CN111625764B

CN111625764B - Mobile data calibration method, device, electronic equipment and storage medium

Info

Publication number: CN111625764B
Application number: CN202010436230.8A
Authority: CN
Inventors: 彭新建; 蒋弘刚
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2020-05-21
Filing date: 2020-05-21
Publication date: 2023-05-12
Anticipated expiration: 2040-05-21
Also published as: CN111625764A

Abstract

The embodiment of the disclosure relates to a calibration method and device of mobile data, electronic equipment and a storage medium. The method comprises the following steps: receiving historical movement data and historical positioning data acquired by a measurement terminal in a historical period; the historical movement data comprise historical IMU data acquired by the measurement terminal; fitting the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function; transmitting the fitted nonlinear calibration function to a terminal to be calibrated; the fitted nonlinear calibration function is used for indicating the terminal to be calibrated to calibrate initial movement data acquired by the terminal to be calibrated by utilizing the fitted nonlinear calibration function, so as to obtain calibrated movement data; the initial movement data comprises initial IMU data of the terminal to be calibrated. By adopting the method, the calculation complexity can be reduced, and the calibration cost can be saved.

Description

Mobile data calibration method, device, electronic equipment and storage medium

Technical Field

The embodiment of the disclosure relates to the technical field of data calibration, in particular to a method and a device for calibrating mobile data, electronic equipment and a storage medium.

Background

With the development of technology, IMUs (inertial measurement unit, inertial measurement units) are widely used in a variety of fields. For example, in the autopilot field and the driving behavior detection field, IMU data may be used for detection of object gestures; in the aerospace field and the indoor navigation field, IMU data may be used for real-time positioning.

In the related art, the IMU includes an accelerometer for detecting three-axis acceleration of an object in a carrier coordinate system, and a gyroscope for detecting angular velocity of the object with respect to a geographic coordinate system. However, because IMU is often interfered by noise, errors often occur in measured IMU data, so in order to ensure the application accuracy of IMU data, the industry usually calibrates the IMU data. At present, a conversion matrix method, a six-sided calibration method and other calibration methods are often adopted to calibrate the acceleration.

However, the above calibration methods all need to perform matrix calculation, and have high calculation complexity and high calibration cost.

Disclosure of Invention

The embodiment of the disclosure provides a calibration method, a device, electronic equipment and a storage medium for mobile data, which can be used for reducing the calculation complexity and the calibration cost.

In a first aspect, an embodiment of the present disclosure provides a method for calibrating mobile data, the method including:

receiving historical movement data and historical positioning data acquired by a measurement terminal in a historical period; the historical mobile data comprise historical IMU data acquired by the measuring terminal;

fitting the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function;

transmitting the fitted nonlinear calibration function to a terminal to be calibrated; the fitted nonlinear calibration function is used for indicating the terminal to be calibrated to calibrate the initial movement data acquired by the terminal to be calibrated by utilizing the fitted nonlinear calibration function, so as to obtain calibrated movement data; the initial movement data includes initial IMU data of the terminal to be calibrated.

In a second aspect, an embodiment of the present disclosure provides a method for calibrating mobile data, the method including:

collecting initial movement data of a terminal to be calibrated; the initial movement data comprise initial IMU data of the terminal to be calibrated;

calibrating initial movement data by using a fitted nonlinear calibration function obtained in advance from a server to obtain calibrated movement data;

The fitted nonlinear calibration function is obtained by fitting the server according to historical movement data and historical positioning data acquired by the measurement terminal in a historical period.

In a third aspect, embodiments of the present disclosure provide a calibration apparatus for mobile data, the apparatus comprising:

the data receiving module is used for receiving the historical movement data and the historical positioning data acquired by the measuring terminal in the historical period; the historical mobile data comprise historical IMU data acquired by the measuring terminal;

the fitting module is used for fitting the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function;

the function sending module is used for sending the fitted nonlinear calibration function to the terminal to be calibrated; the fitted nonlinear calibration function is used for indicating the terminal to be calibrated to calibrate the initial movement data acquired by the terminal to be calibrated by utilizing the fitted nonlinear calibration function, so as to obtain calibrated movement data; the initial movement data includes initial IMU data of the terminal to be calibrated.

In a fourth aspect, an embodiment of the present disclosure provides a calibration apparatus for mobile data, the apparatus comprising:

the data acquisition module is used for acquiring initial movement data of the terminal to be calibrated; the initial movement data comprise initial IMU data of the terminal to be calibrated;

The calibration module is used for calibrating the initial movement data by utilizing the fitted nonlinear calibration function obtained in advance from the server to obtain calibrated movement data;

In a fifth aspect, an embodiment of the disclosure provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method according to any one of the first or second aspects.

In a sixth aspect, embodiments of the present disclosure provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of the first or second aspects.

According to the mobile data calibration method, device, electronic equipment and storage medium, a server receives historical mobile data and historical positioning data acquired by a measurement terminal in a historical period; the historical movement data comprise historical IMU data acquired by the measurement terminal; and fitting the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function, and then, transmitting the fitted nonlinear calibration function to a terminal to be calibrated by the server so as to calibrate the movement data by using the fitted nonlinear calibration function by the terminal to be calibrated.

Drawings

FIG. 1 is an application environment diagram of a method of calibrating mobile data in one embodiment;

FIG. 2 is a flow chart of a method of calibrating movement data in one embodiment;

FIG. 3 is a flow chart of a step of fitting historical movement data and historical positioning data to obtain a fitted nonlinear calibration function in one embodiment;

FIG. 4 is one of the flow diagrams of the steps of constructing a displacement expression of a measurement terminal during a history period based on historical acceleration data and a non-fitted nonlinear calibration function in one embodiment;

FIG. 5 is a second flowchart of a step of constructing a displacement expression of the measurement terminal during a history period based on the history acceleration data and the non-fitted nonlinear calibration function in one embodiment;

FIG. 6 is a schematic diagram of dividing a motion period into 3 sub-periods in one embodiment;

FIG. 7 is a flow chart of a step of constructing a fitting function based on a motion parameter expression and a target motion parameter, performing regression solution on the fitting function, and obtaining a target value of a function constant in one embodiment;

FIG. 8 is a flow chart of a method of calibrating movement data according to another embodiment;

FIG. 9 is one of the block diagrams of the calibration device for movement data in one embodiment;

FIG. 10 is a second block diagram of a calibration device for moving data in one embodiment;

FIG. 11 is a block diagram of a calibration device for movement data in another embodiment;

FIG. 12 is an internal block diagram of an electronic device in one embodiment;

fig. 13 is an internal structural diagram of a server in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the disclosed embodiments and are not intended to limit the disclosed embodiments.

First, before the technical scheme of the embodiments of the present disclosure is specifically described, a description is given of a technical background on which the embodiments of the present disclosure are based.

IMUs, i.e. inertial measurement units, typically consist of accelerometers and gyroscopes. The accelerometer detects the acceleration of the object in three coordinate axes of the carrier coordinate system, and the gyroscope detects the angular velocity of the object relative to the geographic coordinate system. Wherein the carrier coordinate system is a coordinate system with the carrier as a center; the geographic coordinate system may also be called a navigation coordinate system or a ground-fixed coordinate system, and is a local horizontal coordinate system. Typically, the IMU measured data may be referred to as IMU data, which, as noted above, may include acceleration as well as angular velocity.

In practical applications, IMUs are often subject to noise interference, so that errors often occur in measured IMU data. To ensure the accuracy of the application of IMU data, the industry typically calibrates the IMU data. However, the transformation matrix method or the six-sided calibration method adopted at present needs to perform a large amount of matrix calculation, and the larger the matrix is, the higher the calculation complexity is, thus resulting in higher calibration cost.

In the embodiment of the disclosure, the server may receive the historical movement data and the historical positioning data collected by the measurement terminal in the historical period; and fitting the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function, and then, the server can send the fitted nonlinear calibration function to the terminal to be calibrated so that the terminal to be calibrated can calibrate the movement data by using the fitted nonlinear calibration function.

The following describes a technical scheme related to an embodiment of the present disclosure in conjunction with an application environment of the embodiment of the present disclosure.

The calibration method of mobile data provided by the embodiment of the disclosure can be applied to an application environment as shown in fig. 1. Wherein the terminal to be calibrated 101 and the measurement terminal 102 communicate with the server 103 via a network, respectively. The server 103 performs fitting according to the historical movement data and the historical positioning data acquired by the measurement terminal 102 in the historical period to obtain a fitted nonlinear calibration function, and then sends the fitted nonlinear calibration function to the terminal 101 to be calibrated. The terminal 101 to be calibrated calibrates the collected initial movement data according to the fitted nonlinear calibration function, and obtains calibrated movement data. The terminal 101 to be calibrated and the measurement terminal 102 may be the same type of terminal, such as, but not limited to, a notebook computer, a smart phone, a tablet computer, and a portable wearable device, and the server 103 may be implemented by a separate server or a server cluster formed by a plurality of servers.

In one embodiment, as shown in fig. 2, a method for calibrating mobile data is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:

step 201, the server receives historical movement data and historical positioning data collected by the measurement terminal in a historical period.

The historical movement data comprise historical IMU data collected by the measurement terminal and are used for representing the movement state of the measurement terminal in a historical period; optionally, the historical IMU data includes at least historical acceleration data, and optionally, the historical IMU data may also include historical angular velocity data. The historical positioning data is used for representing the geographic position of the measuring terminal in the historical period. For example, the historical positioning data may be GPS data. The measurement terminal is a terminal for collecting historical movement data and historical positioning data, and the measurement terminal can be any terminal which is in communication connection with a server, or can be a terminal which is specified by the server and is specially used for fitting a nonlinear calibration function described below, and the specific type of the measurement terminal is not limited in the embodiment of the disclosure. The historical period may be a period of time of a preset duration before the server fits a nonlinear calibration function described below.

A sensing assembly and a positioning assembly may be installed in the measurement terminal. Wherein the sensing assembly may be used to measure movement data and the positioning assembly may be used to measure positioning data. For example, the sensing component may be an IMU and the positioning component may be a global positioning (Global Positioning System, GPS) module. In the embodiment of the disclosure, the sensing component can collect the historical movement data of the measuring terminal in the historical period, and the positioning component can collect the historical positioning data of the measuring terminal in the historical period.

After the measurement terminal acquires the historical movement data and the historical positioning data in the historical period, the measurement terminal can send the historical movement data and the historical positioning data to the server in real time or periodically, and the server can receive the historical movement data and the historical positioning data sent by the measurement terminal. Alternatively, the server may send an acquisition instruction to the measurement terminal, the measurement terminal may send the historical movement data and the historical positioning data to the server after receiving the acquisition instruction, and the server may receive the historical movement data and the historical positioning data sent by the measurement terminal

In practical application, the measurement terminal may be a smart phone, in which the IMU and the GPS module are installed. The IMU collects acceleration data and angular velocity data (namely historical movement data) of the smart phone in a historical period, and the GPS module collects positioning data (namely historical positioning data) of the smart phone in the historical period. And uploading the acquired acceleration data, angular velocity data and positioning data to a server by the smart phone, and receiving the acceleration data, angular velocity data and positioning data sent by the smart phone by the server.

Step 202, the server fits the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function.

The nonlinear calibration function is a nonlinear function. For example, the nonlinear calibration function may be a unitary multiple function, an exponential function, or the like. The independent variable of the nonlinear calibration function may be uncalibrated movement data and the dependent variable of the nonlinear calibration function may be calibrated movement data. It should be noted that the movement data to be calibrated may be IMU data, wherein the IMU data may include acceleration data, and besides, the IMU data may also include angular velocity data.

In the embodiment of the disclosure, the function constant of the nonlinear calibration function may be obtained by fitting the historical movement data and the historical positioning data by the server, and when the function constant of the nonlinear calibration function is not fitted, the value of the function constant of the nonlinear calibration function is unknown. In the disclosed embodiments, the functional relationship between the independent and dependent variables in the nonlinear calibration function (i.e., the type of function of the nonlinear calibration function) may be preset by a technician.

With nonlinear calibration function being a unitary quadratic function x=ay ² The movement data are acceleration data in IMU data, wherein the independent variable y is uncalibrated acceleration data, the dependent variable x is calibrated acceleration data, the function constants a, b and c of the nonlinear calibration function are obtained by fitting historical movement data and historical positioning data by a server, and when the function constants are not fit, the value of the function constant is unknown.

Because the positioning data is generally accurate, the embodiment of the disclosure can use the historical positioning data as a reference quantity when fitting, so as to solve the function constant in the nonlinear calibration function by using the reference quantity, and after solving to obtain the value of the function constant in the nonlinear calibration function, fitting the nonlinear calibration function can be completed.

And 203, the server sends the fitted nonlinear calibration function to the terminal to be calibrated.

The terminal to be calibrated is a terminal of mobile data to be calibrated, and the fitted nonlinear calibration function is used for indicating the terminal to be calibrated to calibrate initial mobile data (namely mobile data to be calibrated) acquired by the terminal to be calibrated by using the fitted nonlinear calibration function, so as to obtain calibrated mobile data; the initial movement data includes initial IMU data of the terminal to be calibrated.

And after obtaining the fitted nonlinear calibration function, the server sends the fitted nonlinear calibration function to the terminal to be calibrated. The terminal to be calibrated receives and stores the fitted nonlinear calibration function. And then, the terminal to be calibrated calibrates the acquired initial movement data according to the fitted nonlinear calibration function to obtain calibrated movement data.

For example, the initial IMU data is initial acceleration data, and the terminal to be calibrated calibrates the initial acceleration data according to the fitted nonlinear function to obtain calibrated acceleration data. Thus, the gesture of the terminal to be calibrated or real-time positioning and the like can be accurately detected according to the calibrated acceleration data.

In one embodiment, the terminal to be calibrated and the measurement terminal are the same type of terminal, and the terminal to be calibrated and the measurement terminal may be the same terminal or different terminals.

For example, the terminal to be calibrated may be a smartphone A, B, C, and the measurement terminal may be any one of smartphones A, B, C, or may be a smartphone D of the same model as the smartphone A, B, C. Taking the smart phone D as an example of the measurement terminal, the server obtains a fitted nonlinear calibration function according to the historical movement data and the historical positioning data collected by the smart phone D, and then sends the fitted nonlinear calibration function to the smart phone A, B, C. The smart phone A, B, C receives the fitted nonlinear calibration function, and calibrates the collected initial acceleration data according to the fitted nonlinear calibration function to obtain calibrated acceleration data.

In one embodiment, the server may periodically acquire the historical movement data and the historical positioning data of the measurement terminal, and after each time of acquiring the historical movement data and the historical positioning data, perform fitting according to the acquired historical movement data and the historical positioning data to obtain a new nonlinear calibration function, and then, the server may update the nonlinear calibration function obtained by previous fitting by using the new nonlinear calibration function, and send the updated nonlinear calibration function to the terminal to be calibrated.

For example, the server updates the nonlinear calibration functions once a week and sends the new fitted nonlinear calibration functions to the smartphone A, B, C. Alternatively, the server updates the nonlinear calibration function every 10 days, every month, and sends the new fitted nonlinear calibration function to the smartphone A, B, C. The embodiment of the disclosure does not limit the update period in detail, and can be set according to actual situations.

It can be appreciated that the server updates the nonlinear calibration function periodically and sends the new fitted nonlinear calibration function to the terminal to be calibrated, so that the terminal to be calibrated can calibrate the movement data more accurately.

In the calibration method of the mobile data, a server receives historical mobile data and historical positioning data acquired by a measurement terminal in a historical period; the historical movement data comprise historical IMU data acquired by the measurement terminal; and fitting the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function, and then, the server can send the fitted nonlinear calibration function to the terminal to be calibrated so that the terminal to be calibrated can calibrate the movement data by using the fitted nonlinear calibration function.

In one embodiment, as shown in FIG. 3, an alternative process involves fitting historical movement data and historical positioning data to arrive at a fitted nonlinear calibration function. On the basis of the above embodiment, the method may include the following steps:

step 301, the server constructs a movement parameter expression of the measurement terminal in a history period based on the history movement data and the non-fitted nonlinear calibration function.

The movement parameter expression includes a function constant of a nonlinear calibration function, and it should be noted that the movement parameter may be a parameter obtained by performing mathematical calculation on historical movement data and capable of reflecting characteristics of a movement process of the measurement terminal in a historical period.

In an embodiment of the present disclosure, if the historical movement data is historical acceleration data, the movement parameter may be a displacement of the measurement terminal in a historical period, the displacement of the measurement terminal in the historical period may be obtained by performing mathematical calculation on the acceleration data, which may reflect a characteristic of a movement process of the measurement terminal in the historical period, and the movement parameter expression may be a displacement expression of the measurement terminal in the historical period.

In one embodiment of the present disclosure, the server may filter the historical acceleration data; correspondingly, the server can construct a displacement expression of the measurement terminal in the history period based on the filtered history acceleration data and the non-fitted nonlinear calibration function.

The filtering process may include a low-pass filtering process and a kalman filtering process. The low-pass filtering process can remove zero drift value or a value with great noise interference in the historical acceleration data; the Kalman filtering can correct the historical acceleration data, so that the problem of noise caused by the difference of sensors of different measuring terminals is solved.

Step 302, the server determines a target movement parameter of the measurement terminal in a history period according to the history positioning data.

Wherein the movement parameters in the movement parameter expression are the same as the parameter types of the target movement parameters.

In the case where the movement parameter expression is a displacement expression, the target movement parameter is a target displacement of the measurement terminal in the history period. Alternatively, the server may determine a target displacement of the measurement terminal within the historical period from the historical positioning data. For example, the server may determine the target displacement of the measurement terminal in the history period Δt from the history positioning data at the initial time and the history positioning data after Δt.

And 303, constructing a fitting function based on the moving parameter expression and the target moving parameter by the server, carrying out regression solution on the fitting function to obtain a target value of the function constant, and obtaining a nonlinear calibration function after fitting according to the target value of the function constant.

In the case where the movement parameter expression is a displacement expression and the target movement parameter is a target displacement, the process of constructing the fitting function may include: and the server constructs a fitting function according to the displacement expression and the target displacement. Then, as the value of the function constant contained in the displacement expression is unknown, regression solution is carried out on the constructed fitting function, so that the candidate value of the function constant can be obtained; substituting the candidate value of the function constant into the displacement expression to obtain the displacement corresponding to the displacement expression. Judging the fitting degree of the displacement corresponding to the displacement expression and the target displacement, if the fitting degree meets the requirement, finishing the fitting, taking the candidate value of the function constant at the end of the fitting as the target value of the function constant, and substituting the target value of the function constant into the nonlinear calibration function to obtain the nonlinear calibration function after the fitting. And if the fitting degree does not meet the requirement, adjusting the movement parameters in the nonlinear calibration function, and re-fitting.

The fitting function may be a mean square error function, where the mean square error is used to indicate an expected value of the square of the difference between the parameter estimate and the parameter true value. The process of constructing the fitting function by the server may include: and calculating the mean square error between the displacement expression and the target displacement, and then performing deviation derivation on the mean square error to obtain a candidate value of each function constant. And if the difference between the displacement calculated according to the candidate value of the function constant and the displacement expression and the target displacement is smaller than the preset difference, determining that the fitting degree meets the requirement, and ending the fitting. If the difference between the displacement calculated according to the candidate value of the function constant and the displacement expression and the target displacement is not smaller than the preset difference, determining that the fitting degree does not meet the requirement, adjusting the moving parameters in the fitting function, and carrying out regression solution again until the difference between the displacement calculated according to the candidate value of the function constant and the displacement expression and the target displacement is smaller than the preset difference.

In the process of fitting the historical movement data and the historical positioning data to obtain the fitted nonlinear calibration function, the server constructs a movement parameter expression of the measurement terminal in the historical period based on the historical movement data and the non-fitted nonlinear calibration function; then determining target movement parameters of the measurement terminal in a historical period according to the historical positioning data; and then constructing a fitting function based on the moving parameter expression and the target moving parameter, carrying out regression solution on the fitting function to obtain a target value of the function constant, and obtaining a nonlinear calibration function after fitting according to the target value of the function constant. In the embodiment of the disclosure, the fitting is performed by adopting a regression solution mode, so that the calculation complexity is low, and the calculation cost can be saved.

In one embodiment, as shown in FIG. 4, an alternative process involves constructing a displacement expression of the measurement terminal over a historical period based on historical acceleration data and a non-fitted nonlinear calibration function. On the basis of the above embodiment, the method may include the following steps:

in step 401, the server maps the historical acceleration data from the carrier coordinate system to the geographic coordinate system according to the coordinate transformation matrix obtained in advance, and obtains mapped acceleration data.

Wherein the carrier coordinate system is a coordinate system with the carrier as a center; the geographic coordinate system may also be called a navigation coordinate system or a ground-fixed coordinate system, and is a local horizontal coordinate system. The server acquires a coordinate transformation matrix in advance, and maps the historical acceleration data acquired by the measurement terminal from a carrier coordinate system to a geographic coordinate system by utilizing the coordinate transformation matrix to obtain mapped acceleration data. It will be appreciated that the mapping process of the coordinate transformation is a two-dimensional plane mapping the acceleration data from the three-dimensional space of the carrier to the geography.

In practical application, the smart phone moves along with the vehicle, and the mapping process of coordinate conversion is that the server converts acceleration data acquired by the smart phone into a geographic coordinate system where the vehicle is located from a carrier coordinate system where the smart phone is located.

And step 402, the server adopts the initial rotation angle to conduct angle correction on the mapping acceleration data, and the mapping acceleration data after angle correction is obtained.

The rotation angle is the rotation angle of the measurement terminal relative to the Z axis under the carrier coordinate system. After the historical acceleration data is mapped from the carrier coordinate system to the geographic coordinate system, the obtained mapped acceleration data has deviation from the actual direction in the direction, so that the server adopts an initial rotation angle to conduct angle correction on the mapped angular velocity, the direction of the mapped acceleration data is consistent with the actual direction, and the mapped acceleration data after angle correction is obtained.

For example, the initial rotation angle is θ ₁ The historical acceleration data is y, the angle correction is carried out on the historical acceleration data, and the obtained acceleration data after the angle correction can be y' =ycosθ ₁ . The embodiment of the disclosure does not limit the angle correction manner in detail, and may be set according to actual situations.

And step 403, the server constructs a displacement expression corresponding to the measurement terminal under the initial rotation angle based on the angle-corrected mapping acceleration data and the non-fitted nonlinear calibration function.

Substituting the angle-corrected mapped acceleration data into a non-fitted nonlinear calibration function by the server to obtain calibrated acceleration data; then, a displacement expression is constructed according to the calibrated acceleration data. The displacement expression is a displacement expression at an initial rotation angle.

In one embodiment, the historical acceleration data includes historical acceleration values acquired at a plurality of sampling moments in a historical period, as shown in fig. 5, before the step 401, the step of acquiring a coordinate transformation matrix may further include the following steps:

step 404, the server determines a motion period and a stationary period in the history period based on the historical acceleration values acquired at the plurality of sampling moments.

The acceleration values included in the movement period meet the condition that the measurement terminal is in a movement state, and the acceleration values included in the rest period meet the condition that the measurement terminal is in a rest state. The accelerometer in the IMU is a triaxial accelerometer, and the historical acceleration values acquired by the accelerometer comprise historical acceleration values corresponding to three coordinate axes respectively.

The server determining the movement period and the rest period in the history period may specifically include: for each sampling moment in the historical period, the server calculates variances and norms of the variances for the historical acceleration values corresponding to the three coordinate axes respectively; if the norm of the variance is greater than or equal to a preset threshold, determining that the sampling moment is positioned in the motion period; if the norm of the variance is smaller than the preset threshold, determining that the sampling time is in the static period. Among them, norm (norm) is a basic concept in mathematics, which is often used to measure the length or size of each vector in a certain vector space (or matrix).

For example, for the time k1, the server calculates the variance for the historical accelerations corresponding to the three coordinate axes collected at the time k1, respectively, and then calculates the norm of the variance. If the norm of the variance is greater than or equal to a preset threshold, the historical speed value meets the condition that the measuring terminal is in a motion state, and the k1 moment is located in a motion period. If the norm of the variance is smaller than the preset threshold, the historical acceleration value meets the condition that the measuring terminal is in a static state, and the k1 moment is located in a static period. Similarly, the server may determine whether each sampling instant in the history period is in a motion period or a stationary period.

Step 405, the server acquires first historical angular velocity data acquired by the measurement terminal during a movement period and second historical angular velocity data acquired during a stationary period.

Since the acquisition time of the angle data and the acquisition time of the acceleration data have a correspondence, the server can acquire the first historical angular velocity data acquired in the movement period and the second historical angular velocity data acquired in the stationary period from the historical angular velocity data acquired in the historical period by the measurement terminal according to the correspondence after determining the movement period and the stationary period of the historical period.

For example, when the k0 time is in the stationary period and the k1 time is in the moving period in the history period, the history angular velocity data collected at the k0 time is the first history angular velocity data, and the history angular velocity data collected at the k1 time is the second history angular velocity data.

In step 406, the server corrects the first historical angular velocity data by using the second historical angular velocity data, and obtains corrected historical angular velocity data.

Because the historical angular velocity data collected in the stationary period and the historical angular velocity data collected in the moving period are influenced by gravity, the influence of gravity can be removed by subtracting the first historical angular velocity data from the second historical angular velocity data. Optionally, the server calculates an average value of the plurality of second historical angular velocity data in the stationary period to obtain average historical angular velocity data; and then subtracting the average historical angular velocity data from each of the first historical angular velocity data for a plurality of first historical angular velocity data of the movement period to obtain a plurality of corrected historical angular velocity data.

In step 407, the server constructs a coordinate transformation matrix according to the corrected historical angular velocity data.

xis, yis and zis are acceleration values corresponding to three coordinate axes at the initial moment respectively; the data at the initial time is calculated as in the following formulas (1) (2) (3):

/>

Deriving from equations (1) (2) (3) can yield equation (4):

then, performing angle conversion at the initial moment, wherein the rotation angle of the carrier coordinate axis relative to the geographic coordinate axis is shown as a formula (5):

the coordinate transformation matrix (6) at the initial time can be obtained according to the formulas (4) and (5):

determining 3-axis angular rotation of each sampling time relative to the initial time by using average historical angular velocity values i, j and k in the stationary time period and first historical angular velocity data of the moving time period, wherein the sampling frequency is 40Hz, and m is the index of a list where the angle of the current sampling time is located, so as to obtain a formula (7):

deriving equation (8) from equations (6) (7):

in one embodiment, based on the angle-corrected mapped acceleration data and the non-fitted nonlinear calibration function, the server constructs a displacement expression corresponding to the measurement terminal under the initial rotation angle, including: dividing the movement period into a plurality of sub-periods with the same duration; and constructing a displacement expression corresponding to each sub-period of the measurement terminal under the initial rotation angle based on the angle-corrected mapping acceleration value and the non-fitted nonlinear calibration function included in each sub-period.

As shown in fig. 6, the movement period is divided into 3 sub-periods of the same duration. The angle-corrected mapped acceleration value is y', and the non-fitted nonlinear calibration function is x=ay ² +by+c, substituting the angle-corrected mapped acceleration value into the non-fitted nonlinear calibration function to obtain calibrated acceleration data x=ay '' ² +by' +c, and then constructing a displacement expression corresponding to each sub-period of the measurement terminal under the initial rotation angle according to the calibrated acceleration data x, as shown in formula (9):

wherein the moment k0 is the starting moment, the acceleration at moment k0 is 0, x ₁ For the acceleration after calibration at time k1, x ₂ The acceleration after calibration at time k 2. v ₀ Velocity at time k0, v ₁ For the velocity after calibration at time k1, v ₂ The acceleration after calibration at time k 2. S is S ₀ For the displacement expression between the time k0 and the time k1, S ₁ For the displacement expression between the time k1 and the time k2, S ₂ Is a displacement expression between the time k1 and the time k 2.

Calibration acceleration data x=ay' ² The substitution of +by' +c into the displacement expression of each sub-period can result in the formula (10) (11):

wherein j is the sequence number of the displacement expression, dist ₁ Is S ₀ ，Dist ₂ Is S ₁ ，Dist ₃ Is S ₂ 。

In the process of constructing the displacement expression of the measurement terminal in the history period by the history acceleration data and the non-fitted nonlinear calibration function, mapping the history acceleration data from a carrier coordinate system to a geographic coordinate system by a server according to a coordinate conversion matrix obtained in advance to obtain mapped acceleration data; then carrying out angle correction on the mapped acceleration data by adopting an initial rotation angle to obtain angle corrected mapped acceleration data; and then constructing a displacement expression corresponding to the measurement terminal under the initial rotation angle based on the angle-corrected mapping acceleration data and a non-fitted nonlinear calibration function. In the embodiment of the disclosure, coordinate conversion and angle correction are performed on the historical acceleration data, so that a displacement expression of the measurement terminal under an initial rotation angle is constructed, and a displacement expression part for constructing a fitting function is completed. In the process of carrying out coordinate conversion on the historical acceleration data, the influence of gravity is removed based on the historical angular velocity data; in the process of angle correction of the mapping acceleration data, the problem that the acceleration direction after coordinate conversion is inconsistent with the actual direction is solved.

In one embodiment, as shown in fig. 7, an optional process related to constructing a fitting function based on the motion parameter expression and the target motion parameter, and performing regression solution on the fitting function to obtain a target value of the function constant may specifically include the following steps on the basis of the foregoing embodiment:

step 501, the server calculates a mean square error according to the displacement expression corresponding to each sub-period and the target displacement corresponding to each sub-period of the measurement terminal under the initial rotation angle, and obtains a fitting function of the measurement terminal under the initial rotation angle.

And the server determines target displacement corresponding to each sub-period according to the plurality of sub-periods divided by the movement period and the positioning data acquired by the measuring terminal. Then, the server calculates the mean square error between the displacement expression corresponding to each sub-period and the target position corresponding to each sub-period of the measuring terminal under the initial rotation angle, as shown in formula (12):

wherein, the formula (13) is a fitting function of the measuring terminal under the initial rotation angle, N is the sequence number of the subperiod, gps _j Is the target displacement for each sub-period.

Step 502, under the initial rotation angle, the server calculates the partial derivative of each function constant in the fitting function, and obtains the candidate value of the function constant when the partial derivative of the mean square error is zero.

The server obtains a candidate value of the function constant a when the mean square error is zero by solving the bias derivative of the function constant a in the formula (12); performing bias derivative on the function constant b in the formula (12) to obtain a candidate value of the function constant b when the mean square error is zero; and (3) performing bias derivative on the function constant c in the formula (12) to obtain a candidate value of the function constant c when the mean square error is zero.

In step 503, the server substitutes the candidate value of the function constant into the displacement expression corresponding to the initial rotation angle of the measurement terminal, and determines the displacement of the measurement terminal under the initial rotation angle.

The server substitutes the candidate value of the function constant a, the candidate value of the function constant b and the candidate value of the function constant c into the displacement expression (12), so that the displacement of the measurement terminal under the initial rotation angle can be calculated.

Step 504, the server determines whether the difference between the displacement of the measurement terminal at the initial rotation angle and the target displacement is smaller than a preset difference.

The server judges whether the fitting degree meets the requirement according to the difference value between the displacement of the measuring terminal under the initial rotation angle and the target displacement; if the difference is smaller than the preset difference, the fitting degree meets the requirement, and step 505 is executed; if the difference is greater than or equal to the preset difference, the fitting degree is not satisfactory, and step 506 is performed.

In step 505, the server determines a candidate value of the function constant at the initial rotation angle as a target value of the function constant.

If the difference between the displacement of the measuring terminal at the initial rotation angle and the target displacement is smaller than a preset threshold value, the displacement of the measuring terminal at the initial rotation angle calculated by the server is very close to the target displacement determined according to the historical positioning data. And at the moment, after the fitting is finished, determining the candidate value of the function constant under the initial rotation angle as the target value of the function constant, and substituting the target value of the function constant into the nonlinear calibration function to obtain a nonlinear fitted function after the fitting.

Step 506, the server updates the initial corner to obtain a new corner, and obtains a fitting function of the measurement terminal under the new corner based on the new corner; and performing bias guide on each function constant in the fitting function under the new rotation angle until the difference value between the new displacement calculated under the new rotation angle and the target displacement is smaller than a preset difference value, and determining a candidate value of the function constant when the difference value is smaller than the preset difference value.

If the difference between the displacement of the measuring terminal under the initial rotation angle and the target displacement is larger than or equal to the preset difference, the displacement of the measuring terminal under the initial rotation angle calculated by the server is indicated to have deviation with the target displacement determined according to the historical positioning data. At this time, the server determines to update the rotation angle according to the difference between the displacement of the measurement terminal under the initial rotation angle and the target displacement determined according to the historical positioning data, and obtains a new rotation angle.

Then, the server corrects the mapping acceleration data again by adopting the new corner, and the mapping acceleration data after angle correction under the new corner is obtained; then, the server constructs a displacement expression under the new corner according to the angle-corrected mapping angular velocity data under the new corner; and finally, constructing a fitting function under the new corner according to the displacement expression under the new corner, and solving the bias derivative of each function constant in the fitting function under the new corner to obtain a candidate value of the function constant under the new corner.

The server calculates new displacement according to the candidate value of the function constant under the new rotation angle until the difference between the new displacement and the target displacement is smaller than a preset difference value, and determines the candidate value of the function constant at the moment as a target value.

In the process of constructing a fitting function based on the moving parameter expression and the target moving parameter and carrying out regression solving on the fitting function to obtain the target value of the function constant, the server calculates the mean square error according to the displacement expression corresponding to each sub-period of the measuring terminal under the initial rotation angle and the target displacement corresponding to each sub-period to obtain the fitting function of the measuring terminal under the initial rotation angle; then, under the initial rotation angle, each function constant in the fitting function is subjected to partial derivative calculation to obtain a candidate value of the function constant when the partial derivative of the mean square error is zero; substituting the candidate value of the function constant into a displacement expression corresponding to the initial rotation angle of the measurement terminal, and determining the displacement of the measurement terminal under the initial rotation angle; and then, measuring the difference value between the displacement of the terminal under the initial rotation angle and the target displacement to determine whether to adjust the rotation angle, if so, ending fitting until the difference value between the displacement under the new rotation angle and the target displacement is smaller than a preset difference value, and determining the candidate value of the function constant under the new rotation angle as the target value of the function constant. In the embodiment of the disclosure, the fitting of the nonlinear calibration function is performed by solving the fitting function through regression, so that the calculation complexity is low, and the calculation cost can be saved.

In one embodiment, as shown in fig. 8, the calibration method of mobile data is described in connection with a specific scenario, and the method is applied to a terminal to be calibrated, for example, and may include the following steps:

in step 601, the terminal to be calibrated acquires initial movement data of the terminal to be calibrated.

The initial movement data comprise initial IMU data of the terminal to be calibrated.

And pre-installing an IMU in the terminal to be calibrated, and acquiring initial acceleration data by using an accelerometer in the IMU.

And 602, calibrating initial movement data by the terminal to be calibrated by using a fitted nonlinear calibration function obtained in advance from a server, and obtaining calibrated movement data.

And the server periodically transmits the fitted nonlinear calibration function to the terminal to be calibrated, and the terminal to be calibrated receives and stores the fitted nonlinear calibration function. After initial acceleration data are acquired, the terminal to be calibrated calibrates the initial acceleration data by adopting a nonlinear calibration function after fitting, and calibrated acceleration data are obtained. The fitting process of the nonlinear calibration function can be referred to the above embodiments, and will not be described herein.

In one embodiment, an unfired nonlinear calibration function is set in a terminal to be calibrated, a server periodically sends a target value of a function constant in the fitted nonlinear calibration function to the terminal to be calibrated, and the terminal to be calibrated receives the target value of the function constant and substitutes the target value into the unfired nonlinear calibration function to obtain the fitted nonlinear calibration function.

In the calibration method of the movement data, the terminal to be calibrated acquires initial movement data, and the initial movement data is calibrated by using the fitted nonlinear calibration function acquired from the server in advance to obtain calibrated movement data. In the embodiment of the disclosure, the calibration of the mobile data is performed through the fitted nonlinear calibration function, so that the method is simple and easy to operate, and the calculated amount is low, and therefore, the calibration cost can be saved.

It should be understood that, although the steps in the flowcharts of fig. 2-8 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps of fig. 2-8 may include multiple steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the steps or stages in other steps or other steps.

In one embodiment, as shown in fig. 9, there is provided a calibration apparatus for movement data, comprising:

the data receiving module 701 is configured to receive historical movement data and historical positioning data collected by the measurement terminal in a historical period; the historical mobile data comprise historical IMU data acquired by the measuring terminal;

the fitting module 702 is configured to fit the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function;

the function sending module 703 is configured to send the fitted nonlinear calibration function to a terminal to be calibrated; the fitted nonlinear calibration function is used for indicating the terminal to be calibrated to calibrate the initial movement data acquired by the terminal to be calibrated by utilizing the fitted nonlinear calibration function, so as to obtain calibrated movement data; the initial movement data includes initial IMU data of the terminal to be calibrated.

In one embodiment, the fitting module 702 includes:

the mobile parameter expression construction submodule is used for constructing a mobile parameter expression of the measurement terminal in a history period based on the history mobile data and a non-fitted nonlinear calibration function; the motion parameter expression includes a function constant of a nonlinear calibration function;

The target movement parameter determination submodule is used for determining target movement parameters of the measurement terminal in a historical period according to the historical positioning data; wherein, the moving parameter in the moving parameter expression is the same as the parameter type of the target moving parameter;

and the fitting sub-module is used for constructing a fitting function based on the moving parameter expression and the target moving parameter, carrying out regression solution on the fitting function to obtain a target value of the function constant, and obtaining a nonlinear calibration function after fitting according to the target value of the function constant.

In one embodiment, if the historical IMU data includes historical acceleration data, the movement parameter expression is a displacement expression of the measurement terminal in the historical period, and the target movement parameter is a target displacement of the measurement terminal in the historical period.

In one embodiment, the mobile parameter expression construction submodule is specifically configured to map the historical acceleration data from the carrier coordinate system to the geographic coordinate system according to a coordinate transformation matrix obtained in advance to obtain mapped acceleration data; performing angle correction on the mapped acceleration data by adopting an initial rotation angle to obtain angle-corrected mapped acceleration data; the rotation angle is the rotation angle of the measurement terminal relative to the Z axis under the carrier coordinate system; and constructing a displacement expression of the measurement terminal under the initial rotation angle based on the angle-corrected mapping acceleration data and the non-fitted nonlinear calibration function.

In one embodiment, as shown in fig. 10, the historical acceleration data includes historical acceleration values acquired at a plurality of sampling moments in a historical period; the apparatus further comprises:

a period determining module 704, configured to determine a motion period and a stationary period in a history period based on the historical acceleration values acquired at a plurality of sampling moments; each acceleration value included in the movement period meets the condition that the measurement terminal is in a movement state, and each acceleration value included in the static period meets the condition that the measurement terminal is in a static state;

the angular velocity data acquisition module 705 is configured to acquire first historical angular velocity data acquired by the measurement terminal during a motion period and second historical angular velocity data acquired during a stationary period;

the angular velocity data correction module 706 is configured to correct the first historical angular velocity data by using the second historical angular velocity data, so as to obtain corrected historical angular velocity data;

a coordinate transformation matrix construction module 707 for constructing a coordinate transformation matrix according to the corrected historical angular velocity data.

In one embodiment, the moving parameter expression construction submodule is specifically configured to divide a motion period into a plurality of sub-periods with the same duration; and constructing a displacement expression corresponding to each sub-period of the measurement terminal under the initial rotation angle based on the angle-corrected mapping acceleration value and the non-fitted nonlinear calibration function included in each sub-period.

In one embodiment, the historical acceleration values include historical acceleration values corresponding to three coordinate axes respectively; the period determining module includes:

the norm calculation sub-module is used for calculating the variance and the norm of the variance for the historical acceleration values corresponding to the three coordinate axes respectively for each sampling time in the historical period;

a motion period determining submodule, configured to determine that the sampling time is located in the motion period if a norm of the variance is greater than or equal to a preset threshold;

and the stationary period determining submodule is used for determining that the sampling moment is positioned in the stationary period if the norm of the variance is smaller than a preset threshold value.

In one embodiment, the fitting module 702 is specifically configured to calculate a mean square error according to a displacement expression corresponding to each sub-period and a target displacement corresponding to each sub-period of the measurement terminal under the initial rotation angle, so as to obtain a fitting function of the measurement terminal under the initial rotation angle.

In one embodiment, the fitting sub-module is specifically configured to determine a partial derivative of each function constant in the fitting function under an initial rotation angle, so as to obtain a candidate value of the function constant when the partial derivative of the mean square error is zero; substituting the candidate value of the function constant into a displacement expression corresponding to the initial rotation angle of the measurement terminal, and determining the displacement of the measurement terminal under the initial rotation angle; judging whether the difference between the displacement of the measuring terminal under the initial rotation angle and the target displacement is smaller than a preset difference or not; if yes, determining the candidate value of the function constant under the initial rotation angle as the target value of the function constant.

In one embodiment, as shown in fig. 10, the apparatus further comprises:

the corner updating module 708 is configured to update the initial corner to obtain a new corner if not, and obtain a fitting function of the measurement terminal under the new corner based on the new corner;

the fitting sub-module is specifically configured to bias each function constant in the fitting function under the new rotation angle until the difference between the new displacement calculated under the new rotation angle and the target displacement is smaller than a preset difference value, and determine a candidate value of the function constant when the difference value is smaller than the preset difference value as a target value of the function constant.

In one embodiment, the apparatus further comprises:

a filtering processing module 709 for performing filtering processing on the historical acceleration data; wherein the filtering process comprises a low-pass filtering process and a Kalman filtering process;

correspondingly, the above-mentioned movement parameter expression construction submodule is specifically used for constructing the movement parameter expression of the history period based on the history acceleration data after the filtering processing and the non-fitted nonlinear calibration function.

In one embodiment, as shown in fig. 11, there is provided a calibration apparatus for movement data, comprising:

the data acquisition module 801 is configured to acquire initial movement data of a terminal to be calibrated; the initial movement data comprise initial IMU data of the terminal to be calibrated;

A calibration module 802, configured to calibrate the initial movement data by using a fitted nonlinear calibration function obtained in advance from a server, so as to obtain calibrated movement data;

the fitted nonlinear calibration function is obtained by fitting the server according to historical movement data and historical positioning data acquired by the measuring terminal in a historical period.

For specific limitations of the calibration means for the movement data, reference is made to the above limitations of the calibration method for the movement data, which are not repeated here. The various modules in the calibration device for mobile data described above may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in a server, or may be stored in software in a memory in the server, so that the processor may call and execute operations corresponding to the above modules.

Fig. 12 is a block diagram of an electronic device 1300 according to the above embodiments. The electronic device 1300 may be a measurement terminal, a terminal to be calibrated, or the like in the above-described embodiments. Referring to fig. 12, an electronic device 1300 may include one or more of the following components: a processing component 1302, a memory 1304, a power component 1306, a multimedia component 1308, an audio component 1310, an input/output (I/O) interface 1312, a sensor component 1314, and a communication component 1316. Wherein the memory has stored thereon a computer program or instructions that run on the processor.

The processing component 1302 generally controls overall operation of the electronic device 1300, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 1302 may include one or more processors 1320 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 1302 can include one or more modules that facilitate interactions between the processing component 1302 and other components. For example, the processing component 1302 may include a multimedia module to facilitate interaction between the multimedia component 1308 and the processing component 1302.

The memory 1304 is configured to store various types of data to support operations at the electronic device 1300. Examples of such data include instructions for any application or method operating on the electronic device 1300, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 1304 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

The power supply assembly 1306 provides power to the various components of the electronic device 1300. The power components 1306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 1300.

The multimedia component 1308 includes a touch-sensitive display screen that provides an output interface between the electronic device 1300 and a user. In some embodiments, the touch display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1308 includes a front-facing camera and/or a rear-facing camera. When the electronic device 1300 is in an operational mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 1310 is configured to output and/or input audio signals. For example, the audio component 1310 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 1300 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 1304 or transmitted via the communication component 1316. In some embodiments, the audio component 1310 also includes a speaker for outputting audio signals.

The I/O interface 1312 provides an interface between the processing component 1302 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.

The sensor assembly 1314 includes one or more sensors for providing status assessment of various aspects of the electronic device 1300. For example, the sensor assembly 1314 may detect an on/off state of the electronic device 1300, a relative positioning of the components, such as a display and keypad of the electronic device 1300, the sensor assembly 1314 may also detect a change in position of the electronic device 1300 or a component of the electronic device 1300, the presence or absence of a user's contact with the electronic device 1300, an orientation or acceleration/deceleration of the electronic device 1300, and a change in temperature of the electronic device 1300. The sensor assembly 1314 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. The sensor assembly 1314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1314 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 1316 is configured to facilitate communication between the electronic device 1300 and other devices, either wired or wireless. The electronic device 1300 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, or a combination thereof. In one exemplary embodiment, the communication component 1316 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1316 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 1300 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for performing the calibration methods of mobile data described above.

In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, such as memory 1304, including instructions executable by processor 1320 of electronic device 1300 to perform the above-described method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

Fig. 13 is a block diagram of an electronic device according to the above embodiment. Wherein the electronic device may be the server 1400 in the above embodiment. With reference to fig. 13, the server 1400 includes a processing component 1420 that further includes one or more processors and memory resources represented by a memory 1422 for storing instructions or computer programs, such as application programs, executable by the processing component 1420. The application programs stored in memory 1422 can include one or more modules, each corresponding to a set of instructions. Further, the processing component 1420 is configured to execute instructions to perform the calibration method of movement data described above.

The server 1400 may also include a power component 1424 configured to perform power management of the server 1400, a wired or wireless network interface 1426 configured to connect the server 1400 to a network, and an input/output (I/O) interface 1428. The server 1400 may operate an operating system based on storage 1422, such as Window14 14erverTM,Mac O14 XTM,UnixTM,LinuxTM,FreeB14DTM or the like.

In an exemplary embodiment, a storage medium is also provided that includes instructions, such as memory 1422 including instructions, that can be executed by a processor of server 1400 to perform the above-described methods. The storage medium may be a non-transitory computer readable storage medium, which may be, for example, ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided by the present disclosure may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples merely represent a few implementations of the disclosed examples, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made to the disclosed embodiments without departing from the spirit of the disclosed embodiments. Accordingly, the protection scope of the disclosed embodiment patent should be subject to the appended claims.

Claims

1. A method of calibrating mobile data, the method comprising:

receiving historical movement data and historical positioning data acquired by a measurement terminal in a historical period; the historical movement data comprise historical IMU data acquired by the measurement terminal;

transmitting the fitted nonlinear calibration function to a terminal to be calibrated; the fitted nonlinear calibration function is used for indicating the terminal to be calibrated to calibrate initial movement data acquired by the terminal to be calibrated by utilizing the fitted nonlinear calibration function, so as to obtain calibrated movement data; the initial movement data comprise initial IMU data of the terminal to be calibrated;

The method comprises the steps of fitting the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function, and comprises the following steps:

constructing a movement parameter expression of the measurement terminal in the history period based on the history movement data and a non-fitted nonlinear calibration function; the motion parameter expression includes a function constant of the nonlinear calibration function;

determining a target movement parameter of the measurement terminal in the historical period according to the historical positioning data; wherein the moving parameters in the moving parameter expression are the same as the parameter types of the target moving parameters;

and constructing a fitting function based on the moving parameter expression and the target moving parameter, carrying out regression solution on the fitting function to obtain a target value of the function constant, and obtaining a fitted nonlinear calibration function according to the target value of the function constant.

2. The method of claim 1, wherein if the historical IMU data includes historical acceleration data, the movement parameter expression is a displacement expression of the measurement terminal during the historical period, and the target movement parameter is a target displacement of the measurement terminal during the historical period.

3. The method of claim 2, wherein the constructing a movement parameter expression of the measurement terminal during the history period based on the history movement data and a non-fitted non-linear calibration function comprises:

mapping the historical acceleration data from a carrier coordinate system to a geographic coordinate system according to a coordinate transformation matrix obtained in advance to obtain mapped acceleration data;

performing angle correction on the mapped acceleration data by adopting an initial rotation angle to obtain angle-corrected mapped acceleration data; the rotation angle is the rotation angle of the measurement terminal relative to a Z axis under the carrier coordinate system;

and constructing a displacement expression of the measurement terminal under the initial rotation angle based on the angle-corrected mapping acceleration data and the non-fitted nonlinear calibration function.

4. A method according to claim 3, wherein the historical acceleration data comprises historical acceleration values collected at a plurality of sampling instants in the historical period; before the mapping of the historical acceleration data from the carrier coordinate system to the geographic coordinate system according to the pre-acquired coordinate transformation matrix, the method further comprises:

Determining a motion period and a stationary period in the historical period based on the historical acceleration values acquired at the plurality of sampling moments; each acceleration value included in the movement period meets the condition that the measurement terminal is in a movement state, and each acceleration value included in the rest period meets the condition that the measurement terminal is in a rest state;

acquiring first historical angular velocity data acquired by the measuring terminal in the movement period and second historical angular velocity data acquired by the measuring terminal in the static period;

correcting the first historical angular velocity data by using the second historical angular velocity data to obtain corrected historical angular velocity data;

and constructing the coordinate transformation matrix according to the corrected historical angular velocity data.

5. The method of claim 4, wherein constructing a displacement expression of the measurement terminal at the initial rotation angle based on the angle-corrected mapped acceleration data and the non-fitted nonlinear calibration function comprises:

dividing the movement period into a plurality of sub-periods with the same duration;

and constructing a displacement expression corresponding to each sub-period of the measuring terminal under the initial rotation angle based on the angle-corrected mapping acceleration value included in each sub-period and the non-fitted nonlinear calibration function.

6. The method of claim 4, wherein the historical acceleration values comprise historical acceleration values corresponding to three coordinate axes, respectively; the determining a motion period and a stationary period in the history period based on the historical acceleration values acquired at the plurality of sampling moments includes:

calculating a variance and a norm of the variance for the historical acceleration values respectively corresponding to the three coordinate axes for each sampling time in the historical period;

if the norm of the variance is greater than or equal to a preset threshold, determining that the sampling moment is located in the motion period;

and if the norm of the variance is smaller than the preset threshold, determining that the sampling moment is positioned in the static period.

7. The method of claim 6, wherein the constructing a fitting function based on the movement parameter expression and the target movement parameter comprises:

and calculating a mean square error according to the displacement expression corresponding to each sub-period of the measuring terminal under the initial rotation angle and the target displacement corresponding to each sub-period to obtain a fitting function of the measuring terminal under the initial rotation angle.

8. The method of claim 7, wherein performing regression on the fitted function to obtain the target value of the function constant comprises:

Under the initial rotation angle, each function constant in the fitting function is subjected to partial derivative calculation to obtain a candidate value of the function constant when the partial derivative of the mean square error is zero;

substituting the candidate value of the function constant into a displacement expression corresponding to the initial rotation angle of the measurement terminal, and determining the displacement of the measurement terminal under the initial rotation angle;

judging whether the difference between the displacement of the measuring terminal under the initial rotation angle and the target displacement is smaller than a preset difference or not;

if yes, determining the candidate value of the function constant under the initial rotation angle as a target value of the function constant.

9. The method according to claim 8, wherein after said determining whether the difference between the displacement of the measurement terminal at the initial rotation angle and the target displacement is smaller than a preset difference, the method further comprises:

if not, updating the initial corner to obtain a new corner, and obtaining a fitting function of the measurement terminal under the new corner based on the new corner;

and performing bias guide on each function constant in the fitting function under the new rotation angle until the difference between the new displacement calculated under the new rotation angle and the target displacement is smaller than the preset difference value, and determining a candidate value of the function constant when the difference value is smaller than the preset difference value as a target value of the function constant.

10. A method according to claim 3, characterized in that before said constructing a movement parameter expression of the measurement terminal over the history period based on the history movement data and a non-fitted non-linear calibration function, the method further comprises:

filtering the historical acceleration data; wherein the filtering process includes a low-pass filtering process and a Kalman filtering process;

correspondingly, the construction of the movement parameter expression of the measurement terminal in the history period based on the history movement data and the non-fitted nonlinear calibration function comprises the following steps:

and constructing a movement parameter expression of the history period based on the filtered history acceleration data and the non-fitted nonlinear calibration function.

11. A method of calibrating mobile data, the method comprising:

calibrating the initial movement data by using a fitted nonlinear calibration function obtained in advance from a server to obtain calibrated movement data;

The fitted nonlinear calibration function is obtained by the server according to the historical movement data and the historical positioning data acquired by the measuring terminal in the historical period, and the fitting process comprises the following steps:

12. A calibration apparatus for mobile data, the apparatus comprising:

the data receiving module is used for receiving the historical movement data and the historical positioning data acquired by the measuring terminal in the historical period; the historical movement data comprise historical IMU data acquired by the measurement terminal;

the function sending module is used for sending the fitted nonlinear calibration function to a terminal to be calibrated; the fitted nonlinear calibration function is used for indicating the terminal to be calibrated to calibrate initial movement data acquired by the terminal to be calibrated by utilizing the fitted nonlinear calibration function, so as to obtain calibrated movement data; the initial movement data comprise initial IMU data of the terminal to be calibrated;

wherein, the fitting module includes:

a moving parameter expression construction sub-module, configured to construct a moving parameter expression of the measurement terminal in the history period based on the history moving data and a non-fitted nonlinear calibration function; the motion parameter expression includes a function constant of the nonlinear calibration function;

the target movement parameter determination submodule is used for determining target movement parameters of the measuring terminal in the historical period according to the historical positioning data; wherein the moving parameters in the moving parameter expression are the same as the parameter types of the target moving parameters;

And the fitting sub-module is used for constructing a fitting function based on the moving parameter expression and the target moving parameter, carrying out regression solution on the fitting function to obtain a target value of the function constant, and obtaining a fitted nonlinear calibration function according to the target value of the function constant.

13. The apparatus of claim 12, wherein if the historical IMU data includes historical acceleration data, the movement parameter expression is a displacement expression of the measurement terminal during the historical period, and the target movement parameter is a target displacement of the measurement terminal during the historical period.

14. The apparatus of claim 13, wherein the movement parameter expression construction submodule is specifically configured to map the historical acceleration data from a carrier coordinate system to a geographic coordinate system according to a coordinate transformation matrix acquired in advance to obtain mapped acceleration data; performing angle correction on the mapped acceleration data by adopting an initial rotation angle to obtain angle-corrected mapped acceleration data; the rotation angle is the rotation angle of the measurement terminal relative to a Z axis under the carrier coordinate system; and constructing a displacement expression of the measurement terminal under the initial rotation angle based on the angle-corrected mapping acceleration data and the non-fitted nonlinear calibration function.

15. The apparatus of claim 14, wherein the historical acceleration data comprises historical acceleration values collected at a plurality of sampling instants in the historical period; the apparatus further comprises:

the time period determining module is used for determining a motion time period and a static time period in the historical time period based on the historical acceleration values acquired at the plurality of sampling moments; each acceleration value included in the movement period meets the condition that the measurement terminal is in a movement state, and each acceleration value included in the rest period meets the condition that the measurement terminal is in a rest state;

the angular velocity data acquisition module is used for acquiring first historical angular velocity data acquired by the measurement terminal in the movement period and second historical angular velocity data acquired by the measurement terminal in the static period;

the angular velocity data correction module is used for correcting the first historical angular velocity data by utilizing the second historical angular velocity data to obtain corrected historical angular velocity data;

and the coordinate transformation matrix construction module is used for constructing the coordinate transformation matrix according to the corrected historical angular velocity data.

16. The apparatus of claim 15, wherein the movement parameter expression construction submodule is specifically configured to divide the movement period into a plurality of subintervals of the same duration; and constructing a displacement expression corresponding to each sub-period of the measuring terminal under the initial rotation angle based on the angle-corrected mapping acceleration value included in each sub-period and the non-fitted nonlinear calibration function.

17. The apparatus of claim 15, wherein the historical acceleration values comprise historical acceleration values corresponding to three coordinate axes, respectively; the period determination module includes:

a norm calculation sub-module, configured to calculate, for each sampling time in the historical period, a variance and a norm of the variance for the historical acceleration values corresponding to the three coordinate axes respectively;

and the stationary period determining submodule is used for determining that the sampling moment is positioned in the stationary period if the norm of the variance is smaller than the preset threshold value.

18. The apparatus of claim 17, wherein the fitting module is specifically configured to calculate a mean square error according to a displacement expression corresponding to each sub-period and a target displacement corresponding to each sub-period of the measurement terminal at the initial rotation angle, so as to obtain a fitting function of the measurement terminal at the initial rotation angle.

19. The apparatus of claim 18, wherein the fitting sub-module is specifically configured to bias each function constant in the fitting function at the initial rotation angle to obtain a candidate value of the function constant when the partial derivative of the mean square error is zero; substituting the candidate value of the function constant into a displacement expression corresponding to the initial rotation angle of the measurement terminal, and determining the displacement of the measurement terminal under the initial rotation angle; judging whether the difference between the displacement of the measuring terminal under the initial rotation angle and the target displacement is smaller than a preset difference or not; if yes, determining the candidate value of the function constant under the initial rotation angle as a target value of the function constant.

20. The apparatus of claim 19, wherein the apparatus further comprises:

the corner updating module is used for updating the initial corner to obtain a new corner if not, and obtaining a fitting function of the measurement terminal under the new corner based on the new corner;

the fitting sub-module is specifically configured to bias each function constant in the fitting function under the new rotation angle until a difference between the new displacement calculated under the new rotation angle and the target displacement is smaller than the preset difference, and determine a candidate value of the function constant when the difference is smaller than the preset difference as a target value of the function constant.

21. The apparatus of claim 13, wherein the apparatus further comprises:

the filtering processing module is used for carrying out filtering processing on the historical acceleration data; wherein the filtering process includes a low-pass filtering process and a Kalman filtering process;

correspondingly, the mobile parameter expression construction submodule is specifically used for constructing the mobile parameter expression of the history period based on the filtered history acceleration data and the non-fitted nonlinear calibration function.

22. A calibration apparatus for mobile data, the apparatus comprising:

the calibration module is used for calibrating the initial movement data by utilizing the fitted nonlinear calibration function obtained from the server in advance to obtain calibrated movement data;

the fitted nonlinear calibration function is obtained by the server according to the historical movement data and the historical positioning data acquired by the measuring terminal in the historical period, and the fitting process comprises the following steps: constructing a movement parameter expression of the measurement terminal in the history period based on the history movement data and a non-fitted nonlinear calibration function; the motion parameter expression includes a function constant of the nonlinear calibration function; determining a target movement parameter of the measurement terminal in the historical period according to the historical positioning data; wherein the moving parameters in the moving parameter expression are the same as the parameter types of the target moving parameters; and constructing a fitting function based on the moving parameter expression and the target moving parameter, carrying out regression solution on the fitting function to obtain a target value of the function constant, and obtaining a fitted nonlinear calibration function according to the target value of the function constant.

23. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 10 when the computer program is executed or the steps of the method of claim 11 when the computer program is executed by the processor.

24. A storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the method of any of claims 1 to 10; alternatively, the computer program realizes the steps of the method of claim 11 when executed by a processor.