A method, apparatus, and medium for online estimation of IMU lever arm and vehicle body installation angles

Abstract

The application relates to a method, device and medium for online estimation of IMU rod arm and vehicle body installation angle, which comprises the following steps: starting a satellite navigation terminal and an inertial measurement unit, receiving RTK reference station data through a wireless network, and inputting the satellite navigation terminal. Satellite positioning position and heading are acquired and verified, data conversion module is inputted after confirming that the data meet preset standards, converted data is inputted into an error Kalman filter module, a system observation equation is constructed by position error, heading error and converted speed error, and IMU to navigation frame transformation matrix deviation, gyro zero offset, acceleration zero offset, vehicle body to IMU coordinate system transformation matrix deviation and rod arm deviation are estimated. The scheme not only improves the adaptability of the navigation system to dynamic changes, but also significantly improves the navigation and positioning precision in various complex environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle navigation systems, in particular to a method, device and medium for online estimation of IMU lever arm and vehicle body installation angle. BACKGROUND

[0002] With the rapid development of vehicle intelligence, more and more vehicles begin to carry advanced navigation and positioning sensors to support automatic control and path planning functions. In the early stage of intelligent driving technology, host manufacturers or automatic driving kit providers mainly rely on purchasing a combined inertial navigation system that integrates satellite navigation (GNSS) and inertial navigation system (INS) to meet the basic navigation needs. However, this general combined inertial navigation system often cannot meet the specific high-precision positioning needs when dealing with complex and variable actual use scenarios, such as urban highways, rail transit, open-pit mines, etc.

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

[0004] The present application provides a method, device and medium for online estimation of IMU lever arm and vehicle body installation angle, which aims to solve the problem that the existing technology cannot estimate and correct the installation angle deviation and lever arm deviation between the IMU and the vehicle body and the satellite navigation system in real time, thereby improving the navigation and positioning accuracy and robustness of intelligent driving systems in complex and variable environments.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides a method for online estimation of IMU lever arm and vehicle body installation angle, comprising the following steps:

[0006] Starting the satellite navigation terminal and the inertial measurement unit, receiving data from the RTK reference station through the wireless network, and inputting the data into the satellite navigation terminal;

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

[0008] Performing state detection on the received position data, heading data and speed data, and angular velocity data and acceleration data, and confirming whether the data state meets the preset standard;

[0009] Converting the data that meets the preset standard through the data conversion module;

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

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

[0012] 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 are read;

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

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

[0015] If the states of the position information, heading data and speed data are not displayed to reach the preset standards, equipment faults or operation errors need to be checked until the data states are displayed to reach the preset standards.

[0016] Further, the method for performing data conversion on the data reaching the preset standards through the data conversion module comprises:

[0017] The longitude and latitude coordinates in the position information output by the satellite navigation terminal are converted into meter UTM coordinates through a Convert_Geodetic_To_UTM function in the coordinate conversion library;

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

[0019] Further, the system state equation comprises:

[0020]

[0021] wherein, · represents a derivative, Δp is a position deviation, Δv is a speed deviation, Δθ is a transformation matrix to be estimated from an IMU coordinate system to a navigation coordinate system a Lie algebra form of a bias Δb g a gyroscope zero bias deviation Δb ab for the bias of the integrated zero bias g and b a are the gyro zero bias and integrated zero bias respectively, Δα is the transformation matrix from the vehicle coordinate system to the IMU coordinate system to be estimated is the Lie algebra form of the bias, Δl is the link bias, ω is the angular velocity vector, a is the acceleration vector, and × denotes the transformation of a three-dimensional vector into an anti-symmetric matrix.

[0022] Further, 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 coordinate system to the IMU coordinate system, and Log() represents the Lie algebra operation corresponding to the Lie group, represents the transformation matrix from the IMU coordinate system to the vehicle 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 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 coordinate system v, and R represents a rotation matrix, represents the transformation matrix from the global coordinate system to the vehicle coordinate system.

[0025] Further, in the case where there is no on-board wheel speed meter, a software algorithm for transforming the satellite antenna speed observation is used to replace the actual wheel speed sensor, and the transformation method is:

[0026]

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

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

[0029] Further, the signal receiving quality of the satellite navigation terminal is detected before the position, heading data and speed data acquired from the satellite navigation terminal are displayed to reach a preset standard.

[0030] To achieve the above object, the second aspect of the present application provides an electronic device comprising a processor and a memory, wherein the processor is configured to implement the steps of the method for online estimation of IMU lever arm and vehicle installation angle when executing the computer program stored in the memory.

[0031] To achieve the above object, the third aspect of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is configured to implement the steps of the method for online estimation of IMU lever arm and vehicle installation angle when executed by a processor.

[0032] Advantages of the present application:

[0033] Compared with the prior art, the method, device and medium for online estimation of IMU lever arm and vehicle installation angle provided by the present application adopt a real-time error Kalman filtering algorithm to dynamically adjust and compensate the lever arm and installation deviation between the IMU and 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 the vehicle-mounted computer, and then converts these data into information in a unified coordinate system through a coordinate conversion module. By setting up a system state equation, this method includes the installation deviation of the IMU and the lever arm deviation in the state variable, and uses the position and heading data of satellite positioning as observation information. Then, the state estimation is performed through error Kalman filtering to update and correct the deviation in real time. In addition, the present application introduces the dual-satellite antenna heading angle information and transforms the GNSS observation results, which further enhances the stability and robustness of the estimation algorithm. This overall scheme 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. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows.

[0035] Figure 1 is a flow chart of the method for online estimation of IMU lever arm and vehicle installation angle disclosed by the embodiments of the present application.

[0036] Figure 2 is a structural schematic block diagram of an electronic device disclosed by the embodiments of the present application. DETAILED DESCRIPTION

[0037] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the protection scope of the present application.

[0038] According to the embodiments of the present application, it should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a group 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 an order different from here.

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

[0040] The present application adopts the following technical solutions:

[0041] As shown in Figure 1 The present application provides a method for online estimation of IMU rod arm and vehicle body installation angle, comprising 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, the satellite navigation terminal and the inertial measurement unit (IMU) are powered on. 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, ensuring that the received positioning information has higher accuracy and reliability.

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

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

[0046] Step S300, state detection is performed on the received position data, heading data and speed data, and angular velocity data and acceleration data to confirm whether the data state meets the preset standard;

[0047] State detection is performed on the received position data, heading data and speed data to confirm whether these data have met the system preset standard. If the data state shows that the preset standard has not been met, the satellite signal reception quality or the detection equipment should be checked for operation errors or failures. This step ensures the quality of the data processed subsequently, preventing the accuracy and reliability of navigation from being affected by data quality problems.

[0048] Step S400, data conversion is performed on the data that meets the preset standard through a data conversion module;

[0049] The position data, heading data, speed data and IMU angular velocity and acceleration data that are confirmed to be in good condition are converted through a coordinate conversion library. The Convert_Geodetic_To_UTM function in the third-party library such as utm-convert is used to convert the latitude and longitude coordinates into meter units in the UTM coordinate system, and at the same time, the units of acceleration and angular velocity are converted from g and degrees to meters per square second and radians, and the unit of speed is converted from kilometers per hour to meters per second. It is ensured that all data are processed under unified measurement units to ensure the accuracy of the calculation.

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

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

[0052] In the embodiment, the method for detecting the state of the received position data, heading data and speed data, and angular velocity data and acceleration data, as described in step S300, comprises:

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

[0054] Step S302, confirming whether the state of the position data, heading data, speed data, angular velocity data and acceleration data reaches the preset standard;

[0055] Step S303, after the state of the position data, heading data, speed data, angular velocity data and acceleration data is displayed to reach the preset standard, inputting the position data, heading data, speed data, angular velocity data and acceleration data into the coordinate conversion module;

[0056] Step S304, if the state of the position information, heading data and speed data is not displayed to reach the preset standard, checking the equipment failure or operation error until the state of the data is displayed to reach the preset standard.

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

[0058] The system state equation comprises:

[0059]

[0060]

[0061] wherein, · represents derivative, Δp is position deviation, unit: meter (m), Δv is speed deviation, unit: meter per second (m / s), Δθ is the transformation matrix from the IMU coordinate system to the navigation coordinate system to be estimated Deviation in Lie algebra form, dimensionless, Δb g is the gyro zero bias deviation, unit: radian per second (1 / s), Δb a is the accelerometer zero bias deviation, unit: meter per square second (m / s 2 ) g and b a are the gyro zero bias and the 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 Bias Lie algebra form, dimensionless, Dl is the lever arm bias with unit of meter (m), w is the angular velocity vector with unit of radian per second (1 / s), a is the acceleration vector with unit of meter per square second (m / s 2 ), all of the above variables are 3*1 column vectors. x denotes transforming a three-dimensional vector into a skew-symmetric matrix, for example, a three-dimensional vector a = [a0 a1 a2] T , T denotes transposition, and transforming into a skew-symmetric matrix through an operation is as follows:

[0062]

[0063] The system observation equation is as follows:

[0064]

[0065] 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, denotes the conversion matrix from the IMU coordinate system to the navigation coordinate system, is the conversion matrix from the vehicle body coordinate system to the IMU coordinate system, Log() denotes the Lie algebra operation corresponding to the Lie group, denotes the conversion matrix from the IMU coordinate system to the vehicle body coordinate system, denotes the conversion matrix from the navigation coordinate system to the IMU coordinate system, denotes the heading angle observation value of the double 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 denotes a rotation matrix, denotes the conversion matrix from the global coordinate system to the vehicle body coordinate system.

[0066] The transformation mode from to is as follows:

[0067]

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

[0069] The effect of this transformation is equivalent to converting the velocity observations at the satellite antenna position into the velocity observations of the vehicle-mounted wheel speed meter, i.e., the software algorithm of the transformed satellite antenna velocity observation replaces the actual wheel speed sensor in the case of no access to the vehicle-mounted wheel speed meter.

[0070] In the system state equations, i.e., equations (1)-(7), the present method defines specific mathematical models to incorporate the installation biases (Δθ and Δα) of the IMU and the lever arm bias (L) into the state variables. 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 equations, together with the position bias (Δp), the velocity bias (Δv), the gyro zero bias (Δb g ), and the accelerometer zero bias (Δb a ) to form a complete state vector. The system uses the position and heading data provided by satellite navigation as observation information, and estimates these state variables online through the error Kalman filtering algorithm. 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 to environmental changes and equipment state changes in real time, ensuring the accuracy and reliability of the navigation data.

[0071] In the present method, the dual-satellite antenna heading angle information and the transformed GNSS observations are used through equations (8)-(11) to effectively enhance the stability and robustness of the estimation algorithm. Specifically, equations (8) and (9) construct observation equations by using the position and velocity data obtained from the satellite antenna and the IMU, combined with 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 observation value of the satellite antenna heading angle, providing more stable reference information. The conversion in equation (11) converts the GNSS velocity observation into a velocity measurement that matches the vehicle by using the sign function to handle the directionality of the velocity vector. This conversion provides a software algorithm alternative in the case of no direct access to the vehicle-mounted wheel speed meter, thereby reducing the dependence on hardware while enhancing the algorithm's adaptability to different environments and conditions. The comprehensive use of dual-satellite antenna data and 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 acquired by the inertial measurement unit are filtered.

[0073] Preferably, the signal receiving quality of the satellite navigation terminal is detected before the position, heading and speed data acquired from the satellite navigation terminal reach the preset standard.

[0074] In the embodiment, the specific implementation of each unit in the above system embodiment can refer to the description in the above method embodiment, and will not be repeated here.

[0075] Referring to Figure 2 , the embodiment of the present application also provides an electronic device, which can be a server, and the internal structure thereof can be as shown in Figure 2 . The electronic device comprises a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer is used to provide computing and control capabilities. The memory of the electronic device comprises 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 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 the embodiment. The network interface of the electronic device is used to communicate with the external terminal through the network connection. The computer program is executed by the processor to realize the above method.

[0076] Those skilled in the art can understand Figure 2 the structure shown in the embodiment, which is only a block diagram of part of the structure related to the present application scheme, and does not constitute a limitation on the electronic device to which the present application scheme is applied. The embodiment of the present application also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by the processor to realize the above method. It can be understood that the computer readable storage medium in the embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0077] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods 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, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium provided by the present application and used in the embodiments can include non-volatile and / or volatile memory. 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. As an illustration but 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 (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0078] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, device, article or method that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, device, article or method. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, device, article or method that includes the element.

[0079] The above description is only the preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, based on the content of the present application specification and drawings, are also included in the patent protection scope of the present application.

Claims

1. A method of online estimation of IMU lever arm and body mount angle, characterized in that, The method comprises the following steps: starting a satellite navigation terminal and an inertial measurement unit, receiving data from an RTK reference station through a wireless network, and inputting the data into the satellite navigation terminal; obtaining satellite positioning position data, heading data and speed data from the satellite navigation terminal, and obtaining angular velocity data and acceleration data from the inertial measurement unit; performing state detection on the received position data, heading data and speed data, and angular velocity data and acceleration data, and confirming whether the data state reaches a preset standard; performing data conversion on the data reaching the preset standard through a data conversion module; inputting the converted position data, heading data, speed data, acceleration data and angular velocity data into an error Kalman filtering module; performing error Kalman filtering, and estimating the transformation matrix deviation from the IMU coordinate system to the navigation coordinate system, the gyro zero deviation, the accelerometer zero deviation, the transformation matrix deviation from the vehicle body coordinate system to the IMU coordinate system and the lever arm deviation based on a system state equation and a system observation equation.

2. The method of online estimation of IMU lever arm and body mount angle of claim 1, wherein, The method for performing state detection on the received position data, heading data and speed data, and angular velocity data and acceleration data comprises: reading 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; confirming whether the state of the position data, heading data, speed data, angular velocity data and acceleration data reaches a preset standard; after the state display of the position data, heading data, speed data, angular velocity data and acceleration data reaches the preset standard, inputting the position data, heading data, speed data, angular velocity data and acceleration data into a coordinate conversion module; if the state of the position information, heading data and speed data does not reach the preset standard, checking equipment failure or operation error until the data state display reaches the preset standard.

3. The method of online estimation of IMU lever arm and body mount angles of claim 1, wherein, The method for performing data conversion on the data reaching the preset standard through the data conversion module comprises: converting the longitude and latitude coordinates in the position information output by the satellite navigation terminal into meter UTM coordinates through a Convert_Geodetic_To_UTM function in a coordinate conversion library; converting the acceleration unit of the inertial measurement unit from g to meters per second squared, converting the angular velocity unit from degrees to radians, and converting the speed unit from kilometers per hour to meters per second.

4. The method of online estimation of IMU lever arm and body mount angles of claim 1, wherein, The system state equation comprises: where • denotes derivative, Δp is position bias, Δv is velocity bias, and Δθ is the transformation matrix from IMU frame to navigation frame to be estimated Lie algebra form of bias, Δb g is the gyro bias, Δb a is the integrated bias, b g and b a are the gyro bias and integrated bias, respectively, and Δα is the transformation matrix from vehicle body frame to IMU frame to be estimated Lie algebra form of bias, Δl is the lever arm bias, ω is the angular velocity vector, a is the acceleration vector, and × denotes the transformation of a three-dimensional vector into an anti-symmetric matrix.

5. The method of online estimation of IMU lever arm and body mount angles as recited in claim 1, wherein, The system observation equation is: wherein, is a representation of the satellite antenna position in the navigation coordinate system n, is a representation of the IMJ position in the navigation coordinate system n, denotes 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() denotes the Lie algebra operation corresponding to the Lie group, denotes the transformation matrix from the IMU coordinate system to the vehicle body coordinate system, denotes the transformation matrix from the navigation coordinate system to the IMU coordinate system, denotes the transformation matrix from the vehicle body coordinate system to the navigation coordinate system, is a representation of the satellite antenna position in the navigation coordinate system n, is a representation of the satellite antenna position in the vehicle body coordinate system v, R denotes a rotation matrix, denotes the transformation matrix from the global coordinate system to the vehicle body coordinate system.

6. The method of online estimation of IMU lever arm and body mount angles of claim 1, wherein, In the case of no vehicle-mounted wheel speed meter, a software algorithm for transforming satellite antenna speed observation is used to replace the actual wheel speed meter sensor, and the transformation method is: where V x , V y , and V z represent the components of the decomposed velocity vector 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 time and the last time respectively, and sign(x) is a sign function that takes the value +1 when x>0, -1 when x<0, and 0 when x=0.

7. The method of online estimation of IMU lever arm and body mount angles as recited in claim 1, wherein, performing filtering processing on the angular velocity data and acceleration data obtained by the inertial measurement unit.

8. The method of online estimation of IMU lever arm and body mount angles of claim 1, wherein, Before the position, heading data and speed data obtained from the satellite navigation terminal display that they reach the preset standard, detecting the signal receiving quality of the satellite navigation terminal.

9. An electronic device, comprising: The system comprises a processor and a memory, and the processor is used to execute a computer program stored in the memory to realize the steps of the method for online estimating the IMU lever arm and vehicle body installation angle according to any one of claims 1 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program, when run by the processor, performs the steps of the method of any one of claims 1 to 8 for online estimation of the IMU lever arm and vehicle body installation angle.

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