CN108253964A - A kind of vision based on Time-Delay Filter/inertia combined navigation model building method - Google Patents
A kind of vision based on Time-Delay Filter/inertia combined navigation model building method Download PDFInfo
- Publication number
- CN108253964A CN108253964A CN201711477073.XA CN201711477073A CN108253964A CN 108253964 A CN108253964 A CN 108253964A CN 201711477073 A CN201711477073 A CN 201711477073A CN 108253964 A CN108253964 A CN 108253964A
- Authority
- CN
- China
- Prior art keywords
- navigation
- error
- speed
- inertial navigation
- carrier
- 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.)
- Pending
Links
Classifications
-
- 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
- G01C21/165—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 combined with non-inertial navigation instruments
-
- 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
Abstract
The present invention relates to a kind of vision based on Time-Delay Filter/inertia combined navigation model building methods, it is when being corrected in the position using vision guided navigation and velocity information to inertial navigation parameter, the delay time error of integrated navigation is added in filter state equation, estimated by filtering, not only inertial navigation parameter error is estimated, while obtains the time synchronization error of integrated navigation.Solve the problems, such as that integrated navigation parameter calculation is nonsynchronous.The present invention has the functions such as inertial navigation resolving, vision guided navigation parameter calculation, structure Time-Delay Filter.Compared with prior art, present aspect has the advantages that theoretical tight, method novelty, performance efficiency.
Description
Technical field
The present invention relates to integrated navigation field, more particularly to a kind of vision/inertia combined navigation based on Time-Delay Filter
Model building method.
Background technology
Inertial navigation system（Inertial Navigation System INS）Independence, good concealment can be carried continuously
For the navigational parameter of a variety of degree of precision, antijamming capability is good, is a kind of common air navigation aid.But shortcoming be error at any time
Accumulation, the navigation accuracy to work long hours is poor, in order to eliminate accumulative error, needs to introduce other outer navigation system to inertia
Navigation is corrected, and forms the higher integrated navigation system of navigation accuracy.But during integrated navigation, vision guided navigation is adopted due to image
Collection resolves reason with vision guided navigation, and navigational parameter output has delay, when inertial navigation algorithm carries out navigational parameter resolving,
Output also has delay.Therefore it is necessary to solve the problems, such as time synchronization, accurate navigator fix can be only achieved.Do not have still at present
The integrated navigation research of complete vision guided navigation/inertial navigation based on Time-Delay Filter, therefore the present invention has very strong theory
Directive significance.
Invention content
The present invention is intended to provide a kind of vision based on Time-Delay Filter/inertia combined navigation model building method, with solution
The deficiency that certainly navigation accuracy caused by integrated navigation time synchronization error lacks realizes the higher integrated navigation system of precision.
Technical scheme is as follows：
The method that a kind of vision guided navigation/inertial navigation solves navigational parameter, this method include the following steps：
S1, inertial navigation system parameter calculation：Local northeast day geographic coordinate system is chosen as navigational coordinate system, gyroscope is sensitive
The angular speed of carrier, accelerometer carry out the non-gravitation i.e. specific force of sensitive carrier, and the posture of carrier is obtained by attitude algorithm formula
Transition matrix, so as to find out the attitude information of carrier；Carrier speed over the ground and the current position of carrier is obtained in navigation calculation formula
It puts；
S2, vision guided navigation parameter calculation method：Camera is demarcated first, camera imaging model parameter is obtained；Pass through image
Processing Algorithm trains navigation marker, by connecting firmly the vehicle-mounted camera disposed downwards in mobile robot front vertical, obtains and moves
The image sequence on mobile robot passed by road surface during the motion is then based on the feature point tracking matching algorithm of SURF, with
And pixel displacement of the tracked characteristic point between two field pictures, speed is calculated in camera imaging model；By being connected in
Camera in front of carrier, the filmed image during carrier movement, and stencil matching is carried out, successful match performs S3, otherwise holds
Row S1；
S3, structure time delay Kalman filter：The state of time delay Kalman filter be 5 states, the velocity error of inertial navigation, position
Error and the time synchronization error newly increased.The error equation of inertial navigation is taken to be resolved for filter state equation using vision guided navigation
Position that obtained carrier positions, speed and inertial navigation resolves, speed are subtracted each other, and obtained difference is as Kalman filtering
Measurement；
S4, estimated by the filtering of time delay Kalman filter, estimate inertial navigation parameter error and device error and regard
The time delays margin of error of feel/inertia combined navigation so as to carry out output feedback compensation to inertial navigation, obtains more accurately
Navigation accuracy.
The beneficial effects of the invention are as follows：The present invention is for the error accumulation and during integrated navigation at any time of inertial navigation precision
Between the problems such as being delayed, be taken through building the Kalman filter based on time delay, by vision guided navigation to inertial navigation parameter
While carrying out feedback compensation, the error accumulation problem of inertial navigation is not only solved, while it is same to solve the integrated navigation time
The problem of step, substantially increases navigation accuracy.
Description of the drawings
The invention will be further described below in conjunction with the accompanying drawings.
Fig. 1 is system schema block diagram.
Fig. 2 is the fundamental diagram of integrated navigation delay calibration.
Site error estimations of the Fig. 3 without delay calibration.
Site error estimations of the Fig. 4 by delay calibration.
The site error estimation of Fig. 5 integrated navigations.
Fig. 6 integrated navigations velocity error is estimated.
Specific embodiment
The present invention is further described with reference to specific embodiment.
Embodiment 1
The step of building the integrated navigation model method based on Time-Delay Filter for the embodiment as shown in Figure 2：
S1, inertial navigation system parameter calculation：Local northeast day geographic coordinate system is chosen as navigational coordinate system, gyroscope is sensitive
The angular speed of carrier, accelerometer carry out the non-gravitation i.e. specific force of sensitive carrier, and the posture of carrier is obtained by attitude algorithm formula
Transition matrix, so as to find out the attitude information of carrier；Carrier speed over the ground and the current position of carrier is obtained in navigation calculation formula
It puts；
S2, vision guided navigation parameter calculation method：Camera is demarcated first, camera imaging model parameter is obtained；Pass through image
Processing Algorithm trains navigation marker, by connecting firmly the vehicle-mounted camera disposed downwards in mobile robot front vertical, obtains and moves
The image sequence on mobile robot passed by road surface during the motion is then based on the feature point tracking matching algorithm of SURF, with
And pixel displacement of the tracked characteristic point between two field pictures, speed is calculated in camera imaging model；By being connected in
Camera in front of carrier, the filmed image during carrier movement, and stencil matching is carried out, successful match performs S3, otherwise holds
Row S1；
S3, structure time delay Kalman filter：The state of time delay Kalman filter be 5 states, the velocity error of inertial navigation, position
Error and the time synchronization error newly increased.The error equation of inertial navigation is taken to be resolved for filter state equation using vision guided navigation
Position that obtained carrier positions, speed and inertial navigation resolves, speed are subtracted each other, and obtained difference is as Kalman filtering
Measurement；The error equation of inertial navigation system is taken as the state equation of Kalman filter, filter state is velocity error, position
Error and newly-increased integrated navigation time synchronization error Liang ⊿ (t), X=F, wherein, X is error state vector,
Packet inertial navigation site error, velocity errorAnd time synchronization error Liang ⊿ (t)；The position that vision guided navigation is calculated
Put, the position that speed and inertial navigation are calculated, speed measuring value of the difference as Kalman filtering, measurement equation is,,For the east orientation and north orientation speed of inertial navigation,For
The east orientation speed and north orientation speed of vision guided navigation.For the location parameter of inertial navigation,For vision guided navigation
Location parameter, the measurement equation of the extended Kalman filter of time of fusion error are Z=HX+V, wherein Z=, amount
Survey matrix H is 4*5 matrixes：Its nonzero element is：H (1 1)=1, H (2 2)=1, H (3 3)=1, H (4 4)=1, H (1 5)=
, H (2 5)=, H (3 5)=/R, H (4 5)=/ R, whereinAcceleration for INS is flat in filtering cycle
Mean value,For measuring values of the INS in filtering, R is earth radius；
S4, estimated by the filtering of time delay Kalman filter, estimate inertial navigation parameter error and device error and regard
The time delays margin of error of feel/inertia combined navigation so as to carry out output feedback compensation to inertial navigation, obtains more accurately
Navigation accuracy.
Finally illustrate, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although with reference to compared with
The present invention is described in detail in good embodiment, it will be understood by those of ordinary skill in the art that, it can be to the skill of the present invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of technical solution of the present invention, should all be covered at this
In the right of invention.
Claims (4)
1. a kind of vision based on Time-Delay Filter/inertia combined navigation model building method, it is characterized in that, there is following step
Suddenly：
S1, inertial navigation system parameter calculation：Local northeast day geographic coordinate system is chosen as navigational coordinate system, gyroscope is sensitive
The angular speed of carrier, accelerometer carry out the non-gravitation i.e. specific force of sensitive carrier, and the posture of carrier is obtained by attitude algorithm formula
Transition matrix, so as to find out the attitude information of carrier；Carrier speed over the ground and the current position of carrier is obtained in navigation calculation formula
It puts；
S2, vision guided navigation parameter calculation method：Camera is demarcated first, camera imaging model parameter is obtained；Pass through image
Processing Algorithm trains navigation marker, by connecting firmly the vehicle-mounted camera disposed downwards in mobile robot front vertical, obtains and moves
The image sequence on mobile robot passed by road surface during the motion is then based on the feature point tracking matching algorithm of SURF, with
And pixel displacement of the tracked characteristic point between two field pictures, speed is calculated in camera imaging model；By being connected in
Camera in front of carrier, the filmed image during carrier movement, and stencil matching is carried out, successful match performs S3, otherwise holds
Row S1；
S3, structure time delay Kalman filter：The state of time delay Kalman filter be 5 states, the velocity error of inertial navigation, position
Error and the time synchronization error newly increased take the error equation of inertial navigation to be resolved for filter state equation using vision guided navigation
Position that obtained carrier positions, speed and inertial navigation resolves, speed are subtracted each other, and obtained difference is as Kalman filtering
Measurement；
S4, estimated by the filtering of time delay Kalman filter, estimate inertial navigation parameter error and device error and regard
The time delays margin of error of feel/inertia combined navigation so as to carry out output feedback compensation to inertial navigation, obtains more accurately
Navigation accuracy.
2. inertial reference calculation method according to claim 1 is characterized in that：In S1, including：INS Platform is initially aligned, posture
Transformation matrixUpdate, attitude angle extraction, navigational parameter calculate.
3. vision guided navigation parameter calculation method according to claim 1, it is characterized in that：In S2, camera calibration：Pass through mark
Fixed board solves camera calibration the inside and outside parameter of video camera；By the tracking and matching method based on SURF characteristic points, according to figure
As the detection, tracking and matching of the characteristic point between sequence, pixel displacement of the tracked characteristic point between two images is calculated,
The speed and location information of mobile robot are calculated according to the imaging model of video camera.
4. time delay Kalman filter construction method according to claim 1, it is characterized in that：In S3, inertial navigation system is taken
State equation of the error equation as Kalman filter, filter state are velocity error, site error and newly-increased combination
Navigation time synchronous error Liang ⊿ (t), X=F, wherein, X is error state vector, packet inertial navigation site error, speed
Spend errorAnd time synchronization error Liang ⊿ (t)；Position, speed and the inertial navigation that vision guided navigation is calculated calculate
Measuring value of the obtained position, the difference of speed as Kalman filtering, measurement equation are,,、For the east orientation and north orientation speed of inertial navigation,、For the east orientation speed and north orientation speed of vision guided navigation,、For the location parameter of inertial navigation,、For the location parameter of vision guided navigation, the extension karr of time of fusion error
The measurement equation of graceful wave filter be Z=HX+V, wherein Z=, measurement matrix H is 4*5 matrixes：Its nonzero element
For：H (1 1)=1, H (2 2)=1, H (3 3)=1, H (4 4)=1, H (1 5)=H(2 5)=, H (3 5)=/R, H (4
5)=/ R is whereinAverage value of the acceleration in filtering cycle for INS,For INS filtering when measuring value,
R is earth radius.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711477073.XA CN108253964A (en) | 2017-12-29 | 2017-12-29 | A kind of vision based on Time-Delay Filter/inertia combined navigation model building method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711477073.XA CN108253964A (en) | 2017-12-29 | 2017-12-29 | A kind of vision based on Time-Delay Filter/inertia combined navigation model building method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108253964A true CN108253964A (en) | 2018-07-06 |
Family
ID=62724633
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711477073.XA Pending CN108253964A (en) | 2017-12-29 | 2017-12-29 | A kind of vision based on Time-Delay Filter/inertia combined navigation model building method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108253964A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109443353A (en) * | 2018-12-25 | 2019-03-08 | 中北大学 | Vision based on fuzzy self-adaption ICKF-inertia close coupling Combinated navigation method |
| CN109443355A (en) * | 2018-12-25 | 2019-03-08 | 中北大学 | Vision based on adaptive Gauss PF-inertia close coupling Combinated navigation method |
| CN109724598A (en) * | 2019-03-08 | 2019-05-07 | 哈尔滨工程大学 | A kind of estimation and compensation method of the time delay error of GNSS/INS pine combination |
| CN110068325A (en) * | 2019-04-11 | 2019-07-30 | 同济大学 | A kind of lever arm error compensating method of vehicle-mounted INS/ visual combination navigation system |
| CN110375739A (en) * | 2019-06-26 | 2019-10-25 | 中国科学院深圳先进技术研究院 | A kind of mobile terminal vision fusion and positioning method, system and electronic equipment |
| CN110702139A (en) * | 2019-09-29 | 2020-01-17 | 百度在线网络技术（北京）有限公司 | Time delay calibration method and device, electronic equipment and medium |
| CN110880189A (en) * | 2018-09-06 | 2020-03-13 | 舜宇光学（浙江）研究院有限公司 | Combined calibration method and combined calibration device thereof and electronic equipment |
| CN111044069A (en) * | 2019-12-16 | 2020-04-21 | 驭势科技（北京）有限公司 | Vehicle positioning method, vehicle-mounted equipment and storage medium |
| CN111551191A (en) * | 2020-04-28 | 2020-08-18 | 浙江商汤科技开发有限公司 | Sensor external parameter calibration method and device, electronic equipment and storage medium |
| CN114136315A (en) * | 2021-11-30 | 2022-03-04 | 山东天星北斗信息科技有限公司 | Monocular vision-based auxiliary inertial integrated navigation method and system |
| CN116380148A (en) * | 2023-04-06 | 2023-07-04 | 中国人民解放军93209部队 | Two-stage space-time error calibration method and device for multi-sensor target tracking system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104833352A (en) * | 2015-01-29 | 2015-08-12 | 西北工业大学 | Multi-medium complex-environment high-precision vision/inertia combination navigation method |
| CN105910602A (en) * | 2016-05-30 | 2016-08-31 | 南京航空航天大学 | Combined navigation method |
| US20170053538A1 (en) * | 2014-03-18 | 2017-02-23 | Sri International | Real-time system for multi-modal 3d geospatial mapping, object recognition, scene annotation and analytics |
| CN107063246A (en) * | 2017-04-24 | 2017-08-18 | 齐鲁工业大学 | A kind of Loosely coupled air navigation aid of vision guided navigation/inertial navigation |
-
2017
- 2017-12-29 CN CN201711477073.XA patent/CN108253964A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170053538A1 (en) * | 2014-03-18 | 2017-02-23 | Sri International | Real-time system for multi-modal 3d geospatial mapping, object recognition, scene annotation and analytics |
| CN104833352A (en) * | 2015-01-29 | 2015-08-12 | 西北工业大学 | Multi-medium complex-environment high-precision vision/inertia combination navigation method |
| CN105910602A (en) * | 2016-05-30 | 2016-08-31 | 南京航空航天大学 | Combined navigation method |
| CN107063246A (en) * | 2017-04-24 | 2017-08-18 | 齐鲁工业大学 | A kind of Loosely coupled air navigation aid of vision guided navigation/inertial navigation |
Non-Patent Citations (4)
| Title |
|---|
| JE YOUNG LEE 等: "Adaptive GPS/INS integration for relative navigation", 《GPS SOLUTION》 * |
| 张朝飞等: "一种复杂多介质环境下的视觉/惯性自适应组合导航方法 ", 《中国惯性技术学报》 * |
| 秦玉鑫: "移动机器人组合导航系统研究与设计", 《中国优秀硕士学位论文全文数据库》 * |
| 陈林 等: "INS/Vision组合导航中视觉系统动态定位方法研究", 《传感技术学报》 * |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110880189A (en) * | 2018-09-06 | 2020-03-13 | 舜宇光学（浙江）研究院有限公司 | Combined calibration method and combined calibration device thereof and electronic equipment |
| CN110880189B (en) * | 2018-09-06 | 2022-09-09 | 舜宇光学（浙江）研究院有限公司 | Combined calibration method and combined calibration device thereof and electronic equipment |
| CN109443355B (en) * | 2018-12-25 | 2020-10-27 | 中北大学 | Visual-inertial tight coupling combined navigation method based on self-adaptive Gaussian PF |
| CN109443353A (en) * | 2018-12-25 | 2019-03-08 | 中北大学 | Vision based on fuzzy self-adaption ICKF-inertia close coupling Combinated navigation method |
| CN109443353B (en) * | 2018-12-25 | 2020-11-06 | 中北大学 | Visual-inertial tight coupling combined navigation method based on fuzzy self-adaptive ICKF |
| CN109443355A (en) * | 2018-12-25 | 2019-03-08 | 中北大学 | Vision based on adaptive Gauss PF-inertia close coupling Combinated navigation method |
| CN109724598A (en) * | 2019-03-08 | 2019-05-07 | 哈尔滨工程大学 | A kind of estimation and compensation method of the time delay error of GNSS/INS pine combination |
| CN110068325A (en) * | 2019-04-11 | 2019-07-30 | 同济大学 | A kind of lever arm error compensating method of vehicle-mounted INS/ visual combination navigation system |
| CN110375739A (en) * | 2019-06-26 | 2019-10-25 | 中国科学院深圳先进技术研究院 | A kind of mobile terminal vision fusion and positioning method, system and electronic equipment |
| CN110702139A (en) * | 2019-09-29 | 2020-01-17 | 百度在线网络技术（北京）有限公司 | Time delay calibration method and device, electronic equipment and medium |
| CN110702139B (en) * | 2019-09-29 | 2021-08-27 | 百度在线网络技术（北京）有限公司 | Time delay calibration method and device, electronic equipment and medium |
| CN111044069A (en) * | 2019-12-16 | 2020-04-21 | 驭势科技（北京）有限公司 | Vehicle positioning method, vehicle-mounted equipment and storage medium |
| CN111044069B (en) * | 2019-12-16 | 2022-04-29 | 驭势科技（北京）有限公司 | Vehicle positioning method, vehicle-mounted equipment and storage medium |
| CN111551191A (en) * | 2020-04-28 | 2020-08-18 | 浙江商汤科技开发有限公司 | Sensor external parameter calibration method and device, electronic equipment and storage medium |
| CN111551191B (en) * | 2020-04-28 | 2022-08-09 | 浙江商汤科技开发有限公司 | Sensor external parameter calibration method and device, electronic equipment and storage medium |
| CN114136315A (en) * | 2021-11-30 | 2022-03-04 | 山东天星北斗信息科技有限公司 | Monocular vision-based auxiliary inertial integrated navigation method and system |
| CN114136315B (en) * | 2021-11-30 | 2024-04-16 | 山东天星北斗信息科技有限公司 | Monocular vision-based auxiliary inertial integrated navigation method and system |
| CN116380148A (en) * | 2023-04-06 | 2023-07-04 | 中国人民解放军93209部队 | Two-stage space-time error calibration method and device for multi-sensor target tracking system |
| CN116380148B (en) * | 2023-04-06 | 2023-11-10 | 中国人民解放军93209部队 | Two-stage space-time error calibration method and device for multi-sensor target tracking system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108253964A (en) | A kind of vision based on Time-Delay Filter/inertia combined navigation model building method | |
| CN106225784B (en) | Based on inexpensive Multi-sensor Fusion pedestrian dead reckoning method | |
| WO2020087846A1 (en) | Navigation method based on iteratively extended kalman filter fusion inertia and monocular vision | |
| Schall et al. | Global pose estimation using multi-sensor fusion for outdoor augmented reality | |
| Sasani et al. | Improving MEMS-IMU/GPS integrated systems for land vehicle navigation applications | |
| CN110345937A (en) | Appearance localization method and system are determined in a kind of navigation based on two dimensional code | |
| CN106989744A (en) | A kind of rotor wing unmanned aerial vehicle autonomic positioning method for merging onboard multi-sensor | |
| KR101444685B1 (en) | Method and Apparatus for Determining Position and Attitude of Vehicle by Image based Multi-sensor Data | |
| CN110702143B (en) | Rapid initial alignment method for SINS strapdown inertial navigation system moving base based on lie group description | |
| TWI556198B (en) | Positioning and directing data analysis system and method thereof | |
| CN110361010A (en) | It is a kind of based on occupy grating map and combine imu method for positioning mobile robot | |
| CN104937377B (en) | Method and apparatus for the vertical orientation for handling unconfined portable navigating device | |
| CN110325822B (en) | Cradle head pose correction method and cradle head pose correction device | |
| CN108680942A (en) | A kind of inertia/multiple antennas GNSS Combinated navigation methods and device | |
| CN113432609B (en) | Flexible attachment state collaborative estimation method | |
| CN107702712A (en) | Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases | |
| CN109506660A (en) | A kind of posture optimization calculation method for bionic navigation | |
| CN109520476A (en) | Resection dynamic pose measurement system and method based on Inertial Measurement Unit | |
| CN114136315B (en) | Monocular vision-based auxiliary inertial integrated navigation method and system | |
| CN107270896A (en) | A kind of pedestrian's positioning and trace tracking method and system | |
| CN113551665A (en) | High dynamic motion state sensing system and sensing method for motion carrier | |
| CN110763238A (en) | High-precision indoor three-dimensional positioning method based on UWB (ultra wide band), optical flow and inertial navigation | |
| CN110986997A (en) | Method and system for improving indoor inertial navigation precision | |
| CN110986941B (en) | Method for estimating installation angle of mobile phone | |
| CN106643713B (en) | Estimation method and device for zero-speed correction pedestrian locus for smooth and adaptive adjustment of threshold |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180706 |
|
| RJ01 | Rejection of invention patent application after publication |