CN112665612A - Calibration method and device and electronic equipment - Google Patents
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Abstract
The embodiment of the invention relates to the technical field of inertial sensors, in particular to a calibration method, a calibration device and electronic equipment. The method comprises the following steps: acquiring a plurality of data of the inertial sensor rotating around two directions vertical to the gravity direction, wherein the rotation is carried out according to preset actions, and the preset actions comprise a preset angle of rotation and preset static time; estimating the plurality of data according to a preset algorithm to obtain a plurality of error data; acquiring a variance of the plurality of error data; judging whether the variance is smaller than a preset threshold value or not; and if so, obtaining the average value of the error data, thereby realizing the calibration of the inertial sensor. The calibration method provided by the embodiment of the invention is suitable for the inertial sensor.
Description
Technical Field
The embodiment of the invention relates to the technical field of inertial sensors, in particular to a calibration method, a calibration device and electronic equipment.
Background
Inertial sensors (IMU) can provide real-time attitude information for unmanned vehicles. However, due to the imperfect production process of the inertial sensor, the inertial sensor needs to be calibrated before leaving the factory. An uncalibrated inertial sensor inevitably brings accuracy damage to estimation of attitude information, and influences subsequent algorithms such as positioning mapping, planning and the like.
In the process of implementing the invention, the inventor of the invention finds that: currently, there is no calibration method suitable for inertial sensors.
Disclosure of Invention
In view of the foregoing, embodiments of the present invention provide a calibration method, apparatus, and electronic device, which overcome the foregoing problems or at least partially solve the foregoing problems.
According to an aspect of an embodiment of the present invention, there is provided a calibration method, including: acquiring a plurality of data of the inertial sensor rotating around two directions vertical to the gravity direction, wherein the rotation is carried out according to preset actions, and the preset actions comprise a preset angle of rotation and preset static time; estimating the plurality of data according to a preset algorithm to obtain a plurality of error data; acquiring a variance of the plurality of error data; judging whether the variance is smaller than a preset threshold value or not; and if so, obtaining the average value of the error data, thereby realizing the calibration of the inertial sensor.
In an optional manner, the error data includes an accelerometer data error and a gyroscope data error, and the step of estimating the plurality of data according to a preset algorithm to obtain a plurality of error data further includes: calibrating accelerometer data in the data to obtain an accelerometer data error; and calibrating gyroscope data in the data to obtain the gyroscope data error.
In an optional manner, the step of calibrating accelerometer data in the data to obtain the accelerometer data error further includes: dividing the data into static data and motion data, wherein the number of the static data is multiple, and the number of the motion data is multiple; calculating an average value of accelerometer data in the plurality of static data as a first average value; and acquiring the accelerometer data error according to the first mean value and the preset algorithm.
In an alternative, the data has six dimensions, and the step of dividing the data into still data and motion data further comprises: judging whether detail fluctuation amplitudes of the data of the six dimensions are all below a static detail fluctuation threshold value; if yes, judging the data to be static data; if not, the data is judged to be the motion data.
In an optional manner, the step of calibrating gyroscope data in the data to obtain the gyroscope data error further includes: calculating a mean value of gyroscope data in the plurality of static data respectively to be used as a gyroscope zero offset; subtracting a gyroscope zero-bias average number from gyroscope data in the plurality of pieces of motion data to obtain zero-bias removed gyroscope data, wherein the gyroscope zero-bias average number is an average value of zero-bias of two pieces of static data adjacent to the motion data; and calibrating the data of the zero-offset-removed gyroscope to obtain the data error of the gyroscope.
In an optional manner, the step of calibrating gyroscope data in the data to obtain the gyroscope data error further includes: respectively acquiring static acceleration vectors of a plurality of groups of static data; respectively calculating the non-coincidence degree of each static acceleration vector and the static acceleration vectors of all the static data; respectively judging whether the misalignment ratio is greater than a preset value; if so, rejecting the static acceleration vector with the misalignment ratio larger than a preset value; and calibrating the data of the zero-offset-removed gyroscope according to the residual data to obtain the data error of the gyroscope.
According to an aspect of the embodiments of the present invention, there is provided a calibration apparatus for calibrating an inertial sensor, including: the device comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a plurality of data of the inertial sensor rotating around two directions vertical to the gravity direction, the rotation is carried out according to preset actions, and the preset actions comprise a preset angle of rotation and preset static time; the estimation module is used for respectively estimating the plurality of data according to a preset algorithm to obtain a plurality of error data; a second obtaining module, configured to obtain a variance of the plurality of error data; the judging module is used for judging whether the variance is smaller than a preset threshold value or not; and the third acquisition module is used for acquiring the average value of the plurality of error data if the variance is smaller than a preset threshold value, so that the calibration of the inertial sensor is realized.
In an alternative form, the error data includes an accelerometer data error and a gyroscope data error, the estimation module includes: the first calibration unit is used for calibrating the accelerometer data in the data to obtain the accelerometer data error; and the second calibration unit is used for calibrating the gyroscope data in the data to obtain the gyroscope data error.
In an optional manner, the first calibration unit is configured to: dividing the data into static data and motion data, wherein the number of the static data is multiple, and the number of the motion data is multiple; calculating an average value of accelerometer data in the plurality of static data as a first average value; and acquiring the accelerometer data error according to the first mean value and the preset algorithm.
In an alternative manner, the data has six dimensions, and the dividing the data into still data and motion data specifically includes: judging whether detail fluctuation amplitudes of the data of the six dimensions are all below a static detail fluctuation threshold value; if yes, judging the data to be static data; if not, the data is judged to be the motion data.
In an optional manner, the second calibration unit is configured to calculate a mean value of gyroscope data in the plurality of static data as a gyroscope zero offset; subtracting a gyroscope zero-bias average number from gyroscope data in the plurality of pieces of motion data to obtain zero-bias removed gyroscope data, wherein the gyroscope zero-bias average number is an average value of zero-bias of two pieces of static data adjacent to the motion data; and calibrating the data of the zero-offset-removed gyroscope to obtain the data error of the gyroscope.
In an optional manner, the second calibration unit is further configured to obtain stationary acceleration vectors of a plurality of sets of the stationary data respectively; respectively calculating the non-coincidence degree of each static acceleration vector and the static acceleration vectors of all the static data; respectively judging whether the misalignment ratio is greater than a preset value; if so, rejecting the static acceleration vector with the misalignment ratio larger than a preset value; and calibrating the data of the zero-offset-removed gyroscope according to the residual data to obtain the data error of the gyroscope.
According to an aspect of an embodiment of the present invention, there is provided an electronic apparatus including: at least one processor, and a memory communicatively coupled to the at least one processor, the memory storing instructions executable by the at least one processor to enable the at least one processor to perform a method as described above.
The embodiment of the invention has the beneficial effects that: the method comprises the steps that a plurality of data of the inertial sensor rotating around two directions vertical to the gravity direction are obtained, wherein the rotation is carried out according to preset actions, and the preset actions comprise a preset rotating angle and preset static time; estimating the plurality of data according to a preset algorithm to obtain a plurality of error data; acquiring a variance of the plurality of error data; judging whether the variance is smaller than a preset threshold value or not; and if so, obtaining the average value of the error data, thereby realizing the calibration of the inertial sensor. The calibration method provided by the embodiment of the invention is suitable for the inertial sensor.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
Fig. 1 is a schematic flow chart of a calibration method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a process for estimating a plurality of data according to a predetermined algorithm according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a calibration apparatus provided in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a calibration apparatus provided in an embodiment of the present invention;
fig. 5 is a connection relation diagram of each component of the pan/tilt head provided by the embodiment of the invention;
fig. 6 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for descriptive purposes only.
In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a calibration method according to an embodiment of the present invention, where the calibration method is used for calibrating an inertial sensor, and the method includes the following steps:
step S10, acquiring a plurality of data of the inertial sensor rotating around two directions perpendicular to the gravity direction, where the rotation is performed according to a preset action, and the preset action includes a preset angle of rotation and a preset time of stillness.
The calibration of the inertial sensor needs data of three axes, the inertial sensor rotates around two directions vertical to the gravity direction respectively, the gravity direction is taken as one axis, and the other two directions are taken as one axis, so that the data of the gravity direction and the two directions vertical to the gravity direction can be obtained, and the data of the three axes can be obtained.
The inertial sensor includes an accelerometer and a gyroscope, and the data includes accelerometer data and gyroscope data.
The plurality of data may be obtained by manually rotating the inertial sensor manually. Or the inertial sensor can be arranged on the holder, and the rotation of the inertial sensor around two directions vertical to the gravity direction can be realized through the holder.
The preset actions include rotating a preset angle and stationary for a preset time, such as rotating 90 degrees in one of two directions, stationary for 15 seconds, rotating 90 degrees again, stationary for 15 seconds, continuing to rotate 90 degrees, stationary for 15 seconds.
The preset motion of rotating around the other direction of the two directions can try to rotate 90 degrees for 15 seconds, then rotate 90 degrees for 15 seconds, and then rotate 90 degrees for 15 seconds.
And step S20, respectively estimating the data according to a preset algorithm to obtain a plurality of error data.
The data includes accelerometer data and gyroscope data, and the error data includes accelerometer data errors and gyroscope data errors. Referring to fig. 2, step S20 specifically includes the following steps:
step S201, calibrating accelerometer data in the data to obtain the accelerometer data error.
The data may be divided into still data and motion data, the number of the still data being plural, and the number of the motion data being plural. Each stationary data includes accelerometer data and gyroscope data. Each motion data includes accelerometer data and gyroscope data.
In step S201, the data may be divided into a plurality of static data and a plurality of motion data, where the number of the static data is a plurality of the motion data; calculating an average value of accelerometer data in the plurality of static data as a first average value; and acquiring the accelerometer data error according to the first mean value and the preset algorithm.
The data has six dimensions, and in some embodiments, the step of dividing the data into static data and motion data further comprises determining whether the detail fluctuation amplitude of the data in the six dimensions is below a static detail fluctuation threshold; if yes, judging the data to be static data; if not, the data is judged to be the motion data.
Specifically, wavelet decomposition is performed for each dimension, and detail fluctuation amplitudes under different resolutions are decomposed. If at a certain moment, the detail fluctuation amplitude at one resolution of one latitude in the data is larger than the static detail fluctuation threshold value, the data at the moment is considered as motion data. The static detail fluctuation threshold is extracted from a section of static IMU data of about one minute, and the specific method is to carry out wavelet decomposition on the section of static IMU data to decompose detail fluctuation amplitudes under different resolutions, and the detail fluctuation amplitudes can be used as the static detail fluctuation threshold.
Since the number of the static data is multiple, the average value is calculated for the accelerometer data in the multiple static data as the first average value, specifically, the average value is calculated for the accelerometer data in each static data, so as to obtain multiple first average values.
And calculating the average value of the accelerometer data in all the static data so as to obtain the accelerometer zero offset.
The first mean value can eliminate the influence of noise on the preset algorithm in the optimization process.
The first mean value forms an ellipse in three-dimensional space and is not centered at (0, 0, 0), the set optimization function is optimized by using a Levenberg-Marquardt optimization algorithm, and after the optimization is finished and the accelerometer data error of the inertial sensor is compensated, the first mean value falls on a spherical ball and is centered at (0, 0, 0).
When the accelerometer data is calibrated, the preset algorithm is an optimization loss function:
where M represents the number of stationary data in the acquired data, θaccIs a parameter of acceleration, g is a known scalar quantity of the magnitude of local gravitational acceleration,
the first mean value of the static data.
The preset algorithm further comprises the following accelerometer data error model:
h(aS，θacc)＝TaKa(aS+ba)
wherein, aSFor said first mean value, T, of one of the stationary data in the acquired accelerometer dataaIs an accelerometer interaxial misalignment error matrix, KaIs an accelerometer scale error matrix, baIs zero offset for the accelerometer, wherein，Ta、KaAnd baThe accelerometer data that needs calibration.
Wherein, TaThe following formula is satisfied:
aO＝TaaS
wherein alpha isOFor a specific acceleration of the inertial sensor in an orthogonal ideal frame, aSFor said first mean value, a, of one of the stationary data in the acquired accelerometer datayz、αzyAnd alphazxRespectively the acceleration of the different axes of the accelerometer.
And step S202, calibrating gyroscope data in the data to obtain the gyroscope data error.
In step S202, specifically, an average value of gyroscope data in the plurality of static data may be calculated as a gyroscope zero offset; subtracting a gyroscope zero-bias average number from gyroscope data in the plurality of pieces of motion data to obtain zero-bias removed gyroscope data, wherein the gyroscope zero-bias average number is an average value of zero-bias of two pieces of static data adjacent to the motion data; and calibrating the data of the zero-offset-removed gyroscope to obtain the data error of the gyroscope.
Before the above step S202 is executed, the accelerometer data may be compensated and corrected according to the accelerometer data error.
And the acceleration direction of the last static data and the acceleration direction of the next static data are not coincident as a result of integrating the uncalibrated gyroscope data and the uncalibrated gyroscope data, the set optimization function is optimized by utilizing a Levenberg-Marquardt optimization algorithm, and after the optimization is finished, the gyroscope data can accurately describe the rotation relation of the acceleration directions of the two adjacent static areas.
When the gyroscope data is calibrated, the preset algorithm is an optimization loss function:
wherein u isa,kIs the average acceleration vector of the kth stationary zone,
the preset algorithm further comprises the following gyroscope data error model:
ωSfor gyroscope data in the acquired motion data, Ψ [. sup. ]]The method is characterized in that a gyroscope error model is represented, and the error model comprises a gyroscope axis misalignment error matrix TgSum scale error matrix KgWherein, Tg，KgIs the gyroscope data that needs to be calibrated.
Wherein, TgThe following formula is satisfied:
ωO＝TgωS
wherein, ω isOFor a specific angular velocity, ω, of the inertial sensor in an orthogonal ideal frameSFor gyroscope data in the acquired motion data, gammayz、γzyAnd gammazxRespectively the angles of the different axes of the gyroscope.
In some embodiments, when the error of the gyroscope data obtained by subtracting the zero-bias mean of the gyroscope from the gyroscope data in the plurality of data is not ideal, the zero-bias gyroscope data can be calibrated again after removing part of the data. Specifically, the step of calibrating gyroscope data in the data to obtain the gyroscope data error further includes: respectively acquiring static acceleration vectors of a plurality of groups of static data; respectively calculating the non-coincidence degree of each static acceleration vector and the static acceleration vectors of all the static data; respectively judging whether the misalignment ratio is greater than a preset value; if so, rejecting the static acceleration vector with the misalignment ratio larger than a preset value; and calibrating the data of the zero-offset-removed gyroscope according to the residual data to obtain the data error of the gyroscope.
It will be appreciated that if the degree of misalignment of the static acceleration directions is not greater than a predetermined value, then calibration of the gyroscope data is desirable and calibration of the deskewed gyroscope data need not be performed again.
In step S30, the variance of the plurality of error data is acquired.
Step S40, determining whether the variance is smaller than a preset threshold, if yes, executing step S50.
In some embodiments, if the variance is less than a predetermined threshold, the error data is considered reasonable and the inertial sensor is successfully calibrated. If the variance is above a predetermined threshold, the inertial sensor generally needs to be calibrated again.
And step S50, acquiring an average value of the error data, thereby realizing the calibration of the inertial sensor.
In the embodiment of the invention, a plurality of data of the inertial sensor rotating around two directions vertical to the gravity direction are obtained, wherein the rotation is carried out according to preset actions, and the preset actions comprise a preset angle of rotation and a preset static time; estimating the plurality of data according to a preset algorithm to obtain a plurality of error data; acquiring a variance of the plurality of error data; judging whether the variance is smaller than a preset threshold value or not; and if so, obtaining the average value of the error data, thereby realizing the calibration of the inertial sensor. The calibration method provided by the embodiment of the invention is suitable for the inertial sensor.
Example two
Referring to fig. 3, fig. 3 is a schematic diagram of a calibration apparatus according to an embodiment of the present invention, the apparatus 400 includes: a first obtaining module 401, an estimating module 402, a second obtaining module 403, a judging module 404 and a third obtaining module 405. The first obtaining module 401 is configured to obtain a plurality of data of the inertial sensor rotating around two directions perpendicular to a gravity direction, where the rotation is performed according to a preset action, and the preset action includes a preset angle of rotation and a preset static time; an estimation module 402, configured to estimate the multiple pieces of data according to a preset algorithm, respectively, so as to obtain multiple pieces of error data; a second obtaining module 403, configured to obtain a variance of the plurality of error data; a judging module 404, configured to judge whether the variance is smaller than a preset threshold; a third obtaining module 405, configured to obtain an average value of the multiple error data if the variance is smaller than a preset threshold, so as to calibrate the inertial sensor.
In some embodiments, the error data includes an accelerometer data error and a gyroscope data error; the estimation module 402 includes: a first calibration unit 4021 and a second calibration unit 4022. The first calibration unit 4021 is configured to calibrate accelerometer data in the data to obtain an accelerometer data error; the second calibration unit 4022 is configured to calibrate the gyroscope data in the data to obtain the gyroscope data error.
In some embodiments, the first calibration unit 4021 is configured to divide the data into static data and motion data, where the number of the static data is multiple and the number of the motion data is multiple; calculating an average value of accelerometer data in the plurality of static data as a first average value; and acquiring the accelerometer data error according to the first mean value and the preset algorithm.
In some embodiments, the data has six dimensions, and the dividing the data into still data and motion data specifically includes: judging whether detail fluctuation amplitudes of the data of the six dimensions are all below a static detail fluctuation threshold value; if yes, judging the data to be static data; if not, the data is judged to be the motion data.
In some embodiments, the second calibration unit 4022 is configured to calculate a mean value of gyroscope data in the plurality of static data as a gyroscope zero offset; subtracting a gyroscope zero-bias average number from gyroscope data in the plurality of pieces of motion data to obtain zero-bias removed gyroscope data, wherein the gyroscope zero-bias average number is an average value of zero-bias of two pieces of static data adjacent to the motion data; and calibrating the data of the zero-offset-removed gyroscope to obtain the data error of the gyroscope.
In some embodiments, the second calibration unit 4022 is further configured to obtain stationary acceleration vectors of a plurality of sets of the stationary data, respectively; respectively calculating the non-coincidence degree of each static acceleration vector and the static acceleration vectors of all the static data; respectively judging whether the misalignment ratio is greater than a preset value; if so, rejecting the static acceleration vector with the misalignment ratio larger than a preset value; and calibrating the data of the zero-offset-removed gyroscope according to the residual data to obtain the data error of the gyroscope.
In the embodiment of the invention, a first acquisition module is used for acquiring a plurality of data of the inertial sensor rotating around two directions vertical to the gravity direction, wherein the rotation is carried out according to preset actions, and the preset actions comprise a preset rotating angle and a preset static time; estimating the plurality of data through an estimation module according to a preset algorithm to obtain a plurality of error data; acquiring variances of the plurality of error data through a second acquisition module; judging whether the variance is smaller than a preset threshold value through a judging module; and if the variance is smaller than a preset threshold value, acquiring the average value of the plurality of error data through a third acquisition module, thereby realizing the calibration of the inertial sensor. The calibration method provided by the embodiment of the invention is suitable for the inertial sensor.
EXAMPLE III
Referring to fig. 4, fig. 4 is a schematic diagram of a calibration apparatus according to an embodiment of the present invention. The calibration device comprises a holder 100 and an electronic device 200, wherein the holder 100 is used for driving the inertial sensor A to rotate around two directions perpendicular to the gravity direction. The electronic device 200 is used to connect with the inertial sensor a. The electronic device 200 is used for calibrating the inertial sensor a.
With respect to the above-mentioned tripod head 100, in some embodiments, referring to fig. 4 and 5, the tripod head 100 includes a base 10, a support plate 20, a carriage 30, a first driving device 40, a second driving device 50, a control circuit 60, a bracket 70, a fixing plate 80 and a control button 90. The support plate 20 is mounted on the base 10, and the fixing plate 80 is disposed on the support plate 20. The carrier 30 is used for fixing an external object. The first driving device 40 is connected to the carrier 30 through the bracket 70, and is used for driving the carrier 30 to rotate around a first direction. The second driving device 50 is mounted to the supporting plate 20 through the fixing plate 80, and the second driving device 50 is connected to the first driving device 40 for driving the first driving device 40 to rotate around the second direction, so that the bearing member 30 rotates around the second direction. Wherein the first direction is perpendicular to the second direction. The control circuit 60 is respectively connected to the first driving device 40, the second driving device 50 and a control button 90, and the control button 90 is used for controlling the first driving device 40 and the second driving device 50 to be turned on or off through the control circuit 60. The inertial sensor a is mounted on the bearing member 30 of the holder 100, the inertial sensor a can rotate around a first direction and a second direction, and when the holder 100 is placed in the first direction and the second direction, which are respectively perpendicular to the gravity direction, three-axis calibration of the inertial sensor a can be achieved.
It should be noted that, in some embodiments, the fixing plate 80 may not be provided, and the second driving device 50 is directly connected to the supporting plate.
It should be noted that in some embodiments, the bracket 70 may not be provided, and the output shaft of the first driving device 40 is directly connected to the carrier 30.
Referring to fig. 6, the electronic device 200 includes: one or more processors 201 and a memory 202, one for example in fig. 6.
The processor 201 and the memory 202 may be connected by a bus or other means, and the bus connection is taken as an example in the embodiment of the present invention.
The memory 202, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions/modules (e.g., the modules shown in fig. 3) corresponding to the calibration method in the embodiment of the present invention. The processor 201 executes various functional applications and data processing of the calibration apparatus by executing the nonvolatile software programs, instructions and modules stored in the memory 202, so as to implement the calibration method of the above-mentioned method embodiment.
The memory 202 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the calibration device, and the like. Further, the memory 202 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 202 may optionally include memory located remotely from the processor 201, which may be connected to the database access device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 202 and, when executed by the one or more processors 201, perform the calibration method in any of the method embodiments described above.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
The embodiment of the invention provides a nonvolatile computer readable storage medium, wherein the nonvolatile computer readable storage medium stores computer executable instructions, and the computer executable instructions are used by electronic equipment to execute the calibration method in any method embodiment.
An embodiment of the present invention provides a computer program product, including a computer program stored on a non-volatile computer-readable storage medium, where the computer program includes program instructions, which, when executed by a computer, cause the computer to execute the calibration method in any of the above method embodiments.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; within the idea of the invention, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A calibration method for calibrating an inertial sensor, comprising:
acquiring a plurality of data of the inertial sensor rotating around two directions vertical to the gravity direction, wherein the rotation is carried out according to preset actions, and the preset actions comprise a preset angle of rotation and preset static time;
estimating the plurality of data according to a preset algorithm to obtain a plurality of error data;
acquiring a variance of the plurality of error data;
judging whether the variance is smaller than a preset threshold value or not;
and if so, obtaining the average value of the error data, thereby realizing the calibration of the inertial sensor.
2. The method of claim 1, wherein the error data comprises an accelerometer data error and a gyroscope data error, and the step of estimating the plurality of data according to a predetermined algorithm to obtain a plurality of error data further comprises:
calibrating accelerometer data in the data to obtain an accelerometer data error;
and calibrating gyroscope data in the data to obtain the gyroscope data error.
3. The method of claim 2, wherein said step of calibrating accelerometer data in said data to obtain said accelerometer data error further comprises:
dividing the data into static data and motion data, wherein the number of the static data is multiple, and the number of the motion data is multiple;
calculating an average value of accelerometer data in the plurality of static data as a first average value;
and acquiring the accelerometer data error according to the first mean value and the preset algorithm.
4. The method of claim 3, wherein the data has six dimensions, and wherein the step of dividing the data into still data and motion data further comprises:
judging whether detail fluctuation amplitudes of the data of the six dimensions are all below a static detail fluctuation threshold value;
if yes, judging the data to be static data;
if not, the data is judged to be the motion data.
5. The method of claim 3, wherein the step of calibrating the gyroscope data in the data to obtain the gyroscope data error further comprises:
calculating a mean value of gyroscope data in the plurality of static data respectively to be used as a gyroscope zero offset;
subtracting a gyroscope zero-bias average number from gyroscope data in the plurality of pieces of motion data to obtain zero-bias removed gyroscope data, wherein the gyroscope zero-bias average number is an average value of zero-bias of two pieces of static data adjacent to the motion data;
and calibrating the data of the zero-offset-removed gyroscope to obtain the data error of the gyroscope.
6. The method of claim 5, wherein said step of calibrating said gyroscope data in said data to obtain said gyroscope data error further comprises:
respectively acquiring static acceleration vectors of a plurality of groups of static data;
respectively calculating the non-coincidence degree of each static acceleration vector and the static acceleration vectors of all the static data;
respectively judging whether the misalignment ratio is greater than a preset value;
if so, rejecting the static acceleration vector with the misalignment ratio larger than a preset value;
and calibrating the data of the zero-offset-removed gyroscope according to the residual data to obtain the data error of the gyroscope.
7. A calibration device for calibrating an inertial sensor, comprising:
the device comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a plurality of data of the inertial sensor rotating around two directions vertical to the gravity direction, the rotation is carried out according to preset actions, and the preset actions comprise a preset angle of rotation and preset static time;
the estimation module is used for respectively estimating the plurality of data according to a preset algorithm to obtain a plurality of error data;
a second obtaining module, configured to obtain a variance of the plurality of error data;
the judging module is used for judging whether the variance is smaller than a preset threshold value or not;
and the third acquisition module is used for acquiring the average value of the plurality of error data if the variance is smaller than a preset threshold value, so that the calibration of the inertial sensor is realized.
8. The apparatus of claim 7, wherein the error data comprises an accelerometer data error and a gyroscope data error, the estimation module comprising:
the first calibration unit is used for calibrating the accelerometer data in the data to obtain the accelerometer data error;
and the second calibration unit is used for calibrating the gyroscope data in the data to obtain the gyroscope data error.
9. The apparatus of claim 8, wherein the first calibration unit is configured to:
dividing the data into static data and motion data, wherein the number of the static data is multiple, and the number of the motion data is multiple;
calculating an average value of accelerometer data in the plurality of static data as a first average value;
and acquiring the accelerometer data error according to the first mean value and the preset algorithm.
10. A calibration apparatus, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor, the memory storing instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1-6.
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