Vehicle navigation data calibration method and device, vehicle equipment and storage medium

Abstract

The invention provides a navigation data calibration method and device of a vehicle, vehicle equipment and a readable storage medium. The method comprises the following steps: acquiring measurement data of an inertial measurement unit of the vehicle; performing mechanical arrangement processing on measurement data of the inertial measurement unit to obtain position information, speed information and attitude information of the vehicle; processing measurement data of the inertial measurement unit based on a preset state equation to obtain initial calibration parameters of the vehicle; and updating the position information, speed information and attitude information of the vehicle based on the initial calibration parameters of the vehicle, and storing the updated position information, speed information and attitude information of the vehicle and the initial calibration parameters of the vehicle. According to the technical scheme, the calibration precision, the environmental adaptability and the real-time updating capability of the vehicle navigation system are remarkably improved, and safer and more efficient travel experience is provided for users.

Description

Technical Field

[0001] The present application relates to the technical field of calibration of navigation data for vehicles, and particularly to a method, device, vehicle equipment and storage medium for calibrating navigation data of vehicles. Background Art

[0002] With the rapid development of Internet technology, the navigation systems deployed in vehicles have become an indispensable part of modern transportation. These navigation systems can not only provide real-time driving route selection for drivers, but also dynamically adjust according to the set driving route, thus improving travel efficiency and safety. However, the calibration process of current vehicle navigation systems still faces some challenges before leaving the factory. Currently, the calibration of vehicle navigation systems mainly relies on the Global Navigation Satellite System (GNSS). GNSS provides position information by receiving satellite signals, enabling the navigation system to accurately determine the position and driving direction of the vehicle. However, there are several significant problems in the existing calibration process:

[0003] Dependence on GNSS positioning effectiveness: The existing calibration process highly depends on the effectiveness of GNSS signals. In an environment with signal occlusion such as high-rise buildings or tunnels in the city, GNSS signals may be interfered with or completely lost, resulting in the navigation system being unable to perform accurate calibration. Inaccurate calibration parameters: Due to the fluctuations and interference of GNSS signals, the positioning data obtained during the calibration process may not be accurate enough. This inaccuracy directly affects the performance of the navigation system, may lead to incorrect selection of driving routes, and thus affect driving safety and efficiency. Insufficient environmental adaptability: The existing calibration methods have poor adaptability in different environments and cannot effectively cope with the impact of complex urban traffic, bad weather and other factors on GNSS signals. In summary, there are significant deficiencies in the accuracy and reliability of the existing calibration process of vehicle navigation systems, and it is urgent to develop new calibration methods to improve the overall performance and user experience of the navigation system. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a method, device, vehicle equipment and readable storage medium for calibrating navigation data of vehicles to solve the technical problems of low efficiency and low accuracy caused by manually drawing map data in the prior art.

[0005] In the first aspect of the embodiments of the present application, a method for calibrating navigation data of a vehicle is provided, including: obtaining measurement data of an inertial measurement unit of the vehicle; performing mechanical arrangement processing on the measurement data of the inertial measurement unit to obtain the position information, speed information, and attitude information of the vehicle; processing the measurement data of the inertial measurement unit based on a preset state equation to obtain initial calibration parameters of the vehicle; updating the position information, speed information, and attitude information of the vehicle based on the initial calibration parameters of the vehicle, and storing the updated position information, speed information, and attitude information of the vehicle and the initial calibration parameters of the vehicle.

[0006] In the second aspect of the embodiments of the present application, a device for calibrating navigation data of a vehicle is provided, including an acquisition module for obtaining measurement data of an inertial measurement unit of the vehicle; a mechanical arrangement module for performing mechanical arrangement processing on the measurement data of the inertial measurement unit to obtain the position information, speed information, and attitude information of the vehicle; a processing module for processing the measurement data of the inertial measurement unit based on a preset state equation to obtain initial calibration parameters of the vehicle; and an update module for updating the position information, speed information, and attitude information of the vehicle based on the initial calibration parameters of the vehicle, and storing the updated position information, speed information, and attitude information of the vehicle and the initial calibration parameters of the vehicle.

[0007] In the third aspect of the embodiments of the present application, a vehicle device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The vehicle device further includes: an inertial measurement unit, a magnetic sensor, a satellite positioning receiving unit, and a mileage sensor. When the processor executes the computer program to control the inertial measurement unit, the magnetic sensor, the satellite positioning receiving unit, and the mileage sensor, the steps of the method provided in the first aspect as described above are implemented.

[0008] In the fourth aspect of the embodiments of the present application, a readable storage medium is provided. The readable storage medium stores a computer program, and when the computer program is executed by a processor, it controls the inertial measurement unit, the magnetic sensor, the satellite positioning receiving unit, and the mileage sensor to implement the steps of the method provided in the first aspect as described above.

[0009] The beneficial effects of the embodiments of the present application compared with the prior art at least include: By processing the data of the inertial measurement unit, the embodiments of the present application can obtain more accurate vehicle position information, speed information, and attitude information, thereby improving the accuracy of the calibration parameters and reducing the errors caused by unstable GNSS signals. This method no longer completely relies on GNSS signals and can still perform effective calibration in environments where the signal is weak or fails (such as urban canyons, tunnels, etc.). This adaptability enables the navigation system to maintain high performance in various complex environments. The real-time update of vehicle information based on the initial calibration parameters can ensure that the navigation system continuously obtains the latest status information. This dynamic update ability enables the navigation system to better adapt to different driving conditions during driving, improving driving safety and accuracy. By presetting the state equation to process the data of the inertial measurement unit, the calibration process is simplified, the dependence on external signals is reduced, the time and cost required for calibration are lowered, and the production efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings in the following descriptions are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 is the vehicle framework diagram of a method for calibrating navigation data of a vehicle according to an embodiment of the present application

[0012] Figure 2 is the flowchart of a method for calibrating navigation data of a vehicle according to an embodiment of the present application;

[0013] Figure 3 is the calibration algorithm framework diagram of a method for calibrating navigation data of a vehicle according to an embodiment of the present application;

[0014] Figure 4 is the block diagram of a device for calibrating navigation data of a vehicle according to an embodiment of the present application;

[0015] Figure 5 is the structural schematic diagram of a vehicle device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0017] Figure 1 It is a vehicle framework diagram of a method for calibrating navigation data of a vehicle according to an embodiment of the present application.

[0018] As Figure 1 shown, a magnetic sensor, a satellite positioning receiving unit, an inertial sensor, an auxiliary positioning sensor, a wheel speed speed measurement and ranging unit, an axle sensor, a positioning solution processor, and a wireless communication unit can be deployed in the vehicle. In this embodiment, multiple inertial sensors can form an inertial measurement unit. The auxiliary positioning sensor, the wheel speed speed measurement and ranging unit, and the axle sensor can form an odometer sensor. In addition, the vehicle can be equipped with vehicle equipment, which includes the above-mentioned positioning solution processor for receiving measurement data from sensors such as the magnetic sensor, the satellite positioning receiving unit, the inertial sensor, the auxiliary positioning sensor, the wheel speed speed measurement and ranging unit, and the axle sensor. In addition, the measurement data in the satellite unit receiving can be received through the wireless communication unit and differential processing can be performed to obtain the approximate position of the vehicle.

[0019] For example, the positioning solution processor can obtain the measurement data of the inertial measurement unit of the vehicle, perform mechanical arrangement processing on the measurement data of the inertial measurement unit to obtain the position information, speed information, and attitude information of the vehicle, process the measurement data of the inertial measurement unit based on a preset state equation to obtain the initial calibration parameters of the vehicle, update the position information, speed information, and attitude information of the vehicle based on the initial calibration parameters of the vehicle, and store the updated position information, speed information, and attitude information of the vehicle and the initial calibration parameters of the vehicle, which significantly improves the calibration accuracy, environmental adaptability, and real-time update ability of the vehicle navigation system and provides a safer and more efficient travel experience for users.

[0020] For another example, the positioning solution processor can obtain the measurement data of the magnetic sensor of the vehicle, the measurement data of the satellite positioning receiving unit, and the measurement data of the odometer sensor; process the measurement data of the magnetic sensor of the vehicle, the measurement data of the satellite positioning receiving unit, and the measurement data of the odometer sensor based on a preset measurement equation to obtain the navigation observables of the vehicle, so as to calculate the data for subsequent calibration processes.

[0021] Figure 2It is a flowchart of a method for calibrating navigation data of a vehicle according to an embodiment of the present application. The method provided by the embodiment of the present application can be executed by any vehicle device with computer processing capabilities.

[0022] As Figure 2 shown, the method for calibrating navigation data of a vehicle includes steps S210 to S240.

[0023] In step S210, measurement data of the inertial measurement unit of the vehicle is obtained.

[0024] In step S220, the measurement data of the inertial measurement unit is processed by mechanical arrangement to obtain the position information, speed information, and attitude information of the vehicle.

[0025] In step S230, the measurement data of the inertial measurement unit is processed based on a preset state equation to obtain the initial calibration parameters of the vehicle.

[0026] In step S240, the position information, speed information, and attitude information of the vehicle are updated based on the initial calibration parameters of the vehicle, and the updated position information, speed information, and attitude information of the vehicle and the initial calibration parameters of the vehicle are stored.

[0027] This method can process the data of the inertial measurement unit to obtain more accurate position information, speed information, and attitude information of the vehicle, thereby improving the accuracy of the calibration parameters and reducing the errors caused by unstable GNSS signals. This method no longer completely relies on GNSS signals and can still perform effective calibration in environments where the signal is weak or fails (such as urban canyons, tunnels, etc.). This adaptability enables the navigation system to maintain high performance in various complex environments. The real-time update of the vehicle information based on the initial calibration parameters can ensure that the navigation system continuously obtains the latest state information. This dynamic update ability enables the navigation system to better adapt to different driving conditions during driving, improving driving safety and accuracy. Processing the data of the inertial measurement unit through a preset state equation simplifies the calibration process, reduces the dependence on external signals, reduces the time and cost required for calibration, and improves production efficiency.

[0028] In some embodiments, an Inertial Measurement Unit (IMU) can be installed inside a rigid vehicle. By installing the IMU in a rigid structure, the stability and accuracy of the navigation system can be effectively improved. A rigid vehicle refers to a type of vehicle whose body structure is relatively fixed and not easily deformed during driving. Such a vehicle can ensure that the relative positions and attitude relationships between various sensors remain unchanged during driving. Common rigid vehicles include sedans. Due to the rigidity of their overall structure, sedans can effectively maintain the installation position and orientation of the IMU during driving. Trucks, the cargo compartments of trucks are usually of rigid structure, suitable for installing IMUs to obtain accurate motion data. The carriages of single-section trains, train carriages also have high rigidity, suitable for improving navigation accuracy in complex track environments. The IMU can be installed at the center position of the vehicle or near the center of gravity to ensure that the sensor can accurately capture the dynamic changes of the vehicle. The choice of installation position is related to the vehicle's motion. The installation position of the IMU should be as close as possible to the center of gravity of the vehicle to reduce additional errors caused by vehicle motion. Interference between the IMU and other sensors (such as GNSS receivers, vehicle speed sensors, etc.) should be avoided to ensure the independence and accuracy of its data. The acceleration and angular velocity information provided by the IMU can be fused with the data of other vehicle sensors (such as GNSS, vehicle speed sensors, etc.). Through precise sensor layout and stable installation, more efficient data processing can be achieved, thereby improving positioning accuracy and navigation reliability. Installing the inertial measurement unit inside a rigid vehicle can not only ensure the stability of the relative relationship between sensors, but also effectively improve the performance of the vehicle navigation system, providing a solid foundation for achieving high-precision positioning and navigation.

[0029] In some embodiments, the measurement data of the inertial measurement unit is processed by mechanical arrangement to obtain the position information, speed information, and attitude information of the vehicle. For example, the IMU can regularly collect acceleration and angular velocity data. The data collection frequency is usually high to ensure that the subtle changes in the vehicle's dynamic changes can be captured. Before performing mechanical arrangement processing, the raw data needs to be preprocessed first, such as noise reduction, using filtering algorithms (such as Kalman filtering or low-pass filtering) to remove sensor noise and ensure the accuracy of the data. Calibration, calibrate the sensor to eliminate systematic errors, such as bias and calibration errors.

[0030] Use the measurement data of the IMU to estimate the attitude, speed, and position of the vehicle. Specifically, establish an inertial navigation mechanical arrangement equation. Include the attitude transformation matrix from the vehicle body coordinate system b to the navigation coordinate system n Recursive equation, vehicle speed Recursive equation and vehicle position p nThe equation is stored as the value to be modified for navigation in the navigation output area. The mechanical arrangement process can calculate the position information, speed information, and attitude information of the vehicle through the following formula, specifically as follows:

[0031] After receiving the measurement data from the inertial measurement unit, the preset equations in the positioning solution processor can be called, such as the attitude propagation equation, the speed propagation equation, and the position propagation equation. Among them, the attitude propagation equation is In the formula, the n-system is the current navigation coordinate system, the b-system is the vehicle coordinate system, the i-system is the inertial coordinate system, and m-1 and m are the changes at adjacent times. Then, a certain variable above is the rotation matrix when the vehicle rotates from the b-system to the n-system at time m, is the rotation of the vehicle from m-1 to m in the n-system. In the above formula, except the rotation caused by the movement of the device, other rotations are due to the rotation of the earth and the rotation of the navigation system. After the earth's latitude is given, it can be calculated through the physical quantities related to the movement of the earth.

[0032] Among them, the rotation of the device can be sensed by the IMU and has the following equation, That is, the IMU obtains the three-axis angular velocity increment Δθ m-1 and Δθ m , M RV is the change equation for converting the angular velocity increment into a rotation matrix. is the rotation of the navigation system caused by the movement of the device, is the earth rotation angular velocity at time m, and T is the time interval from m-1 to m.

[0033] The speed propagation equation can be expressed as In the formula is the speed of the vehicle in the navigation coordinate system at time m, is the speed change caused by the specific force, In the formula is the specific force obtained by the IMU, is the attitude rotation matrix at the current moment, is the speed change caused by the harmful speed, where represents the earth rotation angular velocity, represents the navigation system rotation angular velocity, v n (t) is the speed in the current navigation coordinate system, and g n (t) represents the gravitational acceleration.

[0034] The position propagation equation can be expressed as where p m is the vehicle position at time m, is the intermediate time matrix linearly extrapolated from M pv(m-1) and is,

[0035] By processing the measurement data of the inertial measurement unit with and without signals as described above, the position information, speed information, and attitude information of the vehicle can be effectively extracted. This process not only improves the accuracy and reliability of the data but also provides important support for enhancing the performance of the vehicle navigation system.

[0036] In some embodiments, based on a preset state equation, the measurement data of the inertial measurement unit is processed to obtain the initial calibration parameters of the vehicle. For example, the preset state equation can be written into the positioning and solution processor of the vehicle device and can be called from the positioning and solution processor during vehicle calibration, and the measurement data of the inertial measurement unit is processed to obtain the initial calibration parameters of the vehicle. In this embodiment, the preset state equation can be the state recurrence equation in the Kalman filter model, and the measurement data of the inertial measurement unit is processed through this state equation to determine whether the navigation system is stable.

[0037] Specifically, the state equation is x k =(I + F k / k-1 )x k-1 + w

[0038]

[0039] In the above formula, x k represents the state quantity at time k (i.e., the above initial calibration parameters), I represents the identity matrix, F k / k-1 represents the state transition matrix, w is the system noise vector, and each state quantity x k is shown in the following table:

[0040]

[0041] The state transition matrix F k / k-1 is shown in the following table

[0042]

[0043] Among them, the first element in each row is equal to the sum of all elements in that row multiplied by the first element in the corresponding column. For example, the first row can be expressed as

[0044] The matrices in the table are respectively, M ap = M 1 + M 2 , M vp = (v n ×)(2M 1 + M 2 ) + M 3 List all the matrices uniformly as

[0045]

[0046] Among them, They are respectively the projections in the n - system of the rotation from the inertial system i - system to the navigation system n - system, the projections in the n - system of the rotation from the i - system to the Earth - centered Earth - fixed system e - system, the projections in the n - system of the rotation from the e - system to the n - system. × represents taking the skew - symmetric matrix of a vector, L represents the latitude where the carrier is located, R Nh , R Mh They are respectively R Mh = R M + h, R Nh = R N + h, R M , R N They are respectively the radius of curvature and the radius of curvature, h is the local elevation, v N , v E They are respectively the north - bound and east - bound velocities, g 0 is the local gravity, β, β 1 , β 2 , β 3 They are respectively the local vertical deflection coefficients, is the matrix about the roll angle γ, pitch angle θ, and heading angle ψ, c ψ , s ψ They are respectively the cosine and sine of the heading angle, c θ , s θ They are respectively the cosine and sine of the pitch angle, c γ , s γ They are respectively the cosine and sine of the roll angle.

[0047] The noise vector w is shown in the following table:

[0048]

[0049] Among them, q gyr , q acc They are respectively the gyro noise and accelerometer noise.

[0050] Based on the above - mentioned embodiments, after calculating the one - step recursive state x k / k-1 at the current k - th moment, it is necessary to calculate the covariance matrix P k / k-1 of x k / k-1 , which is used to characterize the uncertainty of the current state estimation. There is the following equation,

[0051]

[0052] Among them, P k-1 is the covariance matrix at the previous moment, Q is a diagonal matrix with the system noise on the main diagonal. T is the matrix transpose.

[0053] Based on the method in the above embodiments, by updating the position information, speed information, and attitude information based on the initial calibration parameters of the vehicle, not only the real-time performance and accuracy of the navigation system are improved, but also reliable data support is provided for subsequent navigation decisions. For example, the state quantities obtained based on the above state equation can adjust and optimize the position information, speed information, and attitude information, thereby improving the real-time performance and accuracy of the navigation system.

[0054] In some embodiments, the above method further includes: obtaining measurement data of the magnetic sensor of the vehicle, measurement data of the satellite positioning receiving unit, and measurement data of the mileage sensor; processing the measurement data of the magnetic sensor of the vehicle, measurement data of the satellite positioning receiving unit, and measurement data of the mileage sensor based on a preset measurement equation to obtain the navigation observable of the vehicle. For example, the magnetic sensor, satellite positioning receiving unit, and mileage sensor can be respectively installed in the above rigid vehicle. In a vehicle navigation system, the magnetic sensor, satellite positioning receiving unit (e.g., global satellite navigation receiver, i.e., GNSS receiver), and mileage sensor are key components. The magnetic sensor is used to measure the direction and intensity of the earth's magnetic field to help determine the heading information of the vehicle. The magnetic sensor can be installed near the center line of the vehicle, as far as possible from metal components (such as engines, vehicle frames, etc.) and electromagnetic interference sources (such as batteries, generators, etc.) to reduce magnetic field interference. Installed at a certain height from the ground to avoid ground magnetic field interference. For example, bolts or adhesives can be used for fixation to ensure the sensor is stable and not easily affected by vibrations. The satellite positioning receiving unit (GNSS receiver) is used to receive signals from satellites to provide high-precision position information. The GNSS antenna can be installed on the top of the vehicle or near the rearview mirror to ensure the antenna is unobstructed to improve signal reception quality. Avoid installing near metal components inside the vehicle to prevent signal attenuation. For example, a special bracket or adhesive can be used to fix the antenna at the selected position to ensure its stability. The mileage sensor is used to measure the distance traveled by the vehicle, usually implemented by a wheel speed sensor or an encoder. The mileage sensor is usually installed near the wheel and directly connected to the wheel or the transmission system to obtain accurate wheel speed information. For example, the sensor can be fixed to the wheel or the transmission shaft by bolts or clamps to ensure it is not easily loosened during vehicle operation. The installation of the magnetic sensor, satellite positioning receiving unit, and mileage sensor in the rigid vehicle is crucial and directly affects the performance of the navigation system. Reasonably selecting the installation position, ensuring a stable fixation method, and performing necessary calibration can effectively improve the accuracy and reliability of vehicle navigation. During the installation process, the mutual influence between sensors and the influence of external environmental factors on sensor performance should be fully considered.

[0055] The above preset measurement equation can be written into the positioning solution processor of the vehicle device. During vehicle calibration, the measurement equation in the positioning solution processor can be called to process the measurement data of the magnetic sensor of the vehicle, measurement data of the satellite positioning receiving unit, and measurement data of the mileage sensor to obtain the navigation observable of the vehicle. In this embodiment, the above state quantity is estimated based on the measurement data of the magnetic sensor of the vehicle, measurement data of the satellite positioning receiving unit, and measurement data of the mileage sensor through this measurement equation. The measurement equation is: z k = H k x k+v k and let the measurement vector be

[0056] measurement vector z k (i.e., the navigation measurement of the above vehicle) is shown in the following table:

[0057]

[0058]

[0059]

[0060] measurement matrix H k is shown in the following table:

[0061] measurement matrix H k The elements in it can be obtained through the following formula

[0062]

[0063] measurement noise vector v k is shown in the following table:

[0064]

[0065] where are GNSS velocity, position and heading noise, odometer velocity and heading noise respectively.

[0066] Based on the measurement data of the vehicle's magnetic sensor, the measurement data of the satellite positioning receiving unit, and the measurement data of the mileage sensor, the state quantity of the vehicle can be effectively estimated through the above measurement equation, so as to obtain accurate navigation measurement. This process not only improves the positioning accuracy, but also provides important support for the safe driving and intelligent navigation of the vehicle.

[0067] In some embodiments, the above method may further include: determining the state covariance matrix, measurement matrix, and diagonal matrix of measurement noise of the vehicle; updating the initial calibration parameters of the vehicle based on the initial calibration parameters of the vehicle, the navigation measurement of the vehicle, the state covariance matrix of the vehicle, the measurement matrix, and the diagonal matrix of measurement noise to obtain the target calibration parameters of the vehicle. For example, the target calibration parameters of the vehicle can be obtained through the following formula Specifically:

[0068]

[0069] where x k is the initial calibration parameter, z k is the navigation measurement of the vehicle, H k is the measurement matrix, K kis the innovation gain matrix. Additionally, k / k-1 indicates the use of intermediate state variables obtained through recursion. In this embodiment, P k / k-1 is the state covariance matrix of the vehicle, is the measurement matrix, and R k is the diagonal matrix of the measurement noise (i.e., the measurement covariance matrix). Specifically, the state covariance matrix, measurement matrix, and diagonal matrix of the measurement noise of the vehicle can be calculated and obtained according to the corresponding table above.

[0070] By determining the state covariance matrix, measurement matrix, and diagonal matrix of the measurement noise, and combining the initial calibration parameters and navigation observables for updating, the calibration parameters of the vehicle can be effectively adjusted, which not only improves the accuracy of state estimation but also provides reliable parameter support for the real-time navigation of the vehicle.

[0071] In some embodiments, the method further includes: updating the position information, speed information, and attitude information of the vehicle based on the target calibration parameters of the vehicle to obtain the target position information, target speed information, and target attitude information of the vehicle; storing the target position information, target speed information, and target attitude information of the vehicle and the target calibration parameters of the vehicle, which can further improve the accuracy and reliability of the navigation system. At the same time, properly storing these updated information and calibration parameters helps with subsequent real-time navigation and system optimization. For example, based on the target calibration parameters of the vehicle adjust and optimize the position information, speed information, and attitude information of the vehicle, and the results obtained in this way are more accurate and reliable.

[0072] During the vehicle calibration process, it is possible to determine when the calibration ends based on the real-time calculated data, thereby improving the calibration efficiency. The method can also calculate the state covariance matrix of the vehicle at the next moment based on the identity matrix of the vehicle, the state covariance matrix of the vehicle, the measurement protocol variance matrix, and the diagonal matrix of the measurement noise; compare the state covariance matrix of the vehicle at the next moment with a preset threshold, and when the state covariance matrix of the vehicle at the next moment is less than the preset threshold, determine the current moment as the calibration end moment, which can effectively determine the end moment of vehicle calibration. This method not only improves the calibration efficiency but also ensures the high precision and reliability of the vehicle navigation system after calibration. For example, the state covariance matrix P k of the vehicle at the next moment k can be calculated by the following formula:

[0073] P k =(I - K k H k )P k / k-1

[0074] where P kis the state covariance matrix of the vehicle at the next moment, I is the identity matrix of the vehicle, and K k is the innovation gain matrix, and H k is the measurement matrix, and P k / k-1 is the covariance matrix of the vehicle, specifically the covariance matrix of x k / k-1 . In this embodiment, is the measurement matrix, and R k is the diagonal matrix of the measurement noise (i.e., the measurement covariance matrix).

[0075] The state covariance matrix P k of the next moment k is calculated through the above formula, and it is determined whether P k is less than a preset threshold. If it is less, it is determined that the current moment k is the end moment of this calibration process, thereby improving the calibration efficiency.

[0076] In some implementations, after obtaining the measurement data of the vehicle's inertial measurement unit, the measurement data of the magnetic sensor, the measurement data of the satellite positioning receiving unit, and the measurement data of the odometer sensor, all sensor signals are connected to the above positioning and solution processor, and all data is time-calibrated using the GNSS time. Specifically, when all signals reach the positioning and solution processor, the positioning and solution processor records the crystal oscillator count value at this moment in an array when the sensor signal arrives. After the second pulse signal (hereinafter referred to as PPS) in the GNSS board arrives, the crystal oscillator count value at the PPS arrival moment is obtained. Subsequently, the parsed PPS moment time is bound to the crystal oscillator count value, and then all crystal oscillator count values are converted into time values to complete the time synchronization of all sensors.

[0077] Based on the above embodiments, the method further includes determining the time information corresponding to the measurement data of the satellite positioning receiving unit (GNSS) based on the crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit; determining the time information corresponding to the measurement data of the vehicle's inertial measurement unit (IMU) based on the time information and crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit, the crystal oscillator frequency, and the crystal oscillator value corresponding to the measurement data of the vehicle's inertial measurement unit; determining the time information corresponding to the measurement data of the vehicle's magnetic sensor (MAG) based on the time information and crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit, the crystal oscillator frequency, and the crystal oscillator value corresponding to the measurement data of the vehicle's magnetic sensor; determining the time information corresponding to the measurement data of the mileage sensor (DMI) based on the time information and crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit, the crystal oscillator frequency, and the crystal oscillator value corresponding to the measurement data of the mileage sensor. For example, for hardware synchronization of the time of each sensor, the crystal oscillator of the positioning solution processor is used as the unified continuous time count value. After the PPS signal of the GNSS arrives, record the crystal oscillator count value Cnt at this time PPS , and start parsing the moment t of the GNSS at this time k_GNSS , after binding the two, record the crystal oscillator counts Cnt when the IMU, DMI, and MAG messages arrive in this way imu , Cnt dmi , and Cnt mag , then through the crystal oscillator frequency f osm , the message moments t of each sensor can be obtained k_sensor = t k_GNSS + (Cnt sensor - Cnt PPS ) / f osm , where sensor is one of the IMU, DMI, or MAG sensors. At this time, the time calibration of each sensor is completed.

[0078] Through the measurement data of the satellite positioning receiving unit and its corresponding crystal oscillator value, the time information of other sensors (such as the inertial measurement unit, magnetic sensor, and mileage sensor) can be effectively determined. This method ensures the time synchronization of the data of each sensor and provides a basis for subsequent data calibration and precise navigation.

[0079] In some embodiments, before obtaining the measurement data of the inertial measurement unit of the vehicle, the method further includes: obtaining the calibrated driving path of the vehicle after the vehicle starts, where the calibrated driving path includes a straight driving path and a steering driving path; controlling the vehicle to drive according to the straight driving path and the steering driving path according to a preset number of driving times. For example, during the calibration process of the vehicle, in order to ensure that the motion data of the inertial sensor can better estimate the state quantity. The calibrated driving path can specifically make more than three turns (or U-turns) to ensure that there are large angle changes in the vehicle, which is beneficial to the estimation of the gyro zero bias by the inertial sensor. Subsequently, straight driving can be performed, such as driving straight for 400 meters, to ensure that the zero bias parameters of the inertial sensor have time to converge. After three maneuvers, the calibration system parameters are basically stable, and the calibration parameters can be confirmed. In this way, by setting a reasonable calibrated driving path, controlling the number of vehicle driving times, and collecting and processing the IMU measurement data in real time, the calibration of the inertial measurement unit of the vehicle can be effectively completed. Ensure the accuracy and stability of the zero bias parameters of the IMU, and provide reliable data support for the navigation system of the vehicle. In this embodiment, the calibrated driving path can be written into the positioning and solution processor as a configuration parameter in advance and can be directly called during the calibration process.

[0080] The above-recorded algorithm formula can be written into the positioning and solution processor program, and the program can be tested and verified before the calibration starts to ensure that the program can read and write the memory in the positioning and solution processor, and ensure that the configuration parameters can be written, configured, and read during the navigation initialization process after the calibration ends. Specifically, in the way of software engineering, the algorithm is written into the processor according to the requirements, and functions such as configuration input and output, file reading and writing, and algorithm calculation are implemented. The data flow and control flow can meet the requirements of the calibration algorithm. At the same time, the sensor data can be saved offline and then the post-processing program can be used for two-way fusion filtering to more accurately estimate the calibration parameters.

[0081] Reference Figure 3, during the calibration process, corresponding formulas are read from the positioning and calculation processor. For example, by reading the formula for mechanical arrangement, the position information, speed information, and attitude information of the vehicle (such as the initial position, speed, and attitude of INS) can be calculated. The state equation can also be read to calculate the initial calibration parameters (such as the navigation state and calibration parameter error quantity). In addition, the measurement equation can be read to calculate various navigation observables (such as speed observables, heading observables, virtual observables, etc.). Finally, the formula for estimating the initial calibration parameters based on the navigation observables can be read to obtain the target calibration parameters (such as the output of calibration parameters). And the initial position, speed, and attitude of INS are adjusted and optimized according to the target calibration parameters to obtain the integrated navigation output. Additionally, the formula for parsing the measurement data of the satellite positioning receiving unit can be read, processed, and GNSS position, speed, and time are output, that is, the GNSS positioning output.

[0082] Figure 4 is the block diagram of the navigation data calibration device of the vehicle according to an embodiment of the present application, as Figure 4 shown, the navigation data calibration device 400 of the vehicle includes an acquisition module 410, a mechanical arrangement module 420, a processing module 430, and an update module 440.

[0083] Specifically, the acquisition module 410 is configured to acquire the measurement data of the inertial measurement unit of the vehicle.

[0084] The mechanical arrangement module 420 is configured to perform mechanical arrangement processing on the measurement data of the inertial measurement unit to obtain the position information, speed information, and attitude information of the vehicle.

[0085] The processing module 430 is configured to process the measurement data of the inertial measurement unit based on a preset state equation to obtain the initial calibration parameters of the vehicle.

[0086] The update module 440 is configured to update the position information, speed information, and attitude information of the vehicle based on the initial calibration parameters of the vehicle, and store the updated position information, speed information, and attitude information of the vehicle and the initial calibration parameters of the vehicle.

[0087] The navigation data calibration device 400 of the vehicle can process the data of the inertial measurement unit to obtain more accurate vehicle position information, speed information, and attitude information, thereby improving the accuracy of the calibration parameters and reducing the errors caused by unstable GNSS signals. This method no longer relies entirely on GNSS signals and can still perform effective calibration in environments where the signal is weak or fails (such as urban canyons, tunnels, etc.). This adaptability enables the navigation system to maintain high performance in various complex environments. The real-time update of vehicle information based on the initial calibration parameters can ensure that the navigation system continuously obtains the latest status information. This dynamic update ability enables the navigation system to better adapt to different driving conditions during driving, improving driving safety and accuracy. By processing the data of the inertial measurement unit through a preset state equation, the calibration process is simplified, the dependence on external signals is reduced, the time and cost required for calibration are lowered, and production efficiency is improved.

[0088] In some embodiments, the navigation data calibration device 400 is further configured to: acquire the measurement data of the vehicle's magnetic sensor, the measurement data of the satellite positioning receiving unit, and the measurement data of the mileage sensor; process the measurement data of the vehicle's magnetic sensor, the measurement data of the satellite positioning receiving unit, and the measurement data of the mileage sensor based on a preset measurement equation to obtain the navigation observables of the vehicle.

[0089] In some embodiments, the navigation data calibration device 400 is further configured to: determine the state covariance matrix, measurement matrix, and diagonal matrix of measurement noise of the vehicle; update the initial calibration parameters of the vehicle based on the initial calibration parameters of the vehicle, the navigation observables of the vehicle, the state covariance matrix of the vehicle, the measurement protocol variance matrix, and the diagonal matrix of measurement noise to obtain the target calibration parameters of the vehicle.

[0090] In some embodiments, the navigation data calibration device 400 is further configured to: update the position information, speed information, and attitude information of the vehicle based on the target calibration parameters of the vehicle to obtain the target position information, target speed information, and target attitude information of the vehicle; store the target position information, target speed information, and target attitude information of the vehicle and the target calibration parameters of the vehicle.

[0091] In some embodiments, the navigation data calibration device 400 is further configured to: calculate the state covariance matrix of the vehicle at the next moment based on the identity matrix of the vehicle, the state covariance matrix of the vehicle, the measurement matrix, and the diagonal matrix of measurement noise; compare the state covariance matrix of the vehicle at the next moment with a preset threshold, and when the state covariance matrix of the vehicle at the next moment is less than the preset threshold, determine that the current moment is the calibration end moment.

[0092] In some embodiments, the navigation data calibration device 400 is further configured to: determine the time information corresponding to the measurement data of the satellite positioning receiving unit based on the crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit; determine the time information corresponding to the measurement data of the vehicle's inertial measurement unit based on the time information and crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit, the crystal oscillator frequency, and the crystal oscillator value corresponding to the measurement data of the vehicle's inertial measurement unit; determine the time information corresponding to the measurement data of the vehicle's magnetic sensor based on the time information and crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit, the crystal oscillator frequency, and the crystal oscillator value corresponding to the measurement data of the vehicle's magnetic sensor; determine the time information corresponding to the measurement data of the mileage sensor based on the time information and crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit, the crystal oscillator frequency, and the crystal oscillator value corresponding to the measurement data of the mileage sensor.

[0093] In some embodiments, before obtaining the measurement data of the vehicle's inertial measurement unit, the navigation data calibration device 400 is further configured to: obtain the calibrated driving path of the vehicle after the vehicle starts, where the calibrated driving path includes a straight driving path and a turning driving path; control the vehicle to drive according to the straight driving path and the turning driving path according to a preset number of driving times.

[0094] Figure 5 is a schematic structural diagram of a vehicle device according to an embodiment of the present application, as Figure 5 shown, the vehicle device 500 in this embodiment includes: a processor 510, a memory 520, and a computer program 530 stored in the memory 520 and executable on the processor 510. In addition, the vehicle device 500 further includes: an inertial measurement unit, a magnetic sensor, a satellite positioning receiving unit, and a mileage sensor. The processor 510 executes the computer program 530 to control the inertial measurement unit, the magnetic sensor, the satellite positioning receiving unit, and the mileage sensor to implement the steps in each of the above method embodiments at all times. Alternatively, when the processor 510 executes the computer program 530, it implements the functions of each module in each of the above device embodiments. In this embodiment, the processor 510 may be the positioning and solution processor in the above method.

[0095] The vehicle device 500 may be a vehicle device installed in a vehicle or a background server. The vehicle device 500 may include, but is not limited to, the processor 510 and the memory 520. Those skilled in the art can understand that Figure 5 is merely an example of the vehicle device 500, and does not constitute a limitation on the vehicle device 500. It may include more or fewer components than shown in the figure, or different components.

[0096] The processor 510 can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0097] The memory 520 can be an internal storage unit of the vehicle device 500. For example, the hard disk or memory of the vehicle device 500. The memory 520 can also be an external storage device of the vehicle device 500. For example, the plug-in hard disk equipped on the vehicle device 500, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. The memory 520 can also include both the internal storage unit and the external storage device of the vehicle device 500. The memory 520 is used to store computer programs and other programs and data required by the vehicle device.

[0098] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0099] When the integrated module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium (e.g., a computer-readable storage medium). Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of this application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can control the inertial measurement unit, magnetic sensor, satellite positioning receiving unit, and mileage sensor to implement the steps of the above various method embodiments. The computer program can include computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0100] The above embodiments are only used to illustrate the technical solutions of this application, rather than limiting them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for calibrating navigation data of a vehicle, characterized in that: The method comprises: Acquiring measurement data of an inertial measurement unit of the vehicle; Performing mechanical arrangement processing on the measurement data of the inertial measurement unit to obtain the position information, speed information and attitude information of the vehicle; Processing the measurement data of the inertial measurement unit based on a preset state equation to obtain initial calibration parameters of the vehicle; The position information, speed information and posture information of the vehicle are updated based on the initial calibration parameters of the vehicle, and the updated position information, speed information and posture information of the vehicle and the initial calibration parameters of the vehicle are stored.

2. The method according to claim 1, characterized in that: The method further comprises: Acquire measurement data of a magnetic sensor, measurement data of a satellite positioning receiving unit, and measurement data of a mileage sensor of the vehicle; The measurement data of the magnetic sensor of the vehicle, the measurement data of the satellite positioning receiving unit, and the measurement data of the mileage sensor are processed based on a preset measurement equation to obtain the navigation observation value of the vehicle.

3. The method according to claim 2, characterized in that The method further comprises: Determining a state covariance matrix, a measurement matrix, and a diagonal matrix of measurement noise of the vehicle; The initial calibration parameters of the vehicle are updated based on the initial calibration parameters of the vehicle, the navigation observations of the vehicle, the state covariance matrix of the vehicle, the measurement protocol variance matrix, and the diagonal matrix of the measurement noise to obtain the target calibration parameters of the vehicle.

4. The method according to claim 3, characterized in that The method further comprises: Based on the target calibration parameters of the vehicle, the position information, speed information and attitude information of the vehicle are updated to obtain the target position information, target speed information and target attitude information of the vehicle; The target position information, target speed information and target posture information of the vehicle and the target calibration parameters of the vehicle are stored.

5. The method according to claim 4, characterized in that The method further comprises: Calculating a state covariance matrix of the vehicle at a next moment based on the identity matrix of the vehicle, the state covariance matrix of the vehicle, a measurement matrix, and a diagonal matrix of measurement noise; Compare the state covariance matrix of the vehicle at the next moment with a preset threshold, and when the state covariance matrix of the vehicle at the next moment is less than the preset threshold, determine the current moment as the calibration end moment.

6. The method according to claim 2, characterized in that The method further comprises: Determining time information corresponding to the measurement data of the satellite positioning receiving unit based on a crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit; Determine the time information corresponding to the measurement data of the inertial measurement unit of the vehicle based on the time information and crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit and the crystal oscillator frequency and the crystal oscillator value corresponding to the measurement data of the inertial measurement unit of the vehicle; Determine the time information corresponding to the measurement data of the magnetic sensor of the vehicle based on the time information and crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit and the crystal oscillator frequency and the crystal oscillator value corresponding to the measurement data of the magnetic sensor of the vehicle; Based on the time information and crystal oscillator value corresponding to the measurement data of the satellite positioning receiving unit and the crystal oscillator frequency and the crystal oscillator value corresponding to the measurement data of the mileage sensor, the time information corresponding to the measurement data of the mileage sensor is determined.

7. The method according to claim 1, characterized in that Before acquiring the measurement data of the inertial measurement unit of the vehicle, the method further includes: After the vehicle is started, a calibrated driving path of the vehicle is obtained, wherein the calibrated driving path includes a straight driving path and a turning driving path; According to the straight-line driving path and the turning driving path, the vehicle is controlled to travel according to a preset number of travel times.

8. A navigation data calibration device for a vehicle, characterized in that: The device comprises: An acquisition module, used to acquire measurement data of an inertial measurement unit of the vehicle; A mechanical arrangement module, used for performing mechanical arrangement processing on the measurement data of the inertial measurement unit to obtain the position information, speed information and attitude information of the vehicle; A processing module, used for processing the measurement data of the inertial measurement unit based on a preset state equation to obtain initial calibration parameters of the vehicle; An updating module is used to update the position information, speed information and posture information of the vehicle based on the initial calibration parameters of the vehicle, and store the updated position information, speed information and posture information of the vehicle and the initial calibration parameters of the vehicle.

9. A vehicle device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The vehicle equipment also includes: an inertial measurement unit, a magnetic sensor, a satellite positioning receiving unit, and a mileage sensor. When the processor executes the computer program to control the inertial measurement unit, the magnetic sensor, the satellite positioning receiving unit, and the mileage sensor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it controls an inertial measurement unit, a magnetic sensor, a satellite positioning receiving unit, and a mileage sensor to implement the steps of the method according to any one of claims 1 to 7.

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