CN115307628A - Map information simulation method, device and storage medium for integrated navigation positioning platform - Google Patents
Map information simulation method, device and storage medium for integrated navigation positioning platform Download PDFInfo
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- CN115307628A CN115307628A CN202210878391.1A CN202210878391A CN115307628A CN 115307628 A CN115307628 A CN 115307628A CN 202210878391 A CN202210878391 A CN 202210878391A CN 115307628 A CN115307628 A CN 115307628A
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- 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
- G01C21/1652—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 with ranging devices, e.g. LIDAR or RADAR
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
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- 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
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
- G01S19/25—Acquisition or tracking or demodulation of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS
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- 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
- G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
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- 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 application discloses a map information simulation method, a map information simulation device and a storage medium for a combined navigation positioning platform, wherein the method comprises the steps of obtaining drive test data of a test chip and a positioning track of positioning equipment; and calculating the positioning deviation of the drive test data of the test chip and the positioning track of the positioning equipment by taking the positioning track of the positioning equipment as a reference track, and feeding back the positioning deviation as map information. Because the drive test data is processed in the post-processing platform, the test cost of the integrated navigation positioning chip is reduced, and the test period is shortened. Moreover, the positioning track of the high-precision positioning equipment is introduced, so that the positioning precision is improved. In addition, the map information of various scene modes can be simulated, so that the expandability is greatly improved.
Description
Technical Field
The invention relates to the technical field of vehicle-mounted integrated navigation positioning, in particular to a method and a device for simulating map information of an integrated navigation positioning platform and a storage medium.
Background
With the improvement of automobile intellectualization and the requirement of a user for vehicle-mounted positioning, a combined Navigation and positioning module based on a Strapdown Inertial Navigation System (SINS) composed of a Micro-Electro-Mechanical System (MEMS) and an Odometer Dead Reckoning System (ODRS) composed of an automobile Odometer and a Global Navigation Satellite System (GNSS) is widely commercially used.
The automobile mainly depends on SINS and ODRS for combined positioning in the non-signal environment such as the complex tunnel section, but the sensor error of the SINS and the ODRS is large, and the long-time positioning requirement is difficult to meet. With the continuous improvement of the precision of the vehicle-mounted map and the development of vehicle-mounted machine intellectualization, the vehicle-mounted map occupies an important position in automobile navigation. The map manufacturer can perform map matching according to the position reported by the integrated navigation positioning module, and can provide the offset of the vehicle position and the road course as constraints in the situation without GNSS signals, thereby effectively avoiding the torsion of the positioning track during the single inertial navigation work.
The vehicle-mounted map is installed in a vehicle machine (the vehicle machine refers to a vehicle-mounted information entertainment product installed in a vehicle for short, and the vehicle machine can realize information communication between people and the vehicle and between the vehicle and the outside in terms of functions). When carrying out algorithm testing, manufacturers of combined navigation positioning chips often need to rely on a vehicle machine as a test carrier, and transmit a positioning position to a vehicle-mounted map through a vehicle machine system, wherein the vehicle-mounted map relies on a high-precision road track acquired in the early stage, transmits a road offset and road course information with the highest matching rate with a current positioning position according to a preset format, and feeds the road offset and the road course information back to the vehicle-mounted combined navigation positioning chip to carry out corresponding algorithm calculation so as to correct the current offset.
However, the above method has the following problems:
1. the vehicle-mounted test environment in the actual environment is complex, the number of positioning chips in single test depends on the number of vehicle machines, the test cost is high, the period is long, and a driver needs to be equipped for a test vehicle;
2. because the vehicle-mounted map is required to perform real-time position deviation feedback, algorithm iteration cannot be performed on the drive test data in the post-processing platform, and the test repeatability is poor;
3. due to the fact that the map data updating rate of the vehicle-mounted map is low, the reliability of the current algorithm cannot be verified in time aiming at the newly-built tunnel with the complex shape;
4. map matching is only carried out in this kind of single scene in tunnel at present, and later stage along with positioning accuracy's improvement, when extending to carrying out map matching algorithm optimization in other scenes, still need rely on the cooperation of car machine and on-vehicle map once more, and scalability is poor.
Disclosure of Invention
In view of this, the embodiments of the present invention provide the following solutions.
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the invention provides a map information simulation method of a combined navigation positioning platform, which comprises the following steps:
acquiring drive test data of a test chip and a positioning track of positioning equipment;
and calculating the positioning deviation between the drive test data of the test chip and the positioning track of the positioning equipment by taking the positioning track of the positioning equipment as a reference track, and feeding back the positioning deviation as map information.
In an exemplary embodiment, the calculating a positioning deviation between the drive test data of the test chip and the positioning track of the positioning device by using the positioning track of the positioning device as a reference track includes:
determining a combined positioning track according to the drive test data of the test chip;
vertically projecting the track points on the combined positioning track onto the positioning track of the positioning equipment;
and calculating the distance between the track point on the combined positioning track and the projection point of the track point on the positioning track of the positioning equipment, and taking the distance as the positioning deviation.
In an illustrative example, the positioning accuracy of the positioning device is centimeter-level positioning accuracy.
In an exemplary embodiment, the drive test data of the test chip includes at least one of the following data:
inertial navigation data, odometer data, global satellite navigation signals.
In an exemplary embodiment, when the drive test data of the test chip includes a global satellite navigation signal, the SINS/GNSS tight combination navigation mode is entered;
when the drive test data of the test chip does not comprise a global satellite navigation signal, the SINS/ODRS combined navigation mode is entered under the condition of no MAP information, and the SINS/MAP/ODRS combined navigation mode is entered under the condition of MAP information;
and when the drive test data of the test chip does not comprise global satellite navigation signals and milemeter data, the SINS/MAP navigation mode is entered under the condition of MAP information.
In an exemplary embodiment, the entering the SINS/GNSS tight combination navigation mode when the global satellite navigation signal is included in the drive test data of the test chip includes:
selecting a first state vector based on Kalman filtering, subtracting the pseudo-range and the pseudo-range rate calculated by SINS from the pseudo-range and the pseudo-range rate measured by GNSS according to the first state vector to obtain a first difference value, and taking the first difference value as a Kalman filtering observation vector to perform Kalman filtering calculation.
In an exemplary embodiment, when the global satellite navigation signal is not included in the drive test data of the test chip, the SINS/MAP/ODRS combined navigation mode is entered in the presence of MAP information, and the method includes:
selecting a second state vector based on Kalman filtering, subtracting the position calculated by the SINS and the position of the MAP information in a vehicle coordinate system according to the second state vector to obtain a second difference value, and performing Kalman filtering calculation by taking the second difference value as a Kalman filtering observation vector to obtain the position of the SINS fused with the MAP;
and selecting a third state vector based on Kalman filtering, subtracting the position calculated by the ODRS from the position obtained after the SINS and the MAP are fused according to the third state vector to obtain a third difference value, and performing Kalman filtering calculation by taking the third difference value as a Kalman filtering observation vector.
In an exemplary embodiment, when the drive test data of the test chip does not include the global satellite navigation signal and the odometer data, the SINS/MAP navigation mode is entered in the presence of MAP information, and the method includes:
selecting a fourth state vector based on Kalman filtering, subtracting the position calculated by the SINS from the position of the map information in a vehicle coordinate system according to the fourth state vector to obtain a fourth difference value, and performing Kalman filtering calculation by taking the fourth difference value as a Kalman filtering observation vector.
The embodiment of the invention also provides a combined navigation positioning platform map information simulation device, which comprises:
a processor and a computer-readable storage medium having instructions stored therein,
when the instructions are executed by the processor, the combined navigation positioning platform map information simulation method is realized.
Embodiments of the present invention also provide a computer storage medium, having a computer program stored thereon,
when being executed by a processor, the computer program realizes the steps of the map information simulation method of the integrated navigation positioning platform.
The method, the device and the storage medium for simulating the map information of the combined navigation positioning platform acquire the drive test data of the test chip and the positioning track of the positioning equipment; and calculating the positioning deviation between the drive test data of the test chip and the positioning track of the positioning equipment by taking the positioning track of the positioning equipment as a reference track, and feeding back the positioning deviation as map information. Because the drive test data is processed in the post-processing platform, the test cost of the integrated navigation positioning chip is reduced, and the test period is shortened. Moreover, the positioning track of the high-precision positioning equipment is introduced, so that the positioning precision is improved. In addition, the map information of various scene modes can be simulated, so that the expandability is greatly improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a prior art diagram;
FIG. 2 is a schematic diagram of a positioning track of a positioning device and drive test data of a test chip according to an embodiment of the present invention;
FIG. 3 is a flowchart of a combined navigation positioning platform map information simulation method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a track point on a combined positioning track vertically projected onto a positioning track of a positioning device according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating another method for simulating map information of an integrated navigation positioning platform according to an embodiment of the present invention.
Detailed Description
While various embodiments of the invention have been described, the description is intended to be exemplary, rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible that are within the scope of the described embodiments of the invention. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.
The present invention includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements of the present invention that have been disclosed may also be combined with any conventional features or elements to form unique inventive aspects as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the claims. Thus, it should be understood that any of the features shown and/or discussed in this disclosure may be implemented separately or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.
Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Furthermore, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present invention.
The embodiment of the invention provides a combined navigation positioning platform map information simulation method, as shown in fig. 2, comprising the following steps:
and 120, calculating the positioning deviation between the drive test data of the test chip and the positioning track of the positioning equipment by taking the positioning track of the positioning equipment as a reference track, and feeding back the positioning deviation as map information.
In one illustrative example, the map information includes: road type, matching probability, longitude, latitude, altitude offset and road course information.
In an exemplary embodiment, calculating a positioning deviation of the drive test data of the test chip and a positioning track of the positioning device by using the positioning track of the positioning device as a reference track includes:
determining a combined positioning track according to the drive test data of the test chip;
vertically projecting the track points on the combined positioning track onto the positioning track of the positioning equipment;
and calculating the distance between the track point on the combined positioning track and the projection point of the track point on the positioning track of the positioning equipment, and taking the distance as the positioning deviation.
In one illustrative example, as shown in FIG. 3, the combined positioning track is A 1 ～A 3 The positioning track of the positioning equipment is B 1 ～B 3 Combined positioning track A 1 ～A 3 Positioning track B vertically projected to positioning equipment 1 ～B 3 The formed track is B 1 '～B 3 '。
With A 2 B 2 ' As an example, solve for A 2 B 2 The procedure of' is as follows:
(1) solving for the result of
And
determined straight line
Namely, it is
Equivalent to a X + b Y + c =0 (3)
(2) Then
The distance to the straight line is:
(3) then | A 2 B 2 ' I is the transverse road deviation between the two sets of positioning tracks after projection;
(4) road course information selection coordinate point B 2 The course of (2) is used as course information;
(5) the matching probability can be set according to the algorithm requirement.
In one illustrative example, the positioning accuracy of the positioning apparatus is centimeter-level positioning accuracy.
In an exemplary embodiment, the drive test data of the test chip, as shown in fig. 4, includes at least one of the following data:
inertial navigation data, odometry data, global satellite navigation signals.
In an exemplary embodiment, as shown in fig. 5, when the drive test data of the test chip includes a global satellite navigation signal, the SINS/GNSS tight combination navigation mode is entered;
when the drive test data of the test chip does not comprise a global satellite navigation signal, the SINS/ODRS combined navigation mode is entered under the condition of no MAP information, and the SINS/MAP/ODRS combined navigation mode is entered under the condition of MAP information;
and when the drive test data of the test chip does not comprise global satellite navigation signals and odometer data, entering an SINS/MAP navigation mode under the condition of MAP information.
In an illustrative example, drive test data that completes time synchronization and a positioning trace of a positioning device are loaded in a post-processing platform. Firstly, the initialization process of the SINS and the ODRS is completed, including the initialization of the position, the speed and the angle information of the SINS, the estimation of the installation angle of the coordinate system of the SINS and the coordinate system of the automobile, and the initialization of the position and the proportionality coefficient of the ODRS.
In an exemplary embodiment, when the drive test data of the test chip includes a global satellite navigation signal, entering a SINS/GNSS tight integrated navigation mode includes:
selecting a first state vector based on Kalman filtering, subtracting the pseudo range and the pseudo range rate calculated by SINS from the pseudo range and the pseudo range rate measured by GNSS according to the first state vector to obtain a first difference value, and taking the first difference value as a Kalman filtering observation vector to carry out Kalman filtering calculation.
In an exemplary embodiment, kalman filtering is used in the model of the integrated navigation algorithm, and the first state vector selects the SINS position error, velocity error, attitude error, accelerometer zero offset error, gyroscope zero offset error, GNSS clock error, clock drift error:
x is a first state vector of Kalman filtering, wherein delta E, delta N and delta U are east, north and sky position errors, delta V, solved by SINS E ,δV N ,δV U East, north and sky speed errors, calculated for SINS,
Is the attitude error of SINS,
As accelerometer error, ε, for X, Y and Z axes in SINS bx ,ε by ,ε bz As gyroscope errors in X, Y and Z axes in SINS, b clkGNSS Clock error, d, for GNSS system clk Is the clock drift error.
Pseudo range rho calculated by SINS in SINS/GNSS tight combination navigation mode INS And pseudorange rate
Pseudorange rho obtained by measurement with GNSS GNSS And pseudorange rate
The difference is taken as a first difference value Z:
and performing Kalman filtering calculation by taking the first difference value as a Kalman filtering observation vector.
In an exemplary embodiment, when the global satellite navigation signal is not included in the drive test data of the test chip, in the presence of MAP information, the SINS/MAP/ODRS combined navigation mode is entered, which includes:
selecting a second state vector based on Kalman filtering, subtracting the position calculated by the SINS and the position of the MAP information in a vehicle coordinate system according to the second state vector to obtain a second difference value, and performing Kalman filtering calculation by taking the second difference value as a Kalman filtering observation vector to obtain the position of the SINS fused with the MAP;
and selecting a third state vector based on Kalman filtering, subtracting the position calculated by the ODRS from the position after the SINS and the MAP are fused according to the third state vector to obtain a third difference value, and performing Kalman filtering calculation by taking the third difference value as a Kalman filtering observation vector.
In an exemplary embodiment, the SINS/MAP/ODRS combined navigation mode requires that the SINS and MAP be fused prior to the ODRS. And the Kalman filtering is also used in the combined navigation algorithm model, and the SINS position error, the velocity error, the attitude error, the accelerometer zero offset error and the gyroscope zero offset error are selected by the second state vector:
x is a Kalman filtering second state vector, delta E, delta N and delta U are east, north and sky position errors, delta V, solved by SINS E ,δV N ,δV U East, north and sky velocity errors resolved for SINS,
Is the attitude error of SINS,
For the accelerometer errors, ε, for the X, Y and Z axes in SINS bx ,ε by ,ε bz Are gyroscope errors of X, Y and Z axes in SINS.
According to the second state vector, the position estimated by the SINS is differed with the position of the map information in the vehicle coordinate system to obtain a second difference value
Z＝[ΔP Right ] (8)
Wherein, Δ P Right Is namely P SINS And P MAP The amount of deviation in the lateral direction of the vehicle coordinate system.
And performing Kalman filtering calculation by taking the second difference as a Kalman filtering observation vector to obtain the position of the SINS after the SINS is fused with the MAP.
The SINS is further fused with the ODRS after being fused with map information, kalman filtering is used in an integrated navigation algorithm model, and the third state vector selects an ODRS position error, an installation angle deviation of an integrated navigation module coordinate system and a vehicle coordinate system and a milemeter proportionality coefficient error:
x is a Kalman filtering third state vector, wherein delta E, delta N and delta U are east, north and sky position errors solved by ODRS,
And combining the installation angle deviation of the navigation module coordinate system and the vehicle coordinate system, wherein delta k is the error of the odometer proportionality coefficient.
The ODRS is positioned at the position P ODRS Position P after fusing SINS and MAP in the previous step SINS' The difference is taken as a third difference value:
Z＝[P ODRS -P SINS' ] (10)
and performing Kalman filtering calculation by taking the third difference value as a Kalman filtering observation vector.
In an exemplary embodiment, when the drive test data of the test chip does not include the global satellite navigation signal and the odometer data, the SINS/MAP navigation mode is entered in the presence of MAP information, which includes:
selecting a fourth state vector based on Kalman filtering, subtracting the position calculated by the SINS from the position of map information in a vehicle coordinate system according to the fourth state vector to obtain a fourth difference value, and performing Kalman filtering calculation by taking the fourth difference value as a Kalman filtering observation vector.
In an exemplary embodiment, kalman filtering is used in the integrated navigation algorithm model, and the fourth state vector is selected from the group consisting of SINS position error, velocity error, attitude error, accelerometer zero offset error, gyroscope zero offset error:
x is a Kalman filtering fourth state vector, wherein delta E, delta N and delta U are east, north and sky position errors and delta V solved by SINS E ,δV N ,δV U East, north and sky velocity errors resolved for SINS,
Is the attitude error of SINS,
For the accelerometer errors, ε, for the X, Y and Z axes in SINS bx ,ε by ,ε bz Are gyroscope errors in the X, Y and Z axes of the SINS.
And according to the fourth state vector, the position estimated by the SINS and the position of the map information are differentiated in a vehicle coordinate system to obtain a fourth difference value:
Z＝[ΔP Right ] (12)
wherein, Δ P Right Is namely P SINS And P MAP The offset in the lateral direction of the vehicle coordinate system.
And taking the fourth difference as a Kalman filtering observation vector to perform Kalman filtering calculation.
On the other hand, an embodiment of the present invention further provides a combined navigation positioning platform map information simulation apparatus, including:
a processor and a computer-readable storage medium having instructions stored therein,
when the instructions are executed by the processor, the combined navigation positioning platform map information simulation method is realized.
In another aspect, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored,
when being executed by a processor, the computer program realizes the steps of the map information simulation method of the integrated navigation positioning platform.
The map information simulation method, the device and the storage medium of the combined navigation positioning platform obtain the drive test data of the test chip and the positioning track of the positioning equipment; and calculating the positioning deviation of the drive test data of the test chip and the positioning track of the positioning equipment by taking the positioning track of the positioning equipment as a reference track, and feeding back the positioning deviation as map information. Because the drive test data is processed in the post-processing platform, the test cost of the integrated navigation positioning chip is reduced, and the test period is shortened. And moreover, the positioning track of high-precision positioning equipment is introduced, so that the positioning precision is improved. In addition, the map information of various scene modes can be simulated, so that the expandability is greatly improved.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as is well known to those skilled in the art.
Claims (10)
1. A map information simulation method of a combined navigation positioning platform comprises the following steps:
acquiring drive test data of a test chip and a positioning track of positioning equipment;
and calculating the positioning deviation of the drive test data of the test chip and the positioning track of the positioning equipment by taking the positioning track of the positioning equipment as a reference track, and feeding back the positioning deviation as map information.
2. The method of claim 1, wherein,
the calculating a positioning deviation between the drive test data of the test chip and the positioning track of the positioning device by using the positioning track of the positioning device as a reference track comprises:
determining a combined positioning track according to the drive test data of the test chip;
vertically projecting the track points on the combined positioning track onto the positioning track of the positioning equipment;
and calculating the distance between the track point on the combined positioning track and the projection point of the track point on the positioning track of the positioning equipment, and taking the distance as the positioning deviation.
3. The method of claim 1, wherein,
the positioning precision of the positioning equipment is centimeter-level positioning precision.
4. The method of claim 1, wherein,
the drive test data of the test chip comprises at least one of the following data:
inertial navigation data, odometer data, global satellite navigation signals.
5. The method of claim 4, wherein,
when the drive test data of the test chip comprises a global satellite navigation signal, entering an SINS/GNSS tight combination navigation mode;
when the drive test data of the test chip does not comprise a global satellite navigation signal, the SINS/ODRS combined navigation mode is entered under the condition of no MAP information, and the SINS/MAP/ODRS combined navigation mode is entered under the condition of MAP information;
and when the drive test data of the test chip does not comprise global satellite navigation signals and odometer data, entering an SINS/MAP navigation mode under the condition of MAP information.
6. The method of claim 5, wherein,
when the drive test data of the test chip comprises global satellite navigation signals, entering an SINS/GNSS tight combination navigation mode, comprising:
selecting a first state vector based on Kalman filtering, subtracting the pseudo range and the pseudo range rate calculated by SINS from the pseudo range and the pseudo range rate measured by GNSS according to the first state vector to obtain a first difference value, and taking the first difference value as a Kalman filtering observation vector to carry out Kalman filtering calculation.
7. The method of claim 5, wherein,
when the drive test data of the test chip does not include a global satellite navigation signal, under the condition of MAP information, entering an SINS/MAP/ODRS combined navigation mode, including:
selecting a second state vector based on Kalman filtering, subtracting the position calculated by the SINS and the position of the MAP information in a vehicle coordinate system according to the second state vector to obtain a second difference value, and performing Kalman filtering calculation by taking the second difference value as a Kalman filtering observation vector to obtain the position of the SINS fused with the MAP;
and selecting a third state vector based on Kalman filtering, subtracting the position calculated by the ODRS from the position obtained after the SINS and the MAP are fused according to the third state vector to obtain a third difference value, and performing Kalman filtering calculation by taking the third difference value as a Kalman filtering observation vector.
8. The method of claim 5, wherein,
when the drive test data of the test chip does not include global satellite navigation signals and odometer data, the SINS/MAP navigation mode is entered under the condition of MAP information, and the method comprises the following steps:
selecting a fourth state vector based on Kalman filtering, subtracting the position calculated by the SINS from the position of map information in a vehicle coordinate system according to the fourth state vector to obtain a fourth difference value, and performing Kalman filtering calculation by taking the fourth difference value as a Kalman filtering observation vector.
9. A map information simulation device of a combined navigation positioning platform comprises:
a processor and a computer-readable storage medium having instructions stored therein,
the instructions, when executed by the processor, implement the method of any one of claims 1 to 8.
10. A computer-readable storage medium having stored thereon a computer program,
the computer program, when being executed by a processor, realizes the steps of the method as set forth in any one of claims 1 to 8.
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