CN112146678A - Method for determining calibration parameters and electronic equipment - Google Patents

Method for determining calibration parameters and electronic equipment Download PDF

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CN112146678A
CN112146678A CN201910567545.3A CN201910567545A CN112146678A CN 112146678 A CN112146678 A CN 112146678A CN 201910567545 A CN201910567545 A CN 201910567545A CN 112146678 A CN112146678 A CN 112146678A
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measurement data
accelerometer
axis
imu
electronic device
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CN112146678B (en
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单双
陆枭
朱应成
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Huawei Technologies Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass

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Abstract

The embodiment of the application provides a method for determining calibration parameters and electronic equipment, wherein the method is applied to the electronic equipment with an Inertial Measurement Unit (IMU) so as to accurately calibrate measurement data of the IMU, reduce adverse effects of random noise on the IMU and improve the measurement precision of the IMU, and the method comprises the following steps: the method comprises the steps that first original measurement data collected by an IMU are obtained by electronic equipment, and the first original measurement data are collected when the electronic equipment is in a static state; then, performing fast Fourier transform on the first original measurement data to obtain first frequency domain data; then, first reference frequency domain data lower than a set threshold value are obtained from the first frequency domain data, and fast Fourier inverse transformation is carried out on the first reference frequency domain data to obtain first reference measurement data; finally, the electronic device determines calibration parameters of the IMU based on the first reference measurement data.

Description

Method for determining calibration parameters and electronic equipment
Technical Field
The present application relates to the field of terminal devices, and in particular, to a method for determining calibration parameters and an electronic device.
Background
An Inertial Measurement Unit (IMU) is a device that measures the three-axis attitude angle (or angular velocity) and acceleration of an object. Gyroscopes and accelerometers are the main components of the IMU, the accuracy of which directly affects the accuracy of the inertial system. Technologies related to user motion, such as dead reckoning and motion state identification, are widely applied to mobile devices, and the implementation effect of the technologies depends on the accuracy of output signals of a gyroscope and an accelerometer. For the gyroscope and the accelerometer on the mobile device, the main factors affecting the accuracy of the output signal thereof include: the zero point of each axis drifts along with time, the zero point of each axis drifts along with temperature, the sensitivity coefficient of each axis drifts along with temperature, and random noise caused by vibration of other components and parts on the same circuit board with the gyroscope or the accelerometer.
At present, aiming at zero point drift of each axis of the accelerometer along with time or temperature drift, the prior art provides a method for determining calibration parameters, and the method transforms measurement data of the accelerometer in a static state by using a least square and iterative solution method to obtain a scale factor and a zero offset of the accelerometer, so that the measurement data of the accelerometer is calibrated by using the scale factor and the zero offset of the accelerometer.
However, since the accelerometer and the gyroscope both belong to devices with higher sensitivity, even if zero calibration and temperature compensation are performed on each axis of the accelerometer, the influence of random noise caused by vibration of other components cannot be ignored, and an effective solution is not provided in the prior art, so that the data accuracy measured by the accelerometer and the gyroscope is not high.
Disclosure of Invention
The application provides a method for determining calibration parameters and electronic equipment, which are used for generating the calibration parameters so as to accurately calibrate measurement data acquired by an IMU (inertial measurement Unit), reduce adverse effects of random noise on the IMU and improve the measurement accuracy of the IMU.
In a first aspect, an embodiment of the present application provides a method for determining a calibration parameter, where the method is applied to an electronic device, and the method includes: the method comprises the steps that first original measurement data collected by an IMU are obtained by electronic equipment, the first original measurement data are collected when the electronic equipment is in a static state, then fast Fourier transform is carried out on the first original measurement data to obtain first frequency domain data, then first reference frequency domain data lower than a set threshold value are obtained from the first frequency domain data, and fast Fourier inverse transformation is carried out on the first reference frequency domain data to obtain first reference measurement data; finally, the calibration parameters of the IMU are determined according to the first reference measurement data.
In the embodiment of the application, the electronic equipment utilizes the method to accurately calibrate the measurement data acquired by the IMU, so that the adverse effect of random noise on the IMU is reduced, and the measurement precision of the IMU is improved.
In one possible design, the electronic device further acquires second original measurement data acquired by the IMU, and then performs fast Fourier transform on the second original measurement data to obtain second frequency domain data; then, second reference frequency domain data lower than a set threshold value are obtained from the second frequency domain data, and fast Fourier inverse transformation is carried out on the second reference frequency domain data to obtain second reference measurement data; and finally, calibrating the second reference measurement data according to the calibration parameters to obtain a calibrated measurement value.
In the embodiment of the application, the measurement data acquired by the IMU is filtered, and the calibration parameters determined by the method are used for calibrating the filtered measurement data, so that the measurement precision of the IMU is improved.
In one possible design, if the IMU includes an accelerometer, the calibration parameters of the IMU are determined based on the first reference measurement data, according to the following equation:
A＝S(V-O)
wherein V is [ V ]x,Vy,Vz]Is the first reference measurement data of the accelerometer, VxFor the first reference measurement, V, on the x-axis of the accelerometeryFor the first reference measurement, V, on the y-axis of the accelerometerzCorresponding first reference measurement data on a z-axis of the accelerometer; s is a scale factor of the accelerometer, O is a zero offset of the accelerometer, A represents an expected value of the accelerometer in a static state, and A is ═ ax,ay,az]TCan satisfy
Figure BDA0002109948770000021
Wherein g is the actual gravitational acceleration value, axIs the desired value of the accelerometer on the x-axis, ayIs the expected value on the y-axis of the accelerometer, aZIs the expected value on the z-axis of the accelerometer.
In the embodiment of the application, the scale factor and the zero offset of the accelerometer can be determined by using the method, so that the measurement data acquired by the accelerometer can be accurately calibrated.
In one possible design, after determining the scale factor and the zero offset of the accelerometer, the electronic device may calibrate the second reference measurement data according to the scale factor and the zero offset of the accelerometer to obtain a calibrated measurement value, which satisfies the following formula:
Figure BDA0002109948770000022
wherein V is [ V ]x,Vy,Vz]Is the second reference measurement data V of the accelerometerxFor the second reference measurement, V, on the x-axis of the accelerometeryFor the second reference measurement data, V, on the y-axis of the accelerometerzSecond reference measurement data corresponding to the z-axis of the accelerometer;
Figure BDA0002109948770000023
which represents the measured values after the calibration,
Figure BDA0002109948770000024
for the measurements after calibration on the x-axis of the accelerometer,
Figure BDA0002109948770000025
for the measurements after calibration on the accelerometer y-axis,
Figure BDA0002109948770000026
the measurements after calibration on the z-axis of the accelerometer.
In the embodiment of the application, the electronic device determines the calibration parameters by using the method to calibrate the filtered measurement data, so as to improve the measurement accuracy of the IMU.
In one possible design, if the IMU includes a gyroscope, determining calibration parameters of the IMU based on the first reference measurement data, according to the following equation:
Figure BDA0002109948770000027
wherein, CsIs a set of angular velocities in the first reference frequency domain data, N is the number of angular velocities in the set of angular velocities, WiThe ith angular velocity value is from 1 to N; o is zero offset of the gyroscope;
Figure BDA0002109948770000028
wherein, ω isgtIs the actual rotational speed, ω, of the gyroscopex,ωy,ωzRespectively the first reference measurement data S of x, y and z axes collected by the gyroscope under the condition that the turntable rotates at a constant speedx、Sy、SZScale factors of the gyroscope in x, y and z axes, respectively.
In the embodiment of the application, the scale factor and the zero offset of the gyroscope can be determined by using the method, so that the measurement data acquired by the gyroscope can be accurately calibrated.
In one possible design, after determining the scale factor and zero offset of the gyroscope, the electronic device calibrates the second reference measurement data according to the calibration parameter to obtain a calibrated measurement value, which meets the following formula requirement:
w＝S(W-O)
wherein w ═ wx,wy,wz]TFor the angular velocity values of the calibrated gyroscope in the x, y, z axes, S is equal to Sx＝Sy＝SzScale factors of the gyroscope in x, y and z axes, respectively.
In the embodiment of the application, the electronic device determines the calibration parameters by using the method to calibrate the filtered measurement data, so as to improve the measurement accuracy of the IMU.
In a second aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory. Wherein the memory is used to store one or more computer programs; the one or more computer programs stored in the memory, when executed by the processor, enable the electronic device to implement any of the possible design methodologies of any of the aspects described above.
In a third aspect, the present application further provides an apparatus including a module/unit for performing the method of any one of the possible designs of any one of the above aspects. These modules/units may be implemented by hardware, or by hardware executing corresponding software.
In a fourth aspect, this embodiment also provides a computer-readable storage medium, where the computer-readable storage medium includes a computer program, and when the computer program is run on an electronic device, the computer program causes the electronic device to perform any one of the possible design methods of the foregoing aspects.
In a fifth aspect, the present application further provides a method including a computer program product, when the computer program product runs on a terminal, causing an electronic device to execute any one of the possible designs of any one of the above aspects.
These and other aspects of the present application will be more readily apparent from the following description of the embodiments.
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Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a method for determining calibration parameters according to an embodiment of the present disclosure;
FIG. 3a is a schematic time-domain waveform of first raw measurement data provided in an embodiment of the present application;
fig. 3b is a schematic frequency domain waveform of first frequency domain data according to an embodiment of the present disclosure;
FIG. 3c is a schematic time domain waveform of first reference measurement data provided in an embodiment of the present application;
fig. 4 is a schematic view of an applicable scenario provided in the embodiment of the present application;
fig. 5 is a schematic view of another applicable scenario provided in the embodiment of the present application;
FIG. 6 is a schematic diagram of a map construction process provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of another electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. In the description of the embodiments of the present application, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of the present application, "a plurality" means two or more unless otherwise specified.
At present, the IMU is widely applied to the fields of Augmented Reality (AR), Virtual Reality (VR), automation and the like, and AR equipment and VR equipment can be mobile phones, floor sweeping robots, or wearable equipment such as smart watches, VR glasses, sports bracelets and the like, and also can be equipment such as unmanned vehicles and unmanned aerial vehicles. Micro-electro-mechanical systems (MEMS) accelerometers and gyroscopes are basically used in electronic products such as mobile phones and wearable devices, and MEMS is a micro device or system integrating micro sensors, micro actuators, micro mechanical structures, micro power sources, signal processing and control circuits, high-performance electronic integrated devices, interfaces, and communication.
Because there may be components and parts that cause vibrations in equipment such as cell-phone, the robot of sweeping the floor, VR glasses, for example, the motor during operation can cause vibrations in the cell-phone, can cause vibrations when the driving motor among the robot of sweeping the floor moves, and the ray apparatus during operation in the VR glasses can cause vibrations. The vibration of these components can increase the random noise of the operating environment of the IMU, which can result in reduced accuracy of the IMU measurements.
In view of the above problems, embodiments of the present application provide a method for determining calibration parameters, which may be applied to the electronic device with an IMU. The method mainly comprises the following steps: the method comprises the steps that a processor of the electronic equipment firstly obtains first original measurement data collected by the IMU in a static state, then fast Fourier transform is carried out on the first original measurement data to obtain frequency domain data, the frequency domain data are filtered, then fast Fourier inverse transformation is carried out on the filtered frequency domain data to obtain first reference measurement data, and calibration parameters of the IMU are determined based on the first reference measurement data. Because the data is filtered, random noise caused by vibration of other devices can be filtered, and the accuracy of the calibration parameters obtained based on the filtered data is higher, so that errors of the sensor caused by vibration noise are compensated, and the measurement precision of the IMU is improved.
The method for determining the calibration parameters provided by the embodiment of the present application can be applied to any electronic device having an IMU, such as the various AR devices or VR devices described above. A structure of an electronic device to which the embodiment of the present application can be applied is described below, and the electronic device can be a mobile phone. Fig. 1 shows a schematic structural diagram of the electronic device 100.
The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a USB interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 151, a wireless communication module 152, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display 194, a SIM card interface 195, and the like. The sensor module 180 may include a gyroscope sensor 180A, an acceleration sensor 180B, a magnetic field sensor 180C, an electric field sensor 180D, a pressure sensor 180E, a proximity light sensor 180G, a fingerprint sensor 180H, a touch sensor 180K, and a rotation axis sensor 180M (of course, the electronic device 100 may further include other sensors, such as a temperature sensor, a distance sensor, an ambient light sensor, an air pressure sensor, a bone conduction sensor, and the like, which are not shown in the figure).
It is to be understood that the illustrated structure of the embodiment of the present invention does not specifically limit the electronic device 100. In other embodiments of the present application, electronic device 100 may include more or fewer components than shown, or some components may be combined, some components may be split, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
Processor 110 may include one or more processing units, such as: the processor 110 may include an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a memory, a video codec, a Digital Signal Processor (DSP), a baseband processor, and/or a Neural-Network Processing Unit (NPU), etc. The different processing units may be separate devices or may be integrated into one or more processors. The controller may be, among other things, a neural center and a command center of the electronic device 100. The controller can generate an operation control signal according to the instruction operation code and the timing signal to complete the control of instruction fetching and instruction execution.
A memory may also be provided in processor 110 for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory may hold instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to reuse the instruction or data, it can be called directly from memory. Avoiding repeated accesses reduces the latency of the processor 110, thereby increasing the efficiency of the system.
The processor 110 may execute the computer instructions of the method for determining calibration parameters provided by the embodiments of the present application to achieve an improvement in the measurement accuracy of the IMU. When the processor 110 integrates different devices, such as a CPU and a GPU, the CPU and the GPU may cooperate with instructions for executing the method for determining the calibration parameter provided in the embodiment of the present application, for example, part of the algorithm in the method for determining the calibration parameter is executed by the CPU, and another part of the algorithm is executed by the GPU, so as to obtain faster processing efficiency.
The display screen 194 is used to display images, video, and the like. The display screen 194 includes a display panel. The display panel may adopt a Liquid Crystal Display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (active-matrix organic light-emitting diode, AMOLED), a flexible light-emitting diode (FLED), a miniature, a Micro-oeld, a quantum dot light-emitting diode (QLED), and the like. In some embodiments, the electronic device 100 may include 1 or N display screens 194, with N being a positive integer greater than 1.
The cameras 193 (front camera or rear camera, or one camera may be both front camera and rear camera) are used to capture still images or video. In general, the camera 193 may include a photosensitive element such as a lens group including a plurality of lenses (convex lenses or concave lenses) for collecting an optical signal reflected by an object to be photographed and transferring the collected optical signal to an image sensor, and an image sensor. The image sensor generates an original image of an object to be photographed from the optical signal.
The internal memory 121 may be used to store computer-executable program code, which includes instructions. The processor 110 executes various functional applications of the electronic device 100 and data processing by executing instructions stored in the internal memory 121. The internal memory 121 may include a program storage area and a data storage area. Wherein the storage program area may store an operating system, codes of application programs (such as a camera application, a WeChat application, etc.), and the like. The storage data area may store data created during use of the electronic device 100 (e.g., images, videos, etc. captured by a camera application), and the like.
The internal memory 121 may also store code that the embodiments of the present application provide for algorithms to determine calibration parameters. When the codes of the algorithm stored in the internal memory 121 are run by the processor 110, the processor 110 performs time-frequency conversion on the measurement data, filters the converted data, and calculates calibration parameters based on the filtered data.
In addition, the internal memory 121 may include a high-speed random access memory, and may further include a nonvolatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (UFS), and the like.
Of course, the codes of the algorithms provided in the embodiments of the present application may also be stored in the external memory. In this case, the processor 110 may implement the response to the touch operation of the user by executing the code of the algorithm stored in the external memory through the external memory interface 120.
The function of the sensor module 180 is described below.
The gyro sensor 180A (or simply, a gyroscope), which is a primary component of the IMU, may be used to determine the motion pose of the electronic device 100. In some embodiments, the angular velocity of electronic device 100 about three axes (i.e., the x, y, and z axes) may be determined by gyroscope sensor 180A.
The acceleration sensor 180B (or simply, accelerometer), which is the main component of the IMU, can be used to detect the magnitude of acceleration of the electronic device 100 in various directions (typically three axes). I.e., the acceleration sensor 180B may be used to detect the current motion state of the electronic device 100, such as shaking or standing still.
The magnetic field sensor 180C, a device in the IMU, may be used to convert various magnetic fields and the amount of change thereof into electrical signals.
The electric field sensor 180D, which is a device in the IMU, refers to a sensor that senses the strength of the electric field and converts it into a usable output signal.
The pressure sensor 180E, which is a device in the IMU, senses the pressure signal and may convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180E may be disposed on the display screen 194. The pressure sensor 180E can be of various types, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a sensor comprising at least two parallel plates having an electrically conductive material. When a force acts on the pressure sensor 180E, the capacitance between the electrodes changes. The electronic device 100 determines the strength of the pressure from the change in capacitance. When a touch operation is applied to the display screen 194, the electronic apparatus 100 detects the intensity of the touch operation according to the pressure sensor 180A. The electronic apparatus 100 may also calculate the touched position from the detection signal of the pressure sensor 180E. In some embodiments, the touch operations that are applied to the same touch position but different touch operation intensities may correspond to different operation instructions. For example: and when the touch operation with the touch operation intensity smaller than the first pressure threshold value acts on the short message application icon, executing an instruction for viewing the short message. And when the touch operation with the touch operation intensity larger than or equal to the first pressure threshold value acts on the short message application icon, executing an instruction of newly building the short message.
The proximity light sensor 180G may include, for example, a Light Emitting Diode (LED) and a light detector, such as a photodiode. The light emitting diode may be an infrared light emitting diode. The electronic device emits infrared light to the outside through the light emitting diode. The electronic device uses a photodiode to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device. When insufficient reflected light is detected, the electronic device may determine that there are no objects near the electronic device.
The gyro sensor 180A (or the acceleration sensor 180B) may transmit the detected motion state information (such as an angular velocity) to the processor 110. The processor 110 determines whether the electronic device 100 is currently in the handheld state or the tripod state (for example, when the angular velocity is not 0, the electronic device is in the handheld state) based on the motion state information.
The fingerprint sensor 180H is used to collect a fingerprint. The electronic device 100 can utilize the collected fingerprint characteristics to unlock the fingerprint, access the application lock, photograph the fingerprint, answer an incoming call with the fingerprint, and so on.
The touch sensor 180K is also referred to as a "touch panel". The touch sensor 180K may be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is used to detect a touch operation applied thereto or nearby. The touch sensor can communicate the detected touch operation to the application processor to determine the touch event type. Visual output associated with the touch operation may be provided through the display screen 194. In other embodiments, the touch sensor 180K may be disposed on a surface of the electronic device 100, different from the position of the display screen 194.
Illustratively, the display screen 194 of the electronic device 100 displays a main interface including icons for a plurality of applications (e.g., a camera application, a WeChat application, etc.). The user clicks an icon of the sweeping robot application in the main interface through the touch sensor 180K, the processor 110 is triggered to start the sweeping robot application, and the operation map is opened. The display screen 194 displays an interface of the sweeping robot application, such as a job map interface.
The wireless communication function of the electronic device 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 151, the wireless communication module 152, the modem processor, the baseband processor, and the like.
The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the electronic device 100 may be used to cover a single or multiple communication bands. Different antennas can also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.
The mobile communication module 151 may provide a solution including 2G/3G/4G/5G wireless communication applied to the electronic device 100. The mobile communication module 151 may include at least one filter, a switch, a power amplifier, a Low Noise Amplifier (LNA), and the like. The mobile communication module 151 may receive electromagnetic waves from the antenna 1, filter, amplify, etc. the received electromagnetic waves, and transmit the electromagnetic waves to the modem processor for demodulation. The mobile communication module 151 may also amplify the signal modulated by the modem processor, and convert the signal into electromagnetic wave through the antenna 1 to radiate the electromagnetic wave. In some embodiments, at least some of the functional modules of the mobile communication module 151 may be provided in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 151 may be disposed in the same device as at least some of the modules of the processor 110.
The modem processor may include a modulator and a demodulator. The modulator is used for modulating a low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used for demodulating the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then passes the demodulated low frequency baseband signal to a baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and then transferred to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.) or displays an image or video through the display screen 194. In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be provided in the same device as the mobile communication module 151 or other functional modules, independent of the processor 110.
The wireless communication module 152 may provide a solution for wireless communication applied to the electronic device 100, including Wireless Local Area Networks (WLANs) (e.g., wireless fidelity (Wi-Fi) networks), bluetooth (bluetooth, BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), Infrared (IR), and the like. The wireless communication module 152 may be one or more devices integrating at least one communication processing module. The wireless communication module 152 receives electromagnetic waves via the antenna 2, performs frequency modulation and filtering processing on electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 152 may also receive a signal to be transmitted from the processor 110, frequency-modulate it, amplify it, and convert it into electromagnetic waves via the antenna 2 to radiate it.
In addition, the electronic device 100 may implement an audio function through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor. Such as music playing, recording, etc. The electronic device 100 may receive key 190 inputs, generating key signal inputs related to user settings and function control of the electronic device 100. Electronic device 100 may generate a vibration alert (e.g., an incoming call vibration alert) using motor 191. The indicator 192 in the electronic device 100 may be an indicator light, and may be used to indicate a charging status, a power change, or a message, a missed call, a notification, etc. The SIM card interface 195 in the electronic device 100 is used to connect a SIM card. The SIM card can be brought into and out of contact with the electronic apparatus 100 by being inserted into the SIM card interface 195 or being pulled out of the SIM card interface 195.
It should be understood that in practical applications, the electronic device 100 may include more or less components than those shown in fig. 1, and the embodiment of the present application is not limited thereto.
The following describes the technical solution of the present application by taking an example of applying the embodiment of the present application to the electronic device shown in fig. 1. Fig. 2 is a flowchart illustrating a method for determining calibration parameters according to the present application, where the method includes the following steps:
in step 201, when the electronic device is in a static state, the processor 110 of the electronic device acquires first raw measurement data acquired by the IMU.
Specifically, the IMU in the embodiment of the present application includes at least one of the following sensors: a gyro sensor 180A, an acceleration sensor 180B, a magnetic field sensor 180C, an electric field sensor 180D, a pressure sensor 180E, and the like. The processor 110 of the electronic device may trigger each IMU to acquire data according to a set period, or may trigger a corresponding IMU to acquire data when it is detected that a designated function of the electronic device is invoked. Optionally, the specifying function may be called when the posture of the electronic device satisfies a condition that remains unchanged for a set time period. For example, the posture of the electronic device may be kept constant for a certain time in a scene that the user answers a call, reads a short message, takes a picture for focusing, opens a web browser, searches for a wireless signal, and charges the sweeping robot. Therefore, the function corresponding to the service can be artificially set as a specified function which can trigger the IMU to collect data in advance. Of course, the electronic device may use other functions that keep the posture fixed for a certain period of time as the above-mentioned designated function, and the present application is described only by taking the above-mentioned functions as an example, and does not limit the present application.
It should be noted that, in the embodiment of the present application, the electronic device being in a static state may refer to the electronic device being in a completely static state, or may be in a state that the electronic device is close but not completely static, that is, there is a relatively small motion amplitude of the electronic device.
Illustratively, when the IMU is an accelerometer, the first raw measurement data collected by the accelerometer includes accelerometer values in three axes, x, y, and z; when the IMU is a gyroscope, the first raw measurement data collected by the gyroscope includes angular velocity values in three axial directions of the x, y, and z axes.
In step 202, the processor 110 performs fast fourier transform on the first raw measurement data to obtain first frequency domain data.
Exemplarily, assuming that the time domain waveform of the first raw measurement data is as shown in fig. 3a, the frequency domain waveform of the first frequency domain data after the fast fourier transform is as shown in fig. 3 b. As can be seen from fig. 3b, the frequency domain waveform is not smooth, and there are some abrupt changes, i.e. random noise that may be caused by vibrations of other devices.
In step 203, the processor 110 obtains first reference frequency domain data lower than a set threshold from the first frequency domain data, and performs inverse fast fourier transform on the first reference frequency domain data to obtain first reference measurement data.
Exemplarily, assuming that the threshold is set to the value K in fig. 3b, the processor 110 of the electronic device filters out the frequency domain data greater than or equal to the value K in fig. 3b, and obtains the first reference frequency domain data smaller than the value K. Further, the processor 110 of the electronic device performs an inverse fast fourier transform on the filtered first reference frequency domain data to obtain the first reference measurement data corresponding to the time domain waveform (dark region 301) as shown in fig. 3 c. It can be seen from fig. 3c that the waveform of the time domain waveform after filtering is smooth with no abrupt values, i.e. random noise caused by vibrations of other devices has been filtered out.
It should be noted that the set threshold may be a frequency value artificially determined in advance through analysis of the frequency spectrum and stored in the internal memory 121, and the electronic device may filter the random noise (i.e., the above-mentioned mutation value) in the first frequency domain data by using a software filtering unit provided with the set threshold.
At step 204, the processor 110 determines calibration parameters of the IMU based on the first reference measurement data.
Specifically, in one aspect, assuming that the electronic device is VR glasses, an accelerometer is included in an IMU of the electronic device, and the accelerometer of the VR glasses collects accelerometer values in different static states, and then obtains a zero offset and a scale factor of the accelerometer by minimizing a difference value between an actual acceleration and a gravity acceleration. In general, when the temperature is stable, the calibration model equation [1] of the accelerometer can be:
formula [1], (V — O) … …
Wherein V is [ V ]x,Vy,Vz]For the first reference measurement data actually measured (i.e. accelerometer values), VxFor the first reference measurement data on the x-axis, VyFor the first reference measurement data, V, on the y-axiszFirst reference measurement data corresponding to the z-axis; s is a scale factor, O is zero offset, A represents the expected value of the accelerometer in a static state, and A is ═ ax,ay,az]TCan satisfy
Figure BDA0002109948770000081
Wherein g is the actual gravitational acceleration value, axIs the desired value on the x-axis, ayExpected value on the y-axis, aZAs expected on the z-axis.
That is, after the first reference measurement data of the accelerometer is calibrated by the first equation, the measurement value a after calibration is expected to be [ a ═ a%x,ay,az]TCan satisfy
Figure BDA0002109948770000082
Wherein g is the actual gravitational acceleration value. In general, the calibration model shown in equation one can also be replaced by a matrix form, such as equation [2 ]]As shown.
Figure BDA0002109948770000083
And assuming that S is a symmetric matrix, optimizing 9 variables in the variables S and O by minimizing the difference E between the actual acceleration and the gravity acceleration according to the following formula [3] and formula [4], and finally obtaining the zero offset and the scale factor of the accelerometer.
Figure BDA0002109948770000084
Figure BDA0002109948770000085
Wherein, Vj,kRepresenting the output of the j-axis accelerometer at the kth rest attitude, OjDenotes zero offset of the j axis, j is x, y, z, so OjI.e. Ox,Oy,Oz，Sxx,Syy,SzzScale factors, S, representing the x, y, z axesxy,Sxz,SyzRepresenting the influence factor of crosstalk between the xy, xz, yz axes, ekAnd g is an actual gravity acceleration value.
On the other hand, assuming that the electronic device is a VR glasses, the IMU of the electronic device includes a gyroscope, the gyroscope of the VR glasses acquires angular velocity values in different static states, and then the processor 110 substitutes the first reference frequency domain data into the following formula [5] to obtain a zero offset O of the gyroscope in the gyroscope;
Figure BDA0002109948770000086
wherein, CsIs a set of angular velocities in the reference frequency domain data, N is the number of angular velocities in the set of angular velocities, WiFor the ith angular velocity value, i is from 1 to N.
It should be noted that, in the conventional technology, when calibrating a gyroscope, if it is necessary to use a uniform-speed turntable to obtain a scale factor S, the hardware configuration is complicated, in this embodiment of the present application, the scale factor S is set as a diagonal matrix
Figure BDA0002109948770000087
The values on the diagonal are the scale factors for each axis. Under the condition that the rotary table rotates at a constant speed, reading the reading of the gyroscope by using a formula [6]]The scale factor S can be solvedx、Sy、SZ。
That is, the processor 110 substitutes the first reference measurement data into the following equation [6] to obtain the scale factors of the axes of the gyroscope;
Figure BDA0002109948770000091
wherein, ω isgtIs the actual rotational speed, ω, of the gyroscopex,ωy,ωzRespectively are reference measured values S of x, y and z axes respectively collected by the gyroscope under the condition that the turntable rotates at a constant speedx、Sy、SZScale factors of the gyroscope in x, y and z axes.
Further, after determining the calibration parameters of the IMU, the electronic device may also directly calibrate the raw measurement data acquired by the IMU using the calibration parameters. Of course, the electronic device may also calibrate the measurement data filtered in step 203 using the calibration parameter. Specifically, the electronic device may obtain second original measurement data acquired by the IMU, perform fast fourier transform on the second original measurement data to obtain second frequency domain data, obtain second reference frequency domain data lower than a set threshold from the second frequency domain data, and perform inverse fast fourier transform on the second reference frequency domain data to obtain second reference measurement data. Finally, the electronic device calibrates the second reference measurement data by using the calibration parameter obtained in step 204, so as to obtain a calibrated measurement value.
It should be noted that the process of determining the calibration parameter and the process of acquiring the second original measurement data by the IMU do not have a strict sequence, and the calibration parameter may be determined first and then the second original measurement data is acquired, or the second original measurement data may be acquired first and then the calibration parameter is determined, or the calibration parameter may be executed simultaneously.
For example, for the accelerometer, the processor 110 may calibrate the second reference measurement data of the accelerometer in the IMU according to the zero offset O and the scale factor S of the accelerometer in the IMU determined in the above formula [7] to obtain the measurement value after calibration.
Figure BDA0002109948770000092
Wherein V is [ V ]x,Vy,Vz]Is the second reference measurement data of the accelerometer, VxFor the second reference measurement data on the x-axis, VYSecond reference measurement data on the y axis and corresponding second reference measurement data on the z axis are Vz; s is a scale factor, O is zero offset,
Figure BDA0002109948770000093
which represents the measured values after the calibration,
Figure BDA0002109948770000094
for the measured values after calibration on the x-axis,
Figure BDA0002109948770000095
for the measured values after calibration on the y-axis,
Figure BDA0002109948770000096
is the measured value after calibration on the z-axis.
For another example, for a gyroscope, the processor 110 may calibrate the second reference measurement data of the gyroscope according to the zero offset O and the scale factor S of the gyroscope in the IMU determined in the above formula, and according to the following formula [8], to obtain the measurement value after calibration.
Formula [8], (W — O) … … ·
Wherein w ═ wx,wy,wz]TFor the calibrated x, y, z-axis angular velocity values, O is zero offset and S is equal to Sx＝Sy＝SzScale factors for x, y, z axes of the gyroscope。
For example, when determining whether the electronic device is in a stationary state, the application may determine through a specific process.
The first step, the measurement data screening process.
The electronic equipment triggers the IMU to acquire data according to a set period, or triggers the IMU to acquire data when a designated function of the electronic equipment is called, if the IMU comprises an accelerometer and the acquired data comprises original measurement data of the accelerometer, the electronic equipment carries out high-pass filtering on the original measurement data output by the accelerometer, and the acceleration static component of the gravity acceleration is filtered from the original measurement data to obtain an acceleration dynamic component generated due to movement. Similarly, if the IMU further includes a gyroscope, high-pass filtering is performed on the original measurement data output by the gyroscope to obtain an angular velocity dynamic component generated by the motion in the original measurement data of the gyroscope.
Furthermore, the measurement data after the accelerometer or the gyroscope is subjected to high-pass filtering can be smoothed, and available smoothing techniques include low-pass filtering, median filtering, mean filtering and the like.
Furthermore, signals of the acceleration sensor and the gyroscope after smoothing can be rectified, so that the negative half shaft part of the signal waveforms output by the acceleration sensor and the gyroscope is turned to the positive half shaft.
And a second step, measuring data calculation process.
The processor 110 of the electronic device calculates the maximum value and the variance value of the measurement data of the accelerometer after the above-described processing. Similarly, if the IMU further includes a gyroscope, the maximum value and the variance value of the measurement data of the gyroscope after the above processing are calculated in the same manner.
And thirdly, judging the static state.
The electronic device compares the extracted maximum value and the extracted variance value of the measurement data of the accelerometer with a preset maximum value threshold value and a preset variance value threshold value respectively, and if the maximum value of the measurement data of the accelerometer is not greater than the preset maximum value threshold value and the variance value of the measurement data of the acceleration sensor 180B is determined not to be greater than the preset variance value threshold value, the electronic device is determined to be in a still state all the time.
Similarly, if the IMU further includes a gyroscope, the maximum value and the variance value of the measurement data of the gyroscope extracted above are further compared with a preset second maximum value threshold and a preset second variance value threshold, and if the maximum value and the variance value of the measurement data of the accelerometer on the electronic device are not greater than the preset first maximum value threshold and the preset first variance value threshold, and the maximum value and the variance value of the measurement data of the gyroscope on the electronic device are not greater than the preset second maximum value threshold and the preset second variance value threshold, it is determined that the electronic device is always in the stationary state.
Optionally, in the process of determining the calibration parameter, the electronic device may trigger the IMU to acquire raw measurement data of the electronic device in a static state through the method described above, and store the raw measurement data meeting the requirement to the internal memory 121. Once the number of the raw measurement data in the internal memory 121 meets a certain threshold, the processor 110 of the electronic device is triggered to repeatedly execute the above steps 201 to 204, obtain new calibration parameters, and update the new calibration parameters into the parameter pool in the internal memory 121. The IMU detects whether the parameter pool is updated in a timing or real-time mode in the running process, if the parameter pool is updated with calibration parameters, the calibration parameters which are updated last time are extracted from the parameter pool and used as new calibration parameters of the IMU, and the new calibration parameters are used for calibrating subsequent measurement data; if the parameter pool is not updated, the subsequent measurement data continues to be calibrated using the current calibration parameters.
The method for determining the calibration parameter provided by the embodiment of the present application will be described in detail below with reference to the accompanying drawings and application scenarios.
Scene one
The method for determining the calibration parameter provided by the embodiment of the application can be applied to a scene in which the electronic device 100 and the VR glasses 200 are interconnected based on a connecting line as shown in fig. 4. In the scenario shown in fig. 4, the electronic device 100 projects its own screen display content into the VR glasses 200, and the user can watch photos, videos or play games through the VR glasses 200 to enjoy a larger screen experience. In this embodiment, the processor of the VR glasses 200 may collect, according to the method shown in steps 201 to 204 shown in fig. 2, original measurement data measured by the IMU when the VR glasses 200 are in the static state, then filter interference of random noise of components such as an optical machine in the VR glasses 200 on the IMU, and determine a calibration parameter based on the measurement data after filtering, and the VR glasses 200 may calibrate measurement data subsequently collected by the IMU using the calibration parameter, so as to improve measurement accuracy of an accelerometer and a gyroscope in the VR glasses 200.
In addition, the embodiment of the application may also be separately applied to the electronic device 100, specifically, the processor 110 of the electronic device first acquires original measurement data acquired by the IMU when the mobile phone is in a static state, then filters interference caused by noise of components such as a motor in the electronic device 100 to the IMU, and determines the calibration parameter based on the filtered data, and the electronic device 100 may calibrate the measurement data acquired by the IMU by using the calibration parameter, so as to improve measurement accuracy of an accelerometer and a gyroscope in the electronic device 100.
Scene two
On the other hand, the method for determining the calibration parameter provided in the embodiment of the present application may also be applied to the sweeping robot 300 that is performing the cleaning operation as shown in fig. 5.
The sweeping robot starts to use a machine vision technology in order to realize autonomous obstacle avoidance and route planning. The sweeping robot based on the machine vision can acquire images through the camera and plan a path and avoid obstacles based on the acquired images and a reasonable algorithm. Currently, a sweeping robot in the market adopts a simultaneous localization and mapping (SLAM) technology, can observe a space swept by the sweeping robot through a camera, identify a mark object and main characteristics in the space swept by the sweeping robot, and draw a room map for navigation through a triangular localization principle, so as to confirm the position of the robot in the swept space, the swept area, the non-swept area and the like. Due to the fact that the vision robustness of the sweeping robot is poor in scenes such as rapid movement and pure rotation, a plurality of IMU fusion algorithms appear in recent years, and the robustness of the SLAM algorithm is improved.
Specifically, fig. 6 shows a SLAM algorithm, which tracks and locates: when an image is input, a processor of the sweeping robot extracts the feature points of the image, and the initial pose of the camera is obtained according to the initial frame. Based on information acquired by the IMU of the sweeping robot, the estimated pose can be obtained by pre-integrating and filtering the image. And finally, fusing the two poses by the processor, initializing the map, and estimating or repositioning the initial state. When there is more image input, the processor will calculate the pose from frame to track the local map. At the end of the positioning, it is also determined whether a new key frame is inserted.
Local mapping process: and sequentially selecting key frames, deleting invalid map points, and adding valid map points to ensure the quality of the map. Optionally, a Bundle Adjustment (BA) optimization is also performed on the local map.
A closed loop process: the processor can select candidate pixel points among similar image frames, carry out similarity transformation according to the candidate points, calculate the posture among the frames, and fuse and optimize the basic graph of the whole loop so as to ensure the accuracy of the navigation path.
Therefore, in the SLAM algorithm, if the measurement accuracy of the information acquired by the IMU is low, the tracking and positioning process is greatly influenced, and the usability of the constructed map is finally influenced. Therefore, the method for determining the calibration parameters is applied to the tracking and positioning process, that is, the calibration parameters are determined in real time, and the data acquired by the IMU is calibrated, so that the initial state is accurately estimated or repositioned.
In other embodiments of the present application, an embodiment of the present application discloses an electronic device, which may include, as shown in fig. 7: a touch panel 701, wherein the touch panel 701 includes a touch panel 707 and a display screen 708; one or more processors 702; a memory 703; one or more application programs (not shown); and one or more computer programs 704, the IMU705, the various devices described above, may be connected by one or more communication buses 706. Wherein the one or more computer programs 704 are stored in the memory 703 and configured to be executed by the one or more processors 702, the one or more computer programs 704 comprising instructions which may be used to perform the steps as in the respective embodiment of fig. 2.
The embodiment of the present application further provides a computer storage medium, where a computer instruction is stored in the computer storage medium, and when the computer instruction runs on an electronic device, the electronic device is enabled to execute the above related method steps to implement the method for determining the calibration parameter in the above embodiment.
The embodiments of the present application further provide a computer program product, which when run on a computer, causes the computer to execute the above related steps to implement the method for determining the calibration parameter in the above embodiments.
In addition, embodiments of the present application also provide an apparatus, which may be specifically a chip, a component or a module, and may include a processor and a memory connected to each other; the memory is used for storing computer execution instructions, and when the device runs, the processor can execute the computer execution instructions stored in the memory, so that the chip can execute the method for determining the calibration parameters in the above method embodiments.
In addition, the electronic device, the computer storage medium, the computer program product, or the chip provided in the embodiments of the present application are all configured to execute the corresponding method provided above, so that the beneficial effects achieved by the electronic device, the computer storage medium, the computer program product, or the chip may refer to the beneficial effects in the corresponding method provided above, and are not described herein again.
Through the description of the above embodiments, those skilled in the art will understand that, for convenience and simplicity of description, only the division of the above functional modules is used as an example, and in practical applications, the above function distribution may be completed by different functional modules as needed, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, a module or a unit may be divided into only one logic function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another apparatus, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed to a plurality of different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (14)

1. A method of determining calibration parameters for an electronic device having an inertial measurement unit, IMU, the method comprising:
acquiring first original measurement data acquired by the IMU, wherein the first original measurement data is acquired when the electronic equipment is in a static state;
performing fast Fourier transform on the first original measurement data to obtain first frequency domain data;
acquiring first reference frequency domain data lower than a set threshold from the first frequency domain data, and performing inverse fast Fourier transform on the first reference frequency domain data to obtain first reference measurement data;
determining calibration parameters of the IMU based on the first reference measurement data.
2. The method of claim 1, further comprising:
acquiring second original measurement data acquired by the IMU;
performing fast Fourier transform on the second original measurement data to obtain second frequency domain data;
acquiring second reference frequency domain data lower than the set threshold from the second frequency domain data, and performing inverse fast Fourier transform on the second reference frequency domain data to obtain second reference measurement data;
and calibrating the second reference measurement data according to the calibration parameters to obtain a calibrated measurement value.
3. The method of claim 2, wherein the IMU includes an accelerometer, and wherein the determining the calibration parameters of the IMU from the first reference measurement data is in accordance with the following equation:
A＝S(V-O)
wherein V is [ V ]x,Vy,Vz]Is the first reference measurement data, V, of the accelerometerxFor the first reference measurement data, V, on the x-axis of the accelerometeryFor the first reference measurement data, V, on the accelerometer y-axiszCorresponding first reference measurement data on the z-axis of the accelerometer; s is a scale factor of the accelerometer, O is a zero offset of the accelerometer, A represents an expected value of the accelerometer in a static state, and A ═ ax,ay,az]TCan satisfy
Figure FDA0002109948760000011
Wherein g is the actual gravitational acceleration value, axIs a desired value on the x-axis of the accelerometer, ayIs a desired value on the y-axis of the accelerometer, aZIs the expected value on the z-axis of the accelerometer.
4. The method of claim 3, wherein the calibrating the second reference measurement data according to the calibration parameter to obtain the calibrated measurement value satisfies the following formula:
Figure FDA0002109948760000012
wherein V is [ V ]x,Vy,Vz]Is the second reference measurement data V of the accelerometerxFor a second reference measurement, V, on the x-axis of the accelerometeryFor the second reference measurement data, V, on the accelerometer y-axiszIs a stand forSecond reference measurement data corresponding to a z-axis of the accelerometer;
Figure FDA0002109948760000013
which represents the measured values after the calibration,
Figure FDA0002109948760000014
for the measurements after calibration on the x-axis of the accelerometer,
Figure FDA0002109948760000015
for the measurements after calibration on the accelerometer y-axis,
Figure FDA0002109948760000016
and measuring the measured value after the calibration on the z-axis of the accelerometer.
5. The method of claim 2, wherein the IMU includes a gyroscope, and wherein determining calibration parameters for the IMU from the first reference measurement data is in accordance with the following equation:
Figure FDA0002109948760000017
wherein, CsIs the angular velocity set in the first reference frequency domain data, N is the number of angular velocities in the angular velocity set, WiThe ith angular velocity value is from 1 to N; o is the zero bias of the gyroscope;
Figure FDA0002109948760000021
wherein, ω isgtIs the actual rotational speed, ω, of the gyroscopex,ωy,ωzRespectively collecting first reference measurement data S of x, y and z axes of the gyroscope under the condition that the turntable rotates at a constant speedx、Sy、SZAnd the scale factors of the gyroscope on the x axis, the y axis and the z axis respectively.
6. The method of claim 5, wherein the calibrating the second reference measurement data according to the calibration parameter to obtain the calibrated measurement value satisfies the following formula:
w＝S(W-O)
wherein w ═ wx,wy,wz]TFor the angular velocity values of the gyroscope after calibration in the x, y and z axes, S is equal to Sx＝Sy＝SzAnd the scale factors of the gyroscope on the x axis, the y axis and the z axis respectively.
7. An electronic device comprising a processor and a memory;
the memory for storing one or more computer programs;
the memory stores one or more computer programs that, when executed by the processor, cause the electronic device to perform:
acquiring first original measurement data acquired by the IMU, wherein the first original measurement data is acquired when the electronic equipment is in a static state;
performing fast Fourier transform on the first original measurement data to obtain first frequency domain data;
acquiring first reference frequency domain data lower than a set threshold from the first frequency domain data, and performing inverse fast Fourier transform on the first reference frequency domain data to obtain first reference measurement data;
determining calibration parameters of the IMU based on the first reference measurement data.
8. The electronic device of claim 7, wherein the one or more computer programs stored in the memory, when executed by the processor, further cause the electronic device to perform:
acquiring second original measurement data acquired by the IMU;
performing fast Fourier transform on the second original measurement data to obtain second frequency domain data;
acquiring second reference frequency domain data lower than the set threshold from the second frequency domain data, and performing inverse fast Fourier transform on the second reference frequency domain data to obtain second reference measurement data;
and calibrating the second reference measurement data according to the calibration parameters to obtain a calibrated measurement value.
9. The electronic device of claim 7 or 8, wherein the IMU includes an accelerometer, the one or more computer programs stored in the memory, when executed by the processor, cause the electronic device to perform, in particular:
determining calibration parameters of the IMU according to the first reference measurement data, wherein the calibration parameters meet the following formula requirements:
A＝S(V-O)
wherein V is [ V ]x,Vy,Vz]Is the first reference measurement data, V, of the accelerometerxFor the first reference measurement data, V, on the x-axis of the accelerometeryFor the first reference measurement data, V, on the accelerometer y-axiszCorresponding first reference measurement data on the z-axis of the accelerometer; s is a scale factor of the accelerometer, O is a zero offset of the accelerometer, A represents an expected value of the accelerometer in a static state, and A ═ ax,ay,az]TCan satisfy
Figure FDA0002109948760000022
Wherein g is the actual gravitational acceleration value, axIs a desired value on the x-axis of the accelerometer, ayIs a desired value on the y-axis of the accelerometer, aZIs the expected value on the z-axis of the accelerometer.
10. The electronic device of claim 9, wherein the one or more computer programs stored in the memory, when executed by the processor, cause the electronic device to perform, in particular:
according to the calibration parameter, calibrating the second reference measurement data to obtain a calibrated measurement value, wherein the calibrated measurement value meets the requirements of the following formula:
Figure FDA0002109948760000031
wherein V is [ V ]x,Vy,Vz]Is the second reference measurement data V of the accelerometerxFor a second reference measurement, V, on the x-axis of the accelerometeryFor the second reference measurement data, V, on the accelerometer y-axiszSecond reference measurement data corresponding to the z-axis of the accelerometer;
Figure FDA0002109948760000032
which represents the measured values after the calibration,
Figure FDA0002109948760000033
for the measurements after calibration on the x-axis of the accelerometer,
Figure FDA0002109948760000034
for the measurements after calibration on the accelerometer y-axis,
Figure FDA0002109948760000035
and measuring the measured value after the calibration on the z-axis of the accelerometer.
11. The electronic device of claim 7 or 8, wherein the IMU includes a gyroscope, the one or more computer programs stored in the memory, when executed by the processor, cause the electronic device to perform, in particular:
determining calibration parameters of the IMU according to the first reference measurement data, wherein the calibration parameters meet the following formula requirements:
Figure FDA0002109948760000036
wherein, CsIs the angular velocity set in the first reference frequency domain data, N is the number of angular velocities in the angular velocity set, WiThe ith angular velocity value is from 1 to N; o is the zero bias of the gyroscope;
Figure FDA0002109948760000037
wherein, ω isgtIs the actual rotational speed, ω, of the gyroscopex,ωy,ωzRespectively collecting first reference measurement data S of x, y and z axes of the gyroscope under the condition that the turntable rotates at a constant speedx、Sy、SZAnd the scale factors of the gyroscope on the x axis, the y axis and the z axis respectively.
12. The electronic device of claim 11, wherein the one or more computer programs stored in the memory, when executed by the processor, cause the electronic device to perform, in particular:
according to the calibration parameter, calibrating the second reference measurement data to obtain a calibrated measurement value, wherein the calibrated measurement value meets the requirements of the following formula:
w＝S(W-O)
wherein w ═ wx,wy,wz]TFor the angular velocity values of the gyroscope after calibration in the x, y and z axes, S is equal to Sx＝Sy＝SzAnd the scale factors of the gyroscope on the x axis, the y axis and the z axis respectively.
13. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a computer program which, when run on an electronic device, causes the electronic device to carry out the method of determining calibration parameters according to any one of claims 1 to 6.
14. A chip, wherein the chip is coupled to a memory for executing a computer program stored in the memory for performing the method of determining calibration parameters according to any of claims 1 to 6.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113001546A (en) * 2021-03-08 2021-06-22 常州刘国钧高等职业技术学校 Method and system for improving motion speed safety of industrial robot
CN113776575A (en) * 2021-08-16 2021-12-10 深圳市睿联技术股份有限公司 Zero point calibration method, device, apparatus, electronic device, and computer-readable storage medium
CN113932805A (en) * 2021-10-12 2022-01-14 天翼数字生活科技有限公司 Method for improving positioning accuracy and speed of AR virtual object
CN114295147A (en) * 2021-12-17 2022-04-08 杭州海康威视数字技术股份有限公司 Dynamic calibration method, device and equipment for holder
CN114543844A (en) * 2021-04-09 2022-05-27 恒玄科技(上海)股份有限公司 Audio playing processing method and device of wireless audio equipment and wireless audio equipment

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105352487A (en) * 2015-10-13 2016-02-24 上海华测导航技术股份有限公司 Calibration method for precision of attitude measurement system
US20160195398A1 (en) * 2015-01-06 2016-07-07 Honeywell International Inc. Helicopter motion detection during inertial reference system leveling
CN107289930A (en) * 2016-04-01 2017-10-24 南京理工大学 Pure inertia automobile navigation method based on MEMS Inertial Measurement Units
CN107490378A (en) * 2017-07-17 2017-12-19 北京工业大学 A kind of indoor positioning based on MPU6050 and smart mobile phone and the method for navigation
CN108170027A (en) * 2017-12-05 2018-06-15 深圳市道通智能航空技术有限公司 Cradle head controllor control parameter method of adjustment, device, medium and cradle head controllor
CN108875710A (en) * 2018-07-24 2018-11-23 杭州电子科技大学 Elevator door speed of service estimation method based on energy threshold algorithm
CN109425845A (en) * 2017-09-05 2019-03-05 弗兰克公司 The calibration system of voltage measuring apparatus
CN109791048A (en) * 2016-08-01 2019-05-21 无限增强现实以色列有限公司 Usage scenario captures the method and system of the component of data calibration Inertial Measurement Unit (IMU)

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6823279B1 (en) * 2001-07-27 2004-11-23 Trimble Navigation Limted Spectral method for calibrating a multi-axis accelerometer device
CN104164829B (en) * 2014-08-04 2017-06-16 武汉景行致远科技有限公司 Detection method of road-surface evenness and intelligent information of road surface real-time monitoring system based on mobile terminal
CN106767793A (en) * 2017-01-19 2017-05-31 东南大学 A kind of AUV underwater navigation localization methods based on SINS/USBL tight integrations
CN107289951B (en) * 2017-07-31 2020-05-12 电子科技大学 Indoor mobile robot positioning method based on inertial navigation

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160195398A1 (en) * 2015-01-06 2016-07-07 Honeywell International Inc. Helicopter motion detection during inertial reference system leveling
CN105352487A (en) * 2015-10-13 2016-02-24 上海华测导航技术股份有限公司 Calibration method for precision of attitude measurement system
CN107289930A (en) * 2016-04-01 2017-10-24 南京理工大学 Pure inertia automobile navigation method based on MEMS Inertial Measurement Units
CN109791048A (en) * 2016-08-01 2019-05-21 无限增强现实以色列有限公司 Usage scenario captures the method and system of the component of data calibration Inertial Measurement Unit (IMU)
CN107490378A (en) * 2017-07-17 2017-12-19 北京工业大学 A kind of indoor positioning based on MPU6050 and smart mobile phone and the method for navigation
CN109425845A (en) * 2017-09-05 2019-03-05 弗兰克公司 The calibration system of voltage measuring apparatus
CN108170027A (en) * 2017-12-05 2018-06-15 深圳市道通智能航空技术有限公司 Cradle head controllor control parameter method of adjustment, device, medium and cradle head controllor
CN108875710A (en) * 2018-07-24 2018-11-23 杭州电子科技大学 Elevator door speed of service estimation method based on energy threshold algorithm

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CHAN, K.Y.A 等: ""Alignment parameter calibration for IMU using the Taguchi method for image deblurring"", 《MEASUREMENT: JOURNAL OF THE INTERNATIONAL MEASUREMENT CONFEDERATION》 *
张文瑞等: "一种基于六姿态模型的加速度计校准方法研究", 《传感器与微***》 *
郭振昊: ""室内服务机器人定位和导航方案设计与实现"", 《中国优秀博硕士学位论文全文数据库（硕士） 信息科技辑》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113001546A (en) * 2021-03-08 2021-06-22 常州刘国钧高等职业技术学校 Method and system for improving motion speed safety of industrial robot
CN113001546B (en) * 2021-03-08 2021-10-29 常州刘国钧高等职业技术学校 Method and system for improving motion speed safety of industrial robot
CN114543844A (en) * 2021-04-09 2022-05-27 恒玄科技(上海)股份有限公司 Audio playing processing method and device of wireless audio equipment and wireless audio equipment
CN114543844B (en) * 2021-04-09 2024-05-03 恒玄科技(上海)股份有限公司 Audio playing processing method and device of wireless audio equipment and wireless audio equipment
CN113776575A (en) * 2021-08-16 2021-12-10 深圳市睿联技术股份有限公司 Zero point calibration method, device, apparatus, electronic device, and computer-readable storage medium
CN113932805A (en) * 2021-10-12 2022-01-14 天翼数字生活科技有限公司 Method for improving positioning accuracy and speed of AR virtual object
CN113932805B (en) * 2021-10-12 2024-02-23 天翼数字生活科技有限公司 Method for improving positioning accuracy and speed of AR virtual object
CN114295147A (en) * 2021-12-17 2022-04-08 杭州海康威视数字技术股份有限公司 Dynamic calibration method, device and equipment for holder

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