CN117589203B - Gyroscope calibration method - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
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Abstract
The invention provides a gyroscope calibration method, which belongs to the field of inertial sensors and comprises the following steps: setting a gyroscope on a three-axis turntable, randomly rotating the three-axis turntable, and acquiring a first attitude angle of the three-axis turntable after rotating and standing and the angular speed of the gyroscope in the rotating process of the three-axis turntable; integrating the angular velocity of the gyroscope to obtain a second attitude angle of the gyroscope; designing a cost function according to the difference between the first attitude angle and the second attitude angle; constructing a deep learning calibration model through a cost function; and acquiring an original measurement image sequence of the gyroscope, inputting the original measurement image sequence into a deep learning calibration model, and calibrating the gyroscope through the deep learning calibration model. The parameter quantity of the gyroscope deep learning calibration model constructed by the method and the parameter quantity of the classical linear error calibration model are not in one scale level, and are far smaller than the thousands of parameter quantity scales of the existing deep learning calibration model, so that the gyroscope deep learning calibration model can be directly deployed on an embedded platform with limited computing resources.
Description
Technical Field
The invention belongs to the field of inertial sensors, and particularly relates to a gyroscope calibration method.
Background
The calibration of the gyroscope is an indispensable key step before the gyroscope is used, the existing calibration method mostly utilizes an electric turntable to provide reference angular velocity, and then the actual measured value of the gyroscope is compared for calibration model modeling, and the high-precision electric turntable has higher cost.
The problem of high cost can be avoided by using the manual turntable for calibration, at present, when the manual turntable is used for calibration, a gyroscope calibration model based on deep learning can be used for calibration, however, the existing gyroscope calibration model based on deep learning has large parameter, powerful computing platforms such as a desktop CPU (Central processing Unit) or a GPU (graphics processing Unit) are required for providing calculation support, and the gyroscope calibration model cannot be operated on an embedded platform with limited computing resources.
Disclosure of Invention
In order to overcome the defect of larger parameter quantity of the existing gyroscope calibration model based on deep learning, the invention provides a gyroscope calibration method, which comprises the following steps:
setting a gyroscope on a three-axis turntable, randomly rotating the three-axis turntable, and acquiring a first attitude angle of the three-axis turntable after rotating and standing and the angular speed of the gyroscope in the rotating process of the three-axis turntable;
Integrating the angular velocity of the gyroscope to obtain a second attitude angle of the gyroscope;
designing a cost function according to the difference between the first attitude angle and the second attitude angle;
Constructing a deep learning calibration model through the cost function; and acquiring an original measurement image sequence of the gyroscope, inputting the original measurement image sequence into a deep learning calibration model, and calibrating the gyroscope through the deep learning calibration model.
Preferably, the angular velocity of the gyroscope is integrated according to the following formula:
，
Wherein, 、/>And/>Respectively/>The pitch angle, the roll angle and the course angle obtained by integrating the angular velocity of the moment gyroscope are r Gyro,i-1、pGyro,i-1、yGyro,i-1 respectively which are the pitch angle, the roll angle and the course angle obtained by integrating the angular velocity of the moment gyroscope at i-1/>、/>And/>Respectively/>Derivative of pitch angle, roll angle and course angle obtained by integrating angular velocity of moment gyroscope,/>、/>And/>Derivative of pitch angle, roll angle and course angle obtained by integrating angular velocity of gyroscope at moment i-1 respectively,/>、/>And/>Angular velocity measurements at i-1 for the gyroscope x-, y-, and z-axes, respectively,/>、/>And/>The reference roll angle, the reference pitch angle and the reference heading angle provided by the three-axis turntable at the moment i-1 are respectively represented.
Preferably, the second attitude angle is represented by an euler angle, and is specifically written as a vector form:
Wherein, 、/>And/>Respectively/>And integrating the angular speed of the moment gyroscope to obtain a pitch angle, a roll angle and a course angle.
Preferably, the cost function is:
In the method, in the process of the invention, Is the second attitude angle at the moment i+1,/>Representing a first attitude angle vector at time i+1 provided by the three-axis turntable,/>For cross-multiplying symbols, express/>And/>The difference between the two vectors at time i+1.
Preferably, the number of the first attitude angles is a plurality, and the acquiring method includes the following steps:
setting a gyroscope on the three-axis turntable;
randomly rotating the three-axis turntable, and reading a first attitude angle of the three-axis turntable in a static state after rotation by using a data acquisition module in the three-axis turntable;
repeating the first attitude angle acquisition step until the number of the acquired first attitude angles reaches a predetermined number of times.
Preferably, the three-axis turntable is a three-axis manual turntable.
Preferably, the number of the second attitude angles is the same as the number of the first attitude angles.
Preferably, the network structure of the deep learning calibration model is composed of 4 identical convolution operations and 5 network splices.
Preferably, the parameter amount of the deep learning calibration model is 48.
Preferably, the original measurement image sequence is 1 in channel number and 1 in heightImage of width 3,/>The value of (2) is 100-600.
The gyroscope calibration method provided by the invention has the following beneficial effects:
According to the invention, the angular velocity of the gyroscope in the rotation process of the three-axis turntable is integrated, so that a second attitude angle corresponding to the first attitude angle can be obtained; the cost function can be designed through the difference between the first attitude angle and the second attitude angle, and a deep learning calibration model is built through the cost function; the parameter quantity of the built gyroscope deep learning calibration model is only 48, and is far smaller than the thousands of parameter quantity scales of the existing deep learning calibration model, so that the gyroscope deep learning calibration model can be directly deployed on an embedded platform with limited computing resources and can run in real time.
Drawings
In order to more clearly illustrate the embodiments of the present invention and the design thereof, the drawings required for the embodiments will be briefly described below. The drawings in the following description are only some of the embodiments of the present invention and other drawings may be made by those skilled in the art without the exercise of inventive faculty.
FIG. 1 is a flow chart of a method for calibrating a gyroscope according to an embodiment of the present invention;
FIG. 2 is a network structure of a deep learning calibration model;
FIG. 3 is a training loss curve in the present embodiment;
FIG. 4 shows pitch and roll angles integrated before gyroscope calibration;
fig. 5 shows pitch and roll angles integrated after gyroscope calibration.
Detailed Description
The present invention will be described in detail below with reference to the drawings and the embodiments, so that those skilled in the art can better understand the technical scheme of the present invention and can implement the same. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the technical solutions of the present invention and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless explicitly specified or limited otherwise, the terms "connected," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more, and will not be described in detail herein.
Examples
The invention provides a gyroscope calibration method, which is shown in figure 1 and comprises the following steps:
Step 1: and setting the gyroscope on the three-axis turntable, randomly rotating the three-axis turntable, and acquiring a first attitude angle of the three-axis turntable after rotating and standing and the angular speed of the gyroscope in the rotating process of the three-axis turntable. The number of the first attitude angles is a plurality of, and the acquisition method comprises the following steps:
the gyroscope is arranged on the three-axis turntable, and the three-axis turntable is a three-axis manual turntable, so that the problem of high cost caused by using a high-precision electric turntable can be avoided;
randomly rotating the three-axis turntable, and reading a first attitude angle of the three-axis turntable in a static state after rotation by using a data acquisition module in the three-axis turntable;
repeating the first attitude angle acquisition step until the number of the acquired first attitude angles reaches a predetermined number of times.
Step 2: and integrating the angular velocity of the gyroscope to obtain a second attitude angle of the gyroscope.
The angular velocity of the gyroscope is integrated according to the following formula:
，
Wherein, 、/>And/>Respectively/>The pitch angle, the roll angle and the course angle obtained by integrating the angular velocity of the moment gyroscope are r Gyro,i-1、pGyro,i-1、yGyro,i-1 respectively which are the pitch angle, the roll angle and the course angle obtained by integrating the angular velocity of the moment gyroscope at i-1/>、/>And/>Respectively/>Derivative of pitch angle, roll angle and course angle obtained by integrating angular velocity of moment gyroscope,/>、/>And/>Derivative of pitch angle, roll angle and course angle obtained by integrating angular velocity of gyroscope at moment i-1 respectively,/>、/>And/>Angular velocity measurements at i-1 for the gyroscope x-, y-, and z-axes, respectively,/>、/>And/>The reference roll angle, the reference pitch angle and the reference heading angle provided by the three-axis turntable at the moment i-1 are respectively represented.
The second attitude angle obtained is represented by the euler angle, which is written specifically in the form of a vector:
Wherein, 、/>And/>Respectively/>And integrating the angular speed of the moment gyroscope to obtain a pitch angle, a roll angle and a course angle.
Step 3: according to the difference between the first attitude angle and the second attitude angle, a cost function is designed, wherein the cost function is as follows:
In the method, in the process of the invention, Is the second attitude angle at the moment i+1,/>Representing a first attitude angle vector at time i+1 provided by the three-axis turntable,/>For cross-multiplying symbols, express/>And/>The difference between the two vectors at time i+1.
Step 4: and constructing a deep learning calibration model through the cost function, acquiring an original measurement image sequence of the gyroscope, inputting the original measurement image sequence into the deep learning calibration model, and calibrating the gyroscope through the deep learning calibration model.
In this embodiment, the deep learning calibration model is composed of 4 identical convolution operations and 5 network splicing operations, and its structure is shown in fig. 2, specifically, the invention collects reference attitude angle data of the three-axis turntable and original measurement data of the gyroscope, pitch angle and amplitude of the roll angle of the three-axis turntable are both in sine wave motion, the original measurement image sequence of the gyroscope is that the number of channels is 1, and the height is 1An image of width 3, wherein/(The value range of (2) is 100-600, which is used for training the deep learning calibration model, the total number of parameters to be trained is 48, the obtained training loss curve is shown in figure 3, and as can be known from figure 3, the training loss curve is converged rapidly at the beginning of training and gradually becomes stable after the 10 th round.
According to the diagrams shown in fig. 4 and 5, the pitch angle and the roll angle obtained by integrating the gyroscope measurement data before calibration have obvious deviation from the true value; after the calibration model trained by the embodiment is calibrated, the pitch angle and the roll angle obtained by integrating the gyroscope measurement data are highly coincident with the true value, so that the effectiveness of the method is effectively verified.
The network parameters obtained after training the deep learning calibration model are shown in table 1.
TABLE 1 parameters of various convolutional layers obtained by training a deep learning calibration model
Convolutional layer 1
Convolutional layer 2
Convolutional layer 3
Convolutional layer 4
The parameter quantity (48) of the deep learning parameter quantity and the classical linear error calibration model designed based on the patent is in one scale level and is far smaller than the thousands of parameter quantity scales of the existing deep learning calibration model, so that the method can be directly deployed on an embedded platform with limited computing resources and run in real time.
The above embodiments are merely preferred embodiments of the present invention, the protection scope of the present invention is not limited thereto, and any simple changes or equivalent substitutions of technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention disclosed herein are all within the protection scope of the present invention.
Claims (4)
1. The gyroscope calibration method is characterized by comprising the following steps of:
setting a gyroscope on a three-axis turntable, randomly rotating the three-axis turntable, and acquiring a first attitude angle of the three-axis turntable after rotating and standing and the angular speed of the gyroscope in the rotating process of the three-axis turntable;
Integrating the angular velocity of the gyroscope to obtain a second attitude angle of the gyroscope;
designing a cost function according to the difference between the first attitude angle and the second attitude angle;
constructing a deep learning calibration model through the cost function; acquiring an original measurement image sequence of a gyroscope, inputting the original measurement image sequence into a deep learning calibration model, and calibrating the gyroscope through the deep learning calibration model;
integrating the angular velocity of the gyroscope according to the following formula:
Wherein r Gyro,i、pGyro,i and y Gyro,i are pitch angle, roll angle and course angle obtained by integrating the angular velocity of the gyroscope at the moment i, r Gyro,i-1、pGyro,i-1、yGyro,i-1 is pitch angle, roll angle and course angle obtained by integrating the angular velocity of the gyroscope at the moment i-1, And/>Derivative of pitch angle, roll angle and course angle obtained by integrating angular velocity of gyroscope at moment iAnd/>The pitch angle, the roll angle and the derivative of the course angle are obtained by integrating the angular velocity of the gyroscope at the moment i-1, omega x,i-1、ωy,i-1 and omega z,i-1 are respectively measured values of the angular velocity of the gyroscope at the moment i-1, and r Ref,i-1、pRef,i-1 and y Ref,i-1 respectively represent a reference roll angle, a reference pitch angle and a reference course angle provided by a three-axis turntable at the moment i-1;
the second attitude angle is represented by an Euler angle, and is specifically written as a vector form:
vGyro,i＝[rGyro,i pGyro,i yGyro,i]
Wherein r Gyro,i、pGyro,i and y Gyro,i are pitch angle, roll angle and course angle obtained by integrating angular velocity of the gyroscope at moment i respectively;
The cost function is:
where v Gyro,i+1 is the second attitude angle at time i+1, v Ref,i+1＝[rRef,i+1 pRef,i+1 yRef,i+1 represents the first attitude angle vector at time i+1 provided by the three-axis turntable, Is a cross sign, representing the difference between the two vectors v Gyro,i+1 and v Ref,i+1 at time i+1;
The network structure of the deep learning calibration model is formed by 4 identical convolution operations and 5 network splicing;
the parameter quantity of the deep learning calibration model is 48;
The original measurement image sequence is an image with the number of channels being 1, the height being M and the width being 3, and the value range of M is 100-600.
2. The gyroscope calibration method according to claim 1, wherein the number of the first attitude angles is plural, and the acquisition method includes the steps of:
setting a gyroscope on the three-axis turntable;
randomly rotating the three-axis turntable, and reading a first attitude angle of the three-axis turntable in a static state after rotation by using a data acquisition module in the three-axis turntable;
repeating the first attitude angle acquisition step until the number of the acquired first attitude angles reaches a predetermined number of times.
3. The method of calibrating a gyroscope according to claim 1, wherein the three-axis turntable is a three-axis manual turntable.
4. The gyroscope calibration method of claim 1, wherein the number of second attitude angles is the same as the number of first attitude angles.
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