Method, device, electronic device and autonomous driving vehicle for processing positioning data

Abstract

The present disclosure provides a method, device, electronic device and autonomous driving vehicle for positioning data processing, and relates to the field of data processing, and in particular to a method, device, electronic device and autonomous driving vehicle for data processing in vehicle positioning. The specific implementation scheme is: obtaining multiple new information results of a target vehicle, wherein each new information result is obtained by a measurement positioning result and a predicted positioning result of the target vehicle, the measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, the predicted positioning result is obtained based on compensating for the zero bias data of the inertial measurement unit, and the zero bias data is obtained by a target filter fusing multiple measurement positioning results converted to the inertial unit; determining the convergence state of the target filter based on multiple new information results; determining the data state of the zero bias data based on the convergence state; and determining whether to output the predicted positioning result based on the data state.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a data processing method, device, electronic device and autonomous driving vehicle in vehicle positioning. Background Art

[0002] At present, when performing vehicle positioning, the traditional Kalman filtering method is used to calculate the difference between the IMU's predicted value and the measured value as the new information, and the new information is weighted to distribute the correction of the predicted value by combining the current filter prediction matrix and the confidence on both sides. There is no effective screening and processing for the situation in which the filter convergence error in vehicle positioning causes the Inertial Measurement Unit (IMU) zero bias estimation error. Summary of the invention

[0003] The present disclosure provides a method, an apparatus, an electronic device and an autonomous driving vehicle for positioning data processing.

[0004] According to one aspect of the present disclosure, a positioning data processing method is provided, comprising: acquiring multiple new information results of a target vehicle, wherein each new information result is obtained by a measured positioning result and a predicted positioning result of the target vehicle, the measured positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, the predicted positioning result is obtained based on compensating for zero bias data of an inertial measurement unit, and the zero bias data is obtained by fusing multiple measured positioning results converted into the inertial unit by a target filter; determining a convergence state of the target filter based on the multiple new information results; determining a data state of the zero bias data based on the convergence state; and determining whether to output the predicted positioning result based on the data state.

[0005] According to another aspect of the present disclosure, a positioning data processing device is provided, including: an acquisition unit, used to acquire multiple new information results of a target vehicle, wherein each new information result is obtained by a measurement positioning result and a predicted positioning result of the target vehicle, the measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, and the predicted positioning result is obtained based on compensating for zero bias data of an inertial measurement unit, and the zero bias data is obtained by a target filter fusing multiple measurement positioning results converted into the inertial measurement unit; a first determination unit, used to determine a convergence state of the target filter based on multiple information; a second determination unit, used to determine a data state of the zero bias data based on the convergence state; and a third determination unit, used to determine whether to output the predicted positioning result based on the data state.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor performs the method for positioning data processing of an embodiment of the present disclosure.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the method for positioning data processing according to an embodiment of the present disclosure.

[0008] According to another aspect of the present disclosure, an autonomous driving vehicle is provided, comprising a positioning data processing device according to an embodiment of the present disclosure or an electronic device according to an embodiment of the present disclosure.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0011] Figure 1 is a flow chart of a method for positioning data processing according to an embodiment of the present disclosure;

[0012] Figure 2 is a schematic diagram of a fusion filtering quality detection principle based on multi-source information according to an embodiment of the present disclosure;

[0013] Figure 3 is a flow chart for verifying the quality stability of each measurement module according to an embodiment of the present disclosure;

[0014] Figure 4 is a flow chart of a fusion filtering quality detection process based on multi-source information according to an embodiment of the present disclosure;

[0015] Figure 5 is a schematic diagram of a positioning data processing device according to an embodiment of the present disclosure;

[0016] Figure 6 is a schematic block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0018] The following is an introduction to the positioning data processing method of the embodiment of the present disclosure.

[0019] During the autonomous driving process of unmanned vehicles, the positioning system needs to output continuous high-frequency and accurate positioning results in real time to ensure the normal operation of modules such as path planning and perception. Affected by cost and device characteristics, the error term (zero bias) of the accelerometer and gyroscope data output of the IMU carried by the vehicle-side positioning module is large, and the zero bias repeatability is also unstable when it is turned on. It cannot be calibrated offline at the factory, and the positioning module needs to be calibrated online through high-precision measurement values. However, due to the large repeatability of zero bias, sometimes after power-on or when the motion trajectory is a single straight line, the Kalman filter used for positioning has not fully converged. At this time, the zero bias of the IMU's accelerometer is not correctly estimated. When the vehicle turns, it is easy to cause the IMU recursive error to be too large. Even when the measurement values ​​of multiple modules are correct, the filter is still unable to correct the excessive recursive error in time through measurement updates, so the actual positioning drift is too large (0.5-10m). Since the measurement values ​​of the filter are normal and the corresponding confidence is also very small, the confidence output by the Kalman filter is still very small. The positioning does not correctly predict that the positioning has drifted to an unusable state. If it continues to be used downstream, it will cause great safety hazards.

[0020] Figure 1 FIG. 1 is a flow chart of a method for processing positioning data according to an embodiment of the present disclosure. Figure 1 As shown, the method may include the following steps:

[0021] Step S102, obtaining multiple new information results of the target vehicle, wherein each new information result is obtained by a measurement positioning result and a predicted positioning result of the target vehicle, the measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, and the predicted positioning result is obtained based on compensating for the zero bias data of the inertial measurement unit, and the zero bias data is obtained by fusing multiple measurement positioning results converted into the inertial unit by a target filter.

[0022] In the technical solution provided in the above step S102 of the present disclosure, multiple new information results of the target vehicle are obtained, wherein each new information result is obtained by the measurement positioning result and the predicted positioning result of the target vehicle. In the Kalman filter, the new information is the observation value minus the predicted observation value, that is, the new information result can be the measurement positioning result of the target vehicle minus the predicted positioning result. The measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle. The measurement module of the target vehicle can be used to position the target vehicle and then output the measurement positioning result of the target vehicle. The predicted positioning result is obtained based on compensating for the zero bias data of the inertial measurement unit. The inertial measurement unit, i.e., IMU, is a device for measuring the three-axis attitude angle (or angular velocity) and acceleration of an object. The zero bias is the error term output by the IMU data. The predicted positioning result can be obtained by compensating for the error term output by the IMU data. The zero bias data is obtained by fusing multiple measurement positioning results converted to the inertial unit by the target filter.

[0023] In this embodiment, the predicted positioning result is based on compensating the zero bias data of the inertial measurement unit, and the predicted positioning result may be obtained by inertial navigation recursion.

[0024] In this embodiment, the zero bias data is obtained by fusing multiple measurement positioning results converted into the inertial unit by the target filter, and the multiple measurement positioning results can be fused by a Kalman filter to estimate the zero bias data of the IMU.

[0025] In this embodiment, the main components of the inertial measurement unit IMU may be an accelerometer and a gyroscope, and their accuracy directly affects the accuracy of the inertial system. An IMU may include three single-axis accelerometers and three single-axis gyroscopes. The accelerometer detects the acceleration signal of the object in the independent three-axis coordinate system of the carrier (which may be the target vehicle), and the gyroscope detects the angular velocity signal of the carrier relative to the navigation coordinate system. The posture of the object is calculated by measuring the angular velocity and acceleration of the object in three-dimensional space.

[0026] In this embodiment, the target filter can be a Kalman filter. The Kalman filter is an algorithm that uses a linear state equation to make the most accurate estimate of the system state through the system input and output observation data. Since the observation data includes the influence of noise and interference in the system, the optimal estimate can also be regarded as a filtering process. Data filtering is a data processing technology that removes noise and restores real data. Kalman filtering can estimate the state of a dynamic system from a series of three-dimensional data with measurement noise when the measurement variance is known.

[0027] Step S104: determining a convergence state of the target filter based on the multiple innovation results.

[0028] In the technical solution provided in the above step S104 of the present disclosure, the convergence state of the target filter is determined based on multiple new information results. That is, after obtaining multiple new information results of the target vehicle, it is determined based on the multiple new information results whether the Kalman filter has reached a fully converged state. The Kalman filter reaches a fully converged state when the P matrix value output by the Kalman filter is reduced to a stable value.

[0029] Step S106: determining the data state of the zero bias data based on the convergence state.

[0030] In the technical solution provided in the above step S106 of the present disclosure, the data state of the error term of the IMU data output can be determined based on the convergence state of the Kalman filter.

[0031] In this embodiment, it is determined whether a convergence state is reached, that is, it is determined whether the P matrix value output by the Kalman filter is reduced to a stable value.

[0032] In this embodiment, the data status of the zero bias data, that is, whether the error term of the IMU data output is correctly estimated.

[0033] Step S108, determines whether to output the predicted positioning result based on the data state. When the error term of the IMU data output is correctly estimated, the predicted positioning result will not be output based on this data state; when the error term of the IMU data output is not correctly estimated, the predicted positioning result will be output based on this data state.

[0034] In the technical solution provided in the above step S108 of the present disclosure, the output predicted positioning result may be obtained based on compensating the zero bias data of the inertial measurement unit in step S102.

[0035] Through the above steps S102 to S108, multiple new information results of the target vehicle are obtained, wherein each new information result is obtained by the measurement positioning result and the predicted positioning result of the target vehicle, the measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, the predicted positioning result is obtained based on compensating the zero bias data of the inertial measurement unit, and the zero bias data is obtained by the target filter fusing multiple measurement positioning results converted to the inertial unit; the convergence state of the target filter is determined based on the multiple new information results; the data state of the zero bias data is determined based on the convergence state; and whether to output the predicted positioning result is determined based on the data state. In other words, the present disclosure uses the new information results corresponding to multiple measurement values ​​to determine the convergence state of the filter, which can effectively monitor and detect whether the estimated state of the IMU zero bias of the filter is accurate at this time, so as to better predict the positioning state, solve the technical problem of poor reliability in positioning the vehicle, and achieve the technical effect of improving the reliability of positioning the vehicle.

[0036] The above method of this embodiment is further described in detail below.

[0037] As an optional implementation, step S104, determining the convergence state of the target filter based on multiple new information results includes: determining that the corresponding multiple measurement modules meet the target conditions based on the multiple new information results, and then determining the convergence state based on the measurement positioning results of each measurement module, wherein the target conditions are used to characterize the measurement performance of the measurement modules.

[0038] In this embodiment, it is determined based on each new information result whether the corresponding multiple measurement modules meet the target conditions. That is to say, after obtaining multiple new information results of the target vehicle, it is determined based on each new information result whether the corresponding multiple measurement modules meet the target conditions, and then it is determined based on the measurement positioning results of each measurement module whether the filtering has reached a convergence state.

[0039] In this embodiment, the convergence state is determined based on the measurement and positioning results of each measurement module, wherein the convergence state may be that the P matrix value output by the Kalman filter is reduced to a stable value, that is, based on the measurement and positioning results obtained by positioning the target vehicle by each measurement module of the vehicle, it is determined whether the P matrix value output by the Kalman filter is reduced to a stable value, and then the convergence state is determined.

[0040] In this embodiment, the target condition is used to characterize the measurement performance of the measurement module. After the vehicle positioning system is successfully initialized, the measurement performance of each measurement module needs to be verified first. Optionally, the measurement performance of the measurement module can be the quality stability of the measurement module.

[0041] In this embodiment, optionally, the target condition may be that the output quality of each measurement module is stable and consistent, meeting the filtering quality detection condition.

[0042] As an optional implementation, determining the convergence state based on the measurement and positioning results of each measurement module includes: obtaining a first relative quantity between a first measurement frame and a second measurement frame of the measurement and positioning results of each measurement module, wherein the first measurement frame and the second measurement frame are adjacent; obtaining a second relative quantity between a first sub-prediction positioning result corresponding to the first measurement frame and a second sub-prediction positioning result corresponding to the second measurement frame in the predicted positioning result; and determining the convergence state based on the first relative quantity and the second relative quantity corresponding to each measurement module.

[0043] In this embodiment, a first relative quantity between a first measurement frame and a second measurement frame of the measurement positioning result of each measurement module is obtained, wherein the first measurement frame and the second measurement frame are adjacent, that is, after determining that the quality consistency of the corresponding multiple measurement modules has passed based on each new information result, a first relative quantity between a first measurement frame and a second measurement frame in the measurement positioning result output by each measurement module is obtained, wherein the first measurement frame and the second measurement frame are two adjacent frames of measurement values ​​in the measurement positioning result output by the measurement module.

[0044] In this embodiment, optionally, the first measurement frame and the second measurement frame are two most recent frames of measurement values ​​in the measurement positioning results output by the respective measurement modules.

[0045] In this embodiment, optionally, the first relative quantity may be obtained by performing relative pose calculation based on the two most recent frames of measurement values ​​in the measurement positioning results output by the respective measurement modules.

[0046] In this embodiment, a second relative quantity between a first sub-predicted positioning result corresponding to the first measurement frame and a second sub-predicted positioning result corresponding to the second measurement frame in the predicted positioning result is obtained, wherein the first sub-predicted positioning result and the second sub-predicted positioning result are obtained based on compensating for zero bias data of an inertial measurement unit. The inertial measurement unit, i.e., IMU, is a device for measuring the three-axis attitude angle (or angular velocity) and acceleration of an object. The zero bias is an error term output by the IMU data. The first sub-predicted positioning result corresponding to the first measurement frame and the second sub-predicted positioning result corresponding to the second measurement frame in the predicted positioning result can be obtained by compensating for the error term output by the IMU data. The zero bias data is obtained by fusing multiple measurement positioning results converted into the inertial unit by the target filter.

[0047] In this embodiment, optionally, the second relative quantity may be a relative position of fusion positioning between the first measurement frame and the second measurement frame.

[0048] In this embodiment, the convergence state is determined based on the first relative quantity and the second relative quantity corresponding to each measurement module. By acquiring the first relative quantity and the second relative quantity corresponding to each measurement module, the filtering convergence state can be determined.

[0049] In this embodiment, optionally, determining the convergence state based on the first relative quantity and the second relative quantity corresponding to each measurement module includes calculating the difference between the first relative quantity and the second relative quantity.

[0050] As an optional implementation, determining the convergence state based on the first relative quantity and the second relative quantity corresponding to each measurement module includes: obtaining a first difference between the first relative quantity and the second relative quantity corresponding to each measurement module; determining the sum of the squares of a first component of the first difference and a second component of the first difference; determining that the square root of the sum of squares is less than a first target threshold, then determining that the convergence state is used to indicate that the target filter has converged correctly; determining that the square root of the sum of squares is not less than the first target threshold, then determining that the convergence state is used to indicate that the target filter has converged incorrectly.

[0051] In this embodiment, a first difference between a first relative quantity and a second relative quantity corresponding to each measurement module is obtained, that is, after obtaining the first relative quantity between the first measurement frame and the second measurement frame of the measurement positioning result of each measurement module and the second relative quantity between the first sub-prediction positioning result corresponding to the first measurement frame and the second sub-prediction positioning result corresponding to the second measurement frame in the predicted positioning result, the difference between the first relative quantity and the second relative quantity is calculated, and the difference is determined as the first difference, thereby obtaining the first difference.

[0052] In this embodiment, the sum of the squares of the first component of the first difference and the second component of the first difference is determined. That is, after obtaining the first difference between the first relative quantity and the second relative quantity corresponding to each measurement module, the three components of the first difference in the three-dimensional space are determined, and the sum of the squares of the first component and the second component of the first difference is calculated to obtain the value of the sum of the squares.

[0053] In this embodiment, the first component and the second component of the first difference may be two components that determine a plane perpendicular to the direction of gravity.

[0054] In this embodiment, if it is determined that the square root of the sum of squares is less than the first target threshold, then it is determined that the convergence state is used to indicate that the target filter has converged correctly. That is, after obtaining the value of the sum of squares of the first component and the second component of the first difference, the value of the square root of the sum of squares is further calculated. After determining that the value of the square root of the sum of squares is less than the first target threshold, it is determined that the convergence state is used to characterize that the target filter has converged correctly. That is, the convergence state at this time is that the P matrix value output by the target filter is reduced to a stable value.

[0055] In this embodiment, it is determined that the square root of the sum of squares is not less than the first target threshold, and then the convergence state is determined to indicate that the target filter has converged incorrectly, that is, when the value of the square root of the sum of the squares of the first component and the second component of the first difference is not less than the first target threshold, the convergence state at this time is that the target filter has converged incorrectly, that is, the convergence state at this time is that the P matrix value output by the target filter has not decreased to a stable value.

[0056] In this embodiment, the first target threshold may be a maximum value between the product of the calibration coefficient and the fusion horizontal speed of the measurement frame corresponding to each current measurement module and the minimum threshold.

[0057] As an optional implementation, after determining that the convergence state is used to indicate that the target filter has erroneously converged, the method further includes: updating a first original confidence level of a next measurement frame of a current measurement frame in the measurement positioning result based on a square root of a sum of squares to obtain a first target confidence level; and updating the zero bias data based on the next measurement frame and the first target confidence level.

[0058] In this embodiment, the first original confidence of the next measurement frame of the current measurement frame in the measurement positioning result is updated based on the square root of the sum of squares to obtain the first target confidence. That is, after it is determined that the convergence state is used to indicate that the target filter has converged incorrectly, that is, the convergence state is that the P matrix value output by the target filter has not decreased to a stable value, the first original confidence of the next measurement frame of the current measurement frame in the measurement positioning result is updated to obtain the first target confidence. At this time, the first target confidence is the new confidence.

[0059] In this embodiment, the first original confidence of the next measurement frame of the current measurement frame in the measurement positioning result is updated based on the square root of the sum of squares, and the calculation process of obtaining the first target confidence can be: first, the square root of the sum of squares of the first component of the first difference and the second component of the first difference of each measurement module is calculated, and then the average value of the square root of the sum of squares of the first component of the first difference and the second component of the first difference of each measurement module is calculated; secondly, the confidence of the next frame measurement value is unknown, which can be represented by an algebraic representation, and the value of the variance of the sum of squares of the first component and the second component of the next measurement frame is calculated, and the measurement value adjustment coefficient, the confidence of the next frame measurement value and the product of the variance are calculated; finally, the product result is divided by the above average value to obtain the first target confidence.

[0060] In this embodiment, the zero bias data is updated based on the next measurement frame and the first target confidence. That is, after the first target confidence is obtained by calculation, the zero bias data of the IMU is updated based on the next measurement frame of the current measurement frame and the first target confidence.

[0061] In this embodiment, the zero bias data is updated based on the next measurement frame and the first target confidence, and the next measurement and the first target confidence are substituted into the Kalman filter to perform measurement update.

[0062] As an optional implementation, after determining that the convergence state is used to indicate that the target filter has erroneously converged, the method further includes: updating a second original confidence corresponding to the predicted positioning result based on the square root of the sum of squares to obtain a second target confidence; and outputting the second target confidence.

[0063] In this embodiment, the second original confidence corresponding to the predicted positioning result is updated based on the square root of the sum of squares to obtain the second target confidence. After the predicted positioning result is obtained by compensating the zero bias data of the inertial measurement unit, that is, after the predicted positioning result is obtained by compensating the error term output by the IMU data, the second original confidence corresponding to the predicted positioning result is updated based on the square root of the sum of squares to obtain the second target confidence.

[0064] In this embodiment, the second original confidence corresponding to the predicted positioning result is updated based on the square root of the sum of squares, and the calculation process for obtaining the second target confidence can be: first, calculating the square root of the sum of squares of the first component of the first difference and the second component of the first difference of each measurement module, and then calculating the average value of the square root of the sum of squares of the first component of the first difference and the second component of the first difference of each measurement module, and secondly, calculating the product of the amplification factor and the first original confidence, and finally summing the above average values ​​and products to obtain the updated second target confidence.

[0065] In this embodiment, a second target confidence is output, and the measurement confidence of the fused positioning output is re-determined by the above method, and predicted in advance.

[0066] As an optional implementation, determining whether the corresponding multiple measurement modules meet the target conditions based on multiple new information results includes: obtaining the second difference between every two new information results in the multiple new information results to obtain multiple second differences; determining that each second difference satisfies the second target threshold, and then determining that the two measurement modules corresponding to each second difference meet the target conditions.

[0067] In this embodiment, the second difference between every two innovation results in the multiple innovation results is obtained to obtain multiple second differences, that is, the second difference between every two innovation results in the innovation results output by each measurement module is obtained.

[0068] In this embodiment, if it is determined that each second difference satisfies the second target threshold, then it is determined that the two measurement modules corresponding to each second difference meet the target conditions. That is to say, after obtaining the second difference between each two new information results in multiple new information results, it is determined whether each second difference satisfies the second target threshold, and then it is further determined whether the two measurement modules corresponding to each second difference meet the target conditions.

[0069] In this embodiment, it is determined that each second difference satisfies the second target threshold, and then it is determined that the two measurement modules corresponding to each second difference meet the target condition, including: when each measurement module meets the target condition, the number of qualified times is increased by one, and when the filter receives the next frame of measurement value, the step of continuing to determine whether the second difference meets the second target threshold is continued. If the second target condition is met, the number of qualified times is increased by one, and if not, it is reset to zero, until the number of qualified times is greater than a certain set value, then it is determined that each measurement module is stable and consistent, that is, it meets the target condition.

[0070] As an optional implementation, obtaining the second difference between every two innovation results in the multiple innovation results includes: obtaining the second difference between every two innovation results in the multiple innovation results converted into the coordinate system of the inertial measurement unit.

[0071] In this embodiment, among the multiple new information results converted into the coordinate system of the inertial measurement unit, the second difference between every two new information results is obtained, that is, the measurement results output by each measurement module are data of different coordinate systems, wherein the results output by the laser radar module (Laser Radar, referred to as Lidar) and the visual algorithm module are UTM coordinate data, and the results output by the global navigation satellite system (Global Navigation Satellite System, referred to as GNSS) module are longitude and latitude coordinate data, which need to be converted into UTM coordinate data, and the consistency of the new information of each measurement module is verified with each other, and the difference between the new information of each module is solved to obtain the second difference between every two new information results.

[0072] In this embodiment, among the multiple new information results converted into the coordinate system of the inertial measurement unit, it includes: each measurement result is compensated to the center point of the IMU through the respective sensor arm value.

[0073] As an optional implementation, step S106 of determining the data state of the zero bias data of the inertial measurement unit based on the convergence state includes: determining that the convergence state is used to indicate that the target filter converges correctly, and then determining that the data state is an accurate state.

[0074] In this embodiment, the convergence state is determined to indicate that the target filter has converged correctly, and the data state is determined to be an accurate state. That is, when the convergence state is determined to be that the P matrix value output by the target filter decreases to a stable value, indicating that the target filter has converged correctly, the data state is determined to be an accurate state.

[0075] As an optional implementation, step S108, determining whether to output the predicted positioning result based on the data state includes: if the data state is determined to be an accurate state, outputting the predicted positioning result.

[0076] In this embodiment, if the data state is determined to be an accurate state, the predicted positioning result is output, that is, after the filter converges, if the data state is determined to be an accurate state, the predicted positioning result is output to predict the positioning state.

[0077] This embodiment determines that the corresponding multiple measurement modules meet the target conditions based on multiple new information results, and then determines the convergence state based on the measurement positioning results of each measurement module, wherein the target condition is used to characterize the measurement performance of the measurement module; the convergence state is determined to indicate that the target filter has converged correctly, and then the data state is determined to be an accurate state; when the data state is determined to be an accurate state, a predicted positioning result is output, which solves the technical problem of poor reliability in vehicle positioning and achieves the technical effect of improving the reliability of vehicle positioning.

[0078] The above technical solution of the embodiment of the present disclosure is further illustrated below in combination with preferred implementation modes.

[0079] Figure 2 is a schematic diagram of a fusion filtering quality detection principle based on multi-source information according to an embodiment of the present disclosure, such as Figure 2 As shown, the principle may include the following:

[0080] First, the main sensors used for positioning are LiDAR, GNSS, cameras, and IMU;

[0081] Secondly, the output results of the LiDAR and camera algorithm modules are UTM coordinates, and the output of the GNSS module is longitude and latitude coordinates;

[0082] Then, the latitude and longitude coordinates output by the GNSS module need to be converted into UTM coordinates;

[0083] Secondly, the above measurement results are all compensated to the coordinate system of IMU through the arm value of each sensor;

[0084] Finally, the IMU outputs a fused positioning result by fusing the measurement results of LiDAR, vision and GNSS output using Kalman filtering, which is also expressed in UTM coordinates.

[0085] In this embodiment, by converting each measurement result into the IMU coordinate system, using Kalman filtering to fuse the measurement results and outputting the fused positioning result, the quality detection of each measurement module is verified, and then in the case of abnormal IMU zero bias estimation, the positioning reliability of the filter output is adjusted to a result closer to the actual error, and the positioning state is predicted, thereby solving the technical problem of poor reliability of vehicle positioning and achieving the technical effect of improving the reliability of vehicle positioning.

[0086] Figure 3is a flow chart for verifying the quality stability of each measurement module according to an embodiment of the present disclosure, such as Figure 3 As shown, the process may include the following steps:

[0087] Step S302, obtaining new information of the measurement value of each measurement module.

[0088] In the technical solution provided in the above step S302 of the present disclosure, the new information of the measurement value of each measurement module is obtained. Taking the GNSS measurement module as an example, when the output GNSS result is converted into UTM coordinates, each frame of GNSS measurement result is recorded as The corresponding time is recorded as T1, and the fusion positioning result obtained by interpolation of T1 at this time is recorded as The attitude matrix of fusion positioning relative to the navigation coordinate system is: The new information result Δp of the GNSS measurement of this frame is calculated according to the following formula inno-G :

[0089]

[0090] Δp inno-G From the navigation coordinate system to the IMU coordinate system (xyz corresponds to the right-front-up of the IMU coordinate system), we can get the following formula:

[0091]

[0092] Then, within a window period, the LiDAR and visual measurement information closest to GNSS time T1 are obtained in the same way, which are recorded as and Here, the window period represents a time period.

[0093] Optionally, the window period can be 2 seconds. Since the general GNSS output frequency is 1-5 Hz, the LiDAR output frequency is 1-5 Hz, and the visual output frequency is 1-5 Hz, the GNSS, LiDar and visual results can be received in a time length of 2 seconds for comparison.

[0094] Step S304, mutually verifying the consistency of the new information of each measurement module, and calculating the difference between the new information of each module.

[0095] In the technical solution provided in the above step S304 of the present disclosure, the consistency of the innovation information of each measurement module can be verified by calculating the difference between the innovation information of each module, as shown in the following formula:

[0096]

[0097] When the following formula is satisfied, τ represents the maximum allowable horizontal error value (according to the accuracy requirement, here it is 0.5 meters), ε represents the maximum allowable lateral error value (according to the accuracy requirement, here it is 0.3 meters), then it is considered that each measurement module is qualified and consistent, and the qualified N is recorded. count Add once.

[0098]

[0099] Step S306, counting the number of times each measurement module is qualified and consistent, and determining whether it meets the filter quality detection condition;

[0100] In the technical solution provided in the above step S306 of the present disclosure, when the filter receives the next frame of measurement values, the steps S302 and S304 are continued to be executed, and the qualified N count Add once, if not satisfied, set to 0, if qualified N count If it is greater than x times, it is considered that each measurement value module is stable and consistent, and meets the filtering quality detection conditions.

[0101] Optionally, according to actual measurement frequency requirements, χ can be taken as 3.

[0102] In this embodiment, by obtaining the latest information of the measurement value of each measurement module, the consistency of the latest information of each measurement module is verified, the difference between the latest information of each module is solved, and the number of times each measurement module is qualified and consistent is counted, and then it is determined whether the filtering quality detection conditions are met. The quality stability of each measurement module is verified to determine whether the filtering quality detection conditions are met.

[0103] Figure 4 is a flow chart of a fusion filtering quality detection process based on multi-source information according to an embodiment of the present disclosure, such as Figure 3 As shown, the process may include the following steps:

[0104] Step S402, determining that each measurement module is stable and consistent and meets the filter quality detection condition.

[0105] In the technical solution provided in the above step S402 of the present disclosure, Figure 3 Provides specific content to determine that each measurement module is stable and consistent and meets the filter quality detection conditions. When the filter receives the next frame of measurement value, it continues to execute Figure 3 Steps S302 and S304 in the above example satisfy the qualified N count Add once, if not satisfied, set to zero, if qualified N count If the value is greater than x, then it is considered that each measurement value module is stable and consistent, and meets the filtering quality detection condition, and step S404 is executed; otherwise, step S402 is continued, wherein x can be 3 according to the actual measurement frequency requirement.

[0106] Step S404, determine the state of the fused Kalman filter and detect whether the estimation state of the IMU zero bias by the filter is accurate.

[0107] In the technical solution provided in the above step S404 of the present disclosure, when the quality consistency of each measurement module is passed, the measurement values ​​of the last two frames of each measurement module are taken to calculate the relative pose, and compared with the relative pose calculated by the fusion positioning at the corresponding time. Figure 1 As shown, when each module passes the verification of step 2, the visual measurement value is received at T1, and the relative position of the current visual measurement frame and the previous visual measurement frame can be obtained, which is recorded as Δp V , and the relative position of the fusion positioning between the two frames is obtained, recorded as ΔP V , calculate Δp V With ΔP V The difference is as follows:

[0108]

[0109] Similarly, we can get the corresponding LiDAR measurement frame at time T2

[0110]

[0111] The GNSS measurement frame corresponding to time T3

[0112]

[0113] The above variables are judged as follows:

[0114]

[0115] Among them, ξ is the calibration coefficient, which is related to the speed, κ is the bottom threshold, V1 is the fused horizontal speed corresponding to the visual measurement frame at time T1, V2 is the fused horizontal speed corresponding to the LiDAR measurement frame at time T2, and V3 is the fused horizontal speed corresponding to the GNSS measurement frame at time T3.

[0116] Optionally, ξ can be taken as 0.1 seconds and κ can be taken as 0.3 meters.

[0117] If one of the above formulas is satisfied, it is considered that the filtering quality is normal at this time, and the zero bias of the IMU is estimated normally, and no processing is performed, and the process re-enters step 1. If it is not satisfied, it is considered that the filtering error converges at this time, and step S306 is executed.

[0118] Step S406, when the IMU zero bias estimation is abnormal, the positioning confidence of the filter output is adjusted to a result closer to the actual error, and the positioning state is predicted.

[0119] Re-determine the measurement confidence of the fusion positioning output and predict it in advance. At this time, the confidence of the current fusion positioning module to be output is processed as follows. The current fusion position confidence σ is recorded p (Here is the standard deviation, not the variance), record the updated position confidence σ p (new):

[0120]

[0121]

[0122] in, is the magnification factor, optionally, The possible value is 3.0.

[0123] At the same time, the confidence of the measurement is improved, and the ability of fusion positioning to quickly correct the excessive zero deviation of imu is enhanced. The specific operation is as follows: Set the confidence of the next frame measurement value as σ M =[σ Mx σ My σ Mz ] T (Here is the standard deviation, not the variance), to improve the confidence, the process is as follows:

[0124]

[0125] Wherein, γ is the measurement value adjustment coefficient, and optionally, γ can be set to 2.

[0126] After updating the confidence of the measured value, the measured value and the new confidence σ are directly M Substitute (new) into the Kalman filter for measurement update.

[0127] In this embodiment, after determining that each measurement module meets the filter quality detection conditions, the state of the fused Kalman filter is judged, and it is detected whether the filter's estimation state of the IMU zero bias is accurate. In the case of an abnormal IMU zero bias estimation, the positioning confidence of the filter output is adjusted to a result closer to the actual error, and the positioning state is predicted, thereby solving the technical problem of poor reliability in vehicle positioning and achieving the technical effect of improving the reliability of vehicle positioning.

[0128] The present disclosure also provides a method for executing Figure 1 The positioning data processing device shown.

[0129] Figure 5 is a schematic diagram of a positioning data processing device according to an embodiment of the present disclosure. Figure 5As shown, the positioning data processing device 50 may include: an acquisition unit 51 , a first determination unit 52 , a second determination unit 53 , and a third determination unit 54 .

[0130] The acquisition unit 51 is used to acquire multiple new information results of the target vehicle, wherein each new information result is obtained by the measurement positioning result and the predicted positioning result of the target vehicle, the measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, and the predicted positioning result is obtained based on compensating the zero bias data of the inertial measurement unit, and the zero bias data is obtained by fusing multiple measurement positioning results converted into the inertial unit by the target filter.

[0131] The first determining unit 52 is used to determine the convergence state of the target filter based on the multiple innovation results.

[0132] The second determining unit 53 is configured to determine a data state of the zero bias data based on a convergence state.

[0133] The third determination unit 54 is used to determine whether to output the predicted positioning result based on the data state.

[0134] In this embodiment, multiple new information results of the target vehicle are acquired by the acquisition unit 51, the first determination unit 52 determines the convergence state of the target filter based on the multiple new information results, the second determination unit 53 determines the data state of the zero bias data based on the convergence state, and the third determination unit determines whether to output the predicted positioning result based on the data state. In this way, when the IMU zero bias estimation is abnormal, the positioning reliability of the filter output is adjusted to a result closer to the actual error, and the positioning state is predicted, thereby solving the technical problem of poor reliability in vehicle positioning and achieving the technical effect of improving the reliability of vehicle positioning.

[0135] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0136] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0137] An embodiment of the present disclosure provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method for positioning data processing of the embodiment of the present disclosure.

[0138] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0139] Optionally, in this embodiment, the non-volatile storage medium may be used by the device to store a computer program for performing the following steps:

[0140] S102, obtaining multiple new information results of the target vehicle, wherein each new information result is obtained by a measurement positioning result and a predicted positioning result of the target vehicle, the measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, and the predicted positioning result is obtained based on compensating for zero bias data of an inertial measurement unit, and the zero bias data is obtained by fusing multiple measurement positioning results converted into the inertial unit by a target filter;

[0141] S104, determining a convergence state of a target filter based on a plurality of innovation results;

[0142] S106, determining a data state of the zero bias data based on the convergence state;

[0143] S108: Determine whether to output the predicted positioning result based on the data status.

[0144] Alternatively, in this example, the non-transitory computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or apparatuses, or any suitable combination of the above. More specific examples of readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0145] According to an embodiment of the present disclosure, the present disclosure further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0146] S102, obtaining multiple new information results of the target vehicle, wherein each new information result is obtained by a measurement positioning result and a predicted positioning result of the target vehicle, the measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, and the predicted positioning result is obtained based on compensating for zero bias data of an inertial measurement unit, and the zero bias data is obtained by fusing multiple measurement positioning results converted into the inertial unit by a target filter;

[0147] S104, determining a convergence state of a target filter based on a plurality of innovation results;

[0148] S106, determining a data state of the zero bias data based on the convergence state;

[0149] S108: Determine whether to output the predicted positioning result based on the data status.

[0150] According to an embodiment of the present disclosure, the present disclosure also provides an autonomous driving vehicle, comprising a positioning data processing device according to an embodiment of the present disclosure or an electronic device according to an embodiment of the present disclosure.

[0151] Figure 6 is a schematic block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0152] like Figure 6 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0153] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0154] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as the method determining that each measurement module meets the filter quality detection condition. For example, in some embodiments, the method determines that each measurement module meets the filter quality detection condition and can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method described above to determine that each measurement module meets the filter quality detection condition can be executed. Alternatively, in other embodiments, the computing unit 601 may be configured in any other appropriate manner (eg, by means of firmware) to execute the method to determine whether each measurement module meets the filtering quality detection condition.

[0155] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0156] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0157] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0158] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0159] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0160] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0161] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0162] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A positioning data processing method, comprising: Acquire multiple new information results of the target vehicle, wherein each of the new information results is obtained by a measurement positioning result and a predicted positioning result of the target vehicle, the measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, the predicted positioning result is obtained based on compensating for zero bias data of an inertial measurement unit, and the zero bias data is obtained by fusing multiple measurement positioning results converted into the inertial measurement unit by a target filter; Determining a convergence state of the target filter based on the multiple innovation results; Determining a data state of the zero bias data based on the convergence state; Determining whether to output the predicted positioning result based on the data state; Wherein, the measurement module at least includes: a visual algorithm module and a global navigation satellite system module, the measurement and positioning result output by the visual algorithm module is the plane rectangular coordinate data of the target vehicle, the measurement and positioning result output by the global navigation satellite system module is the plane rectangular coordinate data obtained by performing plane rectangular coordinate conversion on the longitude and latitude coordinate data of the target vehicle, and the new information result at least includes: the new information result obtained based on the measurement and positioning result output by the visual algorithm module and the predicted positioning result, and the new information result obtained based on the measurement and positioning result output by the global navigation satellite system module and the predicted positioning result; Determining the convergence state of the target filter based on the multiple new information results includes: obtaining a second difference between every two new information results among the multiple new information results converted into the coordinate system of the inertial measurement unit; determining that each of the second differences satisfies a second target threshold, then determining that the two measurement modules corresponding to each of the second differences meet the target conditions, and determining the convergence state based on the measurement positioning results of each of the measurement modules, wherein the target conditions are used to characterize the measurement performance of the measurement modules.

2. The method according to claim 1, wherein: Determining the convergence state based on the measurement positioning result of each of the measurement modules includes: Acquire a first relative quantity between a first measurement frame and a second measurement frame of the measurement positioning result of each measurement module, wherein the first measurement frame and the second measurement frame are adjacent; Acquire a second relative quantity between a first sub-prediction positioning result corresponding to the first measurement frame and a second sub-prediction positioning result corresponding to the second measurement frame in the predicted positioning result; The convergence state is determined based on the first relative quantity and the second relative quantity corresponding to each of the measurement modules.

3. The method according to claim 2, wherein: Determining the convergence state based on the first relative quantity and the second relative quantity corresponding to each of the measurement modules includes: Acquire a first difference between the first relative quantity and the second relative quantity corresponding to each of the measurement modules; determining a sum of a first component of the first difference and a square of a second component of the first difference; Determining that the square root of the sum of squares is less than a first target threshold, then determining that the convergence state is used to indicate that the target filter has correctly converged; It is determined that the square root of the sum of squares is not less than the first target threshold, and then the convergence state is determined to indicate that the target filter has incorrectly converged.

4. The method according to claim 3, wherein: After determining that the convergence state is used to indicate that the target filter has erroneously converged, the method further includes: Based on the square root of the sum of squares, a first original confidence of a next measurement frame of the current measurement frame in the measurement and positioning result is updated to obtain a first target confidence; The zero bias data is updated based on the next measurement frame and the first target confidence.

5. The method according to claim 3, wherein: After determining that the convergence state is used to indicate that the target filter has erroneously converged, the method further includes: Based on the square root of the sum of squares, a second original confidence level corresponding to the predicted positioning result is updated to obtain a second target confidence level; Output the second target confidence.

6. The method according to any one of claims 1 to 5, wherein: Determining a data state of the zero bias data of the inertial measurement unit based on the convergence state includes: Determining that the convergence state is used to indicate that the target filter has converged correctly, then determining that the data state is an accurate state.

7. The method according to any one of claims 1 to 5, wherein: Determining whether to output the predicted positioning result based on the data state includes: If the data state is determined to be an accurate state, the predicted positioning result is output.

8. A positioning data processing device, comprising: An acquisition unit is used to acquire multiple new information results of a target vehicle, wherein each of the new information results is obtained by a measurement positioning result and a predicted positioning result of the target vehicle, the measurement positioning result is obtained by positioning the target vehicle by a measurement module of the target vehicle, the predicted positioning result is obtained based on compensating for zero bias data of an inertial measurement unit, and the zero bias data is obtained by fusing multiple measurement positioning results converted into the inertial measurement unit by a target filter; A first determining unit, configured to determine a convergence state of the target filter based on the multiple innovation results; A second determining unit, configured to determine a data state of the zero bias data based on the convergence state; A third determining unit, configured to determine whether to output the predicted positioning result based on the data state; Wherein, the measurement module at least includes: a visual algorithm module and a global navigation satellite system module, the measurement and positioning result output by the visual algorithm module is the plane rectangular coordinate data of the target vehicle, the measurement and positioning result output by the global navigation satellite system module is the plane rectangular coordinate data obtained by performing plane rectangular coordinate conversion on the longitude and latitude coordinate data of the target vehicle, and the new information result at least includes: the new information result obtained based on the measurement and positioning result output by the visual algorithm module and the predicted positioning result, and the new information result obtained based on the measurement and positioning result output by the global navigation satellite system module and the predicted positioning result; The first determination unit is further used to determine the convergence state of the target filter based on the multiple new information results by executing the following steps: obtaining a second difference between every two new information results among the multiple new information results converted into the coordinate system of the inertial measurement unit; determining that each of the second differences satisfies a second target threshold, determining that the two measurement modules corresponding to each of the second differences meet the target conditions, and determining the convergence state based on the measurement positioning results of each of the measurement modules, wherein the target condition is used to characterize the measurement performance of the measurement module.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

11. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

12. An autonomous driving vehicle, comprising the positioning data processing device according to claim 8 or the electronic device according to claim 9.

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