Inertial Basis Integrated Navigation Method and Device for Enhancing the Autonomous Characteristics of Lie Groups

Abstract

The present invention belongs to the technical field of inertial navigation. Aiming at the technical problems existing in the existing combined navigation Lie group model, such as insufficient autonomous characteristics and decreased navigation accuracy under large misalignment angle conditions, an inertial-based combined navigation method and device for enhancing the autonomous characteristics of the Lie group are proposed. A Kalman filter system equation for enhancing the autonomous characteristics of the Lie group based on the projection of the world coordinate system is constructed according to the initial navigation state of the moving body and the inertial navigation information, realizing the dual-reference system fusion of the inertial system velocity representation and the earth system attitude / position representation, and enhancing the autonomous characteristics of the combined navigation system. Compared with the traditional method, the present invention can significantly improve the robustness and positioning accuracy of the combined navigation system under large misalignment angle conditions.

Description

Technical Field

[0001] The present invention mainly relates to the field of inertial navigation technology, and particularly to an inertial-based integrated navigation method and device for enhancing the autonomous characteristics of Lie groups. Background Art

[0002] In scenarios where satellite navigation is available, inertial / satellite loose / tight coupled integrated navigation systems can already achieve relatively high navigation accuracy. However, in satellite-denied environments such as tunnels, underground facilities, and electromagnetic countermeasures, autonomous navigation technology faces severe challenges. Most existing land vehicle navigation systems adopt a scheme of fusing speed sensors (such as odometers and Doppler velocimeters) with inertial navigation, and use speed information to constrain the error accumulation of inertial navigation. But this scheme has three significant technical bottlenecks:

[0003] (1) System parameter sensitivity problem: It is necessary to accurately calibrate in advance the installation declination angle, lever arm parameters, scale factors, etc. between the inertial navigation and the speed measurement device. However, in actual applications, vehicle body deformation and road condition changes will cause these parameters to have time-varying offsets, and traditional fixed-parameter models cannot effectively adapt.

[0004] (2) State estimation reliability problem: When the vehicle is stationary or moving in a straight line for a long time, state variables such as gyro zero bias, accelerometer zero bias, and installation angle error show weak observability, and traditional extended Kalman filter (EKF) is prone to estimation divergence under strong nonlinear coupling conditions.

[0005] (3) Difficulty in large misalignment angle alignment: The initial alignment during movement under satellite-denied conditions needs to handle large attitude error scenarios, and existing matrix Lie group models have an inherent defect of insufficient autonomous characteristics, resulting in a significant deterioration in the navigation parameter estimation accuracy under large angle error conditions.

[0006] Especially in the error state conversion process of existing matrix Lie group navigation models, the influence of the reference frame projection relationship and the initial state of the moving body is not fully considered, resulting in insufficient autonomy of the error model and difficulty in maintaining the diffeomorphism characteristics of the Lie group manifold, which has become a key technical bottleneck restricting the integrated navigation accuracy in high-dynamic environments. Summary of the Invention

[0007] Aiming at the autonomy defect of existing matrix Lie group-based navigation models and the applicability problem in large misalignment angle scenarios, the present invention proposes an inertial-based integrated navigation method and device for enhancing the autonomous characteristics of Lie groups to improve navigation performance.

[0008] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0009] On the one hand, the present invention provides an inertial-based integrated navigation method for enhancing the autonomous characteristics of Lie groups, including:

[0010] Receive the inertial navigation system navigation information, odometer navigation information, and positioning system navigation information of the moving object;

[0011] Construct the Kalman filter system equation with enhanced Lie group autonomous characteristics based on the projection of the world coordinate system according to the initial navigation state of the moving object and the inertial navigation information;

[0012] Use the estimated speed of the odometer as the observable quantity to construct a speed observation equation;

[0013] Perform inertial-based integrated navigation according to the Kalman filter system equation with enhanced Lie group autonomous characteristics based on the projection of the world coordinate system and the speed observation equation, and obtain the state and covariance of the inertial-based integrated navigation as the state and covariance for Kalman filter observation update;

[0014] Perform attitude, speed, and position updates according to the Lie group error state transformation.

[0015] In the present invention, the inertial navigation system navigation information includes: navigation timestamp, roll angle, pitch angle, heading angle, longitude, latitude, altitude, and three-dimensional velocity information in the local navigation system;

[0016] The odometer navigation information includes: navigation timestamp, vehicle speed;

[0017] The positioning system navigation information is the navigation information from a satellite positioning system or an acoustic positioning system, including a navigation timestamp and absolute three-dimensional positioning information.

[0018] On the other hand, an inertial-based integrated navigation device with enhanced Lie group autonomous characteristics includes:

[0019] A first module for receiving the inertial navigation system navigation information, odometer navigation information, and positioning system navigation information of the moving object;

[0020] A second module for constructing the Kalman filter system equation with enhanced Lie group autonomous characteristics based on the projection of the world coordinate system according to the initial navigation state of the moving object and the inertial navigation information;

[0021] A third module for using the estimated speed of the odometer as the observable quantity to construct a speed observation equation;

[0022] A fourth module for performing inertial-based integrated navigation according to the Kalman filter system equation with enhanced Lie group autonomous characteristics based on the projection of the world coordinate system and the speed observation equation, and obtaining the state and covariance of the inertial-based integrated navigation as the state and covariance for Kalman filter observation update;

[0023] A fifth module for performing attitude, speed, and position updates according to the Lie group error state transformation.

[0024] On the other hand, the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the inertial-based integrated navigation method for enhancing the autonomous characteristics of Lie groups are implemented.

[0025] On the other hand, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the inertial-based integrated navigation method for enhancing the autonomous characteristics of Lie groups are implemented.

[0026] On the other hand, the present invention provides a computer program product. The computer program product is stored on a computer-readable storage medium and includes computer instructions. When the computer instructions are run by a processor, the computer device is enabled to implement the steps of the inertial-based integrated navigation method for enhancing the autonomous characteristics of Lie groups.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention constructs a Kalman filter system equation based on the projection of the world coordinate system for enhancing the autonomous characteristics of Lie groups according to the initial navigation state of the moving body and the inertial navigation information, realizing the fusion of the double reference systems of the inertial system velocity representation and the earth system attitude / position representation, and enhancing the autonomous characteristics of the integrated navigation system.

[0029] Furthermore, in the present invention, a Lie group Kalman filter (LG-EKF-R) algorithm in the form of right multiplication error is established, which has more advantages in terms of accuracy and computational efficiency compared with the model defined by left error.

[0030] Furthermore, in the error model derivation of the present invention, the influence of the initial velocity and position of the moving body is deducted, thereby simplifying the initial variance setting.

[0031] Compared with the traditional method, the present invention can significantly improve the robustness and positioning accuracy of the integrated navigation system under the condition of large misalignment angles. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. 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 be obtained based on the structures shown in these drawings.

[0033] Figure 1 It is a flowchart of the inertial-based integrated navigation method for enhancing the autonomous characteristics of Lie groups provided for an embodiment;

[0034] Figure 2Schematic diagram of the spatial configuration of vehicle-mounted inertial / odometer integrated navigation

[0035] Figure 3 Vehicle-mounted experimental trajectory diagram

[0036] Figure 4 Comparison diagram of integrated navigation results of the integrated navigation method (LG-EKF-e) with the projection system as the Earth system under the inertial basis, where the initial horizontal attitude error angle is 1° and the heading error angle is 180°, respectively using the Extended Kalman Filter (EKF) and the right error definition of Lie group

[0037] Figure 5 Comparison diagram of integrated navigation results of the integrated navigation method using the velocity error state transformation Kalman filter (ST-EKF) and the filtering method (LG-EKF-w) of the present invention, respectively, where the initial horizontal attitude error angle is 1° and the heading error angle is 180° Detailed implementation manners

[0038] 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 only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention

[0039] The present invention provides an inertial basis integrated navigation method for enhancing the autonomous characteristics of Lie group, aiming to replace the conventional Extended Kalman Filter with a Lie group Kalman filter based on the projection of the world coordinate system. At the same time, the inertial space is used as the velocity reference system, the Earth system is used as the reference system for attitude and position, and the world coordinate system is used as the projection system to achieve more accurate navigation and positioning results

[0040] Refer to Figure 1 , in one embodiment, an inertial basis integrated navigation method for enhancing the autonomous characteristics of Lie group is provided, including:

[0041] Receiving the navigation information of the inertial navigation system, odometer navigation information, and positioning system navigation information of the moving body

[0042] Constructing a system equation of a Kalman filter with enhanced autonomous characteristics of Lie group based on the projection of the world coordinate system according to the initial navigation state of the moving body and the inertial navigation information

[0043] Taking the estimated velocity of the odometer as the observation quantity and constructing a velocity observation equation

[0044] Perform inertial-based integrated navigation according to the Kalman filter system equations and velocity observation equations based on the enhanced Lie group autonomous characteristics projected onto the world coordinate system, and obtain the state and covariance of the inertial-based integrated navigation as the state and covariance for the observation update of the Kalman filter;

[0045] Perform attitude, velocity, and position updates according to the Lie group error state transformation.

[0046] The inertial navigation information in the present invention includes: navigation timestamp, roll angle, pitch angle, heading angle, longitude, latitude, altitude, and three-dimensional velocity information in the local navigation system;

[0047] The odometer navigation information includes: navigation timestamp, vehicle velocity;

[0048] The positioning system navigation information is the navigation information from a satellite positioning system or an acoustic positioning system, including navigation timestamp, absolute three-dimensional positioning information.

[0049] Furthermore, construct the Kalman filter system equations based on the enhanced Lie group autonomous characteristics projected onto the world coordinate system, including:

[0050] Denote the inertial navigation system coordinate system as b system, and the world coordinate system as w system;

[0051] Define the filter state vector , as follows:

[0052] ;

[0053] Where represents the attitude error angle of the inertial navigation system, is the right Lie group velocity error projected onto the w system defined relative to the inertial coordinate system, is the right Lie group position error projected onto the w system defined relative to the w system, is the constant gyro bias, is the constant accelerometer bias, is the odometer scale factor error, are the pitch and yaw misalignment angles and lever arm errors between the inertial navigation system and the odometer, respectively, and are modeled as follows:

[0054] ;

[0055] Where is the correlation time of the first-order Markov process, is the odometer noise.

[0056] The equations of the integrated navigation system based on the enhanced Lie group autonomous characteristics projected in the world coordinate system are as follows:

[0057] ;

[0058] where the inertial navigation system coordinate system is denoted as b system, and the world coordinate system is w system, represents the derivative of the filter state vector ;

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] where is the correlation time of the first-order Markov process, is the projection of the earth rotation vector in the w system, is the direction cosine matrix from the b system to the w system, is the calculated value of the gravitational acceleration projected in the w system, is the projection of the position of the w system relative to the e system in the w system, e system is the earth coordinate system, is the projection of the initial position of the moving body relative to the w system in the w system, is the calculated value of the projection of the velocity of the moving body relative to the inertial coordinate system (i.e., the i system) in the w system, is the calculated value of the projection of the position of the moving body relative to the w system in the w system, represents converting a vector into the corresponding skew-symmetric matrix, represents that this parameter is a calculated value, 、 are the noise vectors of the accelerometer and gyroscope respectively, is the odometer noise.

[0064] Furthermore, the differential equation of the attitude error angle of the inertial navigation system is:

[0065] ;

[0066] where is the projection of the Earth's rotation vector in the w system, is from b system to w system, the calculated value of the direction cosine matrix, is the gyro error.

[0067] The differential equation of the right Lie group velocity error constructed in the present invention is:

[0068] ;

[0069] wherein, is the projection of the attitude error angle of the inertial navigation system relative to the e system in the w system, is the accelerometer error;

[0070] The differential equation of the right Lie group position error is:

[0071] ;

[0072] where is from b system to w system, the calculated value of the direction cosine matrix, is the gyro error.

[0073] The constant zero bias of the gyroscope, the constant zero bias of the accelerometer, the lever arm error, the pitch and yaw installation error angles between the inertial navigation system and the odometer are considered to be constant, and the odometer scale factor error is considered to be a first-order Markov process. Their differential equations are as follows:

[0074] ;

[0075] .

[0076] In the present invention, the speed calculated by the inertial navigation system is corrected by the lever arm to obtain the estimated speed of the odometer. It is expressed in the odometer coordinate system m system as:

[0077] ;

[0078] where represents the equivalent speed projected by the inertial navigation calculation in the odometer m system, m system is the odometer coordinate system.

[0079] According to ;

[0080] Neglecting second-order small quantities, the velocity observation equation is obtained as follows:

[0081] ;

[0082] where represents the projection of the odometer velocity in the m system, represents the velocity output by the odometer in the m system, and the velocity observation matrix

[0083] ;

[0084] is the velocity observation noise, is the attitude transformation matrix from the b system to the m system, is the attitude transformation matrix from the w system to the b system, is the projection of the angular velocity of the moving body relative to the w system in the vehicle coordinate system, ;

[0085] ;

[0086] is the forward velocity of the odometer.

[0087] According to the Lie group error state transformation, attitude, velocity, and position updates are performed, where the attitude, velocity, and position updates are as follows:

[0088] ;

[0089] where represents the projection of the velocity of the moving body relative to the inertial coordinate system in the w system; is the calculated value of the projection of the velocity of the moving body relative to the inertial coordinate system in the w system, is the projection of the position of the moving body relative to the w system in the w system, the position of the moving body relative to the w system in the w system's calculated value.

[0090] The present invention innovatively constructs a composite parameter model for representing the velocity relative to the inertial system and the attitude / position relative to the Earth system, and establishes a canonical Lie algebra mapping of the error state through projection in the world coordinate system, enabling the error state equation to more strictly satisfy the autonomous characteristics of the Lie group manifold;

[0091] The present invention establishes a Lie group Kalman filter (LG-EKF-R) algorithm in the form of right-multiplied error. Compared with the traditional left error model, this architecture has better local linear approximation characteristics, reducing the computational complexity while ensuring the estimation accuracy;

[0092] The initial navigation state decoupling technology in the present invention: In the derivation of the error model, an explicit decoupling method for the initial velocity and position parameters of the moving body is adopted. By introducing the setting of normalized initial variance conditions, the convergence speed of the filter is significantly improved.

[0093] To verify the effectiveness of the inertial-based integrated navigation method provided by the present invention for enhancing the autonomous characteristics of the Lie group, vehicle experiments are used for experimental verification. The vehicle experimental equipment includes a high-precision fiber optic gyro inertial navigation system, a wheel odometer, a satellite receiver, etc., Figure 2 which is a schematic diagram of the spatial configuration of the vehicle inertial / odometer integrated navigation. The original data frequencies of the accelerometers and gyroscopes of the inertial navigation system are 200 Hz, and the zero biases are 0.003° / h and 10 respectively, and the random walks are 0.0003 and respectively. The resolution of the odometer is , and the single-point positioning accuracy of the satellite is better than 1 meter. To increase the credibility and sufficiency of the experiment, a set of open-loop trajectory experiments are carried out, and the experimental trajectory is as Figure 3 shown. Four filtering schemes, namely EKF (Extended Kalman Filter), ST-EKF (Velocity Error State Transformation Kalman Filter), LG-EKF-e (inertial-based integrated navigation method with the projection system defined by the right error of the Lie group and the Earth system as the reference system), and the inertial-based integrated navigation method (LG-EKF-w) provided by the present invention for enhancing the autonomous characteristics of the Lie group, are used for integrated navigation positioning respectively, Figure 4 which is a comparison diagram of the integrated navigation results of the Extended Kalman Filter (EKF) and the inertial-based integrated navigation method with the projection system defined by the right error of the Lie group and the Earth system as the reference system (LG-EKF-e) when the initial horizontal attitude error angle is 1° and the heading error angle is 180°; Figure 5 which is a comparison diagram of the integrated navigation results of the Velocity Error State Transformation Kalman Filter (ST-EKF) and the filtering method of the present invention (LG-EKF-w) when the initial horizontal attitude error angle is 1° and the heading error angle is 180°. It can be seen that the positioning accuracy of the EKF algorithm is the worst, and the LG-EKF-w algorithm has the best positioning result under the condition of a large initial misalignment angle.

[0094] In one embodiment, an inertial-based integrated navigation device for enhancing the autonomous characteristics of a Lie group is provided, including:

[0095] A first module for receiving the navigation information of the inertial navigation system, odometer navigation information, and positioning system navigation information of a moving body;

[0096] A second module for constructing a Kalman filter system equation with enhanced autonomous characteristics of the Lie group projected based on the world coordinate system according to the initial navigation state of the moving body and the inertial navigation information;

[0097] A third module for constructing a velocity observation equation by using the estimated velocity of the odometer as the observable quantity;

[0098] A fourth module for performing inertial-based integrated navigation according to the Kalman filter system equation with enhanced autonomous characteristics of the Lie group projected based on the world coordinate system and the velocity observation equation, and obtaining the state and covariance of the inertial-based integrated navigation as the state and covariance for Kalman filter observation update;

[0099] A fifth module for performing attitude, velocity, and position updates according to the Lie group error state transformation.

[0100] The implementation methods of the above-mentioned modules and the construction of the model can all adopt the methods described in any of the foregoing embodiments, which will not be elaborated herein.

[0101] On the other hand, the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the inertial-based integrated navigation method for enhancing the autonomous characteristics of the Lie group provided in any of the foregoing embodiments are implemented. This computer device can be a server. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer 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 computer device is used to store sample data. The network interface of the computer device is used to communicate with an external terminal through a network connection.

[0102] On the other hand, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the inertial-based integrated navigation method for enhancing the autonomous characteristics of the Lie group provided in any of the foregoing embodiments are implemented.

[0103] 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 used in the various embodiments provided in the present application 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 many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0104] Matters not described in the present invention are well-known technologies.

[0105] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0106] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

[0107] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An inertial base combined navigation method with enhanced Lie group autonomy, characterized in that: include: Receive inertial navigation system navigation information, odometer navigation information, and positioning system navigation information of the moving body; According to the initial navigation state of the moving body and the inertial navigation information, the Kalman filter system equation based on the enhanced Lie group autonomy characteristics of the world coordinate system projection is constructed as follows: ； The coordinate system of the inertial navigation system is b The world coordinate system is w Tie, represents the filter state vector The derivative of ； ； ； ； in is the correlation time of the first-order Markov process, is the Earth's rotation vector w The projection of the system, For b Tie to w The direction cosine matrix of the system, For projection on w The calculated value of the gravitational acceleration of the system, for w Relative e The location of the system is w The projection of the system, e The Earth coordinate system. Relative to the moving body w The initial position of the system is w The projection of the system, is the velocity of the moving body relative to the inertial coordinate system w The calculated value of the projection of the system, Relative to the moving body w The location of the system is w The calculated value of the projection of the system, Indicates converting a vector into its corresponding antisymmetric matrix. , are the noise vector of the accelerometer and the noise vector of the gyroscope, is the odometer noise; Take the estimated speed of the odometer as the observed quantity and construct the speed observation equation; According to the Kalman filter system equation based on the enhanced Lie group autonomy characteristics of the world coordinate system projection and the velocity observation equation, the inertial basis integrated navigation is performed, and the state and covariance of the inertial basis integrated navigation are obtained as the state and covariance of the Kalman filter observation update; According to the Lie group error state transformation, the attitude, velocity and position are updated.

2. The inertial base integrated navigation method with enhanced Lie group autonomy according to claim 1, characterized in that: The inertial navigation system navigation information includes: navigation timestamp, roll angle, pitch angle, heading angle, longitude, latitude, altitude and three-dimensional speed information under the local navigation system; The odometer navigation information includes: navigation timestamp, carrier speed; The positioning system navigation information is navigation information from a satellite positioning system or an acoustic positioning system, including a navigation timestamp and absolute three-dimensional positioning information.

3. The inertial base integrated navigation method with enhanced Lie group autonomy according to claim 2 is characterized in that: Define the filter state vector ,as follows: in represents the attitude error angle of the inertial navigation system, The projection is defined relative to the inertial coordinate system. w The right Lie group velocity error under the system, for relative to w The projection defined by the system is w The right Lie group position error under the system, is the gyroscope constant bias, is the constant zero bias of the accelerometer, is the odometer scale factor error, are the pitch and yaw installation error angles and the lever arm error between the inertial navigation system and the odometer, respectively, and are modeled as follows: in is the correlation time of the first-order Markov process, is the odometer noise.

4. The inertial base integrated navigation method with enhanced Lie group autonomy according to claim 3 is characterized in that: Attitude error angle of inertial navigation system The differential equation is: in is the Earth's rotation vector w The projection of the system, For b Tie to w The calculated value of the direction cosine matrix of the system, is the gyro error.

5. The inertial base integrated navigation method with enhanced Lie group autonomy according to claim 3 is characterized in that: Right Lie group velocity error The differential equation is: in, for relative to e The attitude error angle of the inertial navigation system is w The projection of the system, is the accelerometer error.

6. The inertial base integrated navigation method with enhanced Lie group autonomy according to claim 3 is characterized in that: Right Lie Group Position Error The differential equation is: in For b Tie to w The calculated value of the direction cosine matrix of the system, is the gyro error.

7. The inertial base integrated navigation method with enhanced Lie group autonomy according to claim 3, characterized in that: The gyroscope constant bias, accelerometer constant bias, lever arm error, pitch and yaw installation error angles between the inertial navigation system and the odometer are considered constants, and the odometer scale factor error is considered a first-order Markov process, and its differential equations are as follows: 。 8. The inertial base integrated navigation method with enhanced Lie group autonomy according to any one of claims 4 to 7, characterized in that: The speed calculated by the inertial navigation system is corrected by the lever arm to obtain the estimated speed of the odometer; Construct the velocity observation equation as follows: in Indicates the odometer calculated by the inertial navigation m The equivalent velocity of the projection under the system, m The system is the odometer coordinate system. Indicates that the odometer output is m Velocity under the system; velocity observation matrix ； is the velocity observation noise, For b Tie to m The attitude transformation matrix of the system, For w Tie to b The attitude transformation matrix of the system, Relative to the moving body w The projection of the angular velocity of the system in the carrier coordinate system, ， is the odometer forward speed.

9. The inertial base integrated navigation method with enhanced Lie group autonomy according to claim 8, characterized in that: According to the Lie group error state transformation, the attitude, velocity and position are updated, where the attitude, velocity and position are updated as follows: in The velocity of the moving body relative to the inertial coordinate system is w Projection of the system; is the velocity of the moving body relative to the inertial coordinate system w The calculated value of the projection of the system, Relative to the moving body w The location of the system is w The projection of the system, Relative motion w The location of the system is w The calculated value of the projection of the system.

10. An inertial base integrated navigation device with enhanced Lie group autonomy, characterized in that: include: The first module is used to receive the inertial navigation system navigation information, odometer navigation information, and positioning system navigation information of the moving body; The second module is used to construct the Kalman filter system equation based on the enhanced Lie group autonomy characteristics of the world coordinate system projection according to the initial navigation state of the moving body and the inertial navigation information, as follows: ； The coordinate system of the inertial navigation system is b The world coordinate system is w Tie, represents the filter state vector The derivative of ； ； ； ； in is the correlation time of the first-order Markov process, is the Earth's rotation vector w The projection of the system, For b Tie to w The direction cosine matrix of the system, For projection on w The calculated value of the gravitational acceleration of the system, for w Relative e The location of the system is w The projection of the system, e The Earth coordinate system. Relative to the moving body w The initial position of the system is w The projection of the system, is the velocity of the moving body relative to the inertial coordinate system w The calculated value of the projection of the system, Relative to the moving body w The location of the system is w The calculated value of the projection of the system, Indicates converting a vector into its corresponding antisymmetric matrix. , are the noise vector of the accelerometer and the noise vector of the gyroscope, is the odometer noise; The third module is used to construct the speed observation equation by taking the estimated speed of the odometer as the observed quantity; The fourth module is used to perform inertial basis integrated navigation according to the Kalman filter system equation based on the enhanced Lie group autonomy characteristics of the world coordinate system projection and the velocity observation equation, and obtain the state and covariance of the inertial basis integrated navigation as the state and covariance of the Kalman filter observation update; The fifth module is used to update the attitude, velocity and position according to the Lie group error state transformation.

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