Parameter initialization method, device, equipment and product for integrated navigation

Abstract

Embodiments of the present disclosure relate to a parameter initialization method, device, equipment and product for integrated navigation. The method comprises: obtaining historical navigation data of a mobile terminal device, the historical navigation data comprising data estimated by a filtering model on the mobile terminal device in a historical integrated navigation process, the data comprising a first zero offset of a gyroscope, a second zero offset of an accelerometer, a first installation angle of the mobile terminal device and a first lever arm; and taking at least one of the first zero offset, the second zero offset, the first installation angle and the first lever arm as an initialization parameter of a corresponding estimated variable in the filtering model, and initializing the filtering model; wherein the first lever arm is a vector pointing to a center point of a rear wheel shaft of a vehicle in the historical integrated navigation process. Embodiments of the present disclosure can effectively reduce the problem of filtering divergence or slow convergence caused by too large initialization parameter error.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of navigation, and in particular, to a parameter initialization method, device, equipment and product for integrated navigation. BACKGROUND

[0002] In some scenarios, such as a driving scenario, a mobile terminal device such as a mobile phone can be fixed at a certain position of a vehicle, and navigation data output by an inertial measurement unit and a satellite positioning system on the mobile terminal device can be used to perform integrated navigation by using a Kalman filtering algorithm.

[0003] In the Kalman filtering algorithm, parameters such as a gyroscope bias, an accelerometer bias, an installation angle and a lever arm of the mobile terminal device are generally taken as part of the estimated state. However, in the initialization stage of the Kalman filtering algorithm, the parameters such as the gyroscope bias, the accelerometer bias, the installation angle and the lever arm of the terminal device are unknown, and at this time, the initial values of the parameters such as the gyroscope bias, the accelerometer bias, the installation angle and the lever arm are generally set to 0 or preset values. However, setting the initial values of the parameters such as the gyroscope bias, the accelerometer bias, the installation angle and the lever arm to 0 or preset values can result in a large deviation between the initial values and the actual values, and further result in a slow convergence speed of filtering, or even a filtering divergence problem. Therefore, how to initialize the parameters for integrated navigation and improve the convergence speed of filtering is a technical problem to be solved. SUMMARY

[0004] To solve the above technical problem, embodiments of the present disclosure provide a parameter initialization method, device, equipment and product for integrated navigation.

[0005] A first aspect of embodiments of the present disclosure provides a parameter initialization method for integrated navigation, comprising:

[0006] obtaining historical navigation data of a mobile terminal device, wherein the historical navigation data includes data estimated by a filtering model on the mobile terminal device in a historical integrated navigation process, the data being a first gyroscope bias, a second accelerometer bias, a first installation angle and a first lever arm of the mobile terminal device;

[0007] initializing the filtering model by taking at least one parameter in the first gyroscope bias, the second accelerometer bias, the first installation angle and the first lever arm as an initialization parameter of a corresponding estimated variable in the filtering model;

[0008] The first lever arm refers to a vector pointing to a center point of a rear wheel shaft of the vehicle in the historical integrated navigation process.

[0009] A second aspect of embodiments of the present disclosure provides a parameter initialization device for integrated navigation, comprising:

[0010] The acquisition module is used to acquire historical navigation data of the mobile terminal device. The historical navigation data includes the following data estimated by the filtering model on the mobile terminal device during the historical combined navigation process: the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first installation angle of the mobile terminal device, and the first lever arm.

[0011] An initialization module is used to initialize the filtering model by taking at least one of the first zero bias, the second zero bias, the first mounting angle, and the first lever arm as initialization parameters for the corresponding estimated variables in the filtering model; wherein, the first lever arm refers to the vector from the mobile terminal device to the center point of the rear wheel axle of the vehicle during the historical integrated navigation process.

[0012] A third aspect of this disclosure provides a mobile terminal device, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, can implement the method of the first aspect described above.

[0013] A fourth aspect of this disclosure provides a computer program product stored in a storage medium, which, when run, can implement the method of the first aspect described above.

[0014] A fifth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed, can implement the method described in the first aspect above.

[0015] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0016] In this embodiment, historical navigation data from a mobile terminal device is acquired, and the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first mounting angle of the mobile terminal device, and the first lever arm estimated by the filtering model on the mobile terminal device in the historical navigation data are used to initialize the parameters of the corresponding estimated variables in the filtering model. Since the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first mounting angle of the mobile terminal device, and the first lever arm estimated by the filtering model in the historical navigation data are closer to the actual values ​​than the preset values, the problem of filtering divergence or slow convergence speed caused by excessive initialization parameter errors can be effectively reduced, thereby improving the accuracy of integrated navigation. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0018] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic diagram of a combined navigation system provided in this disclosure embodiment;

[0020] Figure 2 A flowchart of a parameter initialization method for integrated navigation provided in this embodiment of the present disclosure;

[0021] Figure 3 This is a flowchart illustrating the historical navigation data update process provided in this embodiment of the disclosure.

[0022] Figure 4 A flowchart illustrating the historical navigation data update process provided in yet another embodiment of this disclosure;

[0023] Figure 5 A flowchart for determining the confidence level of estimated parameters provided in embodiments of this disclosure;

[0024] Figure 6 A flowchart illustrating the historical navigation data update process provided in yet another embodiment of this disclosure;

[0025] Figure 7 A schematic diagram of the structure of the parameter initialization device for integrated navigation provided in the embodiments of this disclosure;

[0026] Figure 8 This is a schematic diagram of the structure of a mobile terminal device according to an embodiment of this disclosure. Detailed Implementation

[0027] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0028] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0029] To facilitate a better understanding of the technical solutions disclosed herein, some technical terms involved in the embodiments of this disclosure are introduced below.

[0030] A Global Navigation Satellite System (GNSS) is a space-based radio navigation and positioning system that provides users with all-weather three-dimensional coordinates, velocity, and time information at any location on the Earth's surface or in near-Earth space.

[0031] An inertial measurement unit (IMU) is used to measure an object's three-axis attitude angles (or angular rates) and acceleration. Gyroscopes and accelerometers are the main components of an IMU.

[0032] Integrated navigation refers to the technology of combining and optimizing information from multiple navigation systems to output a comprehensive navigation result.

[0033] The installation angle, in integrated navigation, represents the position and attitude of the IMU in the vehicle coordinate system. It can also be understood as the position and attitude of the mobile terminal device equipped with the IMU in the vehicle coordinate system.

[0034] In integrated navigation, a lever is a vector pointing from the IMU to the center point of the vehicle's rear axle, or it can be understood as a vector pointing from a mobile terminal device equipped with an IMU to the center point of the vehicle's rear axle.

[0035] Current technologies typically use a combination of inertial navigation systems and satellite navigation systems for navigation. In some scenarios, such as driving, mobile devices like smartphones can be fixed to a specific location within the vehicle for in-vehicle navigation. Figure 1 A schematic diagram of a combined navigation system provided in this disclosure embodiment, with reference to... Figure 1 After the mobile terminal device activates integrated navigation, it continuously inputs the navigation parameters received from the inertial navigation system 101 and the satellite navigation system 102 into the Kalman filter 103, which then outputs the integrated navigation result 104. The inertial navigation system 101 is a system built using an IMU (Inertial Measurement Unit).

[0036] In the Kalman filter algorithm, parameters such as the gyroscope bias, accelerometer bias, mounting angle, and lever arm of the mobile terminal device are generally used as part of the estimated state. Therefore, while outputting the integrated navigation result 104, the Kalman filter 103 continuously iterates the values ​​of the gyroscope bias, accelerometer bias, mounting angle, and lever arm based on the input navigation parameters until the Kalman filter 103 converges. Since the gyroscope bias, accelerometer bias, mounting angle, and lever arm parameters of the mobile terminal device are unknown during the initialization phase, the initial values ​​of these parameters in the Kalman filter 103 are set to 0 or preset values ​​when integrated navigation is activated. However, setting the initial values ​​of these parameters to 0 or preset values ​​can lead to a large deviation between the initial values ​​and the actual values, causing the Kalman filter 103 to have slow convergence speed or even filter divergence. To address this issue, this disclosure provides a method for initializing parameters in integrated navigation.

[0037] Figure 2 This is a flowchart illustrating a parameter initialization method for integrated navigation provided in an embodiment of this disclosure. This method can be executed by a mobile terminal device, which can be understood as a device with navigation and data processing capabilities, such as a mobile phone or tablet computer. Figure 2 As shown, the method includes:

[0038] S210. Obtain historical navigation data from the mobile terminal device. The historical navigation data includes the following data estimated by the filtering model on the mobile terminal device during the historical integrated navigation process: the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first installation angle of the mobile terminal device, and the first lever arm.

[0039] The first lever arm refers to the vector pointing from the mobile terminal device to the center point of the vehicle's rear axle during the historical integrated navigation process.

[0040] The first lever in this embodiment is a value estimated by the filtering model during historical integrated navigation.

[0041] When mobile terminal devices use a combination of inertial navigation and satellite navigation systems for navigation, the satellite navigation system obtains navigation data by receiving GNSS signals, while the inertial navigation system provides navigation data from its own IMU. Therefore, in scenarios where the mobile terminal device is fixed at a specific location within a vehicle for in-vehicle integrated navigation, the smaller the errors in the gyroscope's zero bias, the accelerometer's zero bias, the mobile terminal device's mounting angle, and the actual distance between the control arm and the control arm, the faster the convergence speed of the filtering model (e.g., the Kalman filter model), and the more accurate the navigation results.

[0042] In practical applications, since the zero bias of the gyroscope and the zero bias of the accelerometer of the mobile terminal device are not strictly calibrated at the factory, and the installation angle and lever arm of the mobile terminal device cannot be manually calibrated every time navigation is performed, the relevant technology sets the zero bias of the gyroscope, the zero bias of the accelerometer, the installation angle and the lever arm to preset values ​​during the initialization phase. The filtering model iteratively updates the preset values ​​based on the continuously input navigation data until the filtering model converges, and then an estimated value close to the actual value can be obtained.

[0043] Because the estimated values ​​obtained by the convergence of the filtering model are closer to the actual values, and the gyroscope and accelerometer zero biases of the mobile terminal device remain essentially unchanged during the two navigation cycles, and given that a user typically places the mobile device in a fixed position within the vehicle for navigation, the mounting angle and lever arm of the mobile terminal device can also be obtained from historical navigation data. Therefore, this embodiment of the present disclosure can obtain at least one of the gyroscope zero bias, accelerometer zero bias, mounting angle, and lever arm from historical navigation data as initialization parameters for the corresponding estimated variables in the filtering model. This reduces the difference between at least some parameters and the true values, thereby improving the convergence speed of the filtering model.

[0044] When a user starts navigation on a mobile terminal device, the mobile terminal device obtains its own historical navigation data, including the zero bias of the gyroscope (hereinafter referred to as the first zero bias), the zero bias of the accelerometer (hereinafter referred to as the second zero bias), the installation angle of the mobile terminal device (hereinafter referred to as the first installation angle), and the lever arm (hereinafter referred to as the first lever arm) estimated by the filtering model during the historical combined navigation process, so as to initialize the parameters of the filtering model in the future using the above historical data from the historical combined navigation process.

[0045] S220. Initialize the filtering model by using at least one of the parameters of the first zero bias, the second zero bias, the first installation angle, and the first lever arm as the initialization parameters of the corresponding estimated variables in the filtering model.

[0046] The mobile terminal device uses at least one of the first zero bias, second zero bias, first installation angle, and first lever arm from historical navigation data as the initialization parameter for the corresponding estimated variable in the current navigation filtering model. For example, the second zero bias is used as the initialization parameter for the estimated variable corresponding to the accelerometer zero bias, the first zero bias is used as the initialization parameter for the estimated variable corresponding to the gyroscope zero bias, the first lever arm is used as the initialization parameter for the estimated variable corresponding to the lever arm, or the first installation angle is used as the initialization parameter for the estimated variable corresponding to the installation angle.

[0047] In one feasible implementation, the historical navigation data also includes the gravity direction vector measured by the accelerometer during the historical integrated navigation process, hereinafter referred to as the first gravity direction vector.

[0048] Specifically, when using historical navigation data as the initialization parameters for the corresponding estimated variables in the filtering model, the embodiments of this disclosure provide the following initialization strategies as examples.

[0049] Strategy S221: In response to the angle between the first gravity direction vector and the second gravity direction vector currently measured by the accelerometer being greater than a preset angle, the first zero bias and the second zero bias are used as initialization parameters for the corresponding estimated variables in the filtering model to initialize the filtering model.

[0050] Because the first gravity direction vector is measured by the accelerometer of the mobile terminal device during historical integrated navigation, if the angle between the second gravity direction vector currently measured by the accelerometer and the first gravity direction vector is greater than a preset angle, it is considered that the user's current orientation of the mobile terminal device differs significantly from the orientation during historical integrated navigation. Because of this significant change in orientation, the mounting angle and lever arm of the mobile terminal device differ considerably from the first mounting angle and first lever arm during the current integrated navigation process.

[0051] The zero bias of the gyroscope and the zero bias of the accelerometer are mainly related to the hardware of the mobile terminal device. Therefore, if the mobile terminal device or its gyroscope and accelerometer are not replaced, the zero bias of the gyroscope and accelerometer will remain roughly unchanged each time it is powered on. Compared with the preset values ​​corresponding to the zero bias of the gyroscope and the zero bias of the accelerometer, the first and second zero biases in the historical navigation data are obviously closer to the actual zero bias of the gyroscope and the accelerometer. Therefore, the first and second zero biases are used as the initialization parameters of the corresponding estimated variables in the filtering model to initialize the filtering model. This makes the initialization parameters corresponding to the zero bias of the gyroscope and the zero bias of the accelerometer in the current integrated navigation model closer to the actual values, resulting in a faster convergence speed of the filtering model.

[0052] In this case, alternatively, strategy S2211 can be further implemented.

[0053] S2211. Initialize the estimated variables corresponding to the installation angle and the estimated variables corresponding to the lever arm in the filtering model to preset values.

[0054] When the angle between the second gravity direction vector and the first gravity direction vector measured by the accelerometer is greater than the preset angle during navigation, it can be assumed that the current installation angle of the mobile terminal device is significantly different from the first installation angle, and the current lever arm is significantly different from the first lever arm. In this case, the estimated variables corresponding to the installation angle and the estimated variables corresponding to the lever arm in the filtering model can be initialized to preset values.

[0055] Strategy S222: In response to the angle between the first gravity direction vector and the second gravity direction vector currently measured by the accelerometer being less than or equal to a preset angle, the first zero bias, the second zero bias, the first installation angle, and the first lever arm are used as initialization parameters for the corresponding estimated variables in the filtering model to initialize the filtering model.

[0056] When the angle between the second gravity direction vector and the first gravity direction vector measured by the accelerometer is less than a preset angle, the user's current pose of the mobile terminal device remains essentially unchanged compared to the pose during historical integrated navigation. Therefore, during the current integrated navigation, the installation angle and lever arm of the mobile terminal device differ little from the first installation angle and the first lever arm. The mobile terminal device uses the first zero bias, the second zero bias, the first installation angle, and the first lever arm as initialization parameters for the corresponding estimated variables in the filtering model. This initializes the filtering model, making the initialization parameters for the gyroscope zero bias, accelerometer zero bias, installation angle, and lever arm in the current integrated navigation model closer to their actual values. This allows for faster convergence of the filtering model in the current integrated navigation.

[0057] This embodiment of the disclosure acquires historical navigation data from a mobile terminal device and uses the filtering model on the mobile terminal device in the historical navigation data to estimate the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first installation angle of the mobile terminal device, and the first lever arm during the integrated navigation process. The parameters of the corresponding estimated variables in the filtering model are then initialized. Since the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first installation angle of the mobile terminal device, and the first lever arm estimated by the filtering model in the historical navigation data are closer to the actual values, the problem of filtering divergence or slow convergence speed caused by excessive initialization parameter errors can be effectively reduced, thereby improving the accuracy of integrated navigation.

[0058] Figure 3 A flowchart illustrating the historical navigation data update process provided in this embodiment of the disclosure. (Refer to...) Figure 3 After initializing the parameters of the filtering model in the current navigation by using at least one of the first zero bias, the second zero bias, the first installation angle, and the first lever arm as initialization parameters for the corresponding estimated variables in the filtering model, the method further includes:

[0059] S310. Obtain the third zero bias of the gyroscope, the fourth zero bias of the accelerometer, the second mounting angle and the second lever of the mobile terminal device estimated by the filtering model after convergence.

[0060] The second lever refers to the vector pointing from the mobile terminal device to the center point of the vehicle's rear axle during the current navigation process.

[0061] The second lever in this embodiment is a value estimated by the filtering model during the current integrated navigation process.

[0062] After the filtering model converges, the parameters in the filtering model tend to stabilize, that is, the estimated variables corresponding to the gyroscope zero bias, the accelerometer zero bias, the installation angle, and the lever arm no longer change.

[0063] Therefore, the third zero bias of the gyroscope, the fourth zero bias of the accelerometer, the second mounting angle and the second lever of the mobile terminal device, estimated by the filtering model after convergence, can be obtained to update historical navigation data.

[0064] S320: Update historical navigation data based on at least one of the following parameters: third zero offset, fourth zero offset, second mounting angle, and second lever arm.

[0065] The mobile terminal device uses parameters estimated by the filtering model after convergence to update the corresponding parameters in the historical navigation data. For example, reliable parameters from the third zero-bias, fourth zero-bias, second installation angle, and second arm can be selected to update the corresponding parameters in the historical navigation data.

[0066] In some embodiments, updating historical navigation data based on at least one of the third zero offset, fourth zero offset, second mounting angle, and second lever arm can include the following two feasible implementation methods.

[0067] Figure 4 A flowchart illustrating the historical navigation data update process, provided as another embodiment of this disclosure. (Refer to...) Figure 4 In a feasible real-time manner, historical navigation data is updated based on at least one of the following parameters: third zero offset, fourth zero offset, second mounting angle, and second lever arm:

[0068] S410. Calculate the first degree of difference between the third zero offset and the first zero offset, the second degree of difference between the second zero offset and the fourth zero offset, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second arm and the first arm.

[0069] For the gyroscope zero bias, accelerometer zero bias, installation angle of the mobile terminal device, and lever arm, the degree of difference between historical navigation data and the estimated data after model convergence in the current integrated navigation is calculated respectively, namely the first degree of difference between the third zero bias and the first zero bias, the second degree of difference between the second zero bias and the fourth zero bias, the third degree of difference between the second installation angle and the first installation angle, and the fourth degree of difference between the second lever arm and the first lever arm.

[0070] For example, the first difference between the third zero offset and the first zero offset can be calculated as the first degree of difference; the second difference between the second zero offset and the fourth zero offset can be calculated as the second degree of difference; the third difference between the second mounting angle and the first mounting angle can be calculated as the third degree of difference; and the fourth difference between the second arm and the first arm can be calculated as the fourth degree of difference. Alternatively, the ratio of the first difference to the first fixed value can be calculated, and this ratio can be used as the first degree of difference. Similarly, the ratio of the second difference to the second fixed value can be calculated as the second degree of difference; the ratio of the third difference to the third fixed value can be calculated as the third degree of difference; and the ratio of the fourth difference to the fourth fixed value can be calculated as the fourth degree of difference.

[0071] S420. Based on the first degree of difference between the third zero offset and the first zero offset, the second degree of difference between the second zero offset and the fourth zero offset, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second arm and the first arm, determine the confidence level of the third zero offset, the fourth zero offset, the second mounting angle, and the second arm.

[0072] This embodiment of the disclosure determines the reliability of the third zero offset, the fourth zero offset, the second mounting angle, and the second lever by measuring the first degree of difference between the third zero offset and the first zero offset, the second degree of difference between the second zero offset and the fourth zero offset, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second lever and the first lever. Based on the reliability, it determines how to select the third zero offset, the fourth zero offset, the second mounting angle, and the second lever to update the corresponding parameters in the historical navigation data.

[0073] Figure 5 A flowchart for determining the confidence level of estimated parameters provided in embodiments of this disclosure. (Refer to...) Figure 5 In a feasible real-time approach, the confidence levels of the third zero offset, fourth zero offset, second mounting angle, and second arm are determined based on the first degree of difference between the third zero offset and the first zero offset, the second degree of difference between the second zero offset and the fourth zero offset, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second arm and the first arm. This includes:

[0074] S421. In response to the first degree of difference being less than or equal to the first difference threshold, the third zero-bias confidence level is higher than the preset confidence level.

[0075] If the first degree of difference is not greater than the first difference threshold, it can be determined that the gyroscope zero bias estimated after the filter model converges in the current integrated navigation process is similar to the gyroscope zero bias estimated after the filter model converges in the historical navigation data. Therefore, it can be determined that the confidence level of the third zero bias is higher than the preset confidence level.

[0076] S422. In response to the second degree of difference being less than or equal to the second difference threshold, the fourth zero-bias confidence level is higher than the preset confidence level.

[0077] If the second degree of difference is not greater than the second difference threshold, it can be determined that the accelerometer zero bias estimated after the filter model converges in the current integrated navigation process is similar to the accelerometer zero bias estimated after the filter model converges in the historical navigation data. Therefore, it can be determined that the fourth zero bias confidence level is higher than the preset confidence level.

[0078] S423. In response to the third difference degree being less than or equal to the third difference threshold, the confidence level of the second installation angle is higher than the preset confidence level.

[0079] If the third degree of difference is not greater than the third difference threshold, it can be determined that the installation angle of the mobile terminal device estimated after the convergence of the filter model in the current integrated navigation process is similar to the installation angle of the mobile terminal device estimated after the convergence of the filter model in the historical navigation data. Therefore, it can be determined that the confidence level of the second installation angle is higher than the preset confidence level.

[0080] S424. In response to the fourth degree of difference being less than or equal to the fourth degree of difference threshold, the confidence level of the second lever arm is higher than the preset confidence level.

[0081] If the fourth difference level is not greater than the fourth difference threshold, it can be determined that the lever arm of the mobile terminal device estimated after the convergence of the filter model in the current integrated navigation process is similar to the lever arm of the mobile terminal device estimated after the convergence of the filter model in the historical navigation data. Therefore, it can be determined that the confidence level of the fourth lever arm is higher than the preset confidence level.

[0082] This embodiment of the disclosure calculates the difference between the estimated variable values ​​after the filter model converges during the current integrated navigation process and the corresponding parameters in the historical navigation data, and then determines whether the difference between the two is greater than the corresponding threshold. In this way, it can determine in detail whether the gyroscope zero bias, accelerometer zero bias, installation angle of the mobile terminal device and lever arm estimated after the filter model converges during the current integrated navigation process are reliable.

[0083] S430 updates historical navigation data based on parameters with higher confidence levels than preset confidence levels, including the third zero-offset, fourth zero-offset, second mounting angle, and second lever.

[0084] The mobile terminal device uses the parameters of the third zero offset, fourth zero offset, second installation angle, and second arm that are higher than the preset confidence level to update the corresponding parameters in the historical navigation data and save the updated historical navigation data.

[0085] For example, if only the confidence level of the third zero bias is higher than the preset confidence level, then the first zero bias in the historical navigation data will be updated to the third zero bias, while the second zero bias, the first installation angle, and the first lever arm in the historical navigation data will not be updated.

[0086] When the confidence levels of the third zero bias and the second lever arm are higher than the preset confidence levels, the first zero bias in the historical navigation data will be updated to the third zero bias, and the first lever arm will be updated to the second lever arm. The second zero bias and the first installation angle in the historical navigation data will not be updated.

[0087] When the confidence levels of the third zero offset, the fourth zero offset, the second mounting angle, and the second lever arm are all higher than the preset confidence levels, the first zero offset, the second zero offset, the first mounting angle, and the first lever arm in the historical navigation data will be updated to the third zero offset, the fourth zero offset, the second mounting angle, and the second lever arm, and so on. This will not be elaborated here.

[0088] This disclosure embodiment determines the confidence levels of the third zero bias, the fourth zero bias, the second mounting angle, and the second lever arm, and then updates the historical navigation data with parameters whose confidence levels are higher than the preset levels, in order to avoid updating the parameters in the historical navigation data with unreliable parameters.

[0089] Figure 6 A flowchart illustrating the historical navigation data update process, provided as another embodiment of this disclosure. (Refer to...) Figure 6 In one feasible implementation, historical navigation data is updated based on at least one of the following parameters: third zero offset, fourth zero offset, second mounting angle, and second lever arm, including:

[0090] S610. Obtain the convergence time of the filtering model in this navigation, where the convergence time refers to the time taken for the filtering model to converge from initialization.

[0091] S620, in response to the convergence time being less than or equal to a preset duration, determines that the confidence levels of the third zero bias, the fourth zero bias, the second mounting angle, and the second lever arm are higher than the preset confidence levels.

[0092] When the convergence time of the filtering model in this navigation is less than or equal to the preset time, it indicates that the convergence time of the filtering model is relatively normal. Correspondingly, the parameters estimated by the filtering model will also be more accurate, and the quality of this integrated navigation is high. Therefore, the overall confidence of the third zero bias, the fourth zero bias, the second installation angle, and the second arm is higher than the preset confidence.

[0093] S630 updates historical navigation data based on the third zero offset, fourth zero offset, second mounting angle, and second lever arm.

[0094] After determining that the confidence levels of the third zero bias, fourth zero bias, second installation angle, and second lever arm are higher than the preset confidence levels based on the convergence time of the filtering model in this navigation, the first zero bias, second zero bias, first installation angle, and first lever arm in the historical navigation data are updated to the third zero bias, fourth zero bias, second installation angle, and second lever arm.

[0095] This embodiment of the disclosure compares whether the convergence time of the filtering model in the current navigation is less than or equal to a preset time, thereby quickly determining from the whole whether the gyroscope zero bias, accelerometer zero bias, installation angle of the mobile terminal device and lever arm estimated after the filtering model converges are reliable, and then determining whether to use them to update the corresponding parameters in the historical navigation data.

[0096] In addition, the reliability of the parameters estimated by the filter model after convergence can be determined by the number of yaws and the yaw distance during this integrated navigation process. The more accurate the parameters estimated by the filter model after convergence, the more accurate the integrated navigation results output by the filter model during the integrated navigation process will be. Therefore, when there are many yaws and / or large yaw distances during this integrated navigation process, it indicates that the integrated navigation results output by the filter model during this integrated navigation process are inaccurate, and correspondingly, the reliability of the parameters estimated by the navigation filter model after convergence is low.

[0097] Figure 7 This is a schematic diagram of the structure of a parameter initialization device for integrated navigation provided in this embodiment. The parameter initialization device for integrated navigation can be a mobile terminal device as described in the above embodiment, or a component or assembly within a mobile terminal device. The parameter initialization device for integrated navigation provided in this embodiment can execute the processing flow provided in the embodiment of the parameter initialization method for integrated navigation, such as... Figure 7 As shown, the parameter initialization device 700 for the integrated navigation system includes:

[0098] The acquisition module 701 is used to acquire historical navigation data of the mobile terminal device. The historical navigation data includes the following data estimated by the filtering model on the mobile terminal device during the historical combined navigation process: the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first installation angle of the mobile terminal device, and the first lever arm.

[0099] The initialization module 702 is used to initialize the filtering model by taking at least one of the first zero bias, the second zero bias, the first installation angle, and the first lever arm as the initialization parameters of the corresponding estimated variables in the filtering model; wherein, the first lever arm refers to the vector pointing from the mobile terminal device to the center point of the rear wheel axle of the vehicle during the historical integrated navigation process.

[0100] In some embodiments, historical navigation data also includes a first gravity direction vector measured by an accelerometer during historical integrated navigation;

[0101] When initializing the filtering model, the initialization module 702 uses at least one of the parameters—first zero bias, second zero bias, first mounting angle, and first lever arm—as initialization parameters for the corresponding estimated variables in the filtering model. Specifically, in response to the angle between the first gravity direction vector and the second gravity direction vector currently measured by the accelerometer being greater than a preset angle, the module initializes the filtering model by using the first zero bias and second zero bias as initialization parameters for the corresponding estimated variables in the filtering model; and in response to the angle between the first gravity direction vector and the second gravity direction vector currently measured by the accelerometer being less than or equal to a preset angle, the module initializes the filtering model by using the first zero bias, second zero bias, first mounting angle, and first lever arm as initialization parameters for the corresponding estimated variables in the filtering model.

[0102] In some embodiments, the initialization module 702 is further configured to initialize the estimated variables corresponding to the installation angle and the estimated variables corresponding to the lever arm in the filtering model to preset values ​​in response to the angle between the first gravity direction vector and the second gravity direction vector currently measured by the accelerometer being greater than a preset angle.

[0103] In some embodiments, the parameter initialization device for the integrated navigation further includes an update module 703, which, after initializing the parameters of the filtering model in the current navigation by using at least one of the first zero bias, the second zero bias, the first mounting angle, and the first lever arm as initialization parameters of the corresponding estimated variables in the filtering model, is used to obtain the third zero bias of the gyroscope, the fourth zero bias of the accelerometer, the second mounting angle of the mobile terminal device, and the second lever arm estimated by the filtering model after convergence. The second lever arm refers to the vector pointing from the mobile terminal device to the center point of the rear wheel axle of the vehicle during the current navigation process. Based on at least one of the third zero bias, the fourth zero bias, the second mounting angle, and the second lever arm, the historical navigation data is updated.

[0104] In some embodiments, when updating historical navigation data based on at least one parameter among the third zero-offset, fourth zero-offset, second mounting angle, and second lever arm, the updating module 703 is specifically used to calculate, respectively, the first degree of difference between the third zero-offset and the first zero-offset, the second degree of difference between the second zero-offset and the fourth zero-offset, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second lever arm and the first lever arm; based on the first degree of difference between the third zero-offset and the first zero-offset, the second degree of difference between the second zero-offset and the fourth zero-offset, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second lever arm and the first lever arm, determine the confidence level of the third zero-offset, the fourth zero-offset, the second mounting angle, and the second lever arm; and update the historical navigation data based on the parameter among the third zero-offset, the fourth zero-offset, the second mounting angle, and the second lever arm whose confidence level is higher than a preset confidence level.

[0105] In some embodiments, when the updating module 703 determines the confidence levels of the third zero bias, the fourth zero bias, the second mounting angle, and the second lever arm based on the first degree of difference between the third zero bias and the first zero bias, the second degree of difference between the second zero bias and the fourth zero bias, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second lever arm and the first lever arm, it is specifically configured to: respond to the first degree of difference being less than or equal to the first difference threshold, then the confidence level of the third zero bias is higher than a preset confidence level; respond to the second degree of difference being less than or equal to the second difference threshold, then the confidence level of the fourth zero bias is higher than a preset confidence level; respond to the third degree of difference being less than or equal to the third difference threshold, then the confidence level of the second mounting angle is higher than a preset confidence level; respond to the fourth degree of difference being less than or equal to the fourth difference threshold, then the confidence level of the second lever arm is higher than a preset confidence level.

[0106] In some embodiments, when updating historical navigation data based on at least one parameter among the third zero bias, the fourth zero bias, the second mounting angle, and the second lever arm, the updating module 703 is specifically used to obtain the convergence time of the filtering model in the current navigation, wherein the convergence time refers to the time taken for the filtering model to converge from initialization; in response to the convergence time being less than or equal to a preset duration, determining that the confidence levels of the third zero bias, the fourth zero bias, the second mounting angle, and the second lever arm are higher than a preset confidence level; and updating the historical navigation data based on the third zero bias, the fourth zero bias, the second mounting angle, and the second lever arm.

[0107] Figure 7 The parameter initialization device for the integrated navigation in the illustrated embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effect are similar, and will not be repeated here.

[0108] This disclosure also provides a mobile terminal device equipped with the parameter initialization device for integrated navigation as described in the above embodiments. By initializing the filtering model on the mobile terminal device through the parameter initialization device for integrated navigation, the filtering convergence speed can be improved.

[0109] Example, Figure 8 This is a schematic diagram of the structure of a mobile terminal device according to an embodiment of this disclosure. See below for details. Figure 8 The diagram illustrates a structural schematic suitable for implementing the mobile terminal device 1400 in the embodiments of this disclosure. The mobile terminal device 1400 in the embodiments of this disclosure may include, but is not limited to, devices with computing and processing capabilities such as PADs (tablet computers) and mobile phones. Figure 8 The mobile terminal device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0110] like Figure 8As shown, the mobile terminal device 1400 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 1401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1402 or a program loaded from a storage device 1408 into a random access memory (RAM) 1403. The RAM 1403 also stores various programs and data required for the operation of the mobile terminal device 1400. The processing unit 1401, ROM 1402, and RAM 1403 are interconnected via a bus 1404. An input / output (I / O) interface 1405 is also connected to the bus 1404.

[0111] Typically, the following devices can be connected to I / O interface 1405: input devices 1406 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1407 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1408 including, for example, magnetic tape, hard disk, etc.; and communication devices 1409. Communication device 1409 allows mobile terminal device 1400 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 8 A mobile terminal device 1400 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0112] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 1409, or installed from storage device 1408, or installed from ROM 1402. When the computer program is executed by processing device 1401, it performs the functions defined in the methods of embodiments of this disclosure.

[0113] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0114] The aforementioned computer-readable medium may be included in the aforementioned mobile terminal device; or it may exist independently and not assembled into the mobile terminal device.

[0115] The aforementioned computer-readable medium carries one or more programs. When the aforementioned one or more programs are executed by a processing device, the processing device causes the following to occur: acquire historical navigation data of a mobile terminal device, the historical navigation data including the following data estimated by the filtering model on the mobile terminal device during historical integrated navigation: the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first mounting angle of the mobile terminal device, and the first lever arm; initialize the filtering model by using at least one parameter of the first zero bias, the second zero bias, the first mounting angle, and the first lever arm as the initialization parameter of the corresponding estimated variable in the filtering model.

[0116] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0117] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0118] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0119] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0120] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0121] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can perform the above-described functions. Figures 2-6 The methods in any of the embodiments are similar in execution and beneficial effects, and will not be described again here.

[0122] This disclosure also provides a computer program product, which is stored in a storage medium. When the program product is run, it can achieve... Figures 2-6 The methods in any of the embodiments are similar in execution and beneficial effects, and will not be described again here.

[0123] 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 one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0124] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A parameter initialization method for integrated navigation, wherein, include: Acquire historical navigation data of the mobile terminal device, wherein the historical navigation data includes the following data estimated by the filtering model on the mobile terminal device during the historical combined navigation process: the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first installation angle of the mobile terminal device, and the first lever arm; The filtering model is initialized by using at least one of the following parameters: the first zero bias, the second zero bias, the first installation angle, and the first lever arm. Wherein, the first lever arm refers to the vector pointing from the mobile terminal device to the center point of the vehicle's rear wheel axle during the historical combined navigation process, and the first mounting angle is the mounting angle of the mobile terminal device.

2. The method according to claim 1, wherein, The historical navigation data also includes the first gravity direction vector measured by the accelerometer during the historical integrated navigation process; The initialization of the filtering model by using at least one of the first zero bias, the second zero bias, the first installation angle, and the first arm as initialization parameters for the corresponding estimated variables in the filtering model includes: In response to the fact that the angle between the first gravity direction vector and the second gravity direction vector currently measured by the accelerometer is greater than a preset angle, the first zero bias and the second zero bias are used as initialization parameters for the corresponding estimated variables in the filtering model to initialize the filtering model; In response to the fact that the angle between the first gravity direction vector and the second gravity direction vector currently measured by the accelerometer is less than or equal to the preset angle, the first zero bias, the second zero bias, the first installation angle and the first lever arm are used as initialization parameters for the corresponding estimated variables in the filtering model, and the filtering model is initialized.

3. The method according to claim 2, wherein, The method further includes: In response to the angle between the first gravity direction vector and the second gravity direction vector currently measured by the accelerometer being greater than a preset angle, the estimated variables corresponding to the installation angle and the estimated variables corresponding to the lever arm in the filtering model are initialized to preset values.

4. The method according to any one of claims 1-3, wherein, After initializing the parameters of the filtering model in the current navigation by using at least one of the first zero bias, the second zero bias, the first mounting angle, and the first arm as initialization parameters for the corresponding estimated variables in the filtering model, the method further includes: The third zero bias of the gyroscope, the fourth zero bias of the accelerometer, the second mounting angle and the second lever arm of the mobile terminal device are estimated by the filtering model after convergence. The second lever arm refers to the vector of the mobile terminal device pointing to the center point of the rear wheel axle of the vehicle during the current navigation process. The historical navigation data is updated based on at least one of the third zero offset, the fourth zero offset, the second mounting angle, and the second lever arm.

5. The method according to claim 4, wherein, The step of updating the historical navigation data based on at least one of the third zero-offset, the fourth zero-offset, the second mounting angle, and the second lever arm includes: Calculate the first degree of difference between the third zero offset and the first zero offset, the second degree of difference between the second zero offset and the fourth zero offset, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second lever arm and the first lever arm, respectively. Based on the first degree of difference between the third zero offset and the first zero offset, the second degree of difference between the second zero offset and the fourth zero offset, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second arm and the first arm, the reliability of the third zero offset, the fourth zero offset, the second mounting angle, and the second arm is determined. The historical navigation data is updated based on parameters whose confidence level is higher than the preset confidence level among the third zero bias, the fourth zero bias, the second mounting angle, and the second lever arm.

6. The method according to claim 5, wherein, The determination of the reliability of the third zero offset, the fourth zero offset, the second mounting angle, and the second lever arm based on the first degree of difference between the third zero offset and the first zero offset, the second degree of difference between the second zero offset and the fourth zero offset, the third degree of difference between the second mounting angle and the first mounting angle, and the fourth degree of difference between the second lever arm and the first lever arm includes: In response to the first degree of difference being less than or equal to the first difference threshold, the third zero-bias confidence level is higher than the preset confidence level; If the second degree of difference is less than or equal to the second difference threshold, then the fourth zero-bias confidence level is higher than the preset confidence level. If the third degree of difference is less than or equal to the third difference threshold, then the confidence level of the second installation angle is higher than the preset confidence level. If the fourth degree of difference is less than or equal to the fourth difference threshold, then the confidence level of the second lever is higher than the preset confidence level.

7. The method according to claim 4, wherein, Updating the historical navigation data based on at least one of the third zero-offset, the fourth zero-offset, the second mounting angle, and the second lever arm includes: Obtain the convergence time of the filtering model in this navigation, where the convergence time refers to the time taken for the filtering model to converge from initialization; In response to the convergence time being less than or equal to a preset duration, the confidence levels of the third zero bias, the fourth zero bias, the second mounting angle, and the second lever arm are determined to be higher than a preset confidence level. The historical navigation data is updated based on the third zero offset, the fourth zero offset, the second mounting angle, and the second lever arm.

8. A parameter initialization device for integrated navigation, wherein, include: The acquisition module is used to acquire historical navigation data of the mobile terminal device. The historical navigation data includes the following data estimated by the filtering model on the mobile terminal device during the historical combined navigation process: the first zero bias of the gyroscope, the second zero bias of the accelerometer, the first installation angle of the mobile terminal device, and the first lever arm. An initialization module is used to initialize the filtering model by taking at least one of the first zero bias, the second zero bias, the first installation angle, and the first arm as the initialization parameters of the corresponding estimated variables in the filtering model. Wherein, the first lever arm refers to the vector pointing from the mobile terminal device to the center point of the vehicle's rear wheel axle during the historical combined navigation process, and the first mounting angle is the mounting angle of the mobile terminal device.

9. A mobile terminal device, wherein, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method as described in any one of claims 1-7.

10. A computer program product, wherein, The program product is stored in a storage medium, and when the program product is run, it implements the method as described in any one of claims 1-7.

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