CN112987061A - Fuzzy fusion positioning method based on GPS and laser radar - Google Patents
Fuzzy fusion positioning method based on GPS and laser radar Download PDFInfo
- Publication number
- CN112987061A CN112987061A CN202110170825.8A CN202110170825A CN112987061A CN 112987061 A CN112987061 A CN 112987061A CN 202110170825 A CN202110170825 A CN 202110170825A CN 112987061 A CN112987061 A CN 112987061A
- Authority
- CN
- China
- Prior art keywords
- gps
- positioning
- data
- laser radar
- positioning data
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 230000004927 fusion Effects 0.000 title claims abstract description 29
- 238000001914 filtration Methods 0.000 claims abstract description 32
- 238000005259 measurement Methods 0.000 claims abstract description 11
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 10
- 230000001133 acceleration Effects 0.000 claims abstract description 7
- 230000000694 effects Effects 0.000 claims abstract description 3
- 230000006870 function Effects 0.000 claims description 24
- 238000012545 processing Methods 0.000 claims description 9
- 238000013507 mapping Methods 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 5
- 230000007547 defect Effects 0.000 claims 1
- 238000010606 normalization Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
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
-
- 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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
-
- 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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
-
- 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
Abstract
The invention relates to a fuzzy fusion positioning method based on a GPS and a laser radar. When the GPS signal is poor or no signal, the laser radar is adopted to carry out feature matching and positioning through a priori map, the position of the unmanned ship is predicted according to the acceleration and the speed measured by the inertial sensing unit, the GPS data and the laser radar positioning data are respectively processed through an extended Carlman filtering method, and then the unmanned ship is finally positioned by fusing through a fuzzy algorithm according to the difference value between the filtered GPS data and the laser radar positioning data and the corresponding measurement data before filtering and the accuracy of the sensor, so that the algorithm is simple, the positioning effect is good, and the practicability is strong.
Description
Technical Field
The invention relates to the field of unmanned driving, in particular to a fuzzy fusion positioning method based on a GPS and a laser radar.
Background
Along with the continuous development of the unmanned technology, the application of unmanned ship is more and more extensive, and the application of unmanned ship is gradually expanded from military field to civil field. No matter be unmanned aerial vehicle or unmanned car, unmanned ship, want to reach better result of use, accurate location is the biggest prerequisite. Traditional GPS positioning can basically meet civil needs in an open environment, but due to the complexity of the actual environment and the need of actual tasks, unmanned ships often inevitably need to pass through places where GPS signals are poor or even unavailable, such as bridge holes, tunnels and the like. Relying on GPS alone can now cause significant positioning errors. Therefore, it is necessary to use a tool that can be localized locally. The traditional GPS integrates INS to carry out positioning navigation, although the GPS can be temporarily replaced to carry out positioning when the GPS signal is poor, the traditional GPS is not suitable for long-time independent positioning due to accumulated error; at present, laser radar is also used as a positioning method without a GPS, but due to the fact that the position of the laser radar on an unmanned ship is unstable, partial data errors are large, most methods do not judge the data errors or simply filter the data errors, and the positioning problem caused by the large errors is difficult to truly eliminate. In addition, the conventional GPS-less positioning navigation may choose to directly discard all GPS data within a certain time period when determining that the GPS signal is not good, without making full use of the GPS data. According to the GPS and laser radar fusion positioning method, all data are utilized to the maximum extent, so that the fused positioning can reach higher precision. Therefore, the existing positioning method needs to be further improved.
Disclosure of Invention
The invention aims to provide a fuzzy fusion positioning method based on a GPS and a laser radar, which aims to solve the problem of accurate positioning of an unmanned ship when a GPS signal is weakened or the GPS signal does not exist, and can still ensure more accurate positioning of the unmanned ship when large errors occur in the GPS positioning or the laser radar positioning.
In order to achieve the purpose, the technical scheme of the invention is as follows: a fuzzy fusion positioning method based on GPS and laser radar comprises the following steps:
the acceleration of the unmanned ship is obtained in real time through the inertial sensing unit so as to calculate and obtain a current prediction position coordinate (namely, the acceleration of the unmanned ship is obtained through the inertial sensing unit and the current prediction position coordinate is estimated by combining the acceleration of the unmanned ship with the position of the unmanned ship at the previous moment);
acquiring the current global position coordinate of the unmanned ship through a GPS (global position System) and filtering GPS positioning data through an extended Kalman filtering method according to the sensor precision of the GPS to obtain the global positioning of the unmanned ship; if the GPS signal is good, the GPS positioning data is used as the unmanned ship positioning result; the extended Kalman filtering method takes the predicted position coordinate as a state predicted value, takes a displacement difference value generated in a period of primary positioning data obtained by a system as process noise, takes GPS data as a measured value and the measurement precision of a GPS sensor as measurement noise, and determines the filtering weight between the state predicted value and the measured value according to the magnitude relation between the process noise and the measurement noise;
if the GPS signal is not good, acquiring surrounding environment point cloud information and distance information through a laser radar, matching the surrounding environment point cloud information and the distance information with a built two-dimensional grid map of the surrounding environment to obtain the current local position coordinate of the unmanned ship, and filtering laser radar positioning data through an extended Kalman filtering method according to the sensor precision of the laser radar to obtain the local positioning of the unmanned ship; the extended kalman filtering method is identical to the method for GPS data filtering except that positioning data obtained by the laser radar is used as a measurement value;
obtaining local positioning data of the laser radar: firstly, according to point cloud information and distance information of a surrounding environment obtained by scanning the surrounding environment by a 360-degree scanning laser radar, establishing a two-dimensional grid map by a cartographer positioning and mapping method; according to the grid map, the laser radar scans the point cloud distribution condition and distance of the surrounding environment to match with the grid map and combines laser odometer information to obtain local accurate positioning of the laser radar in the grid map; obtaining accurate positioning through coordinate conversion according to the global coordinate corresponding to the initial point of the given grid map and the local positioning coordinate in the grid map;
determining a fuzzy subset, a domain of discourse and a membership function of the GPS data for fuzzification through the knowledge of the positioning accuracy of the GPS. Because the data error is larger when the GPS signal is not good, the fuzzy subset used in the GPS data fuzzification is a set { N, Z, P } with the membership of 3, and the domain of discourse value is taken
And R is the positioning accuracy of the GPS sensor. According to the real-time positioning requirement of the unmanned ship, a simpler triangular membership function with smaller calculated amount is adopted, and a specific expression of the triangular membership function is determined according to the fuzzy subset and the discourse domain as follows:
VG is the GPS data innovation value;
if the filtered GPS data innovation value is not in
If not, substituting the innovation value corresponding to the filtered GPS data into the membership function to obtain the membership of the innovation value of the GPS data in each fuzzy value, and performing weighted summation to obtain an unreliable index after the GPS data is fuzzy, wherein the weighted values are respectively assigned as 0.25,0.5 and 0.25;
and determining fuzzy subsets, discourse domains and membership functions used for fuzzification of the laser radar positioning data through the knowledge of the positioning precision of the laser radar. According to the precision of the laser radar sensor, a set { NL, NS, Z, PS, PL } with fuzzy subset membership of 5 is used in fuzzification of the laser radar positioning data, and a domain of discourse value is
And Rp is the precision of the laser radar sensor. And determining a specific expression of the triangular membership function according to the fuzzy subset and the discourse domain as follows:
VP is the laser radar positioning coordinate innovation value, and Rp is the laser radar measurement precision;
if the filtered lidar data innovation value is not in
If so, judging that the laser radar positioning data is unavailable, otherwise substituting the filtered laser radar positioning data into the membership function to obtain the membership of the laser radar positioning data in each fuzzy value, and performing weighted summation to obtain fuzzy laser radar unreliable indexes, wherein the weighted values are respectively assigned to 0.1,0.2,0.4,0.2 and 0.1;
if at least one of the two data is available, performing weight distribution on the available data according to the unreliable index, and simultaneously performing redistribution on the sensor data weight again according to the reliability of the data, namely the innovation value of the filtered data to obtain the final weight, and performing weighted summation on the filtered available sensor data to obtain the final positioning data; and if the two types of positioning data are unavailable, estimating the current position coordinate according to the previous position coordinate and the speed obtained by the inertial sensing unit.
Compared with the prior art, the invention has the following beneficial effects:
1. the method has a multi-level data processing process, and further fuzzy fusion processing is carried out on the GPS data and the laser radar positioning data after the filtering processing is respectively carried out on the GPS data and the laser radar positioning data;
2. the GPS data and the laser radar data are fully utilized for complementary fusion, the problem of positioning failure or large positioning error caused by individual sensor failure or accidental large data deviation is avoided, and the driving safety of the unmanned ship is improved;
3. the simple and efficient fusion strategy enables the requirement of system real-time property to be met while high positioning precision is achieved.
Drawings
FIG. 1 is a flow chart of a GPS and lidar based fuzzy fusion positioning method provided by the invention;
FIG. 2 is a graph of membership functions for fuzzification of the filtered GPS data as provided by the present invention;
FIG. 3 is a membership function graph for fuzzification of the filtered lidar positioning data in accordance with the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The invention provides a fuzzy fusion positioning method based on a GPS and a laser radar, which is characterized by comprising the following steps:
acquiring the acceleration of the unmanned ship in real time through an inertial sensing unit to calculate and obtain a current predicted position coordinate;
acquiring the current global position coordinate of the unmanned ship through a GPS (global position System) and filtering GPS positioning data through an extended Kalman filtering method according to the sensor precision of the GPS to obtain the global positioning of the unmanned ship; if the GPS signal is good, the GPS positioning data is used as the unmanned ship positioning result; if the GPS signal is not good, acquiring surrounding environment point cloud information and distance information through a laser radar, matching the surrounding environment point cloud information and the distance information with a built two-dimensional grid map of the surrounding environment to obtain the current local position coordinate of the unmanned ship, and filtering laser radar positioning data through an extended Kalman filtering method according to the sensor precision of the laser radar to obtain the local positioning of the unmanned ship;
respectively determining membership functions according to the sensor precision of the GPS and the laser radar and the innovation values of the global positioning data and the local positioning data after filtering;
judging the reliability of the global positioning data of the GPS and the local positioning data of the laser radar through a fuzzy algorithm according to the membership function, and judging whether the global positioning data of the GPS and the local positioning data of the laser radar are available or not according to the reliability; if at least one of the global positioning data of the GPS and the local positioning data of the laser radar is available, performing weight distribution on the available data according to the reliability and the respective sensor precision of the GPS and the laser radar, and performing weighted summation on the available data and the corresponding positioning data to obtain a current positioning result; and if the two data are unavailable, taking the predicted position coordinate as the current positioning coordinate.
The following is a specific implementation of the present invention.
Fig. 1 is a flowchart of a fuzzy fusion positioning method based on GPS and lidar according to this embodiment, and as shown in fig. 1, the fuzzy fusion positioning method based on GPS and lidar according to this embodiment includes the following steps:
and S1, obtaining the predicted positioning of the unmanned ship through the inertial sensing unit. And acquiring the advancing acceleration of the unmanned ship through an accelerometer, performing integral calculation, and acquiring the predicted location of the unmanned ship at the current moment by using the position of the unmanned ship at the previous moment. The inertia sensing unit is integrated in the flight control system, and the flight control system is installed in the middle of the interior of the unmanned ship.
And S2, obtaining the global positioning of the unmanned ship through the GPS sensor and carrying out extended Kalman filtering to obtain the filtered global positioning. The GPS is arranged at the front position of the surface of the unmanned ship body.
And when the extended Kalman filtering is carried out, taking the predicted positioning and the global positioning as state predicted values, taking the global positioning as a measured value, taking the position offset estimation in the operating period of the positioning system as process noise, and taking the sensor precision of the GPS as measurement noise.
And judging whether the size of the information value of the filtered global positioning data exceeds a given threshold value, and starting a laser radar positioning module if the size of the information value of the filtered global positioning data exceeds the given threshold value.
And S3, the laser radar positioning module comprises three parts of laser radar mapping, laser radar positioning, coordinate conversion and extended Kalman filtering.
The laser radar mapping method is mainly used for mapping by cartographer positioning, and the boundary of the constructed grid map is clear.
And positioning by the laser radar, wherein the laser radar scans surrounding environment point cloud information and distance information and performs matching according to the grid map to obtain local relative positioning of the unmanned ship in the grid map.
And according to the local relative positioning and the mapping of the origin of the grid map in the global positioning, carrying out coordinate conversion on the local relative positioning to obtain positioning data based on the laser radar.
And the extended Kalman filtering is used for processing according to the positioning data based on the laser radar and the precision of the used laser radar sensor, the positioning data based on the laser radar is used as a measurement value, the precision of the laser radar sensor is used as measurement noise, and the filtered laser radar positioning data is obtained through calculation.
And S4, fusing two groups of positioning data, including fuzzy subsets, domains, determination of membership functions and determination of fusion rules.
And determining a fuzzy subset used in fuzzification of the filtered global positioning data as a set { N, Z, P } with the membership of 3 according to the fusion condition that the error of the filtered global positioning data is large, and dereferencing the domain of interest
And R is the positioning accuracy of the GPS sensor. According to the real-time positioning requirement of the unmanned ship, a simpler triangular membership function with smaller calculated amount is adopted, and the triangular membership function is determined according to the fuzzy subset and the discourse domain as shown in the figure 2, wherein the specific expression is as follows:
VG is the GPS data innovation value, when VG exceeds the given threshold range
If so, the data of the group is not fused.
According to the precision of the laser radar sensor, a set { NL, NS, Z, PS, PL } with fuzzy subset membership of 5 is used in fuzzification of the laser radar positioning data, and a domain of discourse value is
And Rp is the precision of the laser radar sensor. Determining a triangular membership function according to the fuzzy subset and the discourse domain, as shown in fig. 3, the specific expression is as follows:
VP is the laser radar positioning coordinate innovation value, Rp is the laser radar measurement precision, and when VP exceeds a given threshold range
If so, the data of the group is not fused.
When two groups of data meet a given threshold condition, respectively substituting new values corresponding to the two groups of filtered data into corresponding membership functions to calculate membership degrees corresponding to the fuzzy subset members, and carrying out weighted summation with corresponding weight values to obtain credibility of the group of filtered positioning data, wherein the credibility is gR and pR respectively;
normalizing the two groups of data according to the corresponding reliability values gR and pR of the two groups of data and redistributing the normalized reliability values according to the precision of the two sensors, namely
And obtaining the final fusion distribution weight.
The final fusion localization result, i.e., x-x 1-gR + x 2-pR, was calculated from the fusion assignment weights.
If only one group of data meets the given threshold condition, taking the group of filtered positioning data as the final current positioning result; and if the two groups of data do not meet the given threshold condition, taking the predicted positioning value as the final result of the current positioning.
The embodiment combines the GPS and the laser radar for positioning, so that the system positioning is not influenced by the strength of GPS signals and sudden faults of the sensor, and meanwhile, the fusion of the GPS data and the laser radar positioning data is realized by utilizing a simple fuzzy decision method, so that the time cost caused by complex decision calculation is avoided, the requirement of the system real-time performance is met, and a better fusion effect is achieved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (7)
1. A fuzzy fusion positioning method based on GPS and laser radar is characterized by comprising the following steps:
acquiring the acceleration of the unmanned ship in real time through an inertial sensing unit to calculate and obtain a current predicted position coordinate;
acquiring the current global position coordinate of the unmanned ship through a GPS (global position System) and filtering GPS positioning data through an extended Kalman filtering method according to the sensor precision of the GPS to obtain the global positioning of the unmanned ship; if the GPS signal is good, the GPS positioning data is used as the unmanned ship positioning result; if the GPS signal is not good, acquiring surrounding environment point cloud information and distance information through a laser radar, matching the surrounding environment point cloud information and the distance information with a built two-dimensional grid map of the surrounding environment to obtain the current local position coordinate of the unmanned ship, and filtering laser radar positioning data through an extended Kalman filtering method according to the sensor precision of the laser radar to obtain the local positioning of the unmanned ship;
respectively determining membership functions according to the sensor precision of the GPS and the laser radar and the innovation values of the global positioning data and the local positioning data after filtering;
judging the reliability of the global positioning data of the GPS and the local positioning data of the laser radar through a fuzzy algorithm according to the membership function, and judging whether the global positioning data of the GPS and the local positioning data of the laser radar are available or not according to the reliability; if at least one of the global positioning data of the GPS and the local positioning data of the laser radar is available, performing weight distribution on the available data according to the reliability and the respective sensor precision of the GPS and the laser radar, and performing weighted summation on the available data and the corresponding positioning data to obtain a current positioning result; and if the two data are unavailable, taking the predicted position coordinate as the current positioning coordinate.
2. The GPS and lidar based fuzzy fusion positioning method according to claim 1, characterized in that the lidar is installed at a middle position of the top of the unmanned ship, before the unmanned ship is started, a two-dimensional grid map of an environment needing to be tasked is established by remotely controlling the unmanned ship by using a cartographer positioning mapping method, and the lidar scans surrounding environment information and matches with the two-dimensional grid map during autonomous positioning navigation to obtain the current relative positioning of the unmanned ship on the two-dimensional grid map, namely local position coordinates; and filtering the local positioning data of the laser radar through an extended Kalman filtering algorithm according to the difference value between the predicted position coordinate and the local position coordinate of the laser radar and the sensor precision of the laser radar.
3. The fuzzy fusion positioning method based on GPS and lidar as claimed in claim 1, wherein when GPS signal is bad, the fuzzy subset used in the fuzzification of GPS global positioning data is set { N, Z, P } with membership of 3, and domain value is taken
R is the sensor precision of the GPS; according to the sensor precision of the laser radar, a set { NL, NS, Z, PS, PL } with fuzzy subset membership of 5 is used in fuzzification of local positioning data of the laser radar, and a domain value is
Rp is the sensor precision of the laser radar; and adopting a triangular membership function according to the real-time positioning requirement of the unmanned ship.
4. The method according to claim 1, wherein after global positioning data (GPS) or local positioning data of the lidar are determined to be available, new information values corresponding to the corresponding positioning data are substituted into the membership function corresponding to the GPS or local positioning data to obtain membership corresponding to fuzzy integrator, unreliable indexes of the GPS or lidar are obtained in a weighted summation manner, normalization processing is performed on the unreliable indexes, and the normalized unreliable indexes are determined again according to respective sensor accuracies of the GPS and the lidar, so that a final positioning result is obtained by obtaining a fusion weight of the GPS and the lidar and performing weighted summation on the filtered positioning data corresponding to the fusion weight.
5. The method according to claim 4, wherein the innovation value is a difference between the positioning data obtained by the GPS or lidar and the filtered positioning data, that is, the difference between the positioning data obtained by the GPS or lidar and the corresponding filtered positioning data, and a larger difference indicates a larger positioning error of the GPS or lidar and a lower reliability.
6. The GPS-and-lidar-based fuzzy fusion positioning method according to claim 5, wherein the determination of whether the data is available for fusion is based on whether the difference between the GPS or lidar filtered data and the predicted data exceeds a set threshold, the threshold being set to be respectively
And
7. the GPS and lidar based fuzzy fusion positioning method according to claim 1, characterized in that the GPS positioning data or lidar positioning data is processed and fused by an extended Kalman filtering algorithm and a fuzzy algorithm in sequence; the extended Kalman filtering algorithm is used for carrying out preliminary filtering processing on the GPS positioning data or the laser radar positioning data to obtain relatively good positioning data, but cannot completely filter positioning errors caused by measurement values with large errors; the fuzzy algorithm is used for overcoming the defect that the filtering effect of the extended Kalman filtering algorithm is not good enough when a single sensor fails or the data is poor, and the fused data is more accurate than the data using the single sensor through fusion.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110170825.8A CN112987061B (en) | 2021-02-08 | 2021-02-08 | Fuzzy fusion positioning method based on GPS and laser radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110170825.8A CN112987061B (en) | 2021-02-08 | 2021-02-08 | Fuzzy fusion positioning method based on GPS and laser radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112987061A true CN112987061A (en) | 2021-06-18 |
| CN112987061B CN112987061B (en) | 2023-12-29 |
Family
ID=76349219
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110170825.8A Active CN112987061B (en) | 2021-02-08 | 2021-02-08 | Fuzzy fusion positioning method based on GPS and laser radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112987061B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115290098A (en) * | 2022-09-30 | 2022-11-04 | 成都朴为科技有限公司 | Robot positioning method and system based on variable step length |
| CN115727836A (en) * | 2022-11-23 | 2023-03-03 | 锐趣科技(北京)有限公司 | Fusion positioning method and system based on laser reflector and odometer |
| CN116989771A (en) * | 2023-09-18 | 2023-11-03 | 中冶建筑研究总院有限公司 | Ground-air cooperation method, system and device for detecting structural defects |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070222674A1 (en) * | 2006-03-24 | 2007-09-27 | Containertrac, Inc. | Automated asset positioning for location and inventory tracking using multiple positioning techniques |
| CN110018450A (en) * | 2019-04-30 | 2019-07-16 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | AIS is associated with calibration method with radar angular system deviation |
| CN110146910A (en) * | 2019-05-15 | 2019-08-20 | 重庆大学 | A kind of localization method and device merged based on GPS with laser radar data |
| CN111288984A (en) * | 2020-03-04 | 2020-06-16 | 武汉大学 | Multi-vehicle joint absolute positioning method based on Internet of vehicles |
| DE102019000804A1 (en) * | 2019-02-05 | 2020-08-06 | Anavs Gmbh | Method and device for precise position determination and creation of highly current maps with sensor fusion |
| CN111538327A (en) * | 2019-02-06 | 2020-08-14 | 福特全球技术公司 | Vehicle target tracking |
-
2021
- 2021-02-08 CN CN202110170825.8A patent/CN112987061B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070222674A1 (en) * | 2006-03-24 | 2007-09-27 | Containertrac, Inc. | Automated asset positioning for location and inventory tracking using multiple positioning techniques |
| DE102019000804A1 (en) * | 2019-02-05 | 2020-08-06 | Anavs Gmbh | Method and device for precise position determination and creation of highly current maps with sensor fusion |
| CN111538327A (en) * | 2019-02-06 | 2020-08-14 | 福特全球技术公司 | Vehicle target tracking |
| CN110018450A (en) * | 2019-04-30 | 2019-07-16 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | AIS is associated with calibration method with radar angular system deviation |
| CN110146910A (en) * | 2019-05-15 | 2019-08-20 | 重庆大学 | A kind of localization method and device merged based on GPS with laser radar data |
| CN111288984A (en) * | 2020-03-04 | 2020-06-16 | 武汉大学 | Multi-vehicle joint absolute positioning method based on Internet of vehicles |
Non-Patent Citations (3)
| Title |
|---|
| YANG X等: "Research of ship grounding prediction based on fuzzy theory", 《/2011 INTERNATIONAL CONFERENCE OF INFORMATION TECHNOLOGY, COMPUTER ENGINEERING AND MANAGEMENT SCIENCES.》, no. 1, pages 91 - 94, XP032082892, DOI: 10.1109/ICM.2011.103 * |
| 张琳洁: "基于GPS的农机自动导航系统的研究与设计", 《中国优秀硕士学位论文全文数据库 农业科技辑》, no. 08, pages 044 - 22 * |
| 李大伟等: "一种基于卡尔曼滤波与模糊算法的变电站机器人组合导航及控制系统设计", 《智能系统学报》, vol. 8, no. 3, pages 226 - 232 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115290098A (en) * | 2022-09-30 | 2022-11-04 | 成都朴为科技有限公司 | Robot positioning method and system based on variable step length |
| CN115290098B (en) * | 2022-09-30 | 2022-12-23 | 成都朴为科技有限公司 | Robot positioning method and system based on variable step length |
| CN115727836A (en) * | 2022-11-23 | 2023-03-03 | 锐趣科技(北京)有限公司 | Fusion positioning method and system based on laser reflector and odometer |
| CN116989771A (en) * | 2023-09-18 | 2023-11-03 | 中冶建筑研究总院有限公司 | Ground-air cooperation method, system and device for detecting structural defects |
| CN116989771B (en) * | 2023-09-18 | 2024-01-23 | 中冶建筑研究总院有限公司 | Ground-air cooperation method, system and device for detecting structural defects |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112987061B (en) | 2023-12-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112987061A (en) | Fuzzy fusion positioning method based on GPS and laser radar | |
| Wilthil et al. | Radar-based maritime collision avoidance using dynamic window | |
| CN104729506B (en) | A kind of unmanned plane Camera calibration method of visual information auxiliary | |
| Kim et al. | Probabilistic and holistic prediction of vehicle states using sensor fusion for application to integrated vehicle safety systems | |
| CN113029137B (en) | Multi-source information self-adaptive fusion positioning method and system | |
| CN109186610B (en) | Robust BSLAM method for AUV terrain matching navigation | |
| CN112154455B (en) | Data processing method, equipment and movable platform | |
| CN110703762A (en) | Hybrid path planning method for unmanned surface vehicle in complex environment | |
| CN107636751A (en) | Traveling lane discriminating gear and traveling lane method of discrimination | |
| WO2023071442A1 (en) | Data processing method and apparatus | |
| CN111915675B (en) | Particle drift-based particle filtering point cloud positioning method, device and system thereof | |
| KR20190109645A (en) | Apparatus for determining position of vehicle and method thereof | |
| Sabatini et al. | Low-cost navigation and guidance systems for Unmanned Aerial Vehicles. Part 1: Vision-based and integrated sensors | |
| CN108562289A (en) | Quadrotor laser radar air navigation aid in continuous polygon geometry environment | |
| CN113933858A (en) | Abnormal detection method and device of positioning sensor and terminal equipment | |
| Tang et al. | OdoNet: Untethered speed aiding for vehicle navigation without hardware wheeled odometer | |
| US20220063642A1 (en) | Method for Determining an Integrity Range | |
| KR20210073281A (en) | Method and apparatus for estimating motion information | |
| Tsogas et al. | Using digital maps to enhance lane keeping support systems | |
| Li et al. | Estimating localization uncertainty using multi-hypothesis map-matching on high-definition road maps | |
| CN113375679A (en) | Lane level positioning method, device and system and related equipment | |
| CN114415224B (en) | Vehicle fusion positioning system and method in complex limited environment | |
| CN113124881B (en) | Fault recovery method of synchronous positioning and composition system based on magnetic beacon | |
| CN111504332A (en) | Method for determining the position of a vehicle in a digital map | |
| Abdelhafid et al. | Visual and light detection and ranging-based simultaneous localization and mapping for self-driving cars. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |