CN100561130C - A kind of bearing calibration of navigation positioning data - Google Patents
A kind of bearing calibration of navigation positioning data Download PDFInfo
- Publication number
- CN100561130C CN100561130C CNB2006100365631A CN200610036563A CN100561130C CN 100561130 C CN100561130 C CN 100561130C CN B2006100365631 A CNB2006100365631 A CN B2006100365631A CN 200610036563 A CN200610036563 A CN 200610036563A CN 100561130 C CN100561130 C CN 100561130C
- Authority
- CN
- China
- Prior art keywords
- data
- fix
- data points
- navigation positioning
- correcting value
- 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
Abstract
The present invention relates to navigational system, relate in particular to a kind of bearing calibration of navigation positioning data, purpose is to overcome deficiency of the prior art, and providing a kind of can accurately proofread and correct navigation positioning data, particularly is applicable to the calibration steps of the navigation positioning data of farmland operation.Basic inventive concept is to provide relatively accurate electronic chart by map registration for system, with the fix data points and the predetermined known course line contrast that will follow the tracks of of each sensor, makes comprehensive judgement then again, calculates the higher calibration data point of confidence level.The bearing calibration of navigation positioning data provided by the invention is made up of map registration, sensing data pre-service, correcting value decision-making, new four processes of locator data dot generation.
Description
Technical field
The present invention relates to navigational system, relate in particular to a kind of bearing calibration of navigation positioning data, its scope of application is encompassed in the occasion of the autonomous walking of guiding mechanically tracking prebriefed pattern realization under the known environment.
Background technology
In the precision agriculture operation, realize location prescription farming and agricultural feelings automatic information collecting, need a kind of intelligent operation platform that can independently move, this platform is not only wanted the environmental information of energy Real time identification periphery, automatically carry out operations such as field information acquisition, variable input, and want and can walk in the field according to the good path of planning in advance, accurately arrive the destination and finish set job task.Realize the autonomous walking of agricultural intelligent mobile platform, precision navigation is one of crucial technology.The precision of navigator fix directly has influence on the agricultural intelligent mobile platform and carries out the quality of course line from motion tracking.Therefore, improving the precision of navigator fix, is the matter of utmost importance of improving agricultural intelligent mobile platform course line tracking quality.
Many achievements in research have been obtained at differential Global Positioning System (DGPS-differential global positioning system), dead reckoning (DR-dead reckoning), machine vision (machine vision), map match modern navigator fix technical elements such as (MM-map matching) both at home and abroad.Yet single a kind of navigation locating method all exists the shortcoming that self is difficult to overcome, as: DGPS may produce bigger error even loss signal because the mobile terminal receives factors such as error, the atural object of machine inside block; The dead reckoning system As time goes on, its measuring error and the error of calculation can be accumulated; Vision Builder for Automated Inspection is subject to ambient light according to condition effect, and is not suitable for carrying out the planning in macroscopical path in the land for growing field crops; Or the like.Therefore, carry out navigator fix research both at home and abroad, great majority adopt the multisensor combined type navigation locating method based on above-mentioned several different methods.At present, research at multisensor combined type navigation locating method has formed a new research field, it is the fusion of multisensor locator data, known method is broadly divided into two big classes: the one, utilize probabilistic statistical method, comprise estimation theory, Kalman filtering, the Bayes method, statistical decision and other improved methods, indispensable instrument during although the multisensor locator data that probabilistic method become merges, but because the defective of theory of probability itself, often need to do some clear and definite hypothesis when using these class methods, and parameter is undistorted before guaranteeing to test, system modelling is accurate, therefore, many probabilistic methods all are based on a definite probability distribution, to dynamically, uncertain complex work adaptive capacity to environment a little less than.The 2nd, utilize the method for artificial intelligence, comprise D-S evidential reasoning, fuzzy logic, production rule, artificial neural network etc., the method of artificial intelligence has overcome the problem that probabilistic method faced to a certain extent, it is to the expression of information and the more approaching mankind's of processing the mode of thinking, have good adaptive and robustness, but maturation and systematization not enough at present, and there is bigger subjective factor in the description of information.In a word, in known method, all there is its advantage and limitation separately in every kind of method, and the multisensor that does not also have a kind of method in common can be used for handling under all situations at present makes up orientation problem.
Summary of the invention
The objective of the invention is to overcome deficiency of the prior art, providing a kind of can accurately proofread and correct navigation positioning data, particularly is applicable to the calibration steps of the navigation positioning data of farmland operation.
Basic inventive concept of the present invention is to provide relatively accurate electronic chart by map registration for system, fix data points with each sensor contrasts with the predetermined known course line that will follow the tracks of again, make comprehensive judgement then, calculate the higher calibration data point of confidence level.The bearing calibration of navigation positioning data provided by the invention is made up of map registration, sensing data pre-service, correcting value decision-making, new four processes of locator data dot generation, and concrete steps are as follows:
1. the map registration process comprises: map is carried out the correction of coordinate and projection, so that the article coordinate on the map is accurate, make the corresponding relation of coordinate on locator data that sensor obtains and the map accurate;
2. the sensing data preprocessing process comprises: the data that sensor obtains are resolved accordingly, and the filtering gross error, obtain being convenient to the same order of subsequent step processing, the data of same projection coordinate system;
3. the correcting value decision process comprises: known course line on pretreated each sensor fix data points and the map is compared, obtain a series of correlation datas, calculate according to the decision model of setting, judge the confidence level of each fix data points, the corresponding correcting value of making a strategic decision out;
4. new fix data points generative process comprises: confidence level and correcting value according to each fix data points generate calibration data point, the estimated value of calibration data point as current actual position.
Because above-mentioned navigation positioning data bearing calibration adopts course line known in the electronic chart as benchmark, the course line generates by path planning algorithm from electronic chart, therefore, the course line has the precision the same with electronic chart, for the treatment for correcting of navigation positioning data provides accuracy guarantee.At the sensing data pretreatment stage, gross error has been carried out the filtering processing, in time detect misdata, improved the stability of system.In the correcting value decision process, the confidence level of each fix data points by with map on the contrast of known course line draw, therefore, the judged result that system draws is reliable, the new fix data points of Sheng Chenging also is reliable in view of the above.In addition,, therefore, greatly reduce the calculated amount of system, improved the processing capability in real time of system, make system have more practicality because the inventive method has been avoided historical data is carried out operations such as data estimation, reasoning on the statistical significance.By enforcement of the present invention, the effectively saltus step of filtering sensor locator data makes the precision of navigation positioning data can obtain to a certain degree raising, carries out the course line and has good effect from the quality of motion tracking improving machinery.
In order to guarantee that obtaining of data has more reliability, in the map registration process of the present invention, locator data preferably obtains by two or more sensors of various types.Obtain a plurality of data simultaneously, and to a plurality of data are selected or weighted calculation can avoid the error of contingency to cause saltus step.And therefore sensors of various types can avoid same at one time reason owing to be the data that obtain by different approach, stops etc. that as illumination, buildings the sensor errors of the same type that causes further improves measuring reliability.Existing ripe accurate localization data equipment mainly comprises two kinds of range finding positioning equipment and dead reckoning positioning equipments, and sensor of the present invention is preferably in these two kinds of equipment and selects, and guarantees measuring accuracy.The range finding positioning equipment comprises the GPS receiver module, the machine vision location, in ultrasound wave, the laser ranging positioning equipment etc. one or more, the dead reckoning positioning equipment comprises one or more in gyroscope, electronic compass, velograph, the odometer etc.
The present invention in described correcting value decision process, after the pre-service on sensor fix data points and the map known course line compare resulting correlation data, comprise driftage apart from or data point with respect to the orientation in known course line.These two kinds of data can be relatively easy to obtain, and computation model is simple, helps improving processing speed.And these two kinds of data can have very high accuracy by the data that weighted calculation draws after the correction.And adopt the existing ripe decision model that has, comprise based on the linear model algorithm, based on fuzzy logic algorithm, based on genetic algorithm, based on one or more the combination in the neural network algorithm.The input of decision model is: known course line compares resulting correlation data on pretreated each sensor fix data points and the map; The output of decision model is the confidence level or the corresponding correcting value of each fix data points.In the fix data points generative process, as weights, adopt the method for asking each fix data points coordinate figure weighted sum to calculate the coordinate of the fix data points that makes new advances the confidence level of each fix data points or corresponding correcting value.
In described map registration process, locator data is obtained by differential Global Positioning System and dead reckoning system, in the correcting value decision process by judging the confidence level of each fix data points based on fuzzy logic algorithm, the corresponding correcting value of making a strategic decision out.An advantage of the inventive method is can be applied in cheaply in the system, differential Global Positioning System is made of two GPS receiver as described, one one fixing as base station, another one is installed on the mobile platform as movement station, carries out communication by wireless network between two GPS receiver.The system that adopts two GPS receivers to constitute cooperates the method for this patent, can proofread and correct accurately navigation positioning data.The data precision that obtains can with the precision that positions by four above gps satellites under the stationary state, and native system can dynamically adjust, in time rectification error keeps the farming machine to move on default route, adopts the inventive method to greatly reduce cost.
The present invention the control to agricultural machinery, can also be applicable to other field in being applied to general farmland operation, as to control of the movement locus of robot or the like.
The present invention has following outstanding substantive distinguishing features and obvious improvement with respect to prior art.
1. the correction that the inventive method can be correct guarantees that by the navigation positioning data of sensor acquisition agricultural machinery can operate on default course line;
2. the inventive method can be proofreaied and correct the error of navigation positioning data in real time, and exports relatively accurate locator data, can be applicable to the machine control in the motion;
3. adopt the combined type localization method of polytype sensor can further reduce error, and can access the same order of being convenient to subsequent step and handling, the data of same projection coordinate system at the sensing data pretreatment stage;
4. can be suitable for multiple algorithm, help the design of control program.
Description of drawings
Fig. 1 is for carrying out the principle schematic of the embodiment of timing to two fix data points inputs;
Fig. 2 carries out the theory diagram of correcting value decision-making for employing fuzzy logic embodiment illustrated in fig. 1;
Fig. 3 is the subordinate function of fuzzy variable in the fuzzy logic decision shown in Figure 2 system;
Fig. 4 is for realizing the program flow diagram of locator data ambiguity correction algorithm shown in Figure 2 with look-up table;
Fig. 5 positions after the data point treatment for correcting part comparison diagram of flight path behind the DGPS flight path and ambiguity correction for embodiment illustrated in fig. 1;
Fig. 6 is a reasoning language rule table in the fuzzy logic decision shown in Figure 2 system;
Fig. 7 is a DGPS confidence level fuzzy decision Response Table in the fuzzy logic decision shown in Figure 2 system.
Embodiment
Fig. 1 is for carrying out the principle schematic of the embodiment of timing to two fix data points inputs that obtained by DGPS, DR.In this embodiment, the major parameter of the used projected coordinate system of electronic chart registration is:
(1) projection pattern: Gauss-Kruger
(2) central meridian: 114.000000 (3 degree bands)
(3) horizontal offset: 500km
(4) geographic coordinate system: GCS_WGS_1984
(5) the earth reference system: D_WGS_1984
(6) reference ellipsoid: WGS_1984
(7) ellipsoid major axis: 6378137m
(8) flattening of ellipsoid: 0.0033528107.
Fig. 1 a is the process sketch that carries out treatment for correcting embodiment illustrated in fig. 1.In perfect condition, DGPS and DR fix data points P
DGPS, P
DRCoincide together, but because the existence of error, these two fix data points can not coincide together usually.Process according to the new fix data points of these two locator data dot generation is as follows: when sensor measurement provides after vehicle condition data such as the DGPS of front vehicle body data and the speed of a motor vehicle, course, at first carry out dead reckoning according to the speed of a motor vehicle and course, obtain current DR fix data points, then known course line on DGPS, DR fix data points and the map is compared, judge this two fix data points P
DGPS, P
DRConfidence level, generate new fix data points P with this confidence level as weighted value
ADJ, promptly calibration data point is drawn by formula 2.6 cited below, check point P
ADJAs estimated value when the front vehicle body actual position.This check point is output as a result of on the one hand, on the other hand as the starting point of next cycle dead reckoning.
Fuzzy logic has been proved to be to handle the effective way of qualitative term (as possibility or impossible), adopts Mamdani pattern fuzzy logic system to carry out the correcting value decision-making in the present embodiment.The foundation of judging is: the fix data points P of DGPS
DGPSThe fix data points P that obtains with dead reckoning
DRThe coexist side in course line, and both driftages are apart from being more or less the same, current DGPS locator data " probably " is correct, confidence level is than higher, shown in Fig. 1 b; The fix data points P of DGPS
DGPSThe fix data points P that obtains with dead reckoning
DRNot in the same side in course line, and both driftages are apart from differing greatly, and current DGPS locator data " impossible " is correct, and confidence level is very low, shown in Fig. 1 c.Fig. 2 carries out the theory diagram of correcting value decision-making for employing fuzzy logic embodiment illustrated in fig. 1.
The fringe and the domain thereof of the input and output linguistic variable of correcting value decision system shown in Figure 2 are defined as follows:
Input variable is that the t driftage of DGPS constantly is apart from E
DGPS(t) and the driftage of DR apart from E
DR(t).The output language variable is the t confidence level w (t) of DGPS fix data points constantly.
Driftage is apart from being meant the deviation in fix data points and course line, and promptly with minimum value and value between the discrete point on the course line, and agreement: the working direction along the course line is observed fix data points, as if putting the left side that is positioned at the course line, then is designated as the negative bias distance of navigating; If put the right that is positioned at the course line, then be designated as the distance of just going off course.Driftage apart from " just especially big, honest, the center, just little, zero, negative little, negative in, negative big, negative especially big " nine fringes " shelves " describe, the fuzzy subset of correspondence is labeled as " PXB, PB, PM, PS, Z, NS, NM, NB, NXB ".The setting of the domain of driftage distance is by (or row row spacing degree) the B decision of having a lot of social connections, its meaning is: according to general general knowledge, when car body on the road or along row ridge when walking, if fix data points has been run out of the restricted portion of having a lot of social connections, then this fix data points is insecure fully.In order to realize standardized designs, the variation range of going off course apart from E is set at [1,1] interval continually varying amount, the basic domain of promptly choosing the driftage distance is [1,1].Driftage apart from the transform method to basic domain is:
If y is ∈ [1,1],
Then
Confidence level w (t) describes with " very low, lower, low, in, height, higher, very high " seven fringes " shelves ", and corresponding fuzzy subset is labeled as " LT, LR, L, M, H, HR, HT ", and choosing its domain is [0,1].
Fig. 3 is the subordinate function of fuzzy variable in the fuzzy logic decision shown in Figure 2 system, adopts Gauss's subordinate function in the present embodiment, and its expression formula is as follows:
X is used to specify the domain scope of variable in the formula, the shape of σ, c specified function curve, the central point of c determining function curve wherein, the width of σ determining function curve.
Fig. 6 is a reasoning language rule table in the fuzzy logic decision shown in Figure 2 system.Rule in the table is formulated according to such general knowledge: DR piloting confidence level at short notice is higher, if the driftage of DGPS location is apart from the side in the course line, and the driftage of DR location is apart from the opposite side in the course line, then the DGPS location is with a low credibility, both differ far away more, and confidence level is low more; Then with a high credibility conversely.The Mamdani pattern is stuck with paste reasoning algorithm and is adopted minimum operation rule to come ambiguity in definition to contain the fuzzy relation of expression, and for following rule, R:if x is that A then y is B, and fuzzy relation Rc is defined as:
When x was A ', the computing method of fuzzy reasoning conclusion were as follows:
The output result of fuzzy reasoning must pass through de-fuzzy, and the fuzzy decision value is converted to definite confidence value, just can be used to instruct the correction of locator data.Present embodiment adopts gravity model appoach to carry out the de-fuzzy operation, and computing method are:
μ in the formula
0Be the fuzzy set of a certain variable u on domain U.
Through after the aforementioned fuzzy decision, obtained the t confidence value w (t) of DGPS constantly, following step is exactly original DGPS and DR locator data to be proofreaied and correct the fix data points that must make new advances.If P
DGPSBe the data point of DGPS location, P
DRBe the data point of DR location, P
ADJBe the data point after proofreading and correct, the generation method of calibration data point is as follows:
The implementation method of this embodiment related algorithm is as follows:
Because Navigation Control is a real-time control system, therefore can adopt look-up table to realize locator data ambiguity correction algorithm.At first, adopt the method for off-line operation, generate a fuzzy decision Response Table, value according to input directly draws the output valve of fuzzy decision with look-up method in system's operational process then, though this method can be lost some information in discrete quantized, but a kind of advantages of simplicity and high efficiency implementation method, in case set up the fuzzy decision respective table, fuzzy decision algorithm just becomes simple look-up table, and fast operation can satisfy the navigation requirement of control in real time preferably.The fuzzy decision Response Table calculates by the fuzzy control tool box of matlab.Fig. 7 is a DGPS confidence level fuzzy decision Response Table in the fuzzy logic decision shown in Figure 2 system.Fig. 4 is for realizing the program flow diagram of locator data ambiguity correction algorithm shown in Figure 2 with look-up table.
In order to verify the effect of this embodiment, designed following experimental program:
The major equipment that confirmatory experiment adopts is as follows: constitute the DGPS positioning system with two Trimble AG132 GPS receivers, one is placed on engineering college of Agricultural University Of South China soil box laboratory roof as base station, known coordinate is: 113 ° 20.538541 of east longitude, 23 ° 09.581834 of north latitude, another is placed on agricultural intelligent mobile platform (being converted by the Japanese Kubo field SPU-68 type rice transplanter) centroid position as mobile workstation, adopts the communication of GPRS mode between base station and the movement station.The HMR3000 type electronic compass that adopts Honeywell company is measured in the course.Vehicle speed measurement adopts the photo-electric vehicle speed sensor.
For DGPS data relatively with proofread and correct the precision of back data, arranged following experiment: on Agricultural University Of South China forestry institute doorway flat road, chosen the baseline of one section road axis as experimental measurement.Make car body respectively with a plurality of different speed of a motor vehicle along base linc motion, carry out mobile kinetic measurement, during each test is carried out, the centering mark post of car body is aimed at the white linear mark in ground.Frequency with 1Hz receives locator data from the DGPS mobile station receiver, start the locator data ambiguity correction algorithm corresponding program that designs among this embodiment simultaneously, and writes down the locator data after its correction.Carried out confirmatory experiment altogether 9 times.In this tested several times, locator data its precision behind ambiguity correction obviously was better than the precision of original DGPS data, and Fig. 5 is the part comparison diagram of flight path behind DGPS flight path and the ambiguity correction.Distance root mean square poor (the DRMS-distance root mean square error) mean of locator data is from proofreading and correct the 0.568m after preceding 1.021m brings up to correction in 9 tests.Experimental result shows that this method can improve the precision of locator data to a certain extent, proofreaies and correct most of bad point with a low credibility.
Claims (9)
1. the bearing calibration of a navigation positioning data comprises map registration, sensing data pre-service, correcting value decision-making, new four processes of locator data dot generation, it is characterized in that may further comprise the steps:
1. the map registration process comprises: map is carried out the correction of coordinate and projection, so that the article coordinate on the map is accurate, make the corresponding relation of coordinate on locator data that sensor obtains and the map accurate;
2. the sensing data preprocessing process comprises: the data that sensor obtains are resolved accordingly, and the filtering gross error, obtain being convenient to the same order of subsequent step processing, the data of same projection coordinate system;
3. the correcting value decision process comprises: known course line on pretreated each sensor fix data points and the map is compared, obtain a series of correlation datas, calculate according to the decision model of setting, judge the confidence level of each fix data points, the corresponding correcting value of making a strategic decision out;
4. new fix data points generative process comprises: confidence level and correcting value according to each fix data points generate calibration data point, the estimated value of calibration data point as current actual position;
In above-mentioned map registration process, locator data is obtained by two or more sensors of various types.
2. the bearing calibration of navigation positioning data according to claim 1 is characterized in that described locator data is obtained respectively by range finding positioning equipment and dead reckoning positioning equipment.
3. the bearing calibration of navigation positioning data according to claim 2, it is characterized in that described range finding positioning equipment comprises the GPS receiver module, the machine vision location, in ultrasound wave, the laser ranging positioning equipment one or more, described dead reckoning positioning equipment comprises one or more in gyroscope, electronic compass, velograph, the odometer.
4. the bearing calibration of navigation positioning data according to claim 1, it is characterized in that in the described correcting value decision process, after the pre-service on sensor fix data points and the map known course line compare resulting correlation data, comprise driftage apart from or data point with respect to the orientation in known course line.
5. according to the bearing calibration of claim 1 or 2 or 3 or 4 described navigation positioning datas, it is characterized in that described correcting value decision process, described decision model comprises based on the linear model algorithm, based on fuzzy logic algorithm, based on genetic algorithm, based on one or more the combination in the neural network algorithm.
6. the bearing calibration of navigation positioning data according to claim 5 is characterized in that the input of described decision model is: known course line compares resulting correlation data on pretreated each sensor fix data points and the map; The output of decision model is the confidence level or the corresponding correcting value of each fix data points.
7. the bearing calibration of navigation positioning data according to claim 6, it is characterized in that in described new fix data points generative process, as weights, adopt the method for asking each fix data points coordinate figure weighted sum to calculate the coordinate of the fix data points that makes new advances the confidence level of each fix data points or corresponding correcting value.
8. the bearing calibration of navigation positioning data according to claim 5, it is characterized in that in the map registration process, locator data is obtained by differential Global Positioning System and dead reckoning system, in the correcting value decision process by judging the confidence level of each fix data points based on fuzzy logic algorithm, the corresponding correcting value of making a strategic decision out.
9. the bearing calibration of navigation positioning data according to claim 8, it is characterized in that described differential Global Positioning System is made of two GPS receiver, one one fixing as base station, another one is installed on the mobile platform as movement station, carries out communication by wireless network between two GPS receiver.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2006100365631A CN100561130C (en) | 2006-07-18 | 2006-07-18 | A kind of bearing calibration of navigation positioning data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2006100365631A CN100561130C (en) | 2006-07-18 | 2006-07-18 | A kind of bearing calibration of navigation positioning data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1888824A CN1888824A (en) | 2007-01-03 |
| CN100561130C true CN100561130C (en) | 2009-11-18 |
Family
ID=37578113
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2006100365631A Active CN100561130C (en) | 2006-07-18 | 2006-07-18 | A kind of bearing calibration of navigation positioning data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100561130C (en) |
Families Citing this family (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101629828B (en) * | 2008-07-14 | 2014-06-18 | 晨星软件研发(深圳)有限公司 | Navigation device and positioning method thereof |
| CN102620732B (en) * | 2011-01-27 | 2014-03-12 | 迈实电子(上海)有限公司 | Object positioning method and device |
| US9116005B2 (en) | 2009-06-30 | 2015-08-25 | Maishi Electronic (Shanghai) Ltd | Electronic systems for locating objects |
| CN102192727A (en) * | 2010-03-19 | 2011-09-21 | 阿尔卡特朗讯 | Mobile handheld device and method |
| CN102121827B (en) * | 2010-11-29 | 2013-12-18 | 浙江亚特电器有限公司 | Positioning system of mobile robot and positioning method thereof |
| US9250083B2 (en) * | 2013-03-22 | 2016-02-02 | Qualcomm Incorporated | Heading, velocity, and position estimation with vehicle sensors, mobile device, and GNSS inputs |
| US9430822B2 (en) * | 2013-06-14 | 2016-08-30 | Microsoft Technology Licensing, Llc | Mobile imaging platform calibration |
| US20150316577A1 (en) * | 2014-05-02 | 2015-11-05 | Qualcomm Incorporated | Motion direction determination and application |
| CN105045280B (en) * | 2015-08-03 | 2019-01-01 | 中国农业大学 | A kind of self-propelled based on satellite system sprays machine navigation system and method |
| CN105516251B (en) * | 2015-11-26 | 2020-04-10 | 阿坝师范学院 | Positioning calibration method and position service pushing system thereof |
| CN105682222B (en) * | 2016-03-01 | 2019-02-19 | 西安电子科技大学 | A kind of vehicle location positioning information fusion method based on vehicle self-organizing network |
| CN107942364A (en) * | 2016-10-13 | 2018-04-20 | 阿里巴巴集团控股有限公司 | Vehicle positioning method and vehicle positioning system |
| CN107063253A (en) * | 2016-12-22 | 2017-08-18 | 湖南天特智能科技有限公司 | Pass through map scene assisted location method |
| CN107371133B (en) * | 2017-06-14 | 2019-12-27 | 崔兆琦 | Method for improving positioning accuracy of base station |
| CN108007455B (en) * | 2017-11-01 | 2021-02-23 | 千寻位置网络有限公司 | Deviation rectifying method and device of inertial navigation system, navigation and service terminal and memory |
| CN107907895A (en) * | 2017-11-28 | 2018-04-13 | 千寻位置网络有限公司 | High in the clouds position error modification method and system based on convolutional neural networks |
| CN108037520B (en) * | 2017-12-27 | 2021-01-22 | 中国人民解放军战略支援部队信息工程大学 | Neural network-based direct positioning deviation correction method under array amplitude-phase error condition |
| CN108508894A (en) * | 2018-04-03 | 2018-09-07 | 中科微至智能制造科技江苏有限公司 | A kind of robot localization method based on two-dimensional laser |
| CN109444171A (en) * | 2018-09-18 | 2019-03-08 | 山东理工大学 | Integrated Bridges Detection based on unmanned plane |
| CN109737963A (en) * | 2018-11-30 | 2019-05-10 | 苏州极目机器人科技有限公司 | A kind of Map calibration method, mapping mobile terminal and mapping system |
| CN109819757B (en) * | 2019-03-28 | 2020-08-04 | 华中农业大学 | Harvesting boundary positioning and tracking system and method of combined harvester |
| CN110782033A (en) * | 2019-10-28 | 2020-02-11 | 玲睿(上海)医疗科技有限公司 | AGV positioning method based on fuzzy neural network |
| CN110837542A (en) * | 2019-11-06 | 2020-02-25 | 武汉市园林建筑工程公司 | Nursery stock positioning correction method and system |
| 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 |
| CN113206502B (en) * | 2021-06-08 | 2021-10-29 | 中铁电气化铁路运营管理有限公司 | Power supply quality improving device |
-
2006
- 2006-07-18 CN CNB2006100365631A patent/CN100561130C/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN1888824A (en) | 2007-01-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN100561130C (en) | A kind of bearing calibration of navigation positioning data | |
| CN101285686B (en) | Agricultural machines navigation hierarchical positioning process and system | |
| CN109934920B (en) | High-precision three-dimensional point cloud map constructing method based on low-cost equipment | |
| Syed et al. | Fuzzy logic based-map matching algorithm for vehicle navigation system in urban canyons | |
| CN101082493B (en) | Combined positioning method of agricultural machines navigation | |
| CN105628026A (en) | Positioning and posture determining method and system of mobile object | |
| CN110187372B (en) | Combined navigation method and system in low-speed unmanned vehicle park | |
| CN106469505A (en) | A kind of floating wheel paths method for correcting error and device | |
| Pfaff et al. | Towards mapping of cities | |
| CN109807911A (en) | Based on GNSS, UWB, IMU, laser radar, code-disc the multi-environment joint positioning method of outdoor patrol robot | |
| CN104502942A (en) | System and method for positioning agricultural machinery based on satellite navigation and dead reckoning | |
| CN107300696A (en) | A kind of position of mobile robot bearing calibration and device based on RFID | |
| CN101408434A (en) | Zero drift of mobile terminal and double-mould position and single-mould positioning method | |
| Rounsaville et al. | Methods for calculating relative cross-track error for ASABE/ISO Standard 12188-2 from discrete measurements | |
| Liu et al. | Pseudolite constellation optimization for seamless train positioning in GNSS-challenged railway stations | |
| CN110441760A (en) | A kind of large-scale seabed topographic map expansion patterning process based on priori topographic map | |
| US11656364B2 (en) | Real-time correlation of sensed position data with terrestrial features | |
| CN106342230B (en) | A kind of dynamic accurate positioning method of GPS of applicable frigid plateau region | |
| Selloum et al. | Robustification of a map aided location process using road direction | |
| Song et al. | RFID/in-vehicle sensors-integrated vehicle positioning strategy utilising LSSVM and federated UKF in a tunnel | |
| Xi et al. | Map matching algorithm and its application | |
| Busznyák et al. | On-Board Diagnostic-based Positioning as an Additional Information Source of Driver Assistant Systems | |
| Cheng et al. | Graph-based proprioceptive localization using a discrete heading-length feature sequence matching approach | |
| CN104330772B (en) | The bistatic location method of comprehensive trace formula UKF filtering algorithm based on multidirectional optimizing | |
| Yu | Improved positioning of land vehicle in ITS using digital map and other accessory information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |