MEMS-IMU calibration method and device in pipeline surveying and mapping robot

Abstract

The invention discloses an MEMS-IMU calibration method and device in a pipeline surveying and mapping robot, and belongs to the technical field of slope engineering structure reliability design. The device comprises a disc capable of rotating around the center of the disc, two fixing frames arranged on the disc side by side in a spaced mode and a pipeline surveying and mapping robot erected on the two fixing frames and capable of rotating around the axis of the pipeline surveying and mapping robot, and the axis of the pipeline surveying and mapping robot is perpendicular to the radial direction of the disc. The pipeline surveying and mapping robot comprises an MEMS-IMU (Micro Electro Mechanical System-Inertial Measurement Unit), and the MEMS-IMU comprises a three-axis accelerometer and a three-axis gyroscope. According to the method, the actual problem is analyzed and processed by adopting the non-probability reliability, and the engineering problem of serious lack of uncertain parameter statistical data in the reliability design of the slope engineering structure is effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipeline surveying and mapping robots, and particularly relates to a calibration method and device for a MEMS-IMU in a pipeline surveying and mapping robot. Background Art

[0002] In the field of pipeline surveying and mapping robots, accurate navigation and positioning are crucial for obtaining accurate pipeline information. As one of the key sensors of the robot, the performance of the inertial measurement unit (IMU) directly affects the accuracy of the surveying and mapping data.

[0003] Microelectromechanical system (MEMS)-IMU has been widely used in pipeline surveying and mapping robots due to its low cost. However, there are many error sources in MEMS-IMU, which seriously restrict its measurement accuracy. Among them, deterministic errors include bias error, scale factor error, and cross-axis coupling error. The bias error occurs when there is no input. For example, the accelerometer bias is in m / s 2 as the unit, and the gyroscope bias is usually expressed in degrees ° / h; the scale factor error results from the difference between the ideal and actual scale factors; the cross-axis coupling error is due to the imperfect installation of the sensors inside the IMU, resulting in misalignment between the sensitive axis and the mounting platform. These deterministic errors can be removed through calibration, but traditional calibration methods have many limitations.

[0004] Currently, there are mainly three categories of IMU calibration methods: one is the calibration method based on high-precision equipment, which is a standard method commonly used in factories and laboratories. The IMU needs to be placed on a precision turntable and aligned with the instrument axis for calibration (such as the six-position method). Although the calibration parameter accuracy is high, the equipment is expensive, the maintenance cost is high, professional personnel are required for operation, and the IMU needs to be regularly returned to the laboratory for recalibration, which is not suitable for on-site operations; the second is the multi-position calibration method without equipment, which is based on the relationship between the norms of the accelerometer and gyroscope measurement values and the earth's gravity and angular velocity of rotation. No additional equipment is required, but there are defects such as only being able to estimate some error parameters and the gyroscope part of the parameters being unreliable due to the small angular velocity of rotation of the earth. Even with improvements, it often relies on external equipment; the third is the equipment-free calibration method based on Kalman filtering, which aims to estimate the navigation state and calibration parameters within a unified framework. However, some methods are restricted by quasi-static conditions and it is difficult to achieve strict rotation to meet the zero-velocity assumption, and often do not consider the sensor axis misalignment and cross-calibration problems. Even with improvements, there are deficiencies such as data collection relying on specific conditions or not fully considering system observability.

[0005] Prior Art One: Fix the pipeline surveying and mapping robot on a two-axis turntable, control the turntable to move around different axes to multiple specific positions and stop, collect the data of the accelerometer and gyroscope at each position, and calibrate the bias, scale factor, and cross-axis coupling coefficient of the accelerometer in each axis, etc.

[0006] Disadvantages of the prior art 1: This method can obtain relatively accurate calibration results, but it requires a precision turntable, which is expensive, has high maintenance costs, and requires professional technical personnel to operate, and is not suitable for ordinary working scenarios.

[0007] Prior art 2: Rotate the pipeline mapping robot around its own central axis, use the measurement data of the MEMS-IMU as input, and estimate the error parameters based on the Kalman filter algorithm.

[0008] Disadvantages of the prior art 2: Although this method can calibrate the MEMS-IMU without external equipment, there is no input excitation in some directions, which will lead to poor observability of the system. And the worse the observability of the system, the less accurate the estimated calibration parameters will be. Summary of the Invention

[0009] The purpose of the embodiments of the present invention is to provide a calibration method and device for MEMS-IMU in a pipeline mapping robot, which is different from the existing calibration schemes that fix the pipeline mapping robot and rotate it singlely around its axis or use a two-axis turntable with a multi-position method. The scheme provided by the present invention does not require expensive precision equipment, nor a predefined rotation scheme. The calibration process only needs to rotate the equipment by hand, and the required error parameters are obtained by iterative calculation using the Kalman filter. This calibration scheme enables each sensitive axis of the MEMS-IMU in the pipeline mapping robot to be excited, the observability of the system is high, the estimated calibration parameters are accurate, and there is no need to meet the zero-velocity assumption, so that at least one technical problem involved in the background technology can be solved.

[0010] In order to solve the above technical problems, the present invention is implemented as follows:

[0011] The embodiments of the present invention provide a calibration device for MEMS-IMU in a pipeline mapping robot, including a disc that can rotate around its own center, two fixed frames arranged side by side and spaced apart on the disc, and a pipeline mapping robot that is erected on the two fixed frames and can rotate around its own axis. The axis of the pipeline mapping robot is perpendicular to the radial direction of the disc. The pipeline mapping robot includes a MEMS-IMU, and the MEMS-IMU includes a three-axis accelerometer and a three-axis gyroscope.

[0012] The present invention also provides a calibration method for MEMS-IMU in a pipeline mapping robot based on the above-mentioned calibration device for MEMS-IMU in a pipeline mapping robot, including the following steps:

[0013] Step S1, construct an error parameter model and an output model for the triaxial accelerometer and triaxial gyroscope of the MEMS-IMU in the pipeline mapping robot. The error parameter model includes zero-bias error, scale factor error, and cross-axis coupling error;

[0014] Step S2, construct an acceleration error model and a velocity error model caused by the lever-arm effect;

[0015] Step S3, construct a first-order differential error model of gravitational acceleration and output the first-order differential error;

[0016] Step S4, construct a Kalman filter model composed of a state equation and an observation equation;

[0017] Step S5, by manually rotating the pipeline mapping robot and the disk, input the zero-bias error, scale factor error, cross-axis coupling error, acceleration error, velocity error, misalignment angle caused by the non-coincidence of the navigation coordinate system n and the calculated navigation coordinate system system, the lever-arm length, and the first-order differential error into the Kalman filter model, and then perform iterative solution in the Kalman filter model until the output state quantity converges, completing the calibration of the MEMS-IMU in the pipeline mapping robot.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0019] 1. Compared with the traditional calibration scheme of the pipeline mapping robot, the calibration device required by the present invention is simple, does not need to use a precision turntable, does not need a predefined rotation scheme, and only needs to manually and randomly rotate the calibration device to complete the calibration of the pipeline mapping robot;

[0020] 2. The Kalman filter system constructed by the present invention considers the non-coincidence of the navigation coordinate system and the calculated navigation coordinate system, and accordingly introduces the first-order differential error of gravitational acceleration as the observable quantity, greatly improving the observability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings, where:

[0022] Figure 1 is the calibration device for the MEMS-IMU in the pipeline mapping robot provided by the embodiment of the present invention;

[0023] Figure 2 is one of the hardware structure schematic diagrams of the electronic device provided by the embodiment of the present invention;

[0024] Figure 3 This is the second schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present invention. Detailed implementation manners

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0026] The terms "first", "second", etc. in the description and claims of the present invention are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually of the same category, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally means an "or" relationship between the associated objects before and after.

[0027] Please refer to Figure 1 As shown, the embodiment of the present invention provides a calibration device for MEMS-IMU in a pipeline mapping robot, including a disk 1 that can rotate around its own center, two fixed frames 2 arranged side by side and spaced apart on the disk 1, and a pipeline mapping robot that is mounted on the two fixed frames and can rotate around its own axis 3. The axis 3 of the pipeline mapping robot is perpendicular to the radial direction of the disk 1. The pipeline mapping robot includes a MEMS-IMU 4, and the MEMS-IMU includes a three-axis accelerometer and a three-axis gyroscope.

[0028] The present invention also provides a calibration method for MEMS-IMU in a pipeline mapping robot based on the calibration device for MEMS-IMU in the pipeline mapping robot, including the following steps:

[0029] Step S1, constructing an error parameter model and an output model for the three-axis accelerometer and the three-axis gyroscope of the MEMS-IMU in the pipeline mapping robot. The error parameter model includes zero bias error, scale factor error, and cross-axis coupling error;

[0030] Step S2, constructing an acceleration error model and a velocity error model caused by the lever arm effect;

[0031] Step S3: Construct a first-order differential error model of gravitational acceleration and output the first-order differential error;

[0032] Step S4: Construct a Kalman filter model composed of a state equation and an observation equation;

[0033] Step S5: By manually rotating the pipeline mapping robot and the disc, input the zero bias error, scale factor error, cross-axis coupling error, acceleration error, velocity error, misalignment angle where the navigation coordinate system n and the calculated navigation coordinate system system do not coincide, the lever arm length, and the first-order differential error into the Kalman filter model, and then perform iterative solution on the Kalman filter model until the output state quantity converges, completing the calibration of the MEMS-IMU in the pipeline mapping robot.

[0034] In step S1, the error parameter model of the triaxial accelerometer specifically includes:

[0035] The zero bias error of the triaxial accelerometer is expressed by the following formula:

[0036]

[0037] In the formula, and respectively represent the axis zero bias errors of the x, y, and z axes of the triaxial accelerometer;

[0038] The scale factor error of the triaxial accelerometer is expressed by the following formula:

[0039]

[0040] In the formula, and respectively represent the axis scale factor errors of the x, y, and z axes of the triaxial accelerometer;

[0041] The coupling error of the triaxial accelerometer is expressed by the following formula:

[0042]

[0043] In the formula, α yx represents the measured value of the x-axis component of the triaxial accelerometer on its y-axis with respect to the carrier coordinate system; α zx represents the measured value of the x-axis component of the triaxial accelerometer on its z-axis with respect to the carrier coordinate system; α zy represents the measured value of the y-axis component of the triaxial accelerometer on its z-axis with respect to the carrier coordinate system.

[0044] The output model of the triaxial accelerometer is expressed by the following formula:

[0045]

[0046] Wherein, a s is the original output value of the triaxial accelerometer; v a is the measurement noise of the triaxial accelerometer.

[0047] In step S1, the error parameter model of the triaxial gyroscope specifically includes:

[0048] The bias error of the triaxial gyroscope is expressed by the following formula:

[0049]

[0050] Wherein, and respectively represent the axis bias errors of the x, y, and z axes of the triaxial gyroscope;

[0051] The scale factor error of the triaxial gyroscope is expressed by the following formula:

[0052]

[0053] Wherein, and respectively represent the axis scale factor errors of the x, y, and z axes of the triaxial gyroscope;

[0054] The cross-axis coupling error of the triaxial gyroscope is expressed by the following formula:

[0055]

[0056] Wherein, β xy represents the measured value of the y-axis component of the triaxial gyroscope on its x-axis with respect to the vehicle coordinate system; β xz represents the measured value of the z-axis component of the triaxial gyroscope on its x-axis with respect to the vehicle coordinate system; β yx represents the measured value of the x-axis component of the triaxial gyroscope on its y-axis with respect to the vehicle coordinate system; β yz represents the measured value of the z-axis component of the triaxial gyroscope on its y-axis with respect to the vehicle coordinate system; β zx represents the measured value of the x-axis component of the triaxial gyroscope on its z-axis with respect to the vehicle coordinate system; β zy represents the measured value of the y-axis component of the triaxial gyroscope on its z-axis with respect to the vehicle coordinate system.

[0057] The output model of the triaxial gyroscope is expressed by the following formula:

[0058]

[0059] Wherein, ω s is the original output value of the triaxial gyroscope; v ω is the measurement noise of the triaxial gyroscope.

[0060] In step S2, the velocity error model is expressed by the following formula:

[0061]

[0062] wherein, is the transformation from the vehicle coordinate system b to the navigation coordinate system n; l1 is the lever arm length formed by the deviation of the MEMS-IMU from the rotation axis; is the angular velocity of the pipeline mapping robot rotating about its own axis, which is expressed by the following formula:

[0063]

[0064] wherein, ω b is the angular velocity output by the three-axis gyroscope in the vehicle coordinate system b; is the angular velocity of the disc rotation, which is expressed by the following formula:

[0065]

[0066] wherein, v0 is the velocity of the pipeline mapping robot rotating about the disc; l2 is the distance between the pipeline mapping robot and the disc rotation center.

[0067] In step S2, the acceleration error model is expressed by the following formula:

[0068]

[0069] wherein, is the first derivative with respect to time t; represents the tangential acceleration error caused by; represents the normal acceleration error caused by; represents the additional velocity error caused by.

[0070] Step S3 specifically includes:

[0071] Since the navigation coordinate system n does not coincide with the calculated navigation coordinate system system, there is a small angle error whose skew-symmetric matrix is:

[0072]

[0073] According to the DCM chain rule, is expressed as:

[0074]

[0075] For the differential equation is expressed as:

[0076]

[0077] In the navigation coordinate system n, the gravitational acceleration is considered constant, so the first-order differential of the gravitational acceleration However, due to the internal noise of the MEMS-IMU, the first-order differential of the gravitational acceleration estimated by the Kalman filter system is not zero, and its estimated value is expressed as:

[0078]

[0079] In the formula, and respectively represent the estimated gravitational vector and angular velocity vector in the navigation coordinate system n; where:

[0080]

[0081] For the skew-symmetric matrix make the following transformation:

[0082]

[0083] Therefore, is expressed as:

[0084]

[0085] The attitude error is expressed as:

[0086]

[0087] Further expressed as:

[0088]

[0089] Because is a small quantity, make an approximation Therefore, the first-order differential of the gravitational acceleration is expressed by the following formula:

[0090]

[0091] In step S4, construct the state equation, specifically including:

[0092] The error parameters of the three-axis accelerometer and three-axis gyroscope in the MEMS-IMU, the velocity error, the navigation coordinate system n and the calculated navigation coordinate system The misalignment angle that does not coincide and the unknown lever arm length are added as state variables to the Kalman filter. Therefore, the vector x(t) is a 30-dimensional state vector, expressed as:

[0093]

[0094] State equation:

[0095]

[0096] Where:

[0097]

[0098] In the formula, the velocity error vector δv n =(δv n , δv e , δv d ) T ; the misalignment angle error The lever arm length l1=(l 1x , l 1y , l 1z ) T ; v(t) represents the excitation noise inside the system; a n is the acceleration vector in the navigation coordinate system n; [_×] is the skew-symmetric matrix of the _ matrix;

[0099] Select the velocity error and the first-order differential error of the gravitational acceleration as the observed values. Therefore, the observed vector is expressed as:

[0100]

[0101] Where:

[0102]

[0103] The observation equation is expressed as:

[0104] z(t)=Hx(t)+ε(t);

[0105] Where:

[0106]

[0107] In the formula, [_×] is the skew-symmetric matrix of the _ matrix; [_·] is the dot product of the _ matrix; g n is the gravitational acceleration in the navigation coordinate system n, g n =(0,0,g) T ; ε(t) is independent zero-mean Gaussian white noise.

[0108] See also Figure 2As shown in the figure, an embodiment of the present invention further provides an electronic device 600, which includes a processor 601, a memory 602, and a program or instruction stored on the memory 602 and executable on the processor 601. When the program or instruction is executed by the processor 601, it implements each process of the calibration method embodiment of the MEMS-IMU in the above pipeline mapping robot and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0109] It should be noted that the electronic devices in the embodiments of the present invention include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0110] Figure 3 It is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention.

[0111] The electronic device 700 includes, but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, and a processor 710 and other components.

[0112] Those skilled in the art can understand that the electronic device 700 may further include a power source (such as a battery) for supplying power to each component. The power source can be logically connected to the processor 710 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. Figure 3 The structure of the electronic device shown in the figure does not limit the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements, which will not be elaborated here.

[0113] It should be understood that in the embodiments of the present invention, the input unit 704 may include a Graphics Processing Unit (GPU) 7041 and a microphone 7042. The GPU 7041 processes the image data of static images or videos obtained by an image capturing device (such as a camera) in the video capture mode or the image capture mode. The display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 707 includes a touch panel 7071 and other input devices 7072. The touch panel 7071 is also known as a touch screen. The touch panel 7071 may include two parts: a touch detection device and a touch controller. The other input devices 7072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be elaborated here. The memory 709 may be used to store software programs and various data, including but not limited to application programs and operating systems. The processor 710 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor may not be integrated into the processor 710.

[0114] The embodiments of the present invention further provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the calibration method embodiment of the MEMS-IMU in the above pipeline mapping robot, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0115] Among them, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes a computer-readable storage medium, such as a computer Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc, etc.

[0116] The embodiments of the present invention further provide a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run a program or instruction to implement each process of the calibration method embodiment of the MEMS-IMU in the above pipeline mapping robot, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0117] It should be understood that the chip mentioned in the embodiments of the present invention may also be referred to as a system-on-chip, a system chip, a chip system, or a system-on-chip, etc.

[0118] It should be noted that, in this document, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or apparatus that includes a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or apparatus that includes such element.

[0119] In addition, it should be pointed out that the scope of the methods and systems in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0120] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims, and all of them fall within the protection scope of the present invention.

Claims

1. A calibration device for MEMS-IMU in a pipeline surveying and mapping robot, characterized in that It includes a disc that can rotate around its own center, two parallel and spaced fixed frames arranged on the disc, and a pipeline surveying robot mounted on the two fixed frames and capable of rotating around its own axis. The axis of the pipeline surveying robot is perpendicular to the radial direction of the disc. The pipeline surveying robot includes a MEMS-IMU, and the MEMS-IMU includes a three-axis accelerometer and a three-axis gyroscope.

2. A calibration method for the MEMS-IMU in the pipeline mapping robot with the calibration device of the MEMS-IMU in the pipeline mapping robot according to claim 1, characterized in that, It includes the following steps: Step S1, construct an error parameter model and an output model for the three-axis accelerometer and the three-axis gyroscope of the MEMS-IMU in the pipeline surveying robot. The error parameter model includes zero bias error, scale factor error, and cross-axis coupling error; Step S2, construct an acceleration error model and a velocity error model caused by the lever arm effect; Step S3, construct a first-order differential error model of gravitational acceleration and output the first-order differential error; Step S4, construct a Kalman filter model composed of a state equation and an observation equation; Step S5, by manually rotating the pipeline mapping robot and the disc, input the zero bias error, scale factor error, cross-axis coupling error, acceleration error, velocity error, misalignment angle where the navigation coordinate system n and the calculated navigation coordinate system system do not coincide, lever arm length, and first-order differential error into the Kalman filter model, and then perform iterative solution in the Kalman filter model until the output state quantity converges, completing the calibration of the MEMS-IMU in the pipeline mapping robot.

3. The method according to claim 2, characterized in that, In step S1, the error parameter model of the three-axis accelerometer specifically includes: The zero bias error of the three-axis accelerometer is expressed by the following formula: In the formula, and respectively represent the axis zero bias errors of the x, y, and z axes of the triaxial accelerometer; The scale factor error of the three-axis accelerometer is expressed by the following formula: In the formula, and respectively represent the axis scale factor errors of the x, y, and z axes of the triaxial accelerometer; The coupling error of the three-axis accelerometer is expressed by the following formula: where α yx represents the measured value of the x-axis component of the triaxial accelerometer on its y-axis with respect to the x-axis of the carrier coordinate system; α zx represents the measured value of the x-axis component of the triaxial accelerometer on its z-axis with respect to the x-axis of the carrier coordinate system; α zy represents the measured value of the y-axis component of the triaxial accelerometer on its z-axis with respect to the y-axis of the carrier coordinate system.

4. The method according to claim 3, wherein The output model of the three-axis accelerometer is expressed by the following formula: where a s is the original output value of the triaxial accelerometer; v a is the measurement noise of the triaxial accelerometer.

5. The method according to claim 4, characterized in that, In step S1, the error parameter model of the three-axis gyroscope specifically includes: The zero bias error of the three-axis gyroscope is expressed by the following formula: Wherein, and respectively represent the axis zero bias errors of the x, y, and z axes of the three-axis gyroscope; The scale factor error of the three-axis gyroscope is expressed by the following formula: In the formula, and respectively represent the axis scale factor errors of the x, y, and z axes of the three-axis gyroscope; The cross-axis coupling error of the three-axis gyroscope is expressed by the following formula: In the formula, β xy represents the measured value of the y-axis component of the triaxial gyroscope on its x-axis with respect to the carrier coordinate system; β xz represents the measured value of the z-axis component of the triaxial gyroscope on its x-axis with respect to the carrier coordinate system; β yx represents the measured value of the x-axis component of the triaxial gyroscope on its y-axis with respect to the carrier coordinate system; β yz represents the measured value of the z-axis component of the triaxial gyroscope on its y-axis with respect to the carrier coordinate system; β zx represents the measured value of the x-axis component of the triaxial gyroscope on its z-axis with respect to the carrier coordinate system; β zy represents the measured value of the y-axis component of the triaxial gyroscope on its z-axis with respect to the carrier coordinate system.

6. The method according to claim 5, characterized in that, The output model of the three-axis gyroscope is expressed by the following formula: where ω s is the original output value of the three-axis gyroscope; v ω is the measurement noise of the three-axis gyroscope.

7. The method according to claim 6, wherein In step S2, the velocity error model is expressed by the following formula: In the formula, is for converting from the carrier coordinate system b to the navigation coordinate system n; l1 is the lever arm length formed by the deviation of the MEMS-IMU from the rotation axis; is the angular velocity of the pipeline surveying robot rotating around its own axis and is expressed by the following formula: where ω b is the angular velocity output by the three-axis gyroscope in the carrier coordinate system b; is the angular velocity of the disk rotation, which is expressed by the following formula: In the formula, v0 is the velocity of the pipeline surveying robot rotating around the disc; l2 is the distance between the pipeline surveying robot and the center of rotation of the disc.

8. The method according to claim 7, characterized in that, In step S2, the acceleration error model is expressed by the following formula: In the formula, is the first derivative with respect to time t; represents the tangential acceleration error caused; represents the normal acceleration error caused; represents the additional velocity error caused.

9. The method according to claim 8, wherein Step S3 specifically includes: Since the navigation coordinate system n and the calculated navigation coordinate system system do not coincide, there is a small angular error Its skew-symmetric matrix is: According to the DCM chain rule, It is expressed as: For the differential equation is expressed as: In the navigation coordinate system n, the gravitational acceleration is considered constant, so the first-order differential of the gravitational acceleration However, due to the internal noise of the MEMS-IMU, the first-order differential of the gravitational acceleration estimated by the Kalman filter system is not zero, and its estimated value is expressed as: In the formula, and respectively represent the estimated gravity vector and angular velocity vector in the navigation coordinate system n; where: For a skew-symmetric matrix Perform the following transformation: Therefore, It is expressed as: The attitude error is expressed as: Further expressed as: Because is a small quantity and an approximation is made Therefore, the first-order differential of the gravitational acceleration is expressed by the following equation:

10. The method according to claim 9, characterized in that, In step S4, when constructing the state equation, it specifically includes: The error parameters of the three-axis accelerometer and three-axis gyroscope in the MEMS-IMU, the velocity error, the misalignment angle due to the non-coincidence of the navigation coordinate system n and the calculated navigation coordinate system system, and the lever arm length are added as state variables to the Kalman filter. Therefore, the vector x(t) is a 30-dimensional state vector, expressed as: State equation: Among them: In the formula, the velocity error vector δv n =(δv n , δv e , δv d ) T ; the misalignment angle error The lever arm length l1 = (l 1x , l 1y , l 1z ) T ; v(t) represents the excitation noise inside the system; a n is the acceleration vector in the navigation coordinate system n; [_×] is the skew-symmetric matrix of the _ matrix; Select the velocity error and the first-order differential error of gravitational acceleration as the observed values, so the observation vector is expressed as: Among them: The observation equation is expressed as: z(t) = Hx(t) + ε(t); Among them: where, [_×] is the skew-symmetric matrix of the _ matrix; [_·] is the dot product of the _ matrix; g n is the gravitational acceleration in the navigation coordinate system n, g n =(0, 0, g) T ; ε(t) is independent zero-mean Gaussian white noise.

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