CN111625764A

CN111625764A - Calibration method and device for mobile data, electronic equipment and storage medium

Info

Publication number: CN111625764A
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: 2020-09-04
Anticipated expiration: 2040-05-21
Also published as: CN111625764B

Abstract

The embodiment of the disclosure relates to a calibration method and device for mobile data, electronic equipment and a storage medium. The method comprises the following steps: receiving historical movement data and historical positioning data which are acquired by a measuring terminal in a historical time period; the historical mobile data comprises historical IMU data collected by the measuring terminal; fitting the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function; 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 the initial mobile data acquired by the terminal to be calibrated by using the fitted nonlinear calibration function to obtain calibrated mobile 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

Calibration method and device for mobile data, electronic equipment and storage medium

Technical Field

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

Background

With the development of science and technology, an Inertial Measurement Unit (IMU) is widely used in a plurality of fields. For example, in the field of automated driving and the field of driving behavior detection, IMU data may be used for detection of object poses; in the aerospace and indoor navigation fields, IMU data may be used for real-time positioning.

In the related art, the IMU includes an accelerometer for detecting a three-axis acceleration of an object in a carrier coordinate system and a gyroscope for detecting an angular velocity of the object with respect to a geographical coordinate system. However, since the IMU is often subject to noise interference, the measured IMU data is often subject to errors, and therefore, in order to ensure the accuracy of the IMU data application, calibration of the IMU data is often performed in the industry. At present, the acceleration is often calibrated by a calibration method such as a transformation matrix method or a six-surface calibration method.

However, the calibration methods all require matrix calculation, and have high calculation complexity and high calibration cost.

Disclosure of Invention

The embodiment of the disclosure provides a calibration method and device for mobile data, an electronic device and a storage medium, 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 movement data, where the method includes:

receiving historical movement data and historical positioning data which are acquired by a measuring terminal in a historical time period; the historical mobile data comprises historical IMU data collected by a measuring terminal;

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

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 the initial mobile data acquired by the terminal to be calibrated by using the fitted nonlinear calibration function to obtain calibrated mobile 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 movement data, including:

acquiring initial movement data of a terminal to be calibrated; the initial mobile data comprises initial IMU data of the terminal to be calibrated;

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

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

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

the data receiving module is used for receiving historical movement data and historical positioning data which are acquired by the measuring terminal in a historical time period; the historical mobile data comprises historical IMU data collected by a 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 mobile data acquired by the terminal to be calibrated by using the fitted nonlinear calibration function to obtain calibrated mobile 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, including:

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

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

In a fifth aspect, an embodiment of the present 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 implements the method according to any one of the first aspect or the second aspect when executing the computer program.

In a sixth aspect, the disclosed embodiments provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method of any one of the first or second aspects.

According to the calibration method and device for the mobile data, the electronic equipment and the storage medium, the server receives historical mobile data and historical positioning data which are collected by the measuring terminal in a historical time period; the historical mobile data comprises historical IMU data collected by the measuring terminal; the server sends the fitted nonlinear calibration function to the terminal to be calibrated so that the terminal to be calibrated can calibrate the mobile data by using the fitted nonlinear calibration function.

Drawings

FIG. 1 is a diagram of an exemplary implementation of a method for calibrating movement data;

FIG. 2 is a flow diagram illustrating a method for calibrating movement data according to one embodiment;

FIG. 3 is a flowchart illustrating the steps of fitting historical movement data to historical positioning data to obtain a fitted non-linear calibration function according to an embodiment;

FIG. 4 is a flowchart illustrating one of the steps of constructing a displacement expression of the measurement terminal over a history period based on historical acceleration data and an unmounted nonlinear calibration function in one embodiment;

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

FIG. 6 is a diagram illustrating the division of a motion period into 3 sub-periods in one embodiment;

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

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

FIG. 9 is a block diagram of an embodiment of a calibration apparatus for movement data;

FIG. 10 is a second block diagram of an embodiment of a calibration apparatus for motion data;

FIG. 11 is a block diagram showing the structure of a calibration apparatus for moving data according to another embodiment;

FIG. 12 is a diagram of the internal structure of an electronic device in one embodiment;

FIG. 13 is an internal block 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 clearly understood, the embodiments of the present disclosure are described in further detail below with reference to the accompanying drawings and the embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the embodiments of the disclosure and that no limitation to the embodiments of the disclosure is intended.

First, before specifically describing the technical solution of the embodiment of the present disclosure, the technical background on which the embodiment of the present disclosure is based is described.

The IMU, i.e. the inertial measurement unit, is generally composed of an accelerometer and a gyroscope. The accelerometer detects the acceleration of the object in three coordinate axes under a carrier coordinate system, and the gyroscope detects the angular velocity of the object relative to a geographic coordinate system. The carrier coordinate system is a coordinate system taking the carrier as a center; the geographic coordinate system can also be called a navigation coordinate system or a ground-fixed coordinate system, and is a local horizontal coordinate system. Generally, the data measured by the IMU may be referred to as IMU data, which, as noted above, may include acceleration as well as angular velocity.

In practical applications, the IMU is often interfered by noise, so that the measured IMU data often has errors. To ensure the accuracy of the application of IMU data, it is common in the art to calibrate the IMU data. However, the transformation matrix method or the six-sided calibration method adopted at present needs a large amount of matrix calculation, the calculation complexity is high, and the larger the matrix is, the higher the calculation complexity is, thus leading to higher calibration cost.

In the embodiment of the disclosure, the server may receive historical movement data and historical positioning data acquired by the measurement terminal in a historical time period; 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 mobile data by using the fitted nonlinear calibration function.

The following describes technical solutions related to the embodiments of the present disclosure with reference to application environments of the embodiments of the present disclosure.

The calibration method for the movement data provided by the embodiment of the disclosure can be applied to the application environment shown in fig. 1. The terminal 101 to be calibrated and the measurement terminal 102 communicate with the server 103 through the network, respectively. The server 103 fits according to the historical movement data and the historical positioning data acquired by the measurement terminal 102 in the historical time period to obtain a fitted nonlinear calibration function, and then sends the fitted nonlinear calibration function to the terminal 101 to be calibrated. And the terminal 101 to be calibrated calibrates the acquired initial movement data according to the fitted nonlinear calibration function to obtain calibrated movement data. The terminal 101 to be calibrated and the measurement terminal 102 may be the same type of terminal, and may be but not limited to a laptop, a smartphone, a tablet computer, and a portable wearable device, and the server 103 may be implemented by an independent server or a server cluster formed by a plurality of servers.

In one embodiment, as shown in fig. 2, a calibration method for mobile data is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:

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

The historical movement data comprises historical IMU data collected by the measuring terminal and is used for representing the movement state of the measuring terminal in a historical time 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 measuring terminal is a terminal for collecting historical movement data and historical positioning data, the measuring terminal can be any terminal which is in communication connection with the server, and can also be a terminal which is specified by the server and is specially used for fitting a nonlinear calibration function described below. The history period may be a time period of a preset duration before the server fits the non-linear calibration function described below.

A sensing assembly and a positioning assembly may be mounted in the measurement terminal. Wherein, sensing component can be used for measuring the removal data, and locating component can be used for measuring the location data. For example, the sensing component may be an IMU, and the Positioning component may be a Global Positioning System (GPS) module. In the embodiment of the disclosure, historical movement data of the measurement terminal in a historical period can be collected by the sensing component, and historical positioning data of the measurement terminal in the historical period can be collected by the positioning component.

The measurement terminal acquires data in a historical period, after historical mobile data and historical positioning data are obtained, the measurement terminal can send the historical mobile data and the historical positioning data to the server in real time or regularly, and the server can receive the historical mobile data and the historical positioning data sent by the measurement terminal. Or, the server may send an acquisition instruction to the measurement terminal, the measurement terminal sends 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 measuring terminal can be a smart phone, and an IMU and a GPS module are installed in the smart phone. The IMU is used for acquiring acceleration data and angular velocity data (namely historical movement data) of the smart phone in a historical time period, and the GPS module is used for acquiring positioning data (namely historical positioning data) of the smart phone in the historical time period. And then, the smart phone uploads the acquired acceleration data, angular velocity data and positioning data to a server, and the server receives the acceleration data, the angular velocity data and the positioning data sent by the smart phone.

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

The non-linear calibration function is a non-linear function. For example, the non-linear calibration function may be a one-dimensional multi-order function, an exponential function, or the like. The independent variable of the non-linear calibration function may be uncalibrated movement data and the dependent variable of the non-linear calibration function may be calibrated movement data. It is noted that the movement data to be calibrated may be IMU data, wherein IMU data may comprise acceleration data, in addition to angular velocity data.

In the embodiment of the present 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 not fitted, the value of the function constant of the nonlinear calibration function is unknown. In the embodiment of the present disclosure, the functional relationship between the independent variable and the dependent variable in the nonlinear calibration function (i.e., the function type of the nonlinear calibration function) may be preset by a technician.

The non-linear calibration function is a unary quadratic function x-ay²+ by + c, the movement data is, for example, acceleration data in IMU data, where the independent variable y is uncalibrated acceleration data, the dependent variable x is calibrated acceleration data, and the function constants a, b, and c of the nonlinear calibration function are obtained by the server fitting the historical movement data and the historical positioning data, and when the function constants are not fitted, the values of the function constants are unknown.

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

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

The terminal to be calibrated is a terminal for mobile data to be calibrated, and the fitted nonlinear calibration function is used for instructing 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. And 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 the 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. Therefore, the attitude of the terminal to be calibrated or the 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 a smartphone D of the same model as the smartphone A, B, C. Taking the example that the measuring terminal is the smartphone D, the server obtains the fitted nonlinear calibration function according to the historical movement data and the historical positioning data collected by the smartphone D, and then sends the fitted nonlinear calibration function to the smartphone A, B, C. The smartphone A, B, C receives the fitted nonlinear calibration function, and calibrates the acquired initial acceleration data according to the fitted nonlinear calibration function to obtain calibrated acceleration data.

In one embodiment, the server may periodically obtain historical movement data and historical positioning data of the measurement terminal, and after obtaining the historical movement data and the historical positioning data each time, perform fitting according to the obtained historical movement data and the obtained historical positioning data to obtain a new nonlinear calibration function, and then, the server may update the nonlinear calibration function obtained by the last 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 non-linear calibration function once a week and sends the new fitted non-linear calibration function 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 update period is not limited in detail in the embodiments of the present disclosure, and may be set according to actual situations.

The server updates the nonlinear calibration function periodically and sends the fitted nonlinear calibration function to the terminal to be calibrated, so that the terminal to be calibrated can calibrate the mobile data more accurately.

In the mobile data calibration method, a server receives historical mobile data and historical positioning data which are acquired by a measuring terminal in a historical time period; the historical mobile data comprises historical IMU data collected by the measuring terminal; 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 mobile data by using the fitted nonlinear calibration function.

In one embodiment, as shown in FIG. 3, an alternative process involving fitting historical movement data and historical positioning data to obtain 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 the 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 a characteristic of the movement process of the measurement terminal in a historical time 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, and the displacement of the measurement terminal in the historical period may be obtained by performing mathematical calculation on the acceleration data, which can reflect a characteristic of a motion 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 perform filtering processing on the historical acceleration data; correspondingly, the server can construct a displacement expression of the measuring terminal in a historical period based on the filtered historical acceleration data and the fitted nonlinear calibration function.

The filtering process may include a low-pass filtering process and a kalman filtering process, among others. Zero drift values or values with large noise interference in historical acceleration data can be removed through low-pass filtering; the Kalman filtering can correct historical acceleration data, so that the problem of bottom noise caused by difference of sensors of different measurement terminals is solved.

And step 302, the server determines the target movement parameters of the measuring terminal in the historical time period according to the historical positioning data.

And the moving parameters in the moving parameter expression are the same as the parameter types of the target moving parameters.

And under the condition that the movement parameter expression is a displacement expression, the target movement parameter is the target displacement of the measuring terminal in the historical time period. Alternatively, the server may determine the target displacement of the measuring terminal within a historical period of time from the historical positioning data. For example, the server may determine the target displacement of the measurement terminal in the history period Δ t from the history positioning data at the initial time and the history positioning data after Δ t.

Step 303, the server constructs a fitting function based on the mobile parameter expression and the target mobile parameter, performs regression solution on the fitting function to obtain a target value of the function constant, and obtains a fitted nonlinear calibration function according to the target value of the function constant.

In the case that the moving parameter expression is a displacement expression and the target moving 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, the regression solution is carried out on the constructed fitting function, and the candidate value of the function constant can be obtained; and substituting the candidate value of the function constant into the displacement expression to obtain the displacement corresponding to the displacement expression. And judging the fitting degree of the displacement corresponding to the displacement expression and the target displacement, finishing the fitting if the fitting degree meets the requirement, 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 fitted nonlinear calibration function. And if the fitting degree does not meet the requirement, adjusting the movement parameters in the nonlinear calibration function, and fitting again.

The fitting function may be a mean square error function, and the mean square error is used to indicate an expectation value of the square of the difference between the parameter estimation value and the parameter true value. The process of the server building the fitting function may include: and calculating the mean square error between the displacement expression and the target displacement, and then performing partial 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 a preset difference, determining that the fitting degree meets the requirement, and finishing the fitting. 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 not less than the preset difference, determining that the fitting degree does not meet the requirement, adjusting the moving parameters in the fitting function, and performing 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 less than the preset difference.

In the process of fitting the historical mobile data and the historical positioning data to obtain the fitted nonlinear calibration function, the server constructs a mobile parameter expression of the measuring terminal in a historical period based on the historical mobile data and the non-fitted nonlinear calibration function; then determining target movement parameters of the measuring terminal in a historical time period according to historical positioning data; and then constructing a fitting function based on the moving parameter expression and the target moving parameter, performing 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. The embodiment of the disclosure adopts a regression solution mode to perform fitting, so that the calculation complexity is low, and the calculation cost can be saved.

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

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 to obtain mapped acceleration data.

The carrier coordinate system is a coordinate system taking the carrier as a center; the geographic coordinate system can 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 conversion matrix in advance, and maps the historical acceleration data acquired by the measuring terminal from the carrier coordinate system to the geographic coordinate system by using the coordinate conversion matrix to obtain mapped acceleration data. It will be appreciated that the mapping process for the coordinate transformation is a two-dimensional plane that maps acceleration data from the three-dimensional space of the carrier to geography.

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

And step 402, the server performs angle correction on the mapping acceleration data by using the initial corner to obtain the mapping acceleration data after the angle correction.

And the rotation angle is the rotation angle of the measuring terminal relative to the Z axis under the carrier coordinate system. After the historical acceleration data are mapped to the geographic coordinate system from the carrier coordinate system, the obtained mapping acceleration data have deviation in direction from the actual direction, so that the server performs angle correction on the mapping angular speed by adopting an initial corner, the direction of the mapping acceleration data is consistent with the actual direction, and the mapping acceleration data after the angle correction is obtained.

For example, the initial rotation angle is θ₁The historical acceleration data may be y', the angle of the historical acceleration data may be corrected, and the angle-corrected acceleration data may be y ═ ycos θ₁. The angle correction mode in the embodiments of the present disclosure is not limited in detail, and may be set according to actual situations.

And step 403, the server constructs a displacement expression corresponding to the measuring terminal under the initial rotation angle based on the mapping acceleration data after the angle correction and the non-linear calibration function which is not fitted.

The server substitutes the mapping acceleration data after the angle correction into a non-fitted nonlinear calibration function to obtain calibrated acceleration data; and then, constructing a displacement expression 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 collected at a plurality of sampling times in a historical period, and as shown in fig. 5, before step 401, the method may further include the following steps of obtaining a coordinate transformation matrix:

in step 404, the server determines a moving period and a stationary period in the historical period based on the historical acceleration values collected at the plurality of sampling times.

And each acceleration value included in the moving time period meets the condition that the measuring terminal is in the moving state, and each acceleration value included in the static time period meets the condition that the measuring terminal is in the static state. An accelerometer in the IMU is a three-axis accelerometer, and 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 static period in the history period may specifically include: aiming at each sampling moment in a historical period, the server calculates the variance and the norm of the variance for historical acceleration values corresponding to three coordinate axes respectively; if the norm of the variance is greater than or equal to a preset threshold value, determining that the sampling moment is located in the motion period; and if the norm of the variance is smaller than a preset threshold value, determining that the sampling moment is in the static period. Where norm (norm) is a basic concept in mathematics, it 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 of the historical accelerations corresponding to the three coordinate axes acquired at the time k1, and then calculates the norm of the variance. If the norm of the variance is greater than or equal to the preset threshold value, which indicates that the historical speed value meets the condition that the measuring terminal is in the motion state, the moment k1 is in the motion period. If the norm of the variance is smaller than the preset threshold value, the historical acceleration value meets the condition that the measuring terminal is in a static state, and the moment k1 is in a static period. By analogy, the server may determine whether each sampling instant in the history period is in a motion period or a stationary period.

In step 405, the server obtains first historical angular velocity data collected by the measurement terminal in a movement period and second historical angular velocity data collected in a static period.

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

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

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

Since both the historical angular velocity data acquired during the stationary period and the historical angular velocity data acquired during 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 a plurality of second historical angular velocity data in the static period to obtain average historical angular velocity data; then, for a plurality of first historical angular velocity data of the movement period, subtracting the average historical angular velocity data from each first historical angular velocity data 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 are calculated as the following formulas (1), (2) and (3):

the formula (4) can be derived according to the formulas (1), (2) and (3):

and then, carrying out angle conversion at the initial moment, wherein the rotation angle of the carrier coordinate axis relative to the geographic coordinate axis is formula (5):

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

matrix_zyx＝matrix_z*matrix_y*matrix_x

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

deriving formula (8) according to formulas (6) and (7):

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

As shown in fig. 6, the motion period is divided into 3 sub-periods of the same duration. The angle-corrected mapped acceleration value is y', and the unfixed non-linear calibration function is x-ay²+ by + c, the mapped acceleration value after the angle correction is substituted into the non-fitted nonlinear calibration function to obtain the calibrated acceleration data x ═ ay'²And + by' + c, and then constructing a displacement expression corresponding to each sub-period of the measuring terminal under the initial rotation angle according to the calibrated acceleration data x, wherein the displacement expression is as shown in a formula (9):

wherein, the time k0 is the starting time, the acceleration at the time k0 is 0, x₁Acceleration, x, calibrated for time k1₂Is the acceleration calibrated at time k 2. v. of₀Velocity at time k0, v₁Velocity, v, calibrated for time k1₂Is the acceleration calibrated at time k 2. S₀Is a displacement expression from the time k0 to the time k1, S₁Is a displacement expression from the time k1 to the time k2, S₂Is the displacement expression between the time k1 and the time k 2.

Calibrating acceleration data x-ay'²Substituting + by' + c into the displacement expression for each subinterval, equation (10) (11) can be obtained:

wherein j is the serial number of the displacement expression, Dist₁Is S₀，Dist₂Is S₁，Dist₃Is S₂。

In the process of constructing a displacement expression of the measurement terminal in a historical period by the aid of the historical acceleration data and an unmatched nonlinear calibration function, the server maps the historical acceleration data from a carrier coordinate system to a geographic coordinate system according to a coordinate conversion matrix acquired in advance to obtain mapped acceleration data; then, carrying out angle correction on the mapping acceleration data by adopting the initial corner to obtain the mapping acceleration data after the angle correction; and then constructing a corresponding displacement expression of the measuring terminal under the initial rotation angle based on the mapping acceleration data after the angle correction and the non-linear calibration function which is not fitted. In the embodiment of the disclosure, coordinate conversion and angle correction are performed on historical acceleration data, so that a displacement expression of the measurement terminal under an initial rotation angle is constructed, and the construction of a displacement expression part of a fitting function is completed. Wherein, in the process of carrying out coordinate conversion on the historical acceleration data, the influence of gravity is removed on the basis of the historical angular velocity data; in the process of angle correction of the mapping acceleration data, the problem that the direction of the acceleration after coordinate conversion is inconsistent with the actual direction is solved.

In an embodiment, as shown in fig. 7, an optional process of constructing a fitting function based on a movement parameter expression and a target movement parameter, and performing regression solution on the fitting function to obtain a target value of a function constant is involved, and on the basis of the above embodiment, the optional process specifically includes the following steps:

step 501, the server calculates 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, and obtains a fitting function of the measuring 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 motion 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 under the initial rotation angle of the measuring terminal, 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 sub-period, gps_jIs the target displacement for each sub-period.

Step 502, under the initial rotation angle, the server separately 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 calculates the partial derivative of the function constant a in the formula (12) to obtain a candidate value of the function constant a when the mean square error is zero; calculating the partial derivative of 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) calculating the partial derivative of 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.

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

And (4) substituting the candidate values 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) by the server, and calculating the displacement of the measuring terminal under the initial rotation angle.

In 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 predetermined difference, the fitness is not satisfactory, and step 506 is executed.

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 value between the displacement of the measuring terminal under the initial rotation angle and the target displacement is smaller than the preset threshold value, the fact that the displacement of the measuring terminal under the initial rotation angle calculated by the server is very close to the target displacement determined according to the historical positioning data is shown. At this time, the fitting is finished, the candidate value of the function constant under the initial rotation angle is determined as the target value of the function constant, and then the target value of the function constant is substituted into the nonlinear calibration function to obtain the fitted nonlinear fitting function.

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

If the difference value 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 value, the fact that the displacement of the measuring terminal under the initial rotation angle calculated by the server is deviated from the target displacement determined according to the historical positioning data is shown. At the moment, the server determines to update the corner according to the difference value between the displacement of the measuring terminal under the initial corner and the target displacement determined according to the historical positioning data, and a new corner is obtained.

Then, the server corrects the mapping acceleration data again by adopting the new corner to obtain the angle-corrected mapping acceleration data under the new corner; then, the server constructs a displacement expression under a new corner according to the mapping angular speed data after the angle correction 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 partial 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.

And the server calculates new displacement according to the candidate value of the function constant under the new corner until the difference between the new displacement and the target displacement is smaller than a preset difference, 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 mobile parameter expression and the target mobile parameter and performing regression solution on the fitting function to obtain the target value of the function constant, the server calculates 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 of the measuring terminal to obtain the fitting function of the measuring terminal under the initial rotation angle; secondly, respectively solving a partial derivative of each function constant in the fitting function under an 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 measuring terminal under the initial rotation angle, and determining the displacement of the measuring terminal under the initial rotation angle; and then, determining whether the corner is adjusted or not by measuring the difference between the displacement of the terminal under the initial corner and the target displacement, if so, finishing the fitting until the difference between the displacement under the new corner and the target displacement is smaller than a preset difference, and determining the candidate value of the function constant under the new corner as the target value of the function constant. In the embodiment of the disclosure, 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 an embodiment, as shown in fig. 8, a calibration method for mobile data is introduced in combination with a specific scenario, and taking the application of the method to a terminal to be calibrated as an example, the method may include the following steps:

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

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

An IMU is pre-installed in a terminal to be calibrated, and an accelerometer in the IMU acquires initial acceleration data.

Step 602, calibrating the initial mobile data by the terminal to be calibrated by using the fitted nonlinear calibration function obtained from the server in advance, so as to obtain the calibrated mobile data.

The server periodically sends 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 the initial acceleration data are collected, the terminal to be calibrated calibrates the initial acceleration data by adopting the fitted nonlinear calibration function to obtain the calibrated acceleration data. The fitting process of the nonlinear calibration function can be referred to the above embodiments, and is not described herein.

In one embodiment, an unfit 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 unfit nonlinear calibration function to obtain the fitted nonlinear calibration function.

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

It should be understood that although the various steps in the flowcharts of fig. 2-8 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-8 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps or stages.

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

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

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

a function sending module 703, 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 mobile data acquired by the terminal to be calibrated by using the fitted nonlinear calibration function to obtain calibrated mobile 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 measuring terminal in a historical period based on historical mobile data and an unmatched nonlinear calibration function; the movement parameter expression comprises a function constant of the nonlinear calibration function;

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

and the fitting submodule is used for constructing a fitting function based on the moving parameter expression and the target moving parameter, performing 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.

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 a historical period, and the target movement parameter is a target displacement of the measurement terminal in the historical period.

In one embodiment, the movement parameter expression building submodule is specifically configured to map 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; carrying out angle correction on the mapping acceleration data by adopting the initial corner to obtain the mapping acceleration data after the angle correction; the rotation angle is the rotation angle of the measuring terminal relative to the Z axis under the carrier coordinate system; and constructing a displacement expression of the measuring terminal under the initial rotation angle based on the mapping acceleration data after the angle correction and the non-linear calibration function which is not fitted.

In one embodiment, as shown in fig. 10, the historical acceleration data includes historical acceleration values collected at a plurality of sampling times in a historical period; the device also includes:

a period determination module 704, configured to determine a moving period and a stationary period in a historical period based on historical acceleration values collected at multiple sampling moments; each acceleration value included in the moving time period meets the condition that the measuring terminal is in the moving state, and each acceleration value included in the static time period meets the condition that the measuring terminal is in the static state;

an angular velocity data obtaining module 705, configured to obtain first historical angular velocity data collected by the measurement terminal in a motion period and second historical angular velocity data collected in 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;

and a coordinate transformation matrix building module 707, configured to build a coordinate transformation matrix according to the corrected historical angular velocity data.

In one embodiment, the motion parameter expression building 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 measuring terminal under the initial rotation angle based on the mapping acceleration value after the angle correction included in each sub-period and the non-fitted nonlinear calibration function.

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

the norm calculation submodule is used for calculating the variance and the norm of the variance for historical acceleration values respectively corresponding to the three coordinate axes aiming at each sampling moment in the historical time period;

the motion period determination submodule is used for determining that the sampling moment is positioned in the motion period if the norm of the variance is greater than or equal to a preset threshold;

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

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 of the measurement terminal at the initial rotation angle and a target displacement corresponding to each sub-period, so as to obtain a fitting function of the measurement terminal at the initial rotation angle.

In one embodiment, the fitting submodule is specifically configured to separately calculate a partial derivative for each function constant in the fitting function at an initial rotation angle, and 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 measuring terminal under the initial rotation angle, and determining the displacement of the measuring terminal under the initial rotation angle; judging whether the difference value between the displacement of the measuring terminal under the initial rotation angle and the target displacement is smaller than a preset difference value 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:

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

the fitting submodule is specifically configured to solve a partial derivative for each function constant in the fitting function at a new rotation angle until a difference between a new displacement calculated at the new rotation angle and the target displacement is smaller than a 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.

In one embodiment, the apparatus further comprises:

a filtering processing module 709, configured to perform filtering processing on the historical acceleration data; wherein the filtering process comprises a low-pass filtering process and a Kalman filtering process;

correspondingly, the motion parameter expression building submodule is specifically configured to build a motion parameter expression of a historical period based on the filtered historical acceleration data and the non-fitted nonlinear calibration function.

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

a data acquisition module 801, configured to acquire initial movement data of a terminal to be calibrated; wherein the initial movement data comprises 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 from a server in advance, to obtain calibrated movement data;

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

For the specific definition of the calibration means for the movement data, reference may be made to the above definition of the calibration method for the movement data, which is not described herein again. The modules in the calibration device for movement data can be implemented in whole or in part by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the server, and can also be stored in a memory in the server in a software form, so that the processor can call and execute operations corresponding to the modules.

Fig. 12 is a block diagram of an electronic device 1300 shown according to the above-described embodiments. The electronic device 1300 may be the measurement terminal, the terminal to be calibrated, or the like in the above embodiments. Referring to fig. 12, 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 for execution 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 method described above. Further, the processing component 1302 can include one or more modules that facilitate interaction 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 operation 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 non-volatile 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 disks.

The power supply component 1306 provides power to the various components of the electronic device 1300. Power components 1306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for 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 touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect 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. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the electronic device 1300 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

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 further be 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 keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 1314 includes one or more sensors for providing various aspects of state assessment for the electronic device 1300. For example, the sensor assembly 1314 may detect an open/closed state of the electronic device 1300, the relative positioning of components, such as a display and keypad of the electronic device 1300, the sensor assembly 1314 may also detect a change in the position of the electronic device 1300 or a component of the electronic device 1300, the presence or absence of user contact with the electronic device 1300, orientation or acceleration/deceleration of the electronic device 1300, and a change in the temperature of the electronic device 1300. The sensor assembly 1314 may include a proximity sensor configured to detect the presence of a nearby object without 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 gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 1316 is configured to facilitate communications between the electronic device 1300 and other devices in a wired or wireless manner. 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 an 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 communications component 1316 also includes a Near Field Communications (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, micro-controllers, microprocessors, or other electronic components for performing the above-described calibration method of movement data.

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

Fig. 13 is a block diagram of an electronic device shown according to the above-described embodiment. The electronic device may be the server 1400 in the above embodiment. Referring to fig. 13, server 1400 includes a processing component 1420, which further includes one or more processors, and memory resources, represented by memory 1422, for storing instructions or computer programs, e.g., applications, that are executable by processing component 1420. The application programs stored in memory 1422 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1420 is configured to execute instructions to perform the above-described calibration method of movement data.

The server 1400 may also include a power component 1424 configured to perform power management of the device 1400, a wired or wireless network interface 1426 configured to connect the device 1400 to a network, and an input/output (I/O) interface 1428. The server 1400 may operate based on an operating system stored in memory 1422, such as Window 1414 over, Mac O14XTM, UnixTM, Linux, FreeB14DTM, or the like.

In an exemplary embodiment, a storage medium comprising instructions, such as the memory 1422 comprising instructions, executable by the processor of the server 1400 to perform the above-described method is also provided. The storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided by the embodiments of the disclosure may include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express a few implementation modes of the embodiments of the present disclosure, and the description thereof is specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, variations and modifications can be made without departing from the concept of the embodiments of the present disclosure, and these are all within the scope of the embodiments of the present disclosure. Therefore, the protection scope of the patent of the embodiment of the disclosure should be subject to the appended claims.

Claims

1. A method for calibrating movement data, the method comprising:

receiving historical movement data and historical positioning data which are acquired by a measuring terminal in a historical time period; the historical mobile data comprises historical IMU data collected by the measuring terminal;

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 the initial mobile data acquired by the terminal to be calibrated by using the fitted nonlinear calibration function to obtain calibrated mobile data; the initial movement data comprises initial IMU data of the terminal to be calibrated.

2. The method of claim 1, wherein fitting the historical movement data and the historical positioning data to obtain a fitted nonlinear calibration function comprises:

constructing a movement parameter expression of the measuring terminal in the historical time period based on the historical movement data and an unfit nonlinear calibration function; the movement parameter expression comprises a function constant of the nonlinear calibration function;

determining target movement parameters of the measuring terminal in the historical time period according to the historical positioning data; the moving parameter in the moving parameter expression is the same as the parameter type of the target moving parameter;

and constructing a fitting function based on the moving parameter expression and the target moving parameter, performing 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.

3. The method of claim 2, wherein if the historical IMU data comprises 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.

4. The method according to claim 3, wherein the constructing a movement parameter expression of the measuring terminal in the historical period based on the historical movement data and the non-fitted non-linear calibration function comprises:

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

carrying out angle correction on the mapping acceleration data by adopting an initial corner to obtain the mapping acceleration data after the angle correction; the rotation angle is a rotation angle of the measuring terminal relative to a Z axis under the carrier coordinate system;

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

5. The method of claim 4, wherein the historical acceleration data comprises historical acceleration values collected at a plurality of sampling times in the historical period; before the mapping the historical acceleration data from the carrier coordinate system to the geographic coordinate system according to the coordinate transformation matrix acquired in advance, the method further includes:

determining a moving period and a static period in the historical period based on historical acceleration values acquired at the plurality of sampling moments; each acceleration value included in the moving time period meets the condition that the measuring terminal is in a moving state, and each acceleration value included in the static time period meets the condition that the measuring terminal is in a static state;

acquiring first historical angular velocity data acquired by the measuring terminal in the motion 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.

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

dividing the motion period into a plurality of sub-periods 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 mapping acceleration value after the angle correction included in each sub-period and the non-fitted nonlinear calibration function.

7. The method of claim 5, wherein the historical acceleration values comprise historical acceleration values corresponding to three coordinate axes respectively; the determining the moving period and the static period in the historical period based on the historical acceleration values collected at the plurality of sampling moments comprises:

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

if the norm of the variance is greater than or equal to a preset threshold value, 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 located in the static time period.

8. The method of claim 7, wherein 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.

9. The method of claim 8, wherein said regression solving of said fitted function to obtain a target value for said function constant comprises:

respectively solving a partial derivative of each function constant in the fitting function under 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 corresponding displacement expression of the measuring terminal under the initial rotation angle, and determining the displacement of the measuring terminal under the initial rotation angle;

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

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

10. The method according to claim 9, wherein after the 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 measuring terminal under the new corner based on the new corner;

and calculating a partial derivative of 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 the preset difference value, and determining the candidate value of the function constant when the difference value is smaller than the preset difference value as the target value of the function constant.

11. The method of claim 3, wherein prior to said constructing an expression of movement parameters of the measurement terminal over the historical period based on the historical movement data and the 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 constructing a movement parameter expression of the measuring terminal in the historical period based on the historical movement data and the non-fitted non-linear calibration function comprises:

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

12. A method for calibrating movement data, the method comprising:

acquiring initial movement data of a terminal to be calibrated; wherein the initial movement data comprises initial IMU data of the terminal to be calibrated;

13. An apparatus for calibrating movement data, the apparatus comprising:

the data receiving module is used for receiving historical movement data and historical positioning data which are acquired by the measuring terminal in a historical time period; the historical mobile data comprises historical IMU data collected by the measuring 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 the initial mobile data acquired by the terminal to be calibrated by using the fitted nonlinear calibration function to obtain calibrated mobile data; the initial movement data comprises initial IMU data of the terminal to be calibrated.

14. The apparatus of claim 13, wherein the fitting module comprises:

the mobile parameter expression constructing submodule is used for constructing a mobile parameter expression of the measuring terminal in the historical time period based on the historical mobile data and the non-fitted nonlinear calibration function; the movement parameter expression comprises a function constant of the nonlinear calibration function;

the target moving parameter determining submodule is used for determining the target moving parameters of the measuring terminal in the historical time period according to the historical positioning data; the moving parameter in the moving parameter expression is the same as the parameter type of the target moving parameter;

and the fitting submodule is used for constructing a fitting function based on the movement parameter expression and the target movement parameter, performing 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.

15. The apparatus of claim 14, wherein if the historical IMU data comprises historical acceleration data, the motion parameter expression is a displacement expression of the measurement terminal during the historical period, and the target motion parameter is a target displacement of the measurement terminal during the historical period.

16. The device according to claim 15, wherein the movement parameter expression building 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 obtained in advance, so as to obtain mapped acceleration data; carrying out angle correction on the mapping acceleration data by adopting an initial corner to obtain the mapping acceleration data after the angle correction; the rotation angle is a rotation angle of the measuring terminal relative to a Z axis under the carrier coordinate system; and constructing a displacement expression of the measuring terminal under the initial rotation angle based on the mapping acceleration data after the angle correction and the non-fitted nonlinear calibration function.

17. The apparatus of claim 16, wherein the historical acceleration data comprises historical acceleration values collected at a plurality of sampling times in the historical period; the device 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 historical acceleration values acquired at the plurality of sampling moments; each acceleration value included in the moving time period meets the condition that the measuring terminal is in a moving state, and each acceleration value included in the static time period meets the condition that the measuring terminal is in a static state;

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

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

and the coordinate conversion matrix building module is used for building the coordinate conversion matrix according to the corrected historical angular velocity data.

18. The apparatus according to claim 17, wherein the movement parameter expression building submodule is configured to divide the movement time interval into a plurality of sub-time intervals 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 mapping acceleration value after the angle correction included in each sub-period and the non-fitted nonlinear calibration function.

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

the norm calculation submodule is used for calculating a variance and a norm of the variance for historical acceleration values respectively corresponding to the three coordinate axes aiming at each sampling moment in the historical time period;

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

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

20. The apparatus according to claim 19, wherein the fitting module is specifically configured to calculate a mean square error according to a displacement expression corresponding to each sub-period of the measurement terminal at the initial rotation angle and a target displacement corresponding to each sub-period, so as to obtain a fitting function of the measurement terminal at the initial rotation angle.

21. The apparatus according to claim 20, wherein the fitting sub-module is specifically configured to separately calculate a partial derivative for each function constant in the fitting function at the initial rotation angle, and 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 corresponding displacement expression of the measuring terminal under the initial rotation angle, and determining the displacement of the measuring terminal under the initial rotation angle; judging whether the difference value between the displacement of the measuring terminal under the initial rotation angle and the target displacement is smaller than a preset difference value or not; and if so, determining the candidate value of the function constant under the initial rotation angle as the target value of the function constant.

22. The apparatus of claim 21, further comprising:

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

the fitting submodule is specifically configured to calculate a partial derivative for each function constant in the fitting function at the new rotation angle until a difference between the new displacement calculated at 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 the target value of the function constant.

23. The apparatus of claim 15, further comprising:

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

correspondingly, the movement parameter expression construction submodule is specifically configured to construct the movement parameter expression of the history period based on the filtered historical acceleration data and the non-fitted nonlinear calibration function.

24. An apparatus for calibrating movement data, the apparatus comprising:

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

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

25. 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 steps of the method of any of claims 1 to 11 are implemented when the processor executes the computer program or the steps of the method of claim 12 are implemented when the processor executes the computer program.

26. A storage medium having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, realizes the steps of the method of any one of claims 1 to 11; alternatively, the computer program realizes the steps of the method of claim 12 when executed by a processor.