Calibration method and system, electronic equipment and storage medium
By acquiring multi-attitude data of the inertial measurement unit at a preset temperature, establishing an error model and performing temperature mapping compensation, the zero-bias drift problem caused by temperature changes in traditional calibration methods is solved, thereby improving the measurement accuracy and stability of the inertial measurement unit.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-04-03
AI Technical Summary
Traditional inertial measurement unit calibration methods are difficult to effectively compensate for zero drift caused by temperature changes in a constant environment, affecting measurement accuracy and stability.
By acquiring measurement data of the inertial measurement unit under multiple different attitudes at a preset temperature, an initial error model is established. The non-orthogonal error, scale factor error, and zero bias are determined using the least squares method. The zero bias is then dynamically compensated online by combining a temperature mapping model and a filtering algorithm.
It significantly improves the measurement accuracy, stability and robustness of the inertial measurement unit in multi-temperature and complex environments, and effectively suppresses zero drift caused by temperature changes.
Smart Images
Figure CN121783196A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of inertial measurement technology, and more specifically, to a calibration method, system, electronic device, and storage medium. Background Technology
[0002] Inertial Measurement Units (IMUs) are prone to measurement errors due to manufacturing defects or environmental changes. Therefore, it is necessary to calibrate the IMU's axis deviation, dimensional deviation, and zero bias before practical application. Traditional IMU calibration methods are usually performed under constant environmental conditions, which makes it difficult to effectively compensate for zero bias drift caused by temperature changes. Summary of the Invention
[0003] In view of the above, the purpose of this application is to overcome the shortcomings of the prior art and provide a calibration method, system, electronic device, and storage medium. This application provides the following technical solution: In a first aspect, the present invention provides a calibration method, the method comprising: Under a preset temperature, measurement data of the inertial measurement unit in multiple different attitudes are acquired; An initial error model of the inertial measurement unit is obtained. Based on the initial error model, multiple measurement data, and preset constraint relationships, the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at the preset temperature are determined by the least squares method. Substituting the nonorthogonal error and the scaling factor error into the initial error model, the target error model is obtained; Based on the target error model, the zero bias corresponding to the inertial measurement unit at different temperatures is determined. Based on the zero bias of the inertial measurement unit at the preset temperature, and the mapping relationship between different temperatures and each zero bias, a zero bias temperature mapping model is established. Obtain the current temperature, input the current temperature into the zero-bias temperature mapping model, and determine the zero bias of the inertial measurement unit at the current temperature; The inertial measurement unit is calibrated based on the zero bias at the current temperature.
[0004] In one embodiment, determining the zero bias of the inertial measurement unit at different temperatures based on the target error model includes: Measurement data of the inertial measurement unit under different attitudes were collected at different temperatures; Based on the target error model and the measurement data, the zero bias of the inertial measurement unit at different temperatures is determined.
[0005] In one embodiment, before calibrating the inertial measurement unit based on the zero bias at the current temperature, the method further includes: The zero bias at the current temperature is smoothed by filtering.
[0006] In one embodiment, the initial error model is an acceleration error model; the inertial measurement unit includes an accelerometer; the multiple measurement data includes multiple acceleration measurement data; the preset constraint relationship includes acceleration constraint relationships; and determining the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at the preset temperature using a parameter fitting method based on the initial error model, the multiple measurement data, and the preset constraint relationships includes: Based on the initial error model, multiple acceleration measurement data, and the acceleration constraint relationship, the non-orthogonal error, scale factor error, and zero bias of the accelerometer at the preset temperature are determined by the least squares method. The acceleration constraint relationship is that the L2 norm of the acceleration measurement data is equal to the L2 norm of the gravitational acceleration.
[0007] In one embodiment, the inertial measurement unit includes a gyroscope, the multiple measurement data include multiple angular velocity measurement data, the preset constraint relationship includes an attitude consistency constraint relationship, and the step of determining the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at the preset temperature using a parameter fitting method based on the initial error model, the multiple measurement data, and the preset constraint relationship includes: Based on the initial error model, multiple angular velocity measurement data, and the attitude consistency constraint relationship, the non-orthogonal error, scale factor error, and zero bias of the gyroscope at the preset temperature are determined by the least squares method.
[0008] In one embodiment, acquiring measurement data of the inertial measurement unit under multiple different attitudes includes: The inertial measurement unit is controlled to sequentially switch to multiple different preset orthogonal attitudes, and remains stationary for a preset duration in each of the orthogonal attitudes; Multiple measurement data are collected by the inertial measurement unit during the process of switching between stationary and attitude states.
[0009] In a second aspect, the present invention provides a calibration system, the system comprising: The data acquisition module is used to acquire measurement data of the inertial measurement unit in multiple different attitudes at a preset temperature; The first determining module is used to obtain the initial error model of the inertial measurement unit, and determine the non-orthogonal error, scale factor error and zero bias of the inertial measurement unit at the preset temperature by using the least squares method based on the initial error model, multiple measurement data and preset constraint relationships. The model determination module is used to substitute the nonorthogonal error and the scaling factor error into the initial error model to obtain the target error model; The second determining module is used to determine the zero bias corresponding to the inertial measurement unit at different temperatures based on the target error model. The mapping model establishment module is used to establish a zero-bias temperature mapping model based on the zero bias of the inertial measurement unit at the preset temperature, and the mapping relationship between different temperatures and each zero bias. The zero bias determination module is used to acquire the current temperature, input the current temperature into the zero bias temperature mapping model, and determine the zero bias of the inertial measurement unit at the current temperature; The calibration module is used to calibrate the inertial measurement unit based on the zero bias at the current temperature.
[0010] In one embodiment, the second determining module is further configured to: Measurement data of the inertial measurement unit under different attitudes were collected at different temperatures; Based on the target error model and the measurement data, the zero bias of the inertial measurement unit at different temperatures is determined.
[0011] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed on the processor, performs the calibration method described in any of the foregoing embodiments.
[0012] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the calibration method described in any of the foregoing embodiments.
[0013] The beneficial effects of this invention are: by fixing the relatively stable axis offset and scale factor of the inertial measurement unit through a phased calibration strategy, and by combining the temperature-zero offset mapping model and filtering algorithm to achieve dynamic online compensation of the zero offset, the zero offset drift caused by temperature changes is effectively suppressed, and the measurement accuracy, stability and robustness of the inertial measurement unit in multi-temperature and complex environments are significantly improved.
[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 A flowchart of the calibration method provided in an embodiment of this application is shown; Figure 2 A schematic diagram of the calibration system provided in an embodiment of this application is shown; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.
[0017] Explanation of key component symbols: 200 - Calibration system; 210 - Data acquisition module; 220 - First determination module; 230 - Model determination module; 240 - Second determination module; 250 - Mapping model establishment module; 260 - Zero bias determination module; 270 - Calibration module; 300 - Electronic equipment; 301 - Transceiver; 302 - Processor; 303 - Memory. Detailed Implementation
[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0019] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.
[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the template description is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0021] Example 1 Traditional inertial measurement unit (IMU) calibration methods are mostly performed in a constant-temperature environment, which makes it difficult to effectively compensate for zero-bias drift caused by temperature changes, thus affecting the measurement accuracy and stability of the IMU. For further information, please refer to [link to relevant documentation / reference]. Figure 1 This application provides a calibration method, including steps S110 to S170.
[0022] Step S110: At a preset temperature, acquire measurement data of the inertial measurement unit under multiple different attitudes.
[0023] In this embodiment, a preset temperature ensures that the inertial measurement unit (IMU) collects data under stable temperature conditions, avoiding interference from initial temperature fluctuations on the basic calibration parameters. The IMU sequentially switches to multiple different attitudes, which must comprehensively cover the different force and motion states of its three measurement axes. During attitude switching, measurement data from the IMU is collected, specifically including acceleration and angular velocity measurement data, providing a complete data source for subsequent error modeling and parameter estimation.
[0024] Its beneficial effects are that the initial stability of the basic calibration is ensured by setting the temperature environment, and the multi-attitude data acquisition can fully cover the error characteristics of the inertial measurement unit, providing sufficient and reliable data support for the accurate solution of the subsequent axis offset matrix, scale factor matrix and initial zero offset.
[0025] In one embodiment, acquiring measurement data of the inertial measurement unit in multiple different attitudes includes: controlling the inertial measurement unit to sequentially switch to multiple different preset orthogonal attitudes, and remaining stationary for a preset duration in each of the orthogonal attitudes; and collecting multiple measurement data of the inertial measurement unit during the stationary and attitude switching processes.
[0026] In this embodiment, the multiple different attitudes include six mutually perpendicular preset orthogonal attitudes (including front-facing, back-facing, left-facing, right-facing, top-facing, and bottom-facing of the inertial measurement unit). The inertial measurement unit sequentially switches to each preset orthogonal attitude and remains stationary in each preset orthogonal attitude for a preset duration, such as 30 seconds. During the stationary phase of the inertial measurement unit in each preset orthogonal attitude, corresponding static acceleration data and static angular velocity data are collected. During the dynamic phase of the inertial measurement unit switching from one preset orthogonal attitude to another, dynamic angular velocity data is collected synchronously. Finally, the static acceleration data, static angular velocity data, and dynamic angular velocity data are integrated to form a complete set of multiple measurement data.
[0027] Its beneficial effects are that the six preset orthogonal attitudes can cover the error distribution of the three measurement axes of the inertial measurement unit to the greatest extent, the preset static duration requirement ensures that the static data can accurately reflect the real error characteristics under the action of gravity, and the synchronous acquisition of static and dynamic data respectively meets the data source requirements of accelerometer calibration (relying on static gravity modulus constraints) and gyroscope calibration (relying on dynamic angular velocity integral and attitude consistency constraints), providing high-quality data guarantee for the accurate calibration of subsequent basic parameters.
[0028] Step S120: Obtain the initial error model of the inertial measurement unit. Based on the initial error model, multiple measurement data, and preset constraint relationships, determine the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at the preset temperature using the least squares method.
[0029] In this embodiment, the initial error model is: ,in, Represents measurement data, Represents real data. This represents a non-orthogonal error matrix. Represents the scale factor error matrix. This represents a zero-biased vector. Specifically, , , This represents measurement noise. The initial error model includes the mathematical correlation forms of nonorthogonal error, scale factor error, and zero bias correlation, which clearly defines the error propagation relationship between the measured data and the real data. By collecting real inputs and corresponding measurement data from multiple known attitudes of the inertial measurement unit, the nonorthogonal error matrix, scale factor error matrix, and zero bias vector are determined using the least squares algorithm.
[0030] Based on multiple measurement data (including acceleration and angular velocity measurement data) and combined with preset constraints (adapting to different calibration requirements of accelerometers and gyroscopes), the initial error model, measurement data, and constraints are transformed into a set of optimization equations. By minimizing the sum of squared residuals between the measurement data and the model predictions using the least squares method, the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at the preset temperature are obtained.
[0031] Its beneficial effects are that by using the initial error model to clarify the error propagation logic, and by combining the preset constraint relationship to provide a reliable basis for parameter solution, the application of the least squares method can effectively reduce the influence of measurement noise, accurately obtain the basic error parameters, and lay a solid foundation for subsequent fixing of non-orthogonal errors and scale factor errors, and targeted temperature drift zero bias compensation, thus ensuring the accuracy and consistency of the entire calibration process.
[0032] In one embodiment, the initial error model is an acceleration error model, the inertial measurement unit includes an accelerometer, the multiple measurement data includes multiple acceleration measurement data, the preset constraint relationship includes an acceleration constraint relationship, and the step of determining the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at the preset temperature by means of parameter fitting based on the initial error model, the multiple measurement data, and the preset constraint relationship includes: determining the non-orthogonal error, scale factor error, and zero bias of the accelerometer at the preset temperature by means of least squares based on the initial error model, the multiple acceleration measurement data, and the acceleration constraint relationship, wherein the acceleration constraint relationship is: the L2 norm of the acceleration measurement data is equal to the L2 norm of gravitational acceleration.
[0033] In this embodiment, the measurement data in the above-mentioned initial error model are used. Acceleration measurement data By substitution, we obtain the acceleration error model: ,in, This indicates the non-orthogonal error of the accelerometer at the preset temperature. This indicates the scale factor error of the accelerometer at the preset temperature. This indicates the zero bias of the accelerometer at the preset temperature.
[0034] Static acceleration measurement data is filtered from multiple measurement data by using a preset threshold determined by the combination of acceleration variances. Specifically, the acceleration variances on the three axes of the inertial measurement unit are calculated separately, and then the threshold is used to determine whether the inertial measurement unit is stationary. If the variance is less than the threshold P, it indicates that the inertial measurement unit is stationary at this moment, as shown in the following formula:
[0035] Acceleration measurement data when the inertial measurement unit is stationary is selected. The acceleration constraint relationship is that "the L2 norm of the acceleration measurement data equals the L2 norm of gravitational acceleration," as detailed in the following formula:
[0036] Here, N represents the total number of all static sample data, and n=1 to n=N represents the calculation of the difference between the L2 norm squared of the acceleration measurement and the L2 norm squared of the gravitational acceleration for each sample. The optimization objective of the least squares method is to minimize the sum of the squared differences of all samples, thereby optimizing the parameters. This represents gravitational acceleration. Substituting this constraint relationship and static acceleration measurement data into the acceleration error model, an optimization objective for the residual sum of squares is constructed. This objective is then solved using the least squares method to obtain the accelerometer's non-orthogonal error, scale factor error, and zero bias at a preset temperature.
[0037] Its beneficial effects are that it designs a dedicated error model and constraint relationship for the measurement characteristics of the accelerometer, uses the physical characteristics of gravitational acceleration to provide a stable and accurate constraint benchmark, the screening of static acceleration data can avoid dynamic interference, and the application of the least squares method can effectively make the solution parameters converge, ensuring the accuracy of the solution of the three types of error parameters of the accelerometer, and providing a reliable accelerometer error correction basis for the joint calibration of the inertial measurement unit.
[0038] In one embodiment, the inertial measurement unit includes a gyroscope, the plurality of measurement data includes a plurality of angular velocity measurement data, the preset constraint relationship includes an attitude consistency constraint relationship, and the step of determining the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at the preset temperature by means of a parameter fitting method based on the initial error model, the plurality of measurement data, and the preset constraint relationship includes: determining the non-orthogonal error, scale factor error, and zero bias of the gyroscope at the preset temperature by means of a least squares method based on the initial error model, the plurality of angular velocity measurement data, and the attitude consistency constraint relationship.
[0039] In this embodiment, the measurement data in the above-mentioned initial error model are used. Angular velocity measurement data By substitution, we obtain the angular velocity error model: ,in, This indicates the non-orthogonal error of the gyroscope at the preset temperature. This represents the scale factor error of the gyroscope at the preset temperature. This indicates that the gyroscope has zero bias at the preset temperature.
[0040] From multiple measurement data, dynamic angular velocity measurement data corresponding to dynamic segments between two adjacent static attitudes are selected. Simultaneously, attitude references obtained from accelerometer calibration under these two adjacent static attitudes are acquired. Based on attitude consistency constraints, the dynamic angular velocity measurement data are substituted into the angular velocity error model, and the gyroscope-predicted attitude is calculated using an integral algorithm. .
[0041] The formula employs the multi-order Runge-Kutta method. Accelerometer calibration uses data collected in a static state; however, gyroscope calibration relies on dynamic data. The preceding and following segments of the dynamic data are both static data segments, which are numbered. This represents the attitude of the (k-1)th segment after calibration. Since the (k-1)th segment of data is all in a static state, It can also be regarded as the posture of the dynamic data segment at the previous moment. This indicates that the attitude calculated using calibrated acceleration data has high reliability; while This indicates that it is based on the previous time. The attitude is calculated by integrating the gyroscope data collected from the dynamic data. The solution process uses the multi-order Runge-Kutta method for integration calculation.
[0042] Construct an error optimization function between the predicted attitude and the accelerometer attitude reference: The function is solved by the least squares method to determine the non-orthogonal error, scale factor error, and zero bias of the gyroscope at a preset temperature.
[0043] Its beneficial effects are that, taking into account the characteristic that gyroscopes have no natural physical reference when stationary, it effectively compensates for the limitations of gyroscope calibration by associating the precise attitude reference of the accelerometer with the attitude consistency constraint relationship. The application of dynamic angular velocity data can comprehensively reflect the error characteristics of the gyroscope, and the least squares solution method ensures the reliability of error parameter estimation. It achieves accurate calibration of the three types of error parameters of the gyroscope, providing key support for improving the overall measurement accuracy of the inertial measurement unit.
[0044] By combining static acceleration data and dynamic gyroscope data, and employing the multi-order Runge-Kutta method for high-precision integration calculations, the accuracy and stability of attitude estimation can be effectively improved, while reducing the accumulation of errors caused by sensor noise and drift. Simultaneously, the least squares method is introduced to optimize and adjust attitude errors, further enhancing the accuracy and robustness of the calibration results, thereby achieving more reliable and stable sensor calibration and attitude calculation.
[0045] Step S130: Substitute the nonorthogonal error and the scaling factor error into the initial error model to obtain the target error model.
[0046] In this embodiment, the non-orthogonal error and scale factor error of the inertial measurement unit are used as stable basic errors that will not fluctuate significantly with temperature changes. Then, the non-orthogonal error and scale factor error are substituted into the initial error model used in step S120 to replace the corresponding error terms in the initial error model, so that only the zero bias variable that changes with temperature is retained in the initial error model, and finally the target error model is formed.
[0047] Its beneficial effect is that by fixing relatively stable non-orthogonal errors and scaling factor errors, the structure of the error model is simplified, so that the subsequent zero bias correlation calculation does not need to repeatedly consider the influence of the two types of stable errors, and focuses on the discovery of the influence law of temperature on zero bias. This lays a simple and reliable model foundation for the accurate determination of zero bias at different temperatures, and improves the calculation efficiency and pertinence of the subsequent temperature drift compensation process.
[0048] Step S140: Based on the target error model, determine the zero bias corresponding to the inertial measurement unit at different temperatures.
[0049] In this embodiment, at different temperatures, the inertial measurement unit is controlled to switch to multiple preset orthogonal attitudes in sequence. After remaining stationary for a preset time in each attitude, the corresponding acceleration measurement data and angular velocity measurement data are collected. Then, the target error model obtained in step S130 is called. This model has fixed the non-orthogonal error and scale factor error, and only retains the zero bias as a parameter to be determined. The measurement data collected at each temperature are substituted into the target error model, and the zero bias corresponding to the inertial measurement unit at each temperature is obtained by back-calculation through data fitting or parameter solving algorithms.
[0050] Its beneficial effect is that by selectively collecting multi-pose measurement data at different temperatures and combining it with a simplified target error model, it is possible to accurately obtain the complete data relationship between zero bias and temperature.
[0051] In one embodiment, determining the zero bias of the inertial measurement unit (IMU) at different temperatures based on the target error model includes: collecting measurement data of the IMU at different attitudes under different temperatures; and determining the zero bias of the IMU at different temperatures based on the target error model and the measurement data.
[0052] In this embodiment, multiple different temperature values covering actual application scenarios are first set, and temperature environments corresponding to each temperature value are constructed sequentially to ensure that temperature fluctuations in each environment are controlled within a preset range. Under each stable temperature environment, the inertial measurement unit is controlled to switch to multiple preset orthogonal attitudes sequentially, and remains stationary for a preset duration under each orthogonal attitude, collecting corresponding static acceleration measurement data and static angular velocity measurement data. Subsequently, the static acceleration measurement data and static angular velocity measurement data collected at different temperatures are substituted into the target error model. Through parameter solving algorithms such as the least squares method, the accelerometer zero bias and gyroscope zero bias of the inertial measurement unit under that temperature environment are back-calculated. The above process is repeated to complete the determination of the corresponding zero bias under all different temperature environments.
[0053] Step S150: Based on the zero bias of the inertial measurement unit at the preset temperature, and the mapping relationship between different temperatures and each zero bias, establish a zero bias temperature mapping model.
[0054] In this embodiment, the inertial measurement unit zero bias at the preset temperature (i.e., the initial zero bias at the reference temperature) obtained in step S120 is first extracted. Then, the zero bias data at different temperatures and corresponding to each temperature, determined in step S140, are called to form a complete correlation dataset including temperature and zero bias. Subsequently, the distribution characteristics of this correlation dataset are analyzed to determine whether the zero bias changes linearly or nonlinearly with temperature. If it is linear, a linear mapping relationship is constructed, for example: ,in, This indicates the zero offset corresponding to temperature T. preset temperature The zero bias is given by K, which is a temperature coefficient vector describing the slope of the zero bias as a function of temperature.
[0055] If it is nonlinear, a polynomial mapping relationship is constructed, for example:
[0056] Where i is the degree of the polynomial. The slope is the corresponding slope, and n is the polynomial being fitted, which is an nth-degree polynomial. The specific value of n is selected by referring to the collected temperature and the fitting trend of the zero bias.
[0057] The correlation between temperature and zero bias is quantified by data fitting algorithm, and a zero bias temperature mapping model is finally established. This model takes temperature as input and zero bias as output, and clarifies the quantitative correspondence rules between the two.
[0058] Its beneficial effect is that it transforms the abstract law of zero bias change with temperature into a computable quantitative model, providing a standardized tool for real-time acquisition of zero bias at different temperatures, avoiding the subjectivity and uncertainty of zero bias estimation when temperature changes, providing support for dynamic temperature drift compensation, and ensuring the accuracy and repeatability of the compensation process.
[0059] Step S160: Obtain the current temperature, input the current temperature into the zero-bias temperature mapping model, and determine the zero bias of the inertial measurement unit at the current temperature.
[0060] In this embodiment, the current temperature is collected and substituted into the zero-bias temperature mapping model as an input parameter; the zero-bias value corresponding to the inertial measurement unit at the current temperature is obtained by solving the zero-bias temperature mapping model.
[0061] Its beneficial effects are that it realizes the real-time and automatic determination of zero bias at the current temperature, eliminating the need to repeat the complex multi-pose data acquisition and parameter solving process, and greatly improving the efficiency of zero bias acquisition; at the same time, based on the zero bias temperature mapping model, it ensures the accuracy of the zero bias value at the current temperature, providing timely and reliable input for real-time correction of the measurement data of the inertial measurement unit, and ensuring the measurement stability of the inertial measurement unit in a dynamically changing temperature environment.
[0062] Step S170: Calibrate the inertial measurement unit based on the zero bias at the current temperature.
[0063] In this embodiment, the measurement data of the inertial measurement unit at the current temperature is corrected based on the zero bias at the current temperature. The corrected measurement value is... .
[0064] The above formula is the inverse derivation of this formula. , It is the final value obtained by combining axis offset, scale deviation and zero offset (this value is very close to the true value, but not the same as the true value). , This represents the inverse of the axis-biased matrix and the scale-biased matrix.
[0065] In one embodiment, before calibrating the inertial measurement unit based on the zero bias at the current temperature, the method further includes: performing a smoothing filter on the zero bias at the current temperature.
[0066] In this embodiment, the zero bias at the current temperature is input into the preset filtering algorithm, such as Kalman filtering or moving average filtering. The preset filtering algorithm smooths the zero bias at the current temperature, suppressing measurement noise and sudden fluctuations, and obtaining a smoothed zero bias. Finally, the original measurement data of the inertial measurement unit is calibrated based on the smoothed zero bias.
[0067] Its beneficial effect is that by adding smoothing filtering before calibration, interference components in the current temperature zero bias are effectively filtered out, avoiding the influence of noise and sudden changes on the final calibration results, making the zero bias parameters more stable and reliable, thereby improving the robustness of the calibration process and the accuracy of the calibration results, and ensuring the continuity and consistency of the output data of the inertial measurement unit.
[0068] The calibration method provided in this application includes: acquiring measurement data of an inertial measurement unit (IMU) at multiple different attitudes under a preset temperature; acquiring an initial error model of the IMU; determining the non-orthogonal error, scale factor error, and zero bias of the IMU at the preset temperature using the least squares method based on the initial error model, the multiple measurement data, and preset constraint relationships; substituting the non-orthogonal error and the scale factor error into the initial error model to obtain a target error model; determining the zero bias corresponding to the IMU at different temperatures based on the target error model; establishing a zero bias temperature mapping model based on the zero bias of the IMU at the preset temperature and the mapping relationship between different temperatures and each zero bias; acquiring the current temperature; inputting the current temperature into the zero bias temperature mapping model to determine the zero bias of the IMU at the current temperature; and calibrating the IMU based on the zero bias at the current temperature. This application uses a phased calibration strategy to fix the relatively stable axis offset and scale factor of the inertial measurement unit (IMU), and combines a temperature-zero offset mapping model and filtering algorithm to achieve dynamic online zero offset compensation. This effectively suppresses zero offset drift caused by temperature changes and significantly improves the measurement accuracy, stability and robustness of the IMU in multi-temperature and complex environments.
[0069] Example 2 In addition, please see Figure 2 This application also provides a calibration system 200, comprising: The data acquisition module 210 is used to acquire measurement data of the inertial measurement unit under multiple different attitudes at a preset temperature; The first determining module 220 is used to obtain the initial error model of the inertial measurement unit, and determine the non-orthogonal error, scale factor error and zero bias of the inertial measurement unit at the preset temperature by the least squares method based on the initial error model, multiple measurement data and preset constraint relationships. The model determination module 230 is used to substitute the non-orthogonal error and the scaling factor error into the initial error model to obtain the target error model; The second determining module 240 is used to determine the zero bias corresponding to the inertial measurement unit at different temperatures based on the target error model. The mapping model establishment module 250 is used to establish a zero-bias temperature mapping model based on the zero bias of the inertial measurement unit at the preset temperature and the mapping relationship between different temperatures and each zero bias. The zero bias determination module 260 is used to acquire the current temperature, input the current temperature into the zero bias temperature mapping model, and determine the zero bias of the inertial measurement unit at the current temperature; The calibration module 270 is used to calibrate the inertial measurement unit based on the zero bias at the current temperature.
[0070] In one embodiment, the second determining module 240 is further configured to: collect measurement data of the inertial measurement unit under different attitudes at different temperatures; and determine the zero bias of the inertial measurement unit at different temperatures based on the target error model and the measurement data.
[0071] The calibration system 200 provided in this application embodiment can execute the calibration method provided in the above method embodiment 1. To avoid repetition, it will not be described again here.
[0072] Example 3 Furthermore, this embodiment of the invention provides an electronic device 300, including a memory 303 and a processor 302. The memory 303 stores a computer program, and the computer program executes the calibration method provided in Embodiment 1 when it runs on the processor 302.
[0073] For details, please see Figure 3 The electronic device 300 includes a transceiver 301, a bus interface, and a processor 302. The processor 302 is used to: acquire measurement data of an inertial measurement unit (IMU) at multiple different attitudes under a preset temperature; acquire an initial error model of the IMU; determine the non-orthogonal error, scale factor error, and zero bias of the IMU at the preset temperature using the least squares method based on the initial error model, multiple measurement data, and preset constraint relationships; substitute the non-orthogonal error and the scale factor error into the initial error model to obtain a target error model; determine the zero bias of the IMU at different temperatures based on the target error model; establish a zero bias temperature mapping model based on the zero bias of the IMU at the preset temperature and the mapping relationship between different temperatures and each zero bias; acquire the current temperature; input the current temperature into the zero bias temperature mapping model to determine the zero bias of the IMU at the current temperature; and calibrate the IMU based on the zero bias at the current temperature.
[0074] In this embodiment of the application, the electronic device 300 further includes a memory 303. Figure 1In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors 302 (represented by processor 302) and various circuits of memory 303 (represented by memory 303). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 301 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. The processor 302 is responsible for managing the bus architecture and general processing, and the memory 303 can store data used by the processor 302 during operation.
[0075] The electronic device 300 provided in this application embodiment can execute the calibration method provided in the above-described method embodiment 1. To avoid repetition, it will not be described again here.
[0076] Example 4 Furthermore, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the calibration method provided in Embodiment 1.
[0077] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.
[0078] The computer-readable storage medium provided in this embodiment can implement the calibration method provided in Embodiment 1. To avoid repetition, it will not be described again here.
[0079] In all examples shown and described herein, any specific values should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.
[0080] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0081] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.
Claims
1. A calibration method, characterized in that, The method includes: Under a preset temperature, measurement data of the inertial measurement unit in multiple different attitudes are acquired; An initial error model of the inertial measurement unit is obtained. Based on the initial error model, multiple measurement data, and preset constraint relationships, the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at the preset temperature are determined by the least squares method. Substituting the nonorthogonal error and the scaling factor error into the initial error model, the target error model is obtained; Based on the target error model, the zero bias corresponding to the inertial measurement unit at different temperatures is determined. Based on the zero bias of the inertial measurement unit at the preset temperature, and the mapping relationship between different temperatures and each zero bias, a zero bias temperature mapping model is established. Obtain the current temperature, input the current temperature into the zero-bias temperature mapping model, and determine the zero bias of the inertial measurement unit at the current temperature; The inertial measurement unit is calibrated based on the zero bias at the current temperature.
2. The calibration method according to claim 1, characterized in that, The step of determining the zero bias corresponding to the inertial measurement unit at different temperatures based on the target error model includes: Measurement data of the inertial measurement unit under different attitudes were collected at different temperatures; Based on the target error model and the measurement data, the zero bias of the inertial measurement unit at different temperatures is determined.
3. The calibration method according to claim 2, characterized in that, Before calibrating the inertial measurement unit based on the zero bias at the current temperature, the method further includes: The zero bias at the current temperature is smoothed by filtering.
4. The calibration method according to claim 3, characterized in that, The initial error model is an acceleration error model. The inertial measurement unit includes an accelerometer. Multiple measurement data points include multiple acceleration measurement data points. The preset constraint relationship includes acceleration constraint relationships. The step of determining the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at a preset temperature using a parameter fitting method based on the initial error model, the multiple measurement data points, and the preset constraint relationships includes: Based on the initial error model, multiple acceleration measurement data, and the acceleration constraint relationship, the non-orthogonal error, scale factor error, and zero bias of the accelerometer at the preset temperature are determined by the least squares method. The acceleration constraint relationship is that the L2 norm of the acceleration measurement data is equal to the L2 norm of the gravitational acceleration.
5. The calibration method according to claim 3, characterized in that, The inertial measurement unit includes a gyroscope; the multiple measurement data include multiple angular velocity measurement data; the preset constraint relationship includes an attitude consistency constraint relationship; and the step of determining the non-orthogonal error, scale factor error, and zero bias of the inertial measurement unit at a preset temperature using a parameter fitting method based on the initial error model, the multiple measurement data, and the preset constraint relationship includes: Based on the initial error model, multiple angular velocity measurement data, and the attitude consistency constraint relationship, the non-orthogonal error, scale factor error, and zero bias of the gyroscope at the preset temperature are determined by the least squares method.
6. The calibration method according to claim 4 or 5, characterized in that, The acquisition of measurement data from the inertial measurement unit at multiple different attitudes includes: The inertial measurement unit is controlled to sequentially switch to multiple different preset orthogonal attitudes, and remains stationary for a preset duration in each of the orthogonal attitudes; Multiple measurement data are collected by the inertial measurement unit during the process of switching between stationary and attitude states.
7. A calibration system, characterized in that, The system includes: The data acquisition module is used to acquire measurement data of the inertial measurement unit in multiple different attitudes at a preset temperature; The first determining module is used to obtain the initial error model of the inertial measurement unit, and determine the non-orthogonal error, scale factor error and zero bias of the inertial measurement unit at the preset temperature by using the least squares method based on the initial error model, multiple measurement data and preset constraint relationships. The model determination module is used to substitute the nonorthogonal error and the scaling factor error into the initial error model to obtain the target error model; The second determining module is used to determine the zero bias corresponding to the inertial measurement unit at different temperatures based on the target error model. The mapping model establishment module is used to establish a zero-bias temperature mapping model based on the zero bias of the inertial measurement unit at the preset temperature, and the mapping relationship between different temperatures and each zero bias. The zero bias determination module is used to acquire the current temperature, input the current temperature into the zero bias temperature mapping model, and determine the zero bias of the inertial measurement unit at the current temperature; The calibration module is used to calibrate the inertial measurement unit based on the zero bias at the current temperature.
8. The calibration system according to claim 7, characterized in that, The second determining module is further configured to: Measurement data of the inertial measurement unit under different attitudes were collected at different temperatures; Based on the target error model and the measurement data, the zero bias of the inertial measurement unit at different temperatures is determined.
9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when run on the processor, executes the calibration method according to any one of claims 1-6.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the calibration method according to any one of claims 1-6.