Reverse avoidance adaptive scheduling method and system embedded with vehicle-mounted IMU installation angle

Abstract

This invention provides a reverse avoidance adaptive scheduling method and system for embedding the mounting angle of an onboard IMU, comprising: establishing a zero-bias estimation model of the onboard gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state, respectively, and solving for the roll mounting angle, pitch mounting angle, and heading mounting angle; combining the confidence level of the mounting angle with integrated navigation information, fusing and updating the three-axis mounting angle; after each calculation of a new mounting angle, adaptively scheduling a heading mounting angle reverse avoidance method with a preset mode to ensure the correctness of the mounting angle input to the fusion system. This invention addresses the shortcomings of existing onboard IMU mounting angle estimation technologies, requiring no additional hardware. By designing a reverse avoidance adaptive scheduling mechanism, it flexibly switches avoidance modes, optimizes multiple sets of mounting angle fusion logic, corrects heading reversal errors, saves computing power, and achieves high-precision and high-reliability estimation of IMU mounting angles in complex onboard scenarios.

Description

Technical Field

[0001] This invention relates to the field of vehicle navigation integration technology, and in particular to a reverse avoidance adaptive scheduling method and system with embedded vehicle IMU installation angle. Background Technology

[0002] With the rapid iteration of autonomous driving technology and the intelligent connected vehicle industry, the onboard inertial measurement unit (IMU), as the core sensing component of the integrated navigation system, directly determines the reliability of navigation positioning, attitude control, and path planning through its installation accuracy, making it a crucial link in ensuring vehicle driving safety. The IMU provides core motion parameters to the integrated navigation system by measuring the vehicle's angular velocity and acceleration. Accurate estimation of the IMU's installation angles (roll, pitch, and yaw angles) relative to the vehicle's coordinate system is a prerequisite for eliminating measurement errors and ensuring accurate parameter conversion.

[0003] Currently, vehicle-mounted IMUs are widely used in navigation systems of passenger cars, commercial vehicles, and special vehicles. Especially in complex conditions where satellite navigation (GNSS) signals are blocked (such as tunnels, densely populated urban areas with high-rise buildings, and underground parking garages), the autonomous inertial navigation capability of the IMU becomes the core support for maintaining navigation continuity. However, factors such as vibration, bumps, rapid acceleration and deceleration, and physical movement of the IMU during vehicle operation can easily lead to deviations in the installation angle. Among these, a 180° reverse error in the heading installation angle is the most insidious and dangerous systemic deviation, which can directly lead to misjudgment of navigation direction and cause safety hazards such as loss of vehicle control.

[0004] To improve the accuracy of installation angle estimation, the industry generally adopts a multi-batch solution fusion approach, which enhances estimation stability through multi-source data redundancy to address installation angle deviation issues under complex in-vehicle conditions. However, existing technologies struggle to balance real-time performance and accuracy of installation angle estimation under complex in-vehicle conditions, failing to fully meet the high reliability requirements of autonomous driving for integrated navigation systems. Therefore, developing an in-vehicle IMU installation angle fusion and update technology that adapts to complex conditions and balances efficiency and accuracy has become an urgent need in the current in-vehicle navigation field.

[0005] Currently, existing technologies for estimating the mounting angle of vehicle-mounted IMUs mainly fall into two categories. These two methods solve the mounting angle problem based on different technical logics, and the specific technical solutions are as follows: (1) External reference fusion method This method uses the standard GNSS (Global Navigation Satellite System) onboard equipment as a base reference, and additionally deploys hardware such as wheel speed odometers and visual sensors. Through data fusion algorithms such as Kalman filtering and extended Kalman filtering, it establishes an error model between IMU measurements and external reference information (such as GNSS positioning results and odometer speed data), and then inversely calculates the roll, pitch, and yaw angles of installation. Its core logic is to utilize the absolute reference characteristics of external sensors to correct the relative measurement deviation of the IMU, thereby achieving high-precision estimation of the installation angle. The core advantage of this method is its high estimation accuracy and ability to compensate for the limitations of the IMU's own measurements by using multi-source external data; however, it is highly dependent on the deployment of external hardware and signal quality.

[0006] (2) Displacement recursion method for IMU static-start To avoid reliance on additional hardware, a method has emerged that relies solely on IMU data to calculate the three-axis mounting angles. First, using IMU data from a stationary vehicle, the roll and pitch mounting angles are calculated based on the projected geometric relationship between the accelerometer output and the gravity vector. Then, leveraging the vehicle's linear acceleration from a standstill, the horizontal acceleration component output by the IMU is integrated twice to recursively calculate the vehicle's trajectory vector. Finally, the heading mounting angle is calculated using the angle between the trajectory vector's direction and the IMU's forward measurement axis. The core logic relies on the motion characteristic of a stationary start with an initial velocity of 0, using the uniqueness of the displacement trajectory direction to estimate the heading angle, ultimately achieving a complete solution for the three-axis mounting angles without external hardware assistance. This method eliminates the need for additional hardware deployment, reducing system integration costs, but its application scenarios and estimation accuracy are limited by its own technical logic.

[0007] While the two existing technologies mentioned above can achieve the basic solution of the vehicle-mounted IMU mounting angle, they have significant shortcomings in practical engineering applications, and both are problems that this invention can solve: First, both existing technologies share a common drawback: the calculation of the heading mounting angle is based on the core assumption that "the direction of motion acceleration is consistent with the direction of vehicle movement." This assumption no longer holds true under hidden conditions such as sudden deceleration and braking, and reversing, which can easily lead to a systematic error of 180° reversal in the heading mounting angle calculation. Moreover, the motion characteristics of such hidden conditions are highly similar to those of conventional effective calculation conditions, and conventional verification methods cannot identify such anomalies, which will directly affect the accuracy of subsequent navigation calculations and cannot guarantee the stability of vehicle navigation. Second, even if a few technologies attempt to introduce reverse avoidance ideas, they do not design an adaptive scheduling mechanism. The inability to flexibly switch between full reverse avoidance and fast reverse avoidance modes based on the existence of a valid reference installation angle, and the indiscriminate execution of the time-consuming full reverse avoidance process for each batch of installation angle calculation results, leads to a significant increase in computational resource consumption. It is prone to resource competition with the real-time calculation of integrated navigation, resulting in wasted computing power and affecting system real-time performance. Thirdly, the external reference fusion method requires additional hardware deployment, increasing procurement and integration costs, and relies on GNSS signals, making it prone to failure in complex obstruction scenarios. The IMU stationary-start displacement recursion method is not only cumbersome in integral recursion calculation and has a large cumulative error, but also does not consider the weighted fusion of multiple sets of installation angles, resulting in low system reliability. Neither of these two methods forms a closed-loop logic of "calculation-avoidance-fusion-iteration", making it difficult to adapt to the needs of complex vehicle operating conditions.

[0008] In summary, existing technologies generally suffer from the following drawbacks: the lack of a reverse avoidance mechanism for the heading and installation angle and an adaptive scheduling mechanism, and both mainstream methods have their own shortcomings and insufficient reliability, making it difficult to meet the needs of practical applications. Summary of the Invention

[0009] This invention provides a reverse avoidance adaptive scheduling method and system with embedded vehicle-mounted IMU mounting angle, which solves the problems of existing technologies that lack reverse avoidance and adaptive scheduling mechanisms for heading mounting angle, and the shortcomings and insufficient reliability of the two mainstream methods. By designing a reverse avoidance adaptive scheduling mechanism, the avoidance mode can be flexibly switched, multiple sets of mounting angle fusion logic can be optimized, heading reverse errors can be corrected, and computing power can be saved, so as to achieve high-precision and high-reliability estimation of IMU mounting angle in complex vehicle-mounted scenarios.

[0010] In a first aspect, the present invention provides a reverse avoidance adaptive scheduling method embedded in the mounting angle of an onboard IMU, comprising: A zero-bias estimation model for the vehicle-mounted gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state were established to solve for the roll installation angle, pitch installation angle and yaw installation angle. By combining the confidence level of the installation angle with the information from the integrated navigation system, the installation angles of the three axes are fused and updated. After each new installation angle is calculated, the heading installation angle reverse avoidance method of the preset mode is adaptively scheduled to ensure that the installation angle input to the fusion system is correct.

[0011] According to the present invention, a reverse avoidance adaptive scheduling method based on the embedded vehicle-mounted IMU mounting angle is provided, which establishes a zero-bias estimation model of the vehicle-mounted gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state, and solves for the roll mounting angle, pitch mounting angle and yaw mounting angle, including: A zero-bias estimation model for the vehicle-mounted IMU gyroscope is established. Based on the original output of the gyroscope during the vehicle's stationary phase, the zero-bias estimation and measurement data of each axis of the gyroscope are compensated through time averaging and discretization. A correlation model between the vehicle-mounted IMU accelerometer and gravity projection is established. After denoising the accelerometer output, the rotation matrix from the vehicle frame to the IMU frame is constructed. The projection relationship of gravity in the IMU frame is derived. The simultaneous equations are solved to obtain the roll installation angle, pitch installation angle and yaw installation angle.

[0012] Secondly, the present invention also provides a reverse avoidance adaptive scheduling system embedded in the installation angle of an on-board IMU, comprising: A module is established to create a zero-bias estimation model for the vehicle-mounted gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state, and to solve for the roll installation angle, pitch installation angle and yaw installation angle. The calculation module combines the confidence level of the installation angle with the information from the integrated navigation system to fuse and update the installation angles of the three axes. After each new installation angle is calculated, it adaptively schedules the preset mode of the heading installation angle reverse avoidance method to ensure that the installation angle input to the fusion system is correct.

[0013] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the reverse avoidance adaptive scheduling method for embedding the installation angle of the vehicle-mounted IMU as described above.

[0014] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reverse avoidance adaptive scheduling method for embedding the mounting angle of an on-board IMU as described above.

[0015] The present invention provides a reverse avoidance adaptive scheduling method and system for embedded vehicle IMU installation angle. It addresses the shortcomings of existing vehicle IMU installation angle estimation technology. Without additional hardware, it designs a reverse avoidance adaptive scheduling mechanism, flexibly switches avoidance modes, optimizes the fusion logic of multiple sets of installation angles, corrects heading reversal errors, saves computing power, and achieves high-precision and high-reliability estimation of IMU installation angle in complex vehicle scenarios. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the reverse avoidance adaptive scheduling method for embedded vehicle-mounted IMU installation angle provided by the present invention. Figure 2 This is a schematic diagram of the installation angle of the GNSS / INS integrated navigation vehicle IMU provided by the present invention; Figure 3 This is a schematic diagram of the adaptive update process for the vehicle-mounted IMU installation angle in the integrated navigation information judgment provided by the present invention; Figure 4 This is a schematic diagram of the adaptive scheduling process for reverse avoidance of heading installation angle provided by the present invention; Figure 5 This is a schematic diagram of the reverse avoidance adaptive scheduling system with embedded vehicle-mounted IMU installation angle provided by the present invention; Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] Figure 1 This is a flowchart illustrating the reverse avoidance adaptive scheduling method for embedded vehicle-mounted IMU installation angle provided in an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: Step 100: Establish the zero-bias estimation model of the vehicle-mounted gyroscope and the correlation model of the accelerometer and gravity projection in the static state, respectively, and solve for the roll installation angle, pitch installation angle and yaw installation angle; Step 200: Combine the confidence level of the installation angle with the integrated navigation information to fuse and update the three-axis installation angle. After each new installation angle is calculated, adaptively schedule the preset mode of heading installation angle reverse avoidance method to ensure that the installation angle input to the fusion system is correct.

[0020] Specifically, this embodiment of the invention first establishes a zero-bias estimation model for the vehicle-mounted IMU gyroscope. Based on the original output of the gyroscope during the vehicle's stationary phase, time averaging and discretization are used to achieve zero-bias estimation of each axis of the gyroscope and compensation of measurement data, providing accurate input for subsequent installation angle estimation. Then, a correlation model between the vehicle-mounted IMU accelerometer and gravity projection is established. After denoising the accelerometer output, a rotation matrix from the vehicle frame to the IMU frame is constructed, and the projection relationship of gravity in the IMU frame is derived. Simultaneous equations are used to solve for the roll, pitch, and yaw installation angles. Finally, the three-axis installation angles are fused and updated by combining the confidence level of the installation angle with integrated navigation information. After each new installation angle is calculated, two modes of yaw installation angle reverse avoidance methods are adaptively scheduled to ensure the correct installation angle input to the fusion system.

[0021] Based on the above embodiments, step 100 includes: A zero-bias estimation model for the vehicle-mounted IMU gyroscope is established. Based on the original output of the gyroscope during the vehicle's stationary phase, the zero-bias estimation of each axis of the gyroscope and the compensation of measurement data are achieved through time averaging and discretization, providing accurate input for subsequent installation angle estimation.

[0022] The zero bias of a MEMS gyroscope is one of the main error sources affecting the accuracy of inertial navigation. It is defined as the output offset (constant error) of the gyroscope when there is no rotational input. In the initial installation angle estimation of a vehicle, accurate estimation of the gyroscope's zero bias can effectively eliminate rotational errors in the static stage, laying the foundation for subsequent dynamic recursion. This embodiment derives the zero bias estimation formula based on the gyroscope output when the vehicle is stationary.

[0023] Gyroscope measurement model When the vehicle is stationary, the IMU has no rotational motion, and the ideal output of the gyroscope should be 0. However, due to manufacturing process errors and environmental interference in MEMS devices, the actual output includes zero bias and random noise. Its measurement model can be expressed as:

[0024] in: for The IMU gyroscope at the moment The raw output of the axis ( (corresponding to forward, right, and down directions respectively). For the gyroscope Zero offset of the shaft (constant error, does not change with time); The measurement noise of the gyroscope (random error, with a mean of 0 and a variance of 0) (Gaussian distribution).

[0025] The core assumption of this model is that the output of the gyroscope in a static state consists only of zero bias and noise, with no rotational angular velocity input. Therefore, noise can be suppressed and zero bias extracted by time averaging.

[0026] (1) Derivation of the zero bias estimation formula The core idea of ​​zero-bias estimation is: during the static observation period Inside (from) arrive The gyroscope output is integrated over time and averaged. Since the noise mean is 0, the cumulative effect of the noise is suppressed after integration, thus obtaining a zero-biased unbiased estimate.

[0027] For both sides of the measurement model Integral over the interval:

[0028] Analyze the two terms on the right side of the equation: 1. First item: Since it is a constant, the integral result is: ; 2. Second item: The mean is 0, when the observation duration is Over a sufficiently long time (usually 10 seconds), the integral result The impact of noise is negligible.

[0029] Substituting the above result into the integral, and dividing both sides by... The gyroscope's first... Estimation formula for zero axis offset:

[0030] in, Indicates the gyroscope's first The estimated value of the zero-axis bias.

[0031] (2) Zero bias compensation and discretization implementation By compensating the original gyroscope output with the zero-bias estimate, the effective angular velocity output after removing the zero bias can be obtained. The compensation formula is as follows:

[0032] When the vehicle is stationary, the compensated output It should be approximately 0, containing only random noise, to verify the effectiveness of the zero-biased estimation.

[0033] In practical engineering applications, IMU data is acquired through discrete sampling, requiring the continuous integral formula to be discretized. Let the sampling frequency during the stationary phase be... (Sampling interval) ), then in Collected within the time period There are 10 data points. At this point, the discretization formula for the zero-biased estimate is:

[0034] in, Indicates the first The gyroscope at the sampling point The original output value of the axis. This discretization formula is easy to implement in embedded systems and is a commonly used zero-bias estimation method in engineering.

[0035] Furthermore, a correlation model between the vehicle-mounted IMU accelerometer and gravity projection is established. After denoising the accelerometer output, the rotation matrix from the vehicle frame to the IMU frame is constructed, the projection relationship of gravity in the IMU frame is derived, and the simultaneous equations are solved to obtain the roll, pitch, and yaw installation angles.

[0036] When the vehicle is stationary, the IMU has no translational acceleration, and the accelerometer output only reflects the projection of gravity onto the IMU coordinate system. This embodiment derives the mathematical relationship between the accelerometer measurement and the gravity vector when the vehicle is stationary, based on the relationship between coordinate system rotation and gravity projection, providing a theoretical basis for subsequent calculations of roll and pitch angles.

[0037] (1) Accelerometer measurement model When the vehicle is stationary, the translational acceleration of the IMU is 0, and the accelerometer output consists only of gravity projection and random noise. Its measurement model is as follows:

[0038] in: For a moment IMU accelerometer The raw output of the axis; For gravity in the IMU coordinate system The projected components of the axis (constant values, not changing with time). The measurement noise of the accelerometer (random error, with a mean of 0 and a variance of 0) (Gaussian distribution).

[0039] The core assumption of this model is that the output of the accelerometer in a stationary state is only related to the gravity projection and has no translational acceleration input. Therefore, noise can be suppressed by time averaging and the gravity projection component can be extracted.

[0040] To suppress the impact of measurement noise on the extraction of gravity projection components, it is necessary to adjust the static duration. The accelerometer output within the model is time-averaged. The measurement model is then used to measure the accelerometer output on both sides. Integrate over the interval and take the average:

[0041] Analyze the two terms on the right side of the equation: 1. First item: Since it is a constant, the integral result is: ; 2. Second item: The mean is 0, when For a sufficiently long time, The noise impact is negligible.

[0042] Therefore, the accelerometer output after noise reduction Approximately equal to gravity in the IMU coordinate system The projection components of the axis, namely:

[0043] in, These are the time averages of the forward, rightward, and downward outputs of the accelerometer, respectively. The denoised values ​​are used in the subsequent formula derivations.

[0044] (2) Coordinate system projection of the gravity vector like Figure 2 In the schematic diagram of the GNSS / INS integrated navigation vehicle-mounted IMU installation angle shown, the vehicle coordinate system is called the v system, and the carrier (IMU) coordinate system is called the b system. The XYZ axes of the v system point to the lower right front of the vehicle body, while the XYZ axes of the b system do not coincide with the lower right front of the vehicle body, but form an angle. Therefore, it is necessary to construct a rotation matrix from the vehicle system to the IMU system.

[0045] According to the direction cosine matrix, the gravity vector in the vehicle coordinate system ( (system) and IMU coordinate system ( The projection relationships between systems follow standard vector transformation rules.

[0046] First, when the vehicle is stationary on a horizontal surface, gravity acts only along the vehicle's coordinate system. The force acts along the axis (downward). Therefore, the vector expression for gravity in the vehicle coordinate system is:

[0047] in, This is the magnitude of gravitational acceleration. This vector is only... The axle has a component, which is consistent with the force state of a vehicle on a horizontal ground (gravity and ground support force are balanced, and there is no horizontal component).

[0048] Next, we derive the projection of gravity onto the IMU coordinate system. The IMU accelerometer measures specific force, which is the non-gravitational external force per unit mass. In the IMU coordinate system, the specific force equation is: Absolute acceleration when the vehicle is stationary. The equation then simplifies to:

[0049] in, This is the accelerometer output in the IMU coordinate system after noise reduction and zero-bias calibration. It is the projection of the gravity vector into the IMU coordinate system. For example... Figure 3 In the schematic diagram showing the correlation between accelerometer measurements and gravity projection, the specific force output by the accelerometer is in the opposite direction to gravity. The specific force projection is obtained in the b-frame. , , That is the measurement from the accelerometer.

[0050] Based on the above formula, the component expressions of the gravity vector in the IMU coordinate system can be directly obtained:

[0051] Finally, based on the vector transformation relationship of the direction cosine matrix, the vector of gravity in the IMU coordinate system is... It can also be determined by its vector in the vehicle coordinate system. By rotation matrix (From the vehicle system to the IMU system) we get:

[0052] The above derivation establishes the measurement output of the IMU accelerometer and the mounting angle between the IMU and the vehicle (included in...). The direct mathematical connection between the two (in Chinese) laid the foundation for subsequent calculations of roll and pitch angles using static data.

[0053] (3) Expansion of the gravitational projection components The installation deviation of the IMU relative to the vehicle is described by three attitude angles, and the rotation sequence follows the commonly used engineering sequence of "yaw-pitch-roll". Agreement (conforming to the laws governing changes in vehicle motion posture): Heading installation angle : Relative Tie The rotation angle of the axis (downward); Pitch installation angle : Relative Tie Rotation angle of the axis (to the right); Roll installation angle : Relative Tie Rotation angle of the axis (forward).

[0054] Tie Vector transformation of the system is achieved through the composite direction cosine matrix. This matrix is ​​based on " The complete expression for the rotation order derivation is:

[0055] Will and Substituting into the vector transformation formula, we get: The gravity projection components of each axis of the IMU coordinate system are obtained by unfolding. Because... Only The axis has a non-zero component (the third element is...). (The first two elements are 0), therefore matrix multiplication only requires calculating The third column and The product of, i.e.:

[0056] After processing, the relationship between the accelerometer output and the installation angle can be obtained:

[0057] (4) Discretization implementation Similar to gyroscope bias estimation, accelerometer output denoising also requires a discretization formula. Assume data is collected during the stationary phase. If there are 10 data points, the discretization formula for the accelerometer output after denoising is:

[0058] in, Indicates the first The accelerometer at the sampling point is... The original output value of the axis. This formula is easy to implement in embedded systems, and can directly use the sampled data during the stationary phase to calculate the denoised accelerometer output, providing input for subsequent attitude angle calculations.

[0059] (5) Determination of roll and pitch installation angles Based on discretization processing , , The horizontal mounting angle can be solved using the geometric relationships of gravity projection. Since the IMU may be mounted at arbitrary angles (without the assumption of small angles), rigorous derivation of trigonometric functions is required to ensure estimation accuracy in large-angle scenarios.

[0060] The pitch angle is solved using the following formula. :

[0061] Since the range of values ​​for the pitch angle is Within this interval, there is always Therefore, by directly taking the square root of the above equation, we get:

[0062] The expressions for the sine and cosine of the pitch angle can be obtained as follows:

[0063] The formula for calculating the pitch angle is derived through continuous derivation:

[0064] This formula requires no small-angle assumptions, is applicable to any installation orientation, and its output angle range is exactly [value missing]. It perfectly matches the range of values ​​for the pitch angle.

[0065] The expressions for the sine and cosine of the roll angle are:

[0066] The formula for calculating the roll angle is derived through continuous derivation:

[0067] in The system can automatically determine the quadrant of the angle based on the sign of the input parameters, and the output angle range is exactly [range missing]. It can fully cover all possible values ​​of the roll angle.

[0068] (6) Solving for the heading installation angle During the short-term straight-line movement of the vehicle, mechanical arrangement is performed to obtain the horizontal displacement in the IMU coordinate system. Based on the geometric relationship between the horizontal displacement and the heading installation angle, the heading installation angle is solved.

[0069] Heading installation angle Defined as IMU coordinate system ( (system) relative to the vehicle coordinate system ( (system) around The rotation angle of the axis (downward) reflects the forward axis (of the IMU) rotation angle. ) and the vehicle's actual front axle ( Deviation in the horizontal plane. (And roll angle) Pitch angle Unlike in a static state, the gravitational vector acts only in the vertical direction, without an independent horizontal vector to assist it, and therefore cannot be directly solved using accelerometer data. This method utilizes the horizontal acceleration and angular velocity generated by the vehicle's short-term motion (such as straight-line travel) and combines this with the displacement recursion principle in inertial navigation to achieve estimation. This method relies solely on the IMU's own measurement data, requires no additional sensors, and is suitable for engineering application scenarios.

[0070] When the vehicle enters a short-duration motion phase (the duration of motion is denoted as...) If the vehicle is controlled to travel approximately in a straight line (without steering or lateral slip), then its actual direction of motion is along the vehicle coordinate system. Axis (forward), in the horizontal plane ( The acceleration of a plane is only along its direction. The axis has a component, and the angular velocity only revolves around the axis. Axial (downward) variation (angular velocity is 0 during uniform travel). Based on the geometric relationship of coordinate system rotation, the horizontal acceleration measured by the IMU and the subsequently recursively derived horizontal displacement need to be determined through the heading installation angle. It is related to the actual movement state of the vehicle.

[0071] Let the actual horizontal acceleration of the vehicle be... (only along) Axial acceleration, lateral acceleration According to the horizontal plane, The rotational relationship of the shaft, the horizontal acceleration component measured by the IMU ( The mapping relationship between the actual acceleration of the vehicle and the acceleration of the vehicle is as follows:

[0072] In the formula, This is a rotation matrix in the horizontal plane, which physically transforms the acceleration vector from the vehicle coordinate system to the IMU coordinate system. The influence of measurement noise is temporarily ignored here; noise interference will be further suppressed later through the mean effect during the integration process.

[0073] By performing a second integration on the horizontal acceleration measured by the IMU, the horizontal displacement in the IMU coordinate system can be obtained. Because when a vehicle travels in a straight line, the actual horizontal displacement is only along... axis( Lateral displacement The coordinate system transformation relationship of the displacement components is consistent with that of the acceleration, that is:

[0074] in, The actual horizontal displacement of the vehicle (along) axis); and It needs to be calculated by acceleration integration, and the zero bias of the accelerometer horizontal axis needs to be deducted before integration (the zero bias can be estimated by the average acceleration during the stationary phase) to avoid the integral drift caused by the zero bias affecting the displacement accuracy.

[0075] Starting from the horizontal displacement correlation formula, due to the actual horizontal displacement of the vehicle (Short-duration motion can ensure non-zero displacement, usually requiring...) To reduce the impact of noise, dividing the two equations will eliminate the problem. ,get:

[0076] Take the arctangent function of both sides of the equation, using the four-quadrant arctangent function. After correcting the angle range, the formula for calculating the heading installation angle is obtained:

[0077] Output range is It precisely covers all possible values ​​of the heading installation angle, ensuring the accuracy of the heading installation angle calculation when the vehicle is moving forward.

[0078] Based on the above embodiments, step 200 includes: By combining the confidence level of the installation angle with information from the integrated navigation system, the installation angles of the three axes are fused and updated. After each calculation of a new installation angle, two modes of heading installation angle reverse avoidance methods are adaptively scheduled to ensure that the installation angles input to the fusion system are correct.

[0079] (1) Convergence determination of heading installation angle sequence To filter out measurement noise and random disturbances and improve the accuracy and stability of the heading installation angle calculation, a convergence test is performed on the continuous heading installation angle sequence calculated epoch by epoch. The current epoch is then used. Including the recent Each epoch, constructing the heading installation angle sequence. Set the heading angle deviation threshold between adjacent epochs. A sequence is considered convergent if it simultaneously satisfies the following constraints:

[0080] (2) Installation angle confidence test After the heading and installation angle sequence converges, the confidence level of the installation angles is verified to quantitatively evaluate the degree of horizontal attitude disturbance and heading change, eliminating low-confidence operating conditions. A horizontal attitude disturbance threshold is set. Heading and turning disturbance threshold Let the epoch interval of the convergent sequence be... The quantification constraints are:

[0081] In the formula, For the epoch interval corresponding to the convergence sequence of the heading installation angle; for The sum of variances of the roll and pitch angle sequences within the interval quantifies the overall perturbation of the horizontal attitude (the smaller the variance, the more stable the attitude). for The total heading angle within the interval reflects whether the vehicle is maintaining straight-line travel; The variance calculation operator is expressed as follows:

[0082] For the interval epoch number, For sequence The mean; , Horizontal reference coordinate system Next The roll and pitch angles of the epoch. For the first in this coordinate system Epoch Gyroscope Effective output value of axis The epoch sampling interval.

[0083] Construct an installation angle confidence model to assess the reliability of the solution:

[0084] In the formula, For the installation angle confidence, , It is a positive weighting coefficient, which can be adjusted according to the degree of influence of the operating condition disturbance; The larger the value, the higher the reliability of the solution. Set a confidence threshold. , The solution is deemed invalid and the result is discarded. It is determined to be valid and retained.

[0085] (3) Calculation of final value of heading installation angle and integration of three-axis installation angle After the heading installation angle sequence converges and the confidence check passes, The calculated heading and installation angle values ​​for each epoch are filtered using a sliding window mean filter, and then... The mean of each epoch is used as the optimal estimate of the heading angle:

[0086] Roll installation angle calculated using the gravity projection method Pitch installation angle With respect to the above-mentioned optimal heading installation angle By integrating the data, we obtain the three-axis mounting angle vectors of the IMU relative to the vehicle coordinate system:

[0087] (4) Adaptive update of integrated navigation information judgment 1) Collection and filtering of multiple sets of installation angle calculation results Definition of the first The effective solution result is: ,right First, a validity screening of the operating conditions is performed to ensure that the calculation results correspond to normal driving conditions. The specific judgment criteria are as follows: 1. Preliminary Position Information Assessment: Calculation of Position Information Module Length for GNSS-INS Integrated Navigation ,like ( If the location information threshold is used, then the solution condition is determined to be without obvious abnormalities. 2. Stability determination of continuous solutions: To verify the consistency of three consecutive valid solutions, the following conditions must be met simultaneously. and ( (This is the installation angle deviation threshold) to ensure that the solution results are free from sudden fluctuations.

[0088] When both of the above conditions are met, the determination is made. , , This is a fusion-compatible result. Definition For the first If there are three fusionable results, then these three fusionable results are denoted as... , , Only such fusion-compatible results are retained for subsequent weighted fusion.

[0089] 2) Confidence-based weighted fusion calculation Using the confidence level of each set of mounting angles as weight, a two-step weighted fusion of multiple sets of fusionable mounting angles is performed to improve the accuracy and robustness of the final mounting angle: 1. Initial Fusion: After accumulating 3 sets of fusionable results, initial fusion is triggered to build a stable baseline. The fusion formula is:

[0090] In the formula, This represents the total number of fusionable results; for The installation angle is obtained by weighted fusion of the fusion results; This is used as the cumulative confidence weight; this step effectively avoids random disturbances in single-batch calculations by redundantly fusing multiple sets of data.

[0091] 2. Dynamic Iterative Fusion: After the initial fusion is completed, for each newly added set of valid solution results selected by the working conditions... , first determine If this condition is met, lightweight iterative fusion will be initiated, eliminating the need to store historical data. The iterative formula is as follows:

[0092] In the formula, For the first The mounting angle after secondary fusion; For the front The cumulative confidence weight of the sub-fusion The updated cumulative weights are used; the adaptive fusion update of the installation angle is achieved through binary iteration, taking into account the reliability of new data and historical fusion results.

[0093] 3) Outlier detection and adaptive update Outlier detection and adaptive updates are performed on the newly calculated installation angle group to eliminate abnormal operating condition deviations: 1. Outlier detection: If the newly added valid solution does not meet the requirements... If it is, then it is judged as an outlier. They will not be included in the fusion process for the time being, in order to avoid affecting the overall estimation accuracy due to a single abnormal operating condition.

[0094] 2. IMU Movement Detection: An IMU is determined to have physically moved if and only if both of the following conditions are met simultaneously: (1) Temporal anomaly consistency: Two consecutive sets of solution results are outliers, and satisfy the following conditions: This indicates that the installation angle variation is consistent, thus ruling out random anomalies; (2) Location-based supporting evidence: If This indicates that the GNSS-INS integrated navigation position information is outside the normal range, which can further verify that the anomaly was caused by IMU movement.

[0095] When both conditions are met, the fusion benchmark is updated to the latest valid solution; if only one condition is met, it is judged as a normal anomaly, the abnormal data is removed and the current fusion result remains unchanged.

[0096] 3. Adaptive Stable Update: Set a stable threshold for fusion weights. If the cumulative confidence weights satisfy If the installation angle is determined to be stable, dynamic updates are stopped and the current fusion value is used; if the condition is not met, a valid new solution group is included in the fusion, and the weighted fusion value is dynamically updated.

[0097] When the fusion weight Reaching the preset threshold When the fusion result has fully utilized the redundancy of multiple batches of data and the installation angle estimation tends to stabilize, the iterative update is stopped, and the current weighted fusion value is taken as the final optimal estimate of the IMU's three-axis installation angle. .

[0098] Thus, the confidence-weighted adaptive fusion update of the IMU mounting angle has been completed. This method mines the redundancy value of multiple batches of data through staged weighted fusion, and improves robustness by combining outlier detection and IMU movement determination. It achieves high-precision and long-term stable estimation of the mounting angle under complex vehicle conditions, providing reliable support for the engineering application of vehicle IMU mounting angle.

[0099] (5) Reverse avoidance of heading angle The existing vehicle-mounted IMU's heading angle calculation is based on the core assumption that "the direction of motion acceleration is consistent with the direction of vehicle movement". This assumption no longer holds true under hidden conditions such as sudden deceleration and braking, and reversing, which can easily lead to a systematic error of 180° reversal in the heading angle calculation. Moreover, the motion characteristics of such conditions are similar to those of conventional effective calculation conditions, and the accuracy verification cannot identify such anomalies. Therefore, a reverse avoidance method needs to be added to correct this directional deviation.

[0100] 1) Initial state validity verification make , , Let be the north, east, and ground velocity components output by GNSS, respectively. Then the GNSS horizontal velocity can be expressed as: The initial calculation yielded an estimated value for the heading installation angle. Then, the system performs a two-condition check on the initial state. Only when both conditions are met simultaneously can the subsequent recursive process proceed. 1. Velocity Amplitude Condition: The horizontal velocity magnitude of the GNSS must satisfy... ,in To ensure that the vehicle is in an effective state of motion; 2. Normalized velocity accuracy condition: The ratio of the standard deviation of GNSS horizontal velocity to the velocity modulus satisfies... .

[0101] After the verification is passed, the initial heading angle is calculated using GNSS velocity components. Initial pitch angle The initial roll angle is set to zero, and the initial velocity is determined using GNSS observations, thus completing the recursive initial state construction. If the conditions are not met, the system waits for the next moment to re-trigger the verification.

[0102] 2) Parallel recursion of dual heading angle inertial navigation systems Based on the established initial state, pure inertial navigation recursion is performed simultaneously using two sets of heading installation angles: 1. Original installation angle : Take the estimated value of the heading installation angle calculated at the current solution. ; 2 Reverse installation angle Reverse the original installation angle .

[0103] , The corresponding recursive positions are respectively , After reaching the minimum recursion time of 3 seconds, the GNSS quantization and discrimination process begins.

[0104] 3) Output of GNSS Quantization Judgment and Avoidance Results Based on the actual operating performance of GNSS, set the criteria for determining the reliability of GNSS positioning. If the condition is not met, continue the recursion; if the condition is met, proceed to the following judgment step: definition , GNSS position at the same time The difference modulus are respectively , Construct the position deviation ratio: (39) The discrimination threshold is taken as Set abnormal deviation threshold. The discrimination and output rules are as follows: 1. First, determine and If the conditions are met, it means that the recursive errors corresponding to the two installation angles are both large, and the current judgment is deemed to have failed. The current recursive result is cleared, and the process is re-executed after the next initial state verification is met. If the conditions are not met, the process proceeds to the next judgment. 2 If ,Right now Much larger The original installation angle was determined to be correct, and it was directly adopted. Perform subsequent navigation calculations; 3 If ,Right now much smaller The original installation angle was determined to exist. In the opposite direction, Revised to Then input the integrated navigation system; 4. If none of the above conditions are met, the process continues recursively and returns to the beginning of this section to re-execute the GNSS position reliability judgment and subsequent discrimination process.

[0105] (6) Reverse avoidance adaptive scheduling mechanism of embedded installation angle fusion update system The adaptive scheduling mechanism uses the existence of a valid reference installation angle as the core criterion, switching between full reverse avoidance and fast reverse avoidance modes as needed based on the operational scenario. This reduces computational resource consumption while ensuring effective reverse avoidance. The specific judgment rules and execution logic are as follows: 1) Rules for determining the effective reference installation angle The existence of a valid reference installation angle and the corresponding avoidance method are determined by two key conditions. The first type is the determination of integrated navigation information. After the k-th set of installation angles is calculated, if the integrated navigation module is working normally and the information constraints are met... The first type is established when the system possesses a valid reference installation angle after integrated navigation verification, automatically triggering a rapid reverse avoidance process; otherwise, a full reverse avoidance process is initiated. The second type is IMU displacement detection and judgment. When a physical position change of the inertial measurement unit is detected, the original valid reference installation angle becomes invalid. For the newly calculated installation angle data, a full reverse avoidance process is directly executed. The full reverse avoidance relies on dual inertial navigation recursive calculations to complete reverse fault detection and correction.

[0106] 2) Multi-node fast reverse execution logic avoidance When the system has a valid reference installation angle, a lightweight and fast reverse avoidance algorithm is prioritized. A fast discrimination module is embedded at the front end of the three installation angle similarity judgment nodes in the original fusion update system. The core judgment principle is to compare the newly calculated installation angle parameters with the historical reference installation angles. If the heading dimension parameter has changed... If the angle deviation is smaller after the reverse adjustment, then the reverse correction is completed directly.

[0107] The reference installation angles for the three decision nodes are defined as follows: Node 1: Reference installation angle is ,exist and Perform a fast reverse avoidance before similarity judgment; Node 2: Reference mounting angle is the fusion mounting angle. ,exist and Perform a fast reverse avoidance before similarity judgment; Node 3: Reference installation angle is used to mark abnormal installation angles. ,exist and A fast reverse avoidance mechanism is executed before similarity judgment.

[0108] This lightweight discrimination mode does not require complex inertial navigation recursive calculations; it relies solely on angle deviation comparison to complete fault identification, effectively ensuring system operating efficiency and dynamic real-time performance.

[0109] The reverse avoidance adaptive scheduling system with embedded vehicle IMU mounting angle provided by the present invention will be described below. The reverse avoidance adaptive scheduling system with embedded vehicle IMU mounting angle described below can be referred to in correspondence with the reverse avoidance adaptive scheduling method with embedded vehicle IMU mounting angle described above.

[0110] Figure 5 This is a schematic diagram of the reverse avoidance adaptive scheduling system with embedded vehicle-mounted IMU installation angle provided in an embodiment of the present invention, as shown below. Figure 5 As shown, it includes: a setup module 51 and a calculation module 52, wherein: The module 51 is used to establish the zero-bias estimation model of the vehicle-mounted gyroscope and the correlation model of the accelerometer and gravity projection in the static state, respectively, and solve the roll installation angle, pitch installation angle and heading installation angle; the calculation module 52 is used to combine the confidence of the installation angle with the integrated navigation information to fuse and update the three-axis installation angle. After each new installation angle is calculated, the module adaptively schedules the heading installation angle reverse avoidance method of the preset mode to ensure that the installation angle input to the fusion system is correct.

[0111] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communication interface 620, a memory 630, and a communication bus 640. The processor 610, communication interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute an adaptive scheduling method for reverse avoidance based on the embedded vehicle IMU mounting angle. This method includes: establishing a zero-bias estimation model for the vehicle gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state, respectively, and solving for the roll mounting angle, pitch mounting angle, and heading mounting angle; combining the confidence level of the mounting angle with integrated navigation information to fuse and update the three-axis mounting angle; and adaptively scheduling a preset mode of heading mounting angle reverse avoidance method after each new mounting angle is calculated to ensure the correct mounting angle input to the fusion system.

[0112] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements an adaptive scheduling method for inverted avoidance of the embedded vehicle-mounted IMU mounting angle provided by the above methods. The method includes: establishing a zero-bias estimation model of the vehicle-mounted gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state, respectively, and solving for the roll mounting angle, pitch mounting angle, and heading mounting angle; combining the confidence of the mounting angle with the integrated navigation information, fusing and updating the three-axis mounting angle; and adaptively scheduling a heading mounting angle inverted avoidance method of a preset mode after each calculation of the new mounting angle to ensure that the mounting angle input to the fusion system is correct.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for reverse avoidance adaptive scheduling of embedded vehicle IMU installation angle, characterized in that, include: A zero-bias estimation model for the vehicle-mounted gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state were established to solve for the roll installation angle, pitch installation angle and yaw installation angle. By combining the confidence level of the installation angle with the information from the integrated navigation system, the installation angles of the three axes are fused and updated. After each new installation angle is calculated, the heading installation angle reverse avoidance method of the preset mode is adaptively scheduled to ensure that the installation angle input to the fusion system is correct.

2. The reverse-avoidance adaptive scheduling method of claim 1, wherein, A zero-bias estimation model for the vehicle-mounted gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state were established respectively. The roll, pitch, and yaw installation angles were solved, including: A zero-bias estimation model for the vehicle-mounted IMU gyroscope is established. Based on the original output of the gyroscope during the vehicle's stationary phase, the zero-bias estimation and measurement data of each axis of the gyroscope are compensated through time averaging and discretization. A correlation model between the vehicle-mounted IMU accelerometer and gravity projection is established. After denoising the accelerometer output, the rotation matrix from the vehicle frame to the IMU frame is constructed. The projection relationship of gravity in the IMU frame is derived. The simultaneous equations are solved to obtain the roll installation angle, pitch installation angle and yaw installation angle.

3. The reverse-avoidance adaptive scheduling method of claim 1, wherein, Before combining the confidence level of the installation angle with the integrated navigation information, it also includes: Convergence is determined for the continuous heading installation angle sequence calculated epoch by epoch, and the current epoch is used. Including the recent Each epoch, constructing the heading installation angle sequence. Set the heading angle deviation threshold between adjacent epochs. A sequence is considered convergent if it simultaneously satisfies the following constraints: 。 4. The reverse-avoidance adaptive scheduling method of claim 3, wherein, Combining the confidence level of the installation angle with integrated navigation information, including: Set horizontal attitude perturbation threshold Heading and turning disturbance threshold Let the epoch interval of the convergent sequence be... The quantification constraints are: In the formula, For the epoch interval corresponding to the convergence sequence of the heading installation angle; for The sum of variances of the roll and pitch angle sequences within the interval quantifies the overall perturbation of the horizontal attitude. for The total heading angle within the interval reflects whether the vehicle is maintaining straight-line travel; Variance calculation operator: in, For the interval epoch number, For sequence The mean; , Horizontal reference coordinate system Next The roll and pitch angles of the epoch. For the first in this coordinate system Epoch Gyroscope Effective output value of axis The epoch sampling interval; Construct an installation angle confidence model to assess the reliability of the solution: In the formula, For the installation angle confidence, , These are positive weighting coefficients, adjusted according to the degree of influence of the operating condition disturbance; The larger the value, the higher the reliability of the solution. Set a confidence threshold. , The solution is deemed invalid and the result is discarded. It is determined to be valid and retained.

5. The reverse-avoidance adaptive scheduling method of claim 4, wherein, The integrated and updated three-axis mounting angle includes: After the heading installation angle sequence converges and the confidence check passes, The calculated heading and installation angle values ​​for each epoch are filtered using a sliding window mean filter, and then... The mean of each epoch is used as the optimal estimate of the heading angle: The roll installation angle calculated in combination with the gravity projection method The pitch installation angle The optimal estimation value of the heading installation angle The three-axis installation angle vector of the IMU relative to the vehicle coordinate system is obtained by integration 。 6. The reverse-avoidance adaptive scheduling method of claim 5, wherein, Each time a new installation angle is calculated, it includes: Definition of the first The effective solution result is: ,right First, a validity screening of the operating conditions is performed to ensure that the calculation results correspond to normal driving conditions. The specific judgment criteria are as follows: Preliminary Position Information Assessment: Calculation of Position Information Module Length for GNSS-INS Integrated Navigation ,like , If the position information threshold is set, then the calculation condition is determined to be without obvious abnormalities; Continuous solution stability determination: verify the consistency of three consecutive valid solution results, which need to meet With , is the installation angle deviation threshold value, to ensure that the solution result has no sudden fluctuation; When both of the above conditions are met, the determination is made. , , For fusionable results, define For the first If there are three fusionable results, then these three fusionable results are denoted as... , , Preserve this type of fusion result; After collecting three sets of fusionable results, initial fusion is triggered to build a stable baseline. The fusion formula is as follows: In the formula, is the total number of fusable results; is is the installation angle obtained by weighted fusion of the fusable results; is the cumulative confidence weight; After the initial fusion is completed, for each additional set of valid solution results selected by the operating conditions... , first determine If this condition is met, lightweight iterative fusion will be initiated, eliminating the need to store historical data. The iterative formula is as follows: In the formula, For the first The mounting angle after secondary fusion; For the front The cumulative confidence weight of the sub-fusion This is the updated cumulative weight; Outlier detection and adaptive updates are performed on the newly calculated installation angle group to eliminate abnormal operating condition deviations: Outlier detection: If the newly added valid solution does not meet the requirements... If it is, then it is judged as an outlier. They will not be included in the fusion process for the time being to avoid affecting the overall estimation accuracy due to a single abnormal operating condition. IMU relocation determination: A physical relocation of the IMU is determined to have occurred if and only if both of the following conditions are met simultaneously: Temporal anomaly consistency: two consecutive sets of results are outliers, and meet , indicating that the change in installation angle has consistency, and random anomalies can be excluded; Position innovation evidence: If , it indicates that the GNSS-INS integrated navigation position innovation is out of the normal range, which can further verify that the abnormality is caused by IMU movement; When both conditions are met, the fusion benchmark is updated to the latest valid solution; if only one condition is met, it is judged as a normal anomaly, the abnormal data is removed and the current fusion result remains unchanged. Adaptive stable update: Set a stable threshold for fusion weights If the cumulative confidence weights satisfy If the installation angle is determined to be stable, dynamic updates are stopped and the current fusion value is used; if the condition is not met, a valid new solution group is included in the fusion, and the weighted fusion value is dynamically updated. When the fusion weight Reaching the preset threshold When the fusion result has fully utilized the redundancy of multiple batches of data and the installation angle estimation tends to stabilize, the iterative update is stopped, and the current weighted fusion value is taken as the final optimal estimate of the IMU's three-axis installation angle. .

7. The reverse-avoidance adaptive scheduling method of claim 6, wherein, Methods for avoiding obstacles by reverse heading angle include: make , , Let be the north, east, and ground velocity components output by GNSS, respectively. Then the GNSS horizontal velocity can be expressed as: The first calculation yielded an estimated value for the heading installation angle. Then, the system performs a two-condition check on the initial state. When both conditions are met simultaneously, it proceeds to the subsequent recursive process: Speed amplitude condition: the GNSS horizontal speed modulus satisfies wherein to ensure that the vehicle is in a valid motion state; The normalized speed accuracy condition is that the ratio of the GNSS horizontal speed standard deviation to the speed module value satisfies ; After the check is passed, the initial heading angle is calculated using the GNSS speed component , the initial pitch angle , the initial roll angle is set to zero, and the initial speed adopts the GNSS observation value, and the recursive initial state construction is completed. If the condition is not met, wait for the next time to trigger the check again; Based on the established initial state, pure inertial navigation recursion is performed simultaneously using two sets of heading installation angles: original installation angle : take the current calculated heading installation angle estimate ; Reverse installation angle : Reverse the original installation angle ; , The corresponding recursive positions are respectively , After reaching the minimum recursion time of 3 seconds, the GNSS quantization and discrimination process begins. Based on the actual operating performance of GNSS, set the criteria for determining the reliability of GNSS positioning. If the condition is not met, continue the recursion; if the condition is met, proceed to the following judgment step: Definition , The difference between the GNSS position at the same time The difference between the GNSS position at the same time , The position deviation ratio is constructed: wherein the discrimination threshold is taken as ; Setting an abnormal deviation threshold The discrimination and output rules are as follows: First, determine and If the conditions are met, it means that the recursive errors corresponding to the two installation angles are both large, and the current judgment is deemed to have failed. The current recursive result is cleared, and the process is re-executed after the next initial state verification is met. If the conditions are not met, the process proceeds to the next judgment. like ,Right now Much larger The original installation angle was determined to be correct, and it was directly adopted. Perform subsequent navigation calculations; If , i.e. , is much smaller than , it is determined that the original installation angle exists in the opposite direction, and is corrected to after inputting the combined navigation system; If none of the above conditions are met, the process continues recursively, and the GNSS position reliability judgment and subsequent discrimination process is re-executed.

8. The reverse-avoidance adaptive scheduling method of claim 7, wherein, An adaptive scheduling preset mode heading and installation angle reverse avoidance method ensures the correct installation angle of the input fusion system, including: The first mode is determined as the innovation determination for integrated navigation. After the k-th set of installation angles is calculated, if the integrated navigation module is working normally and the innovation constraints are met... The first mode is established when the system has a valid reference installation angle after the combined navigation verification, which automatically triggers the fast reverse avoidance process. If the conditions are not met, the full reverse avoidance is started. The second mode is IMU displacement detection and judgment. When the physical position of the inertial measurement unit is detected to have changed, the original valid reference installation angle becomes invalid. The full reverse avoidance process is directly executed for the newly calculated installation angle data. The full reverse avoidance relies on the dual inertial navigation recursive calculation to complete the reverse fault detection and correction. A multi-node, fast reverse execution avoidance logic is adopted. A fast discrimination module is embedded in the front end of the three installation angle similarity judgment nodes in the original fusion update system. The core judgment principle is to compare the newly calculated installation angle parameters with the historical reference installation angles. If the heading dimension parameter has been changed, the similarity will be compared. If the angle deviation is smaller after reverse adjustment, then reverse correction is completed directly. The reference installation angles for the three judgment nodes are defined as follows: Node 1: Reference installation angle is In With Quick reverse avoidance before similarity determination Node 2: Reference installation angle is fusion installation angle In With Quick reverse avoidance before similarity determination Node 3: Reference installation angle is abnormal marker installation angle In With Quick reverse avoidance is performed before similarity determination with 9. A reverse-avoidance adaptive scheduling system embedded with a vehicle-mounted IMU installation angle, characterized in that, include: A module is established to create a zero-bias estimation model for the vehicle-mounted gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state, and to solve for the roll installation angle, pitch installation angle and yaw installation angle. The calculation module combines the confidence level of the installation angle with the information from the integrated navigation system to fuse and update the installation angles of the three axes. After each new installation angle is calculated, it adaptively schedules the preset mode of the heading installation angle reverse avoidance method to ensure that the installation angle input to the fusion system is correct.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the reverse avoidance adaptive scheduling method for embedding the mounting angle of the vehicle-mounted IMU as described in any one of claims 1 to 8.

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