CN117406259B - Beidou-based intelligent construction site vehicle positioning method and system - Google Patents
Beidou-based intelligent construction site vehicle positioning method and system Download PDFInfo
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- 239000005436 troposphere Substances 0.000 claims description 8
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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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
- G01S19/37—Hardware or software details of the signal processing chain
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Abstract
The invention provides a Beidou-based intelligent construction site vehicle positioning method and system, wherein the method comprises the steps of carrying out error correction processing on Beidou satellite observation data; calculating a first check value and a second check value of the first pseudo moment observed quantity, and eliminating noise of the pseudo moment observed quantity; carrying out coordinate joint calculation on the intelligent construction site vehicle; fusing the first positioning data, the inertial data and the mileage data to obtain fused data; the method can ensure the accuracy of the finally obtained pseudo moment observables to improve the accuracy in the subsequent coordinate calculation, and can avoid the situation of positioning offset and further improve the accuracy of vehicle positioning by carrying out error correction and noise elimination on the observed data.
Description
Technical Field
The invention belongs to the technical field of vehicle positioning, and particularly relates to a Beidou-based intelligent building site vehicle positioning method and system.
Background
The intelligent site platform mainly utilizes technologies such as an intelligent terminal, the Internet of things and mobile interconnection to collect construction process data in real time, utilizes big data and artificial intelligence technologies to analyze the construction process data in real time and controls the construction process.
For the existing intelligent construction site, the material conveying vehicle gradually tends to be unmanned, namely, the AGV is adopted to finish the transportation of building materials, the real-time position of the vehicle needs to be acquired in real time for the AGV in unmanned transportation, so that different transportation vehicles are transported, managed and positioned in a navigation way in the global direction, the condition that the vehicle interferes in the transportation process is avoided, but the accurate positioning of the vehicle cannot be realized under the condition of complex positioning environment based on the existing vehicle positioning, and meanwhile, the positioning precision of the AGV material conveying vehicle is further influenced because errors existing in Beidou satellite positioning are not corrected.
Disclosure of Invention
In order to solve the technical problems, the invention provides a Beidou-based intelligent construction site vehicle positioning method and system, which are used for solving the technical problems in the prior art.
In one aspect, the invention provides the following technical scheme, namely a Beidou-based intelligent construction site vehicle positioning method, which comprises the following steps:
Acquiring Beidou satellite observation data, and performing error correction processing on the Beidou satellite observation data to obtain corrected observation data;
determining a first pseudo-moment observed quantity based on the corrected observed data, calculating a first check value and a second check value of the first pseudo-moment observed quantity, and performing noise rejection on the pseudo-moment observed quantity based on the first check value and the second check value to obtain a second pseudo-moment observed quantity;
performing coordinate joint calculation on the intelligent building site vehicle based on the second pseudo moment observed quantity to obtain first positioning data of the intelligent building site vehicle;
acquiring inertial data and mileage data of the intelligent building site vehicle, and fusing the first positioning data, the inertial data and the mileage data to obtain fused data;
and carrying out particle transformation on the fusion data to obtain a pose particle set, and carrying out weight updating and sampling processing on the pose particle set to obtain second positioning data of the intelligent building site vehicle.
Compared with the prior art, the beneficial effects of this application are: firstly, acquiring Beidou satellite observation data, and performing error correction processing on the Beidou satellite observation data to obtain corrected observation data; then determining a first pseudo-moment observed quantity based on the correction observed data, calculating a first check value and a second check value of the first pseudo-moment observed quantity, and performing noise rejection on the pseudo-moment observed quantity based on the first check value and the second check value to obtain a second pseudo-moment observed quantity; then, carrying out coordinate joint calculation on the intelligent building site vehicle based on the second pseudo moment observed quantity so as to obtain first positioning data of the intelligent building site vehicle; then acquiring inertial data and mileage data of the intelligent building site vehicle, and fusing the first positioning data, the inertial data and the mileage data to obtain fused data; finally, the fusion data are subjected to particle transformation to obtain a pose particle set, the pose particle set is subjected to weight updating and sampling processing to obtain second positioning data of the intelligent construction site vehicle, the accuracy of the finally obtained pseudo moment observed quantity can be ensured by carrying out error correction and noise elimination on the observed data so as to improve the accuracy of the subsequent coordinate calculation, and meanwhile, the first positioning data are subjected to fusion, particle transformation, weight updating and sampling processing, so that the situation of positioning deviation can be avoided, and the vehicle positioning accuracy is further improved.
Preferably, the step of performing error correction processing on the beidou satellite observation data to obtain corrected observation data includes:
and carrying out first error correction processing on the Beidou satellite observation data based on a first preset formula, wherein the first preset formula is as follows:
；
in the method, in the process of the invention,for satellite clock correction value, ++>For clock bias +.>、/>The satellite signal transmitting time and the satellite signal receiving time are respectively +.>For clock drift +.>For frequency drift +.>For the speed of light->Is the eccentric angle of satellite orbit->For the satellite orbit near point angle, +.>Is the gravitational constant->Is a satellite orbit semi-long axis;
and carrying out second error correction processing on the Beidou satellite observation data based on a second preset formula, wherein the second preset formula is as follows:
；
；
in the method, in the process of the invention,for ionosphere correction value, +.>、/>Respectively the +.f in the preset correction model>First network parameter, th->Second network parameters->For the latitude of the ionosphere puncture point, +.>Where the ionosphere is the point of penetration, < +.>、/>First correction constant and second correction constant, respectively, ">For the geographical latitude of the observation station, +.>For satellite altitude, +.>For the earth radius>For ionization layer height, +>Is the satellite azimuth;
And carrying out third error correction processing on the Beidou satellite observation data based on a third preset formula, wherein the third preset formula is as follows:
；
in the method, in the process of the invention,for tropospheric correction values, ++>For the third correction constant, +.>Is ground air pressure->Is the air pressure of water on the ground,for the humidity of the ground->The ground height of the measuring station, the top height of the dry atmosphere layer and the top height of the wet atmosphere layer are respectively measured.
Preferably, the step of determining a first pseudo moment observation based on the corrective observations comprises:
determining a first pseudo-moment observation based on the corrected observation data by a fourth preset formula：
；
In the method, in the process of the invention,for the real distance between the vehicle-mounted receiver and the Beidou satellite, </u >>For the speed of light->Satellite clock correction value, ionosphere correction value, troposphere correction value, ++>Clock error for vehicle-mounted receiver>Other errors.
Preferably, the step of calculating a first test value and a second test value of the first pseudo-moment observed quantity, and performing noise rejection on the pseudo-moment observed quantity based on the first test value and the second test value to obtain a second pseudo-moment observed quantity includes:
calculating a first test value of the first pseudo moment observance quantity：
；
In the method, in the process of the invention,first code deviation of Beidou satellite and vehicle-mounted receiver respectively, < - >Time variable corresponding to first code deviation of Beidou satellite and vehicle-mounted receiver is +.>Is the first observation noise;
calculating a second test value of the first pseudo moment observance quantity；
；
In the method, in the process of the invention,second code deviation of Beidou satellite and vehicle-mounted receiver respectively, < ->Time variable corresponding to second code deviation of Beidou satellite and vehicle-mounted receiver is +.>For the second observation noise +>Ionospheric delay parameters and error terms;
judging the first test valueWhether or not it is greater than a first culling threshold->The second test valueWhether or not it is greater than a second culling threshold->Wherein->；
If the first check valueNo greater than a first culling threshold->And said second test value +.>No greater than a second culling threshold->The corresponding first pseudo-moment observables are retained if the first test value +.>Greater than the first culling threshold->And/or said second test value +.>Greater than the second culling threshold->And eliminating the corresponding first pseudo moment observed quantity to obtain a second pseudo moment observed quantity.
Preferably, the step of performing coordinate joint calculation on the intelligent building site vehicle based on the second pseudo moment observed quantity to obtain first positioning data of the intelligent building site vehicle includes:
Establishing a coordinate solution equation based on the second pseudo moment observables:
；
in the method, in the process of the invention,for the coordinates of the first positioning data, +.>Is->A second pseudo-moment observation quantity,is->Beidou corresponding to second pseudo moment observation quantitySatellite coordinates>Clock error for the vehicle-mounted receiver;
performing taylor expansion on the coordinate solution equation to obtain a linear solution equation;
and solving the linear solving equation by adopting a weighted least square method to obtain first positioning data.
Preferably, the step of obtaining the inertial data and mileage data of the smart site vehicle and fusing the first positioning data, the inertial data and the mileage data to obtain fused data includes:
acquiring inertial data and mileage data of the intelligent building site vehicle, and determining a first positioning data, the inertial data and the mileage dataInitial pose matrix of intelligent site vehicle at moment +.>：
；
In the method, in the process of the invention,is->X-axis, y-axis and z-axis coordinates of the smart site vehicle at the moment in global coordinates,/->Is->Angle of the smart site vehicle at moment in global coordinates,/->Respectively +.>Global linear velocity and global angular velocity of the intelligent site vehicle at the moment;
For the initial pose matrixPerforming motion transformation to obtain a transformation pose matrix +.>：
；
In the method, in the process of the invention,is->X-axis, y-axis and z-axis coordinates of the smart site vehicle at the moment in global coordinates,/->Is->The angle of the smart work site vehicle at the moment in global coordinates,respectively +.>Global linear speed, global angular speed, < > of the smart site vehicle at the moment>Is Gaussian distributed noise;
converting the pose matrixFusing the inertial data and the mileage data to obtain fused data +.>：
；
In the method, in the process of the invention,、/>mileage noise and inertial noise, respectively.
Preferably, the step of performing weight updating and sampling processing on the pose particle set to obtain second positioning data of the intelligent building site vehicle includes:
will be the firstTime pose particle set->Evenly distributed into the global space, wherein,，/>is->The number represents->Particles of the pose at the moment +.>Is particle->A corresponding particle weight;
through the firstIterative estimation of the pose particle set of time +.>Time of day particle distribution to obtain estimated pose particle +.>：
；
In the method, in the process of the invention,respectively +.>Time-of-day smart worksite vehicle x-direction estimated displacement, y-direction estimated displacement, z-direction estimated displacement, angle estimated value, +. >、/>、/>、/>For intelligent construction vehicles at +.>Moment to->Time x-direction displacement change amount, y-direction displacement change amount, z-direction displacement change amount, angle change amount, +.>Respectively x-direction displacement noise, y-direction displacement noise, z-direction displacement noise and angle noise;
based on the firstTime of particle distribution versus pose particle set>The evaluation update is performed on each particle weight of the model to obtain an update weight +.>：
；
In the method, in the process of the invention,for observation, +.>Particles for a given estimated pose>Is sampled under the condition of +.>Is a desired probability density of (2);
based on the update weightResampling all particles in said pose particle set to obtain a sampling weight +.>And sample particle->Based on the sampling weight +.>Calculating the number of effective particles +.>；
；
Determining the effective particle numberWhether or not it is greater than the particle threshold, if the effective particle number +.>Greater than the particle threshold, second positioning data +.>If the effective particle quantity +.>If the particle size is not larger than the particle threshold, adding a plurality of random particles into the pose particle set to obtain an updated particle set, and repeatedly carrying out particle distribution estimation, weight updating and resampling on the updated particle set until the effective particle number is- >Greater than the particle threshold, outputting the corresponding iterative sampling weight +.>And iterate sampling particles->Second positioning data->Wherein->To update the number of particles in the particle set.
In a second aspect, the present invention provides the following technical solutions, a Beidou-based intelligent construction site vehicle positioning system, the system comprising:
the acquisition module is used for acquiring Beidou satellite observation data, and carrying out error correction processing on the Beidou satellite observation data so as to obtain corrected observation data;
the detection module is used for determining a first pseudo-moment observed quantity based on the correction observed data, calculating a first detection value and a second detection value of the first pseudo-moment observed quantity, and carrying out noise rejection on the pseudo-moment observed quantity based on the first detection value and the second detection value so as to obtain a second pseudo-moment observed quantity;
the calculation module is used for carrying out coordinate joint calculation on the intelligent building site vehicle based on the second pseudo moment observed quantity so as to obtain first positioning data of the intelligent building site vehicle;
the fusion module is used for acquiring the inertial data and mileage data of the intelligent building site vehicle, and fusing the first positioning data, the inertial data and the mileage data to obtain fusion data;
And the updating module is used for carrying out particle transformation on the fusion data to obtain a pose particle set, and carrying out weight updating and sampling processing on the pose particle set to obtain second positioning data of the intelligent building site vehicle.
In a third aspect, the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the above-mentioned intelligent building site vehicle positioning method based on beidou when executing the computer program.
In a fourth aspect, the present invention provides a storage medium, where a computer program is stored, where the computer program when executed by a processor implements a beidou-based intelligent construction site vehicle positioning method as described above.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a Beidou-based intelligent construction site vehicle positioning method according to an embodiment of the present invention;
FIG. 2 is a detailed flowchart of step S1 in a Beidou-based intelligent building site vehicle positioning method according to an embodiment of the present invention;
FIG. 3 is a detailed flowchart of step S22 in a Beidou-based intelligent building site vehicle positioning method according to an embodiment of the present invention;
fig. 4 is a detailed flowchart of step S3 in the beidou-based intelligent construction site vehicle positioning method according to the first embodiment of the present invention;
FIG. 5 is a detailed flowchart of step S4 in a Beidou-based intelligent building site vehicle positioning method according to an embodiment of the present invention;
FIG. 6 is a detailed flowchart of step S5 in a Beidou-based intelligent building site vehicle positioning method according to an embodiment of the present invention;
fig. 7 is a block diagram of a Beidou-based intelligent construction site vehicle positioning system according to a second embodiment of the present invention;
fig. 8 is a schematic hardware structure of a computer according to another embodiment of the invention.
Embodiments of the present invention will be further described below with reference to the accompanying drawings.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended to illustrate embodiments of the invention and should not be construed as limiting the invention.
In the description of the embodiments of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate description of the embodiments of the present invention and simplify description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless explicitly defined otherwise.
In the embodiments of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured" and the like are to be construed broadly and include, for example, either permanently connected, removably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the embodiments of the present invention will be understood by those of ordinary skill in the art according to specific circumstances.
Example 1
In a first embodiment of the present invention, as shown in fig. 1, a method for positioning a vehicle on an intelligent construction site based on beidou comprises:
s1, acquiring Beidou satellite observation data, and performing error correction processing on the Beidou satellite observation data to obtain corrected observation data;
specifically, for the observation data of the Beidou satellite, errors of a satellite end, an intermediate transmission and a vehicle-mounted receiver exist in the observation data, if the errors are not corrected, the final coordinate resolving process is affected, so that in the step, error correction processing is performed on the observation data of the Beidou satellite, corrected observation data can be obtained, and the positioning accuracy of the intelligent building site is improved.
As shown in fig. 2, the step S1 includes:
s11, performing first error correction processing on the Beidou satellite observation data based on a first preset formula, wherein the first preset formula is as follows:
；
in the method, in the process of the invention,for satellite clock correction value, ++>For clock bias +.>、/>The satellite signal transmitting time and the satellite signal receiving time are respectively +.>For clock drift +.>For frequency drift +.>For the speed of light->Is the eccentric angle of satellite orbit->For the satellite orbit near point angle, +. >Is the gravitational constant->Is a satellite orbit semi-long axis;
specifically, the first error correction process is to correct the error at the satellite end, and the deviation between the satellite clock and the time of the Beidou system can be corrected by the first preset formula.
S12, performing second error correction processing on the Beidou satellite observation data based on a second preset formula, wherein the second preset formula is as follows:
；
；
in the method, in the process of the invention,for ionosphere correction value, +.>、/>Respectively the +.f in the preset correction model>First network parameter, th->Second network parameters->For the latitude of the ionosphere puncture point, +.>Where the ionosphere is the point of penetration, < +.>、/>First correction constant and second correction constant, respectively, ">For the geographical latitude of the observation station, +.>For satellite altitude, +.>For the earth radius>For ionization layer height, +>Is the satellite azimuth;
wherein the first correction constant is 5×10 -9 The second correction constant is 50400, the second error correction process is to correct the ionosphere, the ionosphere error is generated in the space of 50-100 km above the ground, the signal sent by the Beidou satellite is influenced by positive ions and free electrons in the ionosphere, the propagation path of the signal is deflected, and the ionosphere error can be corrected by using a preset correction model in the navigation circuit, wherein the preset correction model is a Klobuchar model.
S13, performing third error correction processing on the Beidou satellite observation data based on a third preset formula, wherein the third preset formula is as follows:
；
in the method, in the process of the invention,for tropospheric correction values, ++>For the third correction constant, +.>Is ground air pressure->Is the air pressure of water on the ground,for the humidity of the ground->The height of the ground of the measuring station, the height of the top of the dry atmosphere layer and the height of the top of the wet atmosphere layer are respectively;
wherein the third correction constant is 155.2X10 -7 The third error correction process corrects the troposphere error, which is generated below the ground level upper air 40kmm, and the troposphere is connected with the ground, so that a certain temperature difference exists, when a signal passes through the troposphere, the propagation path transmits deflection, so that troposphere delay is generated, and in the application, the third error correction process is performed through a third preset formula.
S2, determining a first pseudo-moment observed quantity based on the correction observed data, calculating a first check value and a second check value of the first pseudo-moment observed quantity, and performing noise rejection on the pseudo-moment observed quantity based on the first check value and the second check value to obtain a second pseudo-moment observed quantity;
specifically, after the corrected observed data are obtained, the first pseudo moment observed quantity can be obtained through calculation, but for the first pseudo moment observed quantity which is actually obtained, rough difference exists, rough turning can reach hundred-meter and kilometer orders, and if the corresponding first pseudo moment observed quantity is not removed, larger errors exist in the subsequent calculation of the first positioning data.
Meanwhile, the step S2 includes: s21, determining a first pseudo moment observation amount based on the correction observation data; s22, calculating a first check value and a second check value of the first pseudo moment observed quantity, and performing noise rejection on the pseudo moment observed quantity based on the first check value and the second check value to obtain a second pseudo moment observed quantity.
The step S21 specifically includes:
determining a first pseudo-moment observation based on the corrected observation data by a fourth preset formula：
；
In the method, in the process of the invention,for the real distance between the vehicle-mounted receiver and the Beidou satellite, </u >>For the speed of light->Satellite clock correction value, ionosphere correction value, troposphere correction value, ++>Clock error for vehicle-mounted receiver>Other errors;
wherein, for the true distance between the vehicle-mounted receiver and the Beidou satelliteIn the sense that the number of the cells,wherein->For the coordinates of the satellite in the earth coordinate system, +.>For the receiver to be at groundCoordinates in a spherical coordinate system.
As shown in fig. 3, the step S22 includes:
s221, calculating a first check value of the first pseudo moment observed quantity：
；
In the method, in the process of the invention,first code deviation of Beidou satellite and vehicle-mounted receiver respectively, < ->Time variable corresponding to first code deviation of Beidou satellite and vehicle-mounted receiver is +. >Is the first observed noise.
S222, calculating a second check value of the first pseudo moment observed quantity；
；
In the method, in the process of the invention,second code deviation of Beidou satellite and vehicle-mounted receiver respectively, < ->Time variable corresponding to second code deviation of Beidou satellite and vehicle-mounted receiver is +.>For the second observation noise +>Ionospheric delay parameters and error terms;
the first code deviation and the second code deviation of the Beidou satellite are respectively the code deviation from the Beidou satellite to the intermediate transmission base station and the code deviation from the intermediate transmission base station to the vehicle-mounted receiver, and the first code deviation and the second code deviation of the vehicle-mounted receiver are respectively the code deviation from the vehicle-mounted receiver to the intermediate transmission base station and the code deviation from the intermediate transmission base station to the Beidou satellite.
S223, judging the first test valueWhether or not it is greater than a first culling threshold->And said second test value +.>Whether or not it is greater than a second culling threshold->Wherein->。
S224, if the first test valueNo greater than a first culling threshold->And the second test valueNo greater than a second culling threshold->Reserving the corresponding first pseudo moment observed quantity, if the first check value isGreater than the first culling threshold->And/or said second test value +.>Greater than the second culling threshold- >And eliminating the corresponding first pseudo moment observed quantity to obtain a second pseudo moment observed quantity.
S3, carrying out coordinate joint calculation on the intelligent building site vehicle based on the second pseudo moment observation quantity so as to obtain first positioning data of the intelligent building site vehicle;
as shown in fig. 4, the step S3 includes:
s31, establishing a coordinate solving equation based on the second pseudo moment observed quantity:
；
in the method, in the process of the invention,for the coordinates of the first positioning data, +.>Is->A second pseudo-moment observation quantity,is->Beidou satellite coordinates corresponding to second pseudo moment observables,/a second pseudo moment observables>Is the onboard receiver clock error.
S32, carrying out Taylor expansion on the coordinate solution equation to obtain a linear solution equation;
the linear solution equation is:
；
wherein,，/>is->Approximate distance between satellite and receiver, < >>For the approximate coordinates of the receiver at each solution, < >>、/>、/>Is the corresponding coordinate variable value when solving.
And S33, solving the linear solving equation by adopting a weighted least square method to obtain first positioning data.
S4, acquiring inertial data and mileage data of the intelligent building site vehicle, and fusing the first positioning data, the inertial data and the mileage data to obtain fused data;
As shown in fig. 5, the step S4 includes:
s41, acquiring inertial data and mileage data of the intelligent building site vehicle, and determining a first positioning data, the inertial data and the mileage dataInitial pose matrix of intelligent site vehicle at moment +.>：
；
In the method, in the process of the invention,is->X-axis, y-axis and z-axis coordinates of the smart site vehicle at the moment in global coordinates,/->Is->Angle of the smart site vehicle at moment in global coordinates,/->Respectively +.>The global linear speed and the global angular speed of the intelligent construction site vehicle at the moment.
S42, the initial pose matrixPerforming motion transformation to obtain a transformation pose matrix +.>：
；
In the method, in the process of the invention,is->X-axis, y-axis and z-axis coordinates of the smart site vehicle at the moment in global coordinates,/->Is->The angle of the smart work site vehicle at the moment in global coordinates,respectively +.>Global linear speed, global angular speed, < > of the smart site vehicle at the moment>Is Gaussian distributed noise;
wherein,for the observation matrix of mileage data, +.>Is an observation matrix of inertial data.
S43, converting the pose matrixFusing the inertial data and the mileage data to obtain fused data +. >：
；
In the method, in the process of the invention,、/>mileage noise and inertial noise, respectively.
S5, carrying out particle transformation on the fusion data to obtain a pose particle set, and carrying out weight updating and sampling processing on the pose particle set to obtain second positioning data of the intelligent construction site vehicle;
as shown in fig. 6, the step S5 includes:
s51, will be the firstTime pose particle set->Evenly distributed into the global space, wherein,，/>is->The number represents->Particles of the pose at the moment +.>Is particle->A corresponding particle weight;
wherein, pose particle setIn the distribution process, a uniform distribution probability strategy is adopted to enable pose particles to be in a group of +.>All particles of (a) are distributed into the global space.
S52, through the firstIterative estimation of the pose particle set of time +.>Time of day particle distribution to obtain estimated pose particle +.>：/>
；
In the method, in the process of the invention,respectively +.>Time-of-day smart worksite vehicle x-direction estimated displacement, y-direction estimated displacement, z-direction estimated displacement, angle estimated value, +.>、/>、/>、/>For intelligent construction vehicles at +.>Moment to->The x-direction displacement variation, y-direction displacement variation, z-direction displacement variation and angle variation at the moment,respectively x-direction displacement noise, y-direction displacement noise, z-direction displacement noise and angle noise;
In particular, by iterative methods, the prior particle distribution at the next moment can be estimated according to the particles in the particles at the previous moment and the motion information, and at the same time, forIn other words, they all satisfy a gaussian distribution.
S53, based on the firstTime of particle distribution versus pose particle set>The evaluation update is performed on each particle weight of the model to obtain an update weight +.>：
；
In the method, in the process of the invention,for observation, +.>Particles for a given estimated pose>Is sampled under the condition of +.>Is a desired probability density of (2);
specifically, the particle weight is evaluated and updated, namely a posterior evaluation process, and the observation result is specifically the observation data obtained by the laser radar.
S54, based on the update weightResampling all particles in said pose particle set to obtain a sampling weight +.>And sample particle->Based on the sampling weight +.>Calculating the number of effective particles +.>；
；
Specifically, in the actual situation of particle distribution estimation and weight updating, the situation that the number of particles is insufficient or exhausted may occur, so that the positioning accuracy and the positioning speed may be affected, and therefore, by calculating the number of effective particles and comparing the number with a particle threshold value, whether random particles need to be added is judged according to a comparison result.
S55, judging the number of the effective particlesWhether or not it is greater than the particle threshold, if the effective particle number +.>Greater than the particle threshold, second positioning data +.>If the effective particle quantity +.>If the particle size is not larger than the particle threshold, adding a plurality of random particles into the pose particle set to obtain updated particle set, and repeatedly carrying out particle on the updated particle setDistribution estimation, weight update and resampling up to the effective particle number +.>Greater than the particle threshold, outputting the corresponding iterative sampling weight +.>And iterate sampling particles->Second positioning data->Wherein, the method comprises the steps of, wherein,to update the number of particles in the particle set;
specifically, when the number of effective particles is sufficient, then the second positioning dataIf the number of effective particles is insufficient, it is indicated that the number of particles is insufficient and particle depletion is likely to occur, and therefore, by adding the corresponding random particles to the particle set, one updated particle set is obtained, and the steps S51 to S54 are repeatedly performed for the updated particle set until the number of effective particles is satisfied>A condition greater than the particle threshold value, and then outputting the corresponding iterative sampling weightAnd iterate sampling particles->At this time, second positioning data +. >。/>
According to the Beidou-based intelligent building site vehicle positioning method provided by the embodiment of the invention, firstly, beidou satellite observation data are acquired, and error correction processing is carried out on the Beidou satellite observation data so as to obtain corrected observation data; then determining a first pseudo-moment observed quantity based on the correction observed data, calculating a first check value and a second check value of the first pseudo-moment observed quantity, and performing noise rejection on the pseudo-moment observed quantity based on the first check value and the second check value to obtain a second pseudo-moment observed quantity; then, carrying out coordinate joint calculation on the intelligent building site vehicle based on the second pseudo moment observed quantity so as to obtain first positioning data of the intelligent building site vehicle; then acquiring inertial data and mileage data of the intelligent building site vehicle, and fusing the first positioning data, the inertial data and the mileage data to obtain fused data; finally, the fusion data are subjected to particle transformation to obtain a pose particle set, the pose particle set is subjected to weight updating and sampling processing to obtain second positioning data of the intelligent construction site vehicle, the accuracy of the finally obtained pseudo moment observed quantity can be ensured by carrying out error correction and noise elimination on the observed data so as to improve the accuracy of the subsequent coordinate calculation, and meanwhile, the first positioning data are subjected to fusion, particle transformation, weight updating and sampling processing, so that the situation of positioning deviation can be avoided, and the vehicle positioning accuracy is further improved.
Example two
As shown in fig. 7, in a second embodiment of the present invention, there is provided a Beidou-based intelligent construction site vehicle positioning system, including:
the acquisition module 1 is used for acquiring Beidou satellite observation data, and carrying out error correction processing on the Beidou satellite observation data to obtain corrected observation data;
the checking module 2 is configured to determine a first pseudo-moment observed quantity based on the corrected observed data, calculate a first check value and a second check value of the first pseudo-moment observed quantity, and perform noise rejection on the pseudo-moment observed quantity based on the first check value and the second check value to obtain a second pseudo-moment observed quantity;
a resolving module 3, configured to perform coordinate joint resolving on the intelligent building site vehicle based on the second pseudo moment observed quantity, so as to obtain first positioning data of the intelligent building site vehicle;
the fusion module 4 is used for acquiring inertial data and mileage data of the intelligent building site vehicle, and fusing the first positioning data, the inertial data and the mileage data to obtain fusion data;
and the updating module 5 is used for carrying out particle transformation on the fusion data to obtain a pose particle set, and carrying out weight updating and sampling processing on the pose particle set to obtain second positioning data of the intelligent building site vehicle.
The acquisition module 1 includes:
the first correction submodule is used for carrying out first error correction processing on the Beidou satellite observation data based on a first preset formula, wherein the first preset formula is as follows:
；
in the method, in the process of the invention,for satellite clock correction value, ++>For clock bias +.>、/>The satellite signal transmitting time and the satellite signal receiving time are respectively +.>For clock drift +.>For frequency drift +.>For the speed of light->Is the eccentric angle of satellite orbit->For the satellite orbit near point angle, +.>Is the gravitational constant->Is a satellite orbit semi-long axis;
the second correction submodule is used for carrying out second error correction processing on the Beidou satellite observation data based on a second preset formula, wherein the second preset formula is as follows:
；/>
；
in the method, in the process of the invention,for ionosphere correction value, +.>、/>Respectively the +.f in the preset correction model>First network parameter, th->Second network parameters->For the latitude of the ionosphere puncture point, +.>Where the ionosphere is the point of penetration, < +.>、/>First correction constant and second correction constant, respectively, ">For the geographical latitude of the observation station, +.>For satellite altitude, +.>For the earth radius>For ionization layer height, +>Is the satellite azimuth;
the third correction submodule is used for carrying out third error correction processing on the Beidou satellite observation data based on a third preset formula, wherein the third preset formula is as follows:
；
In the method, in the process of the invention,for tropospheric correction values, ++>For the third correction constant, +.>Is ground air pressure->Is the air pressure of water on the ground,for the humidity of the ground->The ground height of the measuring station, the top height of the dry atmosphere layer and the top height of the wet atmosphere layer are respectively measured.
The inspection module 2 is used for:
determining a first pseudo-moment observation based on the corrected observation data by a fourth preset formula：
；
In the method, in the process of the invention,for the real distance between the vehicle-mounted receiver and the Beidou satellite, </u >>For the speed of light->Satellite clock correction value, ionosphere correction value, troposphere correction value, ++>Clock error for vehicle-mounted receiver>Other errors.
The inspection module 2 includes:
a first calculation sub-module for calculating a first check value of the first pseudo moment observables：
；
In the method, in the process of the invention,first code deviation of Beidou satellite and vehicle-mounted receiver respectively, < ->Time variable corresponding to first code deviation of Beidou satellite and vehicle-mounted receiver is +.>Is the first observation noise;
a second calculation sub-module for calculating a second test value of the first pseudo moment observed quantity；
；
In the method, in the process of the invention,second code deviation of Beidou satellite and vehicle-mounted receiver respectively, < ->Time variable corresponding to second code deviation of Beidou satellite and vehicle-mounted receiver is +. >For the second observation noise +>Ionospheric delay parameters and error terms;
a first judging sub-module for judging the first test valueWhether or not it is greater than a first reject thresholdValue->And said second test value +.>Whether or not it is greater than a second culling threshold->Wherein->；
A rejecting sub-module for, if the first check valueNo greater than a first culling threshold->And said second test value +.>No greater than a second culling threshold->The corresponding first pseudo-moment observables are retained if the first test value +.>Greater than the first culling threshold->And/or said second test value +.>Greater than the second culling threshold->And eliminating the corresponding first pseudo moment observed quantity to obtain a second pseudo moment observed quantity. />
The resolving module 3 includes:
the establishing sub-module is used for establishing a coordinate solution equation based on the second pseudo moment observed quantity:
；
in the method, in the process of the invention,for the coordinates of the first positioning data, +.>Is->A second pseudo-moment observation quantity,is->Beidou satellite coordinates corresponding to second pseudo moment observables,/a second pseudo moment observables>Clock error for the vehicle-mounted receiver;
the opening submodule is used for carrying out Taylor expansion on the coordinate solution equation so as to obtain a linear solution equation;
and the solving sub-module is used for solving the linear solving equation by adopting a weighted least square method so as to obtain first positioning data.
The fusion module 4 includes:
an initial pose matrix determining sub-module for acquiring inertial data and mileage data of the intelligent building site vehicle and determining a first position based on the first positioning data, the inertial data and the mileage dataInitial pose matrix of intelligent site vehicle at moment +.>：
；
In the method, in the process of the invention,is->X-axis, y-axis and z-axis coordinates of the smart site vehicle at the moment in global coordinates,/->Is->Angle of the smart site vehicle at moment in global coordinates,/->Respectively +.>Global linear velocity and global angular velocity of the intelligent site vehicle at the moment;
a transformation pose matrix determination submodule for determining the initial pose matrixPerforming motion transformation to obtain a transformation pose matrix +.>：
；
In the method, in the process of the invention,is->X-axis sitting of moment intelligent building site vehicle under global coordinatesMark, y-axis coordinate, z-axis coordinate, < >>Is->The angle of the smart work site vehicle at the moment in global coordinates,respectively +.>Global linear speed, global angular speed, < > of the smart site vehicle at the moment>Is Gaussian distributed noise;
a fusion sub-module for transforming the pose matrixFusing the inertial data and the mileage data to obtain fused data +. >：/>
；
In the method, in the process of the invention,、/>mileage noise and inertial noise, respectively.
The update module 5 includes:
a distribution sub-module for dividing the firstTime pose particle set->Evenly distributed into the global space, wherein +.>，/>Is->The number represents->Particles of the pose at the moment +.>Is particle->A corresponding particle weight;
an estimation sub-module for passing throughIterative estimation of the pose particle set of time +.>Time of day particle distribution to obtain estimated pose particle +.>：
；
In the method, in the process of the invention,respectively +.>Time-of-day smart worksite vehicle x-direction estimated displacement, y-direction estimated displacement, z-direction estimated displacement, angle estimated value, +.>、/>、/>、/>For intelligent construction vehicles at +.>Moment to->Time x-direction displacement change amount, y-direction displacement change amount, z-direction displacement change amount, angle change amount, +.>Respectively x-direction displacement noise, y-direction displacement noise, z-direction displacement noise and angle noise;
an update sub-module for based on the firstTime of particle distribution versus pose particle set>The evaluation update is performed on each particle weight of the model to obtain an update weight +.>：
；
In the method, in the process of the invention,for observation, +.>To givePositioning and estimating pose particles->Is sampled under the condition of +.>Is a desired probability density of (2);
a sampling submodule for updating the weight based on the update weight Resampling all particles in said pose particle set to obtain a sampling weight +.>And sample particle->Based on the sampling weight +.>Calculating the number of effective particles +.>；
；
A second judging sub-module for judging the effective particle quantityWhether or not it is greater than the particle threshold, if the effective particle number +.>Greater than the particle threshold, second positioning data +.>If the effective particle quantity +.>Not greater than the particle threshold, thenAdding a plurality of random particles into the pose particle set to obtain updated particle sets, and repeatedly performing particle distribution estimation, weight updating and resampling on the updated particle sets until the effective particle number +.>Greater than the particle threshold, outputting the corresponding iterative sampling weight +.>And iterate sampling particles->Second positioning data->Wherein->To update the number of particles in the particle set. />
In other embodiments of the present invention, a computer is provided in the embodiments of the present invention, including a memory 102, a processor 101, and a computer program stored in the memory 102 and executable on the processor 101, where the processor 101 implements the Beidou-based intelligent worksite vehicle positioning method as described above when executing the computer program.
In particular, the processor 101 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
Memory 102 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 102 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, solid state Drive (Solid State Drive, SSD), flash memory, optical Disk, magneto-optical Disk, tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. Memory 102 may include removable or non-removable (or fixed) media, where appropriate. The memory 102 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 102 is a Non-Volatile (Non-Volatile) memory. In a particular embodiment, the Memory 102 includes Read-Only Memory (ROM) and random access Memory (Random Access Memory, RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, abbreviated PROM), an erasable PROM (Erasable Programmable Read-Only Memory, abbreviated EPROM), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, abbreviated EEPROM), an electrically rewritable ROM (Electrically Alterable Read-Only Memory, abbreviated EAROM), or a FLASH Memory (FLASH), or a combination of two or more of these. The RAM may be Static Random-Access Memory (SRAM) or dynamic Random-Access Memory (Dynamic Random Access Memory DRAM), where the DRAM may be a fast page mode dynamic Random-Access Memory (Fast Page Mode Dynamic Random Access Memory FPMDRAM), extended data output dynamic Random-Access Memory (Extended Date Out Dynamic Random Access Memory EDODRAM), synchronous dynamic Random-Access Memory (Synchronous Dynamic Random-Access Memory SDRAM), or the like, as appropriate.
Memory 102 may be used to store or cache various data files that need to be processed and/or communicated, as well as possible computer program instructions for execution by processor 101.
The processor 101 reads and executes the computer program instructions stored in the memory 102 to implement the above-mentioned intelligent construction site vehicle positioning method based on Beidou.
In some of these embodiments, the computer may also include a communication interface 103 and a bus 100. As shown in fig. 8, the processor 101, the memory 102, and the communication interface 103 are connected to each other via the bus 100 and perform communication with each other.
The communication interface 103 is used to implement communication between modules, devices, units, and/or units in the embodiments of the present application. The communication interface 103 may also enable communication with other components such as: and the external equipment, the image/data acquisition equipment, the database, the external storage, the image/data processing workstation and the like are used for data communication.
Bus 100 includes hardware, software, or both, coupling components of a computer device to each other. Bus 100 includes, but is not limited to, at least one of: data Bus (Data Bus), address Bus (Address Bus), control Bus (Control Bus), expansion Bus (Expansion Bus), local Bus (Local Bus). By way of example, and not limitation, bus 100 may include a graphics acceleration interface (Accelerated Graphics Port), abbreviated AGP, or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industry Standard Architecture, ISA) Bus, a wireless bandwidth (InfiniBand) interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (Micro Channel Architecture, abbreviated MCa) Bus, a peripheral component interconnect (Peripheral Component Interconnect, abbreviated PCI) Bus, a PCI-Express (PCI-X) Bus, a serial advanced technology attachment (Serial Advanced Technology Attachment, abbreviated SATA) Bus, a video electronics standards association local (Video Electronics Standards Association Local Bus, abbreviated VLB) Bus, or other suitable Bus, or a combination of two or more of the foregoing. Bus 100 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
The computer can execute the Beidou-based intelligent building site vehicle positioning method based on the Beidou-based intelligent building site vehicle positioning system, so that the intelligent building site vehicle positioning method is realized.
In still other embodiments of the present invention, in combination with the above-mentioned beidou-based intelligent building site vehicle positioning method, the embodiments of the present invention provide a technical solution, a storage medium, where a computer program is stored on the storage medium, where the computer program is executed by a processor to implement the above-mentioned beidou-based intelligent building site vehicle positioning method.
Those of skill in the art will appreciate that the logic and/or steps represented in the flow diagrams or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (8)
1. A Beidou-based intelligent construction site vehicle positioning method is characterized by comprising the following steps of:
acquiring Beidou satellite observation data, and performing error correction processing on the Beidou satellite observation data to obtain corrected observation data;
determining a first pseudo-range observed quantity based on the corrected observed data, calculating a first check value and a second check value of the first pseudo-range observed quantity, and performing noise rejection on the pseudo-range observed quantity based on the first check value and the second check value to obtain a second pseudo-range observed quantity;
Performing coordinate joint calculation on the intelligent building site vehicle based on the second pseudo-range observed quantity to obtain first positioning data of the intelligent building site vehicle;
acquiring inertial data and mileage data of the intelligent building site vehicle, and fusing the first positioning data, the inertial data and the mileage data to obtain fused data;
performing particle transformation on the fusion data to obtain a pose particle set, and performing weight updating and sampling processing on the pose particle set to obtain second positioning data of the intelligent building site vehicle;
the step of obtaining the inertial data and mileage data of the intelligent building site vehicle and fusing the first positioning data, the inertial data and the mileage data to obtain fused data comprises the following steps:
acquiring inertial data and mileage data of the intelligent building site vehicle, and determining a first positioning data, the inertial data and the mileage dataInitial pose matrix of intelligent site vehicle at moment +.>：
；
In the method, in the process of the invention,is->X-axis, y-axis and z-axis coordinates of the smart site vehicle at the moment in global coordinates,/->Is->Angle of the smart site vehicle at moment in global coordinates,/- >Respectively +.>Global linear velocity and global angular velocity of the intelligent site vehicle at the moment;
for the initial pose matrixPerforming motion transformation to obtain a transformation pose matrix +.>：
；
In the method, in the process of the invention,is->X-axis, y-axis and z-axis coordinates of the smart site vehicle at the moment in global coordinates,/->Is->Angle of the smart site vehicle at moment in global coordinates,/->Respectively +.>Global linear speed, global angular speed, < > of the smart site vehicle at the moment>Is Gaussian distributed noise;
converting the pose matrixFusing the inertial data and the mileage data to obtain fused data +.>：
；
In the method, in the process of the invention,、/>mileage noise and inertia noise respectively;
the step of performing weight updating and sampling processing on the pose particle set to obtain second positioning data of the intelligent building site vehicle comprises the following steps:
will be the firstTime pose particle set->Evenly distributed into the global space, wherein +.>，/>Is->The number represents->Particles of the pose at the moment +.>Is particle->A corresponding particle weight;
through the firstIterative estimation of the pose particle set of time +.>Time of day particle distribution to obtain estimated pose particle +.>：
；
In the method, in the process of the invention,respectively +. >Time-of-day smart worksite vehicle x-direction estimated displacement, y-direction estimated displacement, z-direction estimated displacement, angle estimated value, +.>、/>、/>、/>For intelligent construction vehicles at +.>Moment to->Time x-direction displacement change amount, y-direction displacement change amount, z-direction displacement change amount, angle change amount, +.>Respectively x-direction displacement noise, y-direction displacement noise, z-direction displacement noise and angle noise;
based on the firstTime of particle distribution versus pose particle set>The evaluation update is performed on each particle weight of the model to obtain an update weight +.>：
；
In the method, in the process of the invention,for observation, +.>Particles for a given estimated pose>Is sampled under the condition of +.>Is a desired probability density of (2);
based on the update weightResampling all particles in the pose particle set to obtain sampling weightsAnd sample particle->Based on the sampling weight +.>Calculating the number of effective particles +.>；
；
Determining the effective particle numberWhether or not it is greater than the particle threshold, if the effective particle number +.>Greater than the particle threshold, second positioning data +.>If the effective particle quantity +.>If the particle size is not larger than the particle threshold, adding a plurality of random particles into the pose particle set to obtain an updated particle set, and repeatedly estimating the particle distribution of the updated particle set, updating the weight and resampling until the updated particle set exists Number of effective particles->Greater than the particle threshold, outputting the corresponding iterative sampling weight +.>And iterate sampling particles->Second positioning data->Wherein->To update the number of particles in the particle set.
2. The intelligent building site vehicle positioning method based on Beidou according to claim 1, wherein the step of performing error correction processing on the Beidou satellite observation data to obtain corrected observation data comprises:
and carrying out first error correction processing on the Beidou satellite observation data based on a first preset formula, wherein the first preset formula is as follows:
；
in the method, in the process of the invention,for satellite clock correction value, ++>For clock bias +.>、/>The satellite signal transmitting time and the satellite signal receiving time are respectively +.>For clock drift +.>For frequency drift +.>For the speed of light->Is the eccentric angle of satellite orbit->For the satellite orbit near point angle, +.>Is the gravitational constant->Is a satellite orbit semi-long axis;
and carrying out second error correction processing on the Beidou satellite observation data based on a second preset formula, wherein the second preset formula is as follows:
；
；
in the method, in the process of the invention,for ionosphere correction value, +.>、/>Respectively the +.f in the preset correction model>First network parameter, th->Second network parameters- >For the latitude of the ionosphere puncture point, +.>Where the ionosphere is the point of penetration, < +.>、/>First correction constant and second correction constant, respectively, ">For the geographical latitude of the observation station, +.>For satellite altitude, +.>For the earth radius>For ionization layer height, +>Is the satellite azimuth;
and carrying out third error correction processing on the Beidou satellite observation data based on a third preset formula, wherein the third preset formula is as follows:
；
in the method, in the process of the invention,for tropospheric correction values, ++>For the third correction constant, +.>Is ground air pressure->Is ground water pressure->For the humidity of the ground->The ground height of the measuring station, the top height of the dry atmosphere layer and the top height of the wet atmosphere layer are respectively measured.
3. The method of Beidou-based intelligent worksite vehicle positioning of claim 1, wherein said step of determining a first pseudorange observation based on said rectified observation data includes:
determining a first pseudo-range observed quantity by a fourth preset formula and based on the corrected observed data：
；
In the method, in the process of the invention,for the real distance between the vehicle-mounted receiver and the Beidou satellite, </u >>For the speed of light->Satellite clock correction value, ionosphere correction value, troposphere correction value, ++>Clock error for vehicle-mounted receiver >Other errors.
4. The method for locating a vehicle at a smart worksite based on Beidou according to claim 1, wherein the step of calculating a first check value and a second check value of the first pseudo-range observables, and performing noise rejection on the pseudo-range observables based on the first check value and the second check value to obtain a second pseudo-range observables includes:
calculating a first check value of the first pseudo-range observed quantity：
；
In the method, in the process of the invention,first code deviation of Beidou satellite and vehicle-mounted receiver respectively, < ->For Beidou satellite, vehicle receiverTime variable corresponding to the first code bias, +.>Is the first observation noise;
calculating a second test value of the first pseudo-range observed quantity；
；
In the method, in the process of the invention,second code deviation of Beidou satellite and vehicle-mounted receiver respectively, < ->Time variable corresponding to second code deviation of Beidou satellite and vehicle-mounted receiver is +.>For the second observation noise +>Ionospheric delay parameters and error terms;
judging the first test valueWhether or not it is greater than a first culling threshold->And said second test value +.>Whether or not it is greater than a second culling threshold->Wherein->；
If the first check valueNo greater than a first culling threshold->And said second test value +. >No greater than a second culling threshold->The corresponding first pseudo-range observed quantity is retained, if the first check value +.>Greater than the first culling threshold->And/or said second test value +.>Greater than the second culling threshold->And eliminating the corresponding first pseudo-range observed quantity to obtain a second pseudo-range observed quantity.
5. The method for locating a vehicle at a smart worksite based on Beidou according to claim 1, wherein the step of performing coordinate joint calculation on the vehicle at the smart worksite based on the second pseudo-range observables to obtain first location data of the vehicle at the smart worksite comprises:
establishing a coordinate solution equation based on the second pseudo-range observables:
；
in the method, in the process of the invention,for the coordinates of the first positioning data, +.>Is->Second pseudo-range observables, < >>Is->Beidou satellite coordinates corresponding to second pseudo-range observables,>clock error for vehicle-mounted receiver>Is the speed of light;
performing taylor expansion on the coordinate solution equation to obtain a linear solution equation;
and solving the linear solving equation by adopting a weighted least square method to obtain first positioning data.
6. An intelligent building site vehicle positioning system based on big dipper, characterized in that, the system includes:
The acquisition module is used for acquiring Beidou satellite observation data, and carrying out error correction processing on the Beidou satellite observation data so as to obtain corrected observation data;
the detection module is used for determining a first pseudo-range observed quantity based on the correction observed data, calculating a first detection value and a second detection value of the first pseudo-range observed quantity, and carrying out noise rejection on the pseudo-range observed quantity based on the first detection value and the second detection value to obtain a second pseudo-range observed quantity;
the resolving module is used for carrying out coordinate joint resolving on the intelligent building site vehicle based on the second pseudo-range observed quantity so as to obtain first positioning data of the intelligent building site vehicle;
the fusion module is used for acquiring the inertial data and mileage data of the intelligent building site vehicle, and fusing the first positioning data, the inertial data and the mileage data to obtain fusion data;
the updating module is used for carrying out particle transformation on the fusion data to obtain a pose particle set, and carrying out weight updating and sampling processing on the pose particle set to obtain second positioning data of the intelligent building site vehicle;
the fusion module comprises:
an initial pose matrix determining sub-module for acquiring inertial data and mileage data of the intelligent building site vehicle and determining a first position based on the first positioning data, the inertial data and the mileage data Initial pose matrix of intelligent site vehicle at moment +.>：
；
In the method, in the process of the invention,is->X-axis coordinate and y-axis coordinate of moment intelligent construction site vehicle under global coordinateCoordinates, z-axis coordinates, ">Is->Angle of the smart site vehicle at moment in global coordinates,/->Respectively +.>Global linear velocity and global angular velocity of the intelligent site vehicle at the moment;
a transformation pose matrix determination submodule for determining the initial pose matrixPerforming motion transformation to obtain a transformation pose matrix +.>：
；
In the method, in the process of the invention,is->X-axis, y-axis and z-axis coordinates of the smart site vehicle at the moment in global coordinates,/->Is->Angle of moment intelligent construction site vehicle under global coordinates，/>Respectively +.>Global linear speed, global angular speed, < > of the smart site vehicle at the moment>Is Gaussian distributed noise;
a fusion sub-module for transforming the pose matrixFusing the inertial data and the mileage data to obtain fused data +.>：
；
In the method, in the process of the invention,、/>mileage noise and inertia noise respectively;
the updating module comprises:
a distribution sub-module for dividing the firstTime pose particle set->Evenly distributed into the global space, wherein +.>，/>Is->The number represents- >Particles of the pose at the moment +.>Is particle->A corresponding particle weight;
an estimation sub-module for passing throughIterative estimation of the pose particle set of time +.>Time of day particle distribution to obtain estimated pose particle +.>：
；
In the method, in the process of the invention,respectively +.>Time-of-day smart worksite vehicle x-direction estimated displacement, y-direction estimated displacement, z-direction estimated displacement, angle estimated value, +.>、/>、/>、/>For intelligent construction vehicles at +.>Moment to->Time x-direction displacement change amount, y-direction displacement change amount, z-direction displacement change amount, angle change amount, +.>Respectively x-direction displacement noise, y-direction displacement noise, z-direction displacement noise and angle noise;
an update sub-module for based on the firstTime of particle distribution versus pose particle set>The evaluation update is performed on each particle weight of the model to obtain an update weight +.>：
；
In the method, in the process of the invention,for observation, +.>Particles for a given estimated pose>Is sampled under the condition of +.>Is a desired probability density of (2);
a sampling submodule for updating the weight based on the update weightResampling all particles in said pose particle set to obtain a sampling weight +.>And sample particle->Based on the sampling weight +.>Calculating the number of effective particles +. >；
；
A second judging sub-module for judging the effective particle quantityWhether or not it is greater than the particle threshold, if the effective particle number +.>Greater than the particle threshold, thenTwo positioning data->If the effective particle quantity +.>If the particle size is not larger than the particle threshold, adding a plurality of random particles into the pose particle set to obtain an updated particle set, and repeatedly carrying out particle distribution estimation, weight updating and resampling on the updated particle set until the effective particle number is->Greater than the particle threshold, outputting the corresponding iterative sampling weight +.>And iterate sampling particles->Second positioning data->Wherein->To update the number of particles in the particle set.
7. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the beidou-based intelligent worksite vehicle positioning method of any one of claims 1 to 5.
8. A storage medium having stored thereon a computer program which when executed by a processor implements the beidou-based intelligent worksite vehicle positioning method of any one of claims 1 to 5.
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