Sensor parameter calibration method, device, medium and vehicle based on autonomous driving

Abstract

The present invention relates to the field of autonomous driving technology, and specifically provides a sensor parameter calibration method, device, medium and vehicle based on autonomous driving, aiming to solve the problem of how to realize online calibration of multiple parameters of sensors based on autonomous driving under the premise of ensuring calibration accuracy. To this end, the present invention judges the calibration start condition according to the real-time state of the vehicle, and applies the extended Kalman filter model to iteratively update the parameters to be calibrated contained in the measurement equations and observation equations of multiple sensors until the convergence conditions of the parameters to be calibrated are reached. The present invention can calibrate multiple parameters to be calibrated of multiple sensors in real time online, taking into account the conversion relationship between each sensor and the inertial measurement unit, so that the accuracy of the calibration process is higher, and dynamically turning on or off the calibration process according to the real-time state of the vehicle, taking into account the influence of the real-time state of the vehicle on the online calibration process, so that the calibration process has better robustness.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and specifically provides a sensor parameter calibration method, device, medium and vehicle based on autonomous driving. Background Art

[0002] Autonomous vehicle technology uses a perception system to acquire information about the vehicle itself and the surrounding driving environment, and analyzes, calculates, and processes this information to achieve autonomous driving. Therefore, the accuracy of the parameters of each sensor installed on the vehicle is crucial to autonomous driving technology. Generally, the parameters of each sensor are calibrated offline before the vehicle leaves the factory. However, after leaving the factory, the passage of time can cause changes in factors such as tire pressure, which can further lead to deviations in the actual vehicle sensor parameters, especially external parameters. Therefore, the vehicle sensor parameters need to be calibrated based on the actual changes in the vehicle.

[0003] However, in the prior art, offline calibration is generally used to calibrate the parameters of vehicle sensors. However, the calibration procedure of the offline calibration process is cumbersome, which increases the offline time of the vehicle; and offline calibration also has the problem of limited calibration of external parameters, such as only being able to calibrate the yaw (heading) angle of the IMU (Inertial Measurement Unit) installation error, but not the pitch (pitch) angle; at the same time, offline calibration also has the problem of large calibration error, such as when calibrating the IMU installation error offline, the offline calibration accuracy can only reach 0.3deg, and the calibration error of some parameters will be even greater. There are also methods for online calibration of sensor parameters in the prior art, but they can only calibrate a few parameters, which has certain limitations.

[0004] Accordingly, this field requires a new sensor parameter calibration solution based on autonomous driving to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects, the present invention is proposed to provide a solution or at least partially solve how to achieve online calibration of multiple parameters of sensors based on autonomous driving while ensuring calibration accuracy.

[0006] In a first aspect, the present invention provides a sensor parameter calibration method based on autonomous driving, the method being applied to a vehicle, the method comprising:

[0007] Establishing a measurement equation of the sensor based on an actual measurement state of the sensor;

[0008] Establishing an observation equation of the sensor based on a conversion relationship between a sensor coordinate system of the sensor and an inertial measurement unit coordinate system of the inertial measurement unit, wherein the measurement equation and the observation equation include parameters to be calibrated;

[0009] Determining whether the calibration start condition of the parameters to be calibrated is met according to the real-time status of the vehicle;

[0010] When the calibration start condition is met, the preset extended Kalman filter model is applied to iteratively update the parameters to be calibrated according to the measurement equation and the observation equation until the convergence condition of the parameters to be calibrated is reached, so as to realize the calibration of the parameters to be calibrated.

[0011] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving,

[0012] The establishing of the measurement equation of the sensor based on the actual measurement state of the sensor includes:

[0013] Establishing a measurement equation of the sensor according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit;

[0014] The establishing of the observation equation of the sensor based on the conversion relationship between the sensor coordinate system of the sensor and the inertial measurement unit coordinate system of the inertial measurement unit includes:

[0015] An observation equation of the sensor is established based on a conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and a conversion relationship from the inertial measurement unit coordinate system to a global coordinate system.

[0016] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, the sensor includes a GPS sensor, and the time delay includes a first time delay between the GPS sensor and the inertial measurement unit;

[0017] The establishing of a sensor measurement equation according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit includes:

[0018] Acquire a measured position of the GPS sensor according to an actual measurement state of the GPS sensor, the first time delay, and a coordinate system offset of the GPS sensor;

[0019] acquiring a speed measured by the GPS sensor according to an actual measurement state of the GPS sensor and the first time delay;

[0020] A measurement equation of the GPS sensor is established according to the measured position and the measured speed.

[0021] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving,

[0022] The establishing of the observation equation of the sensor based on the conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the conversion relationship from the inertial measurement unit coordinate system to the global coordinate system includes:

[0023] Acquire an observation position of the GPS sensor according to a conversion relationship from a GPS coordinate system of the GPS sensor to a coordinate system of the inertial measurement unit and a position of the inertial measurement unit in a global coordinate system;

[0024] Obtaining the observed speed of the GPS sensor according to the conversion relationship from the GPS coordinate system to the inertial measurement unit coordinate system and the speed of the inertial measurement unit in the global coordinate system;

[0025] Obtaining an observed heading angle of the GPS sensor according to a conversion relationship from the GPS coordinate system to the inertial measurement unit coordinate system, a rotation matrix from the GPS coordinate system to the global coordinate system, and a rotation matrix from the inertial measurement unit coordinate system to the global coordinate system;

[0026] An observation equation of the GPS sensor is established according to the observed position, the observed speed, and the observed heading angle.

[0027] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, the sensor includes a vehicle-mounted camera, and the time delay includes a second time delay between the vehicle-mounted camera and the inertial measurement unit;

[0028] The establishing of a sensor measurement equation according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit includes:

[0029] Matching the lane lines in the high-precision map with the lane lines captured by the vehicle-mounted camera to obtain the actual measurement status of the matching positioning result;

[0030] Obtaining a positioning position of the matching positioning result in a global coordinate system according to the actual measurement state, the second time delay, and the local map offset of the high-precision map;

[0031] A measurement equation of the vehicle-mounted camera is established according to the lateral positioning position of the positioning position.

[0032] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving,

[0033] The establishing of the observation equation of the sensor based on the conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the conversion relationship from the inertial measurement unit coordinate system to the global coordinate system includes:

[0034] Obtaining an observation position of the vehicle-mounted camera according to a conversion relationship from an image coordinate system of the vehicle-mounted camera to a coordinate system of the inertial measurement unit and a position of the inertial measurement unit in a global coordinate system;

[0035] Obtaining an observation heading angle of the vehicle-mounted camera according to a conversion relationship from the image coordinate system to the inertial measurement unit coordinate system and a rotation matrix from the inertial measurement unit coordinate system to the global coordinate system;

[0036] An observation equation of the vehicle-mounted camera is established according to the observation position and the observation heading angle.

[0037] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, the sensor includes a wheel speed sensor, and the time delay includes a third time delay between the wheel speed sensor and the inertial measurement unit.

[0038] The establishing of a sensor measurement equation according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit includes:

[0039] acquiring a rear wheel axle center speed of the wheel speed sensor according to an actual measurement state of the wheel speed sensor, a speed coefficient of the wheel speed sensor, and the third time delay;

[0040] According to the rear wheel axle center speed, obtaining the forward speed of the rear wheel axle center speed;

[0041] A measurement equation of the wheel speed sensor is established according to the forward speed.

[0042] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving,

[0043] The establishing of the observation equation of the sensor based on the conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the conversion relationship from the inertial measurement unit coordinate system to the global coordinate system includes:

[0044] Obtaining an observed speed of the wheel speed sensor based on a conversion relationship from a wheel speed coordinate system of the wheel speed sensor to a coordinate system of the inertial measurement unit, a speed of the inertial measurement unit in a global coordinate system, and a rotation matrix from the coordinate system of the inertial measurement unit to the global coordinate system;

[0045] An observation equation of the wheel speed sensor is established according to the observed speed.

[0046] In a technical solution of the above-mentioned sensor parameter calibration method for autonomous driving

[0047] The transformation relationship from the sensor coordinate system to the inertial measurement unit coordinate system includes a lever arm value and a rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system.

[0048] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, the rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system is obtained according to the following steps:

[0049] The rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system is obtained according to the rotation matrix from the sensor coordinate system to the rear wheel axle offline installation coordinate system and the installation angle error of the sensor model in the wheel speed coordinate system.

[0050] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, the calibration start condition includes a calibration precondition and a calibration condition of the parameter to be calibrated;

[0051] The determining whether the calibration start condition is met according to the real-time status of the vehicle includes:

[0052] Determining whether a global filter of the extended Kalman filter has converged according to a real-time state of the inertial measurement unit;

[0053] Obtaining, based on the signal quality of data collected by the GPS sensor, a duration of the signal quality being higher than a preset signal quality threshold;

[0054] When the global filter has converged and the duration is longer than a preset duration, it is determined that the calibration prerequisite is met;

[0055] When the calibration prerequisite is met, it is determined whether the calibration condition of the parameter to be calibrated is met according to the real-time state of the vehicle.

[0056] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving,

[0057] The determining, based on the real-time state of the inertial measurement unit, whether the global filter of the extended Kalman filter has converged includes:

[0058] Determining whether the state value of the inertial measurement unit is within a preset range, and if so, determining that a first convergence condition is satisfied;

[0059] determining whether a time delay of an accelerometer of the inertial measurement unit is converged and stable, and if so, determining that a second convergence condition is satisfied;

[0060] determining whether the heading angle of the inertial measurement unit is consistent with the moving direction of the vehicle, and if so, determining that a third convergence condition is satisfied;

[0061] determining whether the position and posture of the inertial measurement unit are consistent with the position and posture of the sensor, and if so, determining that a fourth convergence condition is satisfied;

[0062] When the first convergence condition, the second convergence condition, the third convergence condition and the fourth convergence condition are all satisfied, it is determined that the global filter of the extended Kalman filter has converged.

[0063] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, it is characterized in that the parameter to be calibrated includes a time delay between the sensor and the inertial measurement unit;

[0064] The calibration condition of the time delay is: the speed of the vehicle is greater than a speed threshold; and / or,

[0065] The parameters to be calibrated include a lever arm value between the sensor coordinate system and the inertial measurement unit coordinate system;

[0066] The calibration condition of the lever arm value is: the speed of the vehicle is greater than a speed threshold; and / or,

[0067] The parameters to be calibrated include the wheel speed coefficient of the wheel speed sensor;

[0068] The calibration condition of the wheel speed coefficient is: the speed of the vehicle is greater than a speed threshold.

[0069] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, the time delay includes a first time delay between the GPS sensor and the inertial measurement unit, a second time delay between the on-board camera and the inertial measurement unit, and a third time delay between the wheel speed sensor and the inertial measurement unit;

[0070] The convergence condition of the first time delay is that the covariance of the first time delay is less than a preset ratio of an initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold;

[0071] The convergence condition of the second time delay and the third time delay is: the first time delay has satisfied the convergence condition, the covariance of the second time delay is less than a preset ratio of an initial covariance value, and the jitter of the second time delay within a preset time is less than a jitter threshold;

[0072] The convergence condition of the third time delay is: the first time delay has satisfied the convergence condition, the covariance of the third time delay is less than a preset ratio of the initial covariance value, and the jitter of the third time delay within a preset time is less than a jitter threshold; and / or,

[0073] The convergence condition of the lever arm value is: the covariance of the lever arm value is less than a preset ratio of the initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold; and / or,

[0074] The convergence condition of the wheel speed coefficient is: the covariance of the wheel speed coefficient is less than a preset ratio of the initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold;

[0075] Wherein, the preset ratio is less than one and greater than zero.

[0076] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, the parameters to be calibrated include the installation angle error of the sensor modeled in the wheel speed coordinate system, and the sensors include the inertial measurement unit, the GPS sensor, and the on-board camera; the installation angle error of the inertial measurement unit includes a pitch angle error and a heading angle error, the installation angle error of the GPS sensor includes a heading angle error, and the installation angle error of the on-board camera includes a heading angle error;

[0077] The calibration condition of the pitch angle error of the inertial measurement unit is: the speed of the vehicle is greater than the speed threshold and the vehicle is traveling in a straight line at a constant speed;

[0078] The calibration conditions of the heading angle error of the inertial measurement unit are: the speed of the vehicle is greater than a speed threshold, the acceleration of the vehicle is greater than an acceleration threshold, and the vehicle is traveling in a straight line;

[0079] The calibration condition of the heading angle error of the GPS sensor is: the speed of the vehicle is greater than a speed threshold, and the calibration of the heading angle error of the inertial measurement unit has reached a convergence condition; and / or,

[0080] The parameters to be calibrated include a local map offset of the high-precision map;

[0081] The calibration condition of the local map offset is: the speed of the vehicle is greater than a speed threshold, and the on-board camera obtains a high-quality matching positioning result, wherein the high-quality matching positioning result is judged based on the quality bit of the data of the matching positioning result.

[0082] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, the convergence condition of the pitch angle error of the inertial measurement unit is: the covariance of the pitch angle error is less than a preset ratio of the initial covariance value, and the jitter of the pitch angle error within a preset time is less than a jitter threshold;

[0083] The convergence condition of the heading angle error of the inertial measurement unit is: the covariance of the heading angle error is less than a preset ratio of the initial covariance value, and the jitter of the heading angle error within a preset time is less than a jitter threshold;

[0084] The convergence condition of the heading angle error of the GPS sensor is: the covariance of the heading angle error is less than a preset ratio of the initial covariance value, the jitter of the heading angle error within a preset time is less than a jitter threshold, and the heading angle error of the inertial measurement unit has met the convergence condition; and / or,

[0085] The convergence condition of the local map offset is: the covariance of the local map offset is less than a preset ratio of the initial covariance value, and the jitter of the local map offset within a preset time is less than a jitter threshold;

[0086] Wherein, the preset ratio is less than one and greater than zero.

[0087] In one technical solution of the above-mentioned sensor parameter calibration method for autonomous driving, the extended Kalman filter model is obtained according to the following formula:

[0088] y=zh(x)

[0089]

[0090]

[0091]

[0092]

[0093] Wherein, z is the measurement equation, h(x) is the observation equation, x is the parameter to be calibrated, y is the difference between the measurement equation and the observation equation, H is the Jacobian matrix of the current state, is the Jacobian matrix of the previous state, K is the Kalman gain, is the previous state covariance matrix, R is the observation noise, is the observed calibration quantity, and P is the covariance matrix of the current state.

[0094] In one technical solution of the above-mentioned method for calibrating sensor parameters for autonomous driving, the method further includes:

[0095] When calibrating the parameters to be calibrated of the GPS sensor, applying a Lie algebra solution method to obtain the Jacobian matrix according to the observation equation of the GPS sensor;

[0096] When calibrating the parameters to be calibrated of the vehicle-mounted camera, applying a Lie algebra solution method to obtain the Jacobian matrix according to the observation equation of the vehicle-mounted camera;

[0097] When calibrating the parameters to be calibrated of the wheel speed sensor, a Lie algebra solution method or an Euler angle solution method is applied to obtain the Jacobian matrix according to the observation equation of the wheel speed sensor.

[0098] In a second aspect, a control device is provided, which includes a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the sensor parameter calibration method for autonomous driving described in any one of the technical solutions of the above-mentioned sensor parameter calibration method for autonomous driving.

[0099] In a third aspect, a computer-readable storage medium is provided, which stores a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the sensor parameter calibration method for autonomous driving described in any one of the technical solutions of the above-mentioned sensor parameter calibration method for autonomous driving.

[0100] In a fourth aspect, a vehicle is provided, comprising the control device and the sensor in the above-mentioned control device.

[0101] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0102] In the technical solution of the present invention, the present invention can determine whether the calibration start conditions of the parameters to be calibrated are met based on the real-time state of the vehicle. When the calibration start conditions of the parameters to be calibrated are met, the extended Kalman filter model can be applied to iteratively update the parameters to be calibrated contained in the measurement equations and observation equations of multiple sensors until the convergence conditions of the parameters to be calibrated are reached. Through the above configuration, the present invention can realize real-time online calibration of multiple parameters to be calibrated of multiple sensors. Since the conversion relationship between each sensor and the inertial measurement unit is taken into account, the calibration process is more accurate. At the same time, the calibration process of the parameters to be calibrated can be dynamically turned on or off according to the real-time state of the vehicle, taking into account the impact of the real-time state of the vehicle on the online calibration process, so that the calibration process has better robustness.

[0103] Solution 1. A sensor parameter calibration method based on autonomous driving, characterized in that the method is applied to a vehicle and comprises:

[0104] Establishing a measurement equation of the sensor based on an actual measurement state of the sensor;

[0105] Establishing an observation equation of the sensor based on a conversion relationship between a sensor coordinate system of the sensor and an inertial measurement unit coordinate system of the inertial measurement unit, wherein the measurement equation and the observation equation include parameters to be calibrated;

[0106] Determining whether the calibration start condition of the parameters to be calibrated is met according to the real-time status of the vehicle;

[0107] When the calibration start condition is met, the preset extended Kalman filter model is applied to iteratively update the parameters to be calibrated according to the measurement equation and the observation equation until the convergence condition of the parameters to be calibrated is reached, so as to realize the calibration of the parameters to be calibrated.

[0108] Solution 2. The sensor parameter calibration method based on autonomous driving according to Solution 1 is characterized in that:

[0109] The establishing of the measurement equation of the sensor based on the actual measurement state of the sensor includes:

[0110] Establishing a measurement equation of the sensor according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit;

[0111] The establishing of the observation equation of the sensor based on the conversion relationship between the sensor coordinate system of the sensor and the inertial measurement unit coordinate system of the inertial measurement unit includes:

[0112] An observation equation of the sensor is established based on a conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and a conversion relationship from the inertial measurement unit coordinate system to a global coordinate system.

[0113] Solution 3. The sensor parameter calibration method based on autonomous driving according to Solution 2, wherein the sensor includes a GPS sensor, and the time delay includes a first time delay between the GPS sensor and the inertial measurement unit;

[0114] The establishing of a sensor measurement equation according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit includes:

[0115] Acquire a measured position of the GPS sensor according to an actual measurement state of the GPS sensor, the first time delay, and a coordinate system offset of the GPS sensor;

[0116] acquiring a speed measured by the GPS sensor according to an actual measurement state of the GPS sensor and the first time delay;

[0117] A measurement equation of the GPS sensor is established according to the measured position and the measured speed.

[0118] Solution 4. The sensor parameter calibration method based on autonomous driving according to Solution 3 is characterized in that:

[0119] The establishing of the observation equation of the sensor based on the conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the conversion relationship from the inertial measurement unit coordinate system to the global coordinate system includes:

[0120] Acquire an observation position of the GPS sensor according to a conversion relationship from a GPS coordinate system of the GPS sensor to a coordinate system of the inertial measurement unit and a position of the inertial measurement unit in a global coordinate system;

[0121] Obtaining the observed speed of the GPS sensor according to the conversion relationship from the GPS coordinate system to the inertial measurement unit coordinate system and the speed of the inertial measurement unit in the global coordinate system;

[0122] Obtaining an observed heading angle of the GPS sensor according to a conversion relationship from the GPS coordinate system to the inertial measurement unit coordinate system, a rotation matrix from the GPS coordinate system to the global coordinate system, and a rotation matrix from the inertial measurement unit coordinate system to the global coordinate system;

[0123] An observation equation of the GPS sensor is established according to the observed position, the observed speed, and the observed heading angle.

[0124] Solution 5. The sensor parameter calibration method based on autonomous driving according to Solution 2, wherein the sensor includes a vehicle-mounted camera, and the time delay includes a second time delay between the vehicle-mounted camera and the inertial measurement unit;

[0125] The establishing of a sensor measurement equation according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit includes:

[0126] Matching the lane lines in the high-precision map with the lane lines captured by the vehicle-mounted camera to obtain the actual measurement status of the matching positioning result;

[0127] Obtaining a positioning position of the matching positioning result in a global coordinate system according to the actual measurement state, the second time delay, and the local map offset of the high-precision map;

[0128] A measurement equation of the vehicle-mounted camera is established according to the lateral positioning position of the positioning position.

[0129] Solution 6. The sensor parameter calibration method based on autonomous driving according to Solution 5 is characterized in that:

[0130] The establishing of the observation equation of the sensor based on the conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the conversion relationship from the inertial measurement unit coordinate system to the global coordinate system includes:

[0131] Obtaining an observation position of the vehicle-mounted camera according to a conversion relationship from an image coordinate system of the vehicle-mounted camera to a coordinate system of the inertial measurement unit and a position of the inertial measurement unit in a global coordinate system;

[0132] Obtaining an observation heading angle of the vehicle-mounted camera according to a conversion relationship from the image coordinate system to the inertial measurement unit coordinate system and a rotation matrix from the inertial measurement unit coordinate system to the global coordinate system;

[0133] An observation equation of the vehicle-mounted camera is established according to the observation position and the observation heading angle.

[0134] Solution 7. The sensor parameter calibration method based on autonomous driving according to Solution 2, characterized in that the sensor includes a wheel speed sensor, the time delay includes a third time delay between the wheel speed sensor and the inertial measurement unit,

[0135] The establishing of a sensor measurement equation according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit includes:

[0136] acquiring a rear wheel axle center speed of the wheel speed sensor according to an actual measurement state of the wheel speed sensor, a speed coefficient of the wheel speed sensor, and the third time delay;

[0137] According to the rear wheel axle center speed, obtaining the forward speed of the rear wheel axle center speed;

[0138] A measurement equation of the wheel speed sensor is established according to the forward speed.

[0139] Solution 8. The sensor parameter calibration method based on autonomous driving according to Solution 7 is characterized in that:

[0140] The establishing of the observation equation of the sensor based on the conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the conversion relationship from the inertial measurement unit coordinate system to the global coordinate system includes:

[0141] Obtaining an observed speed of the wheel speed sensor based on a conversion relationship from a wheel speed coordinate system of the wheel speed sensor to a coordinate system of the inertial measurement unit, a speed of the inertial measurement unit in a global coordinate system, and a rotation matrix from the coordinate system of the inertial measurement unit to the global coordinate system;

[0142] An observation equation of the wheel speed sensor is established according to the observed speed.

[0143] Solution 9. The sensor parameter calibration method based on autonomous driving according to any one of Solutions 1 to 8, characterized in that:

[0144] The transformation relationship from the sensor coordinate system to the inertial measurement unit coordinate system includes a lever arm value and a rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system.

[0145] Solution 10. The sensor parameter calibration method based on autonomous driving according to Solution 9 is characterized in that the rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system is obtained according to the following steps:

[0146] The rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system is obtained according to the rotation matrix from the sensor coordinate system to the rear wheel axle offline installation coordinate system and the installation angle error of the sensor model in the wheel speed coordinate system.

[0147] Solution 11. The sensor parameter calibration method based on autonomous driving according to Solution 1, wherein the calibration start condition includes a calibration precondition and a calibration condition of the parameter to be calibrated;

[0148] The determining whether the calibration start condition is met according to the real-time status of the vehicle includes:

[0149] Determining whether a global filter of the extended Kalman filter has converged according to a real-time state of the inertial measurement unit;

[0150] Obtaining, based on the signal quality of data collected by the GPS sensor, a duration of the signal quality being higher than a preset signal quality threshold;

[0151] When the global filter has converged and the duration is longer than a preset duration, it is determined that the calibration prerequisite is met;

[0152] When the calibration prerequisite is met, it is determined whether the calibration condition of the parameter to be calibrated is met according to the real-time state of the vehicle.

[0153] Solution 12. The sensor parameter calibration method based on autonomous driving according to Solution 11 is characterized in that:

[0154] The determining, based on the real-time state of the inertial measurement unit, whether the global filter of the extended Kalman filter has converged includes:

[0155] Determining whether the state value of the inertial measurement unit is within a preset range, and if so, determining that a first convergence condition is satisfied;

[0156] determining whether a time delay of an accelerometer of the inertial measurement unit is converged and stable, and if so, determining that a second convergence condition is satisfied;

[0157] determining whether the heading angle of the inertial measurement unit is consistent with the moving direction of the vehicle, and if so, determining that a third convergence condition is satisfied;

[0158] determining whether the position and posture of the inertial measurement unit are consistent with the position and posture of the sensor, and if so, determining that a fourth convergence condition is satisfied;

[0159] When the first convergence condition, the second convergence condition, the third convergence condition and the fourth convergence condition are all satisfied, it is determined that the global filter of the extended Kalman filter has converged.

[0160] Solution 13. The sensor parameter calibration method based on autonomous driving according to Solution 11, wherein the parameter to be calibrated includes a time delay between the sensor and the inertial measurement unit;

[0161] The calibration condition of the time delay is: the speed of the vehicle is greater than a speed threshold; and / or,

[0162] The parameters to be calibrated include a lever arm value between the sensor coordinate system and the inertial measurement unit coordinate system;

[0163] The calibration condition of the lever arm value is: the speed of the vehicle is greater than a speed threshold; and / or,

[0164] The parameters to be calibrated include the wheel speed coefficient of the wheel speed sensor;

[0165] The calibration condition of the wheel speed coefficient is: the speed of the vehicle is greater than a speed threshold.

[0166] Solution 14. The sensor parameter calibration method based on autonomous driving according to Solution 13, wherein the time delay includes a first time delay between the GPS sensor and the inertial measurement unit, a second time delay between the onboard camera and the inertial measurement unit, and a third time delay between the wheel speed sensor and the inertial measurement unit.

[0167] The convergence condition of the first time delay is that the covariance of the first time delay is less than a preset ratio of an initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold;

[0168] The convergence condition of the second time delay and the third time delay is: the first time delay has satisfied the convergence condition, the covariance of the second time delay is less than a preset ratio of an initial covariance value, and the jitter of the second time delay within a preset time is less than a jitter threshold;

[0169] The convergence condition of the third time delay is: the first time delay has satisfied the convergence condition, the covariance of the third time delay is less than a preset ratio of the initial covariance value, and the jitter of the third time delay within a preset time is less than a jitter threshold; and / or,

[0170] The convergence condition of the lever arm value is: the covariance of the lever arm value is less than a preset ratio of the initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold; and / or,

[0171] The convergence condition of the wheel speed coefficient is: the covariance of the wheel speed coefficient is less than a preset ratio of the initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold;

[0172] Wherein, the preset ratio is less than one and greater than zero.

[0173] Solution 15. The sensor parameter calibration method based on autonomous driving according to Solution 11, characterized in that the parameters to be calibrated include the installation angle errors of the sensors modeled in the wheel speed coordinate system, the sensors including the inertial measurement unit, the GPS sensor, and the onboard camera; the installation angle errors of the inertial measurement unit include a pitch angle error and a heading angle error, the installation angle error of the GPS sensor includes a heading angle error, and the installation angle error of the onboard camera includes a heading angle error;

[0174] The calibration condition of the pitch angle error of the inertial measurement unit is: the speed of the vehicle is greater than the speed threshold and the vehicle is traveling in a straight line at a constant speed;

[0175] The calibration conditions of the heading angle error of the inertial measurement unit are: the speed of the vehicle is greater than a speed threshold, the acceleration of the vehicle is greater than an acceleration threshold, and the vehicle is traveling in a straight line;

[0176] The calibration condition of the heading angle error of the GPS sensor is: the speed of the vehicle is greater than a speed threshold, and the calibration of the heading angle error of the inertial measurement unit has reached a convergence condition; and / or,

[0177] The parameters to be calibrated include a local map offset of the high-precision map;

[0178] The calibration condition of the local map offset is: the speed of the vehicle is greater than a speed threshold, and the on-board camera obtains a high-quality matching positioning result, wherein the high-quality matching positioning result is judged based on the quality bit of the data of the matching positioning result.

[0179] Solution 16. The sensor parameter calibration method based on autonomous driving according to Solution 15 is characterized in that:

[0180] The convergence condition of the pitch angle error of the inertial measurement unit is: the covariance of the pitch angle error is less than a preset ratio of the initial covariance value, and the jitter of the pitch angle error within a preset time is less than a jitter threshold;

[0181] The convergence condition of the heading angle error of the inertial measurement unit is: the covariance of the heading angle error is less than a preset ratio of the initial covariance value, and the jitter of the heading angle error within a preset time is less than a jitter threshold;

[0182] The convergence condition of the heading angle error of the GPS sensor is: the covariance of the heading angle error is less than a preset ratio of the initial covariance value, the jitter of the heading angle error within a preset time is less than a jitter threshold, and the heading angle error of the inertial measurement unit has met the convergence condition; and / or,

[0183] The convergence condition of the local map offset is: the covariance of the local map offset is less than a preset ratio of the initial covariance value, and the jitter of the local map offset within a preset time is less than a jitter threshold;

[0184] Wherein, the preset ratio is less than one and greater than zero.

[0185] Solution 17. The sensor parameter calibration method based on autonomous driving according to Solution 1 is characterized in that the extended Kalman filter model is obtained according to the following steps:

[0186] y=zh(x)

[0187]

[0188]

[0189]

[0190]

[0191] Wherein, z is the measurement equation, h(x) is the observation equation, x is the parameter to be calibrated, y is the difference between the measurement equation and the observation equation, H is the Jacobian matrix of the current state, is the Jacobian matrix of the previous state, K is the Kalman gain, is the previous state covariance matrix, P is the observation noise, is the observed calibration quantity, and P is the covariance matrix of the current state.

[0192] Solution 18. The sensor parameter calibration method based on autonomous driving according to Solution 17, characterized in that the method further comprises:

[0193] When calibrating the parameters to be calibrated of the GPS sensor, applying a Lie algebra solution method to obtain the Jacobian matrix according to the observation equation of the GPS sensor;

[0194] When calibrating the parameters to be calibrated of the vehicle-mounted camera, applying a Lie algebra solution method to obtain the Jacobian matrix according to the observation equation of the vehicle-mounted camera;

[0195] When calibrating the parameters to be calibrated of the wheel speed sensor, a Lie algebra solution method or an Euler angle solution method is applied to obtain the Jacobian matrix according to the observation equation of the wheel speed sensor.

[0196] Solution 19. A control device comprising a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and is characterized in that the program codes are suitable for being loaded and run by the processor to execute the sensor parameter calibration method based on autonomous driving described in any one of Solutions 1 to 18.

[0197] Solution 20. A computer-readable storage medium storing a plurality of program codes, characterized in that the program codes are suitable for being loaded and run by a processor to execute the sensor parameter calibration method based on autonomous driving described in any one of Solutions 1 to 18.

[0198] Solution 21. A vehicle, characterized in that the vehicle includes the control device and sensor described in Solution 19. BRIEF DESCRIPTION OF THE DRAWINGS

[0199] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Among them:

[0200] Figure 1 1 is a flow chart showing the main steps of a sensor parameter calibration method based on autonomous driving according to an embodiment of the present invention;

[0201] Figure 2 1 is a flow chart of the main steps of a sensor parameter calibration method based on autonomous driving according to an embodiment of the present invention. DETAILED DESCRIPTION

[0202] Some embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0203] In the description of the present invention, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, and the like. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "a" and "the" may also include the plural forms.

[0204] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a sensor parameter calibration method based on autonomous driving according to an embodiment of the present invention. Figure 1 As shown, the sensor parameter calibration method based on autonomous driving in an embodiment of the present invention is applied to a vehicle, and the sensor parameter calibration method based on autonomous driving mainly includes the following steps S101 to S104.

[0205] Step S101: establishing a measurement equation of the sensor based on the actual measurement state of the sensor.

[0206] In this embodiment, the measurement equation of the sensor may be established according to the actual measurement state of the sensor.

[0207] In one embodiment, the sensor may include a positioning sensor, such as a GPS (Global Positioning System) sensor; a perception sensor, such as a vehicle-mounted camera; and a wheel speed sensor.

[0208] Step S102: establishing an observation equation of the sensor based on a conversion relationship between the sensor coordinate system of the sensor and the inertial measurement unit coordinate system of the inertial measurement unit, wherein the measurement equation and the observation equation include parameters to be calibrated.

[0209] In this embodiment, the observation equation of the sensor can be established based on the conversion relationship between the sensor coordinate system and the inertial measurement unit (IMU). Among them, the inertial measurement unit is a sensor used to detect and measure the acceleration and rotational motion of the vehicle. During the measurement process, the data update frequency is fast and is not affected by the external environment. Therefore, establishing the observation equation based on the conversion relationship between the sensor and the inertial measurement unit can enable the observation equation to fully consider the relationship between the sensor and the inertial measurement unit, thereby improving the accuracy of the calibration process.

[0210] In one embodiment, multiple parameters to be calibrated included in the sensor measurement equation and the observation equation may be calibrated.

[0211] Step S103: judging whether the calibration start condition of the parameters to be calibrated is met according to the real-time status of the vehicle.

[0212] In this embodiment, whether to start calibration of the parameters to be calibrated can be determined based on the real-time status of the vehicle. That is, during the calibration process of the parameters to be calibrated, calibration cannot be started at any time. It is necessary to determine whether to start calibration based on the real-time status of the vehicle. This fully considers the impact of the real-time status of the vehicle on the calibration process, making the calibration process more stable and improving the robustness of the calibration process.

[0213] Step S104: When the calibration start condition is met, the preset extended Kalman filter model is applied to iteratively update the parameters to be calibrated according to the measurement equation and the observation equation until the convergence condition of the parameters to be calibrated is reached, thereby achieving calibration of the parameters to be calibrated.

[0214] In this embodiment, when the calibration start conditions are met, the extended Kalman filter model can be applied to iteratively update the parameters to be calibrated based on the sensor's measurement and observation equations, bringing the observed results closer to the measured results, thereby achieving calibration of the parameters to be calibrated. When the convergence conditions for the parameters to be calibrated are met, the iterative updates can be stopped. Applying the extended Kalman filter model can achieve a second-order Taylor series expansion for nonlinear systems, achieving higher accuracy.

[0215] Based on the above steps S101 to S104, the embodiment of the present invention can determine whether the calibration start conditions of the parameters to be calibrated are met according to the real-time status of the vehicle. When the calibration start conditions of the parameters to be calibrated are met, the extended Kalman filter model can be applied to iteratively update the parameters to be calibrated contained in the measurement equations and observation equations of multiple sensors until the convergence conditions of the parameters to be calibrated are reached. Through the above configuration, the embodiment of the present invention can realize real-time online calibration of multiple parameters to be calibrated of multiple sensors. Since the conversion relationship between each sensor and the inertial measurement unit is taken into account, the calibration process is more accurate. At the same time, the calibration process of the parameters to be calibrated can be dynamically turned on or off according to the real-time status of the vehicle, taking into account the impact of the real-time status of the vehicle on the online calibration process, so that the calibration process has better robustness.

[0216] Steps S101 to S104 are further described below.

[0217] In one implementation of the embodiment of the present invention, step S101 may be further configured as the following steps:

[0218] The sensor's measurement equation is established based on the actual measurement state of the sensor and the time delay between the sensor and the inertial measurement unit.

[0219] Step S102 can be further configured as the following steps:

[0220] Based on the transformation relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the transformation relationship from the inertial measurement unit coordinate system to the global coordinate system, the observation equation of the sensor is established.

[0221] In one embodiment, the transformation relationship from the sensor coordinate system to the inertial measurement unit coordinate system may include the lever arm value and the rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system. The lever arm (LeverArm) value refers to the vector from the origin of coordinate system A to the origin of coordinate system B when the same carrier in coordinate system A and coordinate system B is in rigid motion (i.e., the relative positions of the two remain unchanged during the motion). The rotation matrix refers to the rotation relationship of the attitude angle between coordinate system A and coordinate system B. When installing the sensor on the vehicle, since the inertial measurement coordinate system cannot completely coincide with the axial direction of the sensor coordinate system of the sensor, the orientation of the inertial measurement unit coordinate system cannot be used as the orientation of the sensor coordinate system to obtain the observation equation. The lever arm value and the rotation matrix are used to realize the conversion of the position, velocity and attitude angle observed by the sensor to the inertial measurement unit coordinate system, and then converted from the inertial measurement unit coordinate system to the global coordinate system, a more accurate observation equation can be obtained.

[0222] In one embodiment, the rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system is obtained according to the following steps:

[0223] The rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system is obtained based on the rotation matrix from the sensor coordinate system to the rear wheel axle offline installation coordinate system and the installation angle error of the sensor modeling in the wheel speed coordinate system.

[0224] In one embodiment, the sensor may include a GPS sensor, a vehicle-mounted camera, and a wheel speed sensor. The actual measurement state of the sensor may include the actual measurement position, actual measurement speed, and actual measurement acceleration of the sensor.

[0225] The following describes the steps for establishing measurement equations and observing observation equations for the GPS sensor, vehicle-mounted camera, and wheel speed sensor, respectively.

[0226] 1.GPS sensor

[0227] The time delay includes a first time delay between the GPS sensor and the inertial measurement unit. The measurement equation of the GPS sensor can be established according to the following steps S1011 to S1013:

[0228] Step S1011: Acquire the measured position of the GPS sensor according to the actual measurement state of the GPS sensor, the first time delay, and the coordinate system offset of the GPS sensor.

[0229] Step S1012: Obtain the measured speed of the GPS sensor according to the actual measurement state of the GPS sensor and the first time delay

[0230] Step S1013: Establishing a measurement equation of the GPS sensor according to the measured position and the measured speed.

[0231] In one embodiment, the measurement equation of the GPS sensor can be obtained according to the following formula (1):

[0232]

[0233] Among them, z g is the measurement equation of the GPS sensor, is the actual measured position of the GPS sensor in global coordinates, is the actual measured speed of the GPS sensor in the global coordinate system, acc gps is the actual measured acceleration of the GPS sensor in the global coordinate system, time bias_g is the first time delay between the GPS sensor and the inertial measurement unit, p offset_gps The coordinate system offset of the GPS sensor.

[0234] The sensor's coordinate system offset may be caused by inconsistent epoch settings in different coordinate systems.

[0235] The observation equation of the GPS sensor can be obtained according to the following steps S1021 to S1024:

[0236] Step S1021: Acquire the observation position of the GPS sensor according to the conversion relationship from the GPS coordinate system of the GPS sensor to the inertial measurement unit coordinate system and the position of the inertial measurement unit in the global coordinate system.

[0237] Step S1022: Obtain the observed speed of the GPS sensor according to the conversion relationship from the GPS coordinate system to the inertial measurement unit coordinate system and the speed of the inertial measurement unit in the global coordinate system.

[0238] Step S1023: Obtain the observed heading angle of the GPS sensor according to the conversion relationship from the GPS coordinate system to the inertial measurement unit coordinate system, the rotation matrix from the GPS coordinate system to the global coordinate system, and the rotation matrix from the inertial measurement unit coordinate system to the global coordinate system.

[0239] Step S1024: Establishing the observation equation of the GPS sensor according to the observed position, observed speed and observed heading angle.

[0240] In one embodiment, the observation equation of the GPS sensor can be obtained according to the following formula (2):

[0241]

[0242] Among them, h g is the observation equation of the GPS sensor, is the observed position of the GPS sensor, is the observed velocity of the GPS sensor, and Λθ is the observed heading angle of the GPS sensor.

[0243] The observed position of the GPS sensor is obtained according to the following formula (3):

[0244]

[0245] in, is the position of the inertial measurement unit in the global coordinate system, is the rotation matrix from the inertial measurement unit to the global coordinate system, is the lever arm value from GPS coordinate system to inertial measurement unit coordinate system, n p is the position observation noise.

[0246] The observed speed of the GPS sensor is obtained according to the following formula (4):

[0247]

[0248] in, is the velocity of the inertial measurement unit in the global coordinate system, n v is the velocity observation noise, |w| × is the antisymmetric matrix of the angular velocity of the inertial measurement unit.

[0249] The observed heading angle of the GPS sensor is obtained according to the following formula (5):

[0250]

[0251] Where Λ=[0 0 1], θ is the attitude angle of the GPS sensor, and LOG is the operation of converting the observed heading angle into the Lie algebra space. is the rotation matrix from the GPS coordinate system to the global coordinate system, is the transpose of the rotation matrix from the GPS coordinate system to the inertial measurement unit coordinate system, is the transpose of the rotation matrix from the inertial measurement unit coordinate system to the global coordinate system, and θ is the attitude angle of the GPS sensor. For GPS sensors, only the heading angle is reliable among the attitude angles, so the Λθ calculation is performed, taking only the third dimension of θ as the GPS sensor's observation angle. The attitude angles include roll, pitch, and yaw, representing the angular relationship between the three axes of the sensor coordinate system and the three axes of the global coordinate system.

[0252] Furthermore, the rotation matrix from the GPS coordinate system to the rear wheel axle offline installation coordinate system, the installation angle error of the GPS sensor model in the wheel speed coordinate system, and the rotation matrix from the wheel speed coordinate system to the inertial measurement unit can be obtained according to the following formula (6):

[0253]

[0254] Among them, w′ is the rear wheel axle offline installation coordinate system, w is the wheel speed coordinate system, Model the installation angle error of the GPS sensor in the wheel speed coordinate system. The rotation matrix from the GPS coordinate system to the rear axle offline installation coordinate system, is the rotation matrix from the wheel speed coordinate system to the inertial measurement unit coordinate system. The wheel speed coordinate system is a virtual coordinate system.

[0255] 2. Car Camera

[0256] The time delay includes a second time delay between the vehicle-mounted camera and the inertial measurement unit, and a measurement equation of the vehicle-mounted camera can be established according to the following steps S1014 to S1016.

[0257] Step S1014: Match the lane lines in the high-precision map with the lane lines captured by the vehicle camera to obtain the actual measurement status of the matching positioning result.

[0258] Step S1015: Obtain the positioning position of the matching positioning result in the global coordinate system according to the actual measurement state, the second time delay and the local map offset of the high-precision map.

[0259] Step S1016: establishing a measurement equation for the vehicle-mounted camera according to the lateral positioning position of the positioning position.

[0260] In one embodiment, the measurement equation of the vehicle-mounted camera is established according to the following formula (7):

[0261]

[0262] Among them, z m is the measurement equation of the vehicle-mounted camera, Λ1=[0 1 0], The rotation matrix of the reference point for matching positioning, To match the positioning result in the global coordinate system, p offset_m is the local map offset of the high-precision map, The second time delay between the onboard camera and the inertial measurement unit, To match the location of the positioning result, To match the speed of positioning results, acc m The acceleration that matches the positioning result.

[0263] In autonomous driving, vehicle positioning is typically achieved by matching environmental images captured by environmental sensors, such as onboard cameras, with high-precision maps. High-precision maps contain lane line location information. By matching the lane line-based results between the onboard camera and the high-precision map, the measurement equation for the onboard camera can be established according to formula (7). Since lane lines are generally parallel to the vehicle, only the lateral position of the matching positioning result can be used.

[0264] The observation equation of the vehicle-mounted camera can be obtained according to the following steps S1025 to S1027:

[0265] Step S1025: Obtain the observation position of the vehicle-mounted camera according to the conversion relationship from the image coordinate system of the vehicle-mounted camera to the coordinate system of the inertial measurement unit and the position of the inertial measurement unit in the global coordinate system.

[0266] Step S1026: Obtain the observation heading angle of the vehicle-mounted camera according to the transformation relationship from the image coordinate system to the inertial measurement unit coordinate system and the rotation matrix from the inertial measurement unit coordinate system to the global coordinate system.

[0267] Step S1027: Establishing the observation equation of the vehicle-mounted camera according to the observation position and the observation heading angle.

[0268] In one embodiment, the observation equation of the vehicle-mounted camera can be obtained according to the following formula (8):

[0269]

[0270] Among them, h m The observation equation of the vehicle-mounted camera, is the observation position of the vehicle-mounted camera, θ2 is the observation heading angle of the vehicle-mounted camera;

[0271] The observation position of the vehicle-mounted camera can be obtained according to the following formula (9):

[0272]

[0273] in, is the position of the inertial measurement unit in the global coordinate system, is the rotation matrix from the inertial measurement unit coordinate system to the global coordinate system, is the lever arm value from the image coordinate system to the inertial measurement unit coordinate system;

[0274] The observation heading angle of the vehicle-mounted camera can be obtained according to the following formula (10):

[0275]

[0276] Where Λ2=[0 0 1], LOG is the operation of converting the observed heading angle into the Lie algebra space, is the rotation matrix from the image coordinate system to the global coordinate system, is the transpose of the rotation matrix from the image coordinate system to the inertial measurement unit coordinate system, is the transpose of the rotation matrix from the inertial measurement unit coordinate system to the global coordinate system.

[0277] Furthermore, the rotation matrix from the image coordinate system to the rear wheel axle offline installation coordinate system, the installation angle error of the vehicle camera model in the wheel speed coordinate system, and the rotation matrix from the wheel speed coordinate system to the inertial measurement unit can be obtained according to the following formula (11):

[0278]

[0279] Among them, w′ is the rear wheel axle offline installation coordinate system, w is the wheel speed coordinate system, Model the installation angle error of the vehicle-mounted camera in the wheel speed coordinate system. The rotation matrix from the image coordinate system to the rear wheel axle offline installation coordinate system, is the rotation matrix from the wheel speed coordinate system to the inertial measurement unit.

[0280] 3. Wheel speed sensor

[0281] The time delay may include a third time delay between the wheel speed sensor and the inertial measurement unit, and the measurement equation of the GPS sensor may be established according to the following steps S1017 to S1019:

[0282] Step S1017: Acquire the rear wheel axle center speed of the wheel speed sensor according to the actual measurement state of the wheel speed sensor, the speed coefficient of the wheel speed sensor, and the third time delay.

[0283] Step S1018: Obtain the forward speed of the rear axle center speed according to the rear axle center speed.

[0284] Step S1019: Establishing a measurement equation of the wheel speed sensor according to the forward speed.

[0285] In one embodiment, the measurement equation of the wheel speed sensor can be obtained according to the following formula (12):

[0286] z w =[speed 0 0] T *scale+acc wheel *time bias_w (12)

[0287] Among them, z w is the measurement equation of the wheel speed sensor, speed is the forward speed of the rear wheel axle center, scale is the speed coefficient of the wheel speed sensor, acc wheel is the acceleration of the rear wheel axle center, time bias_w is the third time delay between the wheel speed sensor and the inertial measurement unit.

[0288] The observation equation of the wheel speed sensor can be established according to the following steps S1028 and S1029:

[0289] Step S1028: Obtain the observed speed of the wheel speed sensor based on the conversion relationship between the wheel speed coordinate system of the wheel speed sensor and the inertial measurement unit coordinate system, the speed of the inertial measurement unit in the global coordinate system, and the rotation matrix from the inertial measurement unit coordinate system to the global coordinate system.

[0290] Step S1029: Establishing the observation equation of the wheel speed sensor according to the observed speed.

[0291] In one embodiment, the observation equation of the wheel speed sensor is obtained according to the following formula (13):

[0292]

[0293] Among them, h w is the observation equation of the wheel speed sensor, Λ=[1 0 0], is the rotation matrix from the wheel speed coordinate system to the inertial measurement unit coordinate system, is the transpose of the rotation matrix from the inertial measurement unit coordinate system to the global coordinate system, is the velocity of the inertial measurement unit in the global coordinate system, is the rotation matrix from the inertial measurement unit coordinate system to the global coordinate system, [w] × is the antisymmetric matrix of the angular velocity of the inertial measurement unit, is the lever arm value from the wheel speed coordinate system to the inertial measurement unit coordinate system, n v is the wheel speed noise of the wheel speed sensor.

[0294] Furthermore, the rotation matrix from the wheel speed sensor to the rear wheel axle offline installation coordinate system and the installation angle error of the sensor model in the wheel speed coordinate system can be used to obtain the rotation matrix from the wheel speed sensor to the inertial measurement unit according to the following formula (14):

[0295]

[0296] Among them, w′ represents the rear wheel axle offline installation coordinate system, w represents the wheel speed coordinate system, is the installation angle error of the wheel speed sensor, The rotation matrix from the rear axle offline installation coordinate system to the inertial measurement unit coordinate system, is the rotation matrix from the wheel speed coordinate system to the inertial measurement unit coordinate system.

[0297] In one embodiment of the present invention, the calibration start condition may include a calibration precondition and a calibration condition of the parameter to be calibrated. Step S103 may further include the following steps S1031 to S1034:

[0298] Step S1031: judging whether the global filter of the Kalman filter has converged according to the real-time status of the inertial measurement unit.

[0299] In this embodiment, step S1031 may further include the following steps S10311 to S10315:

[0300] Step S10311: Determine whether the state value of the inertial measurement unit is within a preset range. If so, determine that the first convergence condition is met.

[0301] In this embodiment, the state value of the inertial measurement unit can be used to determine whether the first convergence condition is met. Thresholds can be set for different state values ​​to determine whether the state value is within a reasonable range based on the threshold. For example, the threshold for the vehicle's longitude and latitude is 180°, meaning that the vehicle's longitude and latitude will not exceed 180°. If all state value averagers are within a preset range, the first convergence condition can be determined to be met.

[0302] Step S10312: Determine whether the time delay of the accelerometer of the inertial measurement unit is converged and stable. If so, determine that the second convergence condition is met.

[0303] In this embodiment, it may be determined whether the time delay of the accelerometer of the inertial measurement unit has converged. If it has converged, it may be determined that the second convergence condition is satisfied.

[0304] Step S10313: Determine whether the heading angle of the inertial measurement unit is consistent with the moving direction of the vehicle. If so, determine that the third convergence condition is met.

[0305] In this embodiment, it can be determined whether the heading angle of the inertial measurement unit is consistent with the moving direction of the vehicle. If they are consistent, it can be determined that the third convergence condition is satisfied.

[0306] Step S10314: Determine whether the posture of the inertial measurement unit is consistent with the posture of the sensor. If so, determine that the fourth convergence condition is met.

[0307] In this embodiment, it is possible to determine whether the position and posture of the inertial measurement unit are consistent with the position and posture of the sensor. If they are consistent, it can be determined that the fourth convergence condition is satisfied. The sensor may include a vehicle-mounted camera and a GPS sensor.

[0308] Step S10315: When the first convergence condition, the second convergence condition, the third convergence condition and the fourth convergence condition are all satisfied, it is determined that the global filter of the extended Kalman filter has converged.

[0309] In this embodiment, calibration can only be performed when the global filter of the Kalman filter has converged. Whether the global filter has converged can be determined based on the first convergence condition, the second convergence condition, the third convergence condition, and the fourth convergence condition. The global filter is used to characterize the positioning state of the inertial measurement unit. Only when the positioning state of the inertial measurement unit is of good quality can the calibration parameters of the sensor to be calibrated be calibrated.

[0310] In one embodiment, the Jacobian matrix of the inertial measurement unit can be obtained by differentiating the observation equation of each sensor to be calibrated with respect to the parameters of the inertial measurement unit, and whether the global filter has converged can be determined based on the Jacobian matrix.

[0311] Step S1032: Obtain, based on the signal quality of the data collected by the GPS sensor, a duration of the signal quality being higher than a preset signal quality threshold.

[0312] In this embodiment, the duration of a good quality GPS signal can be obtained based on the signal quality collected by the GPS sensor. The GPS signal quality can be determined by the signal strength of the GPS signal. If the signal strength is higher than a preset signal quality threshold, it can be considered that the GPS signal is of good quality.

[0313] Step S1033: When the global filter has converged and the duration is longer than the preset duration, it is determined that the calibration prerequisite is met.

[0314] In this embodiment, when the global filter has converged and the duration of the good quality GPS signal is longer than the preset duration, it can be considered that the calibration prerequisite is currently met.

[0315] Step S1034: When the calibration prerequisite is met, determine whether the calibration conditions of the parameters to be calibrated are met based on the real-time status of the vehicle.

[0316] In this embodiment, when the calibration prerequisite is met, it can be determined whether the calibration conditions of the parameters to be calibrated are met based on the real-time status of the vehicle. Specifically:

[0317] The parameters to be calibrated include the time delay between the sensor and the inertial measurement unit, the lever arm value between the sensor coordinate system and the inertial measurement unit coordinate system, and the wheel speed coefficient of the wheel speed sensor:

[0318] The calibration conditions for the time delay are: the vehicle’s speed is greater than the speed threshold;

[0319] The calibration conditions of the lever arm value are: the vehicle speed is greater than the speed threshold;

[0320] The calibration condition of the wheel speed coefficient is: the vehicle speed is greater than the speed threshold.

[0321] In one embodiment, the speed threshold may be 3 m / s.

[0322] In one embodiment, the time delay may include a first time delay between the GPS sensor and the inertial measurement unit, a second time delay between the vehicle-mounted camera and the inertial measurement unit, and a third time delay between the wheel speed sensor and the inertial measurement unit.

[0323] The convergence condition of the first time delay is that the covariance of the first time delay is less than a preset ratio of the initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold.

[0324] The convergence conditions of the second time delay and the third time delay are: the first time delay has met the convergence conditions, the covariance of the second time delay is less than a preset ratio of the initial covariance value, and the jitter of the second time delay within the preset time is less than a jitter threshold.

[0325] The convergence condition of the third time delay is: the first time delay has met the convergence condition, the covariance of the third time delay is less than a preset ratio of the initial covariance value, and the jitter of the third time delay within the preset time is less than a jitter threshold.

[0326] In one embodiment, the convergence condition of the lever arm value is: the covariance of the lever arm value is less than a preset ratio of the initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold.

[0327] In one embodiment, the convergence condition of the wheel speed coefficient is: the covariance of the wheel speed coefficient is less than a preset ratio of the initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold; wherein the preset ratio is less than one and greater than zero.

[0328] In one implementation, the preset ratio may be 10%.

[0329] In one embodiment, the parameters to be calibrated may include the installation angle error of the sensor modeled in the wheel speed coordinate system, the sensors including an inertial measurement unit, a GPS sensor and a vehicle-mounted camera; the installation angle error of the inertial measurement unit includes a pitch angle error and a heading angle error, the installation angle error of the GPS sensor includes a heading angle error, and the installation angle error of the vehicle-mounted camera includes a heading angle error.

[0330] The calibration conditions for the pitch angle error of the inertial measurement unit are: the vehicle speed is greater than the speed threshold and the vehicle is moving in a straight line at a constant speed.

[0331] The calibration conditions for the heading angle error of the inertial measurement unit are: the vehicle speed is greater than the speed threshold, the vehicle acceleration is greater than the acceleration threshold, and the vehicle is traveling in a straight line.

[0332] The calibration conditions for the heading angle error of the GPS sensor are: the speed of the vehicle is greater than the speed threshold, and the calibration of the heading angle error of the inertial measurement unit has reached the convergence condition.

[0333] In one embodiment, the parameters to be calibrated include a local map offset of a high-precision map.

[0334] The calibration conditions for the local map offset are: the vehicle speed is greater than the speed threshold, and the on-board camera obtains a high-quality matching positioning result, where the high-quality matching positioning result is judged based on the quality bit of the matching positioning result data.

[0335] In one embodiment, the speed threshold is 3 m / s and the acceleration threshold is 0.2 m / s.

[0336] In one embodiment, the convergence condition of the pitch angle error of the inertial measurement unit is: the covariance of the pitch angle error is less than a preset ratio of the initial covariance value, and the jitter of the pitch angle error within a preset time is less than a jitter threshold.

[0337] The convergence conditions of the heading angle error of the inertial measurement unit are: the covariance of the heading angle error is less than a preset ratio of the initial covariance value, and the jitter of the heading angle error within a preset time is less than a jitter threshold.

[0338] The convergence conditions of the heading angle error of the GPS sensor are: the covariance of the heading angle error is less than the preset ratio of the initial covariance value, the jitter of the heading angle error within the preset time is less than the jitter threshold, and the heading angle error of the inertial measurement unit has met the convergence conditions.

[0339] The convergence conditions for the local map offset are: the covariance of the local map offset is less than a preset ratio of the initial covariance value, and the jitter of the local map offset within a preset time is less than a jitter threshold. The preset ratio is less than one and greater than zero. Those skilled in the art can configure the jitter threshold and preset ratio based on actual application needs.

[0340] In one implementation, the preset ratio may be 10%.

[0341] In one implementation of the present invention, the extended Kalman filter model is obtained according to the following formulas (15) to (19):

[0342] y=zh(x) (15)

[0343]

[0344]

[0345]

[0346]

[0347] Where z is the measurement equation, h(x) is the observation equation, x is the parameter to be calibrated, y is the difference between the measurement equation and the observation equation, and H is the Jacobian matrix of the current state. is the Jacobian matrix of the previous state, K is the Kalman gain, is the previous state covariance matrix, R is the observation noise, is the observed calibration quantity, and P is the covariance matrix of the current state.

[0348] That is, the measurement equation and observation equation of the above-mentioned sensor can be substituted into the extended Kalman filter model, the Jacobian matrix can be calculated according to the observation equation, the Kalman gain can be updated according to the Jacobian matrix and the covariance matrix, the observation calibration value can be updated according to the Kalman gain and the difference between the measurement and the observation, and the parameters to be calibrated can be updated according to the observation calibration value. Through continuous iterative updates, the observation is gradually brought close to the measurement, so as to realize the calibration of the parameters to be calibrated. The application of the covariance matrix in the iterative update process can realize the estimation of multi-dimensional variables to meet the needs of calibrating multiple parameters to be calibrated at the same time. At the same time, the process of iterative update using the extended Kalman filter model will not affect the operating state of the vehicle, and is therefore suitable for the needs of real-time calibration on the vehicle side.

[0349] In one embodiment, when calibrating the parameters to be calibrated of the GPS sensor, such as time bias_g 、 p offset_gps Using Lie algebraic solutions, the Jacobian matrix can be obtained from the GPS sensor's observation equation. This avoids the gimbal deadlock problem caused by Euler angle solutions when the rotation angle exceeds 90°.

[0350] When calibrating the parameters to be calibrated of the vehicle-mounted camera, such as, p offset_m 、 Etc., the Lie algebra solution method can be applied to obtain the Jacobian matrix according to the observation equation of the vehicle-mounted camera.

[0351] When calibrating the parameters to be calibrated of the wheel speed sensor, the Lie algebra solution method or the Euler angle solution method can be applied to obtain the Jacobian matrix according to the observation equation of the wheel speed sensor.

[0352] When using Lie algebra to solve the Jacobian matrix, you can use scale and time bias_w 、 Waiting for calibration parameters to be calibrated.

[0353] in, It can be expressed by the following formula (20):

[0354]

[0355] Among them, R δyaw is the heading angle error, R δpitch is the pitch angle error, R δrollis the roll angle error.

[0356] When using the Euler angle solution to obtain the Jacobian matrix, you can use the scale and time bias_w 、R δyaw 、R δpitch 、R δroll 、 Waiting for calibration parameters to be calibrated.

[0357] In one embodiment, please refer to the attached Figure 2 , Figure 2 FIG. 1 is a flow chart showing the main steps of a sensor parameter calibration method based on autonomous driving according to an embodiment of the present invention. Figure 2 As shown, the sensor parameter calibration method based on autonomous driving may include the following steps S201 to S205:

[0358] Step S201: Determine the measurement equation of the parameter to be calibrated.

[0359] In this embodiment, step S201 is similar to the aforementioned step S101 and will not be described again for simplicity.

[0360] Step S202: Determine whether the calibration start condition is met; if so, jump to step S203; if not, jump to step S205.

[0361] In this embodiment, step S202 is similar to the aforementioned step S103 and will not be described again for simplicity.

[0362] Step S203: Extended Kalman filter.

[0363] In this embodiment, step S203 is similar to the aforementioned step S104 and will not be described again for simplicity.

[0364] Step S204: Obtain calibration results.

[0365] In this embodiment, after the parameters to be calibrated are calibrated by using an extended Kalman filter, a calibration result can be obtained.

[0366] Step S205: Exit.

[0367] In this embodiment, when the calibration conditions are not met, the calibration process can be exited first.

[0368] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0369] Those skilled in the art will appreciate that all or part of the processes in the method for implementing the above-mentioned embodiment of the present invention may also be accomplished by instructing the relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, it may implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal, and software distribution medium capable of carrying the computer program code. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0370] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the sensor parameter calibration method based on autonomous driving according to the above method embodiment. The processor can be configured to execute the program in the storage device, which includes but is not limited to a program for executing the sensor parameter calibration method based on autonomous driving according to the above method embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device can be a control device device formed by various electronic devices.

[0371] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the sensor parameter calibration method based on autonomous driving according to the above-mentioned method embodiment. The program can be loaded and run by the processor to implement the above-mentioned sensor parameter calibration method based on autonomous driving. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-transitory computer-readable storage medium.

[0372] Furthermore, the present invention also provides a vehicle. In one embodiment of the vehicle according to the present invention, the vehicle includes the control device and the sensor in the above control device embodiment.

[0373] Furthermore, it should be understood that since the configuration of each module is merely for the purpose of illustrating the functional units of the apparatus of the present invention, the physical devices corresponding to these modules may be the processor itself, or a portion of the software in the processor, a portion of the hardware, or a combination of software and hardware. Therefore, the number of modules in the figure is merely illustrative.

[0374] Those skilled in the art will appreciate that the various modules in the device can be adaptively split or merged. Such splitting or merging of specific modules does not cause the technical solution to deviate from the principles of the present invention. Therefore, the technical solutions after splitting or merging will fall within the scope of protection of the present invention.

[0375] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A sensor parameter calibration method based on autonomous driving, characterized in that: The method is applied to a vehicle, and comprises: Establishing a measurement equation of the sensor according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit; Establishing an observation equation for the sensor based on a transformation relationship from a sensor coordinate system to an inertial measurement unit coordinate system and a transformation relationship from the inertial measurement unit coordinate system to a global coordinate system, wherein the measurement equation and the observation equation include parameters to be calibrated; the transformation relationship from the sensor coordinate system to the inertial measurement unit coordinate system includes a lever arm value and a rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system; Determining whether a global filter of the extended Kalman filter has converged based on the real-time state of the inertial measurement unit; Obtaining, based on the signal quality of data collected by the GPS sensor, a duration of the signal quality being higher than a preset signal quality threshold; When the global filter has converged and the duration is longer than a preset duration, it is determined that the calibration prerequisite is met; When the calibration prerequisite is met, judging whether the calibration condition of the parameter to be calibrated is met according to the real-time state of the vehicle; When the calibration start condition is met, a preset extended Kalman filter model is applied to iteratively update the parameter to be calibrated according to the measurement equation and the observation equation until a convergence condition of the parameter to be calibrated is reached, thereby achieving calibration of the parameter to be calibrated; The calibration start condition includes the calibration precondition and the calibration condition of the parameter to be calibrated; The parameters to be calibrated include the time delay between the sensor and the inertial measurement unit; the arm value between the sensor coordinate system and the inertial measurement unit coordinate system; the wheel speed coefficient of the wheel speed sensor; the installation angle error of the sensor modeled in the wheel speed coordinate system, the sensors include the inertial measurement unit, the GPS sensor and the on-board camera; the installation angle error of the inertial measurement unit includes the pitch angle error and the heading angle error, the installation angle error of the GPS sensor includes the heading angle error, the installation angle error of the on-board camera includes the heading angle error, and at least one of the local map offset of the high-precision map.

2. The sensor parameter calibration method based on autonomous driving according to claim 1, characterized in that: The sensor comprises a GPS sensor, the time delay comprises a first time delay between the GPS sensor and the inertial measurement unit; The establishing of a sensor measurement equation according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit includes: Acquire a measured position of the GPS sensor according to an actual measurement state of the GPS sensor, the first time delay, and a coordinate system offset of the GPS sensor; acquiring a speed measured by the GPS sensor according to an actual measurement state of the GPS sensor and the first time delay; A measurement equation of the GPS sensor is established according to the measured position and the measured speed.

3. The sensor parameter calibration method based on autonomous driving according to claim 2, characterized in that: The establishing of the observation equation of the sensor based on the conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the conversion relationship from the inertial measurement unit coordinate system to the global coordinate system includes: Acquire an observation position of the GPS sensor according to a conversion relationship from a GPS coordinate system of the GPS sensor to a coordinate system of the inertial measurement unit and a position of the inertial measurement unit in a global coordinate system; Obtaining the observed speed of the GPS sensor according to the conversion relationship from the GPS coordinate system to the inertial measurement unit coordinate system and the speed of the inertial measurement unit in the global coordinate system; Obtaining an observed heading angle of the GPS sensor according to a conversion relationship from the GPS coordinate system to the inertial measurement unit coordinate system, a rotation matrix from the GPS coordinate system to the global coordinate system, and a rotation matrix from the inertial measurement unit coordinate system to the global coordinate system; An observation equation of the GPS sensor is established according to the observed position, the observed speed, and the observed heading angle.

4. The sensor parameter calibration method based on autonomous driving according to claim 1, characterized in that: The sensor comprises an onboard camera, and the time delay comprises a second time delay between the onboard camera and the inertial measurement unit; The establishing of a sensor measurement equation according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit includes: Matching the lane lines in the high-precision map with the lane lines captured by the vehicle-mounted camera to obtain the actual measurement status of the matching positioning result; Obtaining a positioning position of the matching positioning result in a global coordinate system according to the actual measurement state, the second time delay, and the local map offset of the high-precision map; A measurement equation of the vehicle-mounted camera is established according to the lateral positioning position of the positioning position.

5. The sensor parameter calibration method based on autonomous driving according to claim 4, characterized in that: The establishing of the observation equation of the sensor based on the conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the conversion relationship from the inertial measurement unit coordinate system to the global coordinate system includes: Obtaining an observation position of the vehicle-mounted camera according to a conversion relationship from an image coordinate system of the vehicle-mounted camera to a coordinate system of the inertial measurement unit and a position of the inertial measurement unit in a global coordinate system; Obtaining an observation heading angle of the vehicle-mounted camera according to a conversion relationship from the image coordinate system to the inertial measurement unit coordinate system and a rotation matrix from the inertial measurement unit coordinate system to the global coordinate system; An observation equation of the vehicle-mounted camera is established according to the observation position and the observation heading angle.

6. The sensor parameter calibration method based on autonomous driving according to claim 1, characterized in that: The sensor includes a wheel speed sensor, the time delay includes a third time delay between the wheel speed sensor and the inertial measurement unit, The establishing of a sensor measurement equation according to an actual measurement state of the sensor and a time delay between the sensor and an inertial measurement unit includes: acquiring a rear wheel axle center speed of the wheel speed sensor according to an actual measurement state of the wheel speed sensor, a speed coefficient of the wheel speed sensor, and the third time delay; According to the rear wheel axle center speed, obtaining the forward speed of the rear wheel axle center speed; A measurement equation of the wheel speed sensor is established according to the forward speed.

7. The sensor parameter calibration method based on autonomous driving according to claim 6, characterized in that: The establishing of the observation equation of the sensor based on the conversion relationship from the sensor coordinate system to the inertial measurement unit coordinate system and the conversion relationship from the inertial measurement unit coordinate system to the global coordinate system includes: Obtaining an observed speed of the wheel speed sensor based on a conversion relationship from a wheel speed coordinate system of the wheel speed sensor to a coordinate system of the inertial measurement unit, a speed of the inertial measurement unit in a global coordinate system, and a rotation matrix from the coordinate system of the inertial measurement unit to the global coordinate system; An observation equation of the wheel speed sensor is established according to the observed speed.

8. The sensor parameter calibration method based on autonomous driving according to claim 1, characterized in that: Obtain the rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system by following these steps: The rotation matrix from the sensor coordinate system to the inertial measurement unit coordinate system is obtained according to the rotation matrix from the sensor coordinate system to the rear wheel axle offline installation coordinate system and the installation angle error of the sensor model in the wheel speed coordinate system.

9. The sensor parameter calibration method based on autonomous driving according to claim 8, characterized in that: The determining, based on the real-time state of the inertial measurement unit, whether the global filter of the extended Kalman filter has converged includes: Determining whether the state value of the inertial measurement unit is within a preset range, and if so, determining that a first convergence condition is satisfied; determining whether a time delay of an accelerometer of the inertial measurement unit is converged and stable, and if so, determining that a second convergence condition is satisfied; determining whether the heading angle of the inertial measurement unit is consistent with the moving direction of the vehicle, and if so, determining that a third convergence condition is satisfied; determining whether the position and posture of the inertial measurement unit are consistent with the position and posture of the sensor, and if so, determining that a fourth convergence condition is satisfied; When the first convergence condition, the second convergence condition, the third convergence condition and the fourth convergence condition are all satisfied, it is determined that the global filter of the extended Kalman filter has converged.

10. The sensor parameter calibration method based on autonomous driving according to claim 8, characterized in that: The calibration condition of the time delay is: the speed of the vehicle is greater than a speed threshold; and / or, The calibration condition of the lever arm value is: the speed of the vehicle is greater than a speed threshold; and / or, The calibration condition of the wheel speed coefficient is: the speed of the vehicle is greater than a speed threshold.

11. The sensor parameter calibration method based on autonomous driving according to claim 10, characterized in that: The time delay includes a first time delay between the GPS sensor and the inertial measurement unit, a second time delay between the vehicle-mounted camera and the inertial measurement unit, and a third time delay between the wheel speed sensor and the inertial measurement unit; The convergence condition of the first time delay is that the covariance of the first time delay is less than a preset ratio of an initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold; The convergence condition of the second time delay and the third time delay is: the first time delay has satisfied the convergence condition, the covariance of the second time delay is less than a preset ratio of an initial covariance value, and the jitter of the second time delay within a preset time is less than a jitter threshold; The convergence condition of the third time delay is: the first time delay has satisfied the convergence condition, the covariance of the third time delay is less than a preset ratio of the initial covariance value, and the jitter of the third time delay within a preset time is less than a jitter threshold; and / or, The convergence condition of the lever arm value is: the covariance of the lever arm value is less than a preset ratio of the initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold; and / or, The convergence condition of the wheel speed coefficient is: the covariance of the wheel speed coefficient is less than a preset ratio of the initial covariance value, and the jitter of the first time delay within a preset time is less than a jitter threshold; Wherein, the preset ratio is less than one and greater than zero.

12. The sensor parameter calibration method based on autonomous driving according to claim 8, characterized in that: The calibration condition of the pitch angle error of the inertial measurement unit is: the speed of the vehicle is greater than the speed threshold and the vehicle is traveling in a straight line at a constant speed; The calibration conditions of the heading angle error of the inertial measurement unit are: the speed of the vehicle is greater than a speed threshold, the acceleration of the vehicle is greater than an acceleration threshold, and the vehicle is traveling in a straight line; The calibration condition of the heading angle error of the GPS sensor is: the speed of the vehicle is greater than a speed threshold, and the calibration of the heading angle error of the inertial measurement unit has reached a convergence condition; and / or, The calibration condition of the local map offset is: the speed of the vehicle is greater than a speed threshold, and the on-board camera obtains a high-quality matching positioning result, wherein the high-quality matching positioning result is judged based on the quality bit of the data of the matching positioning result.

13. The sensor parameter calibration method based on autonomous driving according to claim 12, characterized in that: The convergence condition of the pitch angle error of the inertial measurement unit is: the covariance of the pitch angle error is less than a preset ratio of the initial covariance value, and the jitter of the pitch angle error within a preset time is less than a jitter threshold; The convergence condition of the heading angle error of the inertial measurement unit is: the covariance of the heading angle error is less than a preset ratio of the initial covariance value, and the jitter of the heading angle error within a preset time is less than a jitter threshold; The convergence condition of the heading angle error of the GPS sensor is: the covariance of the heading angle error is less than a preset ratio of the initial covariance value, the jitter of the heading angle error within a preset time is less than a jitter threshold, and the heading angle error of the inertial measurement unit has met the convergence condition; and / or, The convergence condition of the local map offset is: the covariance of the local map offset is less than a preset ratio of the initial covariance value, and the jitter of the local map offset within a preset time is less than a jitter threshold; Wherein, the preset ratio is less than one and greater than zero.

14. The sensor parameter calibration method based on autonomous driving according to claim 1, characterized in that: The extended Kalman filter model is obtained by following the steps below: Wherein, z is the measurement equation, h(x) is the observation equation, x is the parameter to be calibrated, y is the difference between the measurement equation and the observation equation, H is the Jacobian matrix of the current state, is the Jacobian matrix of the previous state, K is the Kalman gain, is the previous state covariance matrix, R is the observation noise, is the observed calibration quantity, and P is the covariance matrix of the current state.

15. The sensor parameter calibration method based on autonomous driving according to claim 14, characterized in that: The method further comprises: When calibrating the parameters to be calibrated of the GPS sensor, applying a Lie algebra solution method to obtain the Jacobian matrix according to the observation equation of the GPS sensor; When calibrating the parameters to be calibrated of the vehicle-mounted camera, applying a Lie algebra solution method to obtain the Jacobian matrix according to the observation equation of the vehicle-mounted camera; When calibrating the parameters to be calibrated of the wheel speed sensor, a Lie algebra solution method or an Euler angle solution method is applied to obtain the Jacobian matrix according to the observation equation of the wheel speed sensor.

16. A control device comprising a processor and a storage device, wherein the storage device is adapted to store a plurality of program codes, wherein: The program code is suitable for being loaded and run by the processor to execute the sensor parameter calibration method based on autonomous driving according to any one of claims 1 to 15.

17. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the sensor parameter calibration method based on autonomous driving according to any one of claims 1 to 15.

18. A vehicle, characterized in that: The vehicle includes the control device and the sensor according to claim 16.

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