An IMU installation angle estimation method based on vehicle acceleration and adjacent angle difference

Abstract

The application provides an IMU installation angle estimation method based on vehicle acceleration and adjacent angle difference, comprising: respectively establishing a gyroscope zero offset estimation model of a vehicle and an accelerometer and gravity projection correlation model in a stationary state; determining a straight line acceleration stage detection condition, performing trajectory recursion in a straight line acceleration period to obtain a horizontal displacement vector; calculating a heading installation angle in each epoch through the displacement vector direction, screening a candidate heading installation angle by using a continuous adjacent angle difference, and verifying the candidate heading installation angle result through inertial navigation recursion. The application solves the roll and pitch installation angles in the stationary state, solves the heading installation angle through acceleration in the motion state, realizes integrated estimation of the three installation angles, adapts to the initial calibration scene of the vehicle-mounted inertial navigation, does not need external references such as GNSS and an odometer, and does not limit acceleration in the stationary to start stage, so that the implementation conditions of the installation angle calibration are significantly simplified.

Description

Technical Field

[0001] This invention relates to the field of integrated navigation technology, and in particular to an IMU installation angle estimation method based on vehicle acceleration and adjacent angle differences. Background Technology

[0002] The Inertial Measurement Unit (IMU) is the core sensing component of an inertial navigation system (INS), typically forming a GNSS-INS fusion navigation architecture with a Global Navigation Satellite System (GNSS). This architecture achieves real-time, high-precision calculation of vehicle attitude, position, and motion state by fusing angular motion measurement data from gyroscopes and linear motion measurement data from accelerometers, playing an irreplaceable role in scenarios such as autonomous driving and navigation of special vehicles.

[0003] GNSS provides absolute position and velocity references, effectively suppressing IMU error accumulation. Meanwhile, the IMU maintains short-term navigation accuracy when GNSS signals are interrupted. Their synergy relies on spatial data consistency, with the installation angle between the IMU and the vehicle coordinate system being a key parameter determining the fusion effect. Installation angle deviations cause distortion in the projection of physical quantities output by the IMU across coordinate systems: for example, a heading installation angle offset will cause a deviation between the heading measured by the IMU and the actual direction of vehicle movement, resulting in misalignment between GNSS position updates and IMU motion predictions; roll and pitch installation angle errors will distort the gravity and acceleration components sensed by the accelerometer, leading to attitude drift, position estimation error accumulation, and ultimately compromising the stability and accuracy of the GNSS-INS fusion system. Therefore, the installation angle of the IMU must be calibrated after installation to ensure spatial data consistency, laying the foundation for reliable navigation in complex environments.

[0004] Among current methods for estimating the installation angle of vehicle-mounted IMUs, the mainstream technologies related to this invention can be divided into two categories: (1) External reference fusion method This method uses the standard GNSS 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. 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.

[0005] (2) Heading angle estimation method combining IMU static-start-up acceleration To avoid reliance on additional hardware, a method has emerged that relies solely on IMU data: First, using IMU data during the vehicle's stationary phase, the roll and pitch angles are calculated based on the projected geometric relationship between the accelerometer output and the gravity vector. Then, strictly adhering to the vehicle's "stationary-start" acceleration process (i.e., acceleration with an initial velocity of 0), the horizontal acceleration of the IMU is integrated twice to obtain the trajectory. The heading angle is then calculated using the angle between the trajectory direction and the IMU's forward axis. The core assumption is that "the vehicle accelerates in a straight line forward during stationary start-up," and the uniqueness of the direction is ensured through recursion using a trajectory with an initial velocity of 0, thus completely obtaining the three heading angles.

[0006] Both of the aforementioned existing technologies have significant limitations and are difficult to adapt to the actual needs of complex in-vehicle scenarios: (1) Deficiencies of external reference fusion method Although GNSS is a standard module in vehicle navigation, this method requires the additional deployment of hardware such as wheel speed odometers and visual sensors. This not only increases the hardware procurement cost of the vehicle, but also requires additional installation and debugging (such as matching the odometer with the vehicle wheel diameter and calibrating the visual sensor), increasing the complexity of system integration. At the same time, its performance is heavily dependent on the quality of GNSS signals. In scenarios where GNSS signals are blocked or fail, such as tunnels, underground parking garages, and densely populated areas with tall buildings, external reference information is interrupted, and the installation angle estimation will completely fail, making it impossible to meet the calibration requirements in complex environments.

[0007] (2) Deficiencies of the IMU static-startup acceleration combination method While this method requires no additional hardware and can completely solve for the three mounting angles, it has strict limitations on acceleration conditions: it must rely on a "stationary-start" acceleration process. If the initial acceleration is small (such as a slow start in congested traffic or acceleration on a gentle slope), the acceleration signal is weak and easily interfered with by noise, which will significantly increase the trajectory recursion error and lead to a decrease in the accuracy of the heading mounting angle estimation. At the same time, this method cannot utilize effective data from the vehicle's subsequent movement (such as larger accelerations on straight sections of road). For example, when the mounting angle needs to be recalibrated while the vehicle is in motion, it must first stop to a standstill before accelerating again, which is cumbersome and interrupts the normal driving process, resulting in insufficient adaptability and practicality.

[0008] In summary, existing technologies generally suffer from the following drawbacks: high hardware costs, poor adaptability to various scenarios or dependence on specific startup conditions, and cumbersome operation. These limitations make it difficult to meet the needs of practical applications. Summary of the Invention

[0009] This invention provides an IMU mounting angle estimation method based on vehicle acceleration and adjacent angle differences. This method addresses the shortcomings of existing technologies, such as high hardware costs, poor scene adaptability, reliance on specific startup conditions, and cumbersome operation. It achieves mean calculation of gyroscope output during the vehicle's stationary phase, obtains zero bias of each gyroscope axis, compensates for the measurement data, and uses adjacent angle difference verification to determine the convergence of the mounting angle sequence. At this point, the calculated mounting angle is a stable value, which meets the requirements of real-time system mounting angle calculation.

[0010] In a first aspect, the present invention provides an IMU installation angle estimation method based on vehicle acceleration and adjacent angle differences, comprising: Establish a zero-bias estimation model for the vehicle's gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state, respectively. Determine the detection conditions during the linear acceleration phase, perform trajectory recursion during the linear acceleration period, and obtain the horizontal displacement vector; The heading installation angle is calculated at each epoch by the displacement vector direction. Candidate heading installation angles are selected by the difference between consecutive adjacent angles, and the results of the candidate heading installation angles are verified by inertial navigation recursion.

[0011] According to the present invention, an IMU installation angle estimation method based on vehicle acceleration and adjacent angle difference is provided, which establishes a gyroscope zero-bias estimation model for the vehicle and an accelerometer-gravity projection correlation model under stationary conditions, including: A gyroscope measurement model is constructed, and the average value of the gyroscope output is calculated when the vehicle is stationary. The zero bias of each axis of the gyroscope is obtained, and the measurement data is compensated and discretized. Based on the correlation model of accelerometer and gravity projection under static conditions, the projection of gravity in the IMU frame is derived. By solving the constraint equations, the roll and pitch installation angles are calculated.

[0012] According to the present invention, an IMU mounting angle estimation method based on vehicle acceleration and adjacent angle differences is provided, which determines the detection conditions during the straight-line acceleration phase, performs trajectory recursion during the straight-line acceleration period, and obtains the horizontal displacement vector, including: A single sampling point is defined as an epoch, and a continuous epoch sequence that satisfies the detection conditions of the linear acceleration stage is a valid linear acceleration segment. By recursively extrapolating the horizontal acceleration trajectory of the effective linear acceleration segment, the horizontal displacement vector of any epoch can be obtained.

[0013] According to the present invention, an IMU mounting angle estimation method based on vehicle acceleration and adjacent angle differences is provided, which determines a single sampling point as an epoch, and a continuous epoch sequence that satisfies the detection conditions of the linear acceleration phase as a valid linear acceleration segment, including: The detection conditions for the linear acceleration phase include acceleration significance conditions and angular velocity stability conditions; The acceleration significance condition includes the absolute value of the horizontal acceleration axis component of the IMU after zero bias and gravity compensation exceeding the acceleration threshold. The angular velocity stability condition includes the condition that the cumulative integral value of the angular velocity of each axis of the gyroscope after zero bias compensation is less than the angular velocity threshold.

[0014] According to the present invention, an IMU mounting angle estimation method based on vehicle acceleration and adjacent angle differences is provided, which recursively extrapolates the horizontal acceleration trajectory of the effective linear acceleration segment to obtain the horizontal displacement vector at any epoch, including: When the vehicle accelerates, the actual horizontal direction of motion relative to the vehicle coordinate system By keeping the axes consistent, the horizontal acceleration vector in the vehicle coordinate system can be obtained. , where the lateral acceleration is 0; Considering the heading installation angle The horizontal acceleration transformation relationship between the IMU coordinate system and the vehicle coordinate system is described by a planar rotation matrix, due to the influence of the IMU coordinate system:

[0015] in, , For the first Epoch-processed IMU horizontal acceleration For the first The horizontal acceleration vector of an epoch; The horizontal displacement trajectory in the IMU coordinate system is obtained by performing a second integral on the horizontal acceleration. The initial velocity is set to 0 during the integration process. The integration steps include: Calculate the first epochal velocity ( ), where the initial velocity , This marks the beginning of the acceleration phase. Calculate the first epoch displacement ( ), where the initial displacement ; No. The horizontal displacement vector of the epoch is , will the The deviation between the direction of the horizontal displacement vector at each epoch and the actual direction of vehicle movement is taken as the heading installation angle. .

[0016] According to the present invention, an IMU mounting angle estimation method based on vehicle acceleration and adjacent angle differences is provided. This method calculates the heading mounting angle at each epoch by using the displacement vector direction, filters candidate heading mounting angles using consecutive adjacent angle differences, and verifies the candidate heading mounting angle results via inertial navigation recursion. The method includes: A preliminary solution for the heading angle is performed, and a first-stage consistency verification is conducted to screen stable results, thereby obtaining candidate heading installation angles. The second phase of time stability verification was conducted to screen the final results and obtain the final heading installation angle.

[0017] According to the present invention, an IMU installation angle estimation method based on vehicle acceleration and adjacent angle differences is provided, which performs a preliminary solution for the heading angle and a first-stage consistency verification to screen stable results, thereby obtaining candidate heading installation angles, including: The heading installation angle for each epoch is obtained from the displacement vector direction, and the angle coverage range is determined using the four-quadrant arctangent function. :

[0018] For the first Horizontal displacement vector of an epoch; During the effective acceleration phase, the difference in heading angle between adjacent epochs is calculated sequentially. ; If it is determined that the difference between adjacent heading angles of consecutive first epochs in the sequence is less than the first preset threshold, then the average value of the heading angles of the second epoch in the sequence is extracted to obtain the candidate heading installation angle.

[0019] According to the IMU installation angle estimation method based on vehicle acceleration and adjacent angle difference provided by the present invention, a second-stage time stability verification is performed to screen the final result and obtain the final heading installation angle, including: The new starting point of the sequence is the end of the first phase epoch. Reset initial speed Initial displacement ( This is to eliminate the cumulative effect of previous integration errors; use Using IMU data, when the acceleration is large, the horizontal displacement of subsequent epochs is calculated using the same integration method. And solve for the corresponding heading angle estimate. ; If the deviation between the estimated heading angle and the candidate heading installation angle for a consecutive second number of epochs is less than a second preset threshold, and the second preset threshold is greater than the first preset threshold, then the candidate heading installation angle is determined as the final heading installation angle.

[0020] Secondly, the present invention also provides an IMU installation angle estimation system based on vehicle acceleration and adjacent angle differences, comprising: A module is established to create a gyroscope zero-bias estimation model for the vehicle and an accelerometer-gravity projection correlation model in a stationary state, respectively. The calculation module is used to determine the detection conditions during the linear acceleration phase, and to perform trajectory recursion during the linear acceleration period to obtain the horizontal displacement vector. The estimation module is used to calculate the heading installation angle at each epoch by using the displacement vector direction, filter candidate heading installation angles by the difference between consecutive adjacent angles, and verify the candidate heading installation angle results by inertial navigation recursion.

[0021] 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 IMU installation angle estimation method based on vehicle acceleration and adjacent angle differences as described above.

[0022] The IMU mounting angle estimation method based on vehicle acceleration and adjacent angle difference provided by this invention achieves integrated estimation of three mounting angles by calculating the roll and pitch mounting angles when stationary and the heading mounting angle by calculating the acceleration when in motion. It is suitable for the initial calibration scenario of vehicle inertial navigation, does not require external references such as GNSS or odometers, and the acceleration is not limited to the stage from stationary to startup, which significantly simplifies the implementation conditions of mounting angle calibration. Attached Figure Description

[0023] 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.

[0024] Figure 1 This is a flowchart illustrating the IMU installation angle estimation method based on vehicle acceleration and adjacent angle differences 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 showing the correlation between accelerometer measurements and gravity projection provided by the present invention; Figure 4 This is a schematic diagram showing the relationship between continuous integral of acceleration and heading angle provided by the present invention; Figure 5 This is a schematic diagram of the structure of the IMU installation angle estimation system based on vehicle acceleration and adjacent angle difference 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

[0025] 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.

[0026] To address the following shortcomings in existing technologies: high hardware cost, poor scenario adaptability or dependence on specific startup conditions, and cumbersome operation, this invention provides an auxiliary IMU installation angle estimation method based on vehicle acceleration and adjacent angle difference testing, such as... Figure 1 As shown, it includes: Step 100: Establish the gyroscope zero-bias estimation model and the accelerometer-gravity projection correlation model in a stationary state for the vehicle, respectively; Step 200: Determine the detection conditions for the linear acceleration phase, and perform trajectory recursion during the linear acceleration period to obtain the horizontal displacement vector; Step 300: Calculate the heading installation angle at each epoch using the displacement vector direction, filter candidate heading installation angles using the difference between consecutive adjacent angles, and verify the candidate heading installation angle results through inertial navigation recursion.

[0027] Specifically, this embodiment of the invention first establishes a gyroscope zero-bias estimation model. During the vehicle's stationary phase, the mean value of the gyroscope output is calculated to obtain the zero bias of each gyroscope axis, and the measurement data is compensated. Then, a correlation model between the accelerometer and the gravity projection in a stationary state is established. The projection of gravity in the IMU frame is derived, and the constraint equations are solved to obtain the roll and pitch installation angles. Detection conditions during the linear acceleration phase are set, requiring both significant acceleration and stable angular velocity. Trajectory recursion is performed during the linear acceleration period to obtain the horizontal displacement vector. Finally, the heading installation angle is solved. The heading installation angle is calculated at each epoch using the displacement vector direction, and candidate heading installation angles are selected using the difference between consecutive adjacent angles. The angle results are verified by inertial navigation recursion.

[0028] Based on the above embodiments, step 100 includes: First, a gyroscope zero-bias estimation model is established. The mean value of the gyroscope output is calculated when the vehicle is stationary to obtain the zero bias of each axis of the gyroscope and compensate for the measurement data.

[0029] 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.

[0030] (1) 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: (1) 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).

[0031] 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.

[0032] (2) 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.

[0033] For both sides of the measurement model Integral over the interval: (2) 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.

[0034] Substituting the above result into the integral, and dividing both sides by... The gyroscope's first... Estimation formula for zero axis offset: (3) in, Indicates the gyroscope's first The estimated value of the axis zero bias.

[0035] (3) 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: (4) 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.

[0036] 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: (5) 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.

[0037] Then, a correlation model between the accelerometer and the gravity projection under static conditions is established. The projection of gravity in the IMU frame is derived, and the constraint equations are solved to obtain the roll and pitch installation angles.

[0038] 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.

[0039] When the vehicle is stationary, the IMU has no translational acceleration, and the accelerometer output only reflects the projection of gravity in the IMU coordinate system (ignoring the influence of zero bias). This section 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 the subsequent calculation of roll and pitch angles.

[0040] (1) Accelerometer measurement model (stationary state) 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: (6) in: For a moment IMU accelerometer The raw output of the axis; For gravity in the IMU coordinate system The projection 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).

[0041] 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.

[0042] (2) Accelerometer output noise reduction 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: (7) 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.

[0043] Therefore, the accelerometer output after noise reduction Approximately equal to gravity in the IMU coordinate system The projection components of the axis, namely: (8) 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 derivation.

[0044] (3) Coordinate system projection of the gravity vector 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.

[0045] 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: (9) 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).

[0046] 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: (10) 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.

[0047] Based on the above formula, the component expressions of the gravity vector in the IMU coordinate system can be directly obtained: (11) 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: (12) 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.

[0048] (4) 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).

[0049] 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: (13) Will and Substituting into the vector transformation formula, we get: (14) 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.: (15) After processing, the relationship between the accelerometer output and the installation angle can be obtained: (16) The physical meaning of this expansion is: the accelerometer output (the projection of the supporting force opposite to gravity) is determined by the roll angle. Pitch angle With gravitational acceleration The decision was made jointly, and the specific analysis is as follows: 1. Forward ( (axis) output Only related to pitch angle Related, When the vehicle looks up ( When the vehicle tilts its head down, the forward accelerometer output is positive; when the vehicle tilts its head down (…), the forward accelerometer output is positive. When ), the forward accelerometer output is negative.

[0050] 2. To the right ( (axis) output : with roll angle and pitch angle All are related. When the vehicle tilts to the right ( When the vehicle tilts to the left, the right-hand accelerometer output is negative; when the vehicle tilts to the left... When ), the right-hand accelerometer output is positive.

[0051] 3. downward ( (axis) output : with roll angle and pitch angle All are related. ,because and The absolute values ​​of all of them are less than 1, therefore The absolute value is always less than And it decreases as the installation angle increases.

[0052] This expansion is the core equation for subsequent solutions to roll and pitch angles. By correlating accelerometer measurements with the installation angle, it enables a quantitative conversion from sensor data to attitude angles.

[0053] (5) 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: (17) 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. It can directly use the sampled data during the stationary phase to calculate the denoised accelerometer output, providing input for subsequent attitude angle calculations (note that this value is opposite to the direction of gravity projection).

[0054] (6) Solve for roll and pitch installation angles Pitch angle The solution can be achieved by summing the squares of the last two equations in equation (14): (18) Take the absolute value after taking the square root: (19) Divide the first equation of combination (16) with equation (19), and use... Within the range From the properties of, we can obtain: (20) This formula can be solved directly using the arctangent function, without requiring small angle assumptions, and is applicable to any installation orientation.

[0055] Roll angle The solution can be obtained by dividing the last two equations of equation (16): (twenty one) because The four-quadrant arctangent function needs to be used to ensure quadrant correctness: (twenty two) in The quadrant is automatically determined based on the input sign, and the output range exactly covers all possible values ​​of the roll angle.

[0056] Based on the above embodiments, step 200 includes: The detection conditions for the linear acceleration phase must simultaneously satisfy the requirements of significant acceleration and stable angular velocity. Trajectory recursion is performed during the linear acceleration period to obtain the horizontal displacement vector.

[0057] (1) Detection conditions during linear acceleration The straight-line acceleration phase is the core observation window for estimating the heading and installation angle, and it needs to be dynamically identified through real-time IMU data. A single sampling point is defined as an "epoch" (sampling interval). , usually take (For vehicle-mounted IMUs with sampling rates of 10~100Hz), the effective acceleration phase must simultaneously meet the following conditions: 1. Acceleration significance condition: IMU horizontal acceleration after zero bias and gravity compensation. The absolute value of the axis component must exceed the threshold. (e.g., value) ),Right now ( (This is the current epoch number). This condition ensures that the vehicle has observable forward acceleration, avoiding direction estimation errors caused by weak acceleration signals during the low-speed constant-speed phase.

[0058] 2. Angular velocity stability condition: The cumulative integral value of the angular velocity of each axis of the gyroscope after zero-bias compensation must be less than a threshold value. (e.g., value) ,about ),Right now ( , To accelerate the initial epoch. This condition ensures that the vehicle has no significant steering or attitude sway, maintaining approximately a straight-line motion, which meets the requirement that "the direction of motion is only along..." The theoretical assumption of the "axis".

[0059] The continuous epoch sequence that meets the above conditions is marked as the "effective linear acceleration segment" and used as the basis data for subsequent heading angle estimation.

[0060] (3) Horizontal acceleration and trajectory recursion When a vehicle accelerates in a straight line, its actual horizontal motion direction is strictly along the vehicle coordinate system. The axis (forward) is therefore the horizontal acceleration vector in the vehicle coordinate system is (Lateral acceleration is 0). Considering the heading angle. The horizontal acceleration transformation relationship between the IMU coordinate system and the vehicle coordinate system is described by a planar rotation matrix, due to the influence of the IMU coordinate system: (twenty three) in, , For the first The processed IMU horizontal acceleration at each epoch (zero bias compensation is based on the estimation results during the stationary phase, and gravity compensation utilizes the solved roll angle) With pitch angle To achieve and eliminate the interference of gravity's projection on the horizontal axis.

[0061] The horizontal displacement trajectory in the IMU coordinate system can be obtained by performing a second integration on the horizontal acceleration. The initial velocity is set to 0 during the integration process—this does not affect the estimation of the trajectory direction, as the displacement direction is determined only by the acceleration direction and is independent of the initial velocity magnitude. The specific integration steps are as follows: No. Epoch velocity calculation: ( ), where the initial velocity ( (The starting epoch of the acceleration phase). No. Epoch Displacement Calculation: ( ), where the initial displacement .

[0062] Finally, the first The horizontal displacement vector of the epoch is The deviation of its direction from the actual direction of vehicle movement directly reflects the heading installation angle. .

[0063] Based on the above embodiments, step 300 includes: The heading installation angle is calculated by using the displacement vector direction at each epoch. Candidate heading installation angles are then selected by using the difference between consecutive adjacent angles. The angle results are verified by recursion using inertial navigation.

[0064] like Figure 4 In the diagram showing the relationship between continuous integral of acceleration and heading angle, the heading 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), combined with the displacement recursion principle in inertial navigation, to achieve estimation. In the straight-line travel region, when the acceleration exceeds a threshold, the velocity is obtained by integrating the acceleration, and the horizontal displacement vector is obtained by integrating again, thus calculating the heading angle. This method relies solely on the IMU's own measurement data, requiring no additional sensors, and is suitable for engineering application scenarios.

[0065] (1) Preliminary solution and first-stage verification of the heading installation angle The heading angle for each epoch can be directly calculated from the direction of the displacement vector, and the four-quadrant arctangent function is used to ensure coverage of the angle range. : (twenty four) This formula is derived through displacement. Axial components and The ratio of the axis components determines the angle, which physically represents the relationship between the trajectory direction and the IMU. The included angle of the axis, i.e., the instantaneous estimate of the heading installation angle.

[0066] Since IMU measurement noise may cause instantaneous fluctuations in the estimates, stable results need to be screened through a first-stage consistency verification: during the effective acceleration phase, the difference in heading angle between adjacent epochs is calculated sequentially. ( (This refers to the current epoch number). If there are 10 consecutive epochs... The difference between adjacent values ​​is less than the threshold. (Possible value: 0.2) ),Right now: (25) This indicates that the estimated heading angles over these 10 epochs are stable (without significant jumps). At this point, the mean heading angle of the last 5 epochs in the sequence is calculated: (26) Will Marked as “candidate heading installation angle” (taking the last 5 epochs can further reduce the impact of the earlier integration error).

[0067] (2) Second-stage verification and result confirmation To further ensure the reliability of the candidate values, their temporal stability needs to be verified through a second-stage validation process. 1. Reset initial scoring conditions: End epoch at the end of Phase 1. For a new beginning (referred to as ), reset the initial velocity Initial displacement ( This eliminates the cumulative effects of previous integration errors; 2. Subsequent trajectory recursion: using Using IMU data, when the acceleration is large, the horizontal displacement of subsequent epochs is calculated using the same integration method. And solve for the corresponding heading angle estimate. ; 3. Consistency check: If 5 consecutive epochs... The estimated value With candidate values The deviations are all less than the threshold. (Possible value: 0.4) Slightly larger (with compatibility with integral cumulative error), that is: (27) Then confirm the candidate value For the final heading installation angle .

[0068] The IMU installation angle estimation system based on vehicle acceleration and adjacent angle difference provided by the present invention is described below. The IMU installation angle estimation system based on vehicle acceleration and adjacent angle difference described below can be referred to in correspondence with the IMU installation angle estimation method based on vehicle acceleration and adjacent angle difference described above.

[0069] Figure 5 This is a schematic diagram of the structure of the IMU installation angle estimation system based on vehicle acceleration and adjacent angle differences provided in an embodiment of the present invention, as shown below. Figure 5 As shown, it includes: a setup module 51, a calculation module 52, and an estimation module 53, wherein: Module 51 is used to establish the gyroscope zero-bias estimation model and the accelerometer-gravity projection correlation model in a stationary state for the vehicle, respectively. The calculation module 52 is used to determine the detection conditions during the linear acceleration phase, and to perform trajectory recursion during the linear acceleration period to obtain the horizontal displacement vector. The estimation module 53 is used to calculate the heading installation angle at each epoch by the direction of the displacement vector, filter candidate heading installation angles by the difference between consecutive adjacent angles, and verify the candidate heading installation angle results by inertial navigation recursion.

[0070] 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 IMU installation angle estimation method based on vehicle acceleration and adjacent angle differences. This method includes: establishing a gyroscope zero-bias estimation model for the vehicle and an accelerometer-gravity projection correlation model in a stationary state; determining the detection conditions during the straight-line acceleration phase; performing trajectory recursion during the straight-line acceleration period to obtain the horizontal displacement vector; calculating the heading installation angle at each epoch using the displacement vector direction; filtering candidate heading installation angles using continuous adjacent angle differences; and verifying the candidate heading installation angle results through inertial navigation recursion.

[0071] 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.

[0072] 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 the IMU installation angle estimation method based on vehicle acceleration and adjacent angle differences provided by the above methods. The method includes: establishing a gyroscope zero-bias estimation model for the vehicle and an accelerometer-gravity projection correlation model in a stationary state; determining the detection conditions during the linear acceleration phase; performing trajectory recursion during the linear acceleration period to obtain the horizontal displacement vector; calculating the heading installation angle at each epoch using the direction of the displacement vector; filtering candidate heading installation angles using continuous adjacent angle differences; and verifying the candidate heading installation angle results through inertial navigation recursion.

[0073] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0074] 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.

[0075] 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 estimating the IMU mounting angle based on vehicle acceleration and the difference between adjacent angles, characterized in that, include: Establish a zero-bias estimation model for the vehicle's gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state, respectively. Determine the detection conditions during the linear acceleration phase, perform trajectory recursion during the linear acceleration period, and obtain the horizontal displacement vector; The heading installation angle is calculated at each epoch by the displacement vector direction. Candidate heading installation angles are selected by the difference between consecutive adjacent angles, and the results of the candidate heading installation angles are verified by inertial navigation recursion.

2. The IMU mounting angle estimation method based on vehicle acceleration and adjacent angle differences according to claim 1, characterized in that, A zero-bias estimation model for the vehicle's gyroscope and a correlation model between the accelerometer and gravity projection in a stationary state are established, including: A gyroscope measurement model is constructed, and the average value of the gyroscope output is calculated when the vehicle is stationary. The zero bias of each axis of the gyroscope is obtained, and the measurement data is compensated and discretized. Based on the correlation model of accelerometer and gravity projection under static conditions, the projection of gravity in the IMU frame is derived. By solving the constraint equations, the roll and pitch installation angles are calculated.

3. The IMU mounting angle estimation method based on vehicle acceleration and adjacent angle differences according to claim 1, characterized in that, Determine the detection conditions during the linear acceleration phase, perform trajectory recursion during the linear acceleration period, and obtain the horizontal displacement vector, including: A single sampling point is defined as an epoch, and a continuous epoch sequence that satisfies the detection conditions of the linear acceleration stage is a valid linear acceleration segment. By recursively extrapolating the horizontal acceleration trajectory of the effective linear acceleration segment, the horizontal displacement vector of any epoch can be obtained.

4. The IMU installation angle estimation method based on vehicle acceleration and adjacent angle difference according to claim 3, characterized in that, A single sampling point is defined as an epoch, and a continuous epoch sequence that satisfies the detection conditions of the linear acceleration stage is defined as a valid linear acceleration segment, including: The detection conditions for the linear acceleration phase include acceleration significance conditions and angular velocity stability conditions; The acceleration significance condition includes the absolute value of the horizontal acceleration axis component of the IMU after zero bias and gravity compensation exceeding the acceleration threshold. The angular velocity stability condition includes the condition that the cumulative integral value of the angular velocity of each axis of the gyroscope after zero bias compensation is less than the angular velocity threshold.

5. The IMU mounting angle estimation method based on vehicle acceleration and adjacent angle difference according to claim 4, characterized in that, By recursively extrapolating the horizontal acceleration trajectory of the effective linear acceleration segment, the horizontal displacement vector at any epoch is obtained, including: When the vehicle accelerates, the actual horizontal direction of motion relative to the vehicle coordinate system By keeping the axes consistent, the horizontal acceleration vector in the vehicle coordinate system can be obtained. , where the lateral acceleration is 0; Considering the heading installation angle The horizontal acceleration transformation relationship between the IMU coordinate system and the vehicle coordinate system is described by a planar rotation matrix, due to the influence of the IMU coordinate system: in, , For the first Epoch-processed IMU horizontal acceleration For the first The horizontal acceleration vector of an epoch; The horizontal displacement trajectory in the IMU coordinate system is obtained by performing a second integral on the horizontal acceleration. The initial velocity is set to 0 during the integration process. The integration steps include: Calculate the first epochal velocity ( ), where the initial velocity , This marks the beginning of the acceleration phase. Calculate the first epoch displacement ( ), where the initial displacement ; No. The horizontal displacement vector of the epoch is , will the The deviation between the direction of the horizontal displacement vector at each epoch and the actual direction of vehicle movement is taken as the heading installation angle. .

6. The IMU mounting angle estimation method based on vehicle acceleration and adjacent angle difference according to claim 1, characterized in that, The heading and installation angle are calculated at each epoch using the displacement vector direction. Candidate heading and installation angles are selected using the difference between consecutive adjacent angles. The results of the candidate heading and installation angles are then verified recursively by the inertial navigation system, including: A preliminary solution for the heading angle is performed, and a first-stage consistency verification is conducted to screen stable results, thereby obtaining candidate heading installation angles. The second phase of time stability verification was conducted to screen the final results and obtain the final heading installation angle.

7. The IMU installation angle estimation method based on vehicle acceleration and adjacent angle difference according to claim 6, characterized in that, A preliminary solution for the heading angle is performed, and a first-stage consistency verification is conducted to screen stable results, yielding candidate heading installation angles, including: The heading installation angle for each epoch is obtained from the displacement vector direction, and the angle coverage range is determined using the four-quadrant arctangent function. : For the first Horizontal displacement vector of an epoch; During the effective acceleration phase, the difference in heading angle between adjacent epochs is calculated sequentially. ; If it is determined that the difference between adjacent heading angles of consecutive first epochs in the sequence is less than the first preset threshold, then the average value of the heading angles of the second epoch in the sequence is extracted to obtain the candidate heading installation angle.

8. The IMU mounting angle estimation method based on vehicle acceleration and adjacent angle difference according to claim 7, characterized in that, The second phase of time stability verification was conducted to screen the final results, yielding the final heading installation angle, including: The new starting point of the sequence is the end of the first phase epoch. Reset initial speed Initial displacement ( This is to eliminate the cumulative effect of previous integration errors; use Using IMU data, when the acceleration is large, the horizontal displacement of subsequent epochs is calculated using the same integration method. And solve for the corresponding heading angle estimate. ; If the deviation between the estimated heading angle and the candidate heading installation angle for a consecutive second number of epochs is less than a second preset threshold, and the second preset threshold is greater than the first preset threshold, then the candidate heading installation angle is determined as the final heading installation angle.

9. An IMU installation angle estimation system based on vehicle acceleration and adjacent angle differences, characterized in that, include: A module is established to create a gyroscope zero-bias estimation model for the vehicle and an accelerometer-gravity projection correlation model in a stationary state, respectively. The calculation module is used to determine the detection conditions during the linear acceleration phase, and to perform trajectory recursion during the linear acceleration period to obtain the horizontal displacement vector. The estimation module is used to calculate the heading installation angle at each epoch by using the displacement vector direction, filter candidate heading installation angles by the difference between consecutive adjacent angles, and verify the candidate heading installation angle results by inertial navigation recursion.

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 IMU installation angle estimation method based on vehicle acceleration and adjacent angle difference as described in any one of claims 1 to 8.

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