CN110907978B - Method and device for cloud online optimization of inertial navigation parameters - Google Patents
Method and device for cloud online optimization of inertial navigation parameters Download PDFInfo
- Publication number
- CN110907978B CN110907978B CN201811079911.2A CN201811079911A CN110907978B CN 110907978 B CN110907978 B CN 110907978B CN 201811079911 A CN201811079911 A CN 201811079911A CN 110907978 B CN110907978 B CN 110907978B
- Authority
- CN
- China
- Prior art keywords
- parameter
- inertial navigation
- tunnel
- client
- def
- 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
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
-
- 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
Abstract
The invention provides a method and a device for cloud online optimization of inertial navigation parameters, wherein the method comprises the following steps: the client side sends a service request to the server side for reporting the position; the server side judges whether the client side enters a tunnel scene area, if the client side enters the tunnel scene area, the client side is informed to start uploading data to the server side, otherwise, the client side continues to report the position to the server side for service request; the server side processes the data uploaded by the client side according to the parameter combination to obtain a calculated position of inertial navigation in the tunnel, and meanwhile, the server side processes the uploaded data to obtain a reference true value of the tunnel; and comparing the calculated position with the reference true value to obtain the position error of the inertial navigation in the tunnel, and selecting the parameter combination corresponding to the minimum position error as the optimal parameter to be issued to the client. The invention shortens the mass production period of the product, reduces the optimization time of the inertial navigation parameter from the traditional 72 hours to 5 hours, and improves the optimization efficiency by more than 14 times.
Description
Technical Field
The invention relates to the technical field of inertial navigation parameter optimization, in particular to a method and a device for cloud online inertial navigation parameter optimization.
Background
During the research and development process of the inertial navigation product, the error parameters of inertial devices (gyroscopes and accelerometers) need to be evaluated, and the error parameters mainly comprise zero-bias instability and random walk. The null-bias instability and random walk parameters can be generally obtained by an allan variance evaluation method of a laboratory environment, but the final application environment of a product is different from the indoor laboratory environment, so that vehicle-mounted test data needs to be collected outdoors, a high-precision POS (Position orientation System) reference System is installed as a reference true value, an optimization model is established by using scenes with satellite signal loss, such as tunnels and underground garages, to optimize the null-bias instability and random walk, and the optimal parameters more suitable for the actual application environment are obtained.
At present, in the traditional method, data are collected through field (field vehicle-mounted test), and parameters are optimized through field (indoor single machine establishment optimization model), but the method has lower efficiency and longer parameter updating period. A method for optimizing inertial navigation parameters on line based on a cloud end is not provided. The most similar technical scheme of the invention is that the internet collects data on line, the parameters are adjusted and optimized off line, the algorithm is still operated locally, high-precision POS reference equipment is required to collect data, and the POS data (high-precision GNSS + INS) is subjected to post-processing to obtain a reference true value.
The traditional inertial navigation parameter tuning has the defects that an expensive high-precision POS system is required to be used as a reference standard, the type of a test terminal is single, the time period from data collection to parameter tuning is long, and the final inertial navigation product mass production time period is long.
Disclosure of Invention
The method utilizes the internet to collect the external field test data on line, obtains a basic true value through a server-side post-processing smoothing algorithm, obtains thousands of parameter combinations by setting the numerical range of zero-bias instability and random walk, calculates the inertial navigation by using the data of the scenes of the tunnel and the underground garage, improves the arithmetic efficiency of the algorithm by adopting server-side distributed processing, compares the calculation result with the reference true value to the error distribution of all combinations, further finds the parameter combination with the minimum error, and completes parameter tuning. The method can realize on-line data collection, run the server algorithm, do not need high-precision POS reference equipment, smoothly perform post-processing on GNSS + INS data with common precision to obtain a reference true value, reduce the cost, optimize the parameters in real time, improve the optimization efficiency of inertial navigation parameters and shorten the period from research and development to mass production of inertial navigation products.
The technical scheme adopted by the invention is as follows:
firstly, the service end determines the scene where the client is located according to the position reported by the client, when the client is in the tunnel scene, the service end sends a message to inform the client of the tunnel scene, the client can start to upload GNSS data, gyroscope and accelerometer data and odometer vehicle speed data, then the service end runs a post-processing smoothing algorithm according to the uploaded data to obtain a reference true value, then runs an algorithm library respectively according to different parameter combinations to obtain a calculation result of inertial navigation in the tunnel, then calculates errors of the calculation result of the inertial navigation under different parameter combinations, and selects a parameter with the minimum error, wherein the reliability of the optimal parameter is higher and higher along with the increase of tunnel scene data.
The method has the advantages that the client data can be collected on line by using a crowdsourcing method, the server algorithm is operated in real time to adjust and optimize the parameters, the crowdsourcing data covers more scenes, the types of the terminals are rich, the inertial navigation parameters can be optimized in the mass production of products, and the mass production period of the products is shortened. Experimental results prove that the method for optimizing the inertial navigation parameters on line by using the cloud can reduce the optimization time of the inertial navigation parameters from the traditional 72 hours to 5 hours, and the optimization efficiency is improved by more than 14 times.
Drawings
FIG. 1 is a flow chart of a cloud-based online inertial navigation optimization method according to the present invention;
fig. 2 is a structural diagram of the cloud online optimization inertial navigation device.
Detailed Description
The method and the system utilize the client to report data, and the server runs the server algorithm library and the post-processing algorithm library according to the reported data to adjust and optimize the inertial navigation parameters. And after the parameter is successfully adjusted and optimized, the parameter is broadcasted to the client through the Internet, so that the positioning precision of inertial navigation of the client is greatly improved. The server side collects the client side data on line through the internet, realizes the on-line optimization of the inertial navigation parameters, and broadcasts the optimal parameters to the client side through the internet in real time, so that the inertial navigation precision of the client side is greatly improved. The invention is further illustrated below with reference to the figures and examples.
The first embodiment is as follows:
fig. 1 is a flow chart of a cloud online optimization inertial navigation method according to the present invention, which includes the following steps:
the client reports the position every 15 seconds to carry out service request, the server matches the preset geographic fence according to the reported position, if the client is identified to be in the tunnel scene, the client is informed to start uploading data, and if the client is identified not to be in the tunnel scene, no processing is carried out.
The method comprises the steps that a server processes tunnel scene data reported by a client according to a parameter combination operation algorithm library to obtain a calculated position of Inertial Navigation in a tunnel, the server operates a GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System) post-processing smoothing algorithm to the reported tunnel scene data to obtain a reference true value, the calculated position of Inertial Navigation and the reference true value are compared to obtain a Root Mean Square Error of the tunnel position Error, then a parameter combination corresponding to the smallest RMSE (Root Mean Square Error) is selected as an optimal parameter and is issued to the client, and the optimal parameter is used as a default value of the parameter combination to be iterated.
In order to calculate the inertial navigation calculation error under different parameter combinations, a rule of the parameter combinations needs to be formulated first, and the inertial navigation parameters related in the invention include: gyroscope zero Bias instability GBI (Gyroscope Bias instability), angle Random walk ARW (angle Random walk), accelerometer zero Bias instability ABI (accelerometer Bias instability), velocity Random walk VRW (velocity Random walk), and the variation rules of the four parameters are as follows:
GBIi＝2i*GBIdef
ARWi＝2i*ARWdef
ABIi＝2i*ABIdef
VRWi＝2i*VRWdef
wherein, i ═ {0, ± 1, ± 2 ± 3}, GBIdef、ARWdef、ABIdef、VRWdefIndicating the default values, GBI, of the four parameters, respectivelyi、ARWi、ABIi、VRWiRespectively showing that the four parameters are amplified by different times on the basis of default values to obtain new parameters. Each parameter has 7 changes, the four parameters have 2401 parameter combination modes in total, and the purpose of parameter optimization is to find out the optimal combination of new parameters.
RMSE of tunnel horizontal position errors obtained by different parameter combinations, wherein the root mean square error of 2401 tunnel position errors can be obtained by 2401 parameter combinations, and the RMSE calculation formula is as follows:
wherein k represents the tunnel horizontal position error RMSE, d obtained by combining the kth parametersiAnd n represents the number of position points estimated by inertial navigation.
According to the method, tunnel scene data reported by a client is adopted, tunnel whole-course horizontal positions under different parameter combinations are obtained by operating a server algorithm library, and are compared with reference truth values obtained by post-processing smoothness of a server, so that the whole-course horizontal position error of inertial navigation in the tunnel can be obtained, the parameter combination with the minimum tunnel horizontal position error RMSE is selected as the optimal parameter, the obtained optimal parameter is used as the next default value for iteration, the reliability of parameter optimization is higher as tunnel scene data reported by the client is more and more, and the online optimization of the inertial navigation parameter is finally completed.
Example two:
the invention also provides a cloud online optimization inertial navigation device, as shown in fig. 2, the device comprises: the system comprises a client, a data interaction module and a server.
The client side carries out service requests every 15 seconds through the data interaction module, namely position information (longitude and latitude) is reported, the server side searches according to the position reported by the client side to confirm whether the client side enters a tunnel scene area or not, if the client side enters the tunnel scene area, a scene mode is issued to the client side through the internet, at the moment, the client side starts to upload GNSS data, gyroscope and accelerometer data and odometer vehicle speed data, and the data uploading function is stopped when the position of the client side is not in the tunnel scene area. The server side respectively operates a server side algorithm library according to 2401 parameter combinations by using the collected original data to obtain inertial navigation calculation positions in tunnels under different parameter combinations, meanwhile, a GNSS + INS post-processing smoothing algorithm is used for processing the original data to obtain reference true values of the tunnels, position errors of inertial navigation in the tunnels under different parameter combinations are calculated by comparing the reference true values, a parameter combination corresponding to the minimum inertial navigation error in the tunnels is selected, the parameters are issued to the client side in real time through a data interaction module, meanwhile, the parameter combination returns to the 2401 parameter combinations to be used as default values to continue iteration, and the optimal parameters are calculated to be more and more reliable as more data are reported.
The cloud integration idea is adopted, the client data are collected on line by using the advantages of the cloud, the inertial navigation parameters are adjusted and optimized by operating the server algorithm, online real-time optimization of the parameters can be realized, and the method does not depend on a high-precision POS reference system.
Example three:
the invention also provides a memory storing a computer program executed by a processor to perform the steps of:
the client side sends a service request to the server side for reporting the position;
the server side judges whether the client side enters a tunnel scene area, if the client side enters the tunnel scene area, the client side is informed to start uploading data to the server side, otherwise, the client side continues to report the position to the server side for service request;
the server side processes the data uploaded by the client side according to the parameter combination to obtain a calculated position of inertial navigation in the tunnel, and meanwhile, the server side processes the uploaded data to obtain a reference true value of the tunnel;
and comparing the calculated position with the reference true value to obtain the position error of the inertial navigation in the tunnel, and selecting the parameter combination corresponding to the minimum position error as the optimal parameter to be issued to the client.
The method can select the off-line inertial navigation parameters for tuning, namely, the optimal inertial navigation parameters can be obtained by acquiring a large amount of test equipment data and reference equipment data and tuning the inertial navigation parameters afterwards, and the positioning accuracy of inertial navigation is improved.
The invention preferably adopts C language to realize server algorithm, uses Java to realize engineering, optimizes inertial navigation parameters through real-time online data collection, obviously improves inertial navigation positioning precision of the real-time optimized parameters in actual test, and achieves the best implementation effect.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the present invention, and those skilled in the art can make variations and modifications of the present invention without departing from the spirit and scope of the present invention by using the methods and technical contents disclosed above.
Claims (8)
1. A method for cloud online inertial navigation parameter optimization is characterized by comprising the following steps:
the client side sends a service request to the server side for reporting the position;
the server side judges whether the client side enters a tunnel scene area, if the client side enters the tunnel scene area, the client side is informed to start uploading data to the server side, otherwise, the client side continues to report the position to the server side for service request;
the server side processes the data uploaded by the client side according to the parameter combination to obtain a calculated position of inertial navigation in the tunnel, and meanwhile, the server side operates a GNSS and an inertial navigation system post-processing smoothing algorithm on the uploaded tunnel scene data to obtain a reference true value;
comparing the inertial navigation calculated position with the reference true value to obtain the root mean square error of the position error of the inertial navigation in the tunnel, and selecting the parameter combination corresponding to the minimum root mean square error as the optimal parameter to be issued to the client;
the rule of the parameter combination is as follows:
GBIi＝2i*GBIdef
ARWi＝2i*ARWdef
ABIi＝2i*ABIdef
VRWi＝2i*VRWdef
wherein GBI represents a gyroscope zero-bias instability parameter, ARW represents an angle random walk parameter, ABI represents an accelerometer zero-bias instability parameter, VRW represents a speed random walk parameter, i ═ 0, ± 1, ± 2 ± 3}, GBI represents a zero-bias instability parameter of the gyroscope, and VRW represents a speed random walk parameterdef、ARWdef、ABIdef、VRWdefIndicating the default values, GBI, of the four parameters, respectivelyi、ARWi、ABIi、VRWiRespectively showing that the four parameters are amplified by different times on the basis of default values to obtain new parameters;
the root mean square error calculation formula is as follows:
wherein k represents the tunnel horizontal position error RMSE, d obtained by combining the kth parametersiThe deviation between the position points calculated by inertial navigation and the reference true value is shown, and n represents the number of the position points calculated by inertial navigation;
the method comprises the steps of obtaining the whole-course horizontal position of a tunnel under different parameter combinations by operating a server algorithm library, comparing the whole-course horizontal position with a reference true value obtained by post-processing smoothing of a server, obtaining the whole-course horizontal position error of inertial navigation in the tunnel, selecting the parameter combination with the minimum tunnel horizontal position error RMSE as an optimal parameter, iteration is carried out by taking the obtained optimal parameter as a next default value, and the online optimization of the inertial navigation parameter is finally completed as tunnel scene data reported by clients are more and more, and the reliability of parameter optimization is more and more high.
2. The method for cloud-based online optimization of inertial navigation parameters according to claim 1, wherein the optimal parameters are returned to the parameter combination as default values of the parameter combination for iterative operations.
3. The method for cloud-based online optimization of inertial navigation parameters according to claim 2, wherein the server collects data uploaded by the client via the internet, and sends the optimal parameters to the client via the internet.
4. The method for cloud-based online optimization of inertial navigation parameters according to claim 2, wherein the client reports the location to the server for a service request every 15 seconds.
5. The method for cloud-based online optimization of inertial navigation parameters according to claim 2, wherein the server determines whether the client enters the tunnel scene area according to matching between the position reported by the client and a preset geo-fence.
6. A device for cloud online optimization of inertial navigation parameters is characterized by comprising a server, a data interaction module and a client, wherein the client reports a position to the server through the data interaction module to make a service request, the server judges whether the client enters a tunnel scene area or not, if the navigation system enters the tunnel scene area, the client is informed to upload data through the data interaction module, the server side processes the data uploaded by the client according to the parameter combination to obtain the calculated position of the inertial navigation system in the tunnel, meanwhile, the server operates the GNSS and the inertial navigation system on the uploaded tunnel scene data to obtain a reference true value, comparing the inertial navigation calculated position with the reference true value to obtain the root mean square error of the position error of the inertial navigation in the tunnel, selecting the parameter combination corresponding to the minimum root mean square error as the optimal parameter, and issuing the optimal parameter to the client through the data interaction module;
the rule of the parameter combination is as follows:
GBIi＝2i*GBIdef
ARWi＝2i*ARWdef
ABIi＝2i*ABIdef
VRWi＝2i*VRWdef
wherein GBI represents a gyroscope zero-bias instability parameter, ARW represents an angle random walk parameter, ABI represents an accelerometer zero-bias instability parameter, VRW represents a speed random walk parameter, i ═ 0, ± 1, ± 2 ± 3}, GBI represents a zero-bias instability parameter of the gyroscope, and VRW represents a speed random walk parameterdef、ARWdef、ABIdef、VRWdefIndicating the default values, GBI, of the four parameters, respectivelyi、ARWi、ABIi、VRWiRespectively showing that the four parameters are amplified by different times on the basis of default values to obtain new parameters;
the root mean square error calculation formula is as follows:
wherein k represents the tunnel horizontal position error RMSE, d obtained by combining the kth parametersiThe deviation between the position points calculated by inertial navigation and the reference true value is shown, and n represents the number of the position points calculated by inertial navigation;
the method comprises the steps of obtaining the whole-course horizontal position of a tunnel under different parameter combinations by operating a server algorithm library, comparing the whole-course horizontal position with a reference true value obtained by post-processing smoothing of a server, obtaining the whole-course horizontal position error of inertial navigation in the tunnel, selecting the parameter combination with the minimum tunnel horizontal position error RMSE as an optimal parameter, iteration is carried out by taking the obtained optimal parameter as a next default value, and the online optimization of the inertial navigation parameter is finally completed as tunnel scene data reported by clients are more and more, and the reliability of parameter optimization is more and more high.
7. The cloud-based online inertial navigation parameter optimizing device of claim 6, wherein the optimal parameters are returned to parameter combinations as default values of the parameter combinations for iterative operations.
8. A memory storing a computer program, the computer program performing the steps of:
the client side sends a service request to the server side for reporting the position;
the server side judges whether the client side enters a tunnel scene area, if the client side enters the tunnel scene area, the client side is informed to start uploading data to the server side, otherwise, the client side continues to report the position to the server side for service request;
the server side processes the data uploaded by the client side according to the parameter combination to obtain a calculated position of inertial navigation in the tunnel, and meanwhile, the server side operates a GNSS and an inertial navigation system post-processing smoothing algorithm on the uploaded tunnel scene data to obtain a reference true value;
comparing the inertial navigation calculated position with the reference true value to obtain the root mean square error of the position error of the inertial navigation in the tunnel, and selecting the parameter combination corresponding to the minimum root mean square error as the optimal parameter to be issued to the client;
the rule of the parameter combination is as follows:
GBIi＝2i*GBIdef
ARWi＝2i*ARWdef
ABIi＝2i*ABIdef
VRWi＝2i*VRWdef
wherein GBI represents a gyroscope zero-bias instability parameter, ARW represents an angle random walk parameter, ABI represents an accelerometer zero-bias instability parameter, VRW represents a speed random walk parameter, i ═ 0, ± 1, ± 2 ± 3}, GBI represents a zero-bias instability parameter of the gyroscope, and VRW represents a speed random walk parameterdef、ARWdef、ABIdef、VRWdefIndicating the default values, GBI, of the four parameters, respectivelyi、ARWi、ABIi、VRWiRespectively showing that the four parameters are amplified by different times on the basis of default values to obtain new parameters;
the root mean square error calculation formula is as follows:
wherein k represents the tunnel level obtained by combining the kth parametersPosition error RMSE, diThe deviation between the position points calculated by inertial navigation and the reference true value is shown, and n represents the number of the position points calculated by inertial navigation;
the method comprises the steps of obtaining the whole-course horizontal position of a tunnel under different parameter combinations by operating a server algorithm library, comparing the whole-course horizontal position with a reference true value obtained by post-processing smoothing of a server, obtaining the whole-course horizontal position error of inertial navigation in the tunnel, selecting the parameter combination with the minimum tunnel horizontal position error RMSE as an optimal parameter, iteration is carried out by taking the obtained optimal parameter as a next default value, and the online optimization of the inertial navigation parameter is finally completed as tunnel scene data reported by clients are more and more, and the reliability of parameter optimization is more and more high.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811079911.2A CN110907978B (en) | 2018-09-14 | 2018-09-14 | Method and device for cloud online optimization of inertial navigation parameters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811079911.2A CN110907978B (en) | 2018-09-14 | 2018-09-14 | Method and device for cloud online optimization of inertial navigation parameters |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110907978A CN110907978A (en) | 2020-03-24 |
| CN110907978B true CN110907978B (en) | 2022-02-08 |
Family
ID=69812448
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811079911.2A Active CN110907978B (en) | 2018-09-14 | 2018-09-14 | Method and device for cloud online optimization of inertial navigation parameters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110907978B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111507255B (en) * | 2020-04-16 | 2023-07-28 | 中铁二院工程集团有限责任公司 | Cloud technology-based surface feature information acquisition method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6408245B1 (en) * | 2000-08-03 | 2002-06-18 | American Gnc Corporation | Filtering mechanization method of integrating global positioning system receiver with inertial measurement unit |
| US8212711B1 (en) * | 2009-03-25 | 2012-07-03 | The United States Of America, As Represented By The Secretary Of The Navy | UAV trajectory determination method and system |
| CN103591965A (en) * | 2013-09-12 | 2014-02-19 | 哈尔滨工程大学 | Online calibrating method of ship-based rotary strapdown inertial navigation system |
| CN104215262A (en) * | 2014-08-29 | 2014-12-17 | 南京航空航天大学 | On-line dynamic inertia sensor error identification method of inertia navigation system |
| CN104457789A (en) * | 2014-11-26 | 2015-03-25 | 深圳市华颖泰科电子技术有限公司 | Inertial-navigation-based parameter correcting method and device |
| CN105737858A (en) * | 2016-05-04 | 2016-07-06 | 北京航空航天大学 | Attitude parameter calibration method and attitude parameter calibration device of airborne inertial navigation system |
| CN106972959A (en) * | 2017-03-15 | 2017-07-21 | 千寻位置网络有限公司 | A kind of online service method and system of sensing algorithm |
-
2018
- 2018-09-14 CN CN201811079911.2A patent/CN110907978B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6408245B1 (en) * | 2000-08-03 | 2002-06-18 | American Gnc Corporation | Filtering mechanization method of integrating global positioning system receiver with inertial measurement unit |
| US8212711B1 (en) * | 2009-03-25 | 2012-07-03 | The United States Of America, As Represented By The Secretary Of The Navy | UAV trajectory determination method and system |
| CN103591965A (en) * | 2013-09-12 | 2014-02-19 | 哈尔滨工程大学 | Online calibrating method of ship-based rotary strapdown inertial navigation system |
| CN104215262A (en) * | 2014-08-29 | 2014-12-17 | 南京航空航天大学 | On-line dynamic inertia sensor error identification method of inertia navigation system |
| CN104457789A (en) * | 2014-11-26 | 2015-03-25 | 深圳市华颖泰科电子技术有限公司 | Inertial-navigation-based parameter correcting method and device |
| CN105737858A (en) * | 2016-05-04 | 2016-07-06 | 北京航空航天大学 | Attitude parameter calibration method and attitude parameter calibration device of airborne inertial navigation system |
| CN106972959A (en) * | 2017-03-15 | 2017-07-21 | 千寻位置网络有限公司 | A kind of online service method and system of sensing algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 312 CNKI，AB=优化+最优+调优 AND AB=惯导+惯性导航 AND FT=零偏 AND FT=随机游走;彭卓 等;《清华大学学报(自然科学版)》;20161015;第1065-1078页 * |
| GPS/MIMU深组合技术研究_苗志勇;苗志勇;《中国博士学位论文全文数据库 信息科技辑》;20180615;第15-42页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110907978A (en) | 2020-03-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108732603B (en) | Method and device for locating a vehicle | |
| KR102338270B1 (en) | Method, apparatus, and computer readable storage medium for updating electronic map | |
| US9551561B2 (en) | Determining location using magnetic fields from AC power lines | |
| US11042648B2 (en) | Quantification of privacy risk in location trajectories | |
| JP6531051B2 (en) | Equipment state detection method and apparatus | |
| US20200193170A1 (en) | Determining Position Data | |
| CN114459471B (en) | Positioning information determining method and device, electronic equipment and storage medium | |
| CN114111775A (en) | Multi-sensor fusion positioning method and device, storage medium and electronic equipment | |
| US20230417931A1 (en) | Method for real-time position estimate correction of a movable object | |
| CN110907978B (en) | Method and device for cloud online optimization of inertial navigation parameters | |
| CN115164936A (en) | Global pose correction method and device for point cloud splicing in high-precision map manufacturing | |
| CN104807462A (en) | Method and system for generating indoor geomagnetic navigation reference map | |
| KR102029450B1 (en) | Method and system for providing user location information using gridding map | |
| JP2019174191A (en) | Data structure, information transmitting device, control method, program, and storage medium | |
| CN113503883B (en) | Method for collecting data for constructing map, storage medium and electronic equipment | |
| EP4330632A1 (en) | Methods and systems for determining estimated travel times through a navigable network | |
| CN114689074A (en) | Information processing method and navigation method | |
| CN114528365A (en) | Method and device for identifying parking area on highway, electronic equipment and medium | |
| US20220122316A1 (en) | Point cloud creation | |
| CN113917506A (en) | Ambiguity fixing method and device, electronic equipment and automatic driving equipment | |
| CN113484843A (en) | Method and device for determining external parameters between laser radar and integrated navigation | |
| KR20220132006A (en) | navigation system | |
| WO2019188874A1 (en) | Data structure, information processing device, and map data generation device | |
| CN112241016A (en) | Method and device for determining geographic coordinates of parking map | |
| US20230021637A1 (en) | Methods of Generating and Transmitting Positional Data |
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 | ||
| CB02 | Change of applicant information |
Address after: 200438 9 / F, 10 / F, 11 / F, 12 / F, 38 Lane 1688, Guoquan North Road, Yangpu District, Shanghai Applicant after: QIANXUN SPATIAL INTELLIGENCE Inc. Address before: Room j165, 1st floor, building 64, 1436 Jungong Road, Yangpu District, Shanghai, 200433 Applicant before: QIANXUN SPATIAL INTELLIGENCE Inc. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |