Inertial navigation method and device based on positioning optimization, computer device and medium

Abstract

The embodiment of the application discloses a kind of inertial navigation method, device, computer equipment and medium based on positioning optimization, method includes the each parameter in the real-time acquisition GNSS data is configured in the preset vehicle position parameter library;The each parameter in the real-time acquisition vehicle motion data is configured in the preset vehicle motion parameter library;If the GPS signal parameter in vehicle position parameter library occurs interruption, then activate the preset navigation parameter optimization model;The inertial navigation script corresponding to GNSS data and vehicle motion data is generated and is configured in navigation parameter optimization model;Obtain latitude and longitude parameter and satellite signal parameter when GPS signal parameter occurs interruption and mark as corresponding reference point;Based on reference point, gyroscope reading and vehicle speed parameter, the real-time azimuth angle and vehicle offset amount of the vehicle to be navigated in driving process are calculated to obtain navigation correction parameter.The above method is realized in the case where GPS signal loss, vehicle still keeps accurate positioning, ensure navigation continuity and reliability.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and in particular to an inertial navigation method, device, computer equipment, and medium based on positioning optimization. Background Technology

[0002] In the current technological environment, vehicle navigation systems face the challenge of maintaining high accuracy and reliability in various complex environments. GNSS (Global Navigation Satellite System) systems like GPS are the primary technologies providing positioning, navigation, and time synchronization services, and vehicle navigation systems heavily rely on signals provided by INS (Inertial Navigation System) systems like GPS. However, in environments such as tunnels, urban canyons, and forests, GNSS signals may be lost or become unstable, leading to navigation system failure. In tunnel environments, GPS signals are susceptible to multipath effects, with signals reflecting multiple times, causing phase changes and affecting positioning accuracy. In urban canyons and forests, obstacles such as buildings and trees may block GPS signals, resulting in weak or lost signal reception. While INS can provide navigation services in environments where GPS is unavailable, it accumulates errors over time, requiring periodic calibration or integration with other navigation systems. In summary, the main challenges faced by vehicle navigation systems in complex environments include GNSS signal dependence, multipath effects, building and obstacle obstruction, error accumulation, and the challenges of data fusion techniques. These problems need to be addressed by developing and optimizing new navigation technologies and algorithms to improve the performance and reliability of vehicle navigation systems in complex environments. Summary of the Invention

[0003] This application provides an inertial navigation method, apparatus, computer device, and medium based on positioning optimization, aiming to solve the problem of poor positioning and navigation reliability of vehicle navigation systems in complex environments in existing technologies.

[0004] In a first aspect, embodiments of this application provide an inertial navigation method based on positioning optimization, comprising: configuring various parameters from real-time acquired GNSS data into a preset vehicle position parameter library, wherein the GNSS data includes GPS signal parameters, latitude and longitude parameters, and satellite signal parameters; configuring various parameters from real-time acquired vehicle motion data into a preset vehicle motion parameter library, wherein the vehicle motion data includes vehicle speed parameters and gyroscope readings; if the GPS signal parameters in the vehicle position parameter library are interrupted, activating a preset navigation parameter optimization model based on the vehicle position parameter library and the vehicle motion parameter library; and generating a navigation parameter optimization model that integrates the GNSS data and vehicle motion data. The corresponding inertial navigation script is configured in the navigation parameter optimization model; the latitude and longitude parameters and satellite signal parameters when the GPS signal parameters are interrupted are obtained and marked as the corresponding reference points; based on the reference points, gyroscope readings, and vehicle speed parameters, the real-time azimuth angle and vehicle offset of the vehicle to be navigated during the driving process are calculated to obtain navigation correction parameters; the latitude and longitude parameters corresponding to the reference points, satellite signal parameters, navigation correction parameters, and the latitude and longitude parameters before the update are combined to obtain new GNSS data; a new inertial navigation script is generated based on the new GNSS data and vehicle motion data and input into the navigation parameter optimization model to obtain the corresponding navigation prediction information.

[0005] Secondly, embodiments of this application also provide an inertial navigation device based on positioning optimization, comprising: a first parameter configuration unit, configured to configure various parameters in real-time acquired GNSS data into a preset vehicle position parameter library; a second parameter configuration unit, configured to configure various parameters in real-time acquired vehicle motion data into a preset vehicle motion parameter library; an optimization model activation unit, configured to activate a preset navigation parameter optimization model based on the vehicle position parameter library and the vehicle motion parameter library if the GPS signal parameters in the vehicle position parameter library are interrupted; and a script generation unit, configured to generate an inertial navigation script corresponding to the GNSS data and vehicle motion data and configure it in the navigation parameter optimization library. The model includes: a first marking unit, used to acquire latitude and longitude parameters and satellite signal parameters when GPS signal parameters are interrupted and mark them as corresponding reference points; a calculation unit, used to calculate the real-time azimuth angle and vehicle offset of the vehicle to be navigated during the driving process based on the reference points, gyroscope readings, and vehicle speed parameters, to obtain navigation correction parameters; a data acquisition unit, used to combine the latitude and longitude parameters corresponding to the reference points, satellite signal parameters, navigation correction parameters, and the latitude and longitude parameters before the update to obtain new GNSS data; and a prediction information acquisition unit, used to generate a new inertial navigation script based on the new GNSS data and vehicle motion data and input it into the navigation parameter optimization model to obtain corresponding navigation prediction information.

[0006] Thirdly, embodiments of this application also provide a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0007] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, can implement the above-described method.

[0008] This application provides an inertial navigation method, apparatus, computer device, and medium based on positioning optimization. The method includes configuring various parameters from real-time acquired GNSS data into a preset vehicle position parameter library. GNSS data includes GPS signal parameters, latitude and longitude parameters, and satellite signal parameters. It also includes configuring various parameters from real-time acquired vehicle motion data into a preset vehicle motion parameter library. Vehicle motion data includes vehicle speed parameters and gyroscope readings. If the GPS signal parameters in the vehicle position parameter library are interrupted, a preset navigation parameter optimization model is activated based on the vehicle position parameter library and the vehicle motion parameter library. An inertial navigation script corresponding to the GNSS data and vehicle motion data is generated and configured in the navigation parameter optimization model. The latitude and longitude parameters and satellite signal parameters at the time of GPS signal interruption are acquired and marked as corresponding reference points. Based on the reference points, gyroscope readings, and vehicle speed parameters, the real-time azimuth angle and vehicle offset of the vehicle under navigation during its journey are calculated to obtain navigation correction parameters. New GNSS data is obtained by combining the latitude and longitude parameters corresponding to the reference points, satellite signal parameters, navigation correction parameters, and the latitude and longitude parameters before the update. Finally, a new inertial navigation script is generated based on the new GNSS data and vehicle motion data and input into the navigation parameter optimization model to obtain corresponding navigation prediction information. The aforementioned technical methods enable vehicles to maintain accurate positioning even when GPS signals are lost, ensuring the continuity and reliability of navigation and providing drivers with a seamless navigation experience. By combining GNSS and IMU data and employing a fusion algorithm, positioning accuracy in complex environments is improved. When satellite signals are available, centimeter-level positioning can be provided; even without satellite signals, high positioning accuracy is maintained through IMU data and the fusion algorithm. Using information such as vehicle speed and temperature to calibrate IMU errors effectively reduces error accumulation and improves positioning accuracy. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic diagram illustrating an application scenario of the positioning-optimized inertial navigation method provided in this application embodiment;

[0011] Figure 2 A flowchart illustrating the positioning-optimized inertial navigation method provided in an embodiment of this application;

[0012] Figure 3 A schematic diagram of a sub-process of the positioning-optimized inertial navigation method provided in an embodiment of this application;

[0013] Figure 4 A schematic block diagram of an inertial navigation device based on positioning optimization provided in an embodiment of this application;

[0014] Figure 5 A schematic block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0017] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0018] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0019] This application provides an inertial navigation method, apparatus, computer device, and medium based on positioning optimization.

[0020] The execution entity of the positioning-optimized inertial navigation method can be the positioning-optimized inertial navigation device provided in the embodiments of this application, or a computer device integrating the positioning-optimized inertial navigation device. The positioning-optimized inertial navigation device can be implemented in hardware or software, and the computer device can be a terminal or a server. The terminal can be a smartphone, tablet computer, handheld computer, or laptop computer, etc.

[0021] The aforementioned positioning-optimized inertial navigation method is applied to Figure 5 Among the 500 computer devices.

[0022] Figure 1 This is a flowchart illustrating an inertial navigation method based on positioning optimization provided in an embodiment of this application. The method includes the following steps S110-S180.

[0023] S110. Configure the various parameters in the real-time acquired GNSS data into the preset vehicle position parameter library.

[0024] S120. Configure the various parameters in the real-time acquired vehicle motion data into the preset vehicle motion parameter library.

[0025] S130. If the GPS signal parameters in the vehicle position parameter library are interrupted, the preset navigation parameter optimization model is activated based on the vehicle position parameter library and the vehicle motion parameter library.

[0026] S140. Generate inertial navigation scripts corresponding to GNSS data and vehicle motion data and configure them in the navigation parameter optimization model.

[0027] S150: Obtain the latitude and longitude parameters and satellite signal parameters when the GPS signal parameters are interrupted, and mark them as the corresponding reference points.

[0028] S160. Based on the reference point, gyroscope readings, and vehicle speed parameters, calculate the real-time azimuth angle and vehicle offset of the vehicle to be navigated during its journey to obtain navigation correction parameters.

[0029] S170. Combine the latitude and longitude parameters corresponding to the reference point, satellite signal parameters, navigation correction parameters, and the latitude and longitude parameters before the update to obtain new GNSS data.

[0030] S180. Generate a new inertial navigation script based on the new GNSS data and vehicle motion data, and input it into the navigation parameter optimization model to obtain the corresponding navigation prediction information.

[0031] Specifically, the above method is a vehicle navigation system based on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. Specifically, steps S110 and S120 describe the data collection phase, where real-time GNSS data and vehicle motion data are acquired and configured into corresponding databases. When GPS signals are lost, the system activates a preset navigation parameter optimization model in S130 to compensate for the impact of signal loss. S140 and S150 describe script generation and configuration, where the generated inertial navigation script will be used to simulate the vehicle's trajectory during GPS signal loss. In S160, the system uses reference points (defined by GPS signal parameters and latitude / longitude parameters), gyroscope readings, and vehicle speed parameters to calculate the vehicle's real-time azimuth and offset, thereby obtaining parameters for navigation correction. Subsequently, in S170, these navigation correction parameters are combined with the old latitude / longitude parameters and satellite signal parameters to generate corrected GNSS data. Finally, in S180, a new inertial navigation script is generated using the corrected GNSS data and vehicle motion data, and input into the navigation parameter optimization model to produce navigation prediction information. This allows vehicle navigation to continue even in the event of GPS signal loss, ensuring navigation continuity and accuracy. The advantage of this method is that it can maintain navigation functionality using inertial navigation system (INS) data even in the event of insufficient or interrupted GPS signals, thereby improving the robustness and reliability of the vehicle navigation system. This is particularly useful in scenarios where GPS signals are obstructed or weak, such as urban canyons, areas with tall buildings, and underground parking lots.

[0032] In practical implementation, GPS signals are easily obstructed and interfered with in environments such as urban canyons, areas with high-rise buildings, and underground parking lots, leading to signal strength degradation or even interruption. This affects the accuracy and continuity of GPS data. Inertial Navigation Systems (INS) may experience positioning drift after prolonged operation because INS relies on the accuracy of initial position and velocity; small errors in these initial values ​​accumulate over time, leading to navigation errors. Inertial Measurement Units (IMUs) such as gyroscopes and accelerometers may be affected by noise interference, and their long-term accuracy may also be compromised. Fusion of GNSS and INS data requires complex algorithms to ensure data accuracy and real-time performance. The performance of the algorithm directly impacts the effectiveness of the navigation system. During vehicle operation, large amounts of data need to be processed in real time to quickly generate accurate navigation correction parameters. This requires the navigation system to have high processing power. When GPS signals are restored, the system needs to quickly reinitialize and synchronize GNSS and INS data to ensure navigation continuity. Different vehicles and driving environments may require different navigation parameter optimizations. Therefore, the system needs to be adaptable to different usage scenarios and vehicle types. To address these issues, a series of technical measures are needed, such as improving signal processing algorithms to reduce the impact of interference and obstruction, using advanced filtering techniques to fuse GNSS and INS data, and optimizing algorithms to improve the system's real-time processing capabilities. Furthermore, regular sensor calibration is necessary to reduce errors and drift. These measures can improve the overall performance and reliability of the navigation system.

[0033] The vehicle-mounted navigation system actively collects the following data during operation: latitude and longitude, satellite signal strength, and real-time vehicle speed information. It measures the vehicle's angular velocity to determine steering and directional changes. It measures the vehicle's linear acceleration to determine acceleration and deceleration. Ambient temperature may be used for calibration, as it can affect sensor performance. In environments where GPS signals are lost, such as tunnels and underground parking garages, the system maintains positioning using the following methods: using the last number of satellites before the signal loss as a reference variable; the more satellites, the higher the reliability. It uses the current latitude and longitude values ​​as a derivation benchmark. Combining the gyroscope values ​​and vehicle speed, it calculates the vehicle's azimuth and offset. New GNSS data is calculated every 10ms, overwriting the previous data. GNSS provides high-precision positioning when satellite signals are available, but accuracy decreases in environments with weak signals. IMUs can still provide accurate positioning in complex or lost signal conditions, but they suffer from error accumulation. Therefore, using a fusion navigation algorithm to calibrate the IMU's error based on conditions such as vehicle speed and temperature is crucial for improving positioning accuracy. After the vehicle's infotainment system starts up, these services input the data collected by the hardware into the fusion inertial navigation algorithm module. After algorithmic calculation, new positioning data is generated. The calculated positioning data is reported to CarPlay at a frequency of 10Hz. CarPlay processes the reported data and displays the location information on the map. In this way, even in the event of GPS signal loss, the vehicle's positioning information can still be calculated by fusing data from the IMU and other sensors, and then accurately displayed on the map through CarPlay, thereby improving the user experience.

[0034] In summary, through the S110-S180 process, the vehicle can maintain accurate positioning even when GPS signals are lost, ensuring the continuity and reliability of navigation and providing the driver with a seamless navigation experience. Combining GNSS and IMU data through a fusion algorithm improves positioning accuracy in complex environments. When satellite signals are available, centimeter-level positioning is provided; even without satellite signals, high positioning accuracy is maintained through IMU data and the fusion algorithm. Using information such as vehicle speed and temperature to calibrate IMU errors effectively reduces error accumulation and improves positioning accuracy.

[0035] like Figure 2 As shown, in a more specific embodiment, the execution method S130 further includes execution steps S131-S135.

[0036] S131. Determine whether the GPS signal parameters corresponding to the current time point are correctly configured in the vehicle location parameter library.

[0037] S132. If the GPS signal parameters are not properly configured in the vehicle location parameter database, it is determined that the GPS signal parameters have been interrupted.

[0038] S133. Send a coordination command to the vehicle motion parameter library to activate the navigation parameter optimization model.

[0039] S134. Initialize the navigation parameter optimization model using an inertial navigation algorithm.

[0040] S135. Train the navigation parameter optimization model based on all current data in the obtained vehicle position parameter library and vehicle motion parameter library.

[0041] Specifically, in a more detailed embodiment, steps S131-S135 of method S130 further ensure that the navigation parameter optimization model can be effectively activated and initialized when GPS signal parameters are interrupted, so as to maintain vehicle positioning using the inertial navigation algorithm (INS). The system checks whether the GPS signal parameters in the vehicle position parameter library at the current time point are configured correctly. This involves checking parameters such as GPS signal strength, quality, and satellite visibility to ensure the validity of the GPS data. If the GPS signal parameters are not configured correctly, the system will determine that the GPS signal parameters are interrupted. This may be due to satellite signal obstruction, receiver failure, or other reasons. The system sends a coordination command to the vehicle motion parameter library to activate the navigation parameter optimization model. This model is an algorithmic framework used to fuse GNSS and IMU data to optimize positioning results. The navigation parameter optimization model is initialized using the inertial navigation algorithm. This step includes setting the initial parameters of the model, such as initial position, velocity, acceleration, etc., which will be used for subsequent positioning calculations. The navigation parameter optimization model is trained based on all current data in the vehicle position parameter library and the vehicle motion parameter library. This step involves using IMU data and other sensor data to calibrate and optimize the navigation model, ensuring its accuracy and adaptability. Through these specific steps, the system can maintain and optimize vehicle positioning using IMU and other sensor data and fused navigation algorithms even in the event of GPS signal loss. This not only improves positioning accuracy but also ensures the continuity and reliability of the navigation system.

[0042] like Figure 3 As shown, in a more specific embodiment, before executing method S150, the method further includes executing steps S151-S153.

[0043] S151. Filter the latitude and longitude parameters.

[0044] S152. Determine whether the latitude and longitude parameters meet the preset accuracy requirements.

[0045] S153. If the latitude and longitude parameters meet the accuracy requirements, then mark the obtained latitude and longitude parameters.

[0046] Specifically, in a more detailed embodiment, steps S151-S153 prior to method S150 involve processing and verifying latitude and longitude parameters to ensure their accuracy and reliability. In this step, the system filters the latitude and longitude parameters obtained from the vehicle location parameter database. The purpose of filtering is to reduce noise and random errors, smooth data curves, and thus improve data stability and accuracy. Commonly used filtering techniques include Kalman filtering and particle filtering. The system checks whether the filtered latitude and longitude parameters meet preset accuracy requirements. These accuracy requirements may be based on the specific application scenario and performance standards of the vehicle navigation system. If the accuracy of the latitude and longitude parameters does not meet the requirements, further processing or calibration may be necessary. If the latitude and longitude parameters meet the preset accuracy requirements, the system marks these parameters to indicate that they are accurate and reliable. The marked latitude and longitude parameters can be used for subsequent navigation calculations and positioning services. Through these steps, the system ensures that the latitude and longitude parameters used for navigation and positioning have high accuracy and high reliability. This is crucial for providing accurate location information and ensuring the performance of the vehicle navigation system. These filtered and verified latitude and longitude parameters are especially important in the event of GPS signal loss, as they are a critical data source for maintaining vehicle positioning.

[0047] In a more specific embodiment, the execution method S160 further includes execution steps S161-S164.

[0048] S161. Noise reduction processing is performed on the vehicle speed parameters and gyroscope readings to obtain new vehicle motion data.

[0049] S162. Convert the new vehicle motion data into time frames corresponding to the GNSS data.

[0050] S163. Calculate the acceleration and steering angle of the vehicle to be navigated based on the time frame corresponding to the GNSS data.

[0051] S164. Calculate the real-time azimuth angle and vehicle offset of the vehicle to be navigated based on the acceleration and steering angle of the vehicle to be navigated, and use them as navigation correction parameters.

[0052] Specifically, in a more specific embodiment, steps S161-S164 of method S160 involve processing and calculating vehicle motion data to generate parameters for navigation correction. The system denoises the vehicle speed parameters and gyroscope readings obtained from a vehicle motion parameter library. The purpose of denoising is to eliminate random noise and outliers in the data, thereby improving data quality. Common denoising methods include moving average, median filtering, and wavelet denoising. The system converts the denoised vehicle speed parameters and gyroscope readings into time frames corresponding to the GNSS data. This is done to ensure that the vehicle motion data and GNSS data are time-aligned for subsequent calculations and fusion. Using the converted vehicle motion data and GNSS data, the system calculates the acceleration and steering angle of the vehicle to be navigated. These calculations are based on the vehicle's kinematic model and the position information provided by GNSS. Acceleration and steering angle are important parameters of vehicle motion, crucial for accurately describing the vehicle's motion state. The system calculates the real-time azimuth and vehicle offset of the vehicle under navigation based on its acceleration and steering angle, using these as navigation correction parameters. Finally, based on the calculated acceleration and steering angle, the system calculates the real-time azimuth and vehicle offset of the vehicle under navigation. These parameters serve as navigation correction parameters to correct and improve the vehicle's positioning. The calculation of real-time azimuth and vehicle offset may involve the application of vehicle dynamics models and real-time analysis of the vehicle's trajectory. Through these steps, the system can maintain and improve the vehicle's positioning using vehicle motion data and GNSS information without relying on GPS signals. This is crucial for ensuring the robustness and accuracy of the vehicle navigation system.

[0053] In another, more specific embodiment, the execution method S110 further includes execution steps S111-S115.

[0054] S111. Obtain the raw GNSS data stream of the vehicle to be navigated.

[0055] S112. Parse the raw GNSS data stream to extract the GNSS data.

[0056] S113. Perform quality checks on GNSS data to generate GNSS data detection information, which includes signal strength detection information.

[0057] S114. Determine whether the GPS signal strength in the signal strength detection information reaches the preset strength standard value.

[0058] S115. If the GPS signal strength reaches the preset strength standard value, then configure the GNSS data in the vehicle location parameter library.

[0059] Specifically, in another more specific embodiment, steps S111-S115 of method S110 involve acquiring, parsing, and quality checking GNSS data to ensure that the GPS signal strength meets a preset standard value. In this step, the system acquires raw GNSS data streams from the vehicle's GNSS receiver. These data streams contain the vehicle's positioning information, such as latitude and longitude, satellite signal strength, and the number of satellites. The system parses the raw GNSS data streams to extract useful GNSS data. This data includes the vehicle's current position, speed, timestamp, and satellite ephemeris information. The system performs a quality check on the extracted GNSS data, generating GNSS data detection information. This detection information includes signal strength detection information, used to evaluate the strength and quality of the GPS signal. The system determines whether the GPS signal strength in the signal strength detection information meets a preset strength standard value. This standard value is set based on ensuring the normal operation and positioning accuracy of the navigation system. If the GPS signal strength meets the preset strength standard value, the system configures the GNSS data in the vehicle position parameter library. This is done to ensure that only GNSS data of acceptable quality is used for vehicle positioning calculations. Through these steps, the system ensures that the GNSS data used is accurate and reliable. This high-quality data is crucial for maintaining vehicle positioning in the event of GPS signal loss. Furthermore, these steps help the system automatically identify and avoid using low-quality GNSS data in weak signal environments, thereby improving overall positioning accuracy and system robustness.

[0060] In summary, the above methods can adapt to different vehicles and driving environments in practical implementation, including complex working environments and extreme motion conditions, ensuring positioning accuracy in various scenarios. CarPlay, as an application, can receive and process GNSS data from the vehicle's infotainment system in real time, reporting positioning data at a frequency of 10Hz and accurately displaying location information on the map, providing users with an intuitive and user-friendly interface. By using advanced filtering techniques and data fusion algorithms, the system maintains high stability in various environments, reducing positioning errors and improving navigation accuracy and reliability. In general, the technical effectiveness of the above solutions lies in improving vehicle positioning accuracy in various environments by fusing GNSS and IMU data and combining information such as vehicle speed and temperature for error calibration. Furthermore, it provides intuitive and accurate navigation information through applications such as CarPlay, offering users a seamless and efficient navigation experience.

[0061] Figure 4This is a schematic block diagram of an inertial navigation device based on positioning optimization provided in an embodiment of this application. As shown in the figure, corresponding to the above-described inertial navigation method based on positioning optimization, this application also provides an inertial navigation device 100 based on positioning optimization. This inertial navigation device based on positioning optimization includes a unit for executing the above-described inertial navigation method based on positioning optimization, and the device can be configured in a desktop computer, tablet computer, laptop computer, or other terminal. For details, please refer to... Figure 4 The positioning-optimized inertial navigation device 100 includes a first parameter configuration unit 110, used to configure various parameters from the real-time acquired GNSS data into a preset vehicle position parameter library; a second parameter configuration unit 120, used to configure various parameters from the real-time acquired vehicle motion data into a preset vehicle motion parameter library; an optimization model activation unit 130, used to activate a preset navigation parameter optimization model based on the vehicle position parameter library and the vehicle motion parameter library if the GPS signal parameters in the vehicle position parameter library are interrupted; a script generation unit 140, used to generate inertial navigation scripts corresponding to the GNSS data and vehicle motion data and configure them into the navigation parameter optimization model; and a first marker unit. Unit 150 is used to acquire latitude and longitude parameters and satellite signal parameters when GPS signal parameters are interrupted and mark them as corresponding reference points; Calculation unit 160 is used to calculate the real-time azimuth angle and vehicle offset of the vehicle to be navigated during the driving process based on the reference points, gyroscope readings and vehicle speed parameters, and obtain navigation correction parameters; Data acquisition unit 170 is used to combine the latitude and longitude parameters corresponding to the reference points, satellite signal parameters, navigation correction parameters and the latitude and longitude parameters before the update to obtain new GNSS data; Prediction information acquisition unit 180 is used to generate a new inertial navigation script based on the new GNSS data and vehicle motion data and input it into the navigation parameter optimization model to obtain the corresponding navigation prediction information.

[0062] Furthermore, in a more specific embodiment, the optimization model activation unit 130 in the above-mentioned device further includes a first judgment unit for judging whether the GPS signal parameters corresponding to the current time point are normally configured in the vehicle position parameter library; a judgment unit for judging that the GPS signal parameters are interrupted if the GPS signal parameters are not normally configured in the vehicle position parameter library; an instruction sending unit for sending a cooperative instruction to the vehicle motion parameter library to activate the navigation parameter optimization model; an initialization unit for initializing the navigation parameter optimization model using an inertial navigation algorithm; a model training unit for training the navigation parameter optimization model based on all current data in the acquired vehicle position parameter library and vehicle motion parameter library; related to the above units, the above-mentioned device further includes a filtering processing unit for filtering the latitude and longitude parameters; a second judgment unit for judging whether the latitude and longitude parameters meet the preset accuracy value requirements; and a second marking unit for marking the acquired latitude and longitude parameters if the latitude and longitude parameters meet the accuracy value requirements.

[0063] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned positioning-optimized inertial navigation device and its various units can be referred to the corresponding descriptions in the foregoing method embodiments. For the sake of convenience and brevity, these details will not be repeated here.

[0064] The aforementioned positioning-optimized inertial navigation device can be implemented as a computer program, which can be used in, for example... Figure 5 It runs on the computer device shown.

[0065] Please see Figure 5 This diagram illustrates a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a terminal or a server. The terminal can be an electronic device with communication functions, such as a smartphone, tablet, laptop, desktop computer, personal digital assistant, or wearable device. The server can be a standalone server or a server cluster consisting of multiple servers.

[0066] The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0067] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a positioning-optimized inertial navigation method.

[0068] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0069] The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an inertial navigation method based on positioning optimization.

[0070] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0071] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be 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. The general-purpose processor may be a microprocessor or any conventional processor.

[0072] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0073] Therefore, this application also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When executed by a processor, the program instructions cause the processor to perform the following steps:

[0074] The system configures various parameters from real-time acquired GNSS data into a preset vehicle position parameter library. GNSS data includes GPS signal parameters, latitude and longitude parameters, and satellite signal parameters. It also configures various parameters from real-time acquired vehicle motion data into a preset vehicle motion parameter library. Vehicle motion data includes vehicle speed parameters and gyroscope readings. If the GPS signal parameters in the vehicle position parameter library are interrupted, a preset navigation parameter optimization model is activated based on the vehicle position parameter library and the vehicle motion parameter library. An inertial navigation script corresponding to the GNSS data and vehicle motion data is generated and configured into the navigation parameter optimization model. The latitude and longitude parameters and satellite signal parameters at the time of GPS signal interruption are acquired and marked as corresponding reference points. Based on the reference points, gyroscope readings, and vehicle speed parameters, the real-time azimuth angle and vehicle offset of the vehicle under navigation are calculated to obtain navigation correction parameters. The latitude and longitude parameters corresponding to the reference points, satellite signal parameters, navigation correction parameters, and the latitude and longitude parameters before the update are combined to obtain new GNSS data. A new inertial navigation script is generated based on the new GNSS data and vehicle motion data and input into the navigation parameter optimization model to obtain corresponding navigation prediction information.

[0075] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0076] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0078] The steps in the methods of this application embodiment can be adjusted, merged, or deleted according to actual needs. The units in the apparatus of this application embodiment can be merged, divided, or deleted according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0079] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0080] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An inertial navigation method based on positioning optimization, applied to a vehicle navigation system, wherein the vehicle navigation system is configured in the in-vehicle infotainment system of the vehicle to be navigated, characterized in that, The method includes: The parameters in the real-time acquired GNSS data are configured in a preset vehicle location parameter library. The GNSS data includes GPS signal parameters, latitude and longitude parameters, and satellite signal parameters. The parameters in the real-time acquired vehicle motion data are configured in a preset vehicle motion parameter library. The vehicle motion data includes vehicle speed parameters and gyroscope readings. If the GPS signal parameters in the vehicle position parameter library are interrupted, a preset navigation parameter optimization model is activated based on the vehicle position parameter library and the vehicle motion parameter library. Generate an inertial navigation script corresponding to the GNSS data and the vehicle motion data and configure it in the navigation parameter optimization model; The latitude and longitude parameters and the satellite signal parameters at the time when the GPS signal parameters are interrupted are obtained and marked as the corresponding reference points; Based on the reference point, the gyroscope reading, and the vehicle speed parameter, the real-time azimuth angle and vehicle offset of the vehicle to be navigated during the driving process are calculated to obtain navigation correction parameters; The new GNSS data is obtained by combining the latitude and longitude parameters corresponding to the reference point, the satellite signal parameters, the navigation correction parameters, and the latitude and longitude parameters before the update. A new inertial navigation script is generated based on the new GNSS data and the vehicle motion data, and then input into the navigation parameter optimization model to obtain the corresponding navigation prediction information; The step of configuring various parameters from the real-time acquired GNSS data into a preset vehicle position parameter library includes: Obtain the raw GNSS data stream of the vehicle to be navigated; The raw GNSS data stream is parsed to extract the GNSS data; The GNSS data is subjected to quality checks to generate GNSS data detection information, which includes signal strength detection information. Determine whether the GPS signal strength in the signal strength detection information reaches a preset strength standard value; If the GPS signal strength reaches a preset strength standard value, the GNSS data will be configured in the vehicle location parameter library. The navigation correction parameters are obtained by calculating the real-time azimuth angle and vehicle offset of the vehicle under navigation during its journey based on the reference point, the gyroscope reading, and the vehicle speed parameters, including: The vehicle speed parameters and the gyroscope readings are denoised to obtain new vehicle motion data. The new vehicle motion data is converted into time frames corresponding to the GNSS data; The acceleration and steering angle of the vehicle to be navigated are calculated based on the time frames corresponding to the GNSS data. The real-time azimuth angle and vehicle offset of the vehicle to be navigated are calculated based on the acceleration and steering angle of the vehicle to be navigated, and used as navigation correction parameters.

2. The inertial navigation method based on positioning optimization according to claim 1, characterized in that, If the GPS signal parameters in the vehicle position parameter database are interrupted, a preset navigation parameter optimization model is activated based on the vehicle position parameter database and the vehicle motion parameter database, including: Determine whether the GPS signal parameters corresponding to the current time point are correctly configured in the vehicle location parameter database; If the GPS signal parameters are not properly configured in the vehicle location parameter database, it is determined that the GPS signal parameters have been interrupted. Send a coordination command to the vehicle motion parameter library to activate the navigation parameter optimization model; The navigation parameter optimization model is initialized using an inertial navigation algorithm; The navigation parameter optimization model is trained based on all current data obtained from the vehicle position parameter library and the vehicle motion parameter library.

3. The inertial navigation method based on positioning optimization according to claim 1, characterized in that, Before acquiring the latitude and longitude parameters and the satellite signal parameters when the GPS signal parameters are interrupted and marking them as the corresponding reference points, the method includes: The latitude and longitude parameters are filtered. Determine whether the latitude and longitude parameters meet the preset accuracy requirements; If the latitude and longitude parameters meet the accuracy requirements, then the obtained latitude and longitude parameters are marked.

4. An inertial navigation device based on positioning optimization, employing the inertial navigation method based on positioning optimization as described in any one of claims 1-3, characterized in that, The device includes: The first parameter configuration unit is used to configure various parameters in the real-time acquired GNSS data into a preset vehicle position parameter library. Configuring various parameters in the real-time acquired GNSS data into the preset vehicle position parameter library includes: acquiring the original GNSS data stream of the vehicle to be navigated; parsing the original GNSS data stream to extract the GNSS data; and performing a quality check on the GNSS data to generate GNSS data detection information, the GNSS data detection information including signal strength detection information. Determine whether the GPS signal strength in the signal strength detection information reaches a preset strength standard value; if the GPS signal strength reaches the preset strength standard value, then configure the GNSS data in the vehicle location parameter library; The second parameter configuration unit is used to configure various parameters in the real-time acquired vehicle motion data into a preset vehicle motion parameter library. An optimization model activation unit is used to activate a preset navigation parameter optimization model based on the vehicle position parameter library and the vehicle motion parameter library if the GPS signal parameters in the vehicle position parameter library are interrupted. The script generation unit is used to generate inertial navigation scripts corresponding to the GNSS data and the vehicle motion data and configure them in the navigation parameter optimization model; The first marking unit is used to acquire the latitude and longitude parameters and the satellite signal parameters when the GPS signal parameters are interrupted, and mark them as the corresponding reference points; The calculation unit is used to calculate the real-time azimuth angle and vehicle offset of the vehicle to be navigated during its driving process based on the reference point, the gyroscope reading, and the vehicle speed parameter, to obtain navigation correction parameters; the calculation of the real-time azimuth angle and vehicle offset of the vehicle to be navigated during its driving process based on the reference point, the gyroscope reading, and the vehicle speed parameter to obtain navigation correction parameters includes: performing noise reduction processing on the vehicle speed parameter and the gyroscope reading to obtain new vehicle motion data; converting the new vehicle motion data into a time frame corresponding to the GNSS data; calculating the acceleration and steering angle of the vehicle to be navigated based on the time frame corresponding to the GNSS data; and calculating the real-time azimuth angle and vehicle offset of the vehicle to be navigated based on the acceleration and steering angle of the vehicle to be navigated, as the navigation correction parameters. The data acquisition unit is used to combine the latitude and longitude parameters corresponding to the reference point, the satellite signal parameters, the navigation correction parameters, and the latitude and longitude parameters before the update to obtain new GNSS data; The prediction information acquisition unit is used to generate a new inertial navigation script based on the new GNSS data and the vehicle motion data, and input it into the navigation parameter optimization model to obtain the corresponding navigation prediction information.

5. The inertial navigation device based on positioning optimization according to claim 4, characterized in that, The optimization model activation unit includes: The first judgment unit is used to determine whether the GPS signal parameters corresponding to the current time point are correctly configured in the vehicle location parameter library; The determination unit is used to determine that the GPS signal parameters have been interrupted if the GPS signal parameters are not properly configured in the vehicle location parameter library. The instruction sending unit is used to send a cooperative instruction to the vehicle motion parameter library to activate the navigation parameter optimization model; An initialization unit is used to initialize the navigation parameter optimization model using an inertial navigation algorithm; The model training unit is used to train the navigation parameter optimization model based on all the current data in the acquired vehicle position parameter library and vehicle motion parameter library.

6. The inertial navigation device based on positioning optimization according to claim 5, characterized in that, The device includes: A filtering unit is used to filter the latitude and longitude parameters; The second judgment unit is used to determine whether the latitude and longitude parameters meet the preset accuracy requirements; The second marking unit is used to mark the obtained latitude and longitude parameters if the latitude and longitude parameters meet the accuracy requirements.

7. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions that, when executed by a processor, can implement the method as described in any one of claims 1-3.

Images