Method and device for online estimating mounting angle of IMU lever arm and vehicle body and medium

Abstract

The invention relates to a method, equipment and medium for online estimating mounting angles of an IMU (inertial measurement unit) lever arm and a vehicle body, and the method comprises the following steps: starting a satellite navigation terminal and an inertial measurement unit, receiving RTK (real-time kinematic) base station data through a wireless network, and inputting the RTK base station data into the satellite navigation terminal; the method comprises the following steps: acquiring and verifying satellite positioning position and course, after confirming that data meets a preset standard, inputting the data and speed, angular speed and acceleration data into a data conversion module for conversion, inputting the converted data into an error Kalman filtering module, and constructing a system observation equation according to a position error, a course error and a converted speed error; according to the scheme, the adaptive capacity of the navigation system to dynamic changes is improved, and the navigation and positioning precision in various complex environments is also remarkably improved. The method has the beneficial effects that the adaptive capacity of the navigation system to dynamic changes is improved, and the navigation and positioning precision in various complex environments is also remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle navigation systems, and particularly to a method, device, and medium for online estimating the lever arm of an IMU and the vehicle body mounting angle. Background Art

[0002] With the rapid development of vehicle intelligence, more and more vehicles are starting to be equipped with advanced navigation and positioning sensors to support automatic control and path planning functions. In the initial stage of intelligent driving technology, vehicle manufacturers or autonomous driving kit providers mainly relied on purchasing integrated inertial navigation systems (INS) that have already integrated satellite navigation (GNSS) to meet basic navigation needs. However, when dealing with complex and changing actual usage scenarios, such as urban roads, rail transit, open-pit mines, etc., this general integrated inertial navigation system often fails to meet specific high-precision positioning requirements.

[0003] Most of the existing technologies rely on pre-calibration methods to calibrate the external parameters between the inertial measurement unit (IMU), the satellite navigation system, and the vehicle body. Although this pre-calibration method is effective in some cases, in a real-time dynamic environment, it cannot adapt to the mounting angle deviation and lever arm deviation caused by dynamic changes, and these deviations will directly affect the accuracy and reliability of the navigation system. Summary of the Invention

[0004] The present invention provides a method, device, and medium for online estimating the lever arm of an IMU and the vehicle body mounting angle, aiming to solve the problem in the prior art that the mounting angle deviation and lever arm deviation between the IMU, the vehicle body, and the satellite navigation system cannot be estimated and corrected in real time, thereby improving the navigation and positioning accuracy and robustness of the intelligent driving system in complex and changing environments.

[0005] To achieve the above object, the first aspect of the present invention provides a method for online estimating the lever arm of an IMU and the vehicle body mounting angle, including the following steps:

[0006] Start the satellite navigation terminal and the inertial measurement unit, receive data from the RTK reference station through the wireless network, and input the data into the satellite navigation terminal;

[0007] Obtain the position data, heading data, and speed data of satellite positioning from the satellite navigation terminal, and obtain the angular velocity data and acceleration data from the inertial measurement unit;

[0008] Perform state detection on the received position data, heading data, speed data, angular velocity data, and acceleration data to confirm whether the data status reaches a preset standard;

[0009] Perform data conversion on the data that reaches the preset standard through a data conversion module;

[0010] Input the converted position data, heading data, speed data, acceleration data, and angular velocity data into the error Kalman filter module; through error Kalman filtering, based on the system state equation and the system observation equation, estimate the transformation matrix deviation from the IMU coordinate system to the navigation coordinate system, gyro zero bias deviation, accelerometer zero bias deviation, transformation matrix deviation from the vehicle body coordinate system to the IMU coordinate system, and lever arm deviation.

[0011] Further, the method for performing state detection on the received position data, heading data, speed data, angular velocity data, and acceleration data includes:

[0012] Read the position data, heading data, speed data output by the navigation terminal, and the angular velocity data and acceleration data of the inertial measurement unit;

[0013] Confirm whether the states of the position data, heading data, speed data, angular velocity data, and acceleration data reach the preset standards;

[0014] After the states of the position data, heading data, speed data, angular velocity data, and acceleration data are shown to reach the preset standards, input the position data, heading data, speed data, angular velocity data, and acceleration data into the coordinate conversion module;

[0015] If the states of the position information, heading data, and speed data are not shown to reach the preset standards, it is necessary to check for equipment failures or operation errors until the data states are shown to reach the preset standards.

[0016] Further, the method for performing data conversion on the data that reaches the preset standards through the data conversion module includes:

[0017] Through the Convert_Geodetic_To_UTM function in the coordinate conversion library, convert the longitude and latitude coordinates in the position information output by the satellite navigation terminal into metric UTM coordinates;

[0018] Convert the acceleration unit of the inertial measurement unit from g to meters per second squared, convert the angular velocity unit from degrees to radians, and convert the speed unit from kilometers per hour to meters per second.

[0019] Further, the system state equation includes:

[0020]

[0021] Where, · represents the derivative, Δp is the position deviation, Δv is the speed deviation, Δθ is the Lie algebra form of the transformation matrix deviation to be estimated from the IMU coordinate system to the navigation coordinate system Δb g is the gyro zero bias deviation, Δb aFor the plus-count zero bias, b g and b a are the gyro zero bias and the plus-count zero bias respectively, and Δα is the transformation matrix from the vehicle body coordinate system to the IMU coordinate system to be estimated The Lie algebra form of the bias, Δl is the lever arm bias, ω is the angular velocity vector, a is the acceleration vector, and × represents transforming a three-dimensional vector into an anti-symmetric matrix.

[0022] Furthermore, the system observation equation is:

[0023]

[0024] Wherein, is the representation of the satellite antenna position in the navigation coordinate system n, is the representation of the IMJ position in the navigation coordinate system n, represents the transformation matrix from the IMU coordinate system to the navigation coordinate system, is the transformation matrix from the vehicle body coordinate system to the IMU coordinate system, Log() represents the Lie algebra operation corresponding to the Lie group, represents the transformation matrix from the IMU coordinate system to the vehicle body coordinate system, represents the transformation matrix from the navigation coordinate system to the IMU coordinate system, represents the transformation matrix from the vehicle body coordinate system to the navigation coordinate system, is the representation of the point where the satellite antenna is located in the navigation coordinate system n, is the representation of the point where the satellite antenna is located in the vehicle body coordinate system v, R represents the rotation matrix, represents the transformation matrix from the global coordinate system to the vehicle body coordinate system.

[0025] Furthermore, in the case of no vehicle-mounted wheel speedometer, the software algorithm for transforming the satellite antenna speed observation is used to replace the actual wheel speedometer sensor, and the transformation method is:

[0026]

[0027] Wherein, V x 、V y and V z represent the components of the velocity vector decomposed in the vehicle body coordinate system, V0, V1, and V2 represent three independent velocity components in a certain coordinate system, x is a scalar, are both 3*1 column vectors, P and V represent position and velocity respectively, k and k-1 represent the current moment and the previous moment respectively, sign(x) is the sign function, taking the value +1 when x>0, taking the value -1 when x<0, and taking the value 0 when x = 0.

[0028] Further, filter the angular velocity data and acceleration data acquired by the inertial measurement unit.

[0029] Further, before the position, heading data, and speed data obtained from the satellite navigation terminal are displayed as reaching the preset standard, detect the signal reception quality of the satellite navigation terminal.

[0030] To achieve the above object, a second aspect of the present invention provides an electronic device, including a processor and a memory. When the processor executes the computer program stored in the memory, it implements the steps of the method for online estimating the IMU lever arm and the vehicle body mounting angle.

[0031] To achieve the above object, a third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the method for online estimating the IMU lever arm and the vehicle body mounting angle.

[0032] Advantages of the present invention:

[0033] Compared with the prior art, a method, device, and medium for online estimating the IMU lever arm and the vehicle body mounting angle provided by the present invention adopt a real-time error Kalman filtering algorithm to dynamically adjust and compensate for the lever arm and mounting deviation between the IMU, the vehicle body, and the satellite navigation system. This method first collects the position, heading, speed, acceleration, and angular velocity data output by the satellite navigation terminal and the inertial measurement unit through an in-vehicle computer, and then converts these data into information in a unified coordinate system through a coordinate conversion module. By establishing a system state equation, this method incorporates the mounting deviation and lever arm deviation of the IMU into the state variables and uses the position and heading data of satellite positioning as the observation information. Subsequently, state estimation is performed through error Kalman filtering to update and correct the deviation in real time. In addition, the present invention further enhances the stability and robustness of the estimation algorithm by introducing the dual-satellite antenna heading angle information and transforming the GNSS observation results. This overall solution not only improves the adaptability of the navigation system to dynamic changes but also significantly improves the navigation and positioning accuracy in various complex environments. Description of the Drawings

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments.

[0035] Figure 1 It is a flowchart of a method for online estimating the IMU lever arm and the vehicle body mounting angle disclosed in an embodiment of the present invention.

[0036] Figure 2 It is a structural schematic block diagram of an electronic device disclosed in an embodiment of the present invention. Detailed implementation mode

[0037] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 work shall fall within the protection scope of the present invention.

[0038] According to the embodiments of the present invention, it should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the following manufacturing method, in some cases, the steps shown or described can be executed in a different order from here.

[0039] This patent proposes a real-time online estimation method based on the error Kalman filter algorithm. This method can not only adjust and compensate the lever arm and installation deviation between the IMU, the vehicle body, and the satellite positioning antenna in real time, but also improve the stability and robustness of the algorithm by introducing the transformation of GNSS observation results and using the heading angle information of the dual-satellite antenna, thereby significantly improving the navigation and positioning accuracy of the intelligent driving system.

[0040] The present invention is implemented by adopting the following technical solutions:

[0041] As Figure 1 shown, the present invention provides a method for online estimating the lever arm of the IMU and the installation angle of the vehicle body, including the following steps:

[0042] Step S100: Start the satellite navigation terminal and the inertial measurement unit, receive data from the RTK reference station through the wireless network, and input the data into the satellite navigation terminal;

[0043] First, power on the satellite navigation terminal and the inertial measurement unit (IMU). The satellite navigation terminal is connected to the RTK reference station through the vehicle-mounted wireless network module and receives real-time differential correction data from the reference station. These data are transmitted to the navigation terminal in real time through the wireless network to ensure that the received positioning information has higher accuracy and reliability.

[0044] Step S200: Obtain the position data, heading data, and speed data of satellite positioning from the satellite navigation terminal, and obtain the angular velocity data and acceleration data from the inertial measurement unit;

[0045] After the system successfully starts, the navigation terminal will begin to receive and process signals from the Global Positioning System to obtain the current position, heading data, and speed data. At the same time, the IMU starts to record and output the current angular velocity and acceleration data of the vehicle.

[0046] Step S300: Perform status detection on the received position data, heading data, speed data, as well as angular velocity data and acceleration data to confirm whether the data status reaches the preset standard.

[0047] Perform status detection on the received position data, heading data, and speed data to confirm whether these data have reached the system preset standard. If the data status shows that it has not reached the preset standard, check the satellite signal reception quality or whether there are operation errors or malfunctions in the detection equipment. This step ensures the quality of the data for subsequent processing and prevents the accuracy and reliability of navigation from being affected by data quality problems.

[0048] Step S400: Perform data conversion on the data that reaches the preset standard through the data conversion module.

[0049] Convert the confirmed good-position data, heading data, speed data, as well as the angular velocity and acceleration data of the IMU through the coordinate conversion library. Use a third-party library such as the Convert_Geodetic_To_UTM function in utm-convert to convert the latitude and longitude coordinates into metric units in the UTM coordinate system. At the same time, convert the units of acceleration and angular velocity from g and degrees to meters per square second and radians, and convert the speed unit from kilometers per hour to meters per second. Ensure that all data is processed under a unified measurement unit to guarantee the accuracy of the calculation.

[0050] Step S500: Input the converted position data, heading data, speed data, acceleration data, and angular velocity data into the error Kalman filter module; through error Kalman filtering, based on the system state equation and system observation equation, estimate the transformation matrix deviation from the IMU coordinate system to the navigation coordinate system, gyro zero bias deviation, accelerometer zero bias deviation, transformation matrix deviation from the vehicle body coordinate system to the IMU coordinate system, and lever arm deviation.

[0051] Input the converted data into the error Kalman filter module. This module uses the system state equation and observation equation to estimate various deviations, including the transformation matrix deviation from the IMU coordinate system to the navigation coordinate system, gyro zero bias deviation, accelerometer zero bias deviation, transformation matrix deviation from the vehicle body coordinate system to the IMU coordinate system, and lever arm deviation. During this process, the Kalman filter algorithm adjusts and optimizes the estimated values through iterative calculations according to the real-time input observation data, and finally provides high-precision navigation and positioning results.

[0052] In this embodiment, as described in step S300, the method for performing state detection on the received position data, heading data, speed data, angular velocity data, and acceleration data includes:

[0053] Step S301: Read the position data, heading data, speed data output by the navigation terminal, and the angular velocity data and acceleration data of the inertial measurement unit;

[0054] Step S302: Confirm whether the states of the position data, heading data, speed data, angular velocity data, and acceleration data reach the preset standards;

[0055] Step S303: After the states of the position data, heading data, speed data, angular velocity data, and acceleration data are shown to reach the preset standards, input the position data, heading data, speed data, angular velocity data, and acceleration data into the coordinate conversion module;

[0056] Step S304: If the states of the position information, heading data, and speed data are not shown to reach the preset standards, check for equipment failures or operation errors until the data states are shown to reach the preset standards.

[0057] In this embodiment, as described in step S500, input the converted data into the error Kalman filter module for online error estimation and real-time motion state calculation, where:

[0058] The system state equation includes:

[0059]

[0060]

[0061] Among them, · represents the derivative, Δp is the position deviation, with the unit of meter (m), Δv is the speed deviation, with the unit of meter per second (m / s), Δθ is the Lie algebra form of the deviation of the transformation matrix from the IMU coordinate system to the navigation coordinate system, dimensionless, Δb is the gyro zero bias deviation, with the unit of radian per second (1 / s), Δb g is the accelerometer zero bias deviation, with the unit of meter per second squared (m / s a )), b 2 ) and b g and b a are the gyro zero bias and accelerometer zero bias respectively, with the same unit as Δb g and Δb a , Δα is the transformation matrix from the vehicle body coordinate system to the IMU coordinate system to be estimated The Lie - algebra form of the deviation, dimensionless, where Δl is the lever - arm deviation with the unit of meter (m), ω is the angular - velocity vector with the unit of radian per second (1 / s), and a is the acceleration vector with the unit of meter per second squared (m / s 2 ), and all the above variables are 3×1 column vectors. × represents the transformation of a three - dimensional vector into an anti - symmetric matrix. For example, the three - dimensional vector a = [a0 a1 a2] T , T represents the transpose, and through the operation, it is transformed into an anti - symmetric matrix as follows:

[0062]

[0063] The system observation equation is:

[0064]

[0065] where, is the representation of the satellite - antenna position in the navigation coordinate system n, is the representation of the IMJ position in the navigation coordinate system n, represents the transformation matrix from the IMU coordinate system to the navigation coordinate system, is the transformation matrix from the vehicle - body coordinate system to the IMU coordinate system, Log() represents the Lie - algebra operation corresponding to the Lie group, represents the transformation matrix from the IMU coordinate system to the vehicle - body coordinate system, represents the transformation matrix from the navigation coordinate system to the IMU coordinate system, represents the heading - angle observation value of the dual - satellite antenna, is the representation of the point where the satellite antenna is located in the navigation coordinate system n, is the representation of the point where the satellite antenna is located in the vehicle - body coordinate system v, R represents the rotation matrix, represents the transformation matrix from the global coordinate system to the vehicle - body coordinate system.

[0066] From to the transformation method is:

[0067]

[0068] where, V x 、V y and V z represent the components of the velocity vector decomposed in the vehicle - body coordinate system, V0, V1, and V2 represent three independent velocity components in a certain coordinate system, x is a scalar, are all 3×1 column vectors, P and V represent position and velocity respectively, k and k - 1 represent the current moment and the previous moment respectively, and sign(x) is the sign function, which takes the value +1 when x > 0, -1 when x < 0, and 0 when x = 0.

[0069] The effect of this transformation is equivalent to converting the velocity observation at the satellite antenna position into the velocity observation of the vehicle wheel speedometer. That is, in the case where the vehicle wheel speedometer is not connected, the software algorithm of the satellite antenna velocity observation through the transformation replaces the actual wheel speedometer sensor.

[0070] In the system state equation, namely formulas (1)-(7), this method incorporates the installation biases (Δθ and Δα) of the IMU and the lever arm bias (L) into the state variables by defining a specific mathematical model. Δθ represents the Lie algebra form of the transformation matrix bias from the IMU coordinate system to the navigation coordinate system, while Δα represents the Lie algebra form of the transformation matrix bias from the vehicle body coordinate system to the IMU coordinate system, and L represents the lever arm bias. These biases are incorporated into the state equation and, together with the position bias (Δp), velocity bias (Δv), gyro zero bias (Δb g ), and accelerometer zero bias (Δb a ), form a complete state vector. The system uses the position and heading data provided by satellite navigation as observation information and, through the error Kalman filter algorithm, online estimates these state variables. This method can dynamically adjust and compensate for the navigation biases caused by installation errors and lever arm errors, thereby improving the accuracy and robustness of the entire navigation system. In this way, the system can respond in real time to environmental changes and equipment state changes, ensuring the accuracy and reliability of navigation data.

[0071] In this method, by using the dual-satellite antenna heading angle information and the transformed GNSS observation results through formulas (8)-(11), the stability and robustness of the estimation algorithm are effectively enhanced. Specifically, formulas (8) and (9) construct the observation equations by using the position and velocity data obtained from the satellite antenna and the IMU and combining the heading angle information measured by the satellite navigation system. These observation equations include the expressions of the satellite antenna position and velocity in the navigation coordinate system ( and ) and the expressions of the IMU position and velocity in the navigation coordinate system ( and ). Equation (10) uses the heading angle information to calculate the observed value of the satellite antenna heading angle, providing a more stable reference information. The transformation in equation (11), by using the sign function to handle the directionality of the velocity vector, converts the GNSS velocity observation into a velocity measurement matching the vehicle. This transformation provides a software algorithm alternative for the case where the vehicle wheel speedometer is not directly connected, thereby reducing the dependence on hardware while enhancing the adaptability of the algorithm to different environments and conditions. The comprehensive utilization of this dual-satellite antenna data and the transformed GNSS data significantly improves the accuracy and robustness of the estimation algorithm in the face of complex dynamic environments.

[0072] Preferably, the angular velocity data and acceleration data obtained by the inertial measurement unit are filtered.

[0073] Preferably, before the position, heading, and speed data obtained from the satellite navigation terminal are displayed as reaching the preset standard, the signal reception quality of the satellite navigation terminal is detected.

[0074] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the description in the above method embodiment, and details are not described herein again.

[0075] Refer to Figure 2 , an electronic device is further provided in an embodiment of the present invention. The electronic device may be a server, and its internal structure may be as Figure 2 shown. The electronic device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store the corresponding data in this embodiment. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0076] Those skilled in the art can understand that Figure 2 the structure shown in

[0077] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0078] It should be noted that in this article, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, apparatus, article, or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, apparatus, article, or method including that element.

[0079] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for online estimation of IMU arm and vehicle body mounting angles, characterized in that: The steps include: Starting the satellite navigation terminal and the inertial measurement unit, receiving data from the RTK base station via a wireless network, and inputting the data into the satellite navigation terminal; Acquire satellite positioning position data, heading data and speed data from the satellite navigation terminal, and acquire angular velocity data and acceleration data from the inertial measurement unit; Perform status detection on the received position data, heading data and speed data, as well as angular velocity data and acceleration data to confirm whether the data status meets the preset standard; The data that meets the preset standards is converted through the data conversion module; Input the converted position data, heading data, velocity data, acceleration data and angular velocity data to the error Kalman filter module; Through error Kalman filtering, based on the system state equation and system observation equation, the transformation matrix deviation from the IMU coordinate system to the navigation system, the gyro zero bias deviation, the added zero bias deviation, the transformation matrix deviation from the vehicle coordinate system to the IMU coordinate system and the lever arm deviation are estimated.

2. The method for online estimation of IMU arm and vehicle body mounting angle according to claim 1, characterized in that: The method for performing status detection on the received position data, heading data and speed data, as well as angular velocity data and acceleration data includes: Read the position data, heading data and speed data output by the navigation terminal and the angular velocity data and acceleration data of the inertial measurement unit; Confirm whether the status of position data, heading data, speed data, angular velocity data and acceleration data meets the preset standards; After the status display of the position data, the heading data, the speed data, the angular velocity data and the acceleration data reaches a preset standard, the position data, the heading data, the speed data, the angular velocity data and the acceleration data are input into the coordinate conversion module; If the status of the position information, heading data, and speed data does not meet the preset standard, it is necessary to check for equipment failure or operation error until the data status meets the preset standard.

3. The method for online estimation of IMU arm and vehicle body mounting angle according to claim 1, characterized in that: The method of performing data conversion on the data that meets the preset standard through the data conversion module includes: The longitude and latitude coordinates in the location information output by the satellite navigation terminal are converted into metric UTM coordinates through the Convert_Geodetic_To_UTM function in the coordinate conversion library; Convert the IMU acceleration units from g to meters per second squared, and convert the angular velocity units from degrees to radians, and the velocity units from kilometers per hour to meters per second.

4. The method for online estimation of IMU arm and vehicle body installation angle according to claim 1, characterized in that: The system state equation includes: Among them, · represents the derivative, Δp is the position deviation, Δv is the velocity deviation, and Δθ is the transformation matrix to be estimated from the IMU coordinate system to the navigation system The Lie algebraic form of the deviation, Δb g is the gyro bias error, Δb a is the added zero bias error, b g and b a They are gyro bias and adder bias respectively, Δα is the transformation matrix from the vehicle coordinate system to the IMU coordinate system to be estimated Lie algebraic form of the deviation, Δl is the lever arm deviation, ω is the angular velocity vector, a is the acceleration vector, and × represents the transformation of the three-dimensional vector into an antisymmetric matrix.

5. The method for online estimation of IMU arm and vehicle body mounting angle according to claim 1, characterized in that: The system observation equation is: in, is the representation of the satellite antenna position in the navigation coordinate system n, is the representation of the IMJ position in the navigation coordinate system n, Represents the transformation matrix from the IMU coordinate system to the navigation coordinate system, is the transformation matrix from the vehicle coordinate system to the IMU coordinate system, Log() represents the Lie algebra operation corresponding to the Lie group, Represents the transformation matrix from the IMU coordinate system to the vehicle body coordinate system, Represents the transformation matrix from the navigation coordinate system to the IMU coordinate system, represents the transformation matrix from the vehicle coordinate system to the navigation coordinate system, is the representation of the satellite antenna point in the navigation coordinate system n, is the representation of the satellite antenna point in the vehicle coordinate system v, R represents the rotation matrix, Represents the transformation matrix from the global coordinate system to the vehicle body coordinate system.

6. The method for online estimation of IMU arm and vehicle body mounting angle according to claim 1, characterized in that: In the absence of an on-board wheel speed meter, the actual wheel speed meter sensor is replaced by a software algorithm that transforms the satellite antenna speed observation. The transformation method is: Among them, V x 、V y and V z Represents the components of the velocity vector decomposed in the vehicle body coordinate system, V0, V1 and V2 represent three independent velocity components in a certain coordinate system, x is a scalar, They are all 3*1 column vectors, P and V represent position and velocity respectively, k and k-1 represent the current moment and the previous moment respectively, sign(x) is the sign function, which takes the value +1 when x>0, -1 when x<0, and 0 when x=0.

7. The method for online estimation of IMU arm and vehicle body mounting angles according to claim 1, characterized in that: The angular velocity data and acceleration data obtained by the inertial measurement unit are filtered.

8. The method for online estimation of IMU arm and vehicle body mounting angles according to claim 1, characterized in that: Before the position, heading data and speed data obtained from the satellite navigation terminal are displayed as reaching a preset standard, the signal reception quality of the satellite navigation terminal is detected.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor is used to implement the steps of the method for online estimating the IMU lever arm and vehicle body installation angle as claimed in any one of claims 1 to 8 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for online estimation of the IMU arm and vehicle body installation angle according to any one of claims 1 to 8 are performed.

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