fitting plane information;

[0154] Based on the coordinate information of the radar scanning points in the point cloud data, determining the distance between each radar scanning point and the plane indicated by the fitting plane information;

[0155] Based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information, point cloud data is screened, and based on the screened point cloud data, the step of plane fitting based on the point cloud data is returned to until a preset iteration condition is reached, and the ground parameter information is determined based on the last screened point cloud data.

[0156] In a possible implementation manner, the second determination module 703 is further configured to perform at least one of the following data screening processes before performing plane fitting based on the point cloud data:

[0157] Downsampling the point cloud data so that the distribution density of radar scanning points in the point cloud data meets a preset condition;

[0158] According to the preset installation height of the radar device, a target coordinate range is determined, and radar scanning points whose corresponding coordinate information is within the target coordinate range are screened out.

[0159] In a possible implementation manner, the position data is height data;

[0160] The second determining module 703 is further configured to:

[0161] Based on the height data in the position data and the corresponding relationship between the position data and the point cloud data, the point cloud data is divided into a plurality of collection intervals.

[0162] In a possible implementation manner, the second determining module 703, when dividing the point cloud data into a plurality of collection intervals based on the height data in the position data and the correspondence between the position data and the point cloud data, is configured to:

[0163] Filtering the height data to determine at least one extreme point in the height data retained after filtering;

[0164] The point cloud data is divided into multiple collection intervals using the collection time points corresponding to the extreme value points as the dividing points of the collection intervals.

[0165] In a possible implementation manner, the second determination module 703, when determining ground parameter information representing the ground based on the point cloud data and determining initial position and posture data of the radar device based on the position data, is configured to:

[0166] For each acquisition interval, determining ground parameter information corresponding to the acquisition interval based on the point cloud data in the acquisition interval, and determining initial posture data of the radar device corresponding to the acquisition interval based on the position data of the radar device when the point cloud data in the acquisition interval is acquired;

[0167] The adjustment module 704, when adjusting the external parameter data representing the relative position relationship between the positioning device and the radar device based on the ground parameter information and the initial posture data, is used to:

[0168] Based on the ground parameter information and the initial posture data corresponding to each acquisition interval, the external parameter data characterizing the relative position relationship between the positioning device and the radar device is adjusted.

[0169] In a possible implementation, the adjustment module 704, when adjusting the external parameter data characterizing the relative position relationship between the positioning device and the radar device based on the ground parameter information and the initial posture data corresponding to each acquisition interval, is used to:

[0170] Determine the optimized posture data corresponding to each acquisition interval based on the ground parameter information and the initial posture data corresponding to each acquisition interval;

[0171] The external parameter data is adjusted based on the initial posture data corresponding to each acquisition interval and the optimized posture data.

[0172] In a possible implementation, the adjustment module 704, when determining the optimized posture data corresponding to each acquisition interval based on the ground parameter information and the initial posture data corresponding to each acquisition interval, is used to:

[0173] For any acquisition interval, based on the ground parameter information corresponding to the acquisition interval and the initial pose data, determine the pose data that minimizes the value of the objective function, and use the pose data that minimizes the value of the objective function as the optimized pose data corresponding to the acquisition interval, wherein the objective function is the sum of the absolute value of the difference between the ground parameter information before and after the pose data optimization and the absolute value of the difference between the pose data before and after the pose data optimization.

[0174] In a possible implementation, the adjustment module 704, when adjusting the external parameter data based on the initial posture data corresponding to each acquisition interval and the optimized posture data, is used to:

[0175] Based on the initial posture data corresponding to each acquisition interval and the optimized posture data, determining a first average posture change amount within the acquisition interval;

[0176] Determine, based on the first average posture change amount in each acquisition interval, a second average posture change amount corresponding to the multiple acquisition intervals;

[0177] Based on the second average posture change, the external parameter data is adjusted.

[0178] In a possible implementation, the adjustment module 704, when adjusting the extrinsic parameter data based on the second average posture change, is configured to:

[0179] The product of the second average posture change and the external parameter data before adjustment is determined as the adjusted external parameter data.

[0180] In a possible implementation manner, the positioning data includes a plurality of first acquisition time points and position data acquired at each first acquisition time point, and the point cloud data includes a plurality of second acquisition time points and point cloud data acquired at each second acquisition time point;

[0181] The first determination module 702, when determining the position data of the radar device when the positioning device collects the positioning data based on the positioning data and the point cloud data, is used to:

[0182] For each second acquisition time point, two first acquisition time points with the shortest time interval from the second acquisition time point are respectively determined, wherein the two first acquisition time points include a first acquisition time point before the second acquisition time point and a first acquisition time point after the second acquisition time point;

[0183] Based on the position data respectively corresponding to the two first collection time points, the position data of the radar device when collecting the point cloud data at the second collection time point is determined.

[0184] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0185] In some embodiments, the functions or templates contained in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0186] Based on the same technical concept, the embodiment of the present disclosure also provides a computer device. Figure 8 As shown, it is a schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure, including a processor 801, a memory 802, and a bus 803. Among them, the memory 802 is used to store execution instructions, including a memory 8021 and an external memory 8022; the memory 8021 here is also called an internal memory, which is used to temporarily store the operation data in the processor 801, and the data exchanged with the external memory 8022 such as a hard disk. The processor 801 exchanges data with the external memory 8022 through the memory 8021. When the computer device 800 is running, the processor 801 communicates with the memory 802 through the bus 803, so that the processor 801 executes the following instructions:

[0187] Acquire positioning data collected by a positioning device and point cloud data collected by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle;

[0188] Based on the positioning data and the point cloud data, determining the position data of the radar device when the positioning device collects the positioning data;

[0189] Based on the point cloud data, determining ground parameter information characterizing the ground, and based on the position data, determining initial position and posture data of the radar device;

[0190] Based on the ground parameter information and the initial posture data, external parameter data characterizing the relative position relationship between the positioning device and the radar device is adjusted.

[0191] The specific processing process performed by the processor 801 may refer to the description in the above method embodiment, which will not be elaborated here.

[0192] The present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the data processing method described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0193] The computer program product of the data processing method provided in the embodiment of the present disclosure includes a computer-readable storage medium storing a program code, and the instructions included in the program code can be used to execute the steps of the data processing method described in the above method embodiment. The computer program product can be implemented specifically by hardware, software or a combination thereof. In an optional embodiment, the computer program product is specifically embodied as a computer storage medium, and in another optional embodiment, the computer program product is specifically embodied as a software product, such as a software development kit (Software Development Kit, SDK) and the like.

[0194] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0195] 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 solution of this embodiment.

[0196] In addition, each functional unit in each embodiment of the present disclosure 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.

[0197] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for 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 the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0198] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A data processing method, characterized in that: include: Acquire positioning data collected by a positioning device and point cloud data collected by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle; Based on the positioning data and the point cloud data, determining the position data of the radar device when the positioning device collects the positioning data; Based on the height data in the position data and the corresponding relationship between the position data and the point cloud data, dividing the point cloud data into a plurality of collection intervals; For each acquisition interval, determining ground parameter information corresponding to the acquisition interval based on the point cloud data in the acquisition interval, and determining initial posture data of the radar device corresponding to the acquisition interval based on the position data of the radar device when the point cloud data in the acquisition interval is acquired; Determine the optimized posture data corresponding to each acquisition interval based on the ground parameter information and the initial posture data corresponding to each acquisition interval; Based on the initial posture data corresponding to each acquisition interval and the optimized posture data, the external parameter data characterizing the relative position relationship between the positioning device and the radar device is adjusted.

2. The method according to claim 1, characterized in that The point cloud data includes coordinate information of multiple radar scanning points; The determining, based on the point cloud data, ground parameter information characterizing the ground includes: Performing plane fitting based on the point cloud data to obtain fitting plane information; Based on the coordinate information of the radar scanning points in the point cloud data, determining the distance between each radar scanning point and the plane indicated by the fitting plane information; Based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information, point cloud data is screened, and based on the screened point cloud data, the step of plane fitting based on the point cloud data is returned to until a preset iteration condition is reached, and the ground parameter information is determined based on the last screened point cloud data.

3. The method according to claim 2, characterized in that Before performing plane fitting based on the point cloud data, at least one of the following data screening processes is performed: Downsampling the point cloud data so that the distribution density of radar scanning points in the point cloud data meets a preset condition; According to the preset installation height of the radar device, a target coordinate range is determined, and radar scanning points whose corresponding coordinate information is within the target coordinate range are screened out.

4. The method according to claim 1, characterized in that: The step of dividing the point cloud data into a plurality of collection intervals based on the height data in the position data and the corresponding relationship between the position data and the point cloud data includes: Filtering the height data to determine at least one extreme point in the height data retained after filtering; The point cloud data is divided into multiple collection intervals using the collection time points corresponding to the extreme value points as the dividing points of the collection intervals.

5. The method according to claim 1, characterized in that Determining optimized posture data corresponding to each acquisition interval based on the ground parameter information and the initial posture data corresponding to each acquisition interval includes: For any acquisition interval, based on the ground parameter information corresponding to the acquisition interval and the initial pose data, determine the pose data that minimizes the value of the objective function, and use the pose data that minimizes the value of the objective function as the optimized pose data corresponding to the acquisition interval, wherein the objective function is the sum of the absolute value of the difference between the ground parameter information before and after the pose data optimization and the absolute value of the difference between the pose data before and after the pose data optimization.

6. The method according to claim 1, characterized in that The adjusting the external parameter data based on the initial posture data corresponding to each acquisition interval and the optimized posture data includes: Based on the initial posture data corresponding to each acquisition interval and the optimized posture data, determining a first average posture change amount within the acquisition interval; Determine, based on the first average posture change amount in each acquisition interval, a second average posture change amount corresponding to the multiple acquisition intervals; Based on the second average posture change, the external parameter data is adjusted.

7. The method according to claim 6, characterized in that The adjusting the external parameter data based on the second average posture change includes: The product of the second average posture change and the external parameter data before adjustment is determined as the adjusted external parameter data.

8. The method according to claim 1, characterized in that The positioning data includes a plurality of first acquisition time points and position data acquired at each first acquisition time point, and the point cloud data includes a plurality of second acquisition time points and point cloud data acquired at each second acquisition time point; The determining, based on the positioning data and the point cloud data, the position data of the radar device when the positioning device collects the positioning data comprises: For each second acquisition time point, two first acquisition time points with the shortest time interval from the second acquisition time point are respectively determined, wherein the two first acquisition time points include a first acquisition time point before the second acquisition time point and a first acquisition time point after the second acquisition time point; Based on the position data respectively corresponding to the two first collection time points, the position data of the radar device when collecting the point cloud data at the second collection time point is determined.

9. A data processing device, characterized in that: include: An acquisition module, used to acquire positioning data collected by a positioning device and point cloud data collected by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle; A first determination module, configured to determine, based on the positioning data and the point cloud data, position data of the radar device when the positioning device collects the positioning data; A second determination module is used to divide the point cloud data into a plurality of collection intervals based on the height data in the position data and the correspondence between the position data and the point cloud data; for each collection interval, determine the ground parameter information corresponding to the collection interval based on the point cloud data in the collection interval, and determine the initial posture data of the radar device corresponding to the collection interval based on the position data of the radar device when collecting the point cloud data in the collection interval; An adjustment module is used to determine the optimized posture data corresponding to each acquisition interval based on the ground parameter information and the initial posture data corresponding to each acquisition interval; based on the initial posture data corresponding to each acquisition interval and the optimized posture data, adjust the external parameter data characterizing the relative position relationship between the positioning device and the radar device.

10. A computer device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the data processing method as described in any one of claims 1 to 8 are performed.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the data processing method according to any one of claims 1 to 8.

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