A method and apparatus for calibrating a lever arm value

Abstract

The application discloses a rod arm value calibration method and device, the method comprises the following steps: acquiring first data collected by an IMU and second data collected by a GNSS; the first data comprises IMU data corresponding to multiple IMU sampling moments, and the second data comprises GNSS data corresponding to multiple GNSS sampling moments; constructing a target function according to prior state information, a rod arm value to be optimized and IMU state increment residuals between multiple adjacent GNSS sampling moments, the prior state information and the IMU state increment residuals are obtained according to the first data and the second data; the prior state information represents state prediction information of a carrier at a first moment; the first moment is an initial moment of a target time period; the IMU and the GNSS are installed on the carrier; and the rod arm value is solved to minimize a function value of the target function, and the rod arm value is used for positioning of the carrier. By implementing the application, fast and accurate calibration of the rod arm value can be realized, and the measurement accuracy of the rod arm value is improved.

Description

Technical Field

[0001] The present application relates to the field of integrated navigation and positioning, and in particular to a lever arm value calibration method and device. Background Art

[0002] For integrated navigation, for example, the integrated navigation of the Global Navigation Satellite System (GNSS) and the Micro Electro Mechanical System (MEMS)-Inertial Measurement Unit (IMU), the lever arm value is an important parameter. The lever arm value represents the physical distance between the MEMS-IMU and the GNSS. In the integrated navigation, the lever arm value can be used to compensate the position and speed of the GNSS to the MEMS-IMU to eliminate the cumulative error of the MEMS-IMU that increases over time. Therefore, if the lever arm value is inaccurate, the result of the integrated navigation solution will be inaccurate, affecting the estimation accuracy of the carrier's speed, attitude, etc.

[0003] Typically, lever arm values ​​can be obtained through manual measurement, but this method is not only difficult to measure but also has low accuracy. Alternatively, lever arm values ​​can be estimated using pre-acquired baseline data through Kalman filtering, but this method requires high MEMS-IMU accuracy and the data acquisition process is complex. Summary of the Invention

[0004] The embodiments of the present application disclose a lever arm value calibration method and device, which can accurately and easily obtain the lever arm value used for combined navigation solution, which is beneficial to improving the accuracy of carrier positioning.

[0005] In a first aspect, an embodiment of the present application provides a lever arm value calibration method, the method comprising: obtaining first data collected by an IMU and second data collected by a GNSS; the first data comprises IMU data corresponding to a plurality of IMU sampling moments, and the second data comprises GNSS data corresponding to a plurality of GNSS sampling moments; constructing an objective function based on prior state information, the lever arm value to be optimized, and the IMU state incremental residual between each adjacent GNSS sampling moment in a target time period, the prior state information and the IMU state incremental residual being obtained based on the first data and the second data; the prior state information represents the state prediction information of the carrier at the first moment; the first moment is the initial moment of the target time period; the IMU and GNSS are installed on the carrier; solving the lever arm value to minimize the function value of the objective function, and the lever arm value is used for positioning the carrier.

[0006] Among them, the carrier can be a vehicle, a small drone, a sanitation robot, a robot used for express delivery, and other equipment or devices that require real-time positioning, and this application does not make specific limitations.

[0007] In addition, the IMU state increment residual between adjacent GNSS sampling moments can be represented according to the motion state information of the IMU at two adjacent GNSS sampling moments and the motion state increment of the IMU between the two adjacent GNSS sampling moments.

[0008] In the above method, by constructing an objective function that includes prior state information and the IMU state incremental residual between each adjacent GNSS sampling time period in the target time period, the objective function also includes an arm value variable (i.e., the arm value to be optimized), and the arm value corresponding to the minimum function value of the objective function is used as the calibrated arm value. This makes it possible to conveniently, accurately and quickly obtain the arm value between the IMU and GNSS antenna, thereby improving the measurement accuracy of the arm value.

[0009] In a possible implementation of the first aspect, the prior state information is obtained by weighted summing the forward combined navigation solution result corresponding to the first moment and the backward combined navigation solution result corresponding to the first moment, and the prior state information includes the prior position, prior speed and prior attitude of the carrier at the first moment.

[0010] By implementing the above implementation method, the prior state information is obtained by weighted fusion processing of the results of the forward and backward combined navigation solutions. The prior state information provides an accurate initial value for the optimization process within the target time period, which can not only effectively offset the accumulated error of the IMU, but also improve the accuracy of the arm value to be solved.

[0011] In a possible implementation manner of the first aspect, the a priori state information further includes one or more of a priori accelerometer bias of the carrier at the first moment and a priori gyroscope bias of the carrier at the first moment.

[0012] By implementing the above implementation method, the measurement error of the accelerometer in the IMU can be effectively corrected based on the prior accelerometer zero bias, and the measurement error of the gyroscope in the IMU can be effectively corrected based on the prior gyroscope zero bias, which is beneficial to improving the accuracy of the acceleration data and angular velocity data measured by the IMU.

[0013] In a possible implementation of the first aspect, the target time period includes a second moment, the second moment and the first moment are adjacent GNSS sampling moments within the target time period, and the IMU state incremental residual between the first moment and the second moment is obtained based on the first state variable to be estimated of the IMU at the first moment, the second state variable to be estimated of the IMU at the second moment, and the IMU data corresponding to each IMU sampling moment between the first moment and the second moment.

[0014] The first state variable to be estimated includes the IMU's position to be estimated, velocity to be estimated, and attitude to be estimated at the first moment. The second state variable to be estimated includes the IMU's position to be estimated, velocity to be estimated, and attitude to be estimated at the second moment. In some possible embodiments, the first state variable to be estimated also includes one or more of the IMU's accelerometer bias to be estimated and gyroscope bias to be estimated at the first moment, and the second state variable to be estimated also includes one or more of the IMU's accelerometer bias to be estimated and gyroscope bias to be estimated at the second moment.

[0015] In a possible implementation of the first aspect, the objective function also includes a GNSS factor corresponding to each GNSS sampling moment, the first state variable to be estimated includes the IMU's estimated position at the first moment, the GNSS factor corresponding to the first moment is the difference between the carrier's estimated position at the first moment and the position information in the GNSS data corresponding to the first moment, and the carrier's estimated position at the first moment is represented by the IMU's estimated position at the first moment and the lever arm value to be optimized.

[0016] By implementing the above-mentioned implementation method and introducing GNSS into the objective function, the objective function can represent the error between the estimated position of the carrier at a certain GNSS sampling moment and the position measured by GNSS at the same GNSS sampling moment based on the GNSS factor, which is conducive to improving the measurement accuracy of the arm value, thereby improving the accuracy of carrier positioning.

[0017] In a possible implementation of the first aspect, the objective function is the sum of a first function, a second function, and a third function. The first function is used to represent the difference between the prior state information and the first state variable to be estimated. The second function is used to represent the sum of the GNSS factors corresponding to each GNSS sampling moment within the target time period. The third function is used to represent the sum of the IMU state incremental residuals between each adjacent GNSS sampling moment within the target time period.

[0018] By implementing the above implementation method, the arm value between the IMU and the GNSS antenna can be accurately and quickly obtained according to the objective function, thereby improving the measurement accuracy of the arm value and thus improving the accuracy of carrier positioning.

[0019] In a possible implementation manner of the first aspect, the first data and the second data are acquired by the carrier during a U-turn and / or a turn.

[0020] By implementing the above-described method, the carrier acquires first and second data while performing a U-turn and / or a turn, effectively identifying potential inertial navigation errors, such as gyroscope and accelerometer bias and attitude errors. Furthermore, compared to large maneuvers like figure-eight turns, U-turns and turns are simpler and faster, reducing operational difficulty and improving the efficiency of collecting IMU and GNSS data.

[0021] In the second aspect, an embodiment of the present application provides a device for calibrating a lever arm value, which includes: an acquisition unit for acquiring first data collected by an IMU and second data collected by a GNSS; the first data includes IMU data corresponding to multiple IMU sampling moments, and the second data includes GNSS data corresponding to multiple GNSS sampling moments; a construction unit for constructing an objective function based on prior state information, the lever arm value to be optimized, and the IMU state incremental residual between each adjacent GNSS sampling moment in a target time period, and the prior state information and the IMU state incremental residual are obtained based on the first data and the second data; the prior state information represents the state prediction information of the carrier at the first moment; the first moment is the initial moment of the target time period; the IMU and GNSS are installed on the carrier; a processing unit for solving the lever arm value to minimize the function value of the objective function, and the lever arm value is used for positioning the carrier.

[0022] In a possible implementation of the second aspect, the prior state information is obtained by weighted summing the forward combined navigation solution result corresponding to the first moment and the backward combined navigation solution result corresponding to the first moment, and the prior state information includes the prior position, prior speed and prior attitude of the carrier at the first moment.

[0023] In a possible implementation manner of the second aspect, the a priori state information further includes one or more of a priori accelerometer bias of the carrier at the first moment and a priori gyroscope bias of the carrier at the first moment.

[0024] In a possible implementation of the second aspect, the target time period includes a second moment, the second moment and the first moment are adjacent GNSS sampling moments within the target time period, and the IMU state incremental residual between the first moment and the second moment is obtained based on the first state variable to be estimated of the IMU at the first moment, the second state variable to be estimated of the IMU at the second moment, and the IMU data corresponding to each IMU sampling moment between the first moment and the second moment.

[0025] In a possible implementation of the second aspect, the objective function also includes a GNSS factor corresponding to each GNSS sampling moment, the first state variable to be estimated includes the IMU's estimated position at the first moment, the GNSS factor corresponding to the first moment is the difference between the carrier's estimated position at the first moment and the position information in the GNSS data corresponding to the first moment, and the carrier's estimated position at the first moment is represented by the IMU's estimated position at the first moment and the lever arm value to be optimized.

[0026] In a possible implementation of the second aspect, the objective function is the sum of a first function, a second function, and a third function. The first function is used to represent the difference between the prior state information and the first state variable to be estimated. The second function is used to represent the sum of the GNSS factors corresponding to each GNSS sampling moment within the target time period. The third function is used to represent the sum of the IMU state incremental residuals between each adjacent GNSS sampling moment within the target time period.

[0027] In a possible implementation manner of the second aspect, the first data and the second data are acquired by the carrier during a U-turn and / or a turn.

[0028] In a third aspect, an embodiment of the present application provides a device comprising a processor and a memory, wherein the processor and the memory are connected or coupled together via a bus; wherein the memory is used to store program instructions; and the processor calls the program instructions in the memory to execute the method in the first aspect or any possible implementation of the first aspect.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores program code for execution by a device, wherein the program code includes instructions for executing the method in the first aspect or any possible implementation of the first aspect.

[0030] In a fifth aspect, embodiments of the present application provide a computer software product, comprising program instructions. When the computer software product is executed by a device, the device performs the method described in the first aspect or any possible embodiment of the first aspect. The computer software product may be a software installation package. When the method provided in any possible embodiment of the first aspect is required, the computer software product may be downloaded and executed on the device to implement the method described in the first aspect or any possible embodiment of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 This is a schematic diagram of the installation of related sensors of integrated navigation on a carrier;

[0033] Figure 2 This is a schematic diagram of a system architecture provided by an embodiment of the present application;

[0034] Figure 3 This is a block diagram of a calibrator provided in an embodiment of the present application;

[0035] Figure 4 It is a schematic diagram of an IMU-GNSS integrated navigation solution;

[0036] Figure 5 This is a flow chart of a lever arm value calibration method provided in an embodiment of the present application;

[0037] Figure 6 is a schematic structural diagram of a computing device provided in an embodiment of the present application;

[0038] Figure 7 This is a functional structure diagram of a computing device provided in this embodiment of the present application. DETAILED DESCRIPTION

[0039] The terms used in the embodiments of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "first", "second", etc. in the description and claims of the embodiments of this application are used to distinguish different objects, rather than to describe a specific order.

[0040] To facilitate understanding, the following first introduces relevant terms that may be involved in the embodiments of this application.

[0041] (1)IMU

[0042] An inertial measurement unit (IMU), also known as an inertial sensor, mainly includes two inertial elements: an accelerometer and a gyroscope. The accelerometer is used to detect the acceleration signal of the carrier relative to the inertial coordinate system, and the gyroscope is used to detect the angular velocity signal of the carrier relative to the inertial coordinate system. The detected acceleration and angular velocity signals can be processed to calculate the position, velocity, attitude and other information of the carrier.

[0043] It should be noted that the IMU provides relative positioning information, that is, it measures the displacement of the carrier relative to the starting point. Using the IMU alone cannot obtain the absolute position of the carrier. In addition, although the update frequency of the IMU is high, the IMU will be affected by the accumulation of errors, resulting in the navigation positioning accuracy decreasing over time, so it can only be relied on for positioning in a very short period of time. Therefore, the IMU is usually used in conjunction with the Global Positioning System (GPS) or GNSS, which is called a combined inertial navigation. The MEMS-IMUI (hereinafter referred to as IMU) can be called a six-axis inertial sensor, which is mainly composed of a three-axis accelerometer and a three-axis gyroscope.

[0044] (2)GNSS

[0045] The Global Navigation Satellite System (GNSS) refers to all satellite navigation systems, including global, regional and augmented ones, such as the United States' GPS, Russia's Glonass, Europe's Galileo, China's BeiDou Navigation Satellite System, and related augmentation systems, such as the United States' Wide Area Augmentation System (WAAS), Europe's Geostationary Navigation Overlay Service (EGNOS) and Japan's Multi-Functional Satellite Augmentation System (MSAS). It also covers other satellite navigation systems under construction or to be built in the future.

[0046] GNSS is a space-based radio navigation and positioning system that provides users with all-weather three-dimensional coordinate, velocity, and time information anywhere on the Earth's surface or in near-Earth space. In other words, GNSS can provide the carrier's absolute position information in the world coordinate system. Understandably, when the signal is good, GNSS alone can produce reliable navigation results. However, when the number of searchable satellites is less than four, GNSS cannot perform navigation calculations. Therefore, GNSS and IMU navigation are combined. GNSS can provide accurate position information to the IMU as a reference, while the IMU can output position information at a high sampling rate. Furthermore, when GNSS is interrupted by signal obstruction or interference, the IMU can continue to operate, improving the stability of the positioning system.

[0047] (3) Carrier coordinate system (b system)

[0048] The origin of the carrier coordinate system coincides with the center of the carrier, where the x-axis points to the right along the horizontal axis of the carrier, the y-axis points forward along the vertical axis of the carrier, and the z-axis points upward along the numerical axis. This is called the right-front-up coordinate system, and the carrier coordinate system is also called the b system.

[0049] (4) Navigation coordinate system (n system)

[0050] The navigation coordinate system is a coordinate system used to facilitate the solution of the inertial navigation system. It is related to the position of the carrier. For the strapdown inertial navigation system, the geographic coordinate system (also known as the northeast celestial geographic coordinate system) is generally selected as the navigation coordinate system.

[0051] As can be seen from the above, IMU is usually combined with other sensors (such as GPS or GNSS, etc.) for navigation. Let’s take the combined navigation of IMU and GNSS as an example to illustrate. Figure 1 , Figure 1 This diagram illustrates the installation of the relevant sensors for integrated navigation within a vehicle. For example, a vehicle is used as an example. In practical applications, since GNSS systems need to receive external satellite signals, the GNSS antenna is placed above the vehicle to provide position and velocity information, minimizing signal obstruction by the vehicle itself. To accurately reflect the vehicle's attitude, the IMU is typically mounted and fixed inside the vehicle, for example, at the vehicle's center of gravity. Therefore, the IMU and GNSS sensors are not installed in the same position on the vehicle. The vehicle's state observations output by the GNSS are measured with the GNSS antenna's location as the center, while the vehicle's state observations output by the IMU are measured with the IMU's installation location as the center. Consequently, the position and velocity measured by the IMU and GNSS sensors differ. The three-dimensional distance between the IMU and GNSS is called the lever arm value. Knowing this lever arm value allows the GNSS position and velocity to be compensated to the IMU. Generally, the lever arm value is expressed as the (x, y, z) coordinates of the GNSS antenna's phase center in the IMU's coordinate system.

[0052] The lever arm value can be obtained by manually measuring the distance between the IMU and the GNSS antenna. However, if the IMU is located inside the vehicle and the GNSS antenna is located on top of the vehicle, not only is it structurally inconvenient to measure, but the measured lever arm value is also inaccurate. Alternatively, the lever arm value can be estimated using Kalman filtering on pre-collected basic vehicle data. However, this method requires high IMU accuracy. Generally, only medium- to high-precision fiber-grade IMUs can effectively estimate the lever arm value between the IMU and the antenna. This means that for low-precision IMUs, the lever arm value cannot be estimated using Kalman filtering. Furthermore, the vehicle must perform large maneuvers such as figure-eight maneuvers in advance to collect basic data, making the data collection process complex.

[0053] In response to the above problems, an embodiment of the present application provides a lever arm value calibration method, which can conveniently and accurately calibrate the lever arm value between the IMU and the GNSS antenna, thereby improving the applicability of the lever arm value calibration method.

[0054] See also Figure 2 , Figure 2 This is a schematic diagram of a system architecture provided by an embodiment of the present application. The system is used to solve the lever arm value. Figure 2 As shown, the system includes at least an IMU, a high-precision locator and a calibrator, wherein the IMU and the high-precision locator communicate with the calibrator respectively by wired or wireless means. The high-precision locator can be a GNSS or GPS. In addition, the IMU and the high-precision locator are located on the same carrier, but the IMU and the high-precision locator are installed at different positions on the same carrier respectively. The calibrator can be located on the same carrier as the IMU and the high-precision locator, or it can be a third device independent of the carrier where the IMU and the high-precision locator are located. This application does not make specific restrictions.

[0055] Among them, the carrier can be a vehicle, a small drone, a sanitation robot, a robot used for express delivery, and other devices or equipment that require real-time positioning.

[0056] An IMU can be a MEMS-IMU, also known as an inertial measurement unit or inertial sensor. It is generally installed at the center of gravity of the carrier and is a device used to measure the carrier's angular velocity and acceleration. An IMU primarily consists of a gyroscope and an accelerometer. The gyroscope is used to detect angular velocity, and the accelerometer is used to detect acceleration. In addition, the IMU has a high update frequency, generally reaching 100Hz, which means that the IMU can measure data 100 times in 1 second, or once every 0.01 seconds. It should be noted that the angular velocity output by the gyroscope is an instantaneous quantity. The angular velocity must be integrated with time to calculate the angle change (or angle increment). Adding the angle change to the initial angle yields the target angle at the current moment. Similarly, for the calculation of acceleration, refer to the description of the angular velocity calculation process above.

[0057] The high-precision locator can be GNSS or GPS. The high-precision locator is generally installed on the top of the carrier to facilitate the reception of satellite signals. The high-precision locator is used to measure the position and speed of the carrier, where the position includes longitude, latitude and altitude (altitude). In addition, the update frequency of the high-precision locator is lower than that of the IMU. For example, the update frequency of GNSS is generally 1Hz, that is, it is updated once every 1 second. In the following description, the high-precision locator may be explained using GNSS as an example, but the embodiments of the present application do not limit the high-precision locator to only GNSS.

[0058] The calibrator can be integrated into a vehicle terminal such as an on-board computer, an on-board industrial control computer (IPC), or a computing device independent of the carrier, such as a server or a computer. The calibrator can be implemented by software and / or hardware methods, which is not specifically limited in the embodiments of the present application. The calibrator is used to determine the initial value of the global optimization based on the IMU data collected by the IMU at each IMU sampling moment and the GNSS data collected by the GNSS at each GNSS sampling moment within a preset time period, and to perform a global optimization process to solve the lever arm value between the IMU and the GNSS, wherein the IMU data includes acceleration and angular velocity, and the GNSS data includes position and velocity measured by the GNSS. It can be understood that since the update frequency of the IMU is higher than the update frequency of the GNSS, the number of IMU sampling moments within the preset time period is greater than the number of GNSS sampling moments within the preset time period.

[0059] See also Figure 3 , Figure 3 is the above Figure 2 An example of a block diagram of the composition of a calibrator, the calibrator specifically includes a forward integrated navigation solution module, a reverse integrated navigation solution module, a forward and reverse weighted fusion module and a global optimization module, wherein the output of the forward integrated navigation solution module and the reverse integrated navigation solution module are respectively connected to the input of the forward and reverse weighted fusion module, and the output of the forward and reverse weighted fusion module is connected to the input of the global optimization module.

[0060] The forward integrated navigation solution module is used to perform forward integrated navigation solution based on the data output by the GNSS and the data output by the IMU to obtain a forward integrated navigation solution result (hereinafter referred to as the forward result). The forward result is the state information of the carrier at a preset time calculated by the forward integrated navigation solution, and the state information includes position, velocity, and attitude. In some possible embodiments, the forward result also includes one or more of the accelerometer bias and gyroscope bias of the carrier at the preset time, wherein the accelerometer bias and gyroscope bias can be referred to as IMU bias. For the specific calculation process of the forward result, please refer to the relevant description in the first part below.

[0061] The backward integrated navigation solution module is used to perform a backward integrated navigation solution based on the data output by the GNSS and the IMU to obtain a backward integrated navigation solution result (hereinafter referred to as the backward result). The backward result is the state information of the vehicle at a preset time calculated by the backward integrated navigation solution. The state information includes position, velocity, and attitude. In some possible embodiments, the backward result also includes one or more of the accelerometer bias and gyroscope bias of the vehicle at the preset time. For details on the calculation process of the backward result, please refer to the relevant description in the first section below.

[0062] It should be noted that the initial value of the lever arm is used in both the forward combined navigation solution process and the reverse combined navigation solution process. The initial value of the lever arm can be set to (0, 0, 0), or (0.5, 0.5, 0) or other values, which is not specifically limited in the embodiments of the present application.

[0063] The forward and reverse weighted fusion module is used to perform weighted fusion on the forward results output by the forward integrated navigation solution module and the backward results output by the reverse integrated navigation solution module to obtain prior state information. The prior state information represents the state prediction information of the carrier at a preset time, which includes a priori position, a priori velocity, and a priori attitude. In some possible embodiments, the prior state information also includes one or more of the carrier's priori accelerometer bias and a priori gyroscope bias at the preset time, wherein the priori accelerometer bias and the priori gyroscope bias can be referred to as priori IMU bias. The preset time is used as the initial time of the global optimization module, and the prior state information is output to the global optimization module as the initial value of the global optimization module.

[0064] The global optimization module establishes an objective function including lever arm value variables, IMU state incremental residuals, etc. based on the first data collected by the IMU, the second data collected by the GNSS, the prior state information output by the forward and reverse weighted fusion module, and the initial value of the lever arm, and solves the lever arm value in the objective function to minimize the function value of the objective function.

[0065] In the embodiment of the present application, the calibration process of the lever arm value mainly includes three parts:

[0066] Part 1: Calculate the prior state information at the initial moment in the global time period. This process can use forward and backward solution methods and weighted fusion processing. The prior state information can provide an accurate initial value for the global optimization module.

[0067] Part 2: Constructing prior factors, GNSS factors and IMU pre-integration factors;

[0068] Part 3: Based on the prior, GNSS factor and IMU pre-integration factor, an objective function is constructed that includes the IMU state increment residual, prior state information and the arm value to be solved, and the objective function is solved to obtain the target arm value.

[0069] The following is a brief introduction to the specific contents of each part:

[0070] Part 1:

[0071] It should be noted that before calculating the prior state information, it is necessary to first obtain the preset time period [t1,t N], where the IMU’s update frequency is higher than the GNSS’s, meaning that the IMU’s sampling times are greater than the GNSS’s sampling times in the same time period. N ], based on the sampling time of GNSS, assuming [t1,t N ], there are N GNSS sampling moments, namely t1, t2, t3, ..., t N-1 ,t N , then data 2 includes the preset time period [t1,t N ], wherein the GNSS data includes the position and speed collected by the GNSS, and data 1 includes the preset time period [t1,t N ] corresponds to the IMU data of each IMU sampling moment within the time frame. The IMU data includes the angular velocity and acceleration collected by the IMU. Since the update frequency of the IMU is higher than the update frequency of the GNSS, each GNSS sampling moment corresponds to a set of GNSS data, and there are multiple IMU sampling moments between two adjacent GNSS sampling moments. Each IMU sampling moment corresponds to a set of IMU data, so there are multiple sets of IMU data between two adjacent GNSS sampling moments. It should be noted that the preset time period can be 3 minutes, 5 minutes, 10 minutes, 30 minutes or other values, and the embodiments of the present application do not make specific limitations.

[0072] For example, assuming the preset time period is 9:00 AM - 9:05 AM, which is 5 minutes long, if the GNSS update frequency is 1 Hz and the IMU update frequency is 10 Hz, that is, the GNSS collects data every 1 second and the IMU collects data every 0.1 seconds. It can be understood that from 9:00:00 AM to 9:00:01 AM, the GNSS collects data once, and the IMU collects data 10 times. Therefore, 9:00:01 AM corresponds to one set of GNSS data, and the time period from 9:00:00 AM to 9:00:01 AM corresponds to 10 sets of IMU data.

[0073] The method for solving the prior state information can be: using a preset time period [t1,t N Any GNSS sampling time t within ] T As an example, according to the initial value of the lever arm, the above data 1 and the above data 2, the forward combined navigation solution is obtained to obtain t T The forward result at time t is obtained by performing backward combined navigation solution based on the initial value of the lever arm, the above data 1 and the above data 2. T The backward result at time t is obtained by weighted fusion of the forward result and the backward result. TPrior state information at time t T Greater than t1 and t T Less than t N , t T The prior state information at time t includes the carrier calculated by the integrated navigation T The position, velocity, attitude and IMU bias at any moment. The IMU bias specifically includes the accelerometer bias and gyroscope bias.

[0074] Among them, the initial value of the lever arm is pre-set, and the initial value of the lever arm can be (0, 0, 0), or (0.5, 0.5, 0) or other values, which is not specifically limited in the embodiment of the present application.

[0075] In addition, [t1,t N ] are in chronological order. Therefore, the so-called forward integrated navigation solution refers to solving the data corresponding to each GNSS sampling moment in sequence according to the positive time sequence of the N GNSS sampling moments, and the so-called backward integrated navigation solution refers to solving the data corresponding to each GNSS sampling moment in sequence according to the reverse time sequence of the N GNSS sampling moments.

[0076] See also Figure 4 , Figure 4 It is a schematic block diagram of IMU-GNSS integrated navigation solution, such as Figure 4 As shown, in general, the process of combined navigation solution can be: T As an example, the IMU output is t T The angular velocity and acceleration corresponding to the moment, the IMU algorithm is based on the t output of the IMU T The angular velocity and acceleration corresponding to the moment and the position corresponding to the previous moment are obtained by IMU at t T The position, velocity, attitude and IMU zero bias at the moment; In addition, the IMU will also t T The angular velocity and acceleration corresponding to the moment are input into the combined navigation Kalman filter, and the GNSS collects the t T The position and speed at the moment are also input into the integrated navigation Kalman filter. The integrated navigation Kalman filter is based on the t input by the IMU. T Angular velocity, acceleration, and GNSS input t corresponding to the moment T Calculation of position, velocity and lever arm value at time t T The error at the moment, where t T The error at the moment includes position error, velocity error, attitude error and IMU zero bias error. Finally, according to t T The error at time t of the IMU algorithm output T The position, velocity, attitude and IMU zero bias at the moment are compensated or corrected to output tT The combined navigation solution result at time t can also be called the carrier at t T The state information at time t T Position, velocity, attitude and IMU bias at all times.

[0077] In a specific implementation, the data corresponding to N GNSS sampling moments are used to obtain t by forward integrated navigation solution. T The forward result process at time t is: calculate the state information of the carrier at time t1, the state information at time t2, ..., t T-1 Status information at time t T The status information at the moment t is obtained in this way T The state information at time t can be called the carrier T The forward result at time t is described in detail below. T The calculation process of the forward result at time t: T The IMU data (i.e. acceleration and angular velocity) corresponding to the moment and t T The previous moment (ie t T-1 The position of the carrier is calculated by IMU at t T Status information at the moment, including position, velocity, attitude and IMU zero bias, and also combined with the arm value and GNSS at t T The position and velocity measured at time t are obtained T The error at the moment, finally, using t T The error at time t T The state information at time t is corrected to obtain the carrier T The state information at the moment, the carrier calculated in this way is T The state information at time t can also be called the carrier T The forward result at time t. It should be noted that the above t T The previous moment (ie t T-1 The position of the carrier at time t T-1 It should be noted that the difference between adjacent moments is the sampling period of GNSS.

[0078] In a specific implementation, the data corresponding to N GNSS sampling moments are used to obtain t by backward integrated navigation solution. T The process of the backward result at time t is: calculate the carrier at t N Status information at time t N-1 Status information at time t N-2 Status information at time t T+1 Status information at time t T The status information at the moment t is obtained in this wayT The state information at a moment can be called a carrier t T The backward result of time t is described in detail below. T The calculation process of the backward result at time t: T The IMU data (i.e. acceleration and angular velocity) corresponding to the moment and t T The next moment (ie t T+1 The position of the carrier is calculated by IMU at t T The status information at the moment includes position, velocity, attitude and IMU zero bias. The position and velocity measured by GNSS at the Tth moment are also combined to obtain t T The error at the moment, finally, using t T The error at time t T The state information at time t is corrected to obtain the carrier T The state information at the moment, the carrier calculated in this way is T The state information at time t can also be called the carrier T It should be noted that the above t T The next moment (ie t T+1 The position of the carrier at time t T+1 It should be noted that the difference between adjacent moments is the sampling period of GNSS.

[0079] In a specific implementation, after obtaining the carrier at t T The forward result and carrier at time t T After obtaining the backward result at time t, it is necessary to perform weighted fusion processing on the forward result and the backward result to obtain the carrier at time t T The fusion result at time t T The fusion result at each moment is used as the prior state information of the subsequent global optimization module. The specific method can be: in the forward integrated navigation solution process, the error covariance matrix of the forward solution can also be obtained from the integrated navigation Kalman filter; in the backward integrated navigation solution process, the error covariance matrix of the backward solution can also be obtained from the integrated navigation Kalman filter; the forward result and the backward result are weighted according to the error covariance matrix of the forward solution and the error covariance matrix of the backward solution, and the larger the error corresponding to the error covariance matrix, the smaller the weight of the solution result corresponding to the error covariance matrix in the prior state information. The weighting process can refer to formula (1):

[0080]

[0081] Among them, X 前 Indicates that the carrier is in t T The forward result at time t, W前 Represents the weight of the forward result, X 后 Indicates that the carrier is in t T The backward result at the moment, W 后 represents the weight of the backward result, Represents the prior state information, that is, the result of weighted fusion of the forward result and the backward result. Including prior position, prior velocity, prior attitude, prior accelerometer bias and prior gyroscope bias, For the specific expression of , please refer to the relevant description in the subsequent third part.

[0082] It can be understood that in the forward integrated navigation solution, from time t1 to time t T At the moment, the uncertainty of the state in the early stage leads to a large estimation error; in the backward integrated navigation solution, from t N Time to t T The error corresponding to each moment is also gradually reduced. Therefore, by weighting the forward and backward results, the error of the fused solution can be made smaller. In addition, the forward and backward combined navigation solution and weighted fusion processing can effectively offset the accumulated error of the IMU, thereby improving the accuracy of the integrated navigation solution.

[0083] It can be seen that the prior state information obtained through the forward and backward combined navigation solution and weighted fusion processing is an accurate combined navigation solution result at a certain moment. Using the prior state information as the initial value of the global optimization module can effectively improve the accuracy of the lever arm value estimated by the subsequent global optimization module.

[0084] Part II:

[0085] Before introducing the construction of the prior factors, GNSS factors and IMU pre-integration factors, it should be noted that the prior state information (with t T The time corresponding to the moment) will be used as the initial value for the subsequent global optimization solution, that is, t T The time is taken as the starting time or initial time of global optimization. Therefore, the global time period corresponding to the global optimization process is [t T , t N ].

[0086] In the time period [t T , t N], in addition to the arm value, the variables to be optimized also include the state variables to be estimated of the IMU at each GNSS sampling moment, and the state variables to be estimated at each GNSS sampling moment include the IMU's position to be estimated, the velocity to be estimated, and the attitude to be estimated at that moment. In some possible embodiments, the state variables to be estimated at each GNSS sampling moment also include the accelerometer bias to be estimated and the gyroscope bias to be estimated of the IMU at that moment, wherein the accelerometer bias to be estimated and the gyroscope bias to be estimated can be referred to as the IMU bias to be estimated. The state variables to be estimated at each GNSS sampling moment can be expressed as the following formula (2):

[0087]

[0088] Where k is the time period [t T , t N ] any GNSS sampling moment, X k Represents the state variable to be estimated of IMU at time k, P k Represents the estimated position of IMU at time k, V k Indicates the estimated velocity of IMU at time k, It represents the estimated posture of IMU at time k, which can also be called the estimated posture of IMU at time k. k Indicates the estimated accelerometer bias of the IMU at time k, Bg k Indicates the gyroscope bias to be estimated by the IMU at time k.

[0089] In the embodiment of the present application, a priori factors are established based on the priori state information and the state variables to be estimated of the IMU at the initial time within the global time period; a GNSS factor including the lever arm value to be solved is established, and the GNSS factor represents the difference between the position to be estimated at the carrier at time k and the position measured by the GNSS at time k, where k is [t T , t N ] any GNSS sampling moment within GNSS sampling time; construct the IMU pre-integration factor using the state variables to be estimated corresponding to adjacent GNSS sampling moments and multiple sets of IMU data between adjacent GNSS sampling moments.

[0090] It should be noted that since the global time period is [t T , t N ], then the state variable to be estimated at the initial moment of the IMU in the above global time period is the state variable of the IMU at t T The state variables to be estimated at time t. The following describes the IMU pre-integration factor, prior factor and GNSS factor respectively:

[0091] (1) IMU pre-integration factor

[0092] The IMU pre-integration factor is used to represent the IMU state incremental residual between adjacent GNSS sampling moments, where the IMU state incremental residual includes position incremental residual, velocity incremental residual, and attitude incremental residual. In some possible embodiments, the IMU state incremental residual also includes one or more of an accelerometer zero bias value and a gyroscope zero bias value.

[0093] Among them, the position increment residual can be obtained based on the two positions to be estimated corresponding to adjacent GNSS sampling moments and the position increment of the IMU between the adjacent GNSS sampling moments; the velocity increment residual can be obtained based on the two velocities to be estimated corresponding to adjacent GNSS sampling moments and the velocity increment of the IMU between the adjacent GNSS sampling moments; the attitude increment residual can be obtained based on the two attitudes to be estimated corresponding to adjacent GNSS sampling moments and the attitude increment of the IMU between the adjacent GNSS sampling moments; the accelerometer zero bias value can be obtained from the two accelerometer zero biases to be estimated corresponding to adjacent GNSS sampling moments; the gyroscope zero bias value can be obtained from the two gyroscope zero biases to be estimated corresponding to adjacent GNSS sampling moments. It should be noted that the position increment, velocity increment and attitude increment between adjacent GNSS sampling moments can be obtained through the IMU data corresponding to each IMU sampling moment between the adjacent GNSS sampling moments.

[0094] IMU pre-integration factor r imu =[r imu_p , r imu_v , r imu_a , r imu_Ba , r imu_Bg ], where r imu_p Represents the position increment residual between adjacent GNSS sampling moments, r imu_v Represents the velocity increment residual between adjacent GNSS sampling moments, r imu_a represents the attitude incremental residual between adjacent GNSS sampling moments, r imu_Ba Indicates the accelerometer zero bias value at adjacent GNSS sampling moments, r imu_Bg Indicates the gyroscope zero bias value at adjacent GNSS sampling moments. imu_p 、r imu_v 、r imu_a 、r imu_Ba 、r imu_Bg The expression of can refer to the following formula (3):

[0095]

[0096] Among them, time i and time j are global time periods [t T , t N ] any two adjacent GNSS sampling moments, and moment j is the next moment after moment i, Represents the rotation transformation matrix from the carrier coordinate system to the navigation coordinate system, which can also be called the attitude matrix. j and P i Represent the estimated position of IMU at time j and the estimated position of IMU at time i, V j and V i They represent the estimated velocity of the IMU at time j in the navigation coordinate system and the estimated velocity of the IMU at time i in the navigation coordinate system, respectively. and They represent the estimated attitude (or quaternion) of the IMU from the carrier coordinate system to the navigation coordinate system at time j and time i, respectively. j and Ba i They represent the estimated accelerometer bias of the IMU at time j and time i in the carrier coordinate system, respectively, and Bg j and Bg i They represent the estimated gyroscope bias of IMU at time j and time i in the carrier coordinate system, respectively. n represents the acceleration due to gravity, and Δt represents the time interval between time i and time j. It represents the position increment obtained by integrating the IMU data in the carrier coordinate system at time i, It represents the velocity increment obtained by integrating the IMU data in the carrier coordinate system at time i, It represents the attitude increment obtained by integrating the IMU data in the carrier coordinate system at time i.

[0097] It should be noted that the above formula (3) can also be called the IMU pre-integration factor corresponding to time i or the IMU state incremental residual corresponding to time i. The IMU state incremental residual corresponding to time i is generated based on the state variable to be estimated at time i, the state variable to be estimated at time j, and the IMU data corresponding to each IMU sampling time between time i and time j.

[0098] Among them, the above formula (3) and It can be expressed as the following formula (4)-formula (6) respectively. It should be noted that t is the IMU sampling time between any two adjacent GNSS sampling times [i, j], t = i + h * Δt', Where Δt′ is the sampling period of the IMU.

[0099]

[0100]

[0101]

[0102] in, Represents the acceleration collected by the IMU at time t, is the attitude quaternion of the IMU carrier coordinate system at time t relative to the carrier coordinate system at time i, Indicates the angular velocity collected by the IMU at time t.

[0103] It should be noted that the construction of the IMU pre-integration factor makes the inertial integral increment Only the accelerometer measurements within the [i, j] time period and gyroscope measurements It is related to the initial value P of the integral i 、V i 、 Not relevant.

[0104] (2) Prior factors

[0105] Prior factor r prior Represents the difference between the prior state information and the state variables to be estimated at the initial moment of the global time period IMU, r prior It can be expressed as shown in formula (7):

[0106]

[0107] in, represents the prior state information, represents the prior position, represents the prior velocity, represents the prior attitude, represents the prior accelerometer bias, Represents the prior gyroscope zero bias; X1 represents the state variable to be estimated at the initial moment of the IMU in the global time period, Among them, P1 represents the position to be estimated by IMU at the initial moment, V1 represents the velocity to be estimated by IMU at the initial moment, Indicates the estimated posture of IMU at the initial moment, Ba 1 Indicates the estimated accelerometer bias of the IMU at the initial moment, Bg 1 Indicates the gyroscope bias to be estimated at the initial moment of IMU. T is the global time period [t T , t N ], that is, X1 is equivalent to

[0108] (3) GNSS factor

[0109] GNSS factor r gnssIndicates the difference between the estimated position of the carrier at time k and the position measured by GNSS at time k. In addition, the variable of the lever arm value L is introduced into the GNSS factor, r gnss It can be expressed as shown in formula (8):

[0110]

[0111] Where k is the time period [t T , t N ] any GNSS sampling moment, P k represents the estimated position of the IMU at time k, L represents the lever arm value to be optimized, represents the estimated position of the carrier at time k, represents the position measured by GNSS at time k.

[0112] Part III:

[0113] After obtaining the above-mentioned prior state information and constructing the IMU pre-integration factor, prior factor and GNSS factor, the global time period [t T , t N ] corresponds to the objective function containing the IMU motion state residual, prior state information and the arm value to be solved, and the objective function is solved to obtain the target arm value.

[0114] In a specific implementation, all variables to be optimized in the objective function are solved so that the function value of the objective function is minimized. The lever arm value corresponding to the minimum value of the objective function is the target lever arm value. Among them, the variables to be optimized in the objective function include the lever arm value and [t T , t N ]The state variables to be estimated at each GNSS sampling moment within the time period.

[0115] The objective function can be the sum of the first function, the second function and the third function, where the first function is the prior factor and the second function is used to represent the global time period [t T , t N ] is the sum of the GNSS factors corresponding to each GNSS sampling moment within the time period [t T , t N The objective function is expressed as follows:

[0116]

[0117] Where L represents the lever arm value to be optimized, Indicates that IMU is at t T The state variables to be estimated at time , Indicates that IMU is at t T+1 The state variables to be estimated at time , Indicates that IMU is at t N The state variable to be estimated at time t, the first function mentioned above is the first term in formula (9) σ prior Represents the covariance information of the prior factor. The second function mentioned above is the second term in formula (9) σ gnss_k represents the covariance information corresponding to GNSS at time k. The second function mentioned above is the third term in formula (9) σ imu_k Indicates the covariance information of the IMU pre-integration factor corresponding to time k.

[0118] In the embodiment of the present application, the objective function f(L, X T ,X T+1 ,...,X N ) corresponds to the minimum value of L, Among them, L is the final lever arm value.

[0119] It should be noted that the multivariate least squares method can be used to find the optimal solution for all variables in the objective function so that the function value of the objective function is minimized. The lever arm value corresponding to the minimum value of the objective function is the target lever arm value. In some possible embodiments, the lever arm value corresponding to the minimum value can also be obtained by solving the objective function for the minimum value through an iterative method, a derivative method, etc., which is not specifically limited in the present embodiment.

[0120] As can be seen, by implementing the embodiments of the present application, the lever arm initial value is used to perform forward and backward integrated navigation solutions on the IMU data and GNSS data, respectively. The results of the forward and backward solutions are weighted and fused to obtain prior state information, which is used as the initial value for global optimization. This effectively eliminates the accumulated error of the IMU and improves the accuracy of the lever arm value subsequently solved. In addition, an objective function containing the IMU state incremental residual, prior state information, and lever arm value corresponding to the global time period is constructed, and the objective function is solved for its minimum value to obtain the lever arm value. This allows for convenient, accurate, and quick acquisition of the lever arm value between the IMU and GNSS antennas, thereby improving the accuracy of the integrated navigation solution.

[0121] See also Figure 5 , Figure 5 This embodiment of the present application provides a lever arm value calibration method, which, when applied to a calibrator, can accurately and easily obtain the lever arm value used for combined navigation solution, thereby improving the accuracy of the navigation solution. The method includes but is not limited to the following steps:

[0122] S101: Acquire first data collected by an IMU and second data collected by a GNSS.

[0123] In an embodiment of the present application, the first data and the second data are data related to the motion state of the carrier collected by the IMU and GNSS respectively within a preset time period, wherein the first data includes IMU data corresponding to multiple IMU sampling moments within the preset time period, and the second data includes GNSS data corresponding to multiple GNSS sampling moments within the preset time period, the IMU data includes angular velocity and acceleration, and the GNSS data includes position and velocity. It can be understood that since the update frequency of the IMU is higher than the update frequency of the GNSS, the number of IMU sampling moments within the preset time period is greater than the number of GNSS sampling moments within the preset time period, and each GNSS sampling moment corresponds to a set of GNSS data, and there are multiple IMU sampling moments between two adjacent GNSS sampling moments, so two adjacent GNSS sampling moments correspond to multiple sets of IMU data.

[0124] It should be noted that the first data is equivalent to the data 1 in the above embodiment, and the second data is equivalent to the data 2 in the above embodiment.

[0125] The so-called acquisition of the first data collected by the IMU and the second data collected by the GNSS may be: the calibrator receives the first data sent by the IMU and receives the second data sent by the GNSS.

[0126] In some possible embodiments, the first data and the second data can be acquired separately by the calibrator, or can be acquired simultaneously by the calibrator, which is not specifically limited in the embodiments of the present application. In some possible embodiments, the first data can be sent to the calibrator by the IMU once, or the first data can be sent to the calibrator by the IMU multiple times. Accordingly, the acquisition action of the calibrator can be performed once or multiple times. Similarly, the second data can be sent to the calibrator by the GNSS once, or the second data can be sent to the calibrator by the GNSS multiple times. This is not specifically limited in the embodiments of the present application.

[0127] It should be noted that the IMU and GNSS are installed at different locations on the same carrier. The first and second data are acquired when the carrier containing the IMU and GNSS performs actions such as linear acceleration, cornering, and U-turns within a preset time period. The execution of these actions can trigger possible errors in the inertial navigation system, such as non-orthogonal error projection of the x / z axes, y-axis accelerometer scale factor, gyroscope and accelerometer zero bias, attitude error, and non-orthogonal error. However, there is no need for the carrier to perform a large figure-eight maneuver, which reduces the operational difficulty and improves the efficiency of collecting IMU and GNSS data.

[0128] S102. Constructing an objective function based on the prior state information, the lever arm value to be optimized, and the IMU state incremental residual between each adjacent GNSS sampling time within the target time period, wherein the prior state information and the IMU state incremental residual are obtained based on the first data and the second data.

[0129] In the embodiment of the present application, an objective function is constructed based on the prior state information, the lever arm value to be optimized, and the IMU state incremental residual between each adjacent GNSS sampling moment in the target time period. The prior state information and the IMU state incremental residual are obtained based on the first data and the second data. The prior state information represents the state prediction information of the carrier at the first moment, and the first moment is the initial moment of the target time period. In addition, the preset time period in S101 includes the target time period. It should be noted that the preset time period is equivalent to [t1,t N ], the target time period is equivalent to the global time period [t T , t N ], the first moment is equivalent to t in the above embodiment T time.

[0130] The a priori state information includes the a priori position, a priori velocity, and a priori attitude of the carrier at the first moment. In some possible embodiments, the a priori state information also includes one or more of the a priori accelerometer bias and the a priori gyroscope bias of the carrier at the first moment. The a priori accelerometer bias and the a priori gyroscope bias can be referred to as a priori IMU bias. The carrier is equipped with the aforementioned IMU and GNSS.

[0131] The prior state information is obtained by weighted summing the forward integrated navigation solution result corresponding to the first moment and the backward integrated navigation solution result corresponding to the first moment. It should be noted that the forward integrated navigation solution result corresponding to the first moment is equivalent to the above t T The forward result at the moment, the backward combined navigation solution corresponding to the first moment is equivalent to the above t T The backward result at the moment, the acquisition process of the forward integrated navigation solution result corresponding to the first moment can refer to the above embodiment t T For the description of the forward result at the moment, the acquisition process of the backward combined navigation solution result corresponding to the first moment can refer to the above embodiment t T A description of the backward consequences of the moment.

[0132] In one specific implementation, the target time period includes a second moment, the second moment and the first moment are two adjacent GNSS sampling moments within the target time, and the IMU state incremental residual between the first moment and the second moment is obtained based on the first state variable to be estimated of the IMU at the first moment, the second state variable to be estimated of the IMU at the second moment, and the IMU data corresponding to each IMU sampling moment between the first moment and the second moment.

[0133] The first state variable to be estimated includes the IMU's position to be estimated, velocity to be estimated, and attitude to be estimated at the first moment. The second state variable to be estimated includes the IMU's position to be estimated, velocity to be estimated, and attitude to be estimated at the second moment. In some possible embodiments, the first state variable to be estimated also includes one or more of the IMU's accelerometer bias to be estimated and gyroscope bias to be estimated at the first moment, and the second state variable to be estimated also includes one or more of the IMU's accelerometer bias to be estimated and gyroscope bias to be estimated at the second moment.

[0134] It should be noted that the first state variable to be estimated at the first moment is equivalent to the above t T The state variable to be estimated at the moment, the second state variable to be estimated at the second moment is equivalent to the above t T+1 The state variables to be estimated at time t.

[0135] In one specific implementation, the objective function also includes a GNSS factor corresponding to each GNSS sampling moment within the target time period, the first state variable to be estimated includes the IMU's estimated position at the first moment, the GNSS factor corresponding to the first moment is the difference between the carrier's estimated position at the first moment and the position information in the GNSS data corresponding to the first moment, and the carrier's estimated position at the first moment is represented by the IMU's estimated position at the first moment and the lever arm value to be optimized.

[0136] In one specific implementation, the objective function is the sum of a first function, a second function, and a third function. The first function represents the difference between the prior state information and the first state variable to be estimated; the second function represents the sum of the GNSS factors corresponding to each GNSS sampling time within the target time period; and the third function represents the sum of the IMU state incremental residuals between adjacent GNSS sampling times within the target time period. It should be noted that the first function is equivalent to the prior factor in the above embodiment. The objective function can be described in the third section of the above embodiment and will not be further elaborated here.

[0137] In a specific implementation, the first data and the second data may be acquired by the carrier during a U-turn and / or a turn.

[0138] S103. Solve the lever arm value in the objective function to minimize the function value of the objective function.

[0139] This step is specifically described in the third part of the above embodiment and will not be repeated here. After obtaining the lever arm value, the lever arm value can be used to locate the carrier, which can effectively reduce the horizontal and vertical errors in the combined navigation positioning result.

[0140] As can be seen, the implementation of the present application embodiment obtains prior state information through forward and backward combined navigation settlement and weighted fusion processing, providing an accurate initial value for the global optimization process and effectively improving the accuracy of the lever arm value subsequently solved. Furthermore, by constructing an objective function corresponding to the target time period, including the IMU state incremental residual, prior state information, and lever arm value, and performing a minimum solution on the objective function to obtain the lever arm value, this allows for convenient, accurate, and rapid acquisition of the lever arm value between the IMU and GNSS antenna, thereby improving the accuracy of the combined navigation solution.

[0141] See also Figure 6 , Figure 6 1 is a schematic diagram of the structure of a computing device provided in an embodiment of the present application. The computing device 30 includes at least a processor 110, a memory 111 and a receiver 112. The receiver 112 can also be replaced by a communication interface for providing information input to the processor 110. Optionally, the memory 111, the receiver 112, and the processor 110 are connected or coupled via a bus. The computing device 30 can be Figure 2 In the embodiment of the present application, the computing device 30 is used to implement the above Figure 5 The method described in the embodiment.

[0142] The receiver 112 is configured to receive first data sent by the IMU, the first data including the angular velocity and acceleration collected by the IMU within a preset time period. The receiver 112 is also configured to receive second data sent by the GNSS, the second data including the position and velocity collected by the GNSS within a preset time period. The receiver 112 may be a wired interface or a wireless interface. The wired interface may be an Ethernet interface, a local interconnect network (LIN), or the like, and the wireless interface may be a cellular network interface or a wireless local area network interface. The wireless interface may be configured to receive and send information according to one or more other types of wireless communications (e.g., protocols), such as Bluetooth, IEEE 802.11 communication protocol, cellular technology, Worldwide Interoperability for Microwave Access (WiMAX) or LTE (Long Term Evolution), ZigBee protocol, Dedicated Short Range Communications (DSRC), and RFID (Radio Frequency Identification) communications, among others.

[0143] The specific implementation of the processor 110 performing each operation can refer to the specific operations such as performing forward and backward combined navigation solutions on the first data and the second data in the above method embodiment, constructing the objective function, and solving the lever arm value in the objective function. For example, the processor 110 can be used to perform Figure 5 The processor 110 may be composed of one or more general-purpose processors, such as a central processing unit (CPU), or a combination of a CPU and a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0144] The memory 111 may include volatile memory, such as random access memory (RAM); the memory 111 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); the memory 111 may also include a combination of the above types. The memory 111 can store programs and data, wherein the stored programs include: forward integrated navigation solution algorithm, backward integrated navigation solution algorithm, least squares algorithm, etc., and the stored data includes: IMU data, GNSS data, initial value of the lever arm, etc. The memory 111 can exist independently or be integrated into the processor 110.

[0145] also, Figure 6 is merely an example of a computing device 30 that may include Figure 6 More or fewer components may be displayed, or components may be configured differently. Figure 6 The various components shown in the figure can be implemented by hardware, software, or a combination of hardware and software.

[0146] See, Figure 7 4 is a functional structural diagram of a computing device provided in an embodiment of the present application, wherein the computing device 41 includes an acquisition unit 410, a construction unit 411, and a processing unit 412. The computing device 41 can be implemented by hardware, software, or a combination of hardware and software.

[0147] Among them, the acquisition unit 410 is used to obtain the first data collected by the IMU and the second data collected by the GNSS; the first data includes IMU data corresponding to multiple IMU sampling moments, and the second data includes GNSS data corresponding to multiple GNSS sampling moments; the construction unit 411 is used to construct the objective function according to the prior state information, the arm value to be optimized and the IMU state incremental residual between each adjacent GNSS sampling moment in the target time period, and the prior state information and the IMU state incremental residual are obtained based on the first data and the second data; the prior state information represents the state prediction information of the carrier at the first moment; the first moment is the initial moment of the target time period; the IMU and GNSS are installed on the carrier; the processing unit 412 is used to solve the arm value to minimize the function value of the objective function, and the arm value is used for positioning the carrier.

[0148] The functional modules of the computing device 41 can be used to implement Figure 5 The method described in the embodiment. Figure 5 In an embodiment, the acquiring unit 410 may be used to execute S101 , the constructing unit 411 may be used to execute S102 , and the processing unit 412 may be used to execute S103 .

[0149] In the embodiments described above, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0150] It should be noted that, those skilled in the art can see that all or part of the steps in the various methods of the above embodiments can be completed by a program to instruct relevant hardware. The program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0151] The technical solution of the present application, 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. The computer software product is stored in a storage medium and includes a number of instructions for enabling a device (which can be a personal computer, a server, or a network device, a robot, a single-chip microcomputer, a chip, a robot, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

Claims

1. A lever arm value calibration method, characterized in that: The method comprises: Acquire first data collected by the IMU and second data collected by the GNSS, wherein the first data includes IMU data corresponding to multiple IMU sampling moments, and the second data includes GNSS data corresponding to multiple GNSS sampling moments; An objective function is constructed based on prior state information, a lever arm value to be optimized, and an IMU state incremental residual between each adjacent GNSS sampling moment within a target time period, wherein the prior state information and the IMU state incremental residual are obtained based on the first data and the second data; the prior state information represents predicted state information of a carrier at a first moment; the first moment is an initial moment of the target time period; the IMU and the GNSS are installed on the carrier; the objective function includes a GNSS factor corresponding to each GNSS sampling moment within the target time period, wherein the GNSS factor corresponding to the first moment is used to indicate the difference between the to-be-estimated position of the carrier and the position measured by the GNSS at the first moment, and the representation of the to-be-estimated position of the carrier is associated with the lever arm value to be optimized; The lever arm value is solved to minimize the function value of the objective function, and the lever arm value is used for positioning the carrier.

2. The method according to claim 1, characterized in that The prior state information is obtained by weighted summing a forward integrated navigation solution result corresponding to the first moment and a backward integrated navigation solution result corresponding to the first moment, and the prior state information includes a priori position, a priori speed and a priori posture of the carrier at the first moment.

3. The method according to claim 2, characterized in that The a priori state information further includes one or more of a priori accelerometer bias of the carrier at the first moment and a priori gyroscope bias of the carrier at the first moment.

4. The method according to any one of claims 1 to 3, characterized in that The target time period includes a second moment, the second moment and the first moment are adjacent GNSS sampling moments within the target time period, and the IMU state incremental residual between the first moment and the second moment is obtained based on the first state variable to be estimated of the IMU at the first moment, the second state variable to be estimated of the IMU at the second moment, and the IMU data corresponding to each IMU sampling moment between the first moment and the second moment.

5. The method according to claim 4, characterized in that The first state variable to be estimated includes the estimated position of the IMU at the first moment, the GNSS factor corresponding to the first moment is the difference between the estimated position of the carrier at the first moment and the position information in the GNSS data corresponding to the first moment, and the estimated position of the carrier at the first moment is represented by the estimated position of the IMU at the first moment and the lever arm value to be optimized.

6. The method according to claim 5, characterized in that The objective function is the sum of a first function, a second function, and a third function. The first function is used to represent the difference between the prior state information and the first state variable to be estimated. The second function is used to represent the sum of the GNSS factors corresponding to each GNSS sampling moment within the target time period. The third function is used to represent the sum of the IMU state incremental residuals between each adjacent GNSS sampling moment within the target time period.

7. The method according to any one of claims 1 to 6, characterized in that The first data and the second data are acquired by the carrier when performing a U-turn and / or a turning.

8. A device for calibrating lever arm value, characterized in that: The device comprises: an acquisition unit, configured to acquire first data collected by the IMU and second data collected by the GNSS; the first data including IMU data corresponding to a plurality of IMU sampling moments, and the second data including GNSS data corresponding to a plurality of GNSS sampling moments; a construction unit, configured to construct an objective function based on prior state information, a lever arm value to be optimized, and an IMU state incremental residual between each adjacent GNSS sampling moment within a target time period, wherein the prior state information and the IMU state incremental residual are obtained based on the first data and the second data; the prior state information represents predicted state information of a carrier at a first moment; the first moment is an initial moment of the target time period; the IMU and the GNSS are installed on the carrier; the objective function includes a GNSS factor corresponding to each GNSS sampling moment within the target time period, wherein the GNSS factor corresponding to the first moment is used to indicate the difference between the to-be-estimated position of the carrier and the position measured by the GNSS at the first moment, and the representation of the to-be-estimated position of the carrier is associated with the lever arm value to be optimized; A processing unit is used to solve the lever arm value to minimize the function value of the objective function, and the lever arm value is used for positioning the carrier.

9. The device according to claim 8, characterized in that The prior state information is obtained by weighted summing a forward integrated navigation solution result corresponding to the first moment and a backward integrated navigation solution result corresponding to the first moment, and the prior state information includes a priori position, a priori speed and a priori posture of the carrier at the first moment.

10. The device according to claim 9, characterized in that The a priori state information further includes one or more of a priori accelerometer bias of the carrier at the first moment and a priori gyroscope bias of the carrier at the first moment.

11. The device according to any one of claims 8 to 10, characterized in that: The target time period includes a second moment, the second moment and the first moment are adjacent GNSS sampling moments within the target time period, and the IMU state incremental residual between the first moment and the second moment is obtained based on the first state variable to be estimated of the IMU at the first moment, the second state variable to be estimated of the IMU at the second moment, and the IMU data corresponding to each IMU sampling moment between the first moment and the second moment.

12. The device according to claim 11, characterized in that The first state variable to be estimated includes the estimated position of the IMU at the first moment, the GNSS factor corresponding to the first moment is the difference between the estimated position of the carrier at the first moment and the position information in the GNSS data corresponding to the first moment, and the estimated position of the carrier at the first moment is represented by the estimated position of the IMU at the first moment and the lever arm value to be optimized.

13. The device according to claim 12, characterized in that The objective function is the sum of a first function, a second function, and a third function. The first function is used to represent the difference between the prior state information and the first state variable to be estimated. The second function is used to represent the sum of the GNSS factors corresponding to each GNSS sampling moment within the target time period. The third function is used to represent the sum of the IMU state incremental residuals between each adjacent GNSS sampling moment within the target time period.

14. The device according to any one of claims 8 to 13, characterized in that The first data and the second data are acquired by the carrier when performing a U-turn and / or a turning.

15. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program instructions, and the program instructions are used to implement the method according to any one of claims 1 to 7.

16. A device for calibrating lever arm value, characterized in that: The apparatus comprises a memory and a processor, wherein the memory stores computer program instructions, and the processor executes the computer program instructions to enable the apparatus to perform the method according to any one of claims 1 to 7.

17. A computer program product, characterized in that When the computer program product is executed on a processor, the apparatus for lever arm value calibration is caused to perform the method according to any one of claims 1 to 7.

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