Real-time calibration method and system for inertial measurement unit, and terminal

Abstract

The invention provides a real-time calibration method and system for an inertial measurement unit, and a terminal, and the method comprises the steps: carrying out the first correction of an acceleration measurement value and an angular velocity measurement value which are output by a to-be-calibrated inertial measurement unit and are obtained in real time based on a temperature error compensation data table, and carrying out the second correction through employing a multi-sensor fusion filtering algorithm; on one hand, a temperature variable is introduced to carry out a multi-temperature zero offset test and a multi-temperature multi-axis rotation excitation test, a temperature error compensation data table is established, and the influence of temperature on the performance of the to-be-calibrated inertial measurement unit is fully considered; on the other hand, a multi-sensor fusion filtering algorithm is adopted, interference of vibration noise can be suppressed, the attitude calculation precision of the to-be-calibrated inertial measurement unit is improved, and therefore the calibration precision of the to-be-calibrated inertial measurement unit is guaranteed; the technical problems that an existing inertial measurement unit calibration method is low in calibration precision and poor in real-time performance are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural machinery navigation, and particularly relates to an inertial measurement unit real-time calibration method, system and terminal. BACKGROUND

[0002] An IMU (Inertial Measurement Unit) is an electronic device that measures the motion state of an object in three-dimensional space through inertial sensors. It usually includes sensors such as accelerometers and gyroscopes. The accelerometer is used to measure linear acceleration and detect inertial force based on Newton's second law, which can be used for attitude calculation and motion tracking. The gyroscope is used to measure angular velocity and can be used for rotation state monitoring through Coriolis force or optical effect.

[0003] The existing agricultural machinery navigation system usually uses high-precision inertial measurement units, which are costly. However, low-cost inertial measurement units have high sensor noise and zero bias errors, and temperature changes will further cause sensor zero bias and sensitivity drift, thus causing the inertial measurement unit pose calculation to deviate seriously from the true value, which limits its application in agricultural machinery navigation systems.

[0004] The existing calibration methods for inertial measurement units include static calibration methods such as six-position method and two-position method, and dynamic calibration methods such as turntable rate method and regular hexahedron tool method, but the calibration accuracy is generally low and the errors cannot be corrected in real time. SUMMARY

[0005] In view of the above-mentioned shortcomings of the prior art, the present application aims to provide an inertial measurement unit real-time calibration method, system and terminal, which can solve the technical problems of low calibration accuracy and poor real-time performance of the existing inertial measurement unit calibration methods.

[0006] To achieve the above object and other related objects, the first aspect of the present application provides a real-time calibration method for an inertial measurement unit, which is used for calibrating an inertial measurement unit arranged in a navigation system of an agricultural machine in real time, comprising: collecting a working temperature of a to-be-calibrated inertial measurement unit in real time, and obtaining an acceleration measurement value and an angular velocity measurement value actually output by the to-be-calibrated inertial measurement unit; based on a pre-constructed temperature error compensation data table, performing a first correction on the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature to obtain an initial acceleration correction value and an initial angular velocity correction value; and according to the initial acceleration correction value and the initial angular velocity correction value, performing a second correction on the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature by using a multi-sensor fusion filtering algorithm to obtain a target acceleration correction value and a target angular velocity correction value.

[0007] In some embodiments of the first aspect of the present application, the temperature error compensation data table is constructed in the following manner: the to-be-calibrated inertial measurement unit is fixed on a turntable, and the turntable is kept stationary; a multi-temperature zero offset test is performed on the to-be-calibrated inertial measurement unit to collect a plurality of sets of acceleration measurement data at different preset temperatures, and a least square method is used to calculate acceleration error parameters at different preset temperatures; the turntable is controlled to rotate at a preset angular velocity parameter, a multi-temperature multi-axis rotation excitation test is performed on the to-be-calibrated inertial measurement unit to collect a plurality of sets of angular velocity measurement data at different preset temperatures, and a least square method is used to calculate angular velocity error parameters at different preset temperatures; and according to the acceleration error parameters and the angular velocity error parameters at different preset temperatures, a temperature error compensation data table of the to-be-calibrated inertial measurement unit is constructed.

[0008] In some embodiments of the first aspect of the present application, the acceleration error parameters at a specified preset temperature are calculated by using a least square method according to a plurality of sets of acceleration measurement data collected at the specified preset temperature in the following manner: an acceleration error model of the to-be-calibrated inertial measurement unit is constructed; the plurality of sets of acceleration measurement data at the specified preset temperature are input into the acceleration error model to obtain a set of acceleration error equations of the to-be-calibrated inertial measurement unit; an acceleration loss function of the to-be-calibrated inertial measurement unit at the preset temperature is defined, and the optimal acceleration error parameters of the to-be-calibrated inertial measurement unit at the preset temperature are solved by minimizing the acceleration loss values of the plurality of sets of acceleration measurement data.

[0009] In some embodiments of the first aspect of the application, the way of calculating the angular velocity error parameters of the IMU at the specified preset temperature according to the plurality of sets of angular velocity measurement data collected at the specified preset temperature comprises: constructing an angular velocity error model of the IMU to be calibrated; inputting the plurality of sets of angular velocity measurement data at the specified preset temperature into the angular velocity error model to obtain a set of angular velocity error equations of the IMU to be calibrated; defining an angular velocity loss function of the IMU to be calibrated at the preset temperature, and solving the optimal acceleration error parameters of the IMU to be calibrated at the preset temperature by minimizing the angular velocity loss value of the plurality of sets of angular velocity measurement data.

[0010] In some embodiments of the first aspect of the application, the way of performing the first correction on the acceleration measurement value and the angular velocity measurement value of the IMU to be calibrated at the working temperature based on the pre-constructed temperature error compensation data table to obtain the corresponding acceleration initial correction value and the angular velocity initial correction value comprises: calculating the acceleration error parameters and the angular velocity error parameters of the IMU to be calibrated at the working temperature by using an interpolation method based on the temperature error compensation data table; constructing an acceleration error model of the IMU to be calibrated, and inputting the acceleration error parameters and the acceleration measurement value into the acceleration error model to calculate and obtain the corresponding acceleration initial correction value; constructing an angular velocity error model of the IMU to be calibrated, and inputting the angular velocity error parameters and the angular velocity measurement value into the angular velocity error model to calculate and obtain the corresponding angular velocity initial correction value.

[0011] In some embodiments of the first aspect of the application, the way of correcting the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature by using the multi-sensor fusion filtering algorithm according to the acceleration initial correction value and the angular velocity initial correction value comprises: constructing a state equation of the to-be-calibrated inertial measurement unit, inputting the acceleration initial correction value and the angular velocity initial correction value obtained by the first correction into the state equation, and calculating to obtain a position initial prediction value and a velocity initial prediction value of the to-be-calibrated inertial measurement unit; obtaining a position actual observation value and a velocity actual observation value collected by a GPS device arranged in the agricultural machinery navigation system, and calculating the residual of the position initial prediction value and the position actual observation value and the residual of the velocity initial prediction value and the velocity actual observation value respectively; iteratively updating the state equation based on the residual minimization principle to obtain the optimal estimated acceleration error parameter and angular velocity error parameter of the to-be-calibrated inertial measurement unit at the working temperature; constructing an acceleration error model of the to-be-calibrated inertial measurement unit, inputting the acceleration error parameter and the acceleration initial correction value into the acceleration error model, and calculating to obtain the corresponding acceleration target correction value; constructing an angular velocity error model of the to-be-calibrated inertial measurement unit, inputting the angular velocity error parameter and the angular velocity initial correction value into the angular velocity error model, and calculating to obtain the corresponding angular velocity target correction value.

[0012] In some embodiments of the first aspect of the application, the way of iteratively updating the state equation comprises: constructing an error covariance model, and predicting the error covariance matrix of the position prediction value and the velocity prediction value calculated in the current iteration according to the error covariance model; calculating the Kalman gain of the state equation in the current iteration respectively, and updating the state equation according to the Kalman gain; correcting the position prediction value and the velocity prediction value according to the updated state equation until the residual of the corrected position prediction value and the position actual observation value and the residual of the corrected velocity prediction value and the velocity actual observation value are minimized; obtaining the acceleration error parameter and the angular velocity error parameter estimated in the current iteration according to the updated state equation.

[0013] In some embodiments of the first aspect of the application, the acceleration error parameter comprises an acceleration scale factor, an acceleration cross-axis non-orthogonal error parameter, and an acceleration bias error parameter; and the angular velocity error parameter comprises an angular velocity scale factor, an angular velocity cross-axis non-orthogonal error parameter, and an angular velocity bias error parameter.

[0014] To achieve the above object and other related objects, the second aspect of the present application provides an inertial measurement unit real-time calibration system for calibrating an inertial measurement unit arranged in an agricultural machinery navigation system in real time, comprising: a data acquisition module, configured to acquire a working temperature of a to-be-calibrated inertial measurement unit in real time, and acquire an acceleration measurement value and an angular velocity measurement value actually output by the to-be-calibrated inertial measurement unit; a first data correction module connected to the data acquisition module, configured to correct the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature for the first time based on a pre-constructed temperature error compensation data table, and obtain corresponding acceleration initial correction values and angular velocity initial correction values; and a second data correction module connected to the first data correction module, configured to correct the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature for the second time according to the acceleration initial correction values and the angular velocity initial correction values by using a multi-sensor fusion filtering algorithm, and obtain corresponding acceleration target correction values and angular velocity target correction values.

[0015] To achieve the above object and other related objects, the third aspect of the present application provides an inertial measurement unit real-time calibration terminal, comprising: a processor and a memory; the memory is configured to store a computer program; and the processor is configured to execute the computer program stored in the memory, so that the terminal executes the inertial measurement unit real-time calibration method according to any one of the above embodiments.

[0016] As described above, the present application provides an inertial measurement unit real-time calibration method, system and terminal, which corrects the acceleration measurement value and the angular velocity measurement value actually output by the to-be-calibrated inertial measurement unit for the first time based on a temperature error compensation data table, and corrects the acceleration measurement value and the angular velocity measurement value for the second time by using a multi-sensor fusion filtering algorithm; the present application has the following beneficial effects: on the one hand, the temperature variable is introduced to perform multi-temperature zero offset test and multi-temperature multi-axis rotation excitation test, a temperature error compensation data table is established, and the influence of temperature on the performance of the to-be-calibrated inertial measurement unit is fully considered; on the other hand, the multi-sensor fusion filtering algorithm is used, which can suppress the interference of vibration noise and improve the attitude solution accuracy of the to-be-calibrated inertial measurement unit, so as to ensure the calibration accuracy of the to-be-calibrated inertial measurement unit and solve the technical problems of low calibration accuracy and poor real-time performance existing in the existing inertial measurement unit calibration method. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of an inertial measurement unit real-time calibration method according to an embodiment of the present application is shown.

[0018] Figure 2 A flowchart showing a method for constructing a temperature error compensation data table according to an embodiment of the present application

[0019] Figure 3 A flowchart showing a method for correcting a multi-sensor fusion filtering algorithm according to an embodiment of the present application.

[0020] Figure 4 A structural diagram of an inertial measurement unit real-time calibration system according to an embodiment of the present application.

[0021] Figure 5 A structural diagram of an inertial measurement unit real-time calibration terminal according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application will be described in detail with specific reference to particular examples. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in the specification. The present application can also be implemented or applied in other different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0023] With the continuous development of information technology, agricultural machinery intelligentization is the core direction of modern agricultural development, and the agricultural machinery navigation system is one of the core technologies of modern agricultural intelligentization and precision, which can realize autonomous path planning, precision operation and unmanned operation of agricultural machinery by integrating satellite positioning, sensors, artificial intelligence and other technologies. The agricultural machinery navigation system is usually provided with a GPS device, an inertial measurement unit, a visual sensor, a laser radar, an ultrasonic sensor, etc., for precise positioning and path planning. The inertial measurement unit includes an accelerometer for measuring linear acceleration and a gyroscope for measuring angular velocity and other sensors.

[0024] Based on the positioning accuracy requirement of the agricultural machinery navigation system, if a high-precision inertial measurement unit is selected, the cost is high; if a low-cost inertial measurement unit is selected, the output acceleration value and angular velocity value thereof need to be calibrated to improve the positioning accuracy and meet the application requirement. However, the existing calibration methods for the inertial measurement unit, including static calibration methods such as six-position method and two-position method, and dynamic calibration methods such as turntable rate method and regular hexahedron tooling method, generally have the technical problems of low calibration accuracy and inability to correct errors in real time.

[0025] To solve the above problems, the application provides an inertial measurement unit real-time calibration method, system and terminal, which aims to correct the acceleration measurement value and angular velocity measurement value output by the real-time obtained to-be-calibrated inertial measurement unit twice, including first correction based on temperature error compensation data table and second correction by using a multi-sensor fusion filtering algorithm, so as to solve the technical problems of low calibration accuracy and poor real-time performance existing in the existing inertial measurement unit calibration method.

[0026] Before the application is further described in detail, the terms and terms involved in the embodiments of the application are explained, and the terms and terms involved in the embodiments of the application are applicable to the following explanations:

[0027] <1>IMU (Inertial Measurement Unit): inertial measurement unit, an electronic device for measuring the motion state of an object in three-dimensional space through inertial sensors.

[0028] <2>GPS (Global Positioning System): Global Positioning System, a satellite global radio navigation positioning system developed by the United States, used for high-precision positioning by receiving signals from at least four satellites using the triangulation principle.

[0029] <3>NED (North-East-Down): North-East-Down coordinate system, a local geographic coordinate system, X axis points to the geographic north direction, Y axis points to the geographic east direction, and Z axis points to the center of the earth.

[0030] In order to make the application purpose, technical scheme and advantages of the application more clear and understandable, the technical scheme of the embodiments of the application is further described in detail by the following embodiments and in conjunction with the drawings. It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application.

[0031] As shown in Figure 1 , a flowchart of an inertial measurement unit real-time calibration method in the embodiments of the application is shown. The inertial measurement unit real-time calibration method in the embodiments is used to calibrate the inertial measurement unit arranged in the agricultural machinery navigation system in real time, including steps S1-S3.

[0032] Step S1: Real-time acquisition of the working temperature of the to-be-calibrated inertial measurement unit, and acquisition of the actual output acceleration measurement value and angular velocity measurement value of the to-be-calibrated inertial measurement unit.

[0033] In an embodiment, a temperature sensor can be arranged on the to-be-calibrated inertial measurement unit to real-time collect the working temperature of the to-be-calibrated inertial measurement unit during operation.

[0034] The accelerometer in the to-be-calibrated inertial measurement unit is configured to measure and output an acceleration measurement value, and the gyroscope is configured to measure and output an angular velocity measurement value.

[0035] Step S2: based on the pre-constructed temperature error compensation data table, performing a first correction on the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature, to obtain a corresponding acceleration initial correction value and an angular velocity initial correction value.

[0036] Specifically, the step S2 includes: constructing a temperature error compensation data table; and based on the temperature error compensation data table, performing a first correction on the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature, to obtain a corresponding acceleration initial correction value and an angular velocity initial correction value.

[0037] In an embodiment, as shown in the figure, the temperature error compensation data table is constructed in steps S21-S23. Figure 2

[0038] Step S21: fixing the to-be-calibrated inertial measurement unit on a turntable, keeping the turntable stationary, and performing a multi-temperature zero bias test on the to-be-calibrated inertial measurement unit to collect multiple sets of acceleration measurement data at different preset temperatures, and using a least squares method to calculate acceleration error parameters at different preset temperatures.

[0039] It should be understood that the inertial measurement unit measures the linear acceleration and angular velocity of an object through sensitive axes. The sensitive axes of the inertial measurement unit usually consist of three orthogonal axes (X, Y, Z), which correspond to the linear acceleration and angular velocity measurements in a three-dimensional space, respectively. Among them, the acceleration measurement value output by the inertial measurement unit (specifically the accelerometer) includes the acceleration component in the X-axis direction (forward and backward direction), the acceleration component in the Y-axis direction (left and right direction), and the acceleration component in the Z-axis direction (vertical direction).

[0040] In this embodiment, the multiple sets of acceleration measurement data collected through the multi-temperature zero bias test include: the actual output of the to-be-calibrated inertial measurement unit X-axis direction (forward and backward direction) acceleration component, Y-axis direction (left and right direction) acceleration component and Z-axis direction (vertical direction) acceleration component.

[0041] When performing a multi-temperature zero bias test on the to-be-calibrated inertial measurement unit, the to-be-calibrated inertial measurement unit is set to be at different preset temperatures, and multiple sets of acceleration measurement data output by the to-be-calibrated inertial measurement unit at each preset temperature are collected. Preferably, the preset temperatures include: 25℃, 40℃ and 55℃. It should be noted that the preset temperature is not limited in the present application, and the user can set it according to the needs.​

[0042] In an embodiment, six-position method is adopted to collect multiple sets of acceleration measurement data output by the to-be-calibrated inertial measurement unit at each preset temperature. Specifically, ① the X axis of the to-be-calibrated inertial measurement unit is aligned with the rotation axis of the turntable, and is stationary for a period of time, and the original output of the accelerometer is collected six times, and the average value is taken as the first set of acceleration measurement data a'1. At this time, the ideal acceleration three-axis true value of the to-be-calibrated inertial measurement unit should be ② the X axis of the to-be-calibrated inertial measurement unit is aligned with the rotation axis of the turntable, and is stationary for a period of time, and the original output of the accelerometer is collected six times, and the average value is taken as the second set of acceleration measurement data a'2. At this time, the ideal acceleration three-axis true value of the to-be-calibrated inertial measurement unit should be ③ the Y axis of the to-be-calibrated inertial measurement unit is aligned with the rotation axis of the turntable, and is stationary for a period of time, and the original output of the accelerometer is collected six times, and the average value is taken as the third set of acceleration measurement data a'3. At this time, the ideal acceleration three-axis true value of the to-be-calibrated inertial measurement unit should be ④ the Y axis of the to-be-calibrated inertial measurement unit is aligned with the rotation axis of the turntable, and is stationary for a period of time, and the original output of the accelerometer is collected six times, and the average value is taken as the fourth set of acceleration measurement data a'4. At this time, the ideal acceleration three-axis true value of the to-be-calibrated inertial measurement unit should be ⑤ the Z axis of the to-be-calibrated inertial measurement unit is aligned with the rotation axis of the turntable, and is stationary for a period of time, and the original output of the accelerometer is collected six times, and the average value is taken as the fifth set of acceleration measurement data a'5. At this time, the ideal acceleration three-axis true value of the to-be-calibrated inertial measurement unit should be ⑥ the Z axis of the to-be-calibrated inertial measurement unit is aligned with the rotation axis of the turntable, and is stationary for a period of time, and the original output of the accelerometer is collected six times, and the average value is taken as the sixth set of acceleration measurement data a'6. At this time, the ideal acceleration three-axis true value of the to-be-calibrated inertial measurement unit should be It should be noted that g is the acceleration of gravity, which is usually taken as 9.81 m / s 2 .

[0043] According to the collected multiple sets of acceleration measurement data at the specified preset temperature, the acceleration error parameter at the preset temperature is calculated by the least square method, which includes the following steps.

[0044] ① An acceleration error model of the to-be-calibrated inertial measurement unit is constructed.

[0045] The accelerometer of the inertial measurement unit is usually affected by acceleration scale factor, acceleration cross-axis misalignment error parameters and acceleration bias error parameters when measuring linear acceleration of an object, and the output acceleration measurement value satisfies the inertial measurement unit acceleration error model with the ideal acceleration true value:

[0046]

[0047] The inertial measurement unit acceleration error model can be simplified as:

[0048]

[0049] wherein A is the ideal acceleration three-axis true value of the inertial measurement unit, Ax, Ay and Az are the ideal acceleration X-axis true value, acceleration Y-axis true value and acceleration Z-axis true value of the inertial measurement unit respectively; a is the actual output acceleration three-axis measurement value of the inertial measurement unit, ax, ay and az are the actual output acceleration X-axis measurement value, acceleration Y-axis measurement value and acceleration Z-axis measurement value of the inertial measurement unit respectively; M is the acceleration error parameter to be estimated of the inertial measurement unit, wherein Kax, Kay and Kaz are the acceleration X-axis scale factor, acceleration Y-axis scale factor and acceleration Z-axis scale factor to be estimated of the inertial measurement unit respectively, Saxy, Saya, Sayx, Saya, Sazx and Sazy are a plurality of acceleration cross-axis misalignment error coefficients to be estimated of the inertial measurement unit, and Bax, Bay and Baz are the acceleration X-axis bias error parameter, acceleration Y-axis bias error parameter and acceleration Z-axis bias error parameter to be estimated of the inertial measurement unit respectively.

[0050] In the embodiment, based on the above inertial measurement unit acceleration error model, the acceleration error model of the inertial measurement unit to be calibrated is constructed, so as to estimate the corresponding acceleration error parameters according to a plurality of groups of acceleration measurement data at a specified preset temperature. The acceleration error model is:

[0051]

[0052] wherein A' is the ideal acceleration three-axis true value of the inertial measurement unit to be calibrated at a specified preset temperature; a' is the actual output acceleration three-axis measurement value of the inertial measurement unit to be calibrated at the preset temperature; M' is the acceleration error parameter to be estimated of the inertial measurement unit to be calibrated at the preset temperature, and the acceleration error parameter includes acceleration scale factor, acceleration cross-axis misalignment error parameters and acceleration bias error parameters.

[0053] ② inputting multiple sets of acceleration measurement data at a specified preset temperature into the acceleration error model to obtain an acceleration error equation of the to-be-calibrated inertial measurement unit.

[0054] Specifically, the acceleration error equation is:

[0055]

[0056] wherein a' is a set of actual output acceleration measurement data of the to-be-calibrated inertial measurement unit at a specified preset temperature, such as the first set of acceleration measurement data a'1, the second set of acceleration measurement data a'2, the third set of acceleration measurement data a'3, the fourth set of acceleration measurement data a'4, the fifth set of acceleration measurement data a'5 and the sixth set of acceleration measurement data a'6; M' is an acceleration error parameter to be estimated of the to-be-calibrated inertial measurement unit at the preset temperature; is a predicted value of each set of acceleration measurement data based on the acceleration error coefficient estimation; A ′ i is an ideal acceleration three-axis true value of each set of acceleration measurement data, such as the acceleration three-axis true value corresponding to the first set of acceleration measurement data a'1 the acceleration three-axis true value corresponding to the second set of acceleration measurement data a'2 the acceleration three-axis true value corresponding to the third set of acceleration measurement data a'3 the acceleration three-axis true value corresponding to the fourth set of acceleration measurement data a'4 the acceleration three-axis true value corresponding to the fifth set of acceleration measurement data a'5 and the acceleration three-axis true value corresponding to the sixth set of acceleration measurement data a'6 ε i is a residual error between the predicted value and the ideal acceleration three-axis true value of each set of acceleration measurement data.

[0057] ③ defining an acceleration loss function of the to-be-calibrated inertial measurement unit at the preset temperature, and solving the optimal acceleration error parameter of the to-be-calibrated inertial measurement unit at the preset temperature by minimizing the acceleration loss value of multiple sets of acceleration measurement data.

[0058] In a specific embodiment, the acceleration loss function is defined as:

[0059]

[0060] wherein S is the acceleration loss value of multiple sets of acceleration measurement data; ε iResiduals between the estimated prediction values of the acceleration measurement data of each group and ideal acceleration three-axis true values; n is the number of groups of acceleration measurement data, and in the above embodiment, six-position method is used to collect multiple groups of acceleration measurement data, and n = 6.

[0061] In this embodiment, the acceleration error parameters at each preset temperature are calculated by using the least square method, which is efficient, can quickly solve the optimal solution of the model, and can adapt to various types of models, and can quickly estimate the acceleration error parameters based on the fitting model.

[0062] Step S22: control the turntable to rotate at a preset angular velocity parameter, perform a multi-temperature multi-axis rotation excitation test on the to-be-calibrated inertial measurement unit, collect multiple groups of angular velocity measurement data at different preset temperatures, and calculate angular velocity error parameters at different preset temperatures by using the least square method.

[0063] It should be understood that the inertial measurement unit measures the linear acceleration and angular velocity of an object through sensitive axes. The sensitive axes of the inertial measurement unit usually consist of three orthogonal axes (X, Y, Z), which correspond to the linear acceleration and angular velocity measurements in three-dimensional space, respectively. Among them, the angular velocity measurement value output by the inertial measurement unit (specifically, the gyroscope) includes the angular velocity component in the X-axis direction (such as roll motion), the angular velocity component in the Y-axis direction (such as pitch motion), and the angular velocity component in the Z-axis direction (such as yaw motion).

[0064] In this embodiment, the multiple groups of angular velocity measurement data collected through the multi-temperature multi-axis rotation excitation test include the angular velocity component in the X-axis direction (such as roll motion), the angular velocity component in the Y-axis direction (such as pitch motion), and the angular velocity component in the Z-axis direction (such as yaw motion) actually output by the to-be-calibrated inertial measurement unit.

[0065] When the multi-temperature multi-axis rotation excitation test is performed on the to-be-calibrated inertial measurement unit, the to-be-calibrated inertial measurement unit is set to be at different preset temperatures, and multiple groups of angular velocity measurement data output by the to-be-calibrated inertial measurement unit at each preset temperature are collected. Preferably, the preset temperatures include 25℃, 40℃ and 55℃. It should be noted that the preset temperatures are not limited in the present application, and can be set according to the needs of the user.

[0066] In an embodiment, the six-position method is used to collect multiple groups of angular velocity measurement data output by the to-be-calibrated inertial measurement unit at each preset temperature. Specifically, ① the X-axis of the to-be-calibrated inertial measurement unit is aligned with the rotation axis of the turntable and faces upwards, the turntable is controlled to rotate at a preset angular velocity parameter ω0, the original output of the gyroscope is collected six times, and the average value is taken as the first group of angular velocity measurement data ω′1. At this time, the ideal angular velocity three-axis true value of the to-be-calibrated inertial measurement unit should be ii. aligning the X axis of the to-be-calibrated inertial measurement unit with the rotation axis of the turntable and pointing downward, controlling the turntable to rotate at a preset angular velocity parameter ω0, collecting the original output of the gyroscope six times, taking the average as a second set of angular velocity measurement data ω'2, at this time, the ideal angular velocity three-axis true value of the to-be-calibrated inertial measurement unit should be iii. aligning the Y axis of the to-be-calibrated inertial measurement unit with the rotation axis of the turntable and pointing upward, controlling the turntable to rotate at a preset angular velocity parameter ω0, collecting the original output of the gyroscope six times, taking the average as a third set of angular velocity measurement data ω'3, at this time, the ideal angular velocity three-axis true value of the to-be-calibrated inertial measurement unit should be iv. aligning the Y axis of the to-be-calibrated inertial measurement unit with the rotation axis of the turntable and pointing downward, controlling the turntable to rotate at a preset angular velocity parameter ω0, collecting the original output of the gyroscope six times, taking the average as a fourth set of angular velocity measurement data ω'4, at this time, the ideal angular velocity three-axis true value of the to-be-calibrated inertial measurement unit should be v. aligning the Z axis of the to-be-calibrated inertial measurement unit with the rotation axis of the turntable and pointing upward, controlling the turntable to rotate at a preset angular velocity parameter ω0, collecting the original output of the gyroscope six times, taking the average as a fifth set of angular velocity measurement data ω'5, at this time, the ideal angular velocity three-axis true value of the to-be-calibrated inertial measurement unit should be vi. aligning the Z axis of the to-be-calibrated inertial measurement unit with the rotation axis of the turntable and pointing downward, controlling the turntable to rotate at a preset angular velocity parameter ω0, collecting the original output of the gyroscope six times, taking the average as a sixth set of angular velocity measurement data ω'6, at this time, the ideal angular velocity three-axis true value of the to-be-calibrated inertial measurement unit should be

[0067] Preferably, ω0 can be 50 deg / s.

[0068] According to the collected multiple sets of angular velocity measurement data at a specified preset temperature, the angular velocity error parameter at the specified preset temperature is calculated by the least square method, which includes the following steps.

[0069] i. constructing an angular velocity error model of the to-be-calibrated inertial measurement unit.

[0070] The gyroscope of the inertial measurement unit is usually affected by the angular velocity scale factor, the angular velocity cross-axis non-orthogonal error parameter, and the angular velocity zero offset error parameter when measuring the angular velocity of an object. The output angular velocity measurement value and the ideal angular velocity true value satisfy the inertial measurement unit angular velocity error model:

[0071]

[0072] The inertial measurement unit angular velocity error model can be simplified as:

[0073]

[0074] wherein, W is the ideal three-axis true value of the angular velocity of the inertial measurement unit, Wx, Wy and Wz are the true value of the X-axis angular velocity, the true value of the Y-axis angular velocity and the true value of the Z-axis angular velocity of the inertial measurement unit, respectively; ω is the three-axis measured value of the actual output angular velocity of the inertial measurement unit, ωx, ωy and ωz are the measured value of the X-axis angular velocity, the measured value of the Y-axis angular velocity and the measured value of the Z-axis angular velocity of the actual output angular velocity of the inertial measurement unit, respectively; N is the angular velocity error parameter to be estimated of the inertial measurement unit, wherein, Kgx, Kgy and Kgz are the X-axis scale factor, the Y-axis scale factor and the Z-axis scale factor of the angular velocity to be estimated of the inertial measurement unit, respectively; Saxy, Sgyz, Sgyx, Sgyz, Sgyx and Sgzy are a plurality of angular velocity cross-axis non-orthogonal error coefficients to be estimated of the inertial measurement unit; Bgx, Bgy and Bgz are the X-axis bias error parameter, the Y-axis bias error parameter and the Z-axis bias error parameter to be estimated of the inertial measurement unit, respectively.

[0075] In the embodiment, based on the above-mentioned angular velocity error model of the inertial measurement unit, an angular velocity error model of the inertial measurement unit to be calibrated is constructed, so as to estimate the corresponding angular velocity error parameter according to a plurality of groups of angular velocity measurement data at a specified preset temperature. The angular velocity error model is:

[0076]

[0077] wherein, W' is the ideal three-axis true value of the angular velocity of the inertial measurement unit to be calibrated at a specified preset temperature; ω' is the three-axis measured value of the actual output angular velocity of the inertial measurement unit to be calibrated at the preset temperature; N' is the angular velocity error parameter to be estimated of the inertial measurement unit to be calibrated at the preset temperature, and the angular velocity error parameter includes: angular velocity scale factor, angular velocity cross-axis non-orthogonal error parameter and angular velocity bias error parameter.

[0078] ②The plurality of groups of angular velocity measurement data are brought into the angular velocity error model to obtain a group of angular velocity error equations of the inertial measurement unit to be calibrated.

[0079] Specifically, the angular velocity error equation is:

[0080]

[0081] wherein ω'i is a set of actual output angular velocity measurement data of the to-be-calibrated inertial measurement unit at a specified preset temperature, such as the first set of angular velocity measurement data ω'1, the second set of angular velocity measurement data ω'2, the third set of angular velocity measurement data ω'3, the fourth set of angular velocity measurement data ω'4, the fifth set of angular velocity measurement data ω'5, and the sixth set of angular velocity measurement data ω'6; and N' is an angular velocity error parameter to be estimated of the to-be-calibrated inertial measurement unit at the preset temperature. is a predicted value estimated based on the angular velocity error coefficient for each set of angular velocity measurement data; and W' i is an ideal angular velocity three-axis true value of each set of angular velocity measurement data, such as the angular velocity three-axis true value corresponding to the first set of angular velocity measurement data ω'1 the angular velocity three-axis true value corresponding to the second set of angular velocity measurement data ω'2 the angular velocity three-axis true value corresponding to the third set of angular velocity measurement data ω'3 the angular velocity three-axis true value corresponding to the fourth set of angular velocity measurement data ω the angular velocity three-axis true value corresponding to the fifth set of angular velocity measurement data ω'5 and the angular velocity three-axis true value corresponding to the sixth set of angular velocity measurement data ω'6 ε' is a residual error between the predicted value estimated for each set of angular velocity measurement data and the ideal angular velocity three-axis true value.

[0082] ③ defining an angular velocity loss function of the to-be-calibrated inertial measurement unit at the preset temperature, and solving the optimal acceleration error parameter of the to-be-calibrated inertial measurement unit at the preset temperature by minimizing the angular velocity loss value of multiple sets of angular velocity measurement data.

[0083] In a specific embodiment, the angular velocity loss function is defined as:

[0084]

[0085] wherein S' is the angular velocity loss value of multiple sets of angular velocity measurement data; and ε' i is a residual error between the predicted value estimated for each set of angular velocity measurement data and the ideal angular velocity three-axis true value; and n is the number of sets of angular velocity measurement data, and in the above embodiment, six-position method is used to collect multiple sets of angular velocity measurement data, and n = 6.

[0086] In this embodiment, the least square method is used to calculate the angular velocity error parameter at each preset temperature, which is efficient in calculation, can quickly solve the optimal solution of the model, and can adapt to various types of models, and can quickly estimate the angular velocity error parameter based on the fitting model.

[0087] Step S23: constructing a temperature error compensation data table of the to-be-calibrated inertial measurement unit according to the acceleration error parameters and the angular velocity error parameters at different preset temperatures.

[0088] It should be noted that the temperature error compensation data table includes acceleration error parameters and angular velocity error parameters at different preset temperatures. The acceleration error parameters at least include an acceleration scale factor, an acceleration cross-axis non-orthogonal error parameter, and an acceleration bias error parameter; and the angular velocity error parameters at least include an angular velocity scale factor, an angular velocity cross-axis non-orthogonal error parameter, and an angular velocity bias error parameter.

[0089] Preferably, the temperature error compensation data table can be stored in a key-value pair data structure, taking the preset temperature as the unique key and the acceleration error parameters and the angular velocity error parameters as the retrieval values. The key-value pair data structure can quickly and efficiently retrieve the acceleration error parameters and the angular velocity error parameters at the working temperature, so as to perform the first correction on the acceleration measurement value and the angular velocity measurement value actually output by the to-be-calibrated inertial measurement unit.

[0090] Based on the temperature error compensation data table, the first correction is performed on the acceleration measurement value and the angular velocity measurement value actually output by the to-be-calibrated inertial measurement unit, which not only considers the acceleration error parameters and the angular velocity error parameters in the measurement process of the to-be-calibrated inertial measurement unit, but also considers the influence of temperature on the performance of the inertial measurement unit, thereby enhancing the adaptability of the to-be-calibrated inertial measurement unit to the environment.

[0091] In an embodiment, the specific correction manner includes the following steps.

[0092] ①Based on the pre-constructed temperature error compensation data table, an interpolation method is used to calculate the acceleration error parameters and the angular velocity error parameters of the to-be-calibrated inertial measurement unit at the working temperature.

[0093] It should be understood that the interpolation method is a mathematical method of constructing a continuous function (such as a polynomial function, a spline function, etc.) through known discrete data points to estimate the numerical value of an unknown point. In this embodiment, the interpolation method is used to construct a continuous function from the discrete data points in the temperature error compensation data table, thereby constructing a temperature error compensation model, so as to input the working temperature of the to-be-calibrated inertial measurement unit actually collected in real time into the temperature error compensation model to obtain the corresponding acceleration error parameters and angular velocity error parameters.

[0094] When the working temperature is exactly a preset temperature in the temperature error compensation data table, a table lookup method can be directly used to obtain the corresponding acceleration error parameters and angular velocity error parameters.

[0095] It should be noted that common interpolation methods include linear interpolation, polynomial interpolation, spline interpolation, nearest neighbor interpolation, and inverse distance weighted interpolation, and the user can select a specific interpolation method according to needs, and the present application is not limited.

[0096] ②An acceleration error model of the to-be-calibrated inertial measurement unit is constructed, and the acceleration error parameter and the acceleration measurement value are input into the acceleration error model to calculate an initial acceleration correction value corresponding thereto.

[0097] In the embodiment, the acceleration error model can be constructed in the same manner as in the above embodiment, as shown in formula (3). The real-time collected acceleration measurement value and the acceleration error parameter at the working temperature are input into formula (3) to calculate an initial acceleration correction value of the to-be-calibrated inertial measurement unit at the working temperature.

[0098] ③An angular velocity error model of the to-be-calibrated inertial measurement unit is constructed, and the angular velocity error parameter and the angular velocity measurement value are input into the angular velocity error model to calculate an initial angular velocity correction value corresponding thereto.

[0099] In the embodiment, the angular velocity error model can be constructed in the same manner as in the above embodiment, as shown in formula (8). The real-time collected angular velocity measurement value and the angular velocity error parameter at the working temperature are input into formula (8) to calculate an initial angular velocity correction value of the to-be-calibrated inertial measurement unit at the working temperature.

[0100] Step S3: According to the initial acceleration correction value and the initial angular velocity correction value, a multi-sensor fusion filtering algorithm is used to perform a second correction on the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature, to obtain a target acceleration correction value and a target angular velocity correction value corresponding thereto.

[0101] After the first correction, the to-be-calibrated inertial measurement unit obtains the initial acceleration correction value and the initial angular velocity correction value with high precision. However, since the to-be-calibrated inertial measurement unit is mainly applied to an agricultural machinery navigation system for measuring the linear acceleration and angular velocity of the agricultural machinery in operation, high-frequency vibration, instantaneous impact, and device aging often occur during the operation of the agricultural machinery. The multi-sensor fusion filtering algorithm is used to further correct the actual output acceleration measurement value and angular velocity measurement value of the to-be-calibrated inertial measurement unit, which can suppress vibration noise and further improve the attitude solving precision of the to-be-calibrated inertial measurement unit, thereby ensuring the calibration precision of the to-be-calibrated inertial measurement unit.

[0102] In an embodiment, the multi-sensor fusion filtering algorithm correction method mainly refers to correcting the predicted value of the to-be-calibrated inertial measurement unit through the sensor data of the GPS device in the agricultural machinery navigation system, and the specific manner is as shown in Figure 3 Steps S31-S35.

[0103] Step S31: Construct the state equation of the to-be-calibrated inertial measurement unit, input the acceleration initial correction value and the angular velocity initial correction value obtained by the first correction into the state equation, and calculate the position initial prediction value and the speed initial prediction value of the to-be-calibrated inertial measurement unit.

[0104] Specifically, the state vector completely describing the motion state of the to-be-calibrated inertial measurement unit is:

[0105] x = [p n ,v n ,q nb ,b g ,b a ] T ; formula (11)

[0106] Wherein, p n ,v n ,q nb is used to describe the pose state of the to-be-calibrated inertial measurement unit; b g ,b a , s a is used to describe the error state of the to-be-calibrated inertial measurement unit. Specifically, p n is the position of the to-be-calibrated inertial measurement unit in the North-East-Ground (NED) coordinate system; v n is the speed of the to-be-calibrated inertial measurement unit in the North-East-Ground (NED) coordinate system; q nb is the quaternion of the inertial measurement unit body coordinate system to the North-East-Ground (NED) coordinate system, which is used to fuse the accelerometer and gyroscope data in the to-be-calibrated inertial measurement unit and estimate the attitude of the to-be-calibrated inertial measurement unit; b g is the angular velocity bias error parameter of the to-be-calibrated inertial measurement unit, b a is the acceleration bias error parameter of the to-be-calibrated inertial measurement unit,

[0107] The state equation of the to-be-calibrated inertial measurement unit based on the state vector x is:

[0108]

[0109]

[0110] Wherein, Rnb is a rotation matrix obtained by quaternion q nb ; a m is an acceleration value of the inertial measurement unit to be calibrated; ω m is an angular velocity value of the inertial measurement unit to be calibrated; g n is a gravity vector, η a , η g are accelerometer measurement noise and gyroscope measurement noise of the inertial measurement unit to be calibrated, respectively; τ a , τ g are accelerometer bias correlation time and gyroscope bias correlation time of the inertial measurement unit to be calibrated, respectively; η ba , η bg are accelerometer Gaussian noise and gyroscope Gaussian noise of the inertial measurement unit to be calibrated, respectively.

[0111] The acceleration initial correction value and the angular velocity initial correction value obtained by the first correction are input into the above formula as a m and ω m , respectively, and other parameters are set as initial values, to obtain position initial prediction value and velocity initial prediction value of the inertial measurement unit to be calibrated.

[0112] Step S32: obtaining position actual observation value and velocity actual observation value collected by the GPS device arranged in the agricultural machinery navigation system, and calculating residual of the position initial prediction value and the position actual observation value and residual of the velocity initial prediction value and the velocity actual observation value, respectively.

[0113] In the embodiment, the position initial prediction value and the velocity initial prediction value obtained by the state equation prediction are corrected by the position actual observation value and the velocity actual observation value collected by the GPS device in the agricultural machinery navigation system. That is, the observation equation is constructed as:

[0114]

[0115] wherein, is the position actual observation value collected by the GPS device; is the velocity actual observation value collected by the GPS device; p n is the position of the inertial measurement unit to be calibrated in North-East-Down (NED) coordinate system; v n is the velocity of the inertial measurement unit to be calibrated in North-East-Down (NED) coordinate system; η p , η v are position measurement noise and velocity measurement noise of the GPS device, respectively.

[0116] Step S33: iteratively update the state equation based on the principle of residual minimization to obtain the acceleration error parameter and the angular velocity error parameter of the to-be-calibrated inertial measurement unit optimally estimated at the working temperature.

[0117] Preferably, the multi-sensor fusion filtering algorithm can be optimized based on the Kalman filtering algorithm, mainly according to the Kalman gain to iteratively update the state equation, and the specific mode comprises the following steps.

[0118] ① Construct an error covariance model, and predict the error covariance matrix of the position prediction value and the velocity prediction value calculated at this iteration respectively according to the error covariance model.

[0119] ② Calculate the Kalman gain of the state equation at this iteration respectively, and update the state equation according to the Kalman gain.

[0120] It should be understood that the Kalman gain is the core parameter of the Kalman filtering algorithm, which is used to dynamically adjust the weight of the prediction value and the observation value to realize the optimization of state estimation. Among them, the more the Kalman gain tends to 1, the more the observation value is trusted, which is suitable for the scene with large prediction uncertainty or small observation noise; the more the Kalman gain tends to 0, the more the prediction value is trusted, which is suitable for the scene with large observation noise or accurate prediction.

[0121] ③ According to the updated state equation, correct the position prediction value and the velocity prediction value until the residual of the corrected position prediction value and the actual observed position value and the residual of the corrected velocity prediction value and the actual observed velocity value are minimized.

[0122] ④ According to the updated state equation, obtain the acceleration error parameter and the angular velocity error parameter estimated at this iteration.

[0123] It should be noted that the user can also correct the residual between the prediction value and the observation value according to the comparison interpolation method, the least square method and the extended Kalman algorithm, so as to update the state equation, and optimally estimate the acceleration error parameter and the angular velocity error parameter. The present application is not specifically limited.

[0124] Step S34: construct an acceleration error model of the to-be-calibrated inertial measurement unit, and input the acceleration error parameter and the acceleration initial correction value into the acceleration error model to calculate the corresponding acceleration target correction value.

[0125] In this embodiment, the acceleration error model can be constructed in the same way as the above embodiment, see formula (3). The acceleration initial correction value and the optimally estimated acceleration error parameter are input into formula (3) to calculate the acceleration target correction value of the to-be-calibrated inertial measurement unit at the working temperature.

[0126] Step S35: constructing an angular velocity error model of the to-be-calibrated inertial measurement unit, inputting the angular velocity initial correction value and the angular velocity error parameter into the angular velocity error model, and calculating an angular velocity target correction value.

[0127] In this embodiment, the angular velocity error model can be constructed in the same manner as in the above embodiment, as shown in formula (8). The angular velocity initial correction value and the optimally estimated angular velocity error parameter are input into formula (8) to calculate the angular velocity target correction value of the to-be-calibrated inertial measurement unit at the working temperature.

[0128] The dynamic error modeling technology is adopted in this application, the temperature variable is introduced for multi-temperature zero bias test and multi-temperature multi-axis rotation excitation test, a temperature error compensation data table is established, the to-be-calibrated inertial measurement unit is corrected for the first time to analyze the influence of temperature on the performance of the to-be-calibrated inertial measurement unit, a multi-sensor fusion filtering algorithm is designed for high-frequency vibration interference in the agricultural machinery operation process, the to-be-calibrated inertial measurement unit is corrected for the second time to suppress vibration noise and further improve the attitude solution accuracy of the to-be-calibrated inertial measurement unit, so as to ensure the calibration accuracy of the to-be-calibrated inertial measurement unit.

[0129] As shown in Figure 4 FIG. 1 shows a structure schematic diagram of an inertial measurement unit real-time calibration system 400 in an embodiment of the application. The inertial measurement unit real-time calibration system 400 comprises a data acquisition module 401, a first data correction module 402 and a second data correction module 403 connected in sequence.

[0130] The data acquisition module 401 is configured to acquire the working temperature of the to-be-calibrated inertial measurement unit in real time, and obtain the actual output acceleration measurement value and the actual output angular velocity measurement value of the to-be-calibrated inertial measurement unit.

[0131] The first data correction module 402 is configured to correct the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature for the first time based on the pre-constructed temperature error compensation data table, and obtain the corresponding acceleration initial correction value and the angular velocity initial correction value.

[0132] In an embodiment, the first data correction module 402 comprises a data table construction unit and a data correction unit connected with the data table construction unit. The data table construction unit is configured to construct a temperature error compensation data table. The data correction unit is configured to correct the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature for the first time based on the temperature error compensation data table, to obtain corresponding acceleration initial correction value and angular velocity initial correction value.

[0133] The second data correction module 403 is configured to correct the acceleration measurement value and the angular velocity measurement value of the to-be-calibrated inertial measurement unit at the working temperature for the second time based on the acceleration initial correction value and the angular velocity initial correction value, to obtain corresponding acceleration target correction value and angular velocity target correction value.

[0134] It should be understood that the above-mentioned embodiment provides the inertial measurement unit real-time calibration system embodiment and the inertial measurement unit real-time calibration method embodiment belong to the same concept, and the implementation process of each module and each unit when realizing specific functions has been described in detail in the above-mentioned method embodiment. In order to be brief, it will not be repeated here.

[0135] It should also be understood that the division of modules and units in the embodiments of the present application is illustrative, and is only a logical function division. In actual implementation, there can be another division mode. In addition, each functional module and each functional unit in each embodiment of the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The above-mentioned integrated module or unit can be realized in the form of hardware or software functional module or unit.

[0136] The inertial measurement unit real-time calibration method provided by the embodiment of the present application can be implemented on the terminal side or the server side. As for the hardware structure of the inertial measurement unit real-time calibration terminal, please refer to Figure 5 An optional hardware structure schematic diagram of the inertial measurement unit real-time calibration terminal 500 provided by the embodiment of the present application is shown in the figure. The inertial measurement unit real-time calibration terminal 500 can be a mobile phone, a computer device, a tablet device, a personal digital processing device, a factory background processing device, etc. The inertial measurement unit real-time calibration terminal 500 comprises at least one processor 501, a memory 502, at least one network interface 504 and a user interface 506. Each component in the terminal is coupled together through a bus system 505. It can be understood that the bus system 505 is used to realize the connection communication between these components. The bus system 505 includes not only a data bus, but also a power bus, a control bus and a status signal bus. However, in order to clearly illustrate, only the data bus is shown in the figure.Figure 5 Various buses are labeled as a bus system.

[0137] The user interface 506 can include a display, a keyboard, a mouse, a trackball, a pointing gun, a key, a button, a touchpad, a touch screen, etc.

[0138] It can be understood that the memory 502 can be a volatile memory or a nonvolatile memory, and can also include both volatile and nonvolatile memories. The nonvolatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), and used as an external cache. By way of example but not limitation, many forms of RAM can be used, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present application is intended to include but not limited to these and any other suitable categories of memory.

[0139] The memory 502 in the embodiments of the present application is used to store various categories of data to support the operation of the inertial measurement unit real-time calibration terminal 500. Examples of these data include: any executable programs used to operate on the inertial measurement unit real-time calibration terminal 500, such as an operating system 5021 and an application program 5022; the operating system 5021 contains various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program 5022 can contain various application programs, such as a media player (MediaPlayer), a browser (Browser), etc., for implementing various application services. The inertial measurement unit real-time calibration method provided by the embodiments of the present application can be included in the application program 5022.

[0140] The inertial measurement unit real-time calibration method disclosed in the embodiments of the present application can be applied to the processor 501 or implemented by the processor 501. The processor 501 can be an integrated circuit chip with a signal processing capability. In the implementation process, each step of the inertial measurement unit real-time calibration method can be completed by an integrated logic circuit of hardware in the processor 501 or an instruction in the form of software. The processor 501 described above can be a general processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 501 can implement or execute the disclosed methods, steps, and logic block diagrams in the embodiments of the present application. The general processor 501 can be a microprocessor or any conventional processor, etc. In combination with the inertial measurement unit real-time calibration method provided in the embodiments of the present application, the steps can be directly embodied as hardware decoding processor execution completion or combined execution completion by hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines the hardware to complete the steps of the foregoing method.

[0141] In the exemplary embodiments, the inertial measurement unit real-time calibration terminal 500 can be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), etc. for executing the foregoing inertial measurement unit real-time calibration method.

[0142] Those of ordinary skill in the art can understand that all or part of the steps of the foregoing method embodiments can be completed by computer program related hardware. The foregoing computer program can be stored in a computer readable storage medium. When the program is executed, the steps of the foregoing method embodiments are executed; and the foregoing storage medium includes ROM, RAM, magnetic or optical disc, and various media that can store program codes.

[0143] In the embodiments provided in the present application, the computer readable and writable storage medium can include a read-only memory, a random access memory, an EEPROM, a CD-ROM or other optical disk storage device, a magnetic disk storage device or other magnetic storage device, a flash memory, a U disk, a mobile hard disk, or any other medium capable of storing desired program code in the form of instructions or data structures and capable of being accessed by a computer. In addition, any connection can be appropriately referred to as a computer readable medium. For example, if instructions are sent from a website, server or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technology such as infrared, radio and microwave, the coaxial cable, fiber optic cable, twisted pair, DSL or wireless technology such as infrared, radio and microwave is included in the definition of the medium. However, it should be understood that the computer readable and writable storage medium and the data storage medium do not include connections, carriers, signals or other transitory media, but are intended for non-transitory, tangible storage media. As used in the application, magnetic disks and optical disks include compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks and Blu-ray discs, wherein magnetic disks typically magnetically copy data, and optical disks optically copy data with a laser.

[0144] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0145] In summary, the present application provides a real-time calibration method and system for an inertial measurement unit, and a terminal. The acceleration measurement value and the angular velocity measurement value output by the to-be-calibrated inertial measurement unit are corrected based on a temperature error compensation data table for the first time, and a multi-sensor fusion filtering algorithm is used for the second time. The present application has the following beneficial effects: on the one hand, temperature variables are introduced for multi-temperature zero offset testing and multi-temperature multi-axis rotation excitation testing, a temperature error compensation data table is established, and the influence of temperature on the performance of the to-be-calibrated inertial measurement unit is fully considered; on the other hand, a multi-sensor fusion filtering algorithm is used, which can suppress the interference of vibration noise and improve the attitude solution accuracy of the to-be-calibrated inertial measurement unit, thereby ensuring the calibration accuracy of the to-be-calibrated inertial measurement unit and solving the technical problems of low calibration accuracy and poor real-time performance of the existing inertial measurement unit calibration method.

[0146] Therefore, the present application effectively overcomes the various shortcomings in the prior art and has high industrial utilization value.

[0147] The above embodiments are only illustrative of the principles of the present application and its effects, and are not intended to limit the present application. Any modification or change made by any person skilled in the art without departing from the spirit and scope of the present application shall be covered by the claims of the present application.

Claims

1. A real-time calibration method for an inertial measurement unit, for real-time calibration of an inertial measurement unit provided in an agricultural machinery navigation system, characterized in that: include: Collecting the operating temperature of the inertial measurement unit to be calibrated in real time, and obtaining the acceleration measurement value and angular velocity measurement value actually output by the inertial measurement unit to be calibrated; Based on a pre-built temperature error compensation data table, performing a first correction on the acceleration measurement value and the angular velocity measurement value of the inertial measurement unit to be calibrated at the operating temperature to obtain corresponding initial acceleration correction values ​​and initial angular velocity correction values; According to the initial acceleration correction value and the initial angular velocity correction value, a multi-sensor fusion filtering algorithm is used to perform a second correction on the acceleration measurement value and the angular velocity measurement value of the inertial measurement unit to be calibrated at the operating temperature to obtain corresponding acceleration target correction value and angular velocity target correction value.

2. The real-time calibration method for an inertial measurement unit according to claim 1, wherein: The temperature error compensation data table is constructed in the following manner: The inertial measurement unit to be calibrated is fixed to a turntable and the turntable is kept stationary, a multi-temperature zero bias test is performed on the inertial measurement unit to collect multiple sets of acceleration measurement data at different preset temperatures, and the acceleration error parameters at different preset temperatures are calculated using the least squares method; Controlling the turntable to rotate at a preset angular velocity parameter, performing a multi-temperature multi-axis rotation excitation test on the inertial measurement unit to be calibrated to collect multiple sets of angular velocity measurement data at different preset temperatures, and calculating the angular velocity error parameters at different preset temperatures using a least squares method; A temperature error compensation data table of the inertial measurement unit to be calibrated is constructed according to the acceleration error parameters and the angular velocity error parameters at different preset temperatures.

3. The real-time calibration method for an inertial measurement unit according to claim 2, wherein: Methods for calculating acceleration error parameters at a predetermined preset temperature using a least squares method based on multiple sets of acceleration measurement data collected at the predetermined preset temperature include: Constructing an acceleration error model of the inertial measurement unit to be calibrated; Inputting multiple sets of acceleration measurement data at a specified preset temperature into the acceleration error model to obtain a set of acceleration error equations of the inertial measurement unit to be calibrated; An acceleration loss function of the inertial measurement unit to be calibrated at the preset temperature is defined, and an optimal acceleration error parameter of the inertial measurement unit to be calibrated at the preset temperature is solved by minimizing the acceleration loss values ​​of multiple sets of acceleration measurement data.

4. The real-time calibration method for an inertial measurement unit according to claim 2, wherein: Methods for calculating the angular velocity error parameter at a predetermined preset temperature using the least squares method based on multiple sets of angular velocity measurement data collected at the predetermined preset temperature include: Constructing an angular velocity error model of the inertial measurement unit to be calibrated; Inputting multiple sets of angular velocity measurement data at a specified preset temperature into the angular velocity error model to obtain a set of angular velocity error equations of the inertial measurement unit to be calibrated; An angular velocity loss function of the inertial measurement unit to be calibrated at the preset temperature is defined, and an optimal acceleration error parameter of the inertial measurement unit to be calibrated at the preset temperature is solved by minimizing angular velocity loss values ​​of multiple sets of angular velocity measurement data.

5. The real-time calibration method for an inertial measurement unit according to claim 1, wherein: Based on a pre-built temperature error compensation data table, a first correction is performed on the acceleration measurement value and the angular velocity measurement value of the inertial measurement unit to be calibrated at the operating temperature to obtain the corresponding initial acceleration correction value and initial angular velocity correction value. The method includes: Based on the temperature error compensation data table, an interpolation method is used to calculate the acceleration error parameter and the angular velocity error parameter of the inertial measurement unit to be calibrated at the operating temperature; Constructing an acceleration error model of the inertial measurement unit to be calibrated, and inputting the acceleration error parameter and the acceleration measurement value into the acceleration error model to calculate and obtain a corresponding initial acceleration correction value; An angular velocity error model of the inertial measurement unit to be calibrated is constructed, and the angular velocity error parameter and the angular velocity measurement value are input into the angular velocity error model to calculate and obtain a corresponding angular velocity initial correction value.

6. The real-time calibration of an inertial measurement unit according to claim 1, characterized in that: A method of performing a second correction on the acceleration measurement value and the angular velocity measurement value of the inertial measurement unit to be calibrated at the operating temperature using a multi-sensor fusion filtering algorithm based on the initial acceleration correction value and the initial angular velocity correction value to obtain the corresponding acceleration target correction value and angular velocity target correction value includes: Constructing a state equation of the inertial measurement unit to be calibrated, inputting an initial correction value of acceleration and an initial correction value of angular velocity obtained by the first calibration into the state equation, and calculating an initial predicted value of position and an initial predicted value of velocity of the inertial measurement unit to be calibrated; Obtaining actual position observation values ​​and actual speed observation values ​​collected by a GPS device provided in the agricultural machinery navigation system, and calculating residuals between the initial position prediction value and the actual position observation value, and between the initial speed prediction value and the actual speed observation value, respectively; Iteratively updating the state equation based on the residual minimization principle to obtain optimally estimated acceleration error parameters and angular velocity error parameters of the inertial measurement unit to be calibrated at the operating temperature; Constructing an acceleration error model of the inertial measurement unit to be calibrated, and inputting the acceleration error parameter and the initial acceleration correction value into the acceleration error model to calculate and obtain a corresponding acceleration target correction value; An angular velocity error model of the inertial measurement unit to be calibrated is constructed, and the angular velocity error parameter and the initial angular velocity correction value are input into the angular velocity error model to calculate and obtain a corresponding angular velocity target correction value.

7. The real-time calibration method for an inertial measurement unit according to claim 6, wherein: The method of iteratively updating the state equation includes: Construct an error covariance model and use it to predict the error covariance matrices of the position prediction value and the speed prediction value calculated in this iteration; Calculating the Kalman gain of the state equation at this iteration respectively, and updating the state equation accordingly; According to the updated state equation, the position prediction value and the speed prediction value are corrected until the residuals between the corrected position prediction value and the actual position observation value and between the corrected speed prediction value and the actual speed observation value are minimized; According to the updated state equation, the acceleration error parameter and angular velocity error parameter estimated in this iteration are obtained.

8. The real-time calibration method for an inertial measurement unit according to any one of claims 1 to 7, characterized in that: The acceleration error parameters include: acceleration scale factor, acceleration cross-axis non-orthogonality error parameter and acceleration zero bias error parameter; The angular velocity error parameters include: an angular velocity scale factor, an angular velocity cross-axis non-orthogonality error parameter, and an angular velocity zero bias error parameter.

9. A real-time calibration system for an inertial measurement unit, used for real-time calibration of an inertial measurement unit provided in an agricultural machinery navigation system, characterized in that: include: A data acquisition module, the data acquisition module is used to collect the operating temperature of the inertial measurement unit to be calibrated in real time, and obtain the acceleration measurement value and angular velocity measurement value actually output by the inertial measurement unit to be calibrated; a first data correction module, connected to the data acquisition module, configured to perform a first correction on the acceleration measurement value and the angular velocity measurement value of the inertial measurement unit to be calibrated at the operating temperature based on a pre-built temperature error compensation data table, to obtain corresponding initial acceleration correction values ​​and initial angular velocity correction values; a second data correction module, the second data correction module being connected to the first data correction module and being configured to perform a second correction on the acceleration measurement value and the angular velocity measurement value of the inertial measurement unit to be calibrated at the operating temperature based on the initial acceleration correction value and the initial angular velocity correction value, using a multi-sensor fusion filtering algorithm to obtain corresponding target acceleration correction value and target angular velocity correction value.

10. A real-time calibration terminal for an inertial measurement unit, characterized in that: include: processor and memory; The memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory, so as to enable the terminal to perform the real-time calibration method for an inertial measurement unit according to any one of claims 1 to 8.

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