Inertial measurement unit error compensation method, equipment and medium

Abstract

The invention discloses an inertial measurement unit error compensation method, equipment and a medium, relates to the field of sensor control, and solves the problems that noise errors of an inertial measurement unit cannot be identified and various errors cannot be compensated in a targeted manner. The method comprises the following steps: collecting three-axis motion data shown by an inertial measurement unit at a fixed sampling frequency for continuous sampling duration; analyzing the three-axis motion data shown by the inertial measurement unit to obtain an Airy variance corresponding to each axis in the three axes of the inertial measurement unit; drawing a double logarithmic curve of the inertial measurement unit corresponding to the three axes, analyzing the double logarithmic curve to obtain a random error corresponding to each axis in the three axes, and compensating the random error; before the inertial measurement unit is used for measuring the to-be-measured target, the environmental data of the environment where the to-be-measured target is located is collected, analyzed and compensated, the noise error corresponding to the inertial measurement unit is recognized, and meanwhile targeted compensation is achieved for various errors.

Description

Technical Field

[0001] The present invention belongs to the technical field of sensor compensation, and in particular relates to an inertial measurement unit error compensation method, device and medium. Background Art

[0002] An inertial measurement unit is an electronic device that is usually composed of three main inertial sensors, including an accelerometer, a gyroscope, and a magnetometer. These sensors work together to measure and track the motion state and direction of the device. The measurement accuracy and stability of the inertial measurement unit are crucial to the performance of the entire system. The research on error compensation methods helps to improve the measurement accuracy of IMU, thereby promoting the development of sensor technology. Through error compensation, the accuracy and stability of the control system can be improved, thereby improving the level of control technology. However, at present, when performing error compensation on inertial measurement equipment, most of them can only eliminate the errors caused by environmental factors, while the random errors caused by the nature of the inertial measurement equipment itself cannot be confirmed and compensated, resulting in inaccurate measurement results; To this end, the present invention proposes an inertial measurement unit error compensation method, device and medium. Summary of the invention

[0003] The purpose of the present invention is to provide an inertial measurement unit error compensation method, device and medium to solve the problems raised in the above background technology.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: An inertial measurement unit error compensation method, the method comprising: Step S1, collecting three-axis motion data displayed by the inertial measurement unit at a fixed sampling frequency and for a continuous sampling duration; Step S2, analyzing the three-axis motion data displayed by the inertial measurement unit to obtain the Allen variance corresponding to each of the three axes of the inertial measurement unit; Step S3, drawing a double logarithmic curve corresponding to the three axes of the inertial measurement unit, and analyzing the double logarithmic curve to obtain the random error corresponding to each of the three axes and compensate for it; Step S4, before using the inertial measurement unit to measure the target to be measured, collect environmental data of the environment where the target to be measured is located, analyze and compensate.

[0005] Furthermore, the three-axis motion data specifically includes the acceleration of the inertial measurement unit on the X-axis, Y-axis and Z-axis and the angular velocity of rotation around the X-axis, Y-axis and Z-axis; the environmental data includes the real-time ambient temperature and average atmospheric pressure of the environment where the target to be measured is located.

[0006] Furthermore, the step S1 includes the following sub-steps: Step S11, fixing the inertial measurement unit on a stationary position and rate turntable, and the position and rate turntable provides a horizontal reference for the inertial measurement unit; Step S12, the position rate turntable is started and preheated for a fixed time YR; Step S13, continuously collecting the real-time acceleration and real-time angular velocity of the three axes corresponding to the inertial measurement unit at a sampling frequency CY for H hours.

[0007] Furthermore, the step S2 includes the following sub-steps: Step S21, acquiring the three-axis motion data of the inertial measurement unit, and obtaining the X-axis angular velocity BX, the Y-axis angular velocity BY, the Z-axis angular velocity BZ, the X-axis acceleration AX, the Y-axis acceleration AY and the Z-axis acceleration AZ of the inertial measurement unit; Step S22, dividing the X-axis angular velocity of the inertial measurement unit into time intervals JG to obtain an X-axis angular velocity sequence XBX, wherein the X-axis angular velocity sequence contains N sampling points of the X-axis angular velocity, where N=1, 2, ..., z, and z is a positive integer; Step S23, divide the X-axis angular velocity sequence into K groups and number them R, each group contains M sampling points, and each group lasts for N×JG time; where R=1, 2, ..., K; M≤(N-1) / 2, K=N / M.

[0008] Furthermore, the step S2 further includes the following sub-steps: Step S24, adding and averaging the X-axis angular velocities corresponding to all the sampling points of the X-axis angular velocity in the first group to obtain the intra-group average value JR1 of the first group; Step S25, and so on, calculate the intra-group average values ​​JR2, ..., JRR, ..., JRK corresponding to the second group to the Kth group; Step S26, subtract the mean values ​​of the groups corresponding to the adjacent groups and take the absolute value to obtain the Allan variance of the group with the smaller number; Step S27, concatenating the Allan variances of adjacent groups to obtain the Allan variance corresponding to the X-axis angular velocity; Step S28, and so on, calculate the Allan variance corresponding to the Y-axis angular velocity, the Allan variance corresponding to the Z-axis angular velocity, the Allan variance corresponding to the X-axis acceleration, the Allan variance corresponding to the Y-axis acceleration, and the Allan variance corresponding to the Z-axis acceleration.

[0009] Furthermore, the step S3 includes the following sub-steps: Step S31, obtaining the Allen variance corresponding to the three axes of the inertial measurement unit, obtaining the Allen variance corresponding to the X-axis angular velocity, the Allen variance corresponding to the Y-axis angular velocity, the Allen variance corresponding to the Z-axis angular velocity, the Allen variance corresponding to the X-axis acceleration, the Allen variance corresponding to the Y-axis acceleration, and the Allen variance corresponding to the Z-axis acceleration; Step S32, performing a square root operation on the Allan variance corresponding to the X-axis angular velocity to obtain the Allan standard deviation corresponding to the X-axis angular velocity; Step S32, drawing a double logarithmic curve diagram corresponding to the X-axis angular velocity with the Allen standard deviation ABZ as the ordinate and the time T as the abscissa; Step S33, calculating the slope of the double logarithmic curve at any time, and then referring to the noise-slope relationship comparison table to obtain the relationship between different random noises and the slope; Step S34: The noise in the output data of the inertial measurement unit is generated by noise sources, and each noise source is independent of each other, so ALF(T)=BZ1 2 (T)+BZ2 2 (T)+BZ3 2 (T)+BZ4 2 (T) + BZ5 2 (T); where ALF(T) is the Allan variance corresponding to the angular velocity of the X-axis, BZ1 2 (T) is the Allan standard deviation of a type of noise, BZ2 2 (T) is the Allan standard deviation of the second type of noise, BZ3 2 (T) is the Allan standard deviation of the three types of noise, BZ4 2 (T) is the standard deviation of the four types of noise, BZ5 2 (T) is the standard deviation of five types of noise; Step S35, sort out ; In the formula, p is twice the slope, p = (-2, -1, 0, 1, 2), C p is a constant.

[0010] Furthermore, the step S3 also includes the following sub-steps: Step S36, using the least squares method to fit the double logarithmic curve corresponding to the X-axis angular velocity, to obtain the C corresponding to the X-axis angular velocity p The noise coefficient corresponding to the X-axis angular velocity is obtained as follows: ; Wherein, ZS1 is the noise coefficient of type 1 noise, ZS2 is the noise coefficient of type 2 noise, ZS3 is the noise coefficient of type 3 noise, ZS4 is the noise coefficient of type 4 noise, and ZS5 is the noise coefficient of type 5 noise; Step S37 is similar to the above, and the noise coefficient corresponding to the Y-axis angular velocity, the noise coefficient corresponding to the Z-axis angular velocity, the noise coefficient corresponding to the X-axis acceleration, the noise coefficient corresponding to the Y-axis acceleration, and the noise coefficient corresponding to the Z-axis acceleration are obtained. Step S38, when measuring the object to be measured, the noise coefficient of the three axes corresponding to the inertial measurement unit is compensated, and the compensation method includes the following: Step S381 designs a noise filter according to different noise coefficients, and sets the parameters, cutoff frequency or order of the noise filter; Step S382, collecting the three-axis motion data of the inertial measurement unit again, and processing it through a noise filter, and then comparing the three-axis motion data before and after filtering to evaluate the filtering effect; Step S383, if the error after filtering is less than the error threshold, the filter setting is deemed complete; if the error after filtering is greater than or equal to the error threshold, the noise filter is redesigned until the noise filter setting is completed; The error is obtained by subtracting the filtered three-axis motion data from the calculated standard three-axis motion data and then dividing it by the standard three-axis motion data.

[0011] Furthermore, the step S4 includes the following sub-steps: Step S41, placing the inertial measurement unit in a temperature-controlled box, raising the temperature from -40°C to +80°C in steps of 10°C, stabilizing each temperature point for 30 minutes, and then recording the three-axis motion data corresponding to the inertial measurement unit; Step S42, fitting the temperature model of zero bias LP and scale factor CD by function: LP=a0+a1×WD+a2×WD 2 ;CD=b0+b1×WD; where WD is the real-time ambient temperature, a0, a1, a2, b0 and b2 are constants with fixed values; Step S43: Similarly, the corresponding zero bias and scale factor are obtained by magic fitting for the average atmospheric pressure; Step S44, measuring the target object, and calculating and compensating the measurement result using the zero bias and scale factor corresponding to the real-time ambient temperature and the average atmospheric pressure.

[0012] A computer device, comprising: A memory storing a computer program; A processor is communicatively connected to the memory, and when the computer program is executed by the processor, the above method is implemented.

[0013] A computer-readable storage medium stores a computer program, which implements the above method when executed by a processor.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention first collects the three-axis motion data shown by the inertial measurement unit at a fixed sampling frequency and a continuous sampling time; then analyzes the three-axis motion data shown by the inertial measurement unit to obtain the Allen variance corresponding to each of the three axes of the inertial measurement unit; and realizes the analysis of the noise error of the inertial measurement unit itself.

[0015] 2. The present invention plots the double logarithmic curves of the three axes corresponding to the inertial measurement unit based on the Allen variance, and analyzes the double logarithmic curves to obtain the random errors corresponding to each of the three axes and compensate for them; at the same time, before using the inertial measurement unit to measure the target to be measured, the environmental data of the environment where the target to be measured is collected for analysis and compensation; the present invention realizes compensation for multiple errors of the inertial measurement unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 The figure is a flowchart of the method of the present invention.

[0018] Figure 2 Schematic diagram of the inertial measurement unit in the present invention.

[0019] Figure 3 It is a schematic diagram of the double logarithmic curve in the present invention.

[0020] Figure 4 It is a schematic diagram of the structure of the computer device in the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1, please refer to Figure 1-Figure 3 As shown, the technical solution provided by the present invention is: an inertial measurement unit error compensation method, by measuring and analyzing the three-axis motion data shown by the inertial measurement unit, obtaining multiple errors corresponding to the inertial measurement unit, and performing targeted compensation for the multiple errors; The inertial measurement unit is a device for measuring the three-axis angular velocity and acceleration of the target object. The inertial measurement unit is composed of an accelerometer and a gyroscope, wherein the accelerometer is used to measure the three-axis acceleration and the gyroscope is used to measure the three-axis angular velocity.

[0023] In this embodiment, the inertial measurement unit error compensation method is as follows: Step S1, collecting three-axis motion data shown by the inertial measurement unit at a fixed sampling frequency and a continuous sampling duration; the three-axis motion data specifically includes the acceleration of the inertial measurement unit on the X-axis, Y-axis and Z-axis and the angular velocity of rotation around the X-axis, Y-axis and Z-axis; In this embodiment, step S1 includes the following sub-steps: Step S11, fixing the inertial measurement unit on a stationary position rate turntable, and the position rate turntable provides a horizontal reference for the inertial measurement unit; wherein the position rate turntable is a detection instrument used to test and analyze the performance of inertial navigation equipment, sensors, etc., which can achieve precise position positioning and rate rotation motion, and provide an accurate motion reference for the device under test; Step S12, the position rate turntable is started and preheated for a fixed time YR; preferably, YR is 20 minutes; Step S13, continuously collecting the real-time acceleration and real-time angular velocity of the three axes corresponding to the inertial measurement unit at a sampling frequency CY for H hours; preferably, CY is 100 Hz and H is 4 hours.

[0024] Step S2, analyzing the three-axis motion data displayed by the inertial measurement unit to obtain the Allen variance corresponding to each of the three axes of the inertial measurement unit; In the present invention, step S2 includes the following sub-steps: Step S21, acquiring the three-axis motion data of the inertial measurement unit, and obtaining the X-axis angular velocity BX, the Y-axis angular velocity BY, the Z-axis angular velocity BZ, the X-axis acceleration AX, the Y-axis acceleration AY and the Z-axis acceleration AZ of the inertial measurement unit; Step S22, dividing the X-axis angular velocity of the inertial measurement unit into time intervals JG to obtain an X-axis angular velocity sequence XBX, wherein the X-axis angular velocity sequence contains N sampling points of the X-axis angular velocity, where N=1, 2, ..., z, and z is a positive integer; Step S23, divide the X-axis angular velocity sequence into K groups and number them R, each group contains M sampling points, and each group lasts for N×JG time; where R=1, 2, ..., K; M≤(N-1) / 2, K=N / M; Step S24, adding and averaging the X-axis angular velocities corresponding to all the sampling points of the X-axis angular velocity in the first group to obtain the intra-group average value JR1 of the first group; Step S25, and so on, calculate the intra-group average values ​​JR2, ..., JRR, ..., JRK corresponding to the second group to the Kth group; Step S26, subtract the group averages corresponding to adjacent groups and take the absolute value to obtain the Allan variance of the group with a smaller number; specifically, if the group average corresponding to group 2 is subtracted from the group average corresponding to group 1, the Allan variance of group 1 is obtained; Among them, the Allan variance is a method used to analyze the noise characteristics in time series data, especially widely used in inertial sensors such as gyroscopes and accelerometers; Step S27, concatenating the Allan variances of adjacent groups to obtain the Allan variance corresponding to the X-axis angular velocity; Step S28, and so on, calculate the Allan variance corresponding to the Y-axis angular velocity, the Allan variance corresponding to the Z-axis angular velocity, the Allan variance corresponding to the X-axis acceleration, the Allan variance corresponding to the Y-axis acceleration, and the Allan variance corresponding to the Z-axis acceleration.

[0025] Step S3, drawing a double logarithmic curve corresponding to the three axes of the inertial measurement unit, and analyzing the double logarithmic curve to obtain the random error corresponding to each of the three axes and compensate for it; In the present invention, step S3 includes the following sub-steps: Step S31, obtaining the Allen variance corresponding to the three axes of the inertial measurement unit, obtaining the Allen variance corresponding to the X-axis angular velocity, the Allen variance corresponding to the Y-axis angular velocity, the Allen variance corresponding to the Z-axis angular velocity, the Allen variance corresponding to the X-axis acceleration, the Allen variance corresponding to the Y-axis acceleration, and the Allen variance corresponding to the Z-axis acceleration; Step S32, performing a square root operation on the Allan variance corresponding to the X-axis angular velocity to obtain the Allan standard deviation corresponding to the X-axis angular velocity; Step S32, drawing a double logarithmic curve diagram corresponding to the X-axis angular velocity with the Allen standard deviation ABZ as the ordinate and the time T as the abscissa; Step S33, calculate the slope of the double logarithmic curve at any time, and then refer to the noise and slope relationship comparison table to obtain the relationship between different random noises and slopes; the common noise and slope relationship comparison table is as follows:

[0026] In the table, the first type of noise is quantization noise, which is an error generated when an analog signal is converted into a digital signal; the second type of noise is angle / velocity random walk. The angle random walk of the gyroscope / velocity random walk of the accelerometer belongs to a high-frequency error term, which is caused when the autocorrelation time is less than the sampling time; the third type of error is zero bias instability noise, which comes from circuit noise, environmental noise and other items that are susceptible to random flicker; the fourth type of noise is angular rate / acceleration random walk, and the fifth type of noise is angular rate / acceleration drift slope; the fourth and fifth types of noise are random noises with no definite source; Step S34, the present invention considers that the noise in the output data of the inertial measurement unit is generated by a specific noise source, and each noise source is independent of each other, so ALF(T)=BZ1 2 (T)+BZ2 2 (T)+BZ3 2 (T)+BZ4 2 (T) + BZ5 2 (T); where ALF(T) is the Allan variance corresponding to the angular velocity of the X-axis; Step S35, sort out ; In the formula, p is twice the slope, p = (-2, -1, 0, 1, 2), C p is a constant; Step S36, using the least squares method to fit the double logarithmic curve corresponding to the X-axis angular velocity, to obtain the C corresponding to the X-axis angular velocity p The noise coefficient corresponding to the X-axis angular velocity is obtained as follows: ; Among them, function fitting is the process of approximating a set of data through a mathematical function. Its goal is to select a mathematical function so that it is as close as possible to the actual observed value on the entire data set. Commonly used fitting methods include linear fitting (linear regression) and nonlinear fitting (such as polynomial regression, exponential regression, logarithmic regression, etc.); Step S37 is similar to the above, and the noise coefficient corresponding to the Y-axis angular velocity, the noise coefficient corresponding to the Z-axis angular velocity, the noise coefficient corresponding to the X-axis acceleration, the noise coefficient corresponding to the Y-axis acceleration, and the noise coefficient corresponding to the Z-axis acceleration are obtained. Step S38, when the object to be measured is subsequently measured, specific compensation is performed on the three-axis noise coefficient corresponding to the inertial measurement unit; Specifically, the compensation methods include: Step S381 sets the parameters of the noise filter, such as the cutoff frequency, the order, etc., according to the noise filter of different designs of the noise coefficient; Step S382, collecting the three-axis motion data of the inertial measurement unit again, and processing it through a noise filter, and then comparing the three-axis motion data before and after filtering to evaluate the filtering effect; Step S383, if the error after filtering is less than the error threshold, the filter setting is deemed complete; if the error after filtering is greater than or equal to the error threshold, the noise filter is redesigned until the noise filter setting is completed; Specifically, the error is obtained by subtracting the filtered three-axis motion data from the calculated standard three-axis motion data and then dividing the difference by the standard three-axis motion data.

[0027] Step S4, before using the inertial measurement unit to measure the target, collect environmental data of the environment where the target is located, analyze and compensate; The environmental data specifically includes the real-time ambient temperature and average atmospheric pressure of the environment where the target to be measured is located; In the present invention, step S4 includes the following sub-steps: Step S41, placing the inertial measurement unit in a temperature-controlled box, raising the temperature from -40°C to +80°C in steps of 10°C, stabilizing each temperature point for 30 minutes, and then recording the three-axis motion data corresponding to the inertial measurement unit; Step S42, fitting the temperature model of zero bias LP and scale factor CD by function: LP=a0+a1×WD+a2×WD 2 ;CD=b0+b1×WD; where WD is the real-time ambient temperature, a0, a1, a2, b0 and b2 are constants with fixed values; Among them, zero bias refers to the deviation of the output value of the inertial measurement unit when there is no external excitation or input, and the scale factor describes the proportional relationship between the sensor output and input; Step S43: Similarly, the corresponding zero bias and scale factor are obtained by magic fitting for the average atmospheric pressure; Step S44, measure the target object, and calculate and compensate the measurement result using the zero bias and scale factor corresponding to the real-time ambient temperature and the average atmospheric pressure; for the error caused by electromagnetic interference, since it can be eliminated by adding shielding equipment, the present invention does not provide a specific compensation method.

[0028] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0029] Embodiment 2, Figure 4 The present invention is a structural diagram of a computer device, which may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The processor may call the logic instructions in the memory to execute an inertial measurement unit error compensation method, which includes: collecting three-axis motion data shown by the inertial measurement unit at a fixed sampling frequency and a continuous sampling time; analyzing the three-axis motion data shown by the inertial measurement unit to obtain the Allen variance corresponding to each of the three axes of the inertial measurement unit; drawing the double logarithmic curves corresponding to the three axes of the inertial measurement unit, and analyzing the double logarithmic curves to obtain the random error corresponding to each of the three axes and compensate for it; before using the inertial measurement unit to measure the target to be measured, collecting the environmental data of the environment where the target to be measured is located, analyzing it and compensating it.

[0030] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0031] On the other hand, the present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute an inertial measurement unit error compensation method provided by the above methods, the method including: collecting three-axis motion data shown by the inertial measurement unit at a fixed sampling frequency and a continuous sampling time; analyzing the three-axis motion data shown by the inertial measurement unit to obtain the Allen variance corresponding to each of the three axes of the inertial measurement unit; drawing double logarithmic curves corresponding to the three axes of the inertial measurement unit, and analyzing the double logarithmic curves to obtain the random error corresponding to each of the three axes and compensate for it; before using the inertial measurement unit to measure the target to be measured, collecting environmental data of the environment where the target to be measured is located for analysis and compensation.

[0032] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute an inertial measurement unit error compensation method provided above, the method comprising: collecting three-axis motion data shown by the inertial measurement unit at a fixed sampling frequency and for a continuous sampling period; analyzing the three-axis motion data shown by the inertial measurement unit to obtain the Allen variance corresponding to each of the three axes of the inertial measurement unit; drawing a double logarithmic curve corresponding to the three axes of the inertial measurement unit, and analyzing the double logarithmic curve to obtain the random error corresponding to each of the three axes and compensate for it; before using the inertial measurement unit to measure the target to be measured, collecting environmental data of the environment where the target to be measured is located for analysis and compensation.

[0033] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0034] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for compensating an inertial measurement unit error, characterized in that: Methods include: Step S1, collecting three-axis motion data displayed by the inertial measurement unit at a fixed sampling frequency and for a continuous sampling duration; Step S2, analyzing the three-axis motion data displayed by the inertial measurement unit to obtain the Allen variance corresponding to each of the three axes of the inertial measurement unit; Step S3, drawing a double logarithmic curve corresponding to the three axes of the inertial measurement unit, and analyzing the double logarithmic curve to obtain the random error corresponding to each of the three axes and compensate for it; Step S4, before using the inertial measurement unit to measure the target, collect environmental data of the environment where the target is located, analyze and compensate.

2. The inertial measurement unit error compensation method according to claim 1, characterized in that: The three-axis motion data specifically includes the acceleration of the inertial measurement unit on the X-axis, Y-axis and Z-axis and the angular velocity of rotation around the X-axis, Y-axis and Z-axis; the environmental data includes the real-time ambient temperature and average atmospheric pressure of the environment where the target to be measured is located.

3. The inertial measurement unit error compensation method according to claim 1, characterized in that: The step S1 includes the following sub-steps: Step S11, fixing the inertial measurement unit on a stationary position and rate turntable, and the position and rate turntable provides a horizontal reference for the inertial measurement unit; Step S12, the position rate turntable is started and preheated for a fixed time YR; Step S13, continuously collecting the real-time acceleration and real-time angular velocity of the three axes corresponding to the inertial measurement unit at a sampling frequency CY for H hours.

4. The inertial measurement unit error compensation method according to claim 2, characterized in that: The step S2 includes the following sub-steps: Step S21, acquiring the three-axis motion data of the inertial measurement unit, and obtaining the X-axis angular velocity BX, the Y-axis angular velocity BY, the Z-axis angular velocity BZ, the X-axis acceleration AX, the Y-axis acceleration AY and the Z-axis acceleration AZ of the inertial measurement unit; Step S22, dividing the X-axis angular velocity of the inertial measurement unit into time intervals JG to obtain an X-axis angular velocity sequence XBX, wherein the X-axis angular velocity sequence contains N sampling points of the X-axis angular velocity, where N=1, 2, ..., z, and z is a positive integer; Step S23, divide the X-axis angular velocity sequence into K groups and number them R, each group contains M sampling points, and each group lasts for N×JG time; where R=1, 2, ..., K; M≤(N-1) / 2, K=N / M.

5. The inertial measurement unit error compensation method according to claim 4, characterized in that: The step S2 further comprises the following sub-steps: Step S24, adding and averaging the X-axis angular velocities corresponding to all the sampling points of the X-axis angular velocity in the first group to obtain the intra-group average value JR1 of the first group; Step S25, and so on, calculate the intra-group average values ​​JR2, ..., JRR, ..., JRK corresponding to the second group to the Kth group; Step S26, subtract the mean values ​​of the groups corresponding to the adjacent groups and take the absolute value to obtain the Allan variance of the group with the smaller number; Step S27, concatenating the Allan variances of adjacent groups to obtain the Allan variance corresponding to the X-axis angular velocity; Step S28, and so on, calculate the Allan variance corresponding to the Y-axis angular velocity, the Allan variance corresponding to the Z-axis angular velocity, the Allan variance corresponding to the X-axis acceleration, the Allan variance corresponding to the Y-axis acceleration, and the Allan variance corresponding to the Z-axis acceleration.

6. The inertial measurement unit error compensation method according to claim 5, characterized in that: The step S3 includes the following sub-steps: Step S31, obtaining the Allen variance corresponding to the three axes of the inertial measurement unit, obtaining the Allen variance corresponding to the X-axis angular velocity, the Allen variance corresponding to the Y-axis angular velocity, the Allen variance corresponding to the Z-axis angular velocity, the Allen variance corresponding to the X-axis acceleration, the Allen variance corresponding to the Y-axis acceleration, and the Allen variance corresponding to the Z-axis acceleration; Step S32, performing a square root operation on the Allan variance corresponding to the X-axis angular velocity to obtain the Allan standard deviation corresponding to the X-axis angular velocity; Step S32, drawing a double logarithmic curve diagram corresponding to the X-axis angular velocity with the Allen standard deviation ABZ as the ordinate and the time T as the abscissa; Step S33, calculating the slope of the double logarithmic curve at any time, and then referring to the noise-slope relationship comparison table to obtain the relationship between different random noises and the slope; Step S34: The noise in the output data of the inertial measurement unit is generated by noise sources, and each noise source is independent of each other, so ALF(T)=BZ1 2 (T)+BZ2 2 (T)+BZ3 2 (T)+BZ4 2 (T) + BZ5 2 (T); where ALF(T) is the Allan variance corresponding to the X-axis angular velocity, BZ1 2 (T) is the Allan standard deviation of a type of noise, BZ2 2 (T) is the Allan standard deviation of the second type of noise, BZ3 2 (T) is the Allan standard deviation of the three types of noise, BZ4 2 (T) is the standard deviation of the four types of noise, BZ5 2 (T) is the standard deviation of five types of noise; Step S35, sort out ; Where p is twice the slope, p = (-2, -1, 0, 1, 2), C p is a constant.

7. The inertial measurement unit error compensation method according to claim 6, characterized in that: The step S3 further comprises the following sub-steps: Step S36, using the least squares method to fit the double logarithmic curve corresponding to the X-axis angular velocity, to obtain the C corresponding to the X-axis angular velocity p The noise coefficient corresponding to the X-axis angular velocity is obtained as follows: ; Wherein, ZS1 is the noise coefficient of type 1 noise, ZS2 is the noise coefficient of type 2 noise, ZS3 is the noise coefficient of type 3 noise, ZS4 is the noise coefficient of type 4 noise, and ZS5 is the noise coefficient of type 5 noise; Step S37 is similar to the above, and the noise coefficient corresponding to the Y-axis angular velocity, the noise coefficient corresponding to the Z-axis angular velocity, the noise coefficient corresponding to the X-axis acceleration, the noise coefficient corresponding to the Y-axis acceleration, and the noise coefficient corresponding to the Z-axis acceleration are obtained. Step S38, when measuring the object to be measured, the noise coefficient of the three axes corresponding to the inertial measurement unit is compensated, and the compensation method includes the following: Step S381, designing a noise filter according to different noise coefficients, and setting parameters, cutoff frequency or order of the noise filter; Step S382, collecting the three-axis motion data of the inertial measurement unit again, and processing it through a noise filter, and then comparing the three-axis motion data before and after filtering to evaluate the filtering effect; Step S383, if the error after filtering is less than the error threshold, the filter setting is deemed complete; if the error after filtering is greater than or equal to the error threshold, the noise filter is redesigned until the noise filter setting is completed; The error is obtained by subtracting the filtered three-axis motion data from the calculated standard three-axis motion data and then dividing it by the standard three-axis motion data.

8. The inertial measurement unit error compensation method according to claim 7, characterized in that: The step S4 includes the following sub-steps: Step S41, placing the inertial measurement unit in a temperature-controlled box, raising the temperature from -40°C to +80°C in steps of 10°C, stabilizing each temperature point for 30 minutes, and then recording the three-axis motion data corresponding to the inertial measurement unit; Step S42, fitting the temperature model of zero bias LP and scale factor CD by function: LP=a0+a1×WD+a2×WD 2 ;CD=b0+b1×WD; where WD is the real-time ambient temperature, a0, a1, a2, b0 and b2 are constants with fixed values; Step S43: Similarly, the corresponding zero bias and scale factor are obtained by magic fitting for the average atmospheric pressure; Step S44, measuring the target object, and calculating and compensating the measurement result using the zero bias and scale factor corresponding to the real-time ambient temperature and the average atmospheric pressure.

9. A computer device, characterized in that: The computer device comprises: A memory storing a computer program; A processor is communicatively connected to the memory, and when the computer program is executed by the processor, the method described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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