CN106643445B - A kind of track roughness measurement method - Google Patents
A kind of track roughness measurement method Download PDFInfo
- Publication number
- CN106643445B CN106643445B CN201611263066.5A CN201611263066A CN106643445B CN 106643445 B CN106643445 B CN 106643445B CN 201611263066 A CN201611263066 A CN 201611263066A CN 106643445 B CN106643445 B CN 106643445B
- Authority
- CN
- China
- Prior art keywords
- angle
- track
- data
- walking mechanism
- angular
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B5/00—Measuring arrangements characterised by the use of mechanical techniques
- G01B5/28—Measuring arrangements characterised by the use of mechanical techniques for measuring roughness or irregularity of surfaces
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Gyroscopes (AREA)
- Length Measuring Devices With Unspecified Measuring Means (AREA)
- Navigation (AREA)
Abstract
The present invention provides a kind of track roughness measurement method, comprising the following steps: (1) will place in orbit with the walking mechanism of acceleration transducer, angular-rate sensor, Magnetic Sensor, and be moved to track corresponding position；(2) initial data for obtaining acceleration transducer, angular-rate sensor, pitch angle, yaw angle and the roll angle of walking mechanism are obtained according to initial data；(3) whether the pitch angle of acquisition, yaw angle, roll angle are located in level threshold value section in judgment step (2), illustrate that track is smooth if being located in level threshold value section, otherwise, track out-of-flatness.The present invention can realize simplification, safety and the efficiency of the operation of track installation detection process with the flatness of intelligent decision track installation.
Description
Technical field
The present invention relates to a kind of track roughness measurement methods.
Background technique
Track machine people's Daily Round Check monitor task all carries out in orbit, therefore robot is for the stabilization of track
There is high requirements for property and flatness.For with ensureing inspection monitor task trouble-free operation, need to track installation work early period
Skill is rigid in checking up, and can have the angular deviation of three-dimensional space during track installation, and common manually takes inclinator
The method of measurement can only obtain deviation of the track on two dimension angular, and measurement error is larger, and process is cumbersome, climb when manually
There are security risk when going to measure, efficiency is lower.
Summary of the invention
In order to solve the problems existing in the prior art, the present invention provides a kind of track roughness measurement method, intelligent decision rail
The flatness of road installation, realizes simplification, safety and the efficiency of the operation of track installation detection process.
Track roughness measurement method provided by the invention, comprising the following steps:
(1) it will be placed in orbit with the walking mechanism of acceleration transducer, angular-rate sensor, Magnetic Sensor, and
It is moved to track corresponding position；
(2) initial data for obtaining acceleration transducer, angular-rate sensor, obtains walking mechanism according to initial data
Pitch angle, yaw angle and roll angle；The pitch angle of pitch angle, yaw angle and the roll angle of walking mechanism and track, yaw angle and
Roll angle is identical.
(3) whether the pitch angle of acquisition, yaw angle, roll angle are located in level threshold value section in judgment step (2), if
Then illustrate that track is smooth in level threshold value section, otherwise, track out-of-flatness.
Preferably, the step (2) specifically includes；
(2.1) initial data of acceleration transducer, angular-rate sensor is obtained as four elements, is made using four element
Four element q are initialized for initial value0、q1、q2、q3, by four element q0、q1、q2、q3Be converted into Eulerian angles θ, ψ,This group of Eulerian angles are made
For first group of attitude angle Q0, θ, ψ,Respectively indicate pitch angle, yaw angle, roll angle；
(2.2) median filtering is carried out to the initial data of acceleration transducer, obtains acceleration information A-data, A-
Data includes to respectively correspond x-axis, y-axis, the A of z-axisx,Ay,AzThree components carry out attitude algorithm using these three components, obtain
Pitching angle theta, roll angleAnd the angle γ of z-axis and acceleration of gravity, wherein
(2.3) it obtains the initial data of Magnetic Sensor and carries out median filtering, obtain magnetic data M-data,
The data include three component Mx,My,Mz, then carry out slope compensation:
Yaw angle is obtained according to slope compensation resultWherein θ,To be obtained in step (2.2)
To pitch angle, roll angle.
(2.4) initial data of angular velocity sensor carries out mean filter and obtains angular velocity data ω-data, to ω-
Corresponding angle Q is obtained after data integral operationk=(ωk-ωb)dt+Qk-1, wherein QkFor the angle at current time；ωkFor angle
The angular speed at velocity sensor current time；ωbFor the offset of current time angular speed；Dt is the sampling period；Qk-1It is previous
The angle value at moment.
(2.5) pitching angle theta, the roll angle for obtaining step (2.2), (2.3), (2.4)Yaw angle ψ, current time
Angle QkIt is merged to obtain second group of attitude angle Q by Kalman filtering algorithm1；
(2.6) by first group of attitude angle Q0With second group of attitude angle Q1It is merged, is merged by single order complementary filter
Data Q afterwardst=α Q0+βQ1, according to data Q after fusiontObtain final accurately pitch angle, roll angle, yaw angle, wherein α, β
Corresponding Q0、Q1Confidence weight, alpha+beta=1.α value range is 0.3-0.5, β value range: 0.7~0.5.
To realize the accurate positionin to uneven track position, positioning system is also equipped in walking mechanism of the present invention
System, the positioning system determine walking mechanism position in orbit for positioning to walking mechanism, when detecting track not
When smooth, the position that can use walking mechanism determines the position of uneven track.When pitch angle, yaw angle, roll angle do not exist
When in level threshold value section, walking mechanism stopping walks about and issues alarm, up to the track be repaired it is smooth, by walking mechanism weight
It newly puts back at this, measures after related data meets the requirements, walking mechanism continues to test lower track section.
For with realizing track installation technique early period stringent control, the present invention is using gyroscope to track stability and smooth
Property measure, data are handled using arm processor, use handheld data analysis liquid crystal display show track flatness letter
Breath, reduces the error of manual measurement.In order to realize the measurement accurate feedback to track three-dimensional perspective deviation, AHRS extension point is used
When algorithm and Kalman filtering algorithm measurement data is handled, use data analysis liquid crystal display departure and aberration curve
As feedback quantity, track flatness is judged using abnormal indicator light, intelligent accurate orients the orbital region of abnormal dip.This hair
Bright to load measuring device using walking mechanism, measuring device, which is expanded, becomes a mobile platform, makes to operate walk away safety, improve
Track installation efficiency.The present invention realizes the flatness of intelligent decision track installation, demarcates with carrying out specific region automatically, and feedback is inclined
Difference is precisely corrected, so that the stability of guide rails of robots and planarization are guaranteed.
Detailed description of the invention
Fig. 1 is flow chart of the present invention；
Fig. 2 is the control interface that handheld data analyzes liquid crystal display；
Fig. 3 is the three-dimension altitude angle interface that handheld data analyzes liquid crystal display；
Fig. 4 is the angle of deviation interface that handheld data analyzes liquid crystal display.
Specific embodiment
Walking mechanism of the invention can be used application No. is 201610186221.1 patent of invention provide robot row
Mechanism is walked, which uses a high-performance brush motor for power, has positioning system below driving wheel, in conjunction with electricity
Machine encoder position ring can precisely acquire the distance of walking, which has eight directive wheels, in walking mechanism and track
Respectively there are 4 directive wheels at the both ends of contact, cooperate, and are close to track, will not generate wobble effects gyro data；Guiding module
Block can be turned an angle in the horizontal plane to adapt to turn to and adjust, it can be achieved that without dead angle steering, will not generate fortune at turning
Dynamic direction stress.Positioning system is installed in walking mechanism, 9 magnet steel is taken to cooperate with Hall sensor, it can be achieved that contactless
Property, in the case that track does not need installation power supply, the distance of mechanism walking is accurately positioned.Be equipped in walking mechanism gyroscope,
Arm processor, 3 axis MEMS module measurement module, can also directly use MPU9250 sensor, sensor integration gyro
Instrument, acceleration transducer and Magnetic Sensor.
Overall plan of the present invention is described as follows:
1, walking mechanism is placed on the track for needing testing flatness；
2, the upper arm processor of walking mechanism, 3 axis MEMS module measurement module are powered on；
3, in the level threshold value section (- 0.6 °~+0.6 °) of handheld data analysis liquid crystal display setting track flatness, the mark
Quasi- threshold interval is to be orbited to guarantee horizontal attitude and set according to machine, is sent on track by wireless module
Walking mechanism；
4, input needs the distance of track to be tested, clicks operation, as shown in Fig. 2, analyzing liquid crystal display for handheld data
Control interface；
5, the walking mechanism of track can move in orbit, when encountering a certain section of flatness not in level threshold value section
It can be automatically stopped walking when region and issue flashing red light；This section of track flatness data is transferred to handheld data analysis liquid crystal
Screen；Due to measuring three roll angle, pitch angle, yaw angle data, as long as in any one data no longer level threshold value section, all
It can stop walking.
6, handheld data analysis liquid crystal display shows track flatness exception information, inclined in handheld data analysis liquid crystal display observation
Residual quantity and aberration curve are illustrated in figure 3 the three-dimension altitude angle interface of handheld data analysis liquid crystal display, it is shown that this section of track
Roll angle, pitch angle, yaw angular data, Fig. 4 show handheld data analysis liquid crystal display angle of deviation interface, show departure
And aberration curve；
7, worker carries out artificial correction in abnormal orbital region, until abnormal flash red, which becomes normal, (is indicating data just
Often), this section of track flatness reaches requirement.
As shown in Figure 1, the present invention obtains accurate pitch angle, roll angle, yaw angle, specific step is as follows:
1. first group of attitude angle Q0
The MPU9250 produced using InvenSense company, MPU9250 carry DMP (digital moving processor) and MPL
(embedded motion process library) handles the acceleration transducer of MPU9250 and the initial data of angular-rate sensor, obtains
It to quaternary number and exports, this quaternary number is used to initialize quaternary number q as initial value0、q1、q2、q3, since Eulerian angles are more intuitive,
xb、yb、zbFor the coordinate of three axis of module carrier, xn、yn、zn: for the coordinate under reference frame, need for quaternary number to be converted into
Eulerian angles:
Firstly, from four element q0、q1、q2、q3It is converted into direction cosine matrix:
Then Eulerian angles are converted by direction cosine matrix again:
T in formulaijThe i-th row, the jth column element of (i, j=1,2,3) expression direction cosine matrix.It can be obtained by this way
One group of attitude angle Q0(including pitch angle, roll angle, yaw angle).
2. second group of attitude angle Q1
1) acceleration information is handled
After obtaining accelerometer initial data, a median filtering is carried out, the acceleration information A- that obtains that treated
Data, A-data include Ax,Ay,Az(corresponding x-axis, y-axis, z-axis) three components, then carry out posture solution using these three components
It calculates:
In formula, θ,Pitch angle and roll angle are respectively corresponded, γ is the angle of z-axis and acceleration of gravity, in this way by adding
The available pitch angle of speedometer and roll angle, and yaw angle is not by calibration, it may appear that obvious drift needs to pass using magnetic
Sensor is corrected.
2) magnetic sensor data is handled
After obtaining magnetic sensor data, median filtering is first carried out, obtain that treated magnetic data M-data, the data packet
Containing three component Mx,My,Mz, then carry out slope compensation:
Wherein θ,To obtain pitch angle, roll angle in step 1), yaw angle can pass through following trigonometric function relationship meter
It calculates:
(5)
3) angular velocity data is handled
It after obtaining angular velocity omega initial data, needs first to carry out a mean filter, noise jamming is removed, after obtaining processing
Then data ω-data can be obtained by corresponding angle by integral:
Qk=(ωk-ωb)dt+Qk-1 (6)
Wherein, QkFor the angle at current time；ωkFor the angular speed at gyroscope current time；ωbFor current time angle speed
The offset of degree；Dt is the sampling period；Qk-1For the angle value of previous moment.
4) Kalman filtering
Angle attitude data is merged using Kalman filtering algorithm, the input quantity of filter is systematic perspective measurement,
Output quantity is the prediction of system state amount, carries out optimal estimation using the statistical property of system noise and observation noise.Assuming that being
The state equation and measurement equation of system are described as follows:
Wherein, Xk, ykRespectively correspond state vector and observation vector；A shifts square for -1 moment of kth to kth moment state
Battle array；B is the gain matrix of input control vector；UkFor input control vector；H is gain matrix of the quantity of state to observed quantity；WkFor
System input noise；vkFor observation noise；It is assumed that system input noise and observation noise all Normal Distributions, input noise
Covariance is Q, and the covariance of observation noise is R, then Kalman filtering can be described as following 5 formula:
State quantity prediction:
Error covariance prediction:
Pk|k-1=APk-1AT+Q (9)
Kalman gain updates:
Kk=Pk|k-1HT[HPk|k-1HT+R]-1 (10)
State updates:
Error covariance updates:
Pk=(1-KkH)Pk|k-1 (12)
In formula,For the prediction to current state true value；For the prediction based on the k-1 moment to k moment state；
A is -1 moment of kth to kth moment state-transition matrix；B is the gain matrix of input control vector；H is quantity of state to observed quantity
Gain matrix；ykFor the observed quantity at kth moment；KkFor kalman gain；PkFor the error covariance square of current state estimated value
Battle array；Pk|k-1For the error co-variance matrix of prediction；I is unit matrix.
Gyroscope angle state indicates that accelerometer and Magnetic Sensor corresponding angle are as observation value of feedback with formula (5).For
Inhibition gyroscopic drift, needs to predict offset, so system state equation are as follows:
T indicates the sampling period in formula；QkFor the angle at current time；ωbFor the offset of current time angular speed, ωk-1、
ωkRespectively correspond the angular speed at k moment and k-1 moment.In optimization process, each initial value is provided that
The angular velocity data, acceleration information and magnetic sensor data of gyroscope are melted by Kalman filtering algorithm
It closes, available second group of attitude angle Q1(including pitch angle, roll angle, yaw angle).
3. single order complementary filter
In order to further increase the accuracy for obtaining attitude angle, by first group of three attitude angle Q0(pitch angle, roll angle,
Yaw angle) and second group of three attitude angle Q1(pitch angle, roll angle, yaw angle) is merged by complementary filter
Data Qt (pitch angle, roll angle, yaw angle) complementary filter formula is as follows:
Qt=α Q0+βQ1 (14)
α, β correspond to Q0、Q1Confidence weight, alpha+beta=1, the general value range that α is set: 0.3~0.5, the value range of β is set:
0.7~0.5；Qt is fused data, can thus obtain a very accurate attitude data, be shown by display screen.
Claims (4)
1. a kind of track roughness measurement method, which comprises the following steps:
(1) it will be placed in orbit with the walking mechanism of acceleration transducer, angular-rate sensor, Magnetic Sensor, and mobile
To track corresponding position；
(2) initial data for obtaining acceleration transducer, angular-rate sensor, the pitching of walking mechanism is obtained according to initial data
Angle, yaw angle and roll angle；
(3) whether the pitch angle of acquisition, yaw angle, roll angle are located in level threshold value section in judgment step (2), if be located at
Then illustrate that track is smooth in level threshold value section, otherwise, track out-of-flatness；
The step (2) specifically includes；
(2.1) initial data of acceleration transducer, angular-rate sensor is obtained as four elements, using four element as just
Four element q of value initialization0、q1、q2、q3, by four element q0、q1、q2、q3Be converted into Eulerian angles θ, ψ,This group of Eulerian angles are as
One group of attitude angle Q0, θ, ψ,Respectively indicate pitch angle, yaw angle, roll angle；
(2.2) median filtering is carried out to the initial data of acceleration transducer, obtains acceleration information A-data, A-data includes
Respectively correspond x-axis, y-axis, the A of z-axisx,Ay,AzThree components carry out attitude algorithm using these three components, obtain pitching angle theta,
Roll angleAnd the angle γ of z-axis and acceleration of gravity, wherein
(2.3) it obtains the initial data of Magnetic Sensor and carries out median filtering, obtain magnetic data M-data, which includes three
Component Mx,My,Mz, then carry out slope compensation:
Yaw angle is obtained according to slope compensation resultWherein θ,To obtain pitching in step (2.2)
Angle, roll angle；
(2.4) initial data of angular velocity sensor carries out mean filter and obtains angular velocity data ω-data, to ω-data
Corresponding angle Q is obtained after integral operationk=(ωk-ωb)dt+Qk-1, wherein QkFor the angle at current time；ωkFor angular speed
The angular speed at sensor current time；λωbFor the offset of current time angular speed；Dt is the sampling period；Qk-1When being previous
The angle value at quarter；
(2.5) pitching angle theta, the roll angle for obtaining step (2.2), (2.3), (2.4)Yaw angle ψ, the angle at current time
QkIt is merged to obtain second group of attitude angle Q by Kalman filtering algorithm1；
(2.6) by first group of attitude angle Q0With second group of attitude angle Q1It is merged by single order complementary filter, number after being merged
According to Qt=α Q0+βQ1, according to data Q after fusiontObtain final accurately pitch angle, roll angle, yaw angle, wherein α, β are corresponding
Q0、Q1Confidence weight, alpha+beta=1.
2. track roughness measurement method as described in claim 1, which is characterized in that α value range is 0.3-0.5, β value
Range: 0.7~0.5.
3. track roughness measurement method as described in claim 1, which is characterized in that it is fixed to be also equipped in the walking mechanism
Position system, the positioning system determine walking mechanism position in orbit for positioning to walking mechanism.
4. track roughness measurement method as described in claim 1, which is characterized in that when pitch angle, yaw angle, roll angle not
When in level threshold value section, walking mechanism stopping walks about and issues alarm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611263066.5A CN106643445B (en) | 2016-12-30 | 2016-12-30 | A kind of track roughness measurement method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611263066.5A CN106643445B (en) | 2016-12-30 | 2016-12-30 | A kind of track roughness measurement method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106643445A CN106643445A (en) | 2017-05-10 |
| CN106643445B true CN106643445B (en) | 2019-04-16 |
Family
ID=58839027
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611263066.5A Active CN106643445B (en) | 2016-12-30 | 2016-12-30 | A kind of track roughness measurement method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106643445B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107339982B (en) * | 2017-07-06 | 2019-10-01 | 湖南迈克森伟电子科技有限公司 | High-speed rail wire plotting method |
| CN107941463B (en) * | 2017-10-26 | 2020-11-10 | 深圳多哚新技术有限责任公司 | Horizontal defect detection method and system for head-mounted equipment |
| CN107796386B (en) * | 2017-10-26 | 2020-10-20 | 深圳多哚新技术有限责任公司 | Horizontal defect detection method and system for PCB of head-mounted equipment |
| CN107843219A (en) * | 2017-10-26 | 2018-03-27 | 西南交通大学 | Track geometry irregularities measurement apparatus and method |
| CN108508928B (en) * | 2018-03-15 | 2024-05-28 | 贺凯 | Scooter road surface identification method |
| CN110542417B (en) * | 2019-09-05 | 2022-12-13 | 武汉理工大学 | Gyroscope linear measurement method and system based on static and dynamic inclinometer correction |
| CN110849299B (en) * | 2019-11-22 | 2021-05-18 | 东南大学 | Track unevenness measuring device and method based on image processing |
| CN111845712B (en) * | 2020-07-28 | 2022-02-01 | 聊城大学 | Control method of anti-rollover control system of tractor |
| CN114485562B (en) * | 2022-01-25 | 2023-09-29 | 福建利利普光电科技有限公司 | Handheld oscilloscope horizontal position adjusting method based on gravity sensing |
| CN115535026B (en) * | 2022-12-05 | 2023-04-04 | 北京智弘通达科技有限公司 | Railway track flatness detection method and system |
| CN116718098B (en) * | 2023-08-09 | 2023-10-17 | 成都国营锦江机器厂 | Online measurement device for coaxiality of main stay bar of helicopter and application method |
| CN118067073B (en) * | 2024-04-16 | 2024-07-12 | 北京城建勘测设计研究院有限责任公司 | Subway track deformation degree monitoring method, system and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000283745A (en) * | 1999-03-31 | 2000-10-13 | Komatsu Engineering Kk | On-board device for measuring shape in road surface extension direction |
| JP2005315675A (en) * | 2004-04-28 | 2005-11-10 | Japan Science & Technology Agency | Road surface flatness measuring device |
| CN102252636A (en) * | 2011-04-12 | 2011-11-23 | 中国航空工业第六一八研究所 | Multi-wavelength detection device and detection method for smoothness of high-speed railway track |
| CN102337710A (en) * | 2010-07-19 | 2012-02-01 | 西安奥通数码科技有限公司 | GPS (Global Positioning System) track irregularity detection system and method |
| CN102607505A (en) * | 2012-03-23 | 2012-07-25 | 中国科学院深圳先进技术研究院 | Road evenness detection method and road evenness detection system |
| CN102628249A (en) * | 2012-04-27 | 2012-08-08 | 重庆邮电大学 | Full-automatic inertial sensing pavement evenness detection system and detection method |
| CN103343498A (en) * | 2013-07-24 | 2013-10-09 | 武汉大学 | Track irregularity detecting system and method based on INS/GNSS |
-
2016
- 2016-12-30 CN CN201611263066.5A patent/CN106643445B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000283745A (en) * | 1999-03-31 | 2000-10-13 | Komatsu Engineering Kk | On-board device for measuring shape in road surface extension direction |
| JP2005315675A (en) * | 2004-04-28 | 2005-11-10 | Japan Science & Technology Agency | Road surface flatness measuring device |
| CN102337710A (en) * | 2010-07-19 | 2012-02-01 | 西安奥通数码科技有限公司 | GPS (Global Positioning System) track irregularity detection system and method |
| CN102252636A (en) * | 2011-04-12 | 2011-11-23 | 中国航空工业第六一八研究所 | Multi-wavelength detection device and detection method for smoothness of high-speed railway track |
| CN102607505A (en) * | 2012-03-23 | 2012-07-25 | 中国科学院深圳先进技术研究院 | Road evenness detection method and road evenness detection system |
| CN102628249A (en) * | 2012-04-27 | 2012-08-08 | 重庆邮电大学 | Full-automatic inertial sensing pavement evenness detection system and detection method |
| CN103343498A (en) * | 2013-07-24 | 2013-10-09 | 武汉大学 | Track irregularity detecting system and method based on INS/GNSS |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106643445A (en) | 2017-05-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106643445B (en) | A kind of track roughness measurement method | |
| CN107655493B (en) | SINS six-position system-level calibration method for fiber-optic gyroscope | |
| CN111811537B (en) | Error compensation method for strapdown inertial navigation and navigation system | |
| CN105910624B (en) | A kind of scaling method of used group of optical laying prism installation error | |
| CN106557089B (en) | A kind of control method and device of unmanned plane independent landing | |
| CN106289246B (en) | A kind of flexible link arm measure method based on position and orientation measurement system | |
| Rohac et al. | Calibration of low-cost triaxial inertial sensors | |
| US9541392B2 (en) | Surveying system and method | |
| JP4989035B2 (en) | Error correction of inertial navigation system | |
| EP2901104B1 (en) | Improved inertial navigation system and method | |
| US20130018582A1 (en) | Inertial Navigation Common Azimuth Reference Determination System and Method | |
| KR20170104623A (en) | Initial alignment of inertial navigation devices | |
| CN109870173A (en) | A kind of track correct method of the submarine pipeline inertial navigation system based on checkpoint | |
| CN102607596B (en) | Strapdown flexible gyro dynamic random drift error testing method based on difference GPS (global position system) observation | |
| CN101571394A (en) | Method for determining initial attitude of fiber strapdown inertial navigation system based on rotating mechanism | |
| CN110411444A (en) | A kind of subsurface digging mobile device inertia navigation positioning system and localization method | |
| CN104316055A (en) | Two-wheel self-balancing robot attitude calculation method based on improved Extended Kalman Filter algorithm | |
| CN109631941A (en) | A kind of Inertial Platform System accelerometer installation error method for precisely marking | |
| CN106153069A (en) | Attitude rectification apparatus and method in autonomous navigation system | |
| CN103968844A (en) | Large ellipse maneuverable spacecraft autonomous navigation method based on low-orbit platform tracking measurement | |
| Wu et al. | The calibration for inner and outer lever-arm errors based on velocity differences of two RINSs | |
| KR20120042394A (en) | System and method for localizationing of autonomous vehicle | |
| Cechowicz | Bias drift estimation for mems gyroscope used in inertial navigation | |
| Zhang et al. | The standing calibration method of MEMS gyro bias for autonomous pedestrian navigation system | |
| KR101435106B1 (en) | Localization system of under water robots |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |