CN115047502B - Mileage stake number automatic measuring and calculating and kilometer stake secondary checking method based on multimode positioning technology - Google Patents
Mileage stake number automatic measuring and calculating and kilometer stake secondary checking method based on multimode positioning technology Download PDFInfo
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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
- G01S19/01—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
- G01S19/254—Acquisition or tracking or demodulation of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS relating to Doppler shift of satellite signals
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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
- G01S19/258—Acquisition or tracking or demodulation of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS relating to the satellite constellation, e.g. almanac, ephemeris data, lists of satellites in view
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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
- G01S19/27—Acquisition or tracking or demodulation of signals transmitted by the system creating, predicting or correcting ephemeris or almanac data within the receiver
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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
- G01S19/40—Correcting position, velocity or attitude
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Abstract
The invention discloses a multi-mode positioning technology-based automatic measuring and calculating of mileage stake marks and a secondary checking method of kilometers stake, which changes original data into a computable longitude and latitude data format through data format conversion according to the original positioning data of a multi-mode positioning module; removing offset data after the processed data passes through a Kalman filter; matching the starting point/end point positions of roads, simultaneously comparing two adjacent longitude and latitude values in the inspection process, stopping for waiting by default when the change is smaller than a threshold value, accumulating/subtracting pile numbers by using positioning data reserved on a calculation curve, performing secondary checking on mileage pile numbers of kilometers piles according to the rule of rounding nearby, and realizing mileage pile number assignment for road facilities through positioning data matching. The invention realizes the functions of automatic and accurate measurement and check of the mileage stake marks in the road inspection maintenance scene based on multi-mode positioning, breaks through the limitation that the measurement of the mileage stake marks of the road depends on manual work and is inaccurate, and greatly improves the intellectualization, the precision and the high efficiency of the management and maintenance of the road foundation facilities.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a multimode positioning technology-based mileage stake mark automatic measurement and calculation and kilometer stake secondary checking method.
Background
At present, the traffic facility collection is mainly carried out by adopting a semi-manual collection mode, an intelligent collection scheme is introduced, after video and GPS positioning devices are carried, collection efficiency is greatly improved, huge gaps exist between traffic facility space positioning and mileage stake mark matching, highway information is usually positioned by mileage stake marks, such as robot detection data, vehicle-mounted equipment collection data and the like, the association between a common positioning system and a traditional traffic mileage stake mark system is not close enough, and the road mileage stake mark is not reserved with public space position information due to factors such as construction, design and construction requirements, so that the existing GPS or Beidou positioning system is difficult to convert the space position data into mileage stake mark positioning, and the original positioning data in the existing positioning module has certain data offset, so that accumulated data errors are easy to generate even if the data offset processing is carried out under the condition of uncorrectable data.
Patent number CN 113792613A discloses a precise positioning algorithm of mileage stake marks based on videos and GPS, the method obtains stake marks, time and time ratio by identifying mileage stake marks on driving videos, carries out unscented Kalman filtering on collected GPS tracks, searches GPS information corresponding to a time stamp, corrects the result to generate a mapping relation table, interpolates and fills up missing stake marks, the method has simple thinking, but has errors corresponding to the GPS information through the time stamp, and interpolation supplementing conditions are not consistent with interpolation modes in a non-uniform form.
Therefore, a mileage stake mark measuring and calculating method based on a multimode positioning technology, which is simple in logic, accurate and reliable, is urgently needed in the existing traffic system.
Disclosure of Invention
The invention aims to provide a multi-mode positioning technology-based automatic measuring and calculating of mileage stake marks and secondary checking method of kilometer stake.
The technical solution for realizing the purpose of the invention is as follows: a multimode positioning technology-based automatic measuring and calculating of mileage stake marks and secondary checking method of kilometer stake comprises the following specific steps:
Step 1, adopting a multimode positioning module, performing accurate screening and format conversion on original positioning data through byte matching, and changing original longitude and latitude data into a longitude and latitude data format which is easy to accurately calculate;
Step 2, performing error elimination on the converted data by using a Kalman filter, performing smoothing treatment on offset data, and performing correction process of the Kalman filter to obtain correction amount of a system state estimated value and an optimal estimated value after correction by processing a measurement residual value, thereby eliminating excessive offset data of a position track point in a positioning module;
Step 3, determining a road starting point/end point, and starting to match mileage stake marks with space positions on the whole road at the position of the road starting point/end point in the management road section, wherein the inspection vehicle runs at the normal speed of the management road;
Step 4, accumulating/subtracting pile numbers according to the longitude and latitude data processed in the step 2 through calculating the positioning data reserved on the curve;
Step 5, calibrating the inspection vehicle according to actual mileage stake marks at the road side when passing through the kilometer stake according to the principle of nearby rounding, judging whether accumulated errors are overlarge, and restarting mileage stake mark measurement from the starting point/end point of the previous kilometer stake when the errors exceed a set threshold value so as to realize secondary calibration of the kilometer stake;
and 6, calculating the distance between the longitude and latitude of the road facility and the longitude and latitude of the mileage stake mark to assign stake marks to the road facility.
Preferably, the raw positioning data in step 1 includes Beidou mode data, GPS mode data, and multimode mode data.
Preferably, in step 1, the specific step of converting the original latitude and longitude data into the latitude and longitude data format easy to calculate accurately is as follows: calculating the latitude and longitude offset of the latitude and longitude coordinates of the WGS84 coordinate system relative to the center point:
Δlon0＝lonWGS84-105
Δlat0＝latWGS84-35
Wherein lon WGS84 is the original WGS84 longitude, lat WGS84 is the original WGS84 latitude, deltalon 0 is the longitude offset of the original WGS84 longitude relative to the center point, deltalat 0 is the latitude offset of the original WGS84 longitude relative to the center point;
directly converting the offset of the longitude and latitude coordinates of the WGS84 coordinate system relative to the central point from longitude to meter, and fitting the offset of the longitude and latitude coordinates of the WGS84 coordinate system of a meter system unit through a sine periodic function and a polynomial;
And obtaining the geodetic coordinate offset of the longitude and latitude coordinates of the large place relative to the WGS84 coordinate system through radian-to-angle conversion, and converting the longitude and latitude decimal part.
Preferably, step 2 performs kalman filtering by calling a filter function, and the kalman filtering corresponds to a prediction equation and a correction equation one by one, and the prediction equation is:
the correction equation is:
Wherein the symbol A represents an estimated value, wherein I is a unit array, And x k represents the posterior state estimates at time k-1 and time k, respectively; /(I)Representing a priori state estimation values at the k moment, and P k-1 and P k represent posterior estimation cooperators at the k-1 moment and the k moment respectively; /(I)Representing a priori estimated covariance at time k; h represents the state variable to measurement transition matrix, K g represents the filter gain moment, A represents the state transition matrix, Q represents the process excitation noise covariance, R represents the measurement noise covariance, B is the matrix that converts the input to state,/>Residual errors representing actual observations and predicted observations;
In each epoch, the Kalman filter predicts and receives the current position, speed and clock error by using a state equation, and predicts and positions and speed information of satellites acquired in satellite ephemeris according to the prior estimated value of the state and the satellite position and speed information, a positioning module predicts pseudo-range and Doppler frequency shift values of each satellite, and obtains measurement residues for the difference between the predicted value and the actual measured value, and a correction process of the Kalman filter obtains a correction amount of a system state estimated value and a corrected optimal estimated value by processing the measurement residues.
Preferably, in step 3, when the road annual report data arrives at the route requiring management and maintenance, a corresponding downlink/uplink acquisition is selected from the start point/end point, and the latitude and longitude position where the start point/end point is located is used as the start point/end point pile number.
Preferably, the concrete method for accumulating/subtracting pile numbers by calculating the positioning data reserved on the curve is as follows:
two adjacent longitude and latitude points are set to be two points A and B, and radian between two points A, B is calculated as follows:
c＝sin(LatA×π/180)×sin(LatB×π/180)+cos(LatA×π/180)×cos(LatB×π/180)×cos((LonA-LonB)×π/180)
The distance between two points A, B is obtained as follows:
Distance＝R*cos-1(c)*π/180
wherein LatA and LatB are latitude coordinates corresponding to two points A and B, lonA and LonB are longitude coordinates corresponding to two points A and B, and R is the earth radius.
Preferably, before calculating the location data retained on the curve, the two adjacent longitude and latitude values are compared, and the vehicle is stopped and waited by default when the change is smaller than the threshold value, and the calculation of the mileage stake number accumulation is started from the coordinates after stopping to restarting.
Compared with the prior art, the invention has the remarkable advantages that:
1. The invention not only adopts the multimode positioning technology to control the original longitude and latitude data to the meter level, but also improves the accuracy of the space position;
2. The invention also removes offset data and secondary check of kilometer piles by an error elimination method, thereby improving the accuracy of mileage pile numbers.
3. The invention reduces the traversal calculation quantity through the road ID field and the like, calculates the distance between the road facility and the longitude and latitude of the mileage stake mark, and realizes the assignment of the mileage stake mark to the road facility.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic diagram of a method for automatically measuring and calculating mileage stake marks and secondarily checking kilometers stake marks based on a multimode positioning technology.
Fig. 2 is a schematic flow chart of data screening and format conversion in a method for automatically measuring and calculating mileage stake marks and secondarily checking kilometers stake marks based on a multimode positioning technology.
Fig. 3 is a schematic flow chart of longitude and latitude coordinate processing in a method for automatically measuring and calculating mileage stake marks and secondarily checking kilometer stake marks based on a multimode positioning technology.
Detailed Description
A multimode positioning technology-based automatic measuring and calculating of mileage stake marks and secondary checking method of kilometer stake comprises the following specific steps:
and step 1, adopting a high-precision multimode positioning module, performing accurate screening and format conversion on original positioning data through byte matching, and changing longitude and latitude data in original $ GNRMC into a longitude and latitude data format easy to accurately calculate.
After the first use module downloads satellite data through cold start, when the multimode positioning module continuously finds three or more satellites, the multimode positioning module starts to work normally. The original positioning data comprises BD (Beidou mode), GP (GPS mode) and GN (multimode mode), positioning requirements under various regions and scenes can be met, the obtained original data comprises various satellite positioning information, the $ GNRMC positioning data combined by various satellite data such as BD (Beidou mode) and GP (GPS mode) are accurately screened out from the various positioning data through a byte matching screening mode, field data ：$XXRMC,<1>,<2>,<3>,<4>,<5>,<6>,<7>,<8>,<9>,<10>,<11>,<12>*hh<CR><LF>, under $ GNRMC are selected to mainly screen <1> UTC time, <3> latitude ddmm.mmmm (degree) format, <4> latitude hemisphere, <5> longitude dddmm.mmmm (degree) format, <6> longitude hemisphere and <9> UTC date, and the longitude and latitude data of an original WGS84 coordinate system are formatted into GCJ02 coordinate system data to be changed into longitude and latitude data format which is easy to calculate, and the detailed conversion steps are as follows:
Since the latitude of China ranges from 73 degrees 33'E to 135 degrees 05' E: 3 DEG 51'N to 53 DEG 33' N. Thus, the WGS84 coordinates (105.0E, 35.0N) are approximately at the center of China. And calculating the latitude and longitude offset of the latitude and longitude coordinates of the WGS84 coordinate system relative to the central point, wherein the latitude and longitude units are degrees.
Δlon0＝lonWGS84-105
Δlat0＝latWGS84-35
Where lon WGS84 is the original WGS84 longitude, lat WGS84 is the original WGS84 latitude, deltalon 0 is the longitude offset of the original WGS84 longitude from the center point, deltalat 0 is the latitude offset of the original WGS84 longitude from the center point.
And then, directly converting the offset of the longitude and latitude coordinates of the WGS84 coordinate system relative to the central point into meters (m) from longitude, and fitting the offset of the longitude and latitude coordinates of the WGS84 coordinate system of one metric unit through a sine periodic function and a polynomial. In practical applications, these two formulas are taken out separately to form two methods: transformLat and transformLon.
Wherein Δlon 1 is the WGS84 coordinate system longitude coordinate offset in metric units, and Δlat 1 is the WGS84 coordinate system latitude coordinate offset in metric units.
The numerator and denominator in the following formula can obtain a dimensionless numerical value, the numerical value is regarded as an radian angle of the longitude and latitude of the big place relative to the longitude and latitude of the WGS84 coordinate plus an offset, and the earth coordinate offset of the longitude and latitude coordinate of the big place relative to the WGS84 coordinate system can be obtained through the conversion from radian to angle.
lonGCJ-02＝lonWGS84+Δlon2
latGCJ-02＝latWGS84+Δlat2
Finally, some conversion of the longitude and latitude fraction is required, essentially without too much offset.
And 2, aiming at the data migration problem of the positioning data, performing error elimination on the converted data by using a Kalman filter, performing smoothing treatment on the migration data, and performing a Kalman filtering correction process to obtain a correction amount of a system state estimated value and a corrected optimal estimated value by processing a measurement residual value to eliminate the excessive migration data of the position track point in the positioning module.
The positioning result of the multimode positioning module data after screening and conversion is that the data is continuously seen to have the situation that the data is possibly shifted because the positioning result is single point by point, and the situation is not in line with the actual situation. The position trajectory is smoothed by a kalman filter in consideration of continuity of the object motion and slowness of motion variation. The kalman filter is used to solve the problem of state estimation in discrete time control described by a linear differential equation, with the goal of minimizing the mean square error of the estimated value of the system state. For a linear discrete system, let its system equation be:
xk＝Axk-1+Buk-1+wk-1
the measurement equation is:
yk＝Hxk+vk
Wherein the random variables w k and v k represent process noise and measurement noise, respectively. It is assumed that they are normally distributed white noise independent of each other, and their covariance matrices are Q and R, respectively. In practice, A, B, H, Q and R may both vary over time. The kalman filtering process can be divided into two steps: prediction and correction. The prediction is to estimate the state value of the previous moment through a state equation, and also estimate an error covariance matrix, and the state value is called a priori value because the state value is not corrected at this time, and is represented by a superscript -. The predictive equation is:
The correction is to calculate the Kalman gain first, then to update the state and update the error covariance matrix according to the Kalman gain by using the measured value and the state predicted value. The correction equation is:
Wherein the symbol A represents an estimated value, wherein I is a unit array, And x k represents the posterior state estimation values at the k-1 time and the k time respectively, which are one of the filtering results, namely the updated result, and are also called optimal estimation. /(I)The a priori state estimate representing time k is the result of an intermediate calculation of the filtering, i.e. the result of time k predicted from the optimal estimate of the previous time (time k-1), is the result of the prediction equation. P k-1 and P k represent the a posteriori estimated covariance at time k-1 and time k, respectively (i.e./>And covariance of x k, representing uncertainty of the state), is one of the results of the filtering. /(I)Representing a priori estimated covariance at time kIs the covariance of the filter). H represents a conversion matrix from a state variable to measurement (observation), represents a relation connecting the state and the observation, is a linear relation in Kalman filtering, is responsible for converting a measured value in m dimension into n dimension so as to conform to the mathematical form of the state variable, and is one of preconditions of filtering. y k represents the measured value (observed value), which is the input of the filtering. K g represents a filter gain matrix, which is an intermediate calculation of the filter, the kalman gain, or the kalman coefficient. A represents a state transition matrix, which is actually a guess model for the state transition of the object. For example, in maneuvering target tracking, a state transition matrix is often used to model the motion of the target, which may be uniform linear motion or uniform acceleration motion. When the state transition matrix does not conform to the state transition model of the target, the filtering diverges very rapidly. Q represents the process excitation noise covariance (covariance of the system process). This parameter is used to represent the error between the state transition matrix and the actual process. Because we cannot directly observe the process signal, the value of Q is difficult to determine. Is the state variable used by the kalman filter to estimate the discrete time process, also known as noise from the prediction model itself. R represents the measurement noise covariance. When the filter is actually implemented, the measurement noise covariance R is generally observed and is a known condition of the filter. B is a matrix that converts the input into a state.The residual errors of actual observation and prediction observation are corrected a priori (predicted) together with the Kalman gain to obtain a posterior.
The Kalman filter provides not only the state update values but also its error covariance, which allows it to automatically adjust the Kalman gain based on the measured values and the error covariance to achieve an optimal estimate. The dynamic variation of the kalman gain is explained colloquially, that is, if the predicted value is compared with the spectrum (the error covariance is small), the filtered result is more informed of the predicted value, whereas if the measured value is compared with the spectrum (the R is smaller), the filtered result is more informed of the measured value. Compared with the least square method, the Kalman filter can be calculated in a recursive manner, the measured value can be updated, and the calculation can be performed without waiting until all the measured values are finished.
And calling a filter function to carry out Kalman filtering, wherein the filter function firstly carries out state prediction, and then calling a filter_function to carry out correction. In each epoch, the Kalman filter predicts and receives states such as current position, speed, clock error and the like by using a state equation, predicts and positions and speed information of satellites acquired in satellite ephemeris according to a priori estimated value of the states, predicts and positions pseudo-range and Doppler frequency shift values of each satellite by using a positioning module, obtains measurement residues for differences between the predicted values and actual measured values, and finally obtains correction amounts of system state estimated values and corrected optimal estimated values by processing the measurement residues.
On the basis of high-precision data based on the multimode positioning module, the initial value in the equation is superior to that of a common module, the precision is higher in the subsequent prediction, the numerical value optimization is reduced in the correction process, and the error elimination precision is controlled at the decimeter level.
And step 3, the patrol personnel determines the starting point/end point of the road, the mileage stake number of the whole road is matched with the space position at the starting point/end point of the road in the management road section, and the patrol vehicle runs at the normal speed of the management road.
When the road annual report data arrives at a route to be managed and maintained, if the road annual report data starts from a starting point, the acquisition direction is descending, the longitude and latitude position of the starting point is taken as a stake mark K0+00, and the mileage stake mark and the space position of the whole road are matched; if the road side is started, the acquisition direction is upward, the longitude and latitude position of the road side is taken as the pile number Km+dd (determined according to the actual mileage pile number or annual report data) and the matching is started.
And 4, accumulating/subtracting the pile number by calculating the positioning data reserved on the curve according to the longitude and latitude data processed in the step 2.
Setting two adjacent longitude and latitude points processed in the step 2 as points A and B, wherein the corresponding latitude is as follows: lat A、LatB, longitude: lon A、LonB. The radian between two points of A, B is calculated as:
c＝sin(LatA×π/180)×sin(LatB×π/180)+cos(LatA×π/180)×cos(LatB×π/180)×cos((LonA-LonB)×π/180)
The distance between A, B points can be obtained as follows:
Distance＝R*cos-1(c)*π/180
The Distance error range calculated by the method can be guaranteed to be below 0.2 meter, the high-precision multimode positioning module adopted by the design can reach the decimeter level through measuring the error precision of data, the refreshing frequency of one time per second ensures that the data cannot be free of data in the whole measuring and calculating process, and the error elimination data in the step 2 are fewer. If the distance is the starting point, performing addition calculation on the pile number K0+00, and performing pile number accumulation on the calculated distance; if the distance starts from the end point, subtracting the pile number Km+dd, and subtracting the pile number from the calculated distance.
Preferably, in the inspection process, two adjacent longitude and latitude values are compared simultaneously, and the vehicle is stopped by default and waits when the change is smaller than the threshold value.
Before calculating the distance between adjacent periodic positioning data, if longitude and latitude coordinate changes are smaller than 0.00001, the default vehicle waits for parking, the data change is due to the data migration problem of the positioning module, the migration errors are prevented from being continuously accumulated when the vehicle passes through a signal lamp or parks, and the accuracy of the mileage stake marks is guaranteed to the greatest extent.
When the patrol vehicle passes through the road intersection, the longitude and latitude change during the waiting period of the red light is as follows:
in the table, the longitude and latitude data change is taken as an example, when the patrol vehicle reaches the point B, the vehicle is in a parking state, and the coordinate change of the point C and the point B is analyzed to find that the coordinate change of the longitude and the latitude is smaller than 0.00001. When the G point is reached, the longitude coordinate change is smaller than 0.00001, but the latitude coordinate change is 0.000042, the distance from the B point to the G point is calculated by the accumulation of the mileage stake marks, and the like.
And 5, accurately calibrating the inspection vehicle according to the actual mileage stake marks at the road side when the inspection vehicle passes through the kilometer stake according to the principle of nearby rounding, judging whether the accumulated error is overlarge, and restarting the mileage stake mark measurement from the starting point/end point of the previous kilometer stake when the error exceeds a set threshold value so as to realize the secondary calibration of the kilometer stake.
In the actual measurement process, based on the existing mileage stake marks on the road side, the mileage stake marks of the starting point and the ending point are accurately checked at first when the measurement is started, and then the kilometer stake in the measurement process can be accurately checked by utilizing the actual mileage stake marks on the road side when the measurement is performed. Setting a threshold value for the accumulated error, and based on the high-precision performance of the multimode positioning module data, kalman filtering, default parking waiting and other error elimination schemes, when the difference between the measured mileage stake marks and the actual mileage stake marks at the road side exceeds 0.5km, considering that the measured mileage stake marks have the problem of overlarge measured errors, and restarting the measurement of the mileage stake marks from the starting point/end point of the road.
For example, in the case that the pile number measured at the position of the first situation is K8+690, the actual mileage pile number at the road side corresponds to K8+000 km, and when the actual kilometer pile number at the road side corresponding to the kilometer pile number measured before is not more than +/-500 meters, the measured mileage pile number is measured again by taking the previous K7+000 km as the starting point.
The pile number measured at the position of the second condition is K8+009, and the road side actual mileage pile number corresponds to K8+000 kilometers, the current pile number can be manually modified to be the actual correct pile number K8+000, the subsequent measurement and calculation is modified, and the accumulation/subtraction is continuously carried out according to the new pile number until the whole road pile number is measured and calculated.
And 6, calculating the distance between the longitude and latitude of the road facility and the longitude and latitude of the mileage stake mark to assign stake marks to the road facility.
And after the mileage stake marks of the whole road are calculated, forming a mileage stake mark and space longitude and latitude matching database. In order to reduce the traversing calculation of the whole database, the mileage stake marks and the road sections where the road facilities are located are determined through the road ID field and the like before calculation, the distance calculation mode between two longitude and latitude points in the step 4 is utilized, the mileage stake marks closest to the corresponding longitude and latitude of the road facilities are obtained through calculation of the longitude and latitude acquired by the road facilities and the database, and the mileage stake mark coordinates are associated, so that the mileage stake marks are assigned to the road facilities, and the management and maintenance efficiency of the road facilities is greatly improved.
Claims (7)
1. A multimode positioning technology-based automatic measuring and calculating of mileage stake marks and secondary checking method of kilometer stake is characterized by comprising the following specific steps:
Step 1, adopting a multimode positioning module, performing accurate screening and format conversion on original positioning data through byte matching, and changing original longitude and latitude data into a longitude and latitude data format which is easy to accurately calculate;
Step 2, performing error elimination on the converted data by using a Kalman filter, performing smoothing treatment on offset data, and performing correction process of the Kalman filter to obtain correction amount of a system state estimated value and an optimal estimated value after correction by processing a measurement residual value, thereby eliminating excessive offset data of a position track point in a positioning module;
Step 3, determining a road starting point/end point, and starting to match mileage stake marks with space positions on the whole road at the position of the road starting point/end point in the management road section, wherein the inspection vehicle runs at the normal speed of the management road;
Step 4, accumulating/subtracting pile numbers according to the longitude and latitude data processed in the step 2 through calculating the positioning data reserved on the curve;
Step 5, calibrating the inspection vehicle according to actual mileage stake marks at the road side when passing through the kilometer stake according to the principle of nearby rounding, judging whether accumulated errors are overlarge, and restarting mileage stake mark measurement from the starting point/end point of the previous kilometer stake when the errors exceed a set threshold value so as to realize secondary calibration of the kilometer stake;
and 6, calculating the distance between the longitude and latitude of the road facility and the longitude and latitude of the mileage stake mark to assign stake marks to the road facility.
2. The method for automatically measuring and calculating the mileage stake marks and secondarily checking the kilometer stake based on the multimode positioning technology as claimed in claim 1, wherein the original positioning data in the step 1 includes Beidou mode data, GPS mode data and multimode mode data.
3. The method for automatically measuring and calculating the mileage stake marks and secondarily checking the kilometer stake marks based on the multimode positioning technology as claimed in claim 1, wherein the specific steps of converting the original longitude and latitude data into the longitude and latitude data format easy to calculate accurately in the step 1 are as follows: calculating the latitude and longitude offset of the latitude and longitude coordinates of the WGS84 coordinate system relative to the center point:
Δlon0＝lonWGS84-105
Δlat0＝latWGS84-35
Wherein lon WGS84 is the original WGS84 longitude, lat WGS84 is the original WGS84 latitude, deltalon 0 is the longitude offset of the original WGS84 longitude relative to the center point, deltalat 0 is the latitude offset of the original WGS84 longitude relative to the center point;
directly converting the offset of the longitude and latitude coordinates of the WGS84 coordinate system relative to the central point from longitude to meter, and fitting the offset of the longitude and latitude coordinates of the WGS84 coordinate system of a meter system unit through a sine periodic function and a polynomial;
And obtaining the geodetic coordinate offset of the longitude and latitude coordinates of the large place relative to the WGS84 coordinate system through radian-to-angle conversion, and converting the longitude and latitude decimal part.
4. The method for automatically measuring and calculating the mileage stake mark and secondarily checking the kilometer stake mark based on the multimode positioning technology as set forth in claim 1, wherein the step 2 is characterized in that a filter function is called to perform Kalman filtering, and the method is in one-to-one correspondence with a prediction equation and a correction equation, wherein the prediction equation is:
the correction equation is:
Wherein the symbol A represents an estimated value, wherein I is a unit array, And x k represents the posterior state estimates at time k-1 and time k, respectively; /(I)Representing a priori state estimation values at the k moment, and P k-1 and P k represent posterior estimation cooperators at the k-1 moment and the k moment respectively; /(I)Representing a priori estimated covariance at time k; h represents the state variable to measurement transition matrix, K g represents the filter gain moment, A represents the state transition matrix, Q represents the process excitation noise covariance, R represents the measurement noise covariance, B is the matrix that converts the input to state,/>Residual errors representing actual observations and predicted observations;
In each epoch, the Kalman filter predicts and receives the current position, speed and clock error by using a state equation, and predicts and positions and speed information of satellites acquired in satellite ephemeris according to the prior estimated value of the state and the satellite position and speed information, a positioning module predicts pseudo-range and Doppler frequency shift values of each satellite, and obtains measurement residues for the difference between the predicted value and the actual measured value, and a correction process of the Kalman filter obtains a correction amount of a system state estimated value and a corrected optimal estimated value by processing the measurement residues.
5. The method for automatically measuring and calculating the mileage stake marks and secondarily checking the kilometer stake marks based on the multimode positioning technology according to claim 1, wherein in the step 3, when the road annual report data reaches a route requiring management and maintenance, corresponding descending/ascending collection is selected from a starting point/terminal point, and the longitude and latitude position of the starting point/terminal point is used as the stake mark of the starting point/terminal point.
6. The method for automatically measuring and calculating the mileage stake marks and secondarily checking the kilometer stake based on the multimode positioning technology as claimed in claim 1, wherein the concrete method for accumulating/subtracting the stake marks by calculating the positioning data reserved on the curve is as follows:
two adjacent longitude and latitude points are set to be two points A and B, and radian between two points A, B is calculated as follows:
c＝sin(LatA×π/180)×sin(LatB×π/180)+cos(LatA×π/180)×cos(LatB×π/180)×cos((LonA-LonB)×π/180)
The distance between two points A, B is obtained as follows:
Distance＝R*cos-1(c)*π/180
wherein LatA and LatB are latitude coordinates corresponding to two points A and B, lonA and LonB are longitude coordinates corresponding to two points A and B, and R is the earth radius.
7. The method for automatically measuring and calculating the mileage stake marks and secondarily checking the kilometer stake marks based on the multimode positioning technology according to claim 1, wherein before the positioning data reserved on the curve are calculated, the adjacent longitude and latitude values are compared, and when the change is smaller than a threshold value, the default parking waiting is performed, and the mileage stake mark accumulation is calculated from the coordinates after the parking to the restarting.
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