Monitoring method and device of inertial measurement unit, terminal equipment and storage medium

Abstract

The invention discloses an inertial measurement unit monitoring method and apparatus, a terminal device and a storage medium. The method comprises the steps of obtaining a data stream of a target inertial measurement unit array; performing anomaly identification on the data stream, and determining abnormal data in the data stream; error calibration is conducted on the abnormal data, a calibration result is obtained and used for subsequent data calculation of the target inertial measurement unit array, a calibration device does not need to be additionally adopted or equipment operation is stopped, the data flow in the operation process of the target inertial measurement unit array is intercepted, abnormal identification is conducted on the data flow, and the target inertial measurement unit array is obtained. Therefore, error identification and calibration can be realized, and real-time detection and calibration of the inertial measurement unit array are realized, so that the accuracy and stability of long-time operation of a system where the inertial measurement unit array is located are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of inertial measurement units, and in particular to a monitoring method, an apparatus, a terminal device and a storage medium for an inertial measurement unit. Background Art

[0002] Compared with a single IMU, the performance and fault tolerance of an inertial measurement unit (IMU) array are significantly improved, and it is usually used to improve the stability and accuracy of the system. Before using an IMU, it needs to be pre-calibrated to eliminate most sources of error, such as scale factor, zero bias, random walk, temperature drift, etc. Among the above error sources, scale factor and zero bias have the most direct impact on the accuracy of the IMU data output. The traditional single IMU calibration method requires precision instruments such as a rate turntable, which is complicated to use and difficult to transplant. The calibration method of the IMU array is similar to that of a single IMU. IMUs usually include sensors such as accelerometers and gyroscopes. Compared with a single IMU, the number of sensors in an IMU array increases, and the probability of increased errors will also be greater than that of a single IMU. If the traditional calibration method is used, the complexity and cost of the IMU array calibration will increase significantly, and real-time performance cannot be guaranteed.

[0003] Therefore, it is necessary to propose a solution to simplify the monitoring method of the IMU array and realize real-time detection and calibration of the IMU array.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present invention is to provide a monitoring method, apparatus, terminal device and storage medium for an inertial measurement unit, aiming to simplify the monitoring method of an IMU array and realize real-time detection and calibration of the IMU array.

[0006] To achieve the above object, the present invention provides a monitoring method of an inertial measurement unit, the monitoring method of the inertial measurement unit comprising:

[0007] Acquire the data stream of the target inertial measurement unit array;

[0008] Performing anomaly identification on the data stream to determine abnormal data in the data stream;

[0009] Performing error calibration on the abnormal data to obtain a calibration result;

[0010] Abnormal sensors in the target inertial measurement unit array are determined based on the calibration results.

[0011] Optionally, the step of acquiring the data stream of the target inertial measurement unit array includes:

[0012] Acquiring each raw inertial measurement unit data generated in the target inertial measurement unit array;

[0013] Acquire fused inertial measurement unit data obtained by the target inertial measurement unit array according to the original inertial measurement unit data.

[0014] Optionally, the step of performing abnormality identification on the data stream and determining abnormal data in the data stream includes:

[0015] Calculating each error result according to the fused inertial measurement unit data and each original inertial measurement unit data;

[0016] Selecting an error result greater than a preset error threshold from among the error results as an abnormal error result;

[0017] The original inertial measurement unit data corresponding to the abnormal error result is used as the abnormal data.

[0018] Optionally, the step of performing error calibration on the abnormal data to obtain a calibration result includes:

[0019] determining a calibration sequence according to the abnormal data;

[0020] The inertial measurement units corresponding to the abnormal data are calibrated in sequence according to the calibration sequence to obtain the calibration result, wherein the inertial measurement unit includes an accelerometer and a gyroscope.

[0021] Optionally, the calibration result includes a calibration factor and calibrated data, and the step of calibrating the inertial measurement unit corresponding to the abnormal data to obtain the calibration result includes:

[0022] Selecting true value data and data to be calibrated corresponding to the inertial measurement unit from the abnormal data;

[0023] Calculate the calibration matrix corresponding to the true value data and the data to be calibrated by the least square method;

[0024] The calibration factors and calibrated data are determined according to the calibration matrix.

[0025] Optionally, after the step of performing error calibration on the abnormal data to obtain a calibration result, the step further includes:

[0026] Determining whether the calibrated data is greater than the preset error threshold, and determining whether the calibration factor is greater than a preset calibration threshold;

[0027] If the calibrated data is not greater than the preset error threshold, and the calibration factor is not greater than the preset calibration threshold, the calibration result is used for subsequent data solution of the target inertial measurement unit array.

[0028] Optionally, after the step of determining whether the calibrated data is greater than the preset error threshold and the step of determining whether the calibration factor is greater than the preset calibration threshold, the step further includes:

[0029] If the calibrated data is greater than the preset error threshold, or the calibration factor is greater than the preset calibration threshold, the abnormal sensor in the target inertial measurement unit array is determined based on the calibration result, and the data corresponding to the abnormal sensor is eliminated in the subsequent data solution of the target inertial measurement unit array, and the calibration result is used for data solution.

[0030] In addition, to achieve the above-mentioned object, the present invention further provides a monitoring device for an inertial measurement unit, the monitoring device for the inertial measurement unit comprising:

[0031] An acquisition module, used for acquiring a data stream of a target inertial measurement unit array;

[0032] An identification module, used to identify abnormalities in the data stream and determine abnormal data in the data stream;

[0033] The calibration module is used to perform error calibration on the abnormal data to obtain a calibration result for subsequent data solution of the target inertial measurement unit array.

[0034] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal device, which includes a memory, a processor, and a monitoring program of an inertial measurement unit stored in the memory and executable on the processor, and the monitoring program of the inertial measurement unit implements the steps of the monitoring method of the inertial measurement unit as described above when executed by the processor.

[0035] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a monitoring program of an inertial measurement unit is stored. When the monitoring program of the inertial measurement unit is executed by a processor, the steps of the monitoring method of the inertial measurement unit as described above are implemented.

[0036] The embodiments of the present invention provide a monitoring method, apparatus, terminal device and storage medium for an inertial measurement unit. The method acquires a data stream of a target inertial measurement unit array; identifies abnormalities in the data stream to determine abnormal data in the data stream; and performs error calibration on the abnormal data to obtain a calibration result for subsequent data resolution of the target inertial measurement unit array. No additional calibration device is required or the operation of the equipment is stopped. The method intercepts the data stream during the operation of the target inertial measurement unit array, identifies abnormalities in the data stream, and thus implements error identification and calibration. This enables real-time detection and calibration of the inertial measurement unit array, thereby improving the accuracy and stability of the system in which the inertial measurement unit array is located during long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the functional modules of the terminal equipment to which the monitoring device of the inertial measurement unit of the present invention belongs;

[0038] Figure 2 A flow chart of an exemplary embodiment of a monitoring method of an inertial measurement unit of the present invention;

[0039] Figure 3 for Figure 2 A schematic diagram of a specific process of step S20 in the embodiment;

[0040] Figure 4 for Figure 2 A schematic diagram of a specific process of step S30 in the embodiment;

[0041] Figure 5 Schematic diagram of an IMU array real-time online detection and calibration device in an embodiment of the present invention;

[0042] Figure 6 It is a schematic diagram of the overall detection process in an embodiment of the present invention;

[0043] Figure 7 Schematic diagram of the cyclic error calibration process in an embodiment of the present invention.

[0044] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0046] The main solution of the embodiment of the present invention is: by acquiring the data stream of the target inertial measurement unit array; performing abnormality identification on the data stream to determine the abnormal data in the data stream; performing error calibration on the abnormal data to obtain a calibration result for subsequent data solution of the target inertial measurement unit array, there is no need to use an additional calibration device or stop the operation of the equipment. By intercepting the data stream during the operation of the target inertial measurement unit array and performing abnormality identification on the data stream, error identification and calibration can be achieved, and real-time detection and calibration of the inertial measurement unit array can be achieved, thereby improving the accuracy and stability of the system in which the inertial measurement unit array is located during long-term operation.

[0047] Technical terms involved in the embodiments of the present invention:

[0048] An Inertial Measurement Unit (IMU) is a device used to measure and record the three-dimensional motion information of an object. It usually consists of a set of inertial sensors, including accelerometers, gyroscopes, and sometimes magnetometers. An IMU tracks the motion of an object by measuring its linear acceleration and angular velocity, and can then estimate the object's position, velocity, and direction by integrating these measurements.

[0049] Before using IMU, it needs to be calibrated in advance to eliminate most sources of error, such as scale factor, zero bias, random walk, temperature drift, etc. Among the above sources of error, scale factor and zero bias have the most direct impact on the accuracy of IMU data output. The traditional single IMU calibration method requires precision instruments such as rate turntables, which are complicated to use and difficult to transplant. The calibration method of IMU array is similar to that of single IMU. During the use of IMU array, due to the increase in the number of sensors, the probability of increased error will be greater than that of a single IMU. If the traditional calibration method is used, the complexity and cost of IMU array calibration will increase significantly, and real-time performance cannot be guaranteed.

[0050] The present invention provides a processing method, function and equipment for real-time online detection and calibration of an IMU array. Compared with traditional solutions, it does not require complex and large calibration devices and any specific data acquisition actions, and does not even require the system or equipment to stop running. It does not require any pre-preparation operations. It only needs to intercept a section of data stream for calculation during the real-time operation of the IMU array. Finally, error identification and calibration, as well as marking and processing of abnormal sensors can be realized, so as to achieve true real-time detection, real-time calibration, and real-time protection.

[0051] Specifically, refer to Figure 1 , Figure 1The functional module diagram of the terminal device to which the monitoring device of the inertial measurement unit of the present invention belongs. The monitoring device of the inertial measurement unit can be a device independent of the terminal device and capable of error calibration, which can be carried on the terminal device in the form of hardware or software. The terminal device can be an intelligent mobile terminal with data processing function such as a mobile phone or a tablet computer, or a fixed terminal device or server with data processing function.

[0052] In this embodiment, the terminal device to which the monitoring apparatus of the inertial measurement unit belongs includes at least an output module 110 , a processor 120 , a memory 130 and a communication module 140 .

[0053] The memory 130 stores an operating system and an inertial measurement unit monitoring program. The inertial measurement unit monitoring device can store the acquired data stream of the target inertial measurement unit array, abnormal identification of the data stream, abnormal data in the data stream, error calibration of the abnormal data, and calibration results in the memory 130. The output module 110 can be a display screen, etc. The communication module 140 can include a WIFI module, a mobile communication module, a Bluetooth module, etc., and communicates with an external device or server through the communication module 140.

[0054] When the monitoring program of the inertial measurement unit in the memory 130 is executed by the processor, the following steps are implemented:

[0055] Acquire the data stream of the target inertial measurement unit array;

[0056] Performing anomaly identification on the data stream to determine abnormal data in the data stream;

[0057] The abnormal data is calibrated for errors to obtain calibration results for subsequent data resolution of the target inertial measurement unit array.

[0058] Furthermore, when the monitoring program of the inertial measurement unit in the memory 130 is executed by the processor, the following steps are also implemented:

[0059] Acquiring each raw inertial measurement unit data generated in the target inertial measurement unit array;

[0060] Acquire fused inertial measurement unit data obtained by the target inertial measurement unit array according to the original inertial measurement unit data.

[0061] Furthermore, when the monitoring program of the inertial measurement unit in the memory 130 is executed by the processor, the following steps are also implemented:

[0062] Calculating each error result according to the fused inertial measurement unit data and each original inertial measurement unit data;

[0063] Selecting an error result greater than a preset error threshold from among the error results as an abnormal error result;

[0064] The original inertial measurement unit data corresponding to the abnormal error result is used as the abnormal data.

[0065] Furthermore, when the monitoring program of the inertial measurement unit in the memory 130 is executed by the processor, the following steps are also implemented:

[0066] determining a calibration sequence according to the abnormal data;

[0067] The inertial measurement units corresponding to the abnormal data are calibrated in sequence according to the calibration sequence to obtain the calibration result, wherein the inertial measurement unit includes an accelerometer and a gyroscope.

[0068] Furthermore, when the monitoring program of the inertial measurement unit in the memory 130 is executed by the processor, the following steps are also implemented:

[0069] Selecting true value data and data to be calibrated corresponding to the inertial measurement unit from the abnormal data;

[0070] Calculate the calibration matrix corresponding to the true value data and the data to be calibrated by the least square method;

[0071] The calibration factors and calibrated data are determined according to the calibration matrix.

[0072] Furthermore, when the monitoring program of the inertial measurement unit in the memory 130 is executed by the processor, the following steps are also implemented:

[0073] Determining whether the calibrated data is greater than the preset error threshold, and determining whether the calibration factor is greater than a preset calibration threshold;

[0074] If the calibrated data is not greater than the preset error threshold, and the calibration factor is not greater than the preset calibration threshold, the calibration result is used for subsequent data solution of the target inertial measurement unit array.

[0075] Furthermore, when the monitoring program of the inertial measurement unit in the memory 130 is executed by the processor, the following steps are also implemented:

[0076] If the calibrated data is greater than the preset error threshold, or the calibration factor is greater than the preset calibration threshold, the abnormal sensor in the target inertial measurement unit array is determined based on the calibration result, and the data corresponding to the abnormal sensor is eliminated in the subsequent data solution of the target inertial measurement unit array, and the calibration result is used for data solution.

[0077] This embodiment adopts the above scheme, specifically by acquiring the data stream of the target inertial measurement unit array; performing abnormality identification on the data stream to determine the abnormal data in the data stream; performing error calibration on the abnormal data to obtain the calibration result, which is used for the subsequent data solution of the target inertial measurement unit array, without the need to additionally use a calibration device or suspend the operation of the equipment. By intercepting the data stream during the operation of the target inertial measurement unit array and performing abnormality identification on the data stream, error identification and calibration can be achieved, and abnormal sensors can be determined, so as to achieve real-time detection and calibration of the inertial measurement unit array, thereby improving the accuracy and stability of the system in which the inertial measurement unit array is located during long-term operation.

[0078] Based on the above terminal device architecture but not limited to the above architecture, an embodiment of the method of the present invention is proposed.

[0079] The execution subject of the method of this embodiment may be a monitoring device or terminal equipment of an inertial measurement unit, etc. This embodiment takes the monitoring device of an inertial measurement unit as an example.

[0080] Reference Figure 2 , Figure 2 The following is a flow chart of an exemplary embodiment of a monitoring method of an inertial measurement unit of the present invention. The monitoring method of the inertial measurement unit comprises:

[0081] Step S10, acquiring a data stream of a target inertial measurement unit array;

[0082] Specifically, an inertial measurement unit (IMU) is a device used to measure and record the three-dimensional motion information of an object.

[0083] Optionally, the main components of an IMU include:

[0084] Accelerometer: An accelerometer measures the linear acceleration of an object on three axes. It can detect changes in the acceleration of an object and thus estimate the object's speed and position.

[0085] Gyroscope: A gyroscope measures the angular velocity of an object as it rotates around three axes. It can detect the rotation or turn of an object and help determine the object's orientation.

[0086] Magnetometer: Some IMUs also include a magnetometer, which measures the Earth's magnetic field, providing information about the direction or orientation of an object. This is useful for positioning and navigation applications.

[0087] Optionally, an IMU tracks the motion of an object by measuring its linear acceleration and angular velocity, and can then estimate the object's position, velocity, and orientation by integrating these measurements. IMUs are commonly used in aerospace, self-driving cars, robots, virtual reality helmets, motion tracking devices, mobile phones, and other applications that require motion measurement. They help devices or systems understand their position and orientation in real time by providing accurate motion information, thereby enabling a variety of control and navigation tasks.

[0088] It is important to note that IMUs typically accumulate errors over a period of time, so in long-running applications, they often need to be combined with other sensors or technologies (such as GPS) to correct these errors to improve the accuracy of the measurements.

[0089] Optionally, an inertial measurement unit array has significantly improved performance and fault tolerance compared to a single IMU, and is often used to improve system stability and accuracy. IMUs can generate a variety of errors during use, and as the system runs, errors may accumulate or change randomly. Error-calibrated IMU sensor results can continue to be used after calibration. Failure to calibrate or calibration factors exceeding the threshold indicate that the IMU chip is faulty and the IMU needs to be marked and excluded from subsequent system operations.

[0090] Optionally, the step of acquiring the data stream of the target inertial measurement unit array includes:

[0091] Acquiring each raw inertial measurement unit data generated in the target inertial measurement unit array;

[0092] Acquire fused inertial measurement unit data obtained by the target inertial measurement unit array according to the original inertial measurement unit data.

[0093] Specifically, the target inertial measurement unit array in the embodiment of the present invention is composed of N IMUs, and the system finally fuses the N IMUs into one IMU data output. There are many methods of IMU fusion, such as the mean method, Kalman filtering, and neural network algorithms. Various methods can greatly improve the accuracy of the system. Therefore, the single IMU output by fusion has the characteristics of high precision, and its data can be considered to be closest to the true value. The embodiment of the present invention does not specify a specific IMU fusion method, and the high-precision IMU data obtained using any fusion algorithm is applicable to the calibration method proposed by the present invention.

[0094] Step S20, performing abnormality identification on the data stream to determine abnormal data in the data stream;

[0095] Furthermore, after acquiring the data stream of the target inertial measurement unit array, the data stream can be identified for abnormality to determine the abnormal data in the data stream.

[0096] Optionally, based on the acquired IMU array data, each IMU data can be compared with the fused data to obtain N deviation results. Each deviation result can be compared with a preset error threshold to obtain the number n of IMUs exceeding the threshold. If n is greater than 0, the cyclic error calibration is entered. If n is equal to 0, the process is terminated, wherein the IMU data exceeding the threshold is abnormal data.

[0097] Step S30, performing error calibration on the abnormal data to obtain a calibration result for subsequent data resolution of the target inertial measurement unit array.

[0098] Furthermore, after performing anomaly identification on the data stream and determining the abnormal data in the data stream, error calibration can be performed on the abnormal data to obtain a calibration result.

[0099] Optionally, in the embodiment of the present invention, the least square method is used to calculate the calibration matrix corresponding to the true value data and the data to be calibrated, and then the calibration factor and the calibrated data are determined according to the calibration matrix, and the calibration factor and the calibrated data are used as the calibration result.

[0100] Optionally, after error calibration is performed on the abnormal data to obtain a calibration result, the calibration result can be used for subsequent data solution of the target inertial measurement unit array, or the abnormal sensor in the target inertial measurement unit array can be determined based on the calibration result.

[0101] Optionally, after the step of performing error calibration on the abnormal data to obtain a calibration result, the step further includes:

[0102] Determining whether the calibrated data is greater than the preset error threshold, and determining whether the calibration factor is greater than a preset calibration threshold;

[0103] If the calibrated data is not greater than the preset error threshold, and the calibration factor is not greater than the preset calibration threshold, the calibration result is used for subsequent data solution of the target inertial measurement unit array.

[0104] Optionally, after the step of determining whether the calibrated data is greater than the preset error threshold and the step of determining whether the calibration factor is greater than the preset calibration threshold, the step further includes:

[0105] If the calibrated data is greater than the preset error threshold, or the calibration factor is greater than the preset calibration threshold, the abnormal sensor in the target inertial measurement unit array is determined based on the calibration result, and the data corresponding to the abnormal sensor is eliminated in the subsequent data solution of the target inertial measurement unit array, and the calibration result is used for data solution.

[0106] Specifically, the calibrated IMU data is compared with the fused data. If the error is less than the threshold, the current IMU calibration is considered completed, and the next IMU calibration process is entered until all IMU data with errors exceeding the threshold are calibrated. If the calibration result is greater than the error threshold, or the factor of the calibration matrix is ​​greater than the threshold Δk, the calibrated IMU sensor is considered to be abnormal and marked. The data of the IMU sensor is eliminated in the subsequent algorithm fusion to reduce the risk of system abnormality.

[0107] In this embodiment, by acquiring the data stream of the target inertial measurement unit array; performing abnormality identification on the data stream to determine abnormal data in the data stream; performing error calibration on the abnormal data to obtain a calibration result for subsequent data solution of the target inertial measurement unit array, there is no need to additionally use a calibration device or stop the operation of the equipment. By intercepting the data stream during the operation of the target inertial measurement unit array and performing abnormality identification on the data stream, error identification and calibration can be achieved, and real-time detection and calibration of the inertial measurement unit array can be achieved, thereby improving the accuracy and stability of the system in which the inertial measurement unit array is located during long-term operation.

[0108] Reference Figure 3 , Figure 3 for Figure 2 The specific flow chart of step S20 in the embodiment. Figure 2 In the embodiment shown, in this embodiment, the above step S20 includes:

[0109] Step S201, calculating and obtaining error results according to the fused inertial measurement unit data and the original inertial measurement unit data;

[0110] Step S202, selecting the error results greater than a preset error threshold among the error results as abnormal error results;

[0111] Step S203: using the original inertial measurement unit data corresponding to the abnormal error result as the abnormal data.

[0112] Specifically, the IMU array data obtained in the embodiment of the present invention can be recorded as IMU_rawn and IMU_fusion, where IMU_fusion represents the data obtained after IMU fusion, and IMU_rawn is one of N IMU data.

[0113] Optionally, N deviation results Δerr can be obtained through (IMU_fusion-IMU_rawn), and compared with the preset error threshold Δvalue, the number of IMUs exceeding the threshold n can be obtained. If n is greater than 0, the cyclic error calibration is entered, and if n is equal to 0, the process ends.

[0114] Optionally, during the cyclic error calibration process, if the error exceeds the threshold, that is, n is greater than 0, when n = 1, only one calibration is required to complete the system calibration. When n>1, the K corresponding to the IMU unit with the largest error can be found first. n , enter the calibration process, and then calibrate the remaining super-threshold IMU units K in turn after calibration n-1 , until the last super-threshold IMU unit K0.

[0115] Reference Figure 4 , Figure 4 for Figure 2 The specific flow chart of step S30 in the embodiment. Figure 2 In the embodiment shown, in this embodiment, the above step S30 includes:

[0116] Step S301, determining a calibration sequence according to the abnormal data;

[0117] Step S302: calibrate the inertial measurement unit corresponding to the abnormal data in sequence according to the calibration sequence to obtain the calibration result, wherein the inertial measurement unit includes an accelerometer and a gyroscope.

[0118] Optionally, the calibration result includes a calibration factor and calibrated data, and the step of calibrating the inertial measurement unit corresponding to the abnormal data to obtain the calibration result includes:

[0119] Selecting true value data and data to be calibrated corresponding to the inertial measurement unit from the abnormal data;

[0120] Calculate the calibration matrix corresponding to the true value data and the data to be calibrated by the least square method;

[0121] The calibration factors and calibrated data are determined according to the calibration matrix.

[0122] Specifically, the error calibration in the embodiment of the present invention adopts the least square method, and the accelerometer is taken as an example for explanation. It is assumed that the accelerometer data after IMU array fusion is ACC_FUSION, and the added data of the IMU with an error exceeding the threshold is ACC_Kn.

[0123]

[0124]

[0125] Optionally, the accelerometer error model can be expressed as: ACC_FUSION = K_acc*ACC_Kn, where K_acc represents the calibration matrix of the sensor. In the embodiment of the present invention, ACC_FUSION is regarded as the true value, ACC_Kn is regarded as the value to be calibrated, and the calibration matrix can be obtained by the least squares method as follows:

[0126]

[0127] Optionally, the calibrated IMU data is compared with ACC_FUSION. If it is less than the error threshold Δvalue, the current IMU calibration is considered completed, and the next IMU calibration process is entered until all IMUs with errors exceeding the threshold are calibrated. If the calibration result is greater than the error threshold, or the factor of the calibration matrix is ​​greater than the threshold Δk, the calibrated IMU sensor is considered to be abnormal and marked. The data of the IMU sensor is eliminated in the subsequent algorithm fusion to reduce the risk of system abnormality. The calibration principle of the gyroscope is the same as that of the accelerometer.

[0128] Through the above scheme, this embodiment specifically obtains each error result by calculating according to the fused inertial measurement unit data and the original inertial measurement unit data; selects the error result greater than the preset error threshold among the error results as the abnormal error result; uses the original inertial measurement unit data corresponding to the abnormal error result as the abnormal data; determines the calibration sequence according to the abnormal data; calibrates the inertial measurement units corresponding to the abnormal data in sequence according to the calibration sequence to obtain the calibration result, utilizes the cyclic processing of data and fusion results between multiple sensors to realize error identification and calibration, and finally realizes the sensor error calibration and abnormal sensor marking, thereby improving the accuracy and stability of the system during long-term operation.

[0129] In addition, an embodiment of the present invention further provides a monitoring device for an inertial measurement unit, wherein the monitoring device for the inertial measurement unit comprises:

[0130] An acquisition module, used for acquiring a data stream of a target inertial measurement unit array;

[0131] An identification module, used to identify abnormalities in the data stream and determine abnormal data in the data stream;

[0132] The calibration module is used to perform error calibration on the abnormal data to obtain a calibration result for subsequent data solution of the target inertial measurement unit array.

[0133] Reference Figure 5 , Figure 5 Schematic diagram of the IMU array real-time online detection and calibration device in an embodiment of the present invention, such as Figure 5As shown, in the embodiment of the present invention, a real-time online detection and calibration device for an IMU array is proposed, which is mainly composed of two parts. The first part is an IMU array composed of multiple IMU sensors. The IMU array generates a high-precision IMU sensor through a certain fusion algorithm; the second part is a data processing module, which includes functions such as data input and analysis, error detection, error cycle calibration and marking, and also includes a peripheral power supply module and an external interface module. The proposed real-time online detection and calibration function can improve the accuracy and stability of this device.

[0134] Reference Figure 6 , Figure 6 FIG. 1 is a schematic diagram of the overall detection process in an embodiment of the present invention. Figure 6 As shown, the IMU array in the embodiment of the present invention is composed of N IMUs. The system finally fuses the N IMUs into one IMU data output. There are many methods for IMU fusion, such as the mean method, Kalman filtering, and neural network algorithm. Each method can greatly improve the accuracy of the system. Therefore, the single IMU output by fusion has the characteristics of high precision, and its data can be considered to be closest to the true value. The main detection methods include:

[0135] (1) Obtain IMU array data, which are IMU_rawn and IMU_fusion. IMU_fusion represents the data obtained after IMU fusion, and IMU_rawn is one of the N IMU data.

[0136] (2) Identification and judgment of abnormal IMU data: N deviation results Δerr can be obtained through (IMU_fusion-IMU_rawn). By comparing it with the preset error threshold Δvalue, the number of IMUs exceeding the threshold n can be obtained. If n is greater than 0, the cyclic error calibration is entered. If n is equal to 0, the process ends.

[0137] (3) Cyclic error calibration, refer to Figure 7 , Figure 7 FIG. 1 is a schematic diagram of a cyclic error calibration process in an embodiment of the present invention, as shown in FIG. Figure 7 As shown, if there is an error exceeding the threshold, that is, n is greater than 0, when n=1, only one calibration is required to complete the system calibration. When n>1, the embodiment of the present invention first finds the IMU unit K with the largest error. n , enter the calibration process, and then calibrate the remaining super-threshold IMU units K in turn after calibration n-1 , until the last super-threshold IMU unit K0.

[0138] (4) The error calibration adopts the least square method. In the embodiment of the present invention, the accelerometer in the IMU sensor is taken as an example for explanation. It is assumed that the accelerometer data after the IMU array fusion is ACC_FUSION, and the accelerometer data of the IMU with an error exceeding the threshold is ACC_Kn.

[0139]

[0140]

[0141] The accelerometer error model can be expressed as: ACC_FUSION = K_acc*ACC_Kn, where K_acc represents the calibration matrix of the accelerometer. In the embodiment of the present invention, ACC_FUSION is regarded as the true value, and ACC_Kn is regarded as the value to be calibrated. It is not difficult to obtain the calibration matrix by the least square method:

[0142]

[0143] (5) Determine the calibration factor and effect, and compare the calibrated IMU data with ACC_FUSION. If it is less than the error threshold Δvalue, the current IMU calibration is considered to be completed, and the next IMU calibration process is entered until all IMUs with errors exceeding the threshold are calibrated. If the calibration result is greater than the error threshold, or the factor of the calibration matrix is ​​greater than the threshold Δk, it is considered that the calibrated IMU sensor is abnormal and marked. The data of the IMU sensor is eliminated in the subsequent algorithm fusion to reduce the risk of system abnormality. The calibration principle of the gyroscope is the same as that of the accelerometer.

[0144] In this embodiment, there is no need to use complex instruments or any specific calibration actions. Detection and calibration can be achieved by randomly intercepting a data stream during system operation. The error identification and cyclic calibration processing equipment proposed in the embodiment of the present invention can identify the number of abnormal sensors online, cyclically calibrate abnormal sensors according to the size of the over-threshold error, and mark abnormal IMU sensors, so that real-time monitoring, real-time calibration, and real-time protection can be achieved, and it has the function of active safety protection.

[0145] For the principle and implementation process of implementing the error calibration in this embodiment, please refer to the above embodiments and will not be described in detail here.

[0146] In addition, an embodiment of the present invention also proposes a terminal device, which includes a memory, a processor, and a monitoring program for an inertial measurement unit stored in the memory and executable on the processor, wherein the monitoring program for the inertial measurement unit implements the steps of the monitoring method for the inertial measurement unit as described above when executed by the processor.

[0147] Since all the technical solutions of all the aforementioned embodiments are adopted when the monitoring program of the inertial measurement unit is executed by the processor, it has at least all the beneficial effects brought by all the technical solutions of all the aforementioned embodiments, which will not be described one by one here.

[0148] In addition, an embodiment of the present invention further proposes a computer-readable storage medium, on which a monitoring program for an inertial measurement unit is stored. When the monitoring program for the inertial measurement unit is executed by a processor, the steps of the monitoring method for the inertial measurement unit as described above are implemented.

[0149] Since all the technical solutions of all the aforementioned embodiments are adopted when the monitoring program of the inertial measurement unit is executed by the processor, it has at least all the beneficial effects brought by all the technical solutions of all the aforementioned embodiments, which will not be described one by one here.

[0150] Compared with the prior art, the inertial measurement unit monitoring method, apparatus, terminal device and storage medium proposed in the embodiments of the present invention obtain the data stream of the target inertial measurement unit array; perform abnormality identification on the data stream to determine abnormal data in the data stream; perform error calibration on the abnormal data to obtain a calibration result for subsequent data solution of the target inertial measurement unit array, without the need to additionally use a calibration device or terminate the operation of the equipment. By intercepting the data stream during the operation of the target inertial measurement unit array and performing abnormality identification on the data stream, error identification and calibration can be achieved, and real-time detection and calibration of the inertial measurement unit array can be achieved, thereby improving the accuracy and stability of the system in which the inertial measurement unit array is located during long-term operation.

[0151] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0152] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0153] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, a controlled terminal, or a network device, etc.) to execute the method of each embodiment of the present application.

[0154] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A monitoring method for an inertial measurement unit, characterized in that: The monitoring method of the inertial measurement unit comprises the following steps: Acquire the data stream of the target inertial measurement unit array; Performing anomaly identification on the data stream to determine abnormal data in the data stream; The abnormal data is calibrated for errors to obtain calibration results for subsequent data resolution of the target inertial measurement unit array.

2. The method for monitoring an inertial measurement unit according to claim 1, wherein: The step of acquiring the data stream of the target inertial measurement unit array comprises: Acquiring each raw inertial measurement unit data generated in the target inertial measurement unit array; Acquire fused inertial measurement unit data obtained by the target inertial measurement unit array according to the original inertial measurement unit data.

3. The monitoring method of an inertial measurement unit according to claim 2, characterized in that: The step of identifying anomalies in the data stream and determining abnormal data in the data stream comprises: Calculating each error result according to the fused inertial measurement unit data and each original inertial measurement unit data; Selecting an error result greater than a preset error threshold from among the error results as an abnormal error result; The original inertial measurement unit data corresponding to the abnormal error result is used as the abnormal data.

4. The method for monitoring an inertial measurement unit according to claim 3, characterized in that: The step of performing error calibration on the abnormal data to obtain a calibration result comprises: determining a calibration sequence according to the abnormal data; The inertial measurement units corresponding to the abnormal data are calibrated in sequence according to the calibration sequence to obtain the calibration result, wherein the inertial measurement unit includes an accelerometer and a gyroscope.

5. The monitoring method of an inertial measurement unit according to claim 4, characterized in that: The calibration result includes a calibration factor and calibrated data, and the step of calibrating the inertial measurement unit corresponding to the abnormal data to obtain the calibration result includes: Selecting true value data and data to be calibrated corresponding to the inertial measurement unit from the abnormal data; Calculate the calibration matrix corresponding to the true value data and the data to be calibrated by the least square method; The calibration factors and calibrated data are determined according to the calibration matrix.

6. The monitoring method of an inertial measurement unit according to claim 5, characterized in that: After the step of performing error calibration on the abnormal data to obtain the calibration result, the following step further comprises: Determining whether the calibrated data is greater than the preset error threshold, and determining whether the calibration factor is greater than a preset calibration threshold; If the calibrated data is not greater than the preset error threshold, and the calibration factor is not greater than the preset calibration threshold, the calibration result is used for subsequent data solution of the target inertial measurement unit array.

7. The method for monitoring an inertial measurement unit according to claim 6, characterized in that: After the step of determining whether the calibrated data is greater than the preset error threshold and the step of determining whether the calibration factor is greater than the preset calibration threshold, the method further comprises: If the calibrated data is greater than the preset error threshold, or the calibration factor is greater than the preset calibration threshold, the abnormal sensor in the target inertial measurement unit array is determined based on the calibration result, and the data corresponding to the abnormal sensor is eliminated in the subsequent data solution of the target inertial measurement unit array, and the calibration result is used for data solution.

8. A monitoring device for an inertial measurement unit, characterized in that: The monitoring device of the inertial measurement unit comprises: An acquisition module, used for acquiring a data stream of a target inertial measurement unit array; An identification module, used to identify abnormalities in the data stream and determine abnormal data in the data stream; The calibration module is used to perform error calibration on the abnormal data to obtain a calibration result for subsequent data solution of the target inertial measurement unit array.

9. A terminal device, characterized in that: The terminal device includes a memory, a processor, and a monitoring program for an inertial measurement unit stored in the memory and executable on the processor. When the monitoring program for the inertial measurement unit is executed by the processor, the steps of the monitoring method for an inertial measurement unit as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a monitoring program of an inertial measurement unit, and when the monitoring program of the inertial measurement unit is executed by a processor, the steps of the monitoring method of an inertial measurement unit as described in any one of claims 1 to 7 are implemented.

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