CN112833785A - Track tracking method and system based on filtering fusion - Google Patents
Track tracking method and system based on filtering fusion Download PDFInfo
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- CN112833785A CN112833785A CN202110002878.9A CN202110002878A CN112833785A CN 112833785 A CN112833785 A CN 112833785A CN 202110002878 A CN202110002878 A CN 202110002878A CN 112833785 A CN112833785 A CN 112833785A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/002—Measuring arrangements characterised by the use of optical techniques for measuring two or more coordinates
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/03—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness by measuring coordinates of points
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/26—Measuring arrangements characterised by the use of optical techniques for measuring angles or tapers; for testing the alignment of axes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/02—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/02—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness
- G01B21/04—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness by measuring coordinates of points
- G01B21/045—Correction of measurements
Abstract
The invention discloses a track tracking method and system based on filtering fusion, which relate to the field of track traffic.A sleeper is scanned as the basis of track tracking, and meanwhile, the interval between sleepers is considered, a standard width of the sleeper is arranged between a first laser scanner and a second laser scanner as the interval, so that the continuity of data is ensured, and the track tracking continuity is maintained; by obtaining the comparison between the measured span of the sleeper and the standard width of the sleeper and utilizing the Kalman filtering algorithm, the accumulated error of the encoder is compensated, so that the accuracy of automatic tracking can be ensured after the vehicle runs for a long time.
Description
Technical Field
The invention relates to the field of rail transit, in particular to a method and a system for tracking a rail based on filtering fusion.
Background
Tracking of a working vehicle on a railway is an important task, and the track of the vehicle is detected through various instruments. The offset of the vehicle will result in the accuracy of the operation of the systems on board, such as track alignment of the track laying vehicle, positioning accuracy of the roller placement vehicle, and commonly used positioning instruments including encoders, laser scanners, and the like.
An encoder (encoder) is a device that compiles, converts, and formats signals (e.g., bitstreams) or data into a form of signals that can be communicated, transmitted, and stored. Encoders convert angular or linear displacements, called codewheels, into electrical signals, called coderulers. The encoder can be divided into a contact type and a non-contact type according to a reading mode; encoders can be classified into an incremental type and an absolute type according to their operation principles. The incremental encoder converts displacement into periodic electrical signals, and then converts the electrical signals into counting pulses, and the number of the pulses is used for expressing the magnitude of the displacement. Each position of the absolute encoder corresponds to a certain digital code, so that its representation is only dependent on the start and end positions of the measurement, and not on the intermediate course of the measurement.
When the accumulation type encoder is adopted, no matter the encoder is an angular displacement type encoder or a linear displacement type encoder, errors are accumulated due to the principles of jitter, circular feature transformation and the like, so that the errors of systems for distance measurement, positioning and the like by using the encoder are overlarge, and the accuracy is reduced.
Disclosure of Invention
In order to solve error accumulation caused by conditions of vehicle steering, wheel deformation, vehicle slipping and the like in the track tracking process of an encoder, the invention provides a track tracking method based on filtering fusion, which takes a vehicle running direction as an X axis, a horizontal plane vertical to the X axis as a Y axis, a first laser scanner and a second laser scanner are respectively arranged on two sides of a track, the tire of a vehicle is provided with the encoder, and data is processed through an upper computer, and the method comprises the following steps:
a1: the upper computer receives laser scanning curves of the first laser scanner and the second laser scanner and pulse signals with preset distances as intervals of the encoder;
a2: acquiring the position coordinates of the current jump characteristic points according to the laser scanning curve, and calculating the displacement distance of the tire according to the number of the pulse signals;
a3: acquiring the position deviation of the current jumping feature point and the last jumping feature point according to the position coordinates;
a4: calculating a vehicle offset angle according to the position deviation;
a5: tracking a vehicle track according to the vehicle offset angle and the displacement distance;
while executing the step a2, according to the laser scanning result, the method further comprises the following steps:
b1: when the sleeper is scanned, acquiring position coordinates of a head end characteristic point and a tail end characteristic point of a jump extreme value according to a laser scanning curve;
b2: obtaining the measured width of the sleeper according to the coordinate difference value of the head end characteristic point and the tail end characteristic point, comparing the measured width with the standard width of the sleeper to obtain a difference value, and calibrating an encoder according to the difference value;
c1: when no crosstie is scanned, the encoder is calibrated using a Kalman filter fusion algorithm.
Furthermore, the first laser scanner and the second laser scanner are separated by a distance of a standard crosstie width in the X-axis direction, the first laser scanner and the second laser scanner are equidistant from a track median line, and the scanning range of the laser scanners is the crosstie.
Further, in step A3 or step B3, the
The jumping characteristic point is a data point of a laser scanning curve jumping when the laser scanner scans the crosstie;
the head end characteristic point and the tail end characteristic point are data points at two ends of a laser scanning curve, wherein the data points are continuously positioned at extreme values during the period from the laser scanner to the sleeper.
Further, the step a1 is followed by the steps of:
a11: carrying out moving median denoising on the laser scanning data;
a12: carrying out noise filling and data standardization on the laser scanning data;
a13: and fitting the laser scanning data by using a least square method.
Further, in step a4, the vehicle offset angle is obtained by using a trigonometric function according to the difference between the position deviations of the jump feature points on the X axis and the Y axis,
wherein A is a vehicle offset angle, X is a coordinate difference of the positional deviation on an X axis, Y is a coordinate difference of the positional deviation on a Y axis,
is the displacement distance of the tire.
The invention also provides a track tracking system based on filtering fusion, which takes the running direction of a vehicle as an X axis, the direction of a horizontal plane vertical to the X axis as a Y axis, a first laser scanner and a second laser scanner are respectively arranged at two sides of a track, an encoder is arranged on a tire of the vehicle, and data is processed by an upper computer, wherein:
the first laser scanner and the second laser scanner are used for scanning the sleepers through laser and generating corresponding laser scanning curves;
the encoder is used for measuring the rotation angle of the tire at intervals of a preset distance and feeding back a pulse signal;
the upper computer is used for acquiring the position coordinates of the current jump characteristic points according to the laser scanning curve and calculating the displacement distance of the tire according to the number of the pulse signals;
the upper computer is also used for acquiring the position deviation of the current jump characteristic point according to the position coordinate, calculating a vehicle offset angle by combining the position deviation of the last jump characteristic point, and tracking the vehicle according to the vehicle offset angle and the displacement distance;
the upper computer is further used for obtaining position coordinates of the head end characteristic point and the tail end characteristic point of the jumping extreme value according to the laser scanning curve, obtaining a sleeper measurement width according to a difference value of the position coordinates of the head end characteristic point and the tail end characteristic point, obtaining a difference value between the measurement width and a sleeper standard width, calibrating the encoder by using the difference value when the sleeper is scanned, and calibrating the encoder by using a Kalman filtering algorithm when the sleeper is not scanned.
Furthermore, the first laser scanner and the second laser scanner are separated by a distance of a standard crosstie width in the X-axis direction, the first laser scanner and the second laser scanner are equidistant from a track median line, and the scanning range of the laser scanners is the crosstie.
Further, the
The jumping characteristic point is a data point of a laser scanning curve jumping when the laser scanner scans the crosstie;
the head end characteristic point and the tail end characteristic point are data points at two ends of a laser scanning curve, wherein the data points are continuously positioned at extreme values during the period from the laser scanner to the sleeper.
Further, the upper computer also comprises a preprocessing unit,
the preprocessing unit is used for carrying out moving median denoising on the laser scanning data, filling noise points, standardizing data and fitting the laser scanning data by using a least square method.
Further, the vehicle offset angle is obtained by using a trigonometric function according to the difference between the position deviations of the jump feature points on the X axis and the Y axis, respectively,
wherein A is the offset angle of the vehicle, and X is the position deviation on the X axisY is the coordinate difference of the position deviation on the Y-axis,
is the displacement distance of the tire.
Compared with the prior art, the invention at least has the following beneficial effects:
(1) according to the track tracking method and system based on filtering fusion, a sleeper is scanned to serve as a track tracking basis, meanwhile, the interval between sleepers is considered, the standard width of the sleeper is set between a first laser scanner and a second laser scanner to serve as a distance, the continuity of data is guaranteed, and therefore the track tracking continuity is guaranteed;
(2) and for the unilateral laser, the two conditions of scanning the laser to the crosstie and scanning the laser to the crosstie are divided, when the laser scans the crosstie, the standard distance of the crosstie is utilized to calibrate the output distance of the encoder, and when the laser does not scan the crosstie, the Kalman filtering is utilized to calibrate the output distance of the encoder. The method comprises the steps that the measured span of the sleeper is obtained and compared with the standard width of the sleeper, and the accumulated error of an encoder is compensated by the aid of a Kalman filtering fusion method, so that the accuracy of automatic tracking can be guaranteed after a vehicle runs for a long time;
(3) after calibration, the reliability of the distance value measured by the encoders is greatly improved, and the running distance and the running track of the vehicle can be obtained through the distance values measured by the encoders on the two sides. The cross section of the sleeper is obtained by scanning the sleeper through the laser, the highest point is used as a characteristic point, and the distance and the angle of the vehicle deviating from the center line are judged through the change of the characteristic point, so that the data processing is simplified, the processing speed of the upper computer is accelerated, and the tracking real-time performance is guaranteed.
Drawings
FIG. 1 is a diagram of method steps for a filter fusion based track tracking method;
FIG. 2 is a system diagram of a filter fusion based track tracking system;
FIG. 3 is a schematic view of a laser scanner scan;
drawings and description: 1-sleeper, 2-first laser scanner, 3-second laser scanner, 4-scanning range.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
Example one
In order to solve the problem of error accumulation caused by vehicle steering in the track tracking process of an encoder, as shown in fig. 1, the invention provides a track tracking method based on filter fusion, which takes a vehicle running direction as an X axis, a horizontal plane perpendicular to the X axis as a Y axis, a first laser scanner and a second laser scanner are respectively arranged at two sides of a track, an encoder is arranged on a vehicle tire (because a moving distance is measured according to tire rotation, an angular displacement encoder is adopted in the embodiment), and data is processed through an upper computer, and the track tracking method comprises the following steps:
a1: the upper computer receives laser scanning curves of the first laser scanner and the second laser scanner, and pulse signals with preset distances as intervals are sent by the encoder.
The preset distance is a tire moving distance obtained by combining the tire radius and one pulse signal period of the encoder.
As shown in fig. 3, since the sleepers 1 are spaced apart and adjacent sleepers are spaced apart by a certain width, in order to ensure that the laser scanning curve of the sleepers can maintain continuity to the maximum extent, the present invention is provided with a first laser scanner 2 and a second laser scanner 3 on both sides of the track, which are spaced apart by a distance of one standard width of the sleepers in the X-axis direction, the first laser scanner and the second laser scanner being equidistant from the median line of the track, and the scanning range 4 of the laser scanner being the sleepers. Through the arrangement, the periodicity consistency of the laser scanning curve of the sleeper is ensured, the laser scanning curve can be kept coherent, and the continuity and the timeliness of the finally obtained tracking result are ensured.
Meanwhile, because laser scanning is based on the characteristics of light, and the vehicle and the external environment are likely to interfere with the scanning result, the laser scanning curve needs to be preprocessed after being acquired, and the method comprises the following steps:
a11: carrying out moving median denoising on the laser scanning data;
a12: carrying out noise filling and data standardization on the laser scanning data;
a13: and fitting the laser scanning data by using a least square method.
Dead zones caused by strong light or laser triangulation scanning are removed through median denoising, and then noise filling and standardized processing are carried out on laser scanning data, so that a laser scanning curve is clearer, and data processing of an upper computer is facilitated. And the least square method is adopted to fit the laser scanning curve, so that over-fitting and under-fitting can be avoided.
A2: acquiring the position coordinates of the current jump characteristic points according to the laser scanning curve, and calculating the displacement distance of the tire according to the pulse signals;
a3: acquiring the position deviation of the current jumping feature point and the last jumping feature point according to the position coordinates;
a4: calculating a vehicle offset angle according to the first position deviation and the second position deviation;
a5: and tracking the vehicle track according to the vehicle offset angle and the displacement distance.
The jumping characteristic point is a data point of a laser scanning curve jumping when the laser scanner scans the crosstie. And the vehicle offset angle is obtained by using a trigonometric function according to the difference of the position deviation between the jump characteristic points on the X axis and the Y axis respectively,
wherein A is a vehicle offset angle, X is a coordinate difference of the positional deviation on an X axis, Y is a coordinate difference of the positional deviation on a Y axis,
is the displacement distance of the tyre。
The vehicle offset angle is obtained by adopting a mode of performing trigonometric function calculation on the coordinate difference of the position coordinate on the X axis/Y axis, the data processing is simpler, and meanwhile, the calculation is performed based on the position coordinate of the current moment t and the position coordinate of the last moment t-1, so that the real-time performance of a processing result can be ensured.
While executing the step a2, according to the laser scanning result, the method further comprises the following steps:
b1: when the sleeper is scanned, acquiring position coordinates of a head end characteristic point and a tail end characteristic point of a jump extreme value according to a laser scanning curve;
b2: obtaining the measured width of the sleeper according to the coordinate difference value of the head end characteristic point and the tail end characteristic point, comparing the measured width with the standard width of the sleeper to obtain a difference value, and calibrating an encoder according to the difference value;
c1: when no crosstie is scanned, the encoder is calibrated using a Kalman filter fusion algorithm.
When the accumulation type encoder is adopted, no matter the encoder is an angular displacement type encoder or a linear displacement type encoder, errors are accumulated due to the principles of jitter, circular feature transformation and the like, so that the errors of systems for distance measurement, positioning and the like by using the encoder are overlarge, and the accuracy is reduced. In consideration of the point, the invention adopts a Kalman filtering fusion mode to compensate the accumulated error of the encoder, thereby obtaining a more accurate path.
The Kalman filtering fusion comprises the step of carrying out encoder distance calibration by using a sleeper standard distance, and the formula is as follows:
in the formula, n is the number of encoder pulse signals, l is the standard width of the sleeper, dencodeMeasuring distance, d 'for the encoder'encodeIs the corrected distance.
The method also comprises the following step of calibrating the distance of an encoder of the laser camera, wherein the formula is as follows:
xk＝xk-1+Kk(zk-Hxk-1)
Pk＝Pk-1-KkHPk-1
Kk＝Pk-1H/(Pk-1H2+R)
in the formula, xkIs the k-th new optimal estimate of distance, PkThe original value is 1, and K is continuously updated according to a formula in predictionkFor the k-th Kalman gain, H is the inverse of the laser camera encoder coefficient, zkThe number of pulses of the laser camera encoder read for the k-th time, and R is the noise covariance of the instrument.
Example two
In order to better understand the technical content of the present invention, the present embodiment describes the present invention in the form of a system structure, and as shown in fig. 2, the present invention proposes a track tracking system based on filter fusion, which uses the vehicle driving direction as an X-axis, a horizontal plane perpendicular to the X-axis as a Y-axis, two sides of a track are respectively provided with a first laser scanner and a second laser scanner, a vehicle tire is provided with an encoder, and data is processed by an upper computer, wherein:
the first laser scanner and the second laser scanner are used for scanning the sleepers through laser and generating corresponding laser scanning curves;
the encoder is used for measuring the rotation angle of the tire at intervals of a preset distance and feeding back a pulse signal;
the upper computer is used for acquiring the position coordinates of the current jump characteristic points according to the laser scanning curve and calculating the displacement distance of the tire according to the number of the pulse signals;
the upper computer is also used for acquiring the position deviation of the current jump characteristic point according to the position coordinate, calculating a vehicle offset angle by combining the position deviation of the last jump characteristic point, and tracking the vehicle according to the vehicle offset angle and the displacement distance;
the upper computer is further used for obtaining position coordinates of the head end characteristic point and the tail end characteristic point of the jumping extreme value according to the laser scanning curve, obtaining a sleeper measurement width according to a difference value of the position coordinates of the head end characteristic point and the tail end characteristic point, obtaining a difference value between the measurement width and a sleeper standard width, calibrating the encoder by using the difference value when the sleeper is scanned, and calibrating the encoder by using a Kalman filtering algorithm when the sleeper is not scanned.
The upper computer also comprises a preprocessing unit which is used for carrying out moving median de-noising on the laser scanning data, filling noise points, standardizing data and fitting the laser scanning data by using a least square method.
The first laser scanner and the second laser scanner are spaced at a distance of a standard sleeper width in the X-axis direction, the first laser scanner and the second laser scanner are equidistant from a track median line, and the scanning range of the laser scanner is the sleeper.
Further, the jumping feature point is a data point where a laser scanning curve jumps when the laser scanner scans the crosstie;
the head end characteristic point and the tail end characteristic point are data points at two ends of a laser scanning curve, wherein the data points are continuously positioned at extreme values during the period from the laser scanner to the sleeper.
In summary, according to the track tracking method and system based on filter fusion, the sleepers are scanned to serve as the basis of track tracking, and meanwhile, in consideration of the interval between the sleepers, the standard width of one sleeper is set between the first laser scanner and the second laser scanner to serve as the distance, so that the continuity of data is guaranteed, and the continuity of track tracking is guaranteed.
And for the unilateral laser, the two conditions of scanning the laser to the crosstie and scanning the laser to the crosstie are divided, when the laser scans the crosstie, the standard distance of the crosstie is utilized to calibrate the output distance of the encoder, and when the laser does not scan the crosstie, the Kalman filtering is utilized to calibrate the output distance of the encoder. The former compares the measured span of the sleeper with the standard width of the sleeper, and the latter compensates the accumulated error of the encoder by using a Kalman filtering fusion method, thereby ensuring the accuracy of automatic tracking after the vehicle runs for a long time.
After calibration, the reliability of the distance value measured by the encoders is greatly improved, and the running distance and the running track of the vehicle can be obtained through the distance values measured by the encoders on the two sides. The cross section of the sleeper is obtained by scanning the sleeper through the laser, the highest point is used as a characteristic point, and the distance and the angle of the vehicle deviating from the center line are judged through the change of the characteristic point, so that the data processing is simplified, the processing speed of the upper computer is accelerated, and the tracking real-time performance is guaranteed.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. The utility model provides a track tracking method based on filtering fuses, its characterized in that uses the vehicle direction of travel as the X axle, and the horizontal plane is the Y axle with X axle vertically direction, and the track both sides are equipped with first laser scanner and second laser scanner respectively, are equipped with the encoder on the vehicle tire to through host computer processing data, including the step:
a1: the upper computer receives laser scanning curves of the first laser scanner and the second laser scanner and pulse signals with preset distances as intervals of the encoder;
a2: acquiring the position coordinates of the current jump characteristic points according to the laser scanning curve, and calculating the displacement distance of the tire according to the number of the pulse signals;
a3: acquiring the position deviation of the current jumping feature point and the last jumping feature point according to the position coordinates;
a4: calculating a vehicle offset angle according to the position deviation;
a5: tracking a vehicle track according to the vehicle offset angle and the displacement distance;
while executing the step a2, according to the laser scanning result, the method further comprises the following steps:
b1: when the sleeper is scanned, acquiring position coordinates of a head end characteristic point and a tail end characteristic point of a jump extreme value according to a laser scanning curve;
b2: obtaining the measured width of the sleeper according to the coordinate difference value of the head end characteristic point and the tail end characteristic point, comparing the measured width with the standard width of the sleeper to obtain a difference value, and calibrating an encoder according to the difference value;
c1: when no crosstie is scanned, the encoder is calibrated using a Kalman filter fusion algorithm.
2. The method as claimed in claim 1, wherein the first laser scanner and the second laser scanner are spaced apart from each other by a distance of a standard width of a crosstie in the X-axis direction, the first laser scanner and the second laser scanner are equidistant from a median line of the track, and a scanning range of the laser scanner is the crosstie.
3. The filter fusion-based track tracking method as claimed in claim 1, wherein in step A3 or step B3, the track tracking method is performed in a single step
The jumping characteristic point is a data point of a laser scanning curve jumping when the laser scanner scans the crosstie;
the head end characteristic point and the tail end characteristic point are data points at two ends of a laser scanning curve, wherein the data points are continuously positioned at extreme values during the period from the laser scanner to the sleeper.
4. The method according to claim 1, wherein said step a1 is followed by the steps of:
a11: carrying out moving median denoising on the laser scanning data;
a12: carrying out noise filling and data standardization on the laser scanning data;
a13: and fitting the laser scanning data by using a least square method.
5. The filter fusion-based track tracking method according to claim 1, wherein in step A4, the vehicle offset angle is obtained by a trigonometric function according to the difference between the position deviations of the jump feature points on the X-axis and the Y-axis,
wherein A is a vehicle offset angle, X is a coordinate difference of the positional deviation on an X axis, Y is a coordinate difference of the positional deviation on a Y axis,
is the displacement distance of the tire.
6. The utility model provides a track tracking system based on filtering fuses, its characterized in that to the vehicle direction of travel be the X axle, and the horizontal plane is the Y axle with X axle vertically direction, and the track both sides are equipped with first laser scanner and second laser scanner respectively, are equipped with the encoder on the vehicle tire to through host computer processing data, wherein:
the first laser scanner and the second laser scanner are used for scanning the sleepers through laser and generating corresponding laser scanning curves;
the encoder is used for measuring the rotation angle of the tire at intervals of a preset distance and feeding back a pulse signal;
the upper computer is used for acquiring the position coordinates of the current jump characteristic points according to the laser scanning curve and calculating the displacement distance of the tire according to the number of the pulse signals;
the upper computer is also used for acquiring the position deviation of the current jump characteristic point according to the position coordinate, calculating a vehicle offset angle by combining the position deviation of the last jump characteristic point, and tracking the vehicle according to the vehicle offset angle and the displacement distance;
the upper computer is further used for obtaining position coordinates of the head end characteristic point and the tail end characteristic point of the jumping extreme value according to the laser scanning curve, obtaining a sleeper measurement width according to a difference value of the position coordinates of the head end characteristic point and the tail end characteristic point, obtaining a difference value between the measurement width and a sleeper standard width, calibrating the encoder by using the difference value when the sleeper is scanned, and calibrating the encoder by using a Kalman filtering algorithm when the sleeper is not scanned.
7. The filter fusion based track tracking system according to claim 6, wherein the first laser scanner and the second laser scanner are spaced apart from each other by a distance of a standard width of a crosstie in the X-axis direction, the first laser scanner and the second laser scanner are equidistant from a median line of the track, and a scanning range of the laser scanners is the crosstie.
8. The filter fusion based track tracking system of claim 6, wherein the track tracking system is based on a filter fusion based track tracking system
The jumping characteristic point is a data point of a laser scanning curve jumping when the laser scanner scans the crosstie;
the head end characteristic point and the tail end characteristic point are data points at two ends of a laser scanning curve, wherein the data points are continuously positioned at extreme values during the period from the laser scanner to the sleeper.
9. The filter fusion based track tracking system according to claim 6, wherein the upper computer further comprises a preprocessing unit,
the preprocessing unit is used for carrying out moving median denoising on the laser scanning data, filling noise points, standardizing data and fitting the laser scanning data by using a least square method.
10. The filter fusion-based track tracking system according to claim 6, wherein the vehicle offset angle is obtained by a trigonometric function based on the difference between the position deviations of the jump feature points on the X-axis and the Y-axis respectively,
wherein A is a vehicle offset angle, X is a coordinate difference of the positional deviation on an X axis, Y is a coordinate difference of the positional deviation on a Y axis,
is the displacement of the tyreDistance.
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