Omnibearing performance evaluation method of inertial measurement unit and related device

Abstract

The invention discloses an omnibearing performance evaluation method of an inertial measurement unit and a related device. The method comprises the step of comprehensively evaluating the performance of the IMU module through noise covariance analysis in a static state, time difference stability test, attitude angle integral test under splayed motion and spectrum analysis of an accelerometer. And meanwhile, track integral verification is carried out by combining RTK information obtained when the IMU moves around a splayed shape, so that the positioning precision and stability of the IMU in a complex motion environment are further ensured. Through the multi-dimensional and all-directional testing method, the comprehensive performance of the IMU can be effectively and accurately evaluated, a basis is provided for screening out a high-quality IMU module, and reliable and stable sensor support is provided for an automatic driving vehicle, so that the safety and stability of a whole automatic driving system are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, specifically to technical fields such as multi-dimensional performance analysis and sensor performance evaluation, and especially to a comprehensive performance evaluation method and related devices for an inertial measurement unit. Background Art

[0002] An inertial measurement unit (IMU) consists of three single-axis accelerometers and three single-axis gyroscopes. The accelerometers detect the acceleration signals of an object along three independent axes in the carrier coordinate system, while the gyroscopes detect the angular velocity signals of the carrier relative to the navigation coordinate system. Together, they measure the angular velocity and acceleration of an object in three-dimensional space and use this to calculate the object's attitude. This sensor data is crucial for precise control and positioning of devices and systems, and therefore, IMUs play a crucial role in intelligent driving and automated assisted driving.

[0003] In the field of autonomous driving, selecting a high-performance IMU that provides reliable and stable sensor data is a prerequisite and foundation for its development. Therefore, rigorous factory testing and verification of IMUs is essential. However, existing IMU performance testing methods are limited and ignore important performance evaluation factors, failing to accurately test IMUs. This results in unstable performance and frequent anomalies in factory-installed IMUs, posing safety risks to autonomous driving. Summary of the Invention

[0004] This application provides a comprehensive performance evaluation method and related devices for an inertial measurement unit, so as to evaluate the performance of the IMU from all directions and multiple angles, improve the comprehensiveness and accuracy of the IMU test evaluation, and ensure the reliable control and driving safety of autonomous vehicles.

[0005] The technical solution is as follows:

[0006] In a first aspect, a comprehensive performance evaluation method for an inertial measurement unit is provided, comprising:

[0007] Obtain test data of an 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, each set of IMU data including a timestamp, accelerometer data, and gyroscope data;

[0008] Performing a 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;

[0009] Performing dynamic analysis on the IMU based on the multiple sets of IMU data and RTK data to respectively determine a spectrum result of the IMU during motion, and an angle and a trajectory of the IMU during motion;

[0010] According to the evaluation priority order set for different analysis results, different analysis results are used in turn to conduct a comprehensive evaluation of the performance of the IMU.

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

[0012] Based on the timestamp carried by the test data, respectively calculate the time interval between each adjacent frame IMU data in the test data;

[0013] The time interval is statistically calculated as the time difference between adjacent frames of the IMU stability.

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

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

[0016]

[0017] Where j represents the axis of the accelerometer, which is x, y, z; n is the number of groups of IMU data collected; acc j_i is the value of the i-th accelerometer data on the j-axis; μ acc_j is the average value of the data of the j-axis of the accelerometer;

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

[0019]

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

[0021] In one possible implementation, determining a spectrum result of the IMU during movement specifically includes:

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

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

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

[0025]

[0026] Among them, ρ e+1 is the trajectory of motion at the e+1th moment, ρ e is the trajectory of motion at the e-th moment, v e+1 is the velocity at the e+1th moment, v e is the velocity at the e-th moment, g represents the acceleration due to gravity, Represents the posture matrix, acc mear represents the value of the accelerometer, Δt represents the time difference between the eth moment and the e+1th moment, q e+1 Represents the quaternion at the e+1th moment, q e represents the quaternion at the e-th moment, represents the rotation axis, ω represents the value of the gyroscope, and θ represents the rotation angle; the initialization trajectory, speed, and angle during the movement are all determined by the RTK data.

[0027] In one possible implementation, different analysis results are used in sequence to perform a comprehensive evaluation of the IMU's performance according to the evaluation priority order set for the different analysis results, specifically including:

[0028] Calculating a trajectory closure error after the movement is completed based on the trajectory of the IMU during the movement; determining whether the trajectory closure error is not less than a set percentage of the total movement path, and if so, determining that the IMU has a hardware fault; otherwise, determining that the IMU does not have a hardware fault;

[0029] When it is determined that there is no hardware failure in the IMU, determining whether the maximum deviation of the time difference between adjacent frames of the IMU is less than a set time threshold; if so, determining that the IMU has high data acquisition stability and uniformity; otherwise, determining that the IMU is unusable;

[0030] When determining that the IMU has high stability and uniformity, determining whether the standard deviation of each axis of the accelerometer and gyroscope of the IMU meets the set constraint conditions, if so, determining that the IMU data of the IMU has less noise interference, otherwise, determining that the IMU is unusable;

[0031] When it is determined that the IMU data of the IMU has less noise interference, it is determined whether the total energy of the IMU mid-frequency domain signal greater than the IMU data of 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 inspected, installed with rigidity or increased shock absorption. Otherwise, it is determined that the shock absorption of the IMU is reliable.

[0032] In one possible implementation, determining whether the standard deviation of each axis of the accelerometer and gyroscope of the IMU satisfies a set constraint condition specifically includes:

[0033] Determining whether a z-axis standard deviation of a gyroscope of the IMU is less than a first threshold; if so, determining that the noise of the heading angle of the IMU satisfies a first constraint; otherwise, determining that the IMU is unusable;

[0034] When it is determined that the noise of the heading angle of the IMU satisfies the first constraint condition, determining whether the z-axis standard deviation of the accelerometer of the IMU is less than a second threshold value, if so, determining that the noise of the gravity component of the IMU satisfies the second constraint condition; otherwise, determining 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 x-axis standard deviation and the y-axis standard deviation in the gyroscope of the IMU are less than the third threshold, and whether the x-axis standard deviation and the y-axis standard deviation in the accelerometer are less than the fourth threshold. If so, it is determined that the IMU data of the IMU has less noise interference; otherwise, it is determined that the IMU is unavailable.

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

[0037] An acquisition module is used to acquire test data of an 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;

[0038] a static analysis module, configured to perform static analysis on the IMU based on the multiple sets of IMU data to respectively 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] A dynamic analysis module is used to perform dynamic analysis on the IMU based on the multiple sets of IMU data and RTK data to respectively determine the spectrum results of the IMU during the movement process, and the angle and trajectory of the IMU during the movement process;

[0040] An evaluation module is used to perform a comprehensive evaluation of the performance of the IMU using different analysis results in sequence according to the evaluation priority order set for different analysis results.

[0041] According to a third aspect, an electronic device is provided, including:

[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, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any possible implementation manner and the aspects described above.

[0045] In a fourth aspect, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the method of the above-mentioned aspect and any possible implementation manner.

[0046] In a fifth aspect, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the above-mentioned aspects and any possible implementation method.

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

[0048] It can be seen from the above technical solution that the embodiment of the present application comprehensively evaluates the performance of the IMU module through noise covariance analysis in a stationary state, time difference stability test, attitude angle integral test under figure eight motion, and spectrum analysis of the accelerometer. At the same time, the trajectory integral verification is performed in combination with the RTK information obtained during the figure eight motion, which further ensures the positioning accuracy and stability of the IMU in a complex motion environment. This application uses a multi-dimensional and comprehensive testing method to effectively and accurately evaluate the comprehensive performance of the IMU, provide a basis for screening out high-quality IMU modules, and provide reliable and stable sensor support for autonomous driving vehicles, thereby improving the safety and stability of the entire autonomous driving system.

[0049] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 Schematic diagram of the steps of the comprehensive performance evaluation method of the inertial measurement unit provided in an embodiment of the present application.

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

[0053] Figure 3a and Figure 3b Schematic diagram of the time difference curve drawn for the adjacent frame time difference stability test of module 1 and module 2 respectively.

[0054] Figure 4 The figure below compares the standard deviation curves of the z-axis noise of the accelerometers of module 1 and module 2.

[0055] Figure 5 The figure below compares the standard deviation curves of the z-axis noise of the gyroscopes of module 1 and module 2.

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

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

[0058] Figure 8a and Figure 8b These are the figure-eight trajectory integration schematics of module 1 and module 2 respectively.

[0059] Figure 9 This is a structural block diagram of an all-round performance evaluation device for an inertial measurement unit provided in one embodiment of the present application.

[0060] Figure 10 This is a block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] The following description of exemplary embodiments of the present application is provided in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0062] Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

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

[0064] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0065] In view of the fact that the existing IMU performance test method is single and ignores important performance evaluation factors, it is impossible to accurately test the IMU, which leads to unstable performance and frequent abnormalities of the factory IMU. To this end, the present application proposes a new IMU performance test and evaluation scheme, the inventive concept of which is to comprehensively evaluate the performance of the IMU module through noise covariance analysis in a static state, time difference stability test, attitude angle integral test under figure eight motion, and spectrum analysis of the accelerometer. At the same time, the trajectory integral verification is performed in combination with the RTK information obtained during the figure eight motion, which further ensures the positioning accuracy and stability of the IMU in a complex motion environment. Through a multi-dimensional and comprehensive testing method, the present application can effectively and accurately evaluate the comprehensive performance of the IMU, provide a basis for screening out high-quality IMU modules, and provide reliable and stable sensor support for autonomous driving vehicles, thereby improving the safety and stability of the entire autonomous driving system.

[0066] Reference Figure 1The figure shows a schematic diagram of the steps of the comprehensive performance evaluation method of the inertial measurement unit provided in an embodiment of the present application. The execution subject of the evaluation method can be an IMU comprehensive performance evaluation device, which can be a computing device or software module with data calculation, processing and storage capabilities, such as a computer, tablet computer, smart phone, smart wearable device and other hardware devices, or a software module or component integrated into these hardware devices. This application is not limited to this.

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

[0068] Step 102: 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 a timestamp, accelerometer data, and gyroscope data.

[0069] In the present application scheme, the test process can be divided into two stages, one is a static test stage, and the other is a dynamic test stage. In the static test stage, the IMU to be evaluated is in a stationary state, and the test data of the IMU is obtained at this time. The test data of the static test stage can include multiple sets of IMU data, and each set of IMU data includes: timestamp, accelerometer data and gyroscope data. In the dynamic test stage, the IMU to be evaluated is in motion, which can be specifically a figure eight motion, that is, repeatedly performing a figure eight motion along an approximately identical trajectory. At this time, the test data of the dynamic test stage of the IMU is also obtained. It also includes multiple sets of IMU data, and each set of IMU data includes: timestamp, accelerometer data and gyroscope data, as well as RTK data in the dynamic test stage.

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

[0071] For the RTK data in the dynamic test phase, it 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 attitude represented by the quaternion is (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}, and the three-dimensional velocity (utm_vx, utm_vy, utm_vz) is added to this data format.

[0072] Step 104: Performing a static analysis on the IMU based on the multiple sets of IMU data to respectively 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 the present application, the static analysis of the IMU can mainly include two aspects: first, analyzing the stability of the time difference between adjacent frames, that is, calculating the time interval of the IMU data, and at the same time, drawing a curve of the change of the time interval for evaluation reference. Second, analyzing the noise characteristics of the IMU in a static state, that is, calculating the standard deviation of each axis of the accelerometer and gyroscope to reflect the static noise of the IMU. In fact, in addition to 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.

[0074] Optionally, when determining the time difference stability between adjacent frames of the IMU, the present application can specifically calculate the time interval between each adjacent frame of IMU data in the test data based on the timestamp carried by the test data; and the time interval obtained by statistical calculation is used as the time difference stability between adjacent frames of the IMU.

[0075] In specific implementation, the time difference Δt between adjacent frames can be calculated by the following formula, and then multiple time differences can be counted to analyze 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 They 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 differences (i.e., time intervals) obtained from sampling are uniform. However, if there are problems with the IMU's internal clock or it is subject to external interference, the calculated time intervals can fluctuate significantly, affecting the reliability of the IMU data and the accuracy of subsequent processing. Therefore, in this step, the fluctuations in the calculated time intervals can be used to assess the stability of the IMU's performance. Furthermore, to facilitate observation and analysis, a time interval chart can be plotted to visually reflect the uniformity of IMU sampling and better evaluate IMU performance.

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

[0080]

[0081] Where j represents the axis of the accelerometer, which is x, y, z; n is the number of groups of IMU data collected; acc j_i is the value of the i-th accelerometer data on the j-axis; μ acc_j is the average value of the data of the j-axis of the accelerometer;

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

[0083]

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

[0085] In the test data obtained above, assuming that n sets of IMU data are obtained, the standard deviation of the IMU accelerometer x, y, and z axes can be calculated separately according to the above formula: acc_noise_x , σ acc_noise_y , σ acc_noise_z ; and calculate the standard deviation of the IMU gyroscope x, y, and z axes respectively: σgyr_noise_x , σ gyr_noise_y , σ gyr_noise_z .

[0086] Considering that standard deviation can reflect the degree of data dispersion, namely the stability and accuracy of the accelerometer and gyroscope outputs, the standard deviation of the accelerometer's x, y, and z-axis data can be calculated using the above method to quantify the noise level and stability of the IMU accelerometer, thereby evaluating the IMU's performance based on these factors. It should be understood that when an IMU is stationary and unaffected by external influences, its accelerometer's x and y-axis outputs are close to zero, while the z-axis outputs the current gravity value. If the accelerometer has noise or bias, the output data will fluctuate, resulting in an increased standard deviation. Furthermore, a larger standard deviation of the accelerometer's output data for each axis indicates greater or more interference or noise in the accelerometer, and worse IMU performance. Conversely, a smaller standard deviation of the accelerometer's output data for each axis indicates less or less interference or noise in the accelerometer, and better IMU performance. Similarly, the standard deviation of the gyroscope's x, y, and z-axis data can be calculated using the above formula to quantify the stability and accuracy of the IMU gyroscope, thereby evaluating the IMU's performance based on these factors. It should be understood that when the IMU is in a stationary state and is not affected by external factors, the output of the gyroscope's x, y, and z axes is close to zero; if there is noise in the gyroscope, the output data will fluctuate and the standard deviation will increase. Moreover, the larger the standard deviation of the gyroscope's output data, the greater the interference or noise to the gyroscope, and the worse the IMU performance. Conversely, the smaller the standard deviation of the gyroscope's output data, the less interference or noise to the gyroscope, and the better the IMU performance. It is particularly important to note that in the present application, the gyroscope's z-axis should be strictly controlled. This is because the gyroscope's z-axis will affect the integral of the vehicle's heading angle during actual use, and the greater the noise, the more serious the heading angle drift, and the attitude error will ultimately affect the position integral. Therefore, in order to ensure the accuracy of the subsequent vehicle navigation system, the noise of the gyroscope's z-axis needs to be strictly controlled and evaluated.

[0087] Regarding the standard deviation evaluation method, when implementing it, it can be executed in the set priority order: first evaluate the noise of the gyroscope z-axis, then evaluate the noise of the accelerometer z-axis, and finally evaluate the noise of the accelerometer and gyroscope x and y axes. The evaluation method is still the smaller the noise, the better, that is, the smaller the standard deviation, the better. Among them, the standard deviation of the gyroscope z-axis is best guaranteed to be on the order of 0.025deg / s, and the standard deviation of the accelerometer z-axis is best guaranteed to be on the order of 0.001m / s 2 The x and y axes of the gyroscope are at the order of 0.01deg / s, and the accelerometer is at the order of 0.001m / s 2Magnitude, as long as these indicators meet the magnitude indicators, it means that the IMU is usable. The specific evaluation can be sorted and selected according to the size of the standard deviation.

[0088] It can be seen that through the static analysis method in this step, the noise and stability of the accelerometer and gyroscope in the IMU can be assessed from aspects such as time difference stability and standard deviation, and then the IMU performance can be accurately evaluated from multiple angles, providing a favorable basis for screening high-performance IMUs.

[0089] Step 106: Dynamically analyze the IMU based on the multiple sets of IMU data and RTK data to respectively determine the frequency spectrum results of the IMU during the movement, and the angle and trajectory of the IMU during the movement.

[0090] In this application, dynamic analysis of the IMU can include two aspects: first, spectrum analysis of the accelerometer and gyroscope based on the IMU data generated during motion; second, angle integration and trajectory integration based on the IMU data generated during the figure-eight motion. In addition to the above dynamic analysis, other analyses can also be performed based on the data generated by IMU motion, such as posture analysis or velocity analysis, which is not limited to this in this application.

[0091] Optionally, when determining the spectrum results of the IMU during motion, the present application can 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 specific implementation, the following discrete Fourier transform can be used to convert IMU data from time domain signals to frequency domain signals. For a discrete signal x[n] of length N (for example, the x-axis of the accelerometer), the Fourier transform is:

[0093]

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

[0095] In this application, considering that the accelerometer data and gyroscope data in the IMU data are usually time domain signals, in order to evaluate the noise and stability of the IMU data from a signal perspective, the time domain signal can be converted into a frequency domain signal through Fourier transform. This makes it easier to analyze the frequency domain signal and identify frequency domain components such as vibration, noise, or periodic motion in the signal. At the same time, the source of the noise in the IMU data can be analyzed by analyzing the frequency distribution of the noise in the frequency domain signal. For example, the noise comes from mechanical vibration, electronic noise, etc. When the accelerometer or gyroscope of the IMU behaves abnormally within a certain frequency range, it is determined that there may be a problem with the installation of the IMU. Therefore, the IMU data can be converted from the time domain to the frequency domain. Based on spectrum analysis, on the one hand, the noise of the accelerometer or gyroscope of the IMU can be determined. On the other hand, it can also effectively evaluate the sensor vibration that may be caused when the IMU is installed on the vehicle. Furthermore, it is also possible to make installation improvements or add shock absorption measures to address mechanical vibration caused by unstable installation.

[0096] Optionally, when determining the angle and trajectory of the IMU during motion, the present application solution can specifically determine the motion trajectory of the IMU during motion by the following formula based on multiple sets of IMU data and RTK data during the eight-character motion process: e+1 , speed v e+1 and angle q e+1 :

[0097]

[0098] Among them, ρ e+1 is the trajectory of motion at the e+1th moment, ρ e is the trajectory of motion at the e-th moment, v e+1 is the velocity at the e+1th moment, v e is the velocity at the e-th moment, g represents the acceleration due to gravity, Represents the posture matrix, acc mear represents the value of the accelerometer, Δt represents the time difference between the eth moment and the e+1th moment, q e+1 Represents the quaternion at the e+1th moment, q e represents the quaternion at the e-th moment, represents the rotation axis, ω represents the value of the gyroscope, and θ represents the rotation angle; the initialization trajectory, speed, and angle during the movement are all determined by the RTK data.

[0099] In specific implementations, the IMU's pitch, roll, and heading integration performance can be evaluated based on IMU data generated by the figure-eight motion. Initializing the integration using the position, attitude, and velocity provided by real-time dynamic differential RTK data, combined with the aforementioned formula, effectively ensures that the integrated figure-eight trajectory does not drift excessively due to incorrect initial velocity and attitude. This also ensures that the figure-eight trajectory can be used to evaluate IMU drift and self-noise performance. Furthermore, integrating the figure-eight trajectory offers the following advantages: 1) Comprehensively assessing dynamic performance: The figure-eight trajectory covers a wide range of motion states, enabling comprehensive evaluation of IMU performance under different motion conditions; 2) Detecting error accumulation: Position and attitude estimates can be obtained through integration, enabling detection of error accumulation over long periods of operation, such as attitude drift and position error; and 3) Verifying system stability: The figure-eight trajectory integration verifies the stability of the IMU system, such as its noise suppression and error recovery capabilities under dynamic conditions. In summary, combining the aforementioned formula with the figure-eight trajectory, angle, and velocity integration allows for comprehensive evaluation of IMU performance under dynamic conditions, providing important guidance for IMU selection and application.

[0100] Step 108: Using different analysis results in turn according to the evaluation priority order set for different analysis results, comprehensively evaluate the performance of the IMU.

[0101] In this application scheme, the evaluation priorities of these different analysis results can be ranked according to the size and angle of impact of different analysis results on IMU performance, and the specific details can be flexibly adjusted according to the actual needs of the user.

[0102] Optionally, the present application sets the priority from high to low in the order of trajectory integral generated by the figure eight motion, time difference between adjacent frames, standard deviation of each axis of the accelerometer and gyroscope, and spectrum analysis. In this way, when the performance of the IMU is comprehensively evaluated using different analysis results in turn according to the evaluation priority order set for different analysis results, the trajectory closure error after the movement is completed can be calculated based on the trajectory of the IMU during the movement; it is determined whether the trajectory closure error is not less than the set percentage of the total movement 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 the set time threshold. If so, it is determined that the IMU has a high data acquisition stability. If the IMU data is less noise-interference-free, determine whether the total energy of the IMU data in which the mid-frequency domain signal of the IMU is greater than the set frequency band is greater than the set percentage of the nominal noise baseline. If so, determine that the IMU needs to be inspected, installed with rigidity or have shock absorption increased. Otherwise, determine that the shock absorption of the IMU is reliable.

[0103] When determining the standard deviation of each axis in the accelerometer and gyroscope, the following constraints can be specifically set: First, determine whether the z-axis standard deviation of the IMU's gyroscope is less than a first threshold. If so, determine that the IMU's heading angle noise meets the first constraint; otherwise, determine that the IMU is unusable. Then, if it is determined that the IMU's heading angle noise meets the first constraint, determine whether the z-axis standard deviation of the IMU's accelerometer is less than a second threshold. If so, determine that the IMU's gravity component noise meets the second constraint; otherwise, determine that the IMU is unusable. Further, if it is determined that the IMU's gravity component noise meets the second constraint, determine whether the x-axis standard deviation and y-axis standard deviation of the IMU's gyroscope are less than a third threshold, and whether the x-axis standard deviation and y-axis standard deviation of the accelerometer are less than a fourth threshold. If so, determine that the IMU data of the IMU has low noise interference; otherwise, determine that the IMU is 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 spectral 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 motion 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 motion (spatial "∞" shape 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 within 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 time stamp interval is < 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 satisfied, 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] Set different weights for different types of axes. The weight order is 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 evaluation index can be calculated using the following formula: 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 / s 2 That is, the gyroscope availability value is ≤ 0.022° / s, and the accelerometer availability value is ≤ 0.001m / s 2 When the IMU is available.

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

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

[0117] ②The second constraint

[0118] Determine whether the z-axis standard deviation of the IMU's gyroscope is less than 0.025° / s. If so, the IMU's heading angle noise satisfies the first constraint and has a minimal impact on the long-term stability of the attitude solution. Otherwise, the IMU is deemed unusable.

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

[0120] Further, when determining that the noise of the gravity component of the IMU satisfies the second constraint condition, it is determined whether the x-axis standard deviation and the y-axis standard deviation of the gyroscope of the IMU are less than 0.01° / s, and whether the x-axis standard deviation and the y-axis standard deviation of the accelerometer are less than 0.001m / s 2 If so, it is determined that the IMU data has less noise interference, and finally the IMU is determined to be available. Otherwise, it is determined that the IMU is unavailable.

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

[0122] (4) Spectrum analysis

[0123] If the high-frequency vibration energy (e.g., >100 Hz) 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 be mixed with high-frequency noise. Within this threshold range, the IMU is usable, and the less high-frequency noise, the better.

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

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

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

[0127] Reference Figure 3a and Figure 3b The following figure shows a schematic diagram of the time difference curve drawn for the stability test of the time difference between adjacent frames of module 1 and module 2. The time difference of module 1 is between 0.00284-0.018s, and the time difference of module 2 is between 0.0087-0.0111s. Module 1 has worse stability than module 2, and module 2 performs better than module 1. The horizontal axis represents the serial 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 differences.

[0128] Reference Figure 4 The figure below compares the standard deviation curves of the z-axis noise of the accelerometers of module 1 and module 2. Module 1 (red): 0.0047 m / s² < Module 2 (blue): 0.0080 m / s²; module 1 has superior performance. The horizontal axis represents the serial index of the IMU data, and the vertical axis represents the z-axis acceleration value (in m / s²).

[0129] Reference Figure 5 The figure below shows a comparison of the standard deviation curves of the z-axis gyroscope noise for module 1 and module 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 serial index of the IMU data, and the vertical axis represents the angular velocity along the z-axis (in deg / s).

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

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

[0133] After a comprehensive comparison, it was confirmed that module 2 performed better than module 1. This demonstrates that the comprehensive IMU performance evaluation method of this application solution can accurately, quickly, and efficiently evaluate IMU performance and identify whether an IMU is usable. Furthermore, the optimal IMU can be quickly selected from a wide range of 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 aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0135] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0136] Figure 9FIG. 1 shows a structural block diagram of an all-round performance evaluation device for an inertial measurement unit provided by an embodiment of the present application, as shown in FIG. Figure 9 The all-round performance evaluation device 900 of the inertial measurement unit of this embodiment may include an acquisition module 901 , a static analysis module 902 , a dynamic analysis module 903 and an evaluation module 904 . Among them, the acquisition module 901 is used to obtain test data of the inertial measurement unit IMU to be evaluated; wherein the test data at least includes multiple groups of IMU data and real-time dynamic differential RTK data during movement, and each group of IMU data includes a timestamp, accelerometer data and gyroscope data; the static analysis module 902 is used to perform static analysis on the IMU based on the multiple groups of IMU data, so as to respectively 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 903 is used to perform dynamic analysis on the IMU based on the multiple groups of IMU data and RTK data, so as to respectively determine the spectrum results of the IMU during movement, and the angle and trajectory of the IMU during movement; the evaluation module 904 is used to use different analysis results in accordance with the evaluation priority order set for different analysis results to conduct a comprehensive evaluation of the performance of the IMU.

[0137] It should be noted that part or all of the comprehensive performance evaluation device of the inertial measurement unit of this embodiment can be an application located in the local terminal, or it can also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or it can also be a processing engine located in the network side server, or it can also be a distributed system located on the network side. This embodiment does not specifically limit this.

[0138] It is understandable that the application may be a native program (nativeApp) installed on the local terminal, or may be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0139] Optionally, in a 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; and the time interval obtained by statistical calculation is used as the time difference stability between adjacent frames of the IMU.

[0140] Optionally, in a 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 configured 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 axis of the accelerometer, which is x, y, z; n is the number of groups of IMU data collected; acc j_i is the value of the i-th accelerometer data on the j-axis; μ acc_j is the average value of the data of the j-axis of the accelerometer;

[0143] And, for determining the standard deviation of each axis in the gyroscope of the IMU based on the gyroscope data in the test data using the following formula:

[0144]

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

[0146] Optionally, in a possible implementation of this embodiment, when determining the spectrum results of the IMU during movement, the dynamic analysis module 903 is specifically used to convert the data values ​​of each axis in the accelerometer and gyroscope of the IMU from time domain signals to frequency domain signals by using discrete Fourier transform based on the accelerometer data and gyroscope data in the test data.

[0147] Optionally, in a possible implementation of this embodiment, when determining the angle and trajectory of the IMU during the motion process, the dynamic analysis module 903 is specifically configured to determine the motion trajectory ρ of the IMU during the motion process based on multiple sets of IMU data and RTK data during the figure eight motion process by the following formula: e+1 , speed v e+1 and angle q e+1 :

[0148]

[0149] Among them, ρ e+1 is the trajectory of motion at the e+1th moment, ρ e is the trajectory of motion at the e-th moment, v e+1 is the velocity at the e+1th moment, v e is the velocity at the e-th moment, g represents the acceleration due to gravity, Represents the posture matrix, acc mear represents the value of the accelerometer, Δt represents the time difference between the eth moment and the e+1th moment, q e+1 Represents the quaternion at the e+1th moment, q e represents the quaternion at the e-th moment, represents the rotation axis, ω represents the value of the gyroscope, and θ represents the rotation angle; the initialization trajectory, speed, and angle during the movement are all determined by the RTK data.

[0150] Optionally, in a possible implementation of this embodiment, the evaluation module 904 is specifically used to calculate the trajectory closure error after the movement is completed based on the trajectory of the IMU during the movement, when using different analysis results in sequence according to the evaluation priority order set for different analysis results to perform an all-round evaluation on the performance of the IMU; determine whether the trajectory closure error is not less than a set percentage of the total movement 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 The MU has high data acquisition stability and uniformity, otherwise, the IMU is determined to be unusable; when it is determined that the IMU has high stability and uniformity, determine whether the standard deviation of each axis in the accelerometer and gyroscope of the IMU meets the set constraint conditions, if so, determine that the IMU data of the IMU has less noise interference, otherwise, determine that the IMU is unusable; when it is determined that the IMU data of the IMU has less noise interference, determine whether the total energy of the IMU data whose mid-frequency domain signal is greater than the set frequency band is greater than the set percentage of the standard noise baseline, if so, determine that the IMU needs to be checked, installed with rigidity or increased shock absorption, otherwise, determine that the shock absorption of the IMU is reliable.

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

[0152] In this embodiment, the performance of the IMU module can be comprehensively evaluated through noise covariance analysis in a stationary state, time difference stability test, attitude angle integral test under figure eight motion, and spectrum analysis of the accelerometer. At the same time, the trajectory integral verification is performed in combination with the RTK information obtained during the figure eight motion, which further ensures the positioning accuracy and stability of the IMU in a complex motion environment. This application uses a multi-dimensional and comprehensive testing method to effectively and accurately evaluate the comprehensive performance of the IMU, provide a basis for screening out high-quality IMU modules, and provide reliable and stable sensor support for autonomous driving vehicles, thereby improving the safety and stability of the entire autonomous driving system.

[0153] One embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned comprehensive performance evaluation method of an inertial measurement unit.

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

[0155] An embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the comprehensive performance evaluation method of the inertial measurement unit as described above.

[0156] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved 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 the present 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 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present 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. Various programs and data required for the operation of the electronic device 1000 can also be stored in the RAM 1003. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

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

[0160] The computing unit 1001 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 1001 performs the various methods and processes described above, such as the blind spot detection method. For example, in some embodiments, the comprehensive performance evaluation method of the inertial measurement unit can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the 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 the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the comprehensive performance evaluation method of the inertial measurement unit described above can be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to execute the comprehensive performance evaluation method of the inertial measurement unit in any other appropriate manner (for example, by means of firmware).

[0161] Various embodiments of the systems and techniques described above 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), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, at least one input device, and at least one output device.

[0162] The program code for implementing the methods of the present application can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow charts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0163] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, 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 (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0165] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0166] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0167] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved. This is not a limitation herein.

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

Claims

1. A comprehensive performance evaluation method for an inertial measurement unit, characterized in that: include: Obtain test data of an 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, each set of IMU data including a timestamp, accelerometer data, and gyroscope data; Performing a 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; Performing dynamic analysis on the IMU based on the multiple sets of IMU data and RTK data to respectively determine a spectrum result of the IMU during motion, and an angle and a trajectory of the IMU during motion; According to the evaluation priority order set for different analysis results, different analysis results are used in turn to conduct a comprehensive evaluation of the performance of the IMU.

2. The method according to claim 1, wherein Determining the time difference stability between adjacent frames of the IMU, specifically comprising: Based on the timestamp carried by the test data, respectively calculate the time interval between each adjacent frame IMU data in the test data; The time interval is statistically calculated as the time difference between adjacent frames of the IMU stability.

3. The method according to claim 1, wherein 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 in the accelerometer of the IMU is determined by the following formula: Where j represents the axis of the accelerometer, which is x, y, z; n is the number of groups of IMU data collected; acc j_i is the value of the i-th accelerometer data on the j-axis; μ acc_j is the average value of the data of the accelerometer j axis; Based on the gyroscope data in the test data, the standard deviation of each axis in the IMU's gyroscope is determined by the following formula: Where j represents the axis of the gyroscope, which is x, y, z; n is the total number of IMU data collected; gyr j_i is the value of the i-th gyroscope data on the j-axis; μ gyr_j is the average value of the gyroscope j-axis data.

4. The method according to claim 1, wherein Determine the spectrum result of the IMU during the motion process, specifically including: Based on the accelerometer data and gyroscope data in the test data, discrete Fourier transform is used to convert the data values ​​of each axis in the accelerometer and gyroscope of the IMU from time domain signals to frequency domain signals.

5. The method according to claim 1, wherein Determine the angle and trajectory of the IMU during motion, specifically including: Based on multiple sets of IMU data and RTK data during the eight-character movement, the motion trajectory ρ of the IMU during the movement is determined by the following formula e+1 , speed v e+1 and angle q e+1 : Among them, ρ e+1 is the trajectory of motion at the e+1th moment, ρ e is the trajectory of motion at the e-th moment, v e+1 is the velocity at the e+1th moment, v e is the velocity at the e-th moment, g represents the acceleration due to gravity, Represents the posture matrix, acc mear represents the value of the accelerometer, Δt represents the time difference between the eth moment and the e+1th moment, q e+1 Represents the quaternion at the e+1th moment, q e represents the quaternion at the e-th moment, represents the rotation axis, ω represents the value of the gyroscope, and θ represents the rotation angle; the initialization trajectory, speed, and angle during the movement are all determined by the RTK data.

6. The method according to any one of claims 1 to 5, wherein: The performance of the IMU is comprehensively evaluated using the different analysis results in the order of evaluation priority set for each analysis result, including: Calculating a trajectory closure error after the movement is completed based on the trajectory of the IMU during the movement; determining whether the trajectory closure error is not less than a set percentage of the total movement path, and if so, determining that the IMU has a hardware fault; otherwise, determining that the IMU does not have a hardware fault; When it is determined that there is no hardware failure in the IMU, determining whether the maximum deviation of the time difference between adjacent frames of the IMU is less than a set time threshold; if so, determining that the IMU has high data acquisition stability and uniformity; otherwise, determining that the IMU is unusable; When determining that the IMU has high stability and uniformity, determining whether the standard deviation of each axis of the accelerometer and gyroscope of the IMU meets the set constraint conditions, if so, determining that the IMU data of the IMU has less noise interference, otherwise, determining that the IMU is unusable; When it is determined that the IMU data of the IMU has less noise interference, it is determined whether the total energy of the IMU mid-frequency domain signal greater than the IMU data of 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 inspected, installed with rigidity or increased shock absorption. Otherwise, it is determined that the shock absorption of the IMU is reliable.

7. The method according to claim 6, wherein Determine whether the standard deviation of each axis of the accelerometer and gyroscope of the IMU meets the set constraints, specifically including: Determining whether a z-axis standard deviation of a gyroscope of the IMU is less than a first threshold; if so, determining that the noise of the heading angle of the IMU satisfies a first constraint; otherwise, determining that the IMU is unusable; When it is determined that the noise of the heading angle of the IMU satisfies the first constraint condition, determining whether the z-axis standard deviation of the accelerometer of the IMU is less than a second threshold value, if so, determining that the noise of the gravity component of the IMU satisfies the second constraint condition; otherwise, determining 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 x-axis standard deviation and the y-axis standard deviation in the gyroscope of the IMU are less than the third threshold, and whether the x-axis standard deviation and the y-axis standard deviation in the accelerometer are less than the fourth threshold. If so, it is determined that the IMU data of the IMU has less noise interference; otherwise, it is determined that the IMU is unavailable.

8. An all-round performance evaluation device for an inertial measurement unit, characterized in that: include: An acquisition module is used to acquire test data of an 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, configured to perform static analysis on the IMU based on the multiple sets of IMU data to respectively 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 is used to perform dynamic analysis on the IMU based on the multiple sets of IMU data and RTK data to respectively determine the spectrum results of the IMU during the movement process, and the angle and trajectory of the IMU during the movement process; An evaluation module is used to perform a comprehensive evaluation of the performance of the IMU using different analysis results in sequence 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 execute the method according to any one of claims 1 to 7.

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

Images