State data correction method and device, navigation system, electronic equipment and storage medium

Abstract

The application discloses a state data correction method and device, a navigation system, electronic equipment and a storage medium. The method comprises the following steps: acquiring first state data and N first wheel speed data measured by N wheel speed meters, wherein the first state data is data obtained by processing data measured by an inertial measurement unit; performing first filtering processing on the N first wheel speed data by using the first state data, so as to determine N residual check quantities corresponding to the N first wheel speed data respectively; in the case that M residual check quantities are less than a first threshold value in the N residual check quantities, performing second filtering processing on M first wheel speed data corresponding to the M residual check quantities, so as to obtain a first state feedback quantity; and correcting the first state data according to the first state feedback quantity, so as to obtain target state data to be output. The application uses wheel speed data to correct state data based on multiple wheel speed meters of a vehicle, and can effectively inhibit accumulation of state data errors.

Description

Technical Field

[0001] This application belongs to the field of positioning technology, and in particular relates to a state data correction method, device, navigation system, electronic device and storage medium. Background Technology

[0002] In-vehicle navigation systems typically include inertial measurement units (IMUs), such as strapdown inertial navigation systems (SINS), to measure the vehicle's position, speed, attitude, and other state data. However, the state data obtained by IMUs generally contains errors. In practical applications, especially in complex driving environments such as urban areas, these errors can accumulate rapidly, affecting the vehicle's positioning accuracy. Therefore, suppressing the accumulation of state data errors has become an urgent problem to be solved. Summary of the Invention

[0003] This application provides a state data correction method, apparatus, navigation system, electronic device, and storage medium that can suppress the accumulation of state data errors.

[0004] In a first aspect, embodiments of this application provide a state data correction method, including:

[0005] Acquire first state data and N first wheel speed data measured by N wheel speed gauges. The first state data is the data obtained by processing the data measured by the inertial measurement unit, and N is an integer greater than 1.

[0006] The first state data is used to perform a first filtering process on the N first wheel speed data to determine the N residual check quantities corresponding to each of the N first wheel speed data.

[0007] If M of the N residual check quantities are less than the first threshold, the M first wheel speed data corresponding to the M residual check quantities are subjected to a second filtering process to obtain the first state feedback quantity, where M is an integer less than or equal to N and greater than zero.

[0008] Based on the first state feedback quantity, the first state data is corrected to obtain the target state data to be output.

[0009] Secondly, embodiments of this application provide a state data correction device, comprising:

[0010] The acquisition module is used to acquire first state data and N first wheel speed data measured by N wheel speed meters. The first state data is the data obtained by processing the data measured by the inertial measurement unit, and N is an integer greater than 1.

[0011] The first processing module is used to perform a first filtering process on the N first wheel speed data using the first state data, so as to determine the N residual check quantities corresponding to each of the N first wheel speed data;

[0012] The second processing module is used to perform a second filtering process on the M first wheel speed data corresponding to the M residual check quantities when M of the N residual check quantities are less than the first threshold, so as to obtain a first state feedback quantity, where M is an integer less than or equal to N and greater than zero.

[0013] The first correction module is used to correct the first state data according to the first state feedback quantity to obtain the target state data to be output.

[0014] Thirdly, embodiments of this application provide a navigation system, including a navigation unit, an inertial measurement unit, N wheel speedometers, and the state data correction device described in the second aspect, wherein the state data correction device is communicatively connected to the inertial measurement unit and the N wheel speedometers.

[0015] Fourthly, embodiments of this application provide an electronic device, including: a processor, and a memory storing computer program instructions;

[0016] The processor reads and executes the computer program instructions to implement the state data correction method described in the first aspect.

[0017] Fifthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the state data correction method as described in the first aspect.

[0018] In this embodiment, based on multiple wheel speed meters integrated into the vehicle, the wheel speed data measured by these multiple wheel speed meters is used to correct the state data, thereby effectively suppressing the accumulation of errors in the state data through wheel speed data. During the correction process using wheel speed data, the wheel speed data is filtered using the state data to determine the residual check value of the wheel speed data. This ensures that the wheel speed data involved in the correction has high stability, thus guaranteeing the reliability of the correction. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1This is a flowchart illustrating a state data correction method provided in some embodiments of this application;

[0021] Figure 2 This is a schematic diagram of the geometric relationship of a multi-round tachometer provided in some embodiments of this application;

[0022] Figure 3 This is a system architecture diagram provided in some embodiments of this application;

[0023] Figure 4 These are schematic diagrams of navigation systems provided in some embodiments of this application;

[0024] Figure 5 This is a schematic diagram of the structure of a state data correction device provided in some embodiments of this application;

[0025] Figure 6 This is a schematic diagram of the hardware structure of a state data correction device provided in some embodiments of this application. Detailed Implementation

[0026] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0028] In vehicle navigation applications, integrated navigation systems comprised of Global Navigation Satellite System (GNSS), Strapdown Inertial Navigation System (SINS), and wheel speed sensors have become standard equipment and are widely used in autonomous driving, intelligent vehicle Internet of Things (IoT), and other applications. In these integrated navigation systems, SINS, as an inertial measurement unit, measures the vehicle's position, velocity, attitude, and other state data. Accurate measurement of this state data plays a crucial role in achieving high-precision all-weather positioning. However, the state data obtained from SINS and other inertial measurement units generally contains errors. In practical applications, especially in complex driving environments such as urban areas, these errors can accumulate rapidly, affecting the vehicle's positioning accuracy. Therefore, to achieve high-precision vehicle positioning even in complex environments, it is necessary to correct the state data to suppress the accumulation of errors.

[0029] The inventors analyzed that in integrated navigation systems, GNSS commonly suffers from multipath and signal obstruction issues, making it difficult to use GNSS measurement data to correct state data in certain scenarios. However, vehicles typically have multiple wheel speed sensors, and the multiple wheel speed measurements obtained from these sensors have high confidence and stability; therefore, these multiple wheel speed data can be used to correct state data. Based on this, this application proposes a scheme for correcting state data using wheel speed data.

[0030] To address the problems of the prior art, embodiments of this application provide a state data correction method, apparatus, navigation system, electronic device, and computer storage medium.

[0031] The state data correction method provided in the embodiments of this application will be introduced first below.

[0032] Figure 1 A schematic flowchart of the state data correction method provided in an embodiment of this application is shown.

[0033] like Figure 1 As shown, the method may include the following steps:

[0034] Step 101: Obtain the first state data and N first wheel speed data measured by N wheel speed gauges, where N is an integer greater than 1;

[0035] Step 102: Use the first state data to perform a first filtering process on the N first wheel speed data to determine the N residual check quantities corresponding to each of the N first wheel speed data;

[0036] Step 103: If M of the N residual check quantities are less than the first threshold, perform a second filtering process on the M first wheel speed data corresponding to the M residual check quantities to obtain the first state feedback quantity, where M is an integer less than or equal to N and greater than zero.

[0037] Step 104: Correct the first state data according to the first state feedback quantity to obtain the target state data to be output.

[0038] For ease of description, the following uses the state data correction device as the execution subject to explain the specific process of the state data correction method.

[0039] In step 101, the state data correction device can acquire the first state data and the N first wheel speed data measured by the N wheel speed gauges.

[0040] Here, the first state data is the data obtained by processing the data measured by the inertial measurement unit (INS). For example, the data measured by the INS can be mechanically arranged and state predicted to obtain the first state data; therefore, the first state data can also be understood as the state prediction result. The first state data may include position data, velocity data, and attitude data, etc. The INS can be, for example, a SINS.

[0041] The number of wheel speed meters is generally the same as the number of wheels on a vehicle, with each meter collecting wheel speed data for its corresponding wheel. Wheel speed data, in contrast to state data, can be referred to as observations or observational data.

[0042] In step 102, the state data correction device can use the first state data to perform a first filtering process on the N first wheel speed data. After the first filtering process, the N residual check quantities corresponding to each of the N first wheel speed data can be determined.

[0043] The process of using the first state data to perform the first filtering process on the first wheel speed data can be understood as using the first state data to perform a pre-test diagnosis on the first wheel speed data in order to judge the quality of the current wheel speed data. Therefore, the processing involved in step 102 can also be called the filtering state diagnosis process, and a filtering state diagnosis module can be set in the state data correction device to execute the filtering state diagnosis process.

[0044] For example, the first filtering process can use Kalman filtering, and the residual check value corresponding to the wheel speed data can be calculated using formula (1):

[0045]

[0046] Where σ is the residual check value, v is the information in the Kalman filtering process, H is the design matrix of the observation equation for the wheel speed data, D is the variance matrix of the state data in the Kalman filtering process, and R is the variance matrix of the wheel speed data.

[0047] The smaller the residual check value corresponding to the wheel speed data, the better the quality of the wheel speed data. After calculating the N residual check values ​​corresponding to each of the N first wheel speed data using formula (1), the N residual check values ​​are compared with a pre-set first threshold. If the residual check value is less than the first threshold, it indicates that the quality of the wheel speed data corresponding to that residual check value is good, and the wheel speed data corresponding to that residual check value can be used to correct the state data. If the residual check value is not less than the first threshold, it indicates that the quality of the wheel speed data corresponding to that residual check value is poor, and the wheel speed data corresponding to that residual check value can be discarded. In this way, the wheel speed data participating in the correction can be ensured to have high stability, thereby ensuring the reliability of the correction.

[0048] If M of the N residual check quantities are less than the first threshold, then the M first wheel speed data corresponding to these M residual check quantities can be used to correct the state data. Specifically, in step 103, the state data correction device can perform a second filtering process on these M first wheel speed data to obtain a first state feedback quantity. Then, in step 104, the state data correction device can correct the first state data according to the first state feedback quantity to obtain the target state data to be output.

[0049] For example, the second filtering process can employ Kalman filtering.

[0050] The process of performing a second filtering process on M first wheel speed data to obtain the first state feedback quantity can be understood as a filtering process. Therefore, the processing involved in step 103 can also be called a filtering process, and a filtering module can be set in the state data correction device to perform this filtering process.

[0051] Since the first state feedback quantity is the filtering result of the second filtering process, the process of correcting the first state data based on the first state feedback quantity can be understood as a process of feeding back the first state data using the filtering result. Therefore, the processing involved in step 104 can also be called the filtering result feedback process, and a filtering result feedback module can be set in the state data correction device to execute this filtering result feedback process.

[0052] In this embodiment, based on multiple wheel speed meters integrated into the vehicle, the wheel speed data measured by these multiple wheel speed meters is used to correct the state data, thereby effectively suppressing the accumulation of errors in the state data through wheel speed data. During the correction process using wheel speed data, the wheel speed data is filtered using the state data to determine the residual check value of the wheel speed data. This ensures that the wheel speed data involved in the correction has high stability, thus guaranteeing the reliability of the correction.

[0053] In some embodiments, after step 102, the method further includes:

[0054] If all N residual check quantities are not less than the first threshold, a consistency check is performed on the N first round speed data.

[0055] If the consistency check of the N first round speed data passes, the N first round speed data are updated to obtain the second round speed data;

[0056] The second wheel speed data is subjected to the second filtering process to obtain the second state feedback quantity;

[0057] Based on the second state feedback quantity, the first state data is corrected to obtain the target state data to be output.

[0058] After the filtering state diagnosis process, if all residual check values ​​are not less than the first threshold, it indicates that all first-round velocity data needs to be discarded. The inventors analyzed that this may be due to excessive convergence of the current state data. Therefore, the first-round velocity data can be further screened to determine whether it can be used to correct the state data.

[0059] Specifically, a consistency check can be performed on N first-round speed data. If the consistency check passes, it indicates that these N first-round speed data have good consistency and thus good stability. Therefore, the first-round speed data can be used to correct the state data.

[0060] For example, the process of performing a consistency check on wheel speed data is as follows:

[0061] The maximum value σ of the residual check quantity corresponding to all wheel speed data is calculated using formula (2). max Minimum value σ min and range σ range :

[0062]

[0063] If the range σ range Less than the set threshold and σmin If the speed exceeds the set threshold, the consistency check of the wheel speed data is considered passed.

[0064] In this embodiment, if the consistency check of N first wheel speed data passes, the state data correction device can update the N first wheel speed data to obtain second wheel speed data. Then, the state data correction device can perform a second filtering process on the second wheel speed data to obtain a second state feedback quantity. Subsequently, the state data correction device can correct the first state data based on the second state feedback quantity to obtain the target state data to be output.

[0065] The above process can be understood as a process of updating wheel speed. Therefore, the above process can also be called the wheel speed update process. A wheel speed update control module can be set in the state data correction device to execute the wheel speed update process.

[0066] It should be noted that in this embodiment, the process of "correcting the first state data according to the second state feedback quantity to obtain the target state data to be output" can be the same as the process of "correcting the first state data according to the first state feedback quantity to obtain the target state data to be output".

[0067] Correspondingly, since the second state feedback quantity is the filtering result of the second filtering process, the process of correcting the first state data based on the second state feedback quantity can also be understood as a process of feeding back the first state data using the filtering result. Therefore, this step is also a filtering result feedback process, which can be executed through the filtering result feedback module.

[0068] In this embodiment, when all residual check quantities are not less than the first threshold, the consistency check of the first wheel speed data is performed to further determine whether the first wheel speed data can be used to correct the first state data. This can effectively avoid the situation where wheel speed data is mistakenly rejected due to excessive convergence of state data, thereby enabling wheel speed data to participate more effectively in the correction of state data and further ensuring that the wheel speed data participating in the correction has high stability, thus ensuring the reliability of the correction.

[0069] In some embodiments, updating the N first wheel speed data to obtain second wheel speed data includes:

[0070] The filtered angular velocity is used to transform the N first wheel velocity data to obtain N third wheel velocity data;

[0071] Based on the third wheel speed data of the coaxial wheels, the angular velocity, and the lever arm value between the coaxial wheels, calculate the wheel speed difference test quantity between the coaxial wheels;

[0072] Calculate the average value of the third wheel speed data corresponding to the smallest wheel speed difference test quantity to obtain the second wheel speed data.

[0073] This implementation provides a scheme for updating N first-round speed data.

[0074] After the consistency check of N first-round speed data passes, the optimal data selection can be performed on the N first-round speed data to achieve the update of the N first-round speed data.

[0075] First, the angular velocity output by the gyroscope at the current moment can be low-pass filtered according to formula (3):

[0076]

[0077] in, ω is the filtered angular velocity. k and ω k-1 ω and ω represent the angular velocities at the current and previous moments, respectively, and s is the set filter coefficient.

[0078] Secondly, taking a vehicle with four wheel speedometers as an example, each wheel speedometer can output the forward velocity at the wheel under the vehicle system. For the Ackermann steering system, which is widely used in vehicles, the geometric relationship of each wheel speedometer is shown in [reference needed]. Figure 2 In the diagram, L and K represent the lengths of the vehicle chassis's longitudinal and transverse axes, respectively, O is the instantaneous turning origin, and r... i Let be the instantaneous turning radius of each wheel. Based on the geometric relationship of each wheel speed gauge, the wheel speed data of each wheel speed gauge can be calculated using formula (4):

[0079]

[0080] Among them, v fi and v i This represents the original output speed value of the i-th wheel speed meter (corresponding to the first wheel speed data) and the converted speed value (corresponding to the third wheel speed data).

[0081] Based on this, the wheel speed difference test quantity between coaxial wheels can be calculated using formula (5):

[0082]

[0083] Where C1 is the test quantity for the difference in wheel speed between wheel 1 and wheel 2, and C2 is the test quantity for the difference in wheel speed between wheel 3 and wheel 4. ij This refers to the lever arm value between coaxial wheels.

[0084] If either the test value C1 or C2 is less than the set threshold, then a set of wheel speed data used to construct that test value is considered usable. If both sets of wheel speed values ​​are usable, then the set of wheel speed data corresponding to the smallest test value is selected to obtain the optimal wheel speed data.

[0085] Subsequently, the average value of the selected optimal wheel speed data can be used to obtain the updated wheel speed data, namely the second wheel speed data. This second wheel speed data is equivalent to the forward speed observation value corresponding to the center point of the front axle (when taking the average of v1 and v2) or the rear axle (when taking the average of v3 and v4).

[0086] After updating the second round velocity data from N first round velocity data, the observations can be used for standard Kalman filter observation updates. That is, by performing a second filter on the second round velocity data, the second state feedback quantity can be obtained.

[0087] In this embodiment, the update scheme of selecting the optimal wheel speed data can further ensure the reliability and stability of the wheel speed data used to correct the state data, thereby further ensuring the reliability of the correction.

[0088] It should be noted that, if the consistency check of the N first-round speed data passes, in addition to the above-mentioned update scheme, other update schemes can also be used. For example, the speed data can be updated by averaging the N first-round speed data.

[0089] In some implementations, correcting the first state data based on the first state feedback quantity includes:

[0090] The first state feedback quantity is used to perform closed-loop correction on the velocity and attitude data in the first state data, and

[0091] The position data in the first state data is recursively analyzed using the velocity data after closed-loop correction.

[0092] This implementation provides a scheme for feeding back the filtering result based on the first state feedback quantity.

[0093] The filtering result feedback process can be divided into two parts: filter state feedback correction and state recursion. After obtaining the first state feedback quantity, standard closed-loop corrections can be performed on the velocity data, attitude data, and other state quantities in the first state data. For example, standard closed-loop corrections can be performed on state quantities such as velocity, misalignment angle, and zero bias. This part is the filter state feedback correction. For the position data in the first state data, state recursion can be performed using the velocity data after closed-loop correction.

[0094] In some embodiments, using the velocity data after closed-loop correction to perform state recursion on the position data in the first state data includes:

[0095] Using the velocity data after the closed-loop correction, the time interval between two adjacent second filtering processes, and the position data obtained from the previous correction, the position data in the first state data is recursively analyzed.

[0096] In this implementation, the dynamic model of formula (6) can be used for state recursion:

[0097]

[0098] in, and p k-1 These represent the current position and the previous position, respectively. t represents the velocity after closed-loop correction, and t represents the time interval between two consecutive second filtering processes.

[0099] After the above filtering result feedback process, the target state data to be output can be obtained. After obtaining the target state data, the state data correction device can output the target state data.

[0100] In this embodiment, the position data is corrected by state recursion, which can improve the accuracy of the position data correction.

[0101] In some embodiments, performing a second filtering process on the M first wheel speed data to obtain a first state feedback quantity includes:

[0102] The observation values ​​of the M first-round speed data are weighted to obtain M fourth-round speed data;

[0103] The M fourth-round speed data are subjected to the second filtering process to obtain the first state feedback quantity.

[0104] In this implementation, after obtaining the residual check value of the wheel speed data, the observation values ​​of the M first wheel speed data can also be weighted.

[0105] For example, the observation weights of the first round velocity data corresponding to the residual check quantity can be determined based on the residual check quantity. Specifically, the observation weights can be determined using formula (7).

[0106]

[0107] Among them, std modified and std originalThese are the standard deviations of the observations after weighting and before weighting, respectively. Threshold is a pre-set threshold, and threshold2 is the first threshold involved in this embodiment. A factor of zero means that the observation is discarded.

[0108] After obtaining std modified Then, the fourth round speed data can be obtained from the first round speed data and std. modified These two data points are confirmed.

[0109] In this embodiment, by weighting the observations of the first wheel speed data, the confidence level of the wheel speed data can be optimized, thereby improving the reliability of the wheel speed data and further ensuring the reliability of the correction.

[0110] To better understand the overall technical solution of the embodiments of this application, the following will be used... Figure 3 The system architecture diagram shown is used as an example to illustrate the state data correction process.

[0111] like Figure 3 As shown, the state data correction device 300 includes a filter state diagnosis module 301, a wheel speed update control module 302, a filter module 303, and a filter result feedback module 304. The state data correction device 300 is connected to N wheel speed meters 201 (only one wheel speed meter is shown in the figure) and an inertial measurement unit 202 through the filter state diagnosis module 301.

[0112] The first state data and N first round speed data can be input to the filtering state diagnosis module 301. The filtering state diagnosis module 301 can perform pre-test residual checks to obtain the filtering state diagnosis result. If the filtering state diagnosis result is that the residual check value of M first round speed data among the N first round speed data is less than the first threshold, then the filtering module 303 can filter the M first round speed data to obtain the filtering result. The filtering module 303 can either directly filter the M first round speed data or perform filtering after weighting the observations of the M first round speed data. If the filtering state diagnosis result is that there are no first round speed data among the N first round speed data with a residual check value less than the first threshold, then the round speed update control module 302 can first perform consistency checks, optimal round speed data screening, observation updates, etc., on the N first round speed data to obtain updated round speed data, and then the filtering module 303 can filter the updated round speed data to obtain the filtering result. Finally, the filtering result feedback module 304 can perform state feedback correction, state recursion, and other processing based on the filtering result to obtain the target state data to be output. Thus, it is possible to correct the state data using wheel speed data measured by multiple wheel speed gauges.

[0113] In summary, the embodiments of this application, based on multiple wheel speed meters on the vehicle, use the wheel speed data measured by multiple wheel speed meters to correct the state data, thereby effectively suppressing the accumulation of state data errors.

[0114] like Figure 4 As shown in the figure, this application embodiment also provides a navigation system 400, including a navigation unit 401, an inertial measurement unit 402, N wheel speedometers 403 (only one wheel speedometer is shown in the figure) and a state data correction device 404, the state data correction device 404 being communicatively connected to the inertial measurement unit 402 and the wheel speedometers 403.

[0115] In addition, the status data correction device 404 can also be communicatively connected to the navigation unit 401.

[0116] For example, the navigation unit 401 can be a GNSS, the inertial measurement unit 402 can be a SINS, and the state data correction device 404 can be... Figure 3 The status data correction device is shown.

[0117] In this embodiment, the state data correction device 404 can be combined with the navigation unit 401, the inertial measurement unit 402, and the wheel speed meter 403 to form an in-vehicle integrated navigation system. This can improve the positioning accuracy of the in-vehicle navigation system and achieve seamless high-precision positioning in complex environments around the clock.

[0118] Figure 5 This is a schematic diagram of the state data correction device provided in an embodiment of this application. Figure 5 As shown, the status data correction device 500 includes:

[0119] The acquisition module 501 is used to acquire first state data and N first wheel speed data measured by N wheel speed meters. The first state data is the data obtained by processing the data measured by the inertial measurement unit, and N is an integer greater than 1.

[0120] The first processing module 502 is used to perform a first filtering process on the N first wheel speed data using the first state data, so as to determine the N residual check quantities corresponding to each of the N first wheel speed data.

[0121] The second processing module 503 is used to perform a second filtering process on the M first wheel speed data corresponding to the M residual check quantities when M residual check quantities are less than the first threshold among the N residual check quantities, so as to obtain a first state feedback quantity, where M is an integer less than or equal to N and greater than zero.

[0122] The first correction module 504 is used to correct the first state data according to the first state feedback quantity to obtain the target state data to be output.

[0123] Optionally, the status data correction device 500 also includes:

[0124] The consistency check module is used to perform consistency checks on the N first round speed data when all N residual check quantities are not less than the first threshold.

[0125] The update module is used to update the N first-round speed data to obtain the second-round speed data after the consistency check of the N first-round speed data passes.

[0126] The third processing module is used to perform the second filtering process on the second wheel speed data to obtain the second state feedback quantity;

[0127] The second correction module is used to correct the first state data according to the second state feedback quantity to obtain the target state data to be output.

[0128] Optionally, the update module is specifically used for:

[0129] The filtered angular velocity is used to transform the N first wheel velocity data to obtain N third wheel velocity data;

[0130] Based on the third wheel speed data of the coaxial wheels, the angular velocity, and the lever arm value between the coaxial wheels, calculate the wheel speed difference test quantity between the coaxial wheels;

[0131] Calculate the average value of the third wheel speed data corresponding to the smallest wheel speed difference test quantity to obtain the second wheel speed data.

[0132] Optionally, the first calibration module 504 is specifically used for:

[0133] The first state feedback quantity is used to perform closed-loop correction on the velocity and attitude data in the first state data, and

[0134] The position data in the first state data is recursively analyzed using the velocity data after closed-loop correction.

[0135] Optionally, the first calibration module 504 is specifically used for:

[0136] Using the velocity data after the closed-loop correction, the time interval between two adjacent second filtering processes, and the position data obtained from the previous correction, the position data in the first state data is recursively analyzed.

[0137] Optionally, the second processing module 503 is specifically used for:

[0138] The observation values ​​of the M first-round speed data are weighted to obtain M fourth-round speed data;

[0139] The M fourth-round speed data are subjected to the second filtering process to obtain the first state feedback quantity.

[0140] Figure 5 Each module / unit in the device shown has the function of implementing each step in the method embodiment and can achieve its corresponding technical effect. For the sake of brevity, it will not be described in detail here.

[0141] Figure 6 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0142] like Figure 6 As shown, the electronic device may include a processor 601 and a memory 602 storing computer program instructions.

[0143] Specifically, the processor 601 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0144] Specifically, the processor 601 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0145] Memory 602 may include mass storage for data or instructions. For example, and not limitingly, memory 602 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 602 may include removable or non-removable (or fixed) media, or memory 602 may be non-volatile solid-state memory. Memory 602 may be internal or external to the integrated gateway disaster recovery device.

[0146] In one instance, memory 602 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0147] Memory 602 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the state data correction method according to embodiments of this application.

[0148] Processor 601 reads and executes computer program instructions stored in memory 602 to implement any of the state data correction methods in the above embodiments, and achieves... Figures 1 to 3 The technical effects achieved by executing the methods / steps shown in the examples are not elaborated here for the sake of brevity.

[0149] In one example, the electronic device may also include a communication interface 603 and a bus 610. For example, Figure 6 As shown, the processor 601, memory 602, and communication interface 603 are connected through bus 610 and complete communication with each other.

[0150] The communication interface 603 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0151] Bus 610 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 610 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0152] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0153] The electronic device can execute the state data correction method in the embodiments of this application, thereby achieving the combination Figures 1 to 3 The described state data correction method.

[0154] Furthermore, in conjunction with the state data correction methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the state data correction methods in the above embodiments.

[0155] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0156] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0157] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0158] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0159] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A state data correction method, characterized in that, include: Acquire first state data and N first wheel speed data measured by N wheel speed gauges. The first state data is the data obtained by processing the data measured by the inertial measurement unit, and N is an integer greater than 1. The first state data is used to perform a first filtering process on the N first wheel speed data to determine the N residual check quantities corresponding to each of the N first wheel speed data. If M of the N residual check quantities are less than the first threshold, the M first wheel speed data corresponding to the M residual check quantities are subjected to a second filtering process to obtain the first state feedback quantity, where M is an integer less than or equal to N and greater than zero. Based on the first state feedback quantity, the first state data is corrected to obtain the target state data to be output; The step of correcting the first state data based on the first state feedback quantity includes: The first state feedback quantity is used to perform closed-loop correction on the velocity and attitude data in the first state data, and The position data in the first state data is recursively derived using the velocity data after closed-loop correction. The step of using the velocity data after closed-loop correction to perform state recursion on the position data in the first state data includes: Using the velocity data after the closed-loop correction, the time interval between two adjacent second filtering processes, and the position data obtained from the previous correction, the position data in the first state data is recursively analyzed.

2. The method according to claim 1, characterized in that, After the step of performing a first filtering process on the N first wheel speed data using the first state data to determine the N residual check quantities corresponding to each of the N first wheel speed data, the method further includes: If all N residual check quantities are not less than the first threshold, a consistency check is performed on the N first round speed data. If the consistency check of the N first round speed data passes, the N first round speed data are updated to obtain the second round speed data; The second wheel speed data is subjected to the second filtering process to obtain the second state feedback quantity; Based on the second state feedback quantity, the first state data is corrected to obtain the target state data to be output.

3. The method according to claim 2, characterized in that, The step of updating the N first round speed data to obtain the second round speed data includes: The filtered angular velocity is used to transform the N first wheel velocity data to obtain N third wheel velocity data; Based on the third wheel speed data of the coaxial wheels, the angular velocity, and the lever arm value between the coaxial wheels, calculate the wheel speed difference test quantity between the coaxial wheels; Calculate the average value of the third wheel speed data corresponding to the smallest wheel speed difference test quantity to obtain the second wheel speed data.

4. The method according to claim 1, characterized in that, The step of performing a second filtering process on the M first wheel speed data to obtain a first state feedback quantity includes: The observation values ​​of the M first-round speed data are weighted to obtain M fourth-round speed data; The M fourth-round speed data are subjected to the second filtering process to obtain the first state feedback quantity.

5. A state data correction device, characterized in that, include: The acquisition module is used to acquire first state data and N first wheel speed data measured by N wheel speed meters. The first state data is the data obtained by processing the data measured by the inertial measurement unit, and N is an integer greater than 1. The first processing module is used to perform a first filtering process on the N first wheel speed data using the first state data, so as to determine the N residual check quantities corresponding to each of the N first wheel speed data; The second processing module is used to perform a second filtering process on the M first wheel speed data corresponding to the M residual check quantities when M of the N residual check quantities are less than the first threshold, so as to obtain a first state feedback quantity, where M is an integer less than or equal to N and greater than zero. The first correction module is used to correct the first state data according to the first state feedback quantity to obtain the target state data to be output. The first correction module is specifically used for: The first state feedback quantity is used to perform closed-loop correction on the velocity and attitude data in the first state data, and The position data in the first state data is recursively derived using the velocity data after closed-loop correction. The first correction module is specifically used for: Using the velocity data after the closed-loop correction, the time interval between two adjacent second filtering processes, and the position data obtained from the previous correction, the position data in the first state data is recursively analyzed.

6. A navigation system, characterized in that, It includes a navigation unit, an inertial measurement unit, N wheel speedometers, and a state data correction device as described in claim 5, wherein the state data correction device is communicatively connected to the inertial measurement unit and the N wheel speedometers.

7. An electronic device, characterized in that, Includes: a processor, and memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the state data correction method as described in any one of claims 1 to 4.

8. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement the state data correction method as described in any one of claims 1 to 4.

Images