Service-line calibration method and apparatus for vehicle-mounted IMU, electronic device, and storage medium

Abstract

A service-line calibration method and apparatus for a vehicle-mounted IMU, an electronic device, and a storage medium. An IMU is mounted on a vehicle, and at least a global navigation satellite system (GNSS) is further installed on the vehicle. The method comprises: obtaining stationary IMU data acquired in a stationary state of the vehicle, and, on the basis of the stationary IMU data, calculating a roll initial calibration result and a pitch initial calibration result of the IMU (S110); obtaining GNSS data and dynamic IMU data acquired during driving of the vehicle according to a preset driving requirement (S120); and, on the basis of the GNSS data and the dynamic IMU data, optimizing the roll initial calibration result and the pitch initial calibration result, and calculating a yaw calibration result of the IMU (S130).

Description

A method, apparatus, electronic device, and storage medium for aftermarket calibration of a vehicle-mounted IMU. Technical Field

[0001] This application relates to the field of IMU calibration technology, and more specifically, to a method, apparatus, electronic device, and storage medium for aftermarket calibration of a vehicle-mounted IMU. Background Technology

[0002] An IMU (Inertial Measurement Unit) is an electronic component used to measure the inertial motion of a moving object. It outputs information such as three-axis acceleration and three-axis angular velocity, and is commonly used for measuring attitude angles and motion paths. When an IMU is used in a vehicle, it can measure parameters such as acceleration and pose during vehicle movement. After-sales calibration (SLC) is sometimes required when using an IMU in a vehicle. After-sales calibration occurs when mass-produced vehicles experience sensor aging, damage, or abnormal calibration parameters after delivery to the user. The vehicle needs to be sent to a designated after-sales service shop for sensor repair or replacement. Then, after-sales maintenance personnel drive the vehicle and collect data under specific conditions to complete the calibration in real time during driving. Specifically, the IMU's roll, pitch, and yaw angles need to be calibrated.

[0003] Known IMU aftermarket calibration methods only require IMU data to estimate rotational extrinsic parameters. However, this method is only suitable for high-precision IMUs. Typically, automotive IMUs have lower accuracy, leading to significant errors in aftermarket calibration results. Therefore, improving the accuracy of automotive IMU aftermarket calibration has become a pressing technical problem. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for after-sales calibration of vehicle-mounted IMUs, which can improve the accuracy of after-sales calibration of vehicle-mounted IMUs. The specific technical solution is as follows.

[0005] In a first aspect, embodiments of this application provide an aftermarket calibration method for an inertial measurement unit (IMU) installed in a vehicle, wherein the vehicle is also equipped with at least a Global Navigation Satellite System (GNSS). The method includes:

[0006] Acquire static IMU data collected when the vehicle is stationary, and calculate the initial calibration results of the IMU's roll angle and pitch angle based on the static IMU data;

[0007] Acquire GNSS data and dynamic IMU data collected during the vehicle's operation according to preset driving requirements;

[0008] Based on the GNSS data and dynamic IMU data, the initial calibration results of the roll angle and pitch angle are optimized, and the yaw angle calibration result of the IMU is calculated.

[0009] In this embodiment, the IMU can first be statically calibrated to obtain initial roll and pitch calibration results. Then, dynamic calibration is performed based on GNSS (Global Navigation Satellite System) data and dynamic IMU data to optimize the initial roll and pitch calibration results and obtain yaw calibration results. In other words, this embodiment can perform IMU calibration based on GNSS and IMU fusion positioning. This calibration method can meet the calibration requirements of IMU extrinsic parameters of arbitrary accuracy. Therefore, for vehicle-mounted IMUs with low accuracy, the after-sales calibration accuracy can be improved.

[0010] Optionally, the step of calculating the initial calibration results of the roll angle and pitch angle of the IMU based on the static IMU data includes:

[0011] Obtain the acceleration vector from the static IMU data;

[0012] Calculate the average value of the acceleration vector, multiply the average value by the gravitational acceleration as the rotation angle deviation, and convert the rotation angle deviation into the corresponding quaternion;

[0013] Based on the quaternion, the corresponding roll Euler angle and pitch Euler angle are calculated as the initial calibration results of the IMU's roll angle and pitch angle.

[0014] Optionally, the step of optimizing the initial roll angle calibration result and the initial pitch angle calibration result based on the GNSS data and dynamic IMU data, and calculating the yaw angle calibration result of the IMU, includes:

[0015] Based on the GNSS data and the dynamic IMU data, the roll angle zero bias, as well as the pitch angle observation and yaw angle observation, are obtained based on the constructed Kalman state estimation filter.

[0016] Based on the zero bias of the roll angle, the initial calibration result of the roll angle is calibrated by error elimination to obtain the roll angle calibration result;

[0017] The pitch angle observation value is input into the pitch angle optimizer, and the initial pitch angle calibration result is optimized based on the pitch angle optimizer to obtain the pitch angle calibration result;

[0018] The yaw angle observation value is input into the yaw angle optimizer, and the yaw angle calibration result is obtained based on the yaw angle optimizer.

[0019] Optionally, the step of obtaining the roll angle zero bias, pitch angle observations, and yaw angle observations based on the GNSS data and the dynamic IMU data using the constructed Kalman state estimation filter includes:

[0020] Based on the GNSS data and the dynamic IMU data, estimate the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU;

[0021] Based on the world attitude, calculate the IMU velocity in the IMU coordinate system;

[0022] Based on the rotational extrinsic parameters of the IMU and the vehicle, the IMU velocity is converted into the vehicle velocity of the IMU in the vehicle coordinate system;

[0023] Based on the vehicle speed and according to non-integrity constraints, the pitch angle and yaw angle observations of the IMU are calculated.

[0024] Optionally, the step of estimating the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU based on the GNSS data and the dynamic IMU data includes:

[0025] Based on the dynamic IMU data, the constructed Kalman state estimation filter is predicted, and the Kalman state estimation filter is updated by observation based on the GNSS data to obtain the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU; the state vector of the Kalman state estimation filter includes at least: the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU.

[0026] Optionally, the step of calculating the pitch angle and yaw angle observations of the IMU based on the vehicle speed and according to non-integrity constraints includes:

[0027] The pitch bias and yaw bias of the IMU are calculated using the following formulas:

[0028] Among them, v x Let v be the component of the vehicle's velocity along the x-direction. y Let v be the component of the vehicle's velocity along the y-direction. z Let be the component of the vehicle's velocity along the z-direction.

[0029] Optionally, the pitch angle optimizer includes a pitch angle Kalman filter, and the yaw angle optimizer includes a yaw angle Kalman filter. During the prediction process, the state vector of each Kalman filter remains unchanged.

[0030] Secondly, embodiments of this application provide an aftermarket calibration device for an on-board IMU, wherein the IMU is installed in a vehicle, and the vehicle is at least equipped with a Global Navigation Satellite System (GNSS). The device includes:

[0031] The static calibration module is used to acquire static IMU data collected when the vehicle is stationary, and to calculate the initial calibration results of the IMU's roll angle and pitch angle based on the static IMU data.

[0032] The dynamic data acquisition module is used to acquire GNSS data and dynamic IMU data collected by the vehicle during the driving process according to preset driving requirements;

[0033] The parameter calibration module is used to optimize the initial calibration results of the roll angle and the initial calibration results of the pitch angle based on the GNSS data and the dynamic IMU data, and to calculate the yaw angle calibration results of the IMU.

[0034] Optionally, the static calibration module is specifically used for:

[0035] Obtain the acceleration vector from the static IMU data;

[0036] Calculate the average value of the acceleration vector, multiply the average value by the gravitational acceleration as the rotation angle deviation, and convert the rotation angle deviation into the corresponding quaternion;

[0037] Based on the quaternion, the corresponding roll Euler angle and pitch Euler angle are calculated as the initial calibration results of the IMU's roll angle and pitch angle.

[0038] Optionally, the parameter calibration module is specifically used for:

[0039] Based on the GNSS data and the dynamic IMU data, the roll angle zero bias, as well as the pitch angle observation and yaw angle observation, are obtained based on the constructed Kalman state estimation filter.

[0040] Based on the zero bias of the roll angle, the initial calibration result of the roll angle is calibrated by error elimination to obtain the roll angle calibration result;

[0041] The pitch angle observation value is input into the pitch angle optimizer, and the initial pitch angle calibration result is optimized based on the pitch angle optimizer to obtain the pitch angle calibration result;

[0042] The yaw angle observation value is input into the yaw angle optimizer, and the yaw angle calibration result is obtained based on the yaw angle optimizer.

[0043] Optionally, the parameter calibration module is specifically used for:

[0044] Based on the GNSS data and the dynamic IMU data, estimate the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU;

[0045] Based on the world attitude, calculate the IMU velocity in the IMU coordinate system;

[0046] Based on the rotational extrinsic parameters of the IMU and the vehicle, the IMU velocity is converted into the vehicle velocity of the IMU in the vehicle coordinate system;

[0047] Based on the vehicle speed and according to non-integrity constraints, the pitch angle and yaw angle observations of the IMU are calculated.

[0048] Optionally, the parameter calibration module is specifically used for:

[0049] Based on the dynamic IMU data, the constructed Kalman state estimation filter is predicted, and the Kalman state estimation filter is updated by observation based on the GNSS data to obtain the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU; the state vector of the Kalman state estimation filter includes at least: the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU.

[0050] Optionally, the parameter calibration module is specifically used for:

[0051] The pitch bias and yaw bias of the IMU are calculated using the following formulas:

[0052] Among them, v x Let v be the component of the vehicle's velocity along the x-direction. y Let v be the component of the vehicle's velocity along the y-direction. z Let be the component of the vehicle's velocity along the z-direction.

[0053] Optionally, the pitch angle optimizer includes a pitch angle Kalman filter, and the yaw angle optimizer includes a yaw angle Kalman filter. During the prediction process, the state vector of each Kalman filter remains unchanged.

[0054] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor, wherein the memory and the processor are coupled together;

[0055] The memory is used to store one or more computer instructions;

[0056] The processor is used to execute one or more computer instructions to implement the vehicle-mounted IMU after-sales calibration method as described in the first aspect.

[0057] Fourthly, embodiments of this application provide a computer-readable storage medium storing one or more computer instructions that are executed by a processor to implement the vehicle-mounted IMU after-sales calibration method as described in the first aspect above.

[0058] Fifthly, this application provides a computer program product, which includes a computer program that, when executed by a processor, implements the vehicle-mounted IMU after-sales calibration method described in the first aspect. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0060] Figure 1 is a flowchart illustrating an on-board IMU after-sales calibration method provided in an embodiment of this application;

[0061] Figure 2 is a schematic diagram of a static calibration scenario for a basement according to an embodiment of this application;

[0062] Figure 3 is a schematic diagram of the ground dynamic calibration route according to an embodiment of this application;

[0063] Figure 4 is a structural schematic diagram of a vehicle-mounted IMU after-sales calibration device provided in an embodiment of this application;

[0064] Figure 5 is a schematic diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0065] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0066] It should be noted that the terms "comprising" and "having," and any variations thereof, in the embodiments and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0067] This application discloses a method, apparatus, electronic device, and storage medium for after-sales calibration of vehicle-mounted IMUs, which can improve the accuracy of after-sales calibration of vehicle-mounted IMUs. The embodiments of this application are described in detail below.

[0068] Service-Line Calibration (SLC) refers to the process where, after a mass-produced vehicle is delivered to a user, if the vehicle experiences issues such as sensor aging, damage, or abnormal calibration parameters, it needs to be sent to a designated after-sales service shop for sensor repair or replacement. Then, after-sales maintenance personnel drive the vehicle to collect data under specific scenarios and complete the calibration in real time during the driving process.

[0069] After-sales calibration, because it can be performed by maintenance personnel driving the vehicle, has the advantages of short calibration time. Specifically, roll and pitch can be calibrated statically. However, due to the zero bias in the x and y directions of the IMU acceleration during static calibration, errors will occur in the estimation of the roll and pitch of the IMU rotation extrinsic parameters. Therefore, roll bias compensation and pitch optimization are required. In the embodiments of this application, the zero bias in the y direction of the IMU acceleration can be accurately estimated through dynamic calibration while driving straight on the ground, thereby eliminating the error caused by the zero bias in the y direction introduced in the previous static calibration and obtaining a more accurate roll installation angle estimate. Furthermore, the pitch of the static calibration can be optimized and the yaw can be calibrated through dynamic calibration.

[0070] Figure 1 is a flowchart illustrating an aftermarket calibration method for a vehicle-mounted IMU according to an embodiment of this application. The IMU is installed in a vehicle, which is also equipped with at least a GNSS receiver. The method includes the following steps:

[0071] S110: Acquire static IMU data collected when the vehicle is stationary, and calculate the initial calibration results of the IMU roll angle and pitch angle based on the static IMU data;

[0072] S120: Acquire GNSS data and dynamic IMU data collected during the vehicle's operation according to preset driving requirements;

[0073] S130: Based on GNSS data and dynamic IMU data, optimize the initial calibration results of roll angle and pitch angle, and calculate the yaw angle calibration results of the IMU.

[0074] The GNSS data mentioned above may include: GNSS latitude and longitude, altitude, GNSS status, GNSS hdop value (horizontal accuracy), number of GNSS satellites, and GNSS velocity in the northeast-northeast coordinate system; IMU data may include: IMU's three-axis acceleration data acc_x, acc_y, acc_z, and three-axis gyroscope data gyro_x, gyro_y, gyro_z.

[0075] Due to installation errors, the actual installation position and angle of the IMU differ from the design values. To improve the performance of the autonomous driving system, two attitude angles of the IMU can be statically calibrated: roll and pitch. The vehicle must be placed on a level surface, ensuring its Z-axis is coaxial with the local gravitational acceleration direction. The roll and pitch angles are then calculated by measuring the components of local gravitational acceleration along each axis using the IMU. Furthermore, static calibration introduces a zero bias in the IMU acceleration in the x and y directions, leading to errors in the roll estimation of the IMU's rotational extrinsic parameters. Roll bias compensation is necessary. Specifically, by adding a term `bias_acc` (acceleration zero bias) to the state vector of the extended Kalman filter during dynamic calibration, the zero bias in the x and y directions of the IMU acceleration can be accurately estimated. This eliminates the error introduced by the zero bias in the x and y directions during static calibration, resulting in a more accurate roll installation angle estimate. For example, the roll calibration result can be obtained by subtracting the error caused by the zero bias from the previous calibration result.

[0076] Specifically, taking underground parking garage calibration as an example, the conditions that static calibration needs to meet are explained in Figure 2. Static calibration in underground parking garages with epoxy flooring is recommended. The requirements for calibration are: the slope of the parking space is very small when observed by the naked eye, and the vehicle does not roll downhill when in neutral without the aid of a wheel chock; the contact points of the four wheels with the ground should be as flat as possible without potholes; all four doors and two hoods should be closed, the suspension should be adjusted to normal, and only the driver and passenger seats should be occupied, and the occupants should remain as still as possible during this period; after triggering the IMU after-sales calibration, the vehicle should remain stationary for more than 1 minute; after the static calibration data collection in the underground parking garage is completed (calibration progress reaches 50%), the vehicle should be driven out of the underground parking garage as soon as possible to enter the surface dynamic calibration.

[0077] The specific calibration process includes the following steps: the diagnostic instrument calibration command is triggered; IMU accelerometer data is read for a certain period of time (e.g., 10 seconds); data validity is checked; pitch and roll angles are calculated; the calibration results are returned to the diagnostic instrument, and the process ends.

[0078] The static calibration process is triggered by the operator (driver) of the after-sales 4S store. The data read only requires IMU acceleration data acc_x, acc_y, and acc_z. The validity checks of the above data may include, for example, checking whether the length of the input data meets the 10s requirement; checking whether the number of input data meets the frequency requirement of 10s; and checking whether the standard deviation of the IMU data meets a given threshold, such as 0.5.

[0079] Optionally, variance filtering can be performed on the data, selecting data within 2 sigma to filter out outliers. Then, pitch and roll angles can be calculated based on the collected data. Specifically, the current acceleration vector can be averaged: acc_avg = sigma(acc_x, acc_y, acc_z) / 3. Then, bias_rotation_vec = acc_avg * gravity is calculated. Finally, bias_rotation_vec is converted into a quaternion q = [q...]. w ,q x ,q y ,q z The quaternion is then converted to Euler angles.

[0080] A rotation vector (also known as an axis-angle representation, where the axis is a unit vector and the angle is the rotation angle about that axis) can be converted into a quaternion representation. Given a rotation vector, it is represented by [υ... x ,υ y ,υ z A unit vector composed of [q] and a rotation angle θ can be converted into a quaternion q = [q] using the following formula. w ,q x ,q y ,q z ]:

[0081] Among them, θ = bias_rotation_vec, [υ x ,υ y ,υ z ] is the unit vector of the rotation axis (normalized to length 1). Furthermore, the rotation angle θ should be in radians. In quaternions, q w The real part is represented by the cosine of half the rotation angle. [q] x ,q y ,q z The symbol represents the imaginary component corresponding to the axis of rotation, which is obtained by multiplying each component of the axis of rotation by the sine of half the rotation angle.

[0082] Furthermore, the quaternion is converted to Euler angles roll and pitch using the following formula: roll = arctan2(2·(q) w ·q x +q y ·q z ),1-2·(q x 2 +q y 2 ))

[0083] Where p is the pitch value in radians. The calculated roll and pitch are the initial calibration results for the roll angle and pitch angle, respectively.

[0084] Ground dynamic calibration can select ground scenes for dynamic data collection. The route must follow the pre-selected loop route and cannot be driven arbitrarily. It is recommended to choose urban roads with smooth and open traffic conditions. The route should avoid slopes and should be a square, flat, and open urban loop route close to the underground parking scene. The optimal route length is 1km. Avoid uphill and downhill sections, highways, and elevated roads. The route shape should be square. The recommended route is shown in Figure 3. The vehicle can follow the arrow direction and drive around the loop along route 1-2-3-4.

[0085] During driving, avoid congested areas as much as possible, drive smoothly, and avoid bumps and sudden acceleration / deceleration. Keep lateral and longitudinal acceleration within a certain range. When driving in a straight line, try to maintain a straight line and keep the speed within the predetermined range, especially ensuring it is greater than a certain value. When encountering potholes or uneven road sections or curves, slow down. If the vehicle has active suspension, ensure the suspension gear is locked in the Normal position and avoid manually adjusting the suspension gear while driving.

[0086] After acquiring GNSS and dynamic IMU data corresponding to the vehicle's movement, the initial roll and pitch calibration results can be optimized based on the GNSS and dynamic IMU data, and the IMU yaw calibration result can be calculated. For example, firstly, based on the GNSS and dynamic IMU data and the constructed Kalman state estimation filter, the roll angle zero bias, as well as the pitch and yaw angle observations, can be obtained. Then, based on the roll angle zero bias, the initial roll calibration result is error-eliminating to obtain the roll angle calibration result. Finally, the pitch angle observations are input into the pitch angle optimizer, and the initial pitch angle calibration result is optimized based on the pitch angle optimizer to obtain the pitch angle calibration result. Similarly, the yaw angle observations are input into the yaw angle optimizer, and the yaw angle calibration result is obtained based on the yaw angle optimizer.

[0087] The NHC (Non-Holonomic Constraint) assumes that land vehicles neither jump off the ground nor slide along it; therefore, both of the vehicle's velocity components in the plane perpendicular to the direction of travel (e.g., the x-axis) are zero. Thus, the lateral (y-axis) and vertical (z-axis) velocity components in the vehicle frame can be expressed as: v y ≈0, v z ≈0, where v y and v z These represent the velocity components of the vehicle in the plane perpendicular to the direction of travel (x-axis).

[0088] Based on NHC constraints, in this embodiment of the application, the pitch angle observation value and yaw angle observation value of the IMU, as well as the roll angle zero offset, can be calculated based on GNSS data and dynamic IMU data.

[0089] Specifically, in one implementation, the step of obtaining the roll angle zero bias, pitch angle observations, and yaw angle observations based on the constructed Kalman state estimation filter, according to GNSS data and dynamic IMU data, may include: estimating the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU based on the GNSS data and dynamic IMU data; calculating the IMU velocity in the IMU coordinate system based on the world attitude; converting the IMU velocity into the vehicle velocity in the vehicle coordinate system based on the rotational extrinsic parameters of the IMU and the vehicle; and calculating the pitch angle observations and yaw angle observations of the IMU based on the vehicle velocity and according to non-integrity constraints.

[0090] In other words, GNSS can measure the vehicle's velocity in the world coordinate system. Then, through GNSS and IMU fusion positioning, the IMU's attitude in the world coordinate system can be estimated. Thus, the velocity of GNSS in the world coordinate system can be converted into the velocity of IMU in the IMU coordinate system through the attitude positioning of IMU in the world coordinate system. At the same time, through the rotational extrinsic parameters of IMU and vehicle body, it can be further converted into the velocity of IMU in vehicle body coordinate system. Finally, through the NHC constraint that the lateral and longitudinal velocities of the vehicle body are normally 0, the observed values ​​of the IMU's installation angle pitch and yaw can be obtained.

[0091] In this embodiment, an extended Kalman filter, also known as a Kalman state estimation filter, can be used to estimate the IMU's attitude and obtain its roll angle zero bias. Specifically, the constructed Kalman state estimation filter can be predicted based on dynamic IMU data, and updated based on GNSS data to obtain the IMU's world attitude in the world coordinate system and its roll angle zero bias. The state vector of the Kalman state estimation filter includes at least the IMU's world attitude in the world coordinate system and its roll angle zero bias.

[0092] For example, the filter can include the following 15-dimensional state vector: x = [position(3), orientation(3), velocity(3), bias_acc(3), bias_gyro(3)], where position represents the IMU's position in the world coordinate system, orientation represents the IMU's attitude in the world coordinate system, velocity represents the IMU's velocity in the world coordinate system, bias_acc represents the zero bias of acceleration, and bias_gyro represents the zero bias of gyroscope. Each parameter is a 3-dimensional vector.

[0093] Using dynamic IMU data and GNSS data, the extended Kalman filter is updated for prediction, ultimately estimating the IMU's attitude (orientation_w2i) and velocity (v_w) in the world coordinate system. Further, v_i = orientation_w2i * v_w is calculated to obtain the IMU velocity v_i in the IMU coordinate system. Therefore, based on the IMU's rotational extrinsic parameter orientation_design_i2b relative to the vehicle, v_b = orientation_design_i2b * v_i is calculated to obtain the vehicle's velocity v_b in the vehicle coordinate system. Here, the origin of the vehicle coordinate system is the center of the vehicle's rear axle, the vehicle's forward direction is the positive x-axis, the left side of the vehicle is the positive y-axis, and the vertically upward direction is the positive z-axis.

[0094] After obtaining the vehicle velocity of the IMU in the vehicle coordinate system, the pitch-bias and yaw-bias of the IMU can be calculated using the following formulas:

[0095] Among them, v x Let v be the component of the vehicle's velocity along the x-direction. y Let v be the component of the vehicle's velocity along the y-direction. z Let z be the component of the vehicle's velocity along the z-direction.

[0096] In this embodiment, separate optimizers, such as Kalman filters, can be maintained for pitch and yaw respectively. This allows for the optimization of pitch and yaw using the corresponding optimizers after calculating each observation. For example, for the pitch optimizer, its state vector x = [pitch], the prediction process x remains unchanged, and the update process z = pitch_bias, H = 1. The EKF (Error Kalman Filter) update formula can then be used.

[0097] In this embodiment, the IMU can first be statically calibrated to obtain initial roll and pitch calibration results. Then, dynamic calibration is performed based on GNSS (Global Navigation Satellite System) data and dynamic IMU data to optimize the initial roll and pitch calibration results and obtain yaw calibration results. In other words, this embodiment can perform IMU calibration based on GNSS and IMU fusion positioning. This calibration method can meet the calibration requirements of IMU extrinsic parameters of arbitrary accuracy. Therefore, for vehicle-mounted IMUs with low accuracy, the after-sales calibration accuracy can be improved.

[0098] Figure 4 shows a schematic diagram of a vehicle-mounted IMU aftermarket calibration device according to an embodiment of this application. The IMU is installed in a vehicle, and the vehicle is at least equipped with a Global Navigation Satellite System (GNSS). The device includes:

[0099] The static calibration module 410 is used to acquire static IMU data collected when the vehicle is stationary, and to calculate the initial calibration results of the roll angle and pitch angle of the IMU based on the static IMU data.

[0100] The dynamic data acquisition module 420 is used to acquire GNSS data and dynamic IMU data collected by the vehicle during the driving process according to preset driving requirements;

[0101] The parameter calibration module 430 is used to optimize the initial calibration results of the roll angle and the initial calibration results of the pitch angle based on the GNSS data and the dynamic IMU data, and to calculate the yaw angle calibration results of the IMU.

[0102] Optionally, the static calibration module 410 is specifically used for:

[0103] Obtain the acceleration vector from the static IMU data;

[0104] Calculate the average value of the acceleration vector, multiply the average value by the gravitational acceleration as the rotation angle deviation, and convert the rotation angle deviation into the corresponding quaternion;

[0105] Based on the quaternion, the corresponding roll Euler angle and pitch Euler angle are calculated as the initial calibration results of the IMU's roll angle and pitch angle.

[0106] Optionally, the parameter calibration module 430 is specifically used for:

[0107] Based on the GNSS data and the dynamic IMU data, the roll angle zero bias, as well as the pitch angle observation and yaw angle observation, are obtained based on the constructed Kalman state estimation filter.

[0108] Based on the zero bias of the roll angle, the initial calibration result of the roll angle is calibrated by error elimination to obtain the roll angle calibration result;

[0109] The pitch angle observation value is input into the pitch angle optimizer, and the initial pitch angle calibration result is optimized based on the pitch angle optimizer to obtain the pitch angle calibration result;

[0110] The yaw angle observation value is input into the yaw angle optimizer, and the yaw angle calibration result is obtained based on the yaw angle optimizer.

[0111] Optionally, the parameter calibration module 430 is specifically used for:

[0112] Based on the GNSS data and the dynamic IMU data, estimate the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU;

[0113] Based on the world attitude, calculate the IMU velocity in the IMU coordinate system;

[0114] Based on the rotational extrinsic parameters of the IMU and the vehicle, the IMU velocity is converted into the vehicle velocity of the IMU in the vehicle coordinate system;

[0115] Based on the vehicle speed and according to non-integrity constraints, the pitch angle and yaw angle observations of the IMU are calculated.

[0116] Optionally, the parameter calibration module 430 is specifically used for:

[0117] Based on the dynamic IMU data, the constructed Kalman state estimation filter is predicted, and the Kalman state estimation filter is updated by observation based on the GNSS data to obtain the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU; the state vector of the Kalman state estimation filter includes at least: the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU.

[0118] Optionally, the parameter calibration module 430 is specifically used for:

[0119] The pitch bias and yaw bias of the IMU are calculated using the following formulas:

[0120] Among them, v x Let v be the component of the vehicle's velocity along the x-direction. y Let v be the component of the vehicle's velocity along the y-direction. z Let be the component of the vehicle's velocity along the z-direction.

[0121] Optionally, the pitch angle optimizer includes a pitch angle Kalman filter, and the yaw angle optimizer includes a yaw angle Kalman filter. During the prediction process, the state vector of each Kalman filter remains unchanged.

[0122] In this embodiment, the IMU can first be statically calibrated to obtain initial roll and pitch calibration results. Then, dynamic calibration is performed based on GNSS (Global Navigation Satellite System) data and dynamic IMU data to optimize the initial roll and pitch calibration results and obtain yaw calibration results. In other words, this embodiment can perform IMU calibration based on GNSS and IMU fusion positioning. This calibration method can meet the calibration requirements of IMU extrinsic parameters of arbitrary accuracy. Therefore, for vehicle-mounted IMUs with low accuracy, the after-sales calibration accuracy can be improved.

[0123] The following describes a computer device provided in an embodiment of this application. Please refer to Figure 5, which is a schematic diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes:

[0124] One or more processors 40;

[0125] The processor 40 is coupled to a storage device 41, which is used to store one or more programs.

[0126] When the one or more programs are executed by the one or more processors 40, the electronic device implements the technical solution of the vehicle-mounted IMU after-sales calibration method as described above.

[0127] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the technical solution of the vehicle-mounted IMU after-sales calibration method described above.

[0128] This application provides a computer program product, which includes a computer program that, when executed by a processor, implements the technical solution of the on-board IMU after-sales calibration method described above.

[0129] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0130] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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. Such 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 this application.

Claims

1. A method for after-sales calibration of a vehicle-mounted inertial measurement unit (IMU), characterized in that, The IMU is installed in the vehicle, and the vehicle is also equipped with at least a Global Navigation Satellite System (GNSS). The method includes: Acquire static IMU data collected when the vehicle is stationary, and calculate the initial calibration results of the IMU's roll angle and pitch angle based on the static IMU data; Acquire GNSS data and dynamic IMU data collected during the vehicle's operation according to preset driving requirements; Based on the GNSS data and dynamic IMU data, the initial calibration results of the roll angle and pitch angle are optimized, and the yaw angle calibration result of the IMU is calculated.

2. The method according to claim 1, characterized in that, The steps of calculating the initial calibration results of the roll angle and pitch angle of the IMU based on the static IMU data include: Obtain the acceleration vector from the static IMU data; Calculate the average value of the acceleration vector, multiply the average value by the gravitational acceleration as the rotation angle deviation, and convert the rotation angle deviation into the corresponding quaternion; Based on the quaternion, the corresponding roll Euler angle and pitch Euler angle are calculated as the initial calibration results of the IMU's roll angle and pitch angle.

3. The method according to claim 1, characterized in that, The steps of optimizing the initial roll angle calibration results and initial pitch angle calibration results based on the GNSS data and dynamic IMU data, and calculating the IMU yaw angle calibration results, include: Based on the GNSS data and the dynamic IMU data, the roll angle zero bias, as well as the pitch angle observation and yaw angle observation, are obtained based on the constructed Kalman state estimation filter. Based on the zero bias of the roll angle, the initial calibration result of the roll angle is calibrated by error elimination to obtain the roll angle calibration result; The pitch angle observation value is input into the pitch angle optimizer, and the initial pitch angle calibration result is optimized based on the pitch angle optimizer to obtain the pitch angle calibration result; The yaw angle observation value is input into the yaw angle optimizer, and the yaw angle calibration result is obtained based on the yaw angle optimizer.

4. The method according to claim 3, characterized in that, The step of obtaining the roll angle zero bias, pitch angle observations, and yaw angle observations based on the GNSS data and the dynamic IMU data and the constructed Kalman state estimation filter includes: Based on the GNSS data and the dynamic IMU data, estimate the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU; Based on the world attitude, calculate the IMU velocity in the IMU coordinate system; Based on the rotational extrinsic parameters of the IMU and the vehicle, the IMU velocity is converted into the vehicle velocity of the IMU in the vehicle coordinate system; Based on the vehicle speed and according to non-integrity constraints, the pitch angle and yaw angle observations of the IMU are calculated.

5. The method according to claim 4, characterized in that, The step of estimating the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU based on the GNSS data and the dynamic IMU data includes: Based on the dynamic IMU data, the constructed Kalman state estimation filter is predicted, and the Kalman state estimation filter is updated by observation based on the GNSS data to obtain the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU; the state vector of the Kalman state estimation filter includes at least: the world attitude of the IMU in the world coordinate system and the roll angle zero bias of the IMU.

6. The method according to claim 4, characterized in that, The step of calculating the pitch angle and yaw angle observations of the IMU based on the vehicle speed and according to non-integrity constraints includes: The pitch bias and yaw bias of the IMU are calculated using the following formulas: Among them, v x Let v be the component of the vehicle's velocity along the x-direction. y Let v be the component of the vehicle's velocity along the y-direction. z Let be the component of the vehicle's velocity along the z-direction.

7. The method according to claim 3, characterized in that, The pitch angle optimizer includes a pitch angle Kalman filter, and the yaw angle optimizer includes a yaw angle Kalman filter. During the prediction process, the state vector of each Kalman filter remains unchanged.

8. A vehicle-mounted IMU after-sales calibration device, characterized in that, The IMU is installed in the vehicle, which is also equipped with at least a Global Navigation Satellite System (GNSS). The device includes: The static calibration module is used to acquire static IMU data collected when the vehicle is stationary, and to calculate the initial calibration results of the IMU's roll angle and pitch angle based on the static IMU data. The dynamic data acquisition module is used to acquire GNSS data and dynamic IMU data collected by the vehicle during the driving process according to preset driving requirements; The parameter calibration module is used to optimize the initial calibration results of the roll angle and the initial calibration results of the pitch angle based on the GNSS data and the dynamic IMU data, and to calculate the yaw angle calibration results of the IMU.

9. A computer device, characterized in that, include: The memory and the processor are coupled; The memory is used to store one or more computer instructions; The processor is used to execute one or more computer instructions to implement the vehicle-mounted IMU after-sales calibration method as described in any one of claims 1 to 7.

10. A readable storage medium having stored thereon one or more computer instructions, characterized in that, The instruction is executed by the processor to implement the vehicle-mounted IMU aftermarket calibration method as described in any one of claims 1 to 7.

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