A method for omnidirectional performance evaluation of an inertial measurement unit and related apparatus

Abstract

This application discloses a comprehensive performance evaluation method and related apparatus for inertial measurement units (IMUs). The method includes: comprehensively evaluating the IMU module's performance through noise covariance analysis in a static state, time difference stability testing, attitude angle integration testing during figure-eight motion, and accelerometer spectrum analysis. Simultaneously, trajectory integration verification is performed using RTK information acquired during figure-eight motion, further ensuring the IMU's positioning accuracy and stability in complex motion environments. This application, through multi-dimensional and comprehensive testing methods, can effectively and accurately evaluate the overall performance of the IMU, providing a basis for selecting high-quality IMU modules and providing reliable and stable sensor support for autonomous vehicles, thereby improving the safety and stability of the entire autonomous driving system.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, specifically to the technical fields of multi-dimensional performance analysis and sensor performance evaluation, and particularly to a comprehensive performance evaluation method and related device for an inertial measurement unit. Background Technology

[0002] An inertial measurement unit (IMU) contains three single-axis accelerometers and three single-axis gyroscopes. The accelerometers detect the acceleration signals of an object along three independent axes of the carrier coordinate system, while the gyroscopes detect the angular velocity signals of the carrier relative to the navigation coordinate system. By measuring the angular velocity and acceleration of the object in three-dimensional space, the object's attitude can be calculated. This sensor data is crucial for achieving precise control and positioning of devices and systems; therefore, the IMU plays a vital role in the fields of intelligent driving and autonomous driving assistance.

[0003] In the field of autonomous driving technology, selecting high-performance IMUs is a prerequisite and foundation for the development of autonomous driving technology, providing reliable and stable sensor data. Therefore, rigorous factory testing and verification of IMUs is extremely necessary. However, existing IMU performance testing methods are simplistic and neglect important performance evaluation factors, failing to accurately test IMUs. This results in unstable performance of factory-shipped IMUs, frequent anomalies, and potential safety hazards for autonomous driving. Summary of the Invention

[0004] This application provides a comprehensive performance evaluation method and related apparatus for inertial measurement units (IMUs), which improves the comprehensiveness and accuracy of IMU testing and evaluation by evaluating the IMU performance from multiple angles and perspectives, thereby ensuring the reliability of autonomous vehicle control and driving safety.

[0005] The technical solution is as follows:

[0006] Firstly, a comprehensive performance evaluation method for inertial measurement units is provided, including:

[0007] Acquire test data of the inertial measurement unit (IMU) to be evaluated; wherein the test data includes at least multiple sets of IMU data and real-time dynamic differential RTK data during motion, and each set of IMU data includes timestamps, accelerometer data and gyroscope data;

[0008] Static analysis is performed on the IMU based on the multiple sets of IMU data to determine the stability of the time difference between adjacent frames of the IMU, as well as the standard deviation of each axis in the accelerometer and gyroscope of the IMU.

[0009] Based on the multiple sets of IMU data and RTK data, the IMU is dynamically analyzed to determine the spectral results of the IMU during motion, as well as the angle and trajectory of the IMU during motion.

[0010] The performance of the IMU is comprehensively evaluated using different analysis results in sequence, according to the evaluation priority set for different analysis results.

[0011] In one possible implementation, determining the stability of the time difference between adjacent frames of the IMU specifically includes:

[0012] Based on the timestamps carried in the test data, the time interval between each adjacent frame of IMU data in the test data is calculated respectively;

[0013] The time interval obtained through statistical calculation is used as the time difference stability between adjacent frames of the IMU.

[0014] In one possible implementation, determining the standard deviation of each axis in the IMU's accelerometer and gyroscope specifically includes:

[0015] Based on the accelerometer data in the test data, the standard deviation of each axis of the IMU's accelerometer is determined using the following formula:

[0016]

[0017] Where j represents the axial direction of the accelerometer, taking values ​​of x, y, z; n is the number of IMU data sets acquired; acc j_i μ is the value of the i-th accelerometer data on the j-axis; acc_j It is the average value of the data along the j-axis of the accelerometer;

[0018] Based on the gyroscope data in the test data, the standard deviation of each axis of the IMU's gyroscope is determined using the following formula:

[0019]

[0020] Where j represents the axis of the gyroscope, taking values ​​of x, y, z; n is the total number of IMU data collected; gyr j_i μ is the value of the i-th gyroscope data on the j-axis; gyr_j It is the average value of the data along the j-axis of the gyroscope.

[0021] In one possible implementation, determining the spectral results of the IMU during motion specifically includes:

[0022] Based on the accelerometer and gyroscope data in the test data, the discrete Fourier transform is used to convert the data values ​​of each axis in the IMU's accelerometer and gyroscope from time-domain signals to frequency-domain signals.

[0023] In one possible implementation, determining the angle and trajectory of the IMU during motion specifically includes:

[0024] Based on multiple sets of IMU data and RTK data during the figure-eight motion, the motion trajectory ρ of the IMU during the motion process is determined by the following formula. e+1 speed v e+1 and angle q e+1 :

[0025]

[0026] Where, ρ e+1 It is the trajectory at time e+1, ρ e It is the trajectory of motion at time e, v e+1 It is the velocity at time e+1, v e It is the velocity at time e, and g represents the acceleration due to gravity. Represents the attitude matrix, acc mear The value represents the accelerometer reading, Δt represents the time difference between time e and time e+1, and q represents the accelerometer reading. e+1 Let q represent the quaternion at time e+1. e Describe the quaternion at time e. The axis of rotation is represented by ω, the value of the gyroscope is represented by ω, and the rotation angle is represented by θ. The initial trajectory, speed, and angle during the motion are all determined by the RTK data.

[0027] In one possible implementation, the performance of the IMU is comprehensively evaluated sequentially using different analysis results according to the evaluation priority set for different analysis results, specifically including:

[0028] Based on the trajectory of the IMU during the motion, the trajectory closure error after the motion is completed is calculated; it is determined whether the trajectory closure error is not less than a set percentage of the total motion path. If so, it is determined that the IMU has a hardware fault; otherwise, it is determined that the IMU does not have a hardware fault.

[0029] When it is determined that the IMU does not have a hardware fault, it is determined whether the maximum deviation of the time difference between adjacent frames of the IMU is less than a set time threshold. If so, it is determined that the IMU has high data acquisition stability and uniformity; otherwise, it is determined that the IMU is unusable.

[0030] When it is determined that the IMU has high stability and uniformity, it is judged whether the standard deviation of each axis in the accelerometer and gyroscope of the IMU meets the set constraint conditions. If so, it is determined that the IMU data has low noise interference; otherwise, it is determined that the IMU is unusable.

[0031] When it is determined that the IMU data has relatively low noise interference, it is determined whether the total energy of the IMU data with frequency domain signals greater than the set frequency band is greater than the set percentage of the standard noise baseline. If so, it is determined that the IMU needs to be checked, its installation rigidity needs to be improved, or vibration damping needs to be added. Otherwise, it is determined that the vibration damping of the IMU is reliable.

[0032] In one possible implementation, determining whether the standard deviation of each axis in the IMU's accelerometer and gyroscope meets set constraints specifically includes:

[0033] Determine whether the standard deviation of the z-axis in the gyroscope of the IMU is less than a first threshold. If it is, determine that the noise of the heading angle of the IMU satisfies the first constraint condition; otherwise, determine that the IMU is unusable.

[0034] When it is determined that the noise of the heading angle of the IMU meets the first constraint condition, it is determined whether the standard deviation of the z-axis in the accelerometer of the IMU is less than the second threshold. If it is, it is determined that the noise of the gravity component of the IMU meets the second constraint condition; otherwise, it is determined that the IMU is unusable.

[0035] When it is determined that the noise of the gravity component of the IMU meets the second constraint condition, it is determined whether the standard deviation of the x-axis and the standard deviation of the y-axis in the gyroscope of the IMU are less than the third threshold, and whether the standard deviation of the x-axis and the standard deviation of the accelerometer are less than the fourth threshold. If they are, it is determined that the IMU data of the IMU has small noise interference; otherwise, it is determined that the IMU is unusable.

[0036] Secondly, a comprehensive performance evaluation device for an inertial measurement unit is provided, comprising:

[0037] The acquisition module is used to acquire test data of the inertial measurement unit (IMU) to be evaluated; wherein, the test data includes at least multiple sets of IMU data and real-time dynamic differential RTK data during the motion process, and each set of IMU data includes timestamps, accelerometer data and gyroscope data;

[0038] The static analysis module is used to perform static analysis on the IMU based on the multiple sets of IMU data, so as to determine the time difference stability between adjacent frames of the IMU, and the standard deviation of each axis in the accelerometer and gyroscope of the IMU.

[0039] The dynamic analysis module is used to perform dynamic analysis on the IMU based on the multiple sets of IMU data and RTK data, so as to determine the spectral results of the IMU during the motion, as well as the angle and trajectory of the IMU during the motion.

[0040] The evaluation module is used to comprehensively evaluate the performance of the IMU by sequentially using different analysis results according to the evaluation priority order set for different analysis results.

[0041] Thirdly, an electronic device is provided, comprising:

[0042] At least one processor; and

[0043] A memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the methods described above and any possible implementations.

[0045] Fourthly, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement the aspects described above and any possible implementation thereof.

[0046] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the aspects and any possible implementations described above.

[0047] The beneficial effects of the technical solution provided in this application include at least the following:

[0048] As can be seen from the above technical solutions, the embodiments of this application comprehensively evaluate the performance of the IMU module through noise covariance analysis in a static state, time difference stability testing, attitude angle integration testing under figure-eight motion, and accelerometer spectrum analysis. Simultaneously, trajectory integration verification is performed using RTK information acquired during figure-eight motion, further ensuring the positioning accuracy and stability of the IMU in complex motion environments. This application, through multi-dimensional and comprehensive testing methods, can effectively and accurately evaluate the overall performance of the IMU, providing a basis for selecting high-quality IMU modules and providing reliable and stable sensor support for autonomous vehicles, thereby improving the safety and stability of the entire autonomous driving system.

[0049] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram illustrating the steps of the comprehensive performance evaluation method for an inertial measurement unit provided in the embodiments of this application.

[0052] Figure 2 This is a schematic diagram of a comprehensive performance evaluation process for an IMU provided in another embodiment of this application.

[0053] Figure 3a and Figure 3b The diagram shows the time difference curves plotted for the stability test of the time difference between adjacent frames for module 1 and module 2.

[0054] Figure 4 This is a schematic diagram comparing the standard deviation curves of the z-axis noise of the accelerometers in modules 1 and 2.

[0055] Figure 5 This is a schematic diagram comparing the standard deviation curves of the z-axis noise of the gyroscopes in Module 1 and Module 2.

[0056] Figure 6a and Figure 6b These are schematic diagrams of the spectrum curves of module 1 and module 2, respectively.

[0057] Figure 7a and Figure 7b These are schematic diagrams of the figure-eight angle integrals for module 1 and module 2, respectively.

[0058] Figure 8a and Figure 8b These are schematic diagrams of the figure-eight trajectory integrals for Module 1 and Module 2, respectively.

[0059] Figure 9 This is a structural block diagram of an omnidirectional performance evaluation device for an inertial measurement unit provided in one embodiment of this application.

[0060] Figure 10 This is a block diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0061] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These embodiments should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0062] Obviously, the described embodiments are only some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0063] It should be noted that the terminal devices involved in the embodiments of this application may include, but are not limited to, smart devices such as mobile phones, personal digital assistants (PDAs), wireless handheld devices, and tablet computers; the display devices may include, but are not limited to, personal computers, televisions, and other devices with display functions.

[0064] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0065] Given that existing IMU performance testing methods are simplistic and neglect crucial performance evaluation factors, they fail to accurately test IMUs, leading to unstable performance and frequent anomalies in factory-shipped IMUs. Therefore, this application proposes a novel IMU performance testing and evaluation scheme. The inventive concept involves comprehensively evaluating the IMU module's performance through noise covariance analysis in a static state, time difference stability testing, attitude angle integration testing during figure-eight motion, and accelerometer spectrum analysis. Simultaneously, trajectory integration verification is performed using RTK information acquired during figure-eight motion, further ensuring the IMU's positioning accuracy and stability in complex motion environments. This application, through multi-dimensional and comprehensive testing methods, can effectively and accurately evaluate the overall performance of the IMU, providing a basis for selecting high-quality IMU modules and offering reliable and stable sensor support for autonomous vehicles, thereby improving the safety and stability of the entire autonomous driving system.

[0066] Reference Figure 1The diagram illustrates the steps of an omnidirectional performance evaluation method for an inertial measurement unit (IMU) provided in this embodiment. The entity executing this evaluation method can be an IMU omnidirectional performance evaluation device, which can be a computing device or software module with data computing, processing, and storage capabilities, such as a computer, tablet computer, smartphone, smart wearable device, or other hardware device, or a software module or component integrated into such hardware device. This application does not limit this to any particular type.

[0067] like Figure 1 As shown, the evaluation method may include the following steps:

[0068] Step 102: Obtain test data of the inertial measurement unit (IMU) to be evaluated; wherein the test data includes at least multiple sets of IMU data and real-time dynamic differential RTK data during motion, and each set of IMU data includes timestamps, accelerometer data and gyroscope data.

[0069] In this application, the testing process can be divided into two phases: a static testing phase and a dynamic testing phase. In the static testing phase, the IMU to be evaluated is in a static state, and test data is acquired during this phase. This static testing phase data can include multiple sets of IMU data, each set containing a timestamp, accelerometer data, and gyroscope data. In the dynamic testing phase, the IMU to be evaluated is in motion, specifically in a figure-eight motion, i.e., repeatedly moving along approximately the same trajectory. The dynamic testing phase data also includes multiple sets of IMU data, each set containing a timestamp, accelerometer data, gyroscope data, and RTK data from the dynamic testing phase.

[0070] It should be understood that test data acquired from the IMU, whether during the static or dynamic testing phase, can be stored in the IMU data file defined in the specified file path. The data format can be: IMU_data = {timestamps, acc_x, acc_y, acc_z, gyr_x, gyr_y, gyr_z}, where timestamps are the timestamps, accelerometer data is (acc_x, acc_y, acc_z), and gyroscope data is (gyr_x, gyr_y, gyr_z).

[0071] Similarly, RTK data for the dynamic testing phase can be stored in the RTK data file defined in the specified file path. Its data format can be: RTK_data = {timestamps, utm_px, utm_py, utm_pz, rotation_qx, rotation_qy, rotation_qz, rotation_qw}, where the timestamp is timestamps, the UTM position coordinates are (utm_px, utm_py, utm_pz), and the quaternion represents the pose as (rotation_qx, rotation_qy, rotation_qz, rotation_qw). Alternatively, the RTK data format can be: RTK_data = {timestamps, utm_px, utm_py, utm_pz, rotation_qx, rotation_qy, rotation_qz, rotation_w, utm_vx, utm_vy, utm_vz}, which adds the three-dimensional velocity (utm_vx, utm_vy, utm_vz).

[0072] Step 104: Perform static analysis on the IMU based on the multiple sets of IMU data to determine the time difference stability between adjacent frames of the IMU, and the standard deviation of each axis in the accelerometer and gyroscope of the IMU.

[0073] In this application, static analysis of the IMU mainly includes two aspects: First, analyzing the stability of the time difference between adjacent frames, i.e., calculating the time interval of IMU data, and plotting the variation curve of the time interval for evaluation reference. Second, analyzing the noise characteristics of the IMU in a static state, i.e., calculating the standard deviation of each axis of the accelerometer and gyroscope to reflect the static noise of the IMU. In fact, besides the above-mentioned static analysis method, the performance of the IMU can also be evaluated based on temperature drift or zero-bias stability, etc., and this application does not limit this approach.

[0074] Optionally, when determining the time difference stability between adjacent frames of the IMU, this application may specifically calculate the time interval between IMU data in each adjacent frame of the test data based on the timestamp carried in the test data; the statistically calculated time interval is used as the time difference stability between adjacent frames of the IMU.

[0075] In practice, the time difference Δt between adjacent frames can be calculated using the following formula. Then, multiple time differences can be statistically analyzed to determine the regularity and stability of IMU data sampling based on the uniformity of these time differences:

[0076] Δt=ti+1 -t i

[0077] Among them, t i and t i+1 These represent the i-th time point and the (i+1)-th time point in the IMU data sequence, respectively.

[0078] Considering that sensors in an IMU typically sample at a fixed frequency, ideally, the time difference obtained from sampling, i.e., the time interval, should be uniform. However, if there are problems with the IMU's internal clock or it is subject to external interference, the calculated time interval will fluctuate significantly, thus affecting the reliability of the IMU data and the accuracy of subsequent processing. Therefore, this step can evaluate the IMU's performance stability by statistically analyzing the fluctuations in the obtained time intervals. Furthermore, for easier observation and analysis, a time interval graph can be plotted to visually reflect whether the IMU sampling is uniform, thereby better evaluating the IMU's performance.

[0079] Optionally, when determining the standard deviation of each axis in the accelerometer and gyroscope of the IMU, this application can specifically determine the standard deviation of each axis in the accelerometer of the IMU based on the accelerometer data in the test data using the following formula:

[0080]

[0081] Where j represents the axial direction of the accelerometer, taking values ​​of x, y, z; n is the number of IMU data sets acquired; acc j_i μ is the value of the i-th accelerometer data on the j-axis; acc_j It is the average value of the data along the j-axis of the accelerometer;

[0082] Based on the gyroscope data in the test data, the standard deviation of each axis of the IMU's gyroscope is determined using the following formula:

[0083]

[0084] Where j represents the axis of the gyroscope, taking values ​​of x, y, z; n is the total number of IMU data collected; gyr j_i μ is the value of the i-th gyroscope data on the j-axis; gyr_j It is the average value of the data along the j-axis of the gyroscope.

[0085] In the test data obtained above, assuming n sets of IMU data are acquired, the standard deviations of the IMU accelerometer along the x, y, and z axes can be calculated using the formula above: σ acc_noise_x σ acc_noise_y σ acc_noise_z ; and the standard deviations of the IMU gyroscope's x, y, and z axes were calculated respectively: σgyr_noise_x σ gyr_noise_y σ gyr_noise_z .

[0086] Considering that standard deviation reflects the dispersion of data, i.e., the stability and accuracy of accelerometer and gyroscope outputs, the standard deviation of the x, y, and z-axis data of the accelerometer can be calculated using the above method. This quantifies the noise level and stability of the IMU accelerometer, allowing for an assessment of the IMU's performance based on noise level and stability. It should be understood that when the IMU is stationary and unaffected by external factors, its accelerometer x and y-axis outputs are close to zero, while the z-axis output shows the current gravity value. If the accelerometer has noise or bias, the output data will fluctuate, leading to an increase in standard deviation. Furthermore, a larger standard deviation for each axis of the accelerometer output data indicates greater or more interference or noise affecting the accelerometer, resulting in poorer IMU performance. Conversely, a smaller standard deviation indicates less or no interference or noise affecting the accelerometer, resulting in better IMU performance. Similarly, the standard deviation of the x, y, and z-axis data of the gyroscope can be calculated using the above formula to quantify the stability and accuracy of the IMU gyroscope, allowing for an assessment of the IMU's performance based on stability and accuracy. It should be understood that when the IMU is stationary and unaffected by external influences, its gyroscope x, y, and z axis outputs are close to zero. If the gyroscope is noisy, the output data will fluctuate, and the standard deviation will increase. Furthermore, the larger the standard deviation of the gyroscope's output data for each axis, the greater the interference or noise affecting the gyroscope, and the worse the IMU's performance. Conversely, the smaller the standard deviation of the gyroscope's output data for each axis, the less interference or noise affecting the gyroscope, and the better the IMU's performance. Particularly noteworthy is the need for strict control of the gyroscope's z-axis in this application. This is because the gyroscope's z-axis affects the integral of the vehicle's heading angle during actual use, and the greater the noise, the more severe the heading angle drift, ultimately affecting the position integral due to attitude errors. Therefore, to ensure the accuracy of the subsequent vehicle navigation system, strict control and evaluation of the gyroscope's z-axis noise are necessary.

[0087] Regarding the evaluation method for standard deviation, in practice, it can be implemented according to a set priority order: first, evaluate the noise of the gyroscope's z-axis; second, evaluate the noise of the accelerometer's z-axis; and finally, evaluate the noise of the accelerometer and gyroscope's x and y axes. The evaluation principle remains: the lower the noise, the better; that is, the smaller the standard deviation, the better. Specifically, the optimal standard deviation for the gyroscope's z-axis is on the order of 0.025 deg / s, and the optimal standard deviation for the accelerometer's z-axis is on the order of 0.001 m / s. 2 The speeds are on the order of 0.01 deg / s for the gyroscope's x and y axes, and on the order of 0.001 m / s for the accelerometer. 2In terms of magnitude, as long as these indicators all meet the magnitude criteria, it means that the IMU is usable. The specific evaluation of its merits can be carried out by sorting and selecting according to the size of the standard deviation.

[0088] As can be seen, the static analysis method in this step can be used to assess the noise and stability of the accelerometer and gyroscope in the IMU from aspects such as time difference stability and standard deviation. This allows for a multi-faceted and accurate evaluation of IMU performance, providing a favorable basis for selecting high-performance IMUs.

[0089] Step 106: Perform dynamic analysis on the IMU based on the multiple sets of IMU data and RTK data to determine the spectral results of the IMU during motion, as well as the angle and trajectory of the IMU during motion.

[0090] In this application, dynamic analysis of the IMU can include two aspects: first, performing spectral analysis on the accelerometer and gyroscope based on the IMU data generated during motion; second, performing angle integration and trajectory integration based on the IMU data generated during figure-eight motion. In fact, besides the above dynamic analysis, other analyses can be performed based on the IMU motion data, such as attitude analysis or velocity analysis, and this application does not limit this to any particular analysis.

[0091] Optionally, when determining the spectral results of the IMU during motion, this application may specifically use discrete Fourier transform based on the accelerometer data and gyroscope data in the test data to convert the data values ​​of each axis in the accelerometer and gyroscope of the IMU from time-domain signals to frequency-domain signals.

[0092] In practical implementation, the following discrete Fourier transform can be used to convert IMU data from a time-domain signal to a frequency-domain signal. For a discrete signal x[n] of length N (e.g., the x-axis of an accelerometer), the Fourier transform is as follows:

[0093]

[0094] X[k] is the kth frequency component in the frequency domain, x[n] is the nth sampling point in the time domain (e.g., the nth data value of the x-axis of the accelerometer), N is the length of the data signal, j is the imaginary unit, and k is the frequency index, with a range of 0≤k≤N-1.

[0095] In this application, considering that accelerometer and gyroscope data in IMUs are typically time-domain signals, a Fourier transform can be used to convert the time-domain signal into a frequency-domain signal to assess the noise and stability of the IMU data from a signal perspective. This facilitates analysis of the frequency-domain signal, identifying frequency components such as vibration, noise, or periodic motion. Furthermore, the frequency distribution of noise in the frequency-domain signal can be analyzed to identify the noise source of the IMU data, such as mechanical vibration or electronic noise. If the IMU's accelerometer or gyroscope exhibits abnormal behavior within certain frequency ranges, it indicates a potential problem with the IMU's installation. Therefore, by converting IMU data from the time domain to the frequency domain and performing spectral analysis, the noise level of the IMU's accelerometer or gyroscope can be assessed, and potential sensor vibrations during IMU installation on the vehicle can be effectively evaluated. Moreover, installation improvements or vibration damping measures can be implemented to address mechanical vibrations caused by unstable installation.

[0096] Optionally, when determining the angle and trajectory of the IMU during its movement, this application's solution can specifically determine the IMU's motion trajectory ρ during the movement based on multiple sets of IMU data and RTK data during the figure-eight motion, using the following formulas. e+1 speed v e+1 and angle q e+1 :

[0097]

[0098] Where, ρ e+1 It is the trajectory at time e+1, ρ e It is the trajectory of motion at time e, v e+1 It is the velocity at time e+1, v e It is the velocity at time e, and g represents the acceleration due to gravity. Represents the attitude matrix, acc mear The value represents the accelerometer reading, Δt represents the time difference between time e and time e+1, and q represents the accelerometer reading. e+1 Let q represent the quaternion at time e+1. e Describe the quaternion at time e. The axis of rotation is represented by ω, the value of the gyroscope is represented by ω, and the rotation angle is represented by θ. The initial trajectory, speed, and angle during the motion are all determined by the RTK data.

[0099] In practical implementation, the integral performance of the IMU's pitch, roll, and yaw angles can be evaluated based on the IMU data generated by the figure-eight motion. The position, attitude, and velocity provided by real-time dynamic differential (RTK) data, combined with the above formulas, are used for integration initialization. This effectively ensures that the integrated figure-eight trajectory will not drift excessively due to incorrect initial velocity and attitude, while also ensuring that the IMU's drift and self-noise performance can be evaluated through the figure-eight trajectory. Furthermore, figure-eight trajectory integration offers the following advantages: 1) Comprehensive evaluation of dynamic performance: The figure-eight trajectory covers various motion states, allowing for a comprehensive evaluation of the IMU's performance under different motion conditions; 2) Detection of error accumulation: Integration provides estimates of position and attitude, enabling the detection of error accumulation during long-term operation, such as attitude drift and position error; 3) Verification of system stability: Figure-eight trajectory integration can verify the stability of the IMU system, such as its noise suppression and error recovery capabilities under dynamic conditions. In summary, by combining the above formulas with figure-eight trajectory, angle, and velocity integration, the performance of the IMU under dynamic conditions can be comprehensively evaluated, providing an important basis for IMU selection and application.

[0100] Step 108: According to the evaluation priority order set for different analysis results, use different analysis results in sequence to conduct a comprehensive evaluation of the IMU's performance.

[0101] In this application, the evaluation priority of different analysis results can be ranked based on the magnitude and perspective of their impact on IMU performance. The specific priorities can be flexibly adjusted according to the user's actual needs.

[0102] Optionally, this application prioritizes the trajectory integral generated by the figure-eight motion, the time difference between adjacent frames, the standard deviation of each axis of the accelerometer and gyroscope, and the spectral analysis, from high to low. Thus, when comprehensively evaluating the IMU's performance using different analysis results according to the established evaluation priority order, specifically, based on the IMU's trajectory during motion, the trajectory closure error after completing the motion can be calculated; it can be determined whether the trajectory closure error is not less than a set percentage of the total motion path. If so, it is determined that the IMU has a hardware fault; otherwise, it is determined that the IMU does not have a hardware fault. When it is determined that the IMU does not have a hardware fault, it is determined whether the maximum deviation of the time difference between adjacent frames of the IMU is less than a set time threshold. If so, it is determined that the IMU has high data acquisition stability. If the IMU is determined to have high stability and uniformity, then the standard deviation of each axis in the accelerometer and gyroscope of the IMU is determined to meet the set constraint conditions. If yes, then the IMU data of the IMU is determined to have low noise interference. Otherwise, the IMU is determined to be unusable. If the IMU data of the IMU is determined to have low noise interference, then the total energy of the IMU data with frequency domain signals greater than the set frequency band is determined to be greater than the set percentage of the nominal noise baseline. If yes, then the IMU needs to be checked, its installation rigidity needs to be improved, or vibration damping needs to be added. Otherwise, the vibration damping of the IMU is determined to be reliable.

[0103] When judging the standard deviation of each axis in the accelerometer and gyroscope, the following constraints can be set: First, determine whether the standard deviation of the z-axis in the IMU's gyroscope is less than a first threshold. If so, the noise of the IMU's heading angle meets the first constraint; otherwise, the IMU is deemed unusable. Then, when the noise of the IMU's heading angle meets the first constraint, determine whether the standard deviation of the z-axis in the IMU's accelerometer is less than a second threshold. If so, the noise of the IMU's gravity component meets the second constraint; otherwise, the IMU is deemed unusable. Further, when the noise of the IMU's gravity component meets the second constraint, determine whether the standard deviations of the x-axis and y-axis in the IMU's gyroscope are less than a third threshold, and whether the standard deviations of the x-axis and y-axis in the accelerometer are less than a fourth threshold. If so, the IMU data has minimal noise interference; otherwise, the IMU is deemed unusable.

[0104] In specific implementation, the basic functions of the IMU can be verified first to ensure the integrity of the IMU gyroscope and accelerometer. This standard only needs to consider that the angle change during the figure-eight movement follows a sine function distribution and the trajectory is in the shape of a figure-eight. Then, verify the stability of the time difference of the collected data. After that, evaluate the noise of each axis. Finally, perform spectrum analysis. The following analyzes the specific evaluation process in combination with Figure 2 the flowchart shown below.

[0105] (1) Basic functional verification (dynamic figure-eight movement trajectory integration)

[0106] This evaluation item is to verify the basic functional integrity of the accelerometer and gyroscope and exclude hardware damage. Specifically, the dynamic figure-eight movement (spatial "∞" shaped trajectory) can be used to stimulate the dynamic response of each axis of the IMU, and the trajectory closure error is calculated by integration. If the trajectory closure error (position / attitude drift) is less than 5% of the total figure-eight path length, it indicates that the sensor has no hardware failure. It should be understood that this test only verifies the basic functions and does not reflect the accuracy index. Among them, 5% is the value listed in this embodiment and is not used as a limitation. In actual evaluation scenarios, different percentages can be set according to the type of vehicle end loaded, the type of IMU, and the accuracy.

[0107] (2) Verification of data acquisition stability

[0108] After the verification in (1) is completed and the integration result meets the expectations, the stability of data acquisition is verified. In this embodiment, the set time threshold can be set to 0.1 s. Then, if the maximum deviation ΔT_max of the timestamp interval is less than 0.1 s, it is determined that the IMU is available; otherwise, it is not available.

[0109] Furthermore, if a more refined evaluation is desired, the mean square error (σ_Δt) can be added on this basis. σ_Δt should be less than 1% of the nominal sampling period of the IMU. If it is greater, it is confirmed that the IMU is not available. Or, when both conditions of ΔT_max < 0.1 s and σ_Δt < 1% of the nominal sampling period of the IMU are met, the IMU is available; and the smaller ΔT_max is, the better the IMU is, and the smaller σ_Δt is, the better the IMU is.

[0110] (3) Noise evaluation

[0111] This evaluation item includes the three-axis standard deviation of the accelerometer and the three-axis standard deviation of the gyroscope. Therefore, different constraint conditions can be set according to the influence degree of the axial direction of different sensors on the IMU performance.

[0112] ① The first constraint condition

[0113] Different weights were assigned to different axes, with the weight order being: gyroscope Z-axis noise (0.8) > accelerometer Z-axis noise (0.6) > accelerometer X / Y-axis noise (0.2) > gyroscope X / Y-axis noise (0.1).

[0114] The overall availability assessment index can be expressed by the following formulas: Gyroscope availability value = 0.8 * gyroscope Z-axis noise + 0.1 * gyroscope X-axis noise + 0.1 * gyroscope Y-axis noise ≤ 0.022° / s; Accelerometer availability value = 0.6 * accelerometer Z-axis noise + 0.2 * accelerometer X-axis noise + 0.2 * accelerometer Y-axis noise ≤ 0.001m / s² 2 That is, the availability value of the gyroscope is ≤0.022° / s, and the availability value of the accelerometer is ≤0.001m / s². 2 At that time, the IMU is available.

[0115] Furthermore, the availability weight of the gyroscope is 0.7, and the availability weight of the accelerometer is 0.3. Then, based on availability, the overall availability value is calculated as 0.7 * gyroscope availability value + 0.3 * accelerometer availability value, and the smaller the result, the better.

[0116] It should be understood that the weight values ​​involved in this application are only for illustrative purposes and do not limit their specific values ​​or ranges. They can be flexibly adjusted according to the actual test scenario and test requirements.

[0117] ② Second type of constraint

[0118] Determine if the standard deviation of the z-axis in the IMU's gyroscope is less than 0.025° / s. If so, the noise of the IMU's heading angle satisfies the first constraint condition, and its noise has a small impact on the long-term stability of attitude calculation. Otherwise, the IMU is determined to be unusable.

[0119] Then, after determining that the noise of the IMU's heading angle meets the first constraint condition, it is determined whether the standard deviation of the z-axis in the IMU's accelerometer is less than 0.001 m / s². 2 If so, the noise of the gravity component of the IMU is determined to satisfy the second constraint condition, and its noise will not affect the pitch / roll angle calculation error or has a small impact; otherwise, the IMU is determined to be unusable.

[0120] Furthermore, when determining that the noise of the gravity component of the IMU satisfies the second constraint condition, it is determined whether the standard deviation of the x-axis and y-axis in the IMU's gyroscope is less than 0.01° / s, and whether the standard deviation of the x-axis and y-axis in the accelerometer is less than 0.001m / s. 2 If so, the IMU data is determined to have minimal noise interference, and the IMU is ultimately determined to be usable; otherwise, the IMU is determined to be unusable.

[0121] It should be understood that the values ​​of the first threshold, the second threshold, the third threshold, and the fourth threshold involved in this application are only examples and do not limit their specific values ​​or ranges. They can be flexibly adjusted according to the actual test scenario and test requirements.

[0122] (4) Spectrum Analysis

[0123] If the high-frequency vibration energy (e.g., >100Hz) of the converted frequency domain signal exceeds 20% of the nominal noise baseline, the IMU needs to be replaced or another installation method needs to be selected. This situation is considered to have high-frequency noise interference. Within this threshold range, the IMU can be used, and the less high-frequency noise, the better.

[0124] It should be understood that the percentage of the nominal noise baseline involved in this application is only an example and does not limit its specific value or range. It can be flexibly adjusted according to the actual test scenario and test requirements.

[0125] Therefore, by conducting a comprehensive evaluation from multiple perspectives using the aforementioned different evaluation items and comparing them according to different priorities, high-performance IMUs can be quickly, accurately, and efficiently screened in actual evaluation and testing scenarios, providing reliable and stable sensor support for autonomous vehicles, thereby improving the safety and performance of the entire autonomous driving system.

[0126] The evaluation method of this application is used below to conduct a performance comparison analysis of the two IMU modules (mozu1 and module 2) based on different evaluation items.

[0127] Reference Figure 3a and Figure 3b The figure shows the time difference curves plotted for the stability test of the time difference between adjacent frames for modules 1 and 2, respectively. The time difference for module 1 is 0.00284-0.018s, and the time difference for module 2 is 0.0087-0.0111s. Module 1 is less stable than module 2, and module 2 performs better than module 1. The horizontal axis represents the sequence index of the IMU data, and the vertical axis represents the time difference between adjacent IMU timestamps (in seconds). The red boxes indicate the minimum and maximum inter-frame time difference.

[0128] Reference Figure 4 The figure shows a comparison of the standard deviation curves of the accelerometer z-axis noise for modules 1 and 2. Module 1 (red): 0.0047 m / s^2 < Module 2 (blue): 0.0080 m / s^2; Module 1 has better performance. The horizontal axis represents the IMU data index, and the vertical axis represents the acceleration value in the z-axis direction (unit: m / s^2).

[0129] Reference Figure 5 The figure shows a comparison of the standard deviation curves of the gyroscope z-axis noise for modules 1 and 2. Module 1 (red): 0.0241 deg / s > Module 2 (blue): 0.0146 deg / s; Module 2 has superior performance. The horizontal axis represents the IMU data index, and the vertical axis represents the z-axis angular velocity value (in deg / s).

[0130] Reference Figure 6a and Figure 6b The figures shown are schematic diagrams of the frequency spectrum curves of module 1 and module 2, respectively. Frequency spectrum analysis: Module 2 has lower amplitude in the high-frequency range, less noise, and better performance. The horizontal axis represents the acceleration frequency component along the z-axis (in Hz), and the vertical axis represents the amplitude of the acceleration signal at each frequency component along the z-axis.

[0131] Reference Figure 7a and Figure 7b The figures shown are schematic diagrams of the figure-eight angle integration for module 1 and module 2, respectively. The two modules have comparable performance. The horizontal axis represents time, and the vertical axis represents angle.

[0132] Reference Figure 8a and Figure 8b The figures shown are schematic diagrams of the figure-eight trajectory integration for module 1 and module 2, respectively. The two modules have comparable performance. The horizontal axis represents time, and the vertical axis represents pose.

[0133] After comprehensive comparison, it was confirmed that module 2 outperformed module 1. This demonstrates that the comprehensive IMU performance evaluation method proposed in this application can accurately, quickly, and efficiently evaluate IMU performance and determine IMU usability. Furthermore, it can rapidly select the best-performing IMU from among many IMUs, providing reliable and stable sensor support for autonomous vehicles, thereby improving the safety and stability of the entire autonomous driving system.

[0134] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0135] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0136] Figure 9This application provides a structural block diagram of an omnidirectional performance evaluation device for an inertial measurement unit according to one embodiment of the present application. Figure 9 As shown. The omnidirectional performance evaluation device 900 of the inertial measurement unit in this embodiment may include an acquisition module 901, a static analysis module 902, a dynamic analysis module 903, and an evaluation module 904. The system includes: an acquisition module 901 for acquiring test data of the inertial measurement unit (IMU) to be evaluated; wherein the test data includes at least multiple sets of IMU data and real-time dynamic differential (RTK) data during motion, and each set of IMU data includes a timestamp, accelerometer data, and gyroscope data; a static analysis module 902 for performing static analysis on the IMU based on the multiple sets of IMU data to determine the time difference stability between adjacent frames of the IMU, and the standard deviation of each axis in the accelerometer and gyroscope of the IMU; a dynamic analysis module 903 for performing dynamic analysis on the IMU based on the multiple sets of IMU data and RTK data to determine the spectral results of the IMU during motion, and the angle and trajectory of the IMU during motion; and an evaluation module 904 for performing a comprehensive evaluation of the IMU's performance using different analysis results in sequence according to the evaluation priority order set for different analysis results.

[0137] It should be noted that some or all of the omnidirectional performance evaluation device of the inertial measurement unit in this embodiment may be an application located on the local terminal, or it may be a plugin or software development kit (SDK) or other functional unit set in the application located on the local terminal, or it may be a processing engine located on the network-side server, or it may be a distributed system located on the network side. This embodiment does not make any special limitations on this.

[0138] It is understood that the application may be a native program installed on the local terminal, or it may be a web application of a browser on the local terminal. This embodiment does not limit this.

[0139] Optionally, in one possible implementation of this embodiment, when determining the time difference stability between adjacent frames of the IMU, the static analysis module 902 is specifically used to calculate the time interval between each adjacent frame of IMU data in the test data based on the timestamp carried by the test data; the statistically calculated time interval is used as the time difference stability between adjacent frames of the IMU.

[0140] Optionally, in one possible implementation of this embodiment, when determining the standard deviation of each axis in the accelerometer and gyroscope of the IMU, the static analysis module 902 is specifically used to determine the standard deviation of each axis in the accelerometer of the IMU based on the accelerometer data in the test data using the following formula:

[0141]

[0142] Where j represents the axial direction of the accelerometer, taking values ​​of x, y, z; n is the number of IMU data sets acquired; acc j_i μ is the value of the i-th accelerometer data on the j-axis; acc_j It is the average value of the data along the j-axis of the accelerometer;

[0143] And, based on the gyroscope data in the test data, the standard deviation of each axis in the IMU's gyroscope is determined using the following formula:

[0144]

[0145] Where j represents the axis of the gyroscope, taking values ​​of x, y, z; n is the total number of IMU data collected; gyr j_i μ is the value of the i-th gyroscope data on the j-axis; gyr_j It is the average value of the data along the j-axis of the gyroscope.

[0146] Optionally, in one possible implementation of this embodiment, when determining the spectral results of the IMU during motion, the dynamic analysis module 903 is specifically used to convert the time-domain signals of the accelerometer and gyroscope data of the IMU into frequency-domain signals by using discrete Fourier transform based on the accelerometer data and gyroscope data in the test data.

[0147] Optionally, in one possible implementation of this embodiment, when determining the angle and trajectory of the IMU during its movement, the dynamic analysis module 903 specifically determines the motion trajectory ρ of the IMU during its movement based on multiple sets of IMU data and RTK data during the figure-eight motion, using the following formulas. e+1 speed v e+1 and angle q e+1 :

[0148]

[0149] Where, ρ e+1 It is the trajectory at time e+1, ρ e It is the trajectory of motion at time e, v e+1 It is the velocity at time e+1, v e It is the velocity at time e, and g represents the acceleration due to gravity. Represents the attitude matrix, acc mear The value represents the accelerometer reading, Δt represents the time difference between time e and time e+1, and q represents the accelerometer reading. e+1 Let q represent the quaternion at time e+1. e Describe the quaternion at time e. The axis of rotation is represented by ω, the value of the gyroscope is represented by ω, and the rotation angle is represented by θ. The initial trajectory, speed, and angle during the motion are all determined by the RTK data.

[0150] Optionally, in one possible implementation of this embodiment, when the evaluation module 904 performs a comprehensive evaluation of the IMU's performance using different analysis results sequentially according to the evaluation priority order set for different analysis results, it is specifically used to calculate the trajectory closure error after the IMU completes the motion based on the IMU's trajectory during the motion process; determine whether the trajectory closure error is not less than a set percentage of the total motion path; if so, determine that the IMU has a hardware fault; otherwise, determine that the IMU does not have a hardware fault; when it is determined that the IMU does not have a hardware fault, determine whether the maximum deviation of the time difference between adjacent frames of the IMU is less than a set time threshold; if so, determine that the IMU has a hardware fault. The IMU must possess high data acquisition stability and uniformity; otherwise, the IMU is deemed unusable. When the IMU is determined to have high stability and uniformity, it is determined whether the standard deviation of each axis in the IMU's accelerometer and gyroscope meets the set constraints. If so, the IMU data is determined to have minimal noise interference; otherwise, the IMU is deemed unusable. When the IMU data is determined to have minimal noise interference, it is determined whether the total energy of the IMU data with frequency domain signals exceeding the set frequency band exceeds the set percentage of the standard noise baseline. If so, the IMU needs to be checked, its installation rigidity improved, or vibration damping added; otherwise, the IMU's vibration damping is deemed reliable.

[0151] Optionally, in one possible implementation of this embodiment, when the evaluation module 904 determines whether the standard deviation of each axis in the accelerometer and gyroscope of the IMU meets the set constraint conditions, it is specifically used to determine whether the standard deviation of the z-axis in the gyroscope of the IMU is less than a first threshold. If so, it is determined that the noise of the heading angle of the IMU meets the first constraint condition; otherwise, it is determined that the IMU is unusable. When it is determined that the noise of the heading angle of the IMU meets the first constraint condition, it is determined whether the standard deviation of the z-axis in the accelerometer of the IMU is less than a second threshold. If so, it is determined that the noise of the gravity component of the IMU meets the second constraint condition; otherwise, it is determined that the IMU is unusable. When it is determined that the noise of the gravity component of the IMU meets the second constraint condition, it is determined whether the standard deviation of the x-axis and y-axis in the gyroscope of the IMU is less than a third threshold, and whether the standard deviation of the x-axis and y-axis in the accelerometer is less than a fourth threshold. If so, it is determined that the IMU data of the IMU has relatively small noise interference; otherwise, it is determined that the IMU is unusable.

[0152] In this embodiment, the performance of the IMU module can be comprehensively evaluated through noise covariance analysis in a static state, time difference stability testing, attitude angle integration testing during figure-eight motion, and accelerometer spectrum analysis. Simultaneously, trajectory integration verification is performed using RTK information acquired during figure-eight motion, further ensuring the positioning accuracy and stability of the IMU in complex motion environments. This application, through multi-dimensional and comprehensive testing methods, can effectively and accurately evaluate the overall performance of the IMU, providing a basis for selecting high-quality IMU modules and providing reliable and stable sensor support for autonomous vehicles, thereby improving the safety and stability of the entire autonomous driving system.

[0153] One embodiment of this application provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the omnidirectional performance evaluation method for an inertial measurement unit as described above.

[0154] One embodiment of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for comprehensive performance evaluation of an inertial measurement unit.

[0155] One embodiment of this application provides an electronic device including a processor and a memory, wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the omnidirectional performance evaluation method for an inertial measurement unit as described above.

[0156] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0157] Figure 10 A schematic block diagram of an example electronic device 1000 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0158] like Figure 10 As shown, the electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. The RAM 1003 may also store various programs and data required for the operation of the electronic device 1000. The computing unit 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0159] Multiple components in electronic device 1000 are connected to I / O interface 1005, including: input unit 1006, such as keyboard, mouse, etc.; output unit 1007, such as various types of displays, speakers, etc.; storage unit 1008, such as disk, optical disk, etc.; and communication unit 1009, such as network card, modem, wireless transceiver, etc. Communication unit 1009 allows electronic device 1000 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0160] The computing unit 1001 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1001 performs the various methods and processes described above, such as methods for blind zone detection. For example, in some embodiments, the omnidirectional performance evaluation method of the inertial measurement unit can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1000 via ROM 1002 and / or communication unit 1009. When the computer program is loaded into RAM 1003 and executed by the computing unit 1001, one or more steps of the omnidirectional performance evaluation method of the inertial measurement unit described above can be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured by any other suitable means (e.g., by means of firmware) to perform a comprehensive performance evaluation method for the inertial measurement unit.

[0161] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, at least one input device, and at least one output device.

[0162] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0163] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0164] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0165] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0166] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0167] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0168] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for comprehensive performance evaluation of an inertial measurement unit, characterized in that, include: Acquire test data of the inertial measurement unit (IMU) to be evaluated; wherein the test data includes at least multiple sets of IMU data and real-time dynamic differential RTK data during motion, and each set of IMU data includes timestamps, accelerometer data and gyroscope data; Static analysis is performed on the IMU based on the multiple sets of IMU data to determine the stability of the time difference between adjacent frames of the IMU, as well as the standard deviation of each axis in the accelerometer and gyroscope of the IMU. The IMU is dynamically analyzed based on the multiple sets of IMU data and RTK data. The spectral analysis of the accelerometer and gyroscope is performed based on the IMU data generated during the motion to determine the spectral results of the IMU during the motion. The angle integration and trajectory integration are performed based on the IMU data generated during the figure-eight motion to determine the angle and trajectory of the IMU during the motion. The performance of the IMU is comprehensively evaluated using different analysis results in sequence, according to the evaluation priority set for different analysis results.

2. The method as described in claim 1, characterized in that, Determining the stability of the time difference between adjacent frames of the IMU specifically includes: Based on the timestamps carried in the test data, the time interval between each adjacent frame of IMU data in the test data is calculated respectively; The time interval obtained through statistical calculation is used as the time difference stability between adjacent frames of the IMU.

3. The method as described in claim 1, characterized in that, Determine the standard deviation of each axis in the accelerometer and gyroscope of the IMU, specifically including: Based on the accelerometer data in the test data, the standard deviation of each axis of the IMU's accelerometer is determined using the following formula: in, This indicates the axis of the accelerometer, with values ​​of x, y, and z. This is the number of groups of IMU data collected; It is the first Accelerometer data in The values ​​of the axis; It is an accelerometer The average value of the data on the axis; Based on the gyroscope data in the test data, the standard deviation of each axis of the IMU's gyroscope is determined using the following formula: in, This indicates the axis of the gyroscope, with values ​​of x, y, and z. This is the total amount of IMU data collected; It is the first Each gyroscope data in The values ​​of the axis; It is a gyroscope The average value of the data on the axis.

4. The method as described in claim 1, characterized in that, Based on the IMU data generated during motion, spectral analysis is performed on the accelerometer and gyroscope to determine the IMU's spectral results during motion, specifically including: Based on the accelerometer and gyroscope data in the test data, the discrete Fourier transform is used to convert the data values ​​of each axis in the IMU's accelerometer and gyroscope from time-domain signals to frequency-domain signals.

5. The method as described in claim 1, characterized in that, The angle and trajectory of the IMU during its motion are determined by performing angle integration and trajectory integration based on the IMU data generated during the figure-eight motion. Specifically, this includes: Based on multiple sets of IMU data and RTK data during the figure-eight motion, the motion trajectory of the IMU during the motion process is determined by the following formulas. ,speed and angle : in, It is the trajectory at time e+1. It is the trajectory of motion at time e. It is the velocity at time e+1. It is the velocity at time e. Represents gravitational acceleration. Represents the attitude matrix. This indicates the value of the accelerometer. This represents the time difference between time e and time e+1. Describe the quaternion at time e+1. Describe the quaternion at time e. Indicates the axis of rotation. This indicates the value of the gyroscope. The rotation angle is indicated; the initial trajectory, speed, and angle during the motion are all determined using the RTK data.

6. The method according to any one of claims 1-5, characterized in that, Following the evaluation priority order set for different analysis results, the performance of the IMU is comprehensively evaluated using different analysis results in sequence, specifically including: Based on the trajectory of the IMU during the motion, the trajectory closure error after the motion is completed is calculated; it is determined whether the trajectory closure error is not less than a set percentage of the total motion path. If so, it is determined that the IMU has a hardware fault; otherwise, it is determined that the IMU does not have a hardware fault. When it is determined that the IMU does not have a hardware fault, it is determined whether the maximum deviation of the time difference between adjacent frames of the IMU is less than a set time threshold. If so, it is determined that the IMU has high data acquisition stability and uniformity; otherwise, it is determined that the IMU is unusable. When it is determined that the IMU has high stability and uniformity, it is judged whether the standard deviation of each axis in the accelerometer and gyroscope of the IMU meets the set constraint conditions. If so, it is determined that the IMU data has low noise interference; otherwise, it is determined that the IMU is unusable. When it is determined that the IMU data has relatively low noise interference, it is determined whether the total energy of the IMU data with frequency domain signals greater than the set frequency band is greater than the set percentage of the standard noise baseline. If so, it is determined that the IMU needs to be checked, its installation rigidity needs to be improved, or vibration damping needs to be added. Otherwise, it is determined that the vibration damping of the IMU is reliable.

7. The method as described in claim 6, characterized in that, Determining whether the standard deviation of each axis in the accelerometer and gyroscope of the IMU meets the set constraints specifically includes: Determine whether the standard deviation of the z-axis in the gyroscope of the IMU is less than a first threshold. If it is, determine that the noise of the heading angle of the IMU satisfies the first constraint condition; otherwise, determine that the IMU is unusable. When it is determined that the noise of the heading angle of the IMU meets the first constraint condition, it is determined whether the standard deviation of the z-axis in the accelerometer of the IMU is less than the second threshold. If it is, it is determined that the noise of the gravity component of the IMU meets the second constraint condition; otherwise, it is determined that the IMU is unusable. When it is determined that the noise of the gravity component of the IMU meets the second constraint condition, it is determined whether the standard deviation of the x-axis and the standard deviation of the y-axis in the gyroscope of the IMU are less than the third threshold, and whether the standard deviation of the x-axis and the standard deviation of the accelerometer are less than the fourth threshold. If they are, it is determined that the IMU data of the IMU has small noise interference; otherwise, it is determined that the IMU is unusable.

8. A comprehensive performance evaluation device for an inertial measurement unit, characterized in that, include: The acquisition module is used to acquire test data of the inertial measurement unit (IMU) to be evaluated; wherein, the test data includes at least multiple sets of IMU data and real-time dynamic differential RTK data during the motion process, and each set of IMU data includes timestamps, accelerometer data and gyroscope data; The static analysis module is used to perform static analysis on the IMU based on the multiple sets of IMU data, so as to determine the time difference stability between adjacent frames of the IMU, and the standard deviation of each axis in the accelerometer and gyroscope of the IMU. The dynamic analysis module is used to perform dynamic analysis on the IMU based on the multiple sets of IMU data and RTK data, to perform spectral analysis on the accelerometer and gyroscope based on the IMU data generated during the motion to determine the spectral results of the IMU during the motion, and to perform angle integration and trajectory integration on the IMU data generated during the figure-eight motion to determine the angle and trajectory of the IMU during the motion. The evaluation module is used to comprehensively evaluate the performance of the IMU by sequentially using different analysis results according to the evaluation priority order set for different analysis results.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-7.

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