CN107289951A - A kind of Localization Approach for Indoor Mobile based on inertial navigation - Google Patents
A kind of Localization Approach for Indoor Mobile based on inertial navigation Download PDFInfo
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- CN107289951A CN107289951A CN201710636739.5A CN201710636739A CN107289951A CN 107289951 A CN107289951 A CN 107289951A CN 201710636739 A CN201710636739 A CN 201710636739A CN 107289951 A CN107289951 A CN 107289951A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
Abstract
The present invention discloses a kind of Localization Approach for Indoor Mobile based on inertial navigation, applied to mobile robot field；First, by classifying to the raw acceleration data that the inertial navigation sensors received are exported to sensor states；And according to the acceleration information average value of each frame in the setting time under inactive state as null offset amount, to be corrected to the acceleration information under motion state；Secondly, it have selected Simpson's integration method processing be integrated to acceleration information and obtain displacement；Furthermore, frequency-domain analysis is with the addition of on the basis of time-domain analysis as the method for second of reduction error, by handling and filtering on frequency domain, improved a lot for result precision, the accumulated error for fundamentally solving conventional inertia navigation integral algorithm is excessive so that the problem of having influence on actual use.
Description
Technical field
The invention belongs to mobile robot field, localization for Mobile Robot technology in more particularly to a kind of inertial navigation room.
Background technology
Inertial navigation is by obtaining position to rate integrating and integrating the process of speed of obtaining to total acceleration.It is total to accelerate
Degree refers to acceleration (that is, the specific force acceleration) sum produced by acceleration of gravity and the non-gravity being applied in.Inertial navigation system
System (Inertial Navigation System, INS) includes：Navigational computer for integration；For fixed to integral operation
When precision interval clock, measure specific force acceleration accelerometer group platform；Carried out for a function as calculated position
Acceleration of gravity calculate and stay the Gravity Models software in navigational computer, and in order to be defined as speed calculate one
Attitude reference used in the angle direction for the accelerometer triple divided.In modern INS, attitude reference is by residing at INS
What the Software Integration function in computer was provided, it inputs the inertia angular-rate sensor for having three axles from one.Angular speed is passed
Sensor and accelerometer triple are arranged on a public firm framework, and the framework is every to ensure on INS chassis
A kind of accurate alignment between individual inertial sensor, such arrangement is referred to as strapdown INS.Because inertial sensor is solid securely
It is scheduled in chassis, so being also just firmly fixed in the mobile robot for installing INS.
Theoretical according to inertial navigation, the basic function in INS computers has the integral function that angular speed is transformed to posture
The acceleration magnitude measured is transformed into appropriate navigational coordinate system by (be referred to as posture integration) using attitude data, then by it
The function (being referred to as vector velocity integration) of vector velocity is integrated into, is the function that vector velocity is integrated into position also by navigation
(being referred to as position integration).Thus there are three integral functions, posture function, vector velocity function and position function, each
The required precision of function is very high, to ensure that function error is minimum, accords with the requirement of platform inertial sensor precision.In actual applications,
The pose of given initial machine people, you can obtain the current pose of mobile robot via inertial navigation theory.
In order to reach the function in time-domain integration, it is necessary to carry out numerical integration.The purpose of numerical integration is, by limited
F (x) value is calculated on individual sampled point to approach definite integral of the f (x) on interval [a, b].If a=x0<x1<…<xM=b. claims shape
Such as
And with propertyFormula be numerical integration or area formula.Item E [f] is referred to as product
The truncated error divided, valueReferred to as area node,Referred to as weigh.Number of times by M+1 equidistant points existence anduniquess is small
In the multinomial P equal to MM(x).When with the multinomial come f (x) on approximate [a, b], PM(x) integration is just approximately equal to f
(x) integration, this kind of formula is referred to as newton-Ke Tesi formula.As use sampled point x0=a and xMDuring=b, referred to as closed form ox
Dun-Ke Tesi formula.
If xk=x0+ kh is Equidistant Nodes, and fk=f (xk), Simpson's integral formula is：
The precision of Simpson formula is n=3, if f ∈ C4[a, b], then
For inertial navigation, its shortcoming be it is following some：The same of acceleration information is being gathered from inertial sensor
When, inevitably receive the interference of noise, noise not only comes from sensor quantization operation in itself and circuit noise
Come from transmitting procedure and to be disturbed by external condition；If uncontrolled to noise or be that identification loop inherently has
Null offset characteristic must all influence whether the accuracy of following integration operation, now noise has reformed into trend term,
A velocity error increased over time is shown as when being once integrated to velocity information, this error can be put again after quadratic integral
Greatly, here it is so-called accumulated error.For inertial navigation, accumulated error is to influence the main factor of its precision.
For these interference, appropriate amendment and processing just becomes necessary.Noise signal is eliminated, need to first be observed point
The characteristic of signal is analysed, the characteristics of time domain can not observe noise signal, this relates to enterprising in frequency domain to acceleration signal
Row analysis.Fast Fourier Transform (FFT) (Fast Fourier Transform) be signal transacting with it is most important in data analysis field
One of algorithm.FFT (Fast Fourier Transform (FFT)) inherently discrete Fourier transform (Discrete Fourier
Transform fast algorithm), makes algorithm complex be changed into O (NlogN), discrete Fourier transform from the O (N^2) of script
DFT, the continuous fourier transform as known to being more people, there is following forward and inverse form of Definition：
Forward Discrete Fourier Transform(DFT):
Inverse Discrete Fourier Transform(IDFT):
xnTo XkConversion be exactly conversion of the time domain to frequency domain, this conversion helps to study the power spectrum of signal, and makes certain
The calculating of a little problems is more efficiently.Signal is carried out after Fast Fourier Transform (FFT), it is possible to obtain noise and interference probably distribution
Frequency.In combination with the occasion of inertial sensor actual use, frequency where determining useful signal is adopted according to Nyquist
Sample theorem, i.e. sample frequency need to be more than more than two octave bands, it is determined that the passband width of low pass filter used, you can completion pair
Primary signal carries out effective denoising Processing.
The content of the invention
The present invention is in order to solve the above technical problems, propose a kind of indoor mobile robot positioning side based on inertial navigation
Method, from Simpson's integration method, realizes on the premise of amount of calculation is not increased, significantly mentions precision；And handled on frequency domain
And filter, the accumulated error for solving conventional inertia navigation integral algorithm is excessive so that the problem of having influence on actual use.
The technical solution adopted by the present invention is：A kind of Localization Approach for Indoor Mobile based on inertial navigation, including：
Each frame acceleration information and Eulerian angles that S1, reception are gathered from inertial sensor；
S2, each frame acceleration information obtained to step S1 are corrected；
S3, according to each Eulerian angles the acceleration that the obtained corresponding acceleration informations of step S2 are transformed to relative ground is become
Amount；
S4, the corresponding acceleration variable that is kept in motion to inertial sensor carry out discrete integration, obtain velocity information；
S5, the velocity information obtained to step S4 are enterprising in frequency domain to obtained velocity information by Fast Fourier Transform (FFT)
Row filtering process；Obtain speed frequency-region signal；
S6, the speed frequency-region signal obtained to step S5 carry out Fast Fourier Transform Inverse, obtain speed time-domain signal；
S7, the speed time-domain signal obtained to step S6 carry out discrete integration, obtain initial displacement signal；
S8, the initial displacement signal obtained to step S7 are by Fast Fourier Transform (FFT) to obtained velocity information in frequency domain
Upper carry out filtering process, obtains the frequency-region signal of initial displacement；
S9, the frequency-region signal to initial displacement carry out Fast Fourier Transform Inverse, obtain the time-domain signal of initial displacement.
Further, the step S1 specifically include it is following step by step：
S11, setting serial port protocol are：It is fixed length per frame, sets putting in order raw acceleration data and Eulerian angles；
And initialize frame head；
If S12, the frame head of reception data do not meet preset value, the whole frame data received are given up；
S13, data are received to step S12 according to the carry out cutting operation that puts in order, obtain one group of acceleration information and
Eulerian angles.
Further, the step S2 is specially：
Analyzed by each frame acceleration information received to step S1, judge the state of inertial sensor；The shape
State includes：Inactive state and motion state；
And inertial sensor remains static down, some frame acceleration informations for being collected in setting time are averaged
Value as inertial sensor be kept in motion it is lower correspondence acceleration information null offset amount, subtracted with acceleration information original value
Null offset value is gone to complete zero correction.
Further, the deterministic process of the inactive state or motion state is：If being received in the setting time each
The difference value of acceleration information between frame meets fixed probability density, then judges that inertial sensor remains static；It is no
Then inertial sensor is kept in motion.
Further, step S3 is specially：Step S2 is obtained into acceleration information by following formula according to spin matrix to convert
Into ground reference coordinate system：
Wherein, axAnd ayCoordinate for acceleration information relative to bodywork reference frame；aEAnd aNFor acceleration information relative to
The coordinate of earth axes；Euler is Eulerian angles；E, N are two axial directions of earth axes.
Further, the discrete integration is integrated using Simpson.
Further, the step S4 also includes：If inertial sensor remains static, then corresponding speed is set to
Zero.
Beneficial effects of the present invention：A kind of Localization Approach for Indoor Mobile based on inertial navigation of the present invention, it is first
First, by carrying out state classification to the raw acceleration data that the inertial navigation sensors received are exported；And according to static shape
The acceleration information average value of each frame is as null offset amount in setting time under state, to the acceleration number of degrees under motion state
According to being corrected, the static drift brought by accelerometer self character and null offset are effectively reduced to integral algorithm precision
Influence, significantly reduce accumulated error；Secondly, it have selected such a precision and amount of calculation taken into account of Simpson's integration method
Method, relative to the trapezoidal integration method being widely used, on the premise of no increase how many amount of calculation, significantly improves essence
Degree；Furthermore, frequency-domain analysis is with the addition of on the basis of time-domain analysis as the method for second of reduction error, by frequency domain
Processing and filtering, improve a lot for result precision, fundamentally solve the accumulated error of conventional inertia navigation integral algorithm
It is excessive so that the problem of having influence on actual use.
Brief description of the drawings
Fig. 1 is the main flow chart of the method for the present invention.
Fig. 2 be the present invention relates to Coordinate Conversion schematic diagram.
Embodiment
For ease of skilled artisan understands that the technology contents of the present invention, enter one to present invention below in conjunction with the accompanying drawings
Step explaination.
It is the solution of the present invention flow chart as shown in Figure 1, the technical scheme is that：A kind of room based on inertial navigation
Interior method for positioning mobile robot, including：
Acceleration information and Eulerian angles that S1, reception are gathered from inertial sensor；
The acceleration information of inertial sensor collection, detailed process is as follows：
1) data output format, is set in sensor side, by setting as follows every frame acceleration information can be made isometric, it is convenient
Ensuing data segmentation and processing.
First：Initial bit, frame head, be set to symbol "！”
Second to the 7th：X-direction original acceleration, supplies 6 if without 6；
8th：Space, for being spaced；
9th to 14：Y-direction original acceleration, supplies 6 if without 6；
15th：Space, for being spaced；
Sixteen bit is to the 21st：Euler angles, 6 are supplied less than 6；
22nd, 23：New-line key.
The application uses the serial port protocol as described above per 23 fixed length of frame, is more easy to keep number using such a agreement
According to integrality, processing is also convenient for.But in actual processing procedure, the fixation requirement of serial port protocol is every frame data fixed length,
The specific length per frame is determined according to actual conditions.
2), serial ports receives data：By pre-setting the serial ports attribute such as baud rate, the reception of serial data is completed.At this
Wherein the frame head and frame length of every frame are judged according to the serial port protocol set in 1), prevented due to serial ports single transmission number
According to the imperfect loss of data caused and mistake.If frame head does not meet preset value, the whole frame data received are given up.Such as
The every frame length set in the application is 23, if the data received are less than 23, waits until the data received are big
In 23, therefrom first 23 are extracted and is used as complete data frame.
Specifically, the every frame length set in the application is 23, first is frame head, be set to symbol "！”；Second to
7th is X-direction original acceleration, and 6 are supplied if without 6；8th is space, for being spaced；9th to 14
It is Y-direction original acceleration, 6 is supplied if without 6；15th is space, for being spaced；Sixteen bit is to the 20th
One is Euler angles, and 6 are supplied less than 6；22nd, 23：New-line key.
3), data are split：The whole frame data received to step 2 carry out cutting operation, therefrom extract this patent needs
X-direction linear acceleration, Y-direction linear acceleration and Euler angles (Eulerian angles) information.
Specially：Serial ports is received after every frame initial data；All spaces in the frame data are replaced with into comma (i.e.
", "), several sections of valid data using comma as segmentation are respectively then X-direction linear acceleration, Y-direction linear acceleration and Euler
Angle.
S2, each frame acceleration information obtained to step S1 are corrected；Specially：The initial data received is carried out
Preliminary treatment.
First, progress is state demarcation；Each frame acceleration information received to step S1 carries out condition adjudgement；State
Including：Inactive state and motion state；
Then, each frame acceleration information received to step S1 carries out probability analysis, is found through experiments that whether just
Begin to stand after static or motion, the difference value between accelerometer frame and frame meets fixed probability density：I.e. in certain time
95% both less than 0.035 in the data collected.Find, can be entered by 100 data to being collected into 1S more than
Row analysis, judges that sensor assembly that is to say that robot platform remains static at this moment if condition is met；By now
Motion state is recorded as inactive state, to be used after treating；Otherwise it is recorded as motion state.
Secondly, it is contemplated that identification loop has null offset phenomenon, when judging that sensor is currently at inactive state, note
The average value of the acceleration information collected in record inactive state lower certain time is as the zero of following motion state acceleration
Point drift amount；
Finally, in output campaign state acceleration, obtained null offset value is subtracted with acceleration original value and is completed
Zero correction.
S3, the acceleration variable that the obtained acceleration informations of step S2 are transformed to according to Eulerian angles relative ground；Such as Fig. 2
It is shown.For two dimensional navigation system；N, E are two axles of ground reference system,Deflection angle clockwise for car body relative to N axles
Degree, in the application by the way that car body initial position is aligned into N axles, the Euler angles (Eulerian angles) then obtained with step S1 are replacedAngle carries out ensuing computing.
Can be by relative to a of bodywork reference frame by following conversion according to spin matrixxAnd ayTransform to ground reference coordinate
In system, it is convenient for further positioning and is operated with navigation：
The E relative to ground reference system, N directional accelerations m are obtained according to above formulaEAnd mNOn the basis of, carried out in upper computer end
Superposition operation can obtain E of the mobile robot relative to ground reference system initial position, N directions speed.
S4, the acceleration variable progress discrete integration to being kept in motion, obtain velocity information；If in static shape
State, then corresponding speed be set to zero；Just carry out the calculating of this module when judging and being currently at motion state, reduce as far as possible by
The accumulated error caused after sensor error integration.Simpson's integration is have selected after trapezoidal integration, Simpson's integration is compared
It is used as the integration method of this patent.Its advantage be embodied in amount of calculation do not had more relative to trapezoidal integration it is too many on the premise of,
High two series of computational accuracy.
Integration is carried out once to the acceleration information under motion state and obtains rate signal, velocity information is saved as into the overall situation
Variable is in case track following and motion control link afterwards are used.At the same time also need what data processing step before was recorded
Motion state is judged that speed is directly output as zero if inactive state is recorded as, and skips integration operation.
S5, the velocity information obtained to step S4 are by Fast Fourier Transform (FFT) (FFT) to obtained velocity information in frequency
Filtering process is carried out on domain；Obtain speed frequency-region signal；
S6, the speed frequency-region signal obtained to step S5 carry out Fast Fourier Transform Inverse, obtain speed time-domain signal；
Velocity information after to once integrating carries out Fast Fourier Transform (FFT).The base that the algorithm used extracts for one-dimensional frequency
2FFT algorithms.Operation times can be significantly decreased from fast Fourier method, arithmetic speed is improved.
First, Fast Fourier Transform (FFT), the shape run by actual robot platform are carried out to obtained speed frequency-region signal
State understands that the frequency of effective signal is probably present in 10Hz or so, therefore according to nyquist sampling law, by frequency-domain analysis
Passband be scheduled on 20Hz, choose suitable filter type, signal be filtered on frequency domain, eliminate high-frequency noise.
Then, Fourier inversion is carried out to obtained frequency-region signal, retrieves the signal in time domain.
S7, the speed time-domain signal obtained to step S6 carry out discrete integration, obtain initial displacement signal；
Velocity information that is being obtained using once integration and handling after filtering, carries out a time domain product again on this basis
Get displacement information.Simultaneously in the case of known initial coordinate, displacement information can be equivalent to the seat of robot platform currently
Scale value.
S8, the initial displacement signal obtained to step S7 are by Fast Fourier Transform (FFT) (FFT) to obtained velocity information
Filtering process is carried out on frequency domain, the frequency-region signal of initial displacement is obtained；
Fast Fourier Transform (FFT) is carried out to the displacement information after quadratic integral.The base that the algorithm used extracts for one-dimensional frequency
2FFT algorithms.It is similar with the processing before to velocity information, carry out frequency domain filtering processing.
S9, the frequency-region signal to initial displacement carry out Fast Fourier Transform Inverse, obtain the time-domain signal of initial displacement.
One of ordinary skill in the art will be appreciated that embodiment described here is to aid in reader and understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such especially statement and embodiment.For ability
For the technical staff in domain, the present invention can have various modifications and variations.Within the spirit and principles of the invention, made
Any modification, equivalent substitution and improvements etc., should be included within scope of the presently claimed invention.
Claims (7)
1. a kind of Localization Approach for Indoor Mobile based on inertial navigation, it is characterised in that including：
Each frame acceleration information and Eulerian angles that S1, reception are gathered from inertial sensor；
S2, each frame acceleration information obtained to step S1 are corrected；
S3, the acceleration variable that the obtained corresponding acceleration informations of step S2 are transformed to according to each Eulerian angles relative ground；
S4, the corresponding acceleration variable that is kept in motion to inertial sensor carry out discrete integration, obtain velocity information；
S5, the velocity information that step S4 is obtained is filtered by Fast Fourier Transform (FFT) to obtained velocity information on frequency domain
Ripple processing；Obtain speed frequency-region signal；
S6, the speed frequency-region signal obtained to step S5 carry out Fast Fourier Transform Inverse, obtain speed time-domain signal；
S7, the speed time-domain signal obtained to step S6 carry out discrete integration, obtain initial displacement signal；
S8, the initial displacement signal obtained to step S7 are enterprising in frequency domain to obtained velocity information by Fast Fourier Transform (FFT)
Row filtering process, obtains the frequency-region signal of initial displacement；
S9, the frequency-region signal to initial displacement carry out Fast Fourier Transform Inverse, obtain the time-domain signal of initial displacement.
2. a kind of Localization Approach for Indoor Mobile based on inertial navigation according to claim 1, it is characterised in that
The step S1 specifically include it is following step by step：
S11, setting serial port protocol are：It is fixed length per frame, sets putting in order raw acceleration data and Eulerian angles；And just
Beginningization frame head；
If S12, the frame head of reception data do not meet preset value, the whole frame data received are given up；
S13, data are received to step S12 according to the carry out cutting operation that puts in order, obtain one group of acceleration information and Euler
Angle.
3. a kind of Localization Approach for Indoor Mobile based on inertial navigation according to claim 1, it is characterised in that
The step S2 is specially：
Analyzed by each frame acceleration information received to step S1, judge the state of inertial sensor；The state bag
Include：Inactive state and motion state；
And inertial sensor remains static down, the average value of some frame acceleration informations collected in setting time is made
For inertial sensor be kept in motion it is lower correspondence acceleration information null offset amount, subtract zero with acceleration information original value
Point drift value completes zero correction.
4. a kind of Localization Approach for Indoor Mobile based on inertial navigation according to claim 3, it is characterised in that
The deterministic process of the inactive state or motion state is：If receiving the acceleration information between each frame in the setting time
Difference value meets fixed probability density, then judges that inertial sensor remains static；Otherwise inertial sensor is in motion
State.
5. a kind of Localization Approach for Indoor Mobile based on inertial navigation according to claim 1, it is characterised in that
Step S3 is specially：Step S2 is obtained by following formula by acceleration information according to spin matrix and transforms to ground reference coordinate system
In：
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Wherein, axAnd ayCoordinate for acceleration information relative to bodywork reference frame；aEAnd aNIt is acceleration information relative to ground
The coordinate of coordinate system；Euler is Eulerian angles；E, N are two axial directions of earth axes.
6. a kind of Localization Approach for Indoor Mobile based on inertial navigation according to claim 1, it is characterised in that
The discrete integration is integrated using Simpson.
7. a kind of Localization Approach for Indoor Mobile based on inertial navigation according to claim 1, it is characterised in that
The step S4 also includes：If inertial sensor remains static, then corresponding speed is set to zero.
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| CN109521226A (en) * | 2018-11-29 | 2019-03-26 | 歌尔科技有限公司 | A kind of speed calculation method, system, electronic equipment and readable storage medium storing program for executing |
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| WO2020244521A1 (en) * | 2019-06-06 | 2020-12-10 | 华为技术有限公司 | Motion state determination method and apparatus |
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