Rail parameter measurement system based on multi-source data fusion

Abstract

This invention discloses a track parameter measurement system based on multi-source data fusion, relating to the field of track detection technology. It efficiently and cost-effectively detects tracks, eliminating data errors caused by misalignment between the detected wheels and the track under test. The system includes: an inertial navigation measurement unit, an odometer, a vision measurement unit, a synchronous controller, and a processing computer. The inertial navigation measurement unit collects the relative motion attitude of the vehicle frame traveling on the track under test. The vision measurement module collects attitude change information of the vehicle frame relative to the track under test. The processing computer integrates the above information, performs combined calculations using a Kalman filter algorithm, generates the relative motion trajectory of the vehicle frame in the geodetic coordinate system, corrects the serpentine motion of the vehicle frame using data collected by the vision measurement module, and extracts the track parameters based on the corrected relative motion trajectory.

Description

Technical Field

[0001] This invention relates to the field of track detection technology, and in particular to a track parameter measurement system based on multi-source data fusion. Background Technology

[0002] As a core infrastructure of railway transportation, the technical condition of railway tracks directly affects the safety, economy, and comfort of train operation. With the increase in railway mileage and train speeds, the requirements for the accuracy and efficiency of track inspection are constantly rising. Current track inspection technologies have the following limitations:

[0003] Traditional contact-type track inspection instruments rely on mechanical contact measurement, with a detection speed below 8 km / h, making it difficult to complete large-area inspections within the track maintenance window. They also suffer from high wear rates, requiring contact wheel replacement every 100 km of inspection, increasing maintenance costs. Furthermore, they cannot traverse complex areas such as turnouts and small-radius curves, resulting in blind spots. Environmental factors also contribute to significant errors in track gauge measurement, leading to insufficient accuracy. Because the track gauge measuring wheel of traditional contact-type track inspection instruments needs to be locked to the rail, its speed cannot be increased. However, if it is not locked, the trolley will exhibit serpentine movement, resulting in inaccurate measurement results.

[0004] Non-contact large track inspection vehicles (such as the JG-6 model): The cost per unit is high, making it difficult to popularize at the grassroots level; in addition, non-contact large track inspection vehicles integrate nearly a hundred sensors, requiring professional personnel to operate them, and the training period is long; they are only suitable for periodic inspections on main lines, and cannot meet the daily fine inspection needs of stations, branch lines and other areas; the accuracy is low when conducting high-speed inspections, making it difficult to meet the needs of fine track inspections.

[0005] Existing technologies suffer from a trade-off between efficiency, cost, and accuracy, and there is an urgent need for a detection technology that balances high efficiency, low cost, high accuracy, and adaptability to complex environments. Summary of the Invention

[0006] In view of this, this application provides a track parameter measurement system based on multi-source data fusion, which can efficiently and cost-effectively complete track inspection and correct measurement data deviations caused by the serpentine motion of traditional contact track inspectors due to non-close contact with the rails during the data acquisition process.

[0007] This application provides a track parameter measurement system based on multi-source data fusion. The system includes an inertial navigation measurement unit, an odometer, a visual measurement unit, a synchronization controller, and a processing computer; wherein,

[0008] The inertial navigation measurement unit is used to collect motion information of the vehicle frame body traveling in the area of ​​the rail to be measured. The motion information includes motion attitude and acceleration.

[0009] The odometer is used to collect relative mileage data of the chassis body traveling within the area of ​​the rail to be measured; the relative mileage data is the distance traveled by the chassis body from the starting point to the corresponding position at the current time.

[0010] The visual measurement unit is used to collect attitude change information; the attitude change information is the position change information of the corresponding steel rail to be measured during the movement of the vehicle frame body.

[0011] The synchronization controller is used for system synchronization control to determine the time synchronization of data collected by the odometer, the vision measurement unit, and the inertial navigation measurement unit.

[0012] The processing computer is used to solve the relative mileage data and the motion information through a combined navigation algorithm to generate the relative motion trajectory of the vehicle body; the combined navigation algorithm is based on the Kalman filter algorithm.

[0013] The processing computer is also used to perform serpentine correction on the relative motion trajectory; the serpentine correction of the relative motion trajectory is achieved by using attitude change information to compensate for the spatial displacement caused by the serpentine motion of the chassis body;

[0014] The processing computer calculates the relative track parameters of the rail to be measured based on the corrected relative motion trajectory, which is used to realize the relative measurement of track geometric parameters.

[0015] The processing computer is used to solve the relative mileage data and the motion information through a combined navigation algorithm to generate the relative motion trajectory of the vehicle body; the combined navigation algorithm is based on the Kalman filter algorithm; the processing computer is also used to perform serpentine correction on the relative motion trajectory; the serpentine correction of the relative motion trajectory is achieved by using attitude change information to compensate for the spatial displacement caused by the serpentine motion of the vehicle body;

[0016] In one embodiment, the visual measurement unit comprises multiple line structured light 3D cameras, which are disposed on both sides of the vehicle frame body, with the optical axis of the line structured light 3D cameras set perpendicular to the axis of the rail to be measured; the synchronization controller, processing computer, and odometer are integrated at the bottom of the vehicle frame body, and the synchronization controller is connected to the line structured light 3D cameras via cables; wherein, the line structured light 3D cameras are used to synchronously acquire the original profile data of the rail to be measured.

[0017] In one possible implementation, the processing computer is further configured to perform serpentine correction on the original profile data. The process of performing serpentine correction on the original profile data includes:

[0018] The RANSAC algorithm combined with the least squares method is used to fit the circular arc to the specific three-dimensional point cloud data corresponding to the track bottom, to fit the optimal circular arc model, and to extract the center coordinates of the optimal circular arc model.

[0019] The angle of the serpentine movement of the chassis body is calculated based on the angle between the line connecting the center coordinates of the optimal circular arc model and the initial angle, and the serpentine movement trajectory is obtained by integrating the mileage information obtained from the odometer count.

[0020] The spatial displacement caused by the serpentine motion of the chassis body is compensated in the original profile data; at the same time, a standard reference plane aligned with the track design datum is constructed, the compensated profile point cloud is projected onto the standard reference plane and aligned with the standard profile data of the rail to obtain the geometrically corrected data, which is the corrected profile data.

[0021] In one possible implementation, the processing computer is used to preprocess the corrected profile data, extract the corresponding rail waist, rail bottom and rail head area data through clustering and segmentation, and continue to match different weight coefficients on the rail waist, rail bottom and rail head area data to obtain the matching result, which is the matched profile data.

[0022] The processing computer is also used to calculate the vertical wear and lateral wear of the rail to be measured based on the matched profile data.

[0023] In one possible implementation, the processing computer preprocesses the corrected profile data, extracts corresponding rail waist, rail bottom, and rail head region data through clustering and segmentation, and further matches different weight coefficients to the rail waist, rail bottom, and rail head region data to obtain matching results. The specific process is as follows:

[0024] Create a collection containing the revised profile data. The set of data points obtained by the line structured light 3D camera scanning the rail profile calibration plate is obtained. The following formula is used to iteratively calculate the nearest point of the corrected profile data;

[0025] (1);

[0026] in, Let be a rotation matrix. It is a translation vector. Here, P is the weight of data point i, and P is the set of sampling points in the rail profile calibration plate. It is the first in the rail profile calibration plate During calibration, Q is the set of scan points obtained by the line-structured light 3D camera scanning the rail profile calibration plate. During measurement, Q is the set of scan points obtained by the line-structured light 3D camera scanning the rail, and N is the number of sampling points or scan points in the set. It is the first in the set obtained after scanning the steel rail with a line structured light 3D camera. One scan point.

[0027] In one possible implementation, the relative track parameters include left and right track elevation parameters, left and right track direction parameters, track horizontal parameters, track triangular pit parameters, and track gauge values.

[0028] In one possible implementation, the extraction process of the left and right track elevation parameters, left and right track direction parameters, track horizontal parameters, and track triangular pit parameters includes:

[0029] The processing computer is also used to obtain the rotation matrix and translation vector between the line structured light 3D camera and the laser inertial navigation system; extract the left and right rail vertices from the matched profile data; map the left and right rail vertices to the inertial navigation coordinate system using the rotation matrix and translation vector to obtain the true left and right rail vertices; fuse the true left and right rail vertices and their relative motion trajectories to obtain the true trajectory of the left rail vertex and the true trajectory of the right rail top vertex of the rail to be measured; the laser inertial navigation system is a component of the inertial navigation measurement unit; the sliding window measurement method is used on the true trajectory of the left rail vertex and the true trajectory of the right rail top vertex, combined with the midpoint chord measurement method, to calculate the left and right rail height parameters and left and right rail direction parameters; when calculated by those skilled in the art, the measurement chord length is used, and the size of the sliding window corresponds to the measurement chord length.

[0030] The true trajectories of the left and right rail apexes are calculated to obtain the elevation difference between the left and right rail apexes; thus, the track horizontal parameters are obtained; the triangular pit is calculated based on the algebraic difference of the horizontal difference within the sliding window size. When calculating this, those skilled in the art use the measurement base length, where the sliding window size corresponds to the measurement base length; the base length is the measured distance, for example, it can be set to 2 meters. The algebraic difference of the horizontal difference within the base length refers to the value obtained by subtracting the elevation differences at positions 2 meters apart.

[0031] The process of extracting the track gauge value includes:

[0032] Extract the profile data of the left and right rails of the rail to be measured from the matched profile data, and draw tangents to the vertices of the left and right rail profile data respectively to obtain the first and second tangents. Shift the first and second tangents downward by 16mm at the same cross section, and calculate the coordinates of the intersection point of the left and right rail profile data. Calculate the Euclidean distance based on the coordinates of the intersection point of the left and right rail profile data to obtain the rail gauge value.

[0033] In one possible implementation, the orbital parameter measurement system based on multi-source data fusion further includes a GNSS positioning module;

[0034] The GNSS positioning module is used to collect the absolute spatial position of the vehicle frame body; the absolute spatial position includes the longitude, latitude and altitude information of the vehicle frame body in the geodetic coordinate system;

[0035] The processing computer calculates the relative mileage data, the motion information, and the absolute spatial position using a combined navigation algorithm to generate the absolute motion trajectory of the vehicle body in the geodetic coordinate system.

[0036] The processing computer is also used to process the attitude change information, to compensate for the spatial displacement caused by the serpentine movement of the chassis body, and to correct the absolute motion trajectory;

[0037] The computer calculates the absolute track parameters of the rail to be measured based on the corrected absolute motion trajectory, which is used to realize the absolute measurement of track geometric parameters.

[0038] In one possible implementation, the process by which the synchronization controller performs synchronization control on the system includes:

[0039] The synchronization controller is used to output a single trigger pulse to the odometer, the visual measurement unit, the inertial navigation measurement unit, and the GNSS positioning module when the coded mileage output by the odometer first reaches or exceeds the next untriggered trigger time point in the predetermined mileage sequence, so that the odometer, the visual measurement unit, the inertial navigation measurement unit, and the GNSS positioning module can synchronously collect data; the interval between adjacent trigger time points is set according to a fixed spatial interval.

[0040] In one possible implementation, the GNSS positioning module is further configured to output a timestamp to the inertial navigation measurement unit and the synchronization controller.

[0041] The inertial navigation measurement unit (INS) marks the collected data with timestamps and odometer pulse counts. The processing computer is also used to interpolate the data collected by the GNSS positioning module with the timestamp as the reference time to obtain the data collected by the INS at the reference time, and to align the data collected by the INS with the data collected by the GNSS positioning module.

[0042] Compared to existing technologies, the advantages of this system are:

[0043] The system includes an inertial navigation measurement unit, an odometer, a vision measurement unit, a synchronization controller, and a processing computer. The laser inertial navigation system in the inertial navigation measurement unit acquires the motion attitude of the vehicle frame body, and the odometer acquires the relative mileage data of the vehicle frame body traveling within the area of ​​the rail to be measured. The relative mileage data and the motion information are processed by a combined navigation algorithm to generate the relative motion trajectory of the vehicle frame body. The attitude change information acquired by the vision measurement unit is used to perform serpentine correction on the relative motion trajectory to obtain accurate data, namely the corrected relative motion trajectory, thereby achieving more accurate extraction of relative track parameters.

[0044] Furthermore, the GNSS positioning module acquires the absolute spatial position of the vehicle frame, transforms the data collected by the aforementioned devices into a unified coordinate system, and aligns the multi-source data collected by each device with the timestamp of the GNSS positioning module as a reference. By sequentially performing serpentine correction and matching processing on the original profile data, the accuracy of the profile data is increased, resulting in matched profile data, thereby eliminating data errors caused by the non-close contact between the vehicle frame wheels and the track.

[0045] The above process utilizes a Kalman filter algorithm for integrated navigation, generating the vehicle's trajectory in the geodetic coordinate system. This trajectory is then projected onto the matched profile data to obtain the trajectory's representation within the profile data, thus yielding the trajectory representing the vertex transformation of the rails. Track data is then extracted from this trajectory. This process addresses the issues of low efficiency, rapid wear, and narrow applicability of traditional contact-based equipment, as well as the high cost and complex operation of large-scale non-contact equipment. It offers advantages such as high detection accuracy, high speed, low cost, and adaptability to complex environments, meeting the refined inspection needs of main lines, stations, and branch lines. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the equipment relationships in the track parameter measurement system based on multi-source data fusion proposed in the embodiments of this application;

[0048] Figure 2 This is a schematic diagram of the chassis of a track parameter measurement system based on multi-source data fusion;

[0049] Figure 3 This is an information flow diagram of the orbital parameter measurement system based on multi-source data fusion proposed in the embodiments of this application during operation;

[0050] Figure 4 This is a schematic diagram of the track orientation in one example of this application;

[0051] Figure 5 This is a schematic diagram of the rail track cross-section in one example of this application;

[0052] 1-Line structured light 3D camera, 2-Laser inertial navigation, 3-GNSS positioning module, 4-Bottom of vehicle frame, 11-First line structured light 3D camera, 12-Second line structured light 3D camera, 13-Third line structured light 3D camera. Detailed Implementation

[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0054] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0055] In view of the problems described in the technical background, this application provides an orbital parameter measurement system based on multi-source data fusion to efficiently and cost-effectively detect orbital parameters.

[0056] Example 1

[0057] The orbital parameter measurement system based on multi-source data fusion proposed in this application includes an inertial navigation measurement unit, an odometer, a visual measurement unit, a synchronization controller, and a processing computer.

[0058] The inertial navigation measurement unit is used to collect motion information of the vehicle frame body traveling in the area of ​​the rail to be measured. The motion information includes motion attitude and acceleration.

[0059] The odometer is used to collect relative mileage data of the chassis body traveling within the area of ​​the rail to be measured; the relative mileage data is the distance traveled by the chassis body from the starting point to the corresponding position at the current time.

[0060] The visual measurement unit is used to collect attitude change information; the attitude change information is the position change information of the corresponding steel rail to be measured during the movement of the vehicle frame body.

[0061] In one example, attitude change information can refer to the displacement and deviation of the wheels and the rail under test during the operation of the chassis.

[0062] The synchronization controller is used for system synchronization control to determine the time synchronization of data collected by the odometer, the vision measurement unit, and the inertial navigation measurement unit.

[0063] The processing computer is used to solve the relative mileage data and the motion information through a combined navigation algorithm to generate the relative motion trajectory of the vehicle body; the combined navigation algorithm is based on the Kalman filter algorithm.

[0064] The relative motion trajectory of the frame body is the motion trajectory formed by the frame body traveling from the starting point to the current position point.

[0065] The processing computer is also used to perform serpentine correction on the relative motion trajectory; the serpentine correction of the relative motion trajectory is achieved by using attitude change information to compensate for the spatial displacement caused by the serpentine motion of the chassis body.

[0066] In one example, the lateral sway angle of the car can be detected by two parallel 3D cameras (500mm apart), dynamically compensating for the deviation of the relative motion trajectory and correcting the measurement error caused by the serpentine motion (amplitude ±5mm).

[0067] In one example, serpentine correction of relative motion trajectories can be achieved based on Kalman filtering. The attitude change information is input into the Kalman filter calculation model as an observation to correct the relative motion trajectory.

[0068] The processing computer calculates the relative track parameters of the rail to be measured based on the corrected relative motion trajectory, which is used to realize the relative measurement of track geometric parameters.

[0069] The above process obtains the relative mileage data of the chassis from the starting point through an odometer, obtains the motion information of the chassis body through an inertial navigation measurement unit, collects the attitude change information between the chassis body and the rail through a vision measurement unit, and processes the above information by a computer. In particular, it uses the attitude change information to compensate for the spatial displacement caused by the serpentine motion of the chassis body, and obtains the relative motion trajectory of the chassis body relative to the starting point in the geodetic coordinate system. After compensating for the serpentine motion with the position change information of the chassis body relative to the rail to be measured collected by the vision measurement unit, accurate relative track parameters are obtained based on the relative motion trajectory.

[0070] One example of this application also proposes that the visual measurement unit consists of multiple line structured light 3D cameras, which are disposed on both sides of the vehicle frame body, and the optical axis of the line structured light 3D cameras is set to be perpendicular to the axis of the rail to be measured; the synchronization controller, processing computer and odometer are integrated at the bottom of the vehicle frame body, and the synchronization controller is connected to the line structured light 3D cameras respectively through cables; wherein, the line structured light 3D cameras are used to synchronously acquire the original profile data of the rail to be measured;

[0071] Furthermore, the processing computer is also used to perform serpentine correction on the original profile data. The process of performing serpentine correction on the original profile data includes:

[0072] The RANSAC algorithm combined with the least squares method is used to fit the circular arc to the specific three-dimensional point cloud data corresponding to the track bottom, to fit the optimal circular arc model, and to extract the center coordinates of the optimal circular arc model.

[0073] The angle of the serpentine movement of the chassis body is calculated based on the line connecting the center coordinates of the optimal circular arc model and the initial angle. The serpentine movement trajectory is obtained by integrating the mileage information obtained from the odometer count. The spatial displacement caused by the serpentine movement of the chassis body is compensated in the original profile data. At the same time, a standard reference plane aligned with the track design benchmark is constructed. The compensated profile point cloud is projected onto the standard reference plane and aligned with the standard profile data of the rail to obtain the geometrically corrected data, which is the corrected profile data.

[0074] The processing computer is used to preprocess the corrected profile data, extract the corresponding rail web, rail base, and rail head region data through clustering and segmentation, and further match different weight coefficients to the rail web, rail base, and rail head region data to obtain the matching result, which is the matched profile data; the processing computer is also used to calculate the vertical wear and lateral wear of the rail to be measured based on the matched profile data.

[0075] Furthermore, the processing computer preprocesses the corrected profile data, extracts the corresponding rail waist, rail bottom, and rail head region data through clustering and segmentation, and further matches different weight coefficients to the rail waist, rail bottom, and rail head region data to obtain the matching results. The specific process is as follows:

[0076] Create a collection containing the revised profile data. The set of data points obtained by the line structured light 3D camera scanning the rail profile calibration plate is obtained. The following formula is used to iteratively calculate the nearest point of the corrected profile data;

[0077] (1);

[0078] in, Let be a rotation matrix. It is a translation vector. Here, P is the weight of data point i, and P is the set of sampling points in the rail profile calibration plate. It is the first in the rail profile calibration plate During calibration, Q is the set of scan points obtained by the line-structured light 3D camera scanning the rail profile calibration plate. During measurement, Q is the set of scan points obtained by the line-structured light 3D camera scanning the rail, and N is the number of sampling points or scan points in the set. It is the first in the set obtained after scanning the steel rail with a line structured light 3D camera. One scan point.

[0079] The embodiments of this application obtain the corrected relative motion trajectory and the matched profile data through the corresponding calculations performed by the above-mentioned units. It can further extract several specific relative track parameters that are different from those in the prior art. The relative track parameters include left and right track height parameters, left and right track direction parameters, track horizontal parameters, track triangular pit parameters, and track gauge values.

[0080] The extraction process of the left and right track elevation parameters, left and right track direction parameters, track horizontal parameters, and track triangular pit parameters requires the use of laser inertial navigation, including:

[0081] The processing computer is also used to obtain the rotation matrix and translation vector between the line structured light 3D camera and the laser inertial navigation system; extract the left and right rail vertices from the matched profile data; map the left and right rail vertices to the inertial navigation coordinate system using the rotation matrix and translation vector to obtain the true left and right rail vertices; fuse the true left and right rail vertices with their relative motion trajectories to obtain the true trajectory of the left rail vertex and the true trajectory of the right rail top vertex of the rail to be measured; use the sliding window measurement method and the midpoint chord measurement method to calculate the left and right rail height parameters and left and right rail direction parameters; calculate the left and right rail top elevation difference and the right rail top elevation difference; and then obtain the rail horizontal parameters; calculate the triangular pit based on the algebraic difference of the horizontal difference within the sliding window size.

[0082] The process of extracting the track gauge value does not require the use of laser inertial navigation, and includes:

[0083] Extract the profile data of the left and right rails of the rail to be measured from the matched profile data, and draw tangents to the vertices of the left and right rail profile data respectively to obtain the first and second tangents. Shift the first and second tangents downward by 16mm at the same cross section, and calculate the coordinates of the intersection point of the left and right rail profile data. Calculate the Euclidean distance based on the coordinates of the intersection point of the left and right rail profile data to obtain the rail gauge value.

[0084] Example 2

[0085] In another embodiment of this application, building upon the technology of Embodiment 1, a GNSS positioning module is added to the track parameter measurement system based on multi-source data fusion. The GNSS positioning module acquires the absolute spatial position of the vehicle frame, and then Kalman filtering is used to obtain the absolute trajectory of the vehicle frame in the geodetic coordinate system. Specifically, the absolute spatial position can be used as an observation value and substituted into the Kalman filtering algorithm to iteratively obtain the absolute trajectory.

[0086] Specifically, the computer processes the relative mileage data, the real-time GNSS positioning, the motion attitude, and the position information in the absolute motion trajectory corresponding to the previous moment, using a Kalman filter algorithm to perform combined navigation, obtaining the absolute motion trajectory of the vehicle body in the geodetic coordinate system, such as the motion trajectory of the vehicle body from one geographical coordinate to another. Rail parameters are then extracted from this absolute motion trajectory to achieve absolute measurement of track geometry parameters.

[0087] The GNSS positioning module is used to collect the absolute spatial position of the vehicle frame body; the absolute spatial position includes the longitude, latitude and altitude information of the vehicle frame body in the geodetic coordinate system;

[0088] The processing computer calculates the relative mileage data, the motion information, and the absolute spatial position using a combined navigation algorithm to generate the absolute motion trajectory of the vehicle body in the geodetic coordinate system.

[0089] The processing computer is also used to process the attitude change information, to compensate for the spatial displacement caused by the serpentine movement of the chassis body, and to correct the absolute motion trajectory;

[0090] The processing computer calculates the absolute track parameters of the rail to be measured based on the corrected absolute motion trajectory, which is used to realize the absolute measurement of track geometric parameters.

[0091] It is important to emphasize that the parameter names represented by absolute track parameters and relative track parameters are consistent. For example, the absolute track parameters also include left and right track elevation parameters, left and right track direction parameters, track horizontal parameters, track triangular pit parameters, and track gauge values. In this article, relative track parameters refer to calculating parameters using the relative motion trajectory as known values, while absolute track parameters refer to calculating parameters using the absolute motion trajectory as known values. The absolute motion trajectory is a further option, and the choice between relative track parameters and absolute track parameters can be made on-site based on the actual situation.

[0092] Considering that there are multiple measuring devices in this application, in order to ensure the time consistency of the data, the embodiments of this application also propose an implementation method for synchronous control of the system:

[0093] The synchronization controller is used to output a single trigger pulse to the odometer, the visual measurement unit, the inertial navigation measurement unit, and the GNSS positioning module when the coded mileage output by the odometer first reaches or exceeds the next untriggered trigger time point in the predetermined mileage sequence, so that the odometer, the visual measurement unit, the inertial navigation measurement unit, and the GNSS positioning module can synchronously collect data; the interval between adjacent trigger time points is set according to a fixed spatial interval.

[0094] The GNSS positioning module is also used to output a timestamp to the inertial navigation measurement unit and the synchronization controller;

[0095] After acquiring data, the inertial navigation measurement unit marks the acquired data with timestamps and odometer pulse counts.

[0096] The processing computer is further configured to use the timestamp of the data collected by the GNSS positioning module as the reference time, interpolate the reference time with the data corresponding to the timestamp, obtain the data collected by the inertial navigation measurement unit at the reference time, and align the data collected by the inertial navigation measurement unit with the data collected by the GNSS positioning module.

[0097] Example 3

[0098] Figure 1 This is a schematic diagram of the equipment relationships in a track parameter measurement system based on multi-source data fusion. Figure 1 The diagram only shows the connection relationship and is unrelated to the specific location of the device.

[0099] Figure 2 This is a schematic diagram of the vehicle frame in a track parameter measurement system based on multi-source data fusion. Figure 2 The diagram shows the specific installation locations of the equipment, including: the chassis body, a line structured light 3D camera 1, a laser inertial navigation system 2, and a GNSS positioning module 3; the wheels of the chassis body coincide with the rails, and it also includes a synchronization controller, a processing computer, and an odometer; (Reference) Figure 2 The preferred number of line structured light 3D cameras is 3, which are set on both sides of the vehicle frame body. There are three installation positions: the first line structured light 3D camera position 11, the second line structured light 3D camera position 12, and the third line structured light 3D camera position 13.

[0100] Figure 3 It is an information flow diagram of the orbital parameter measurement system based on multi-source data fusion during operation;

[0101] The aforementioned multiple line structured light 3D cameras, synchronization controllers, processing computers, odometers, laser inertial navigation systems, and GNSS positioning modules cooperate with each other through data transmission to complete the track detection process, which includes:

[0102] S1: When the coded mileage output by the odometer first reaches or exceeds the next untriggered mileage point in the predetermined mileage sequence, the synchronization controller outputs a single trigger pulse to the line structured light 3D camera; the mileage points are generated at fixed spatial intervals.

[0103] For example, assuming a fixed spatial interval of ΔL, the synchronous controller generates mileage points when the odometer outputs the codes 0, ΔL, 2ΔL, and 3ΔL respectively. Mileage points are generated when the odometer first reaches 0, and then when it exceeds ΔL, and so on.

[0104] In one example, the synchronization controller is equipped with a mechanism to prevent repeated triggering, ensuring that each mileage point is triggered only once.

[0105] Specifically, when the coded mileage output by the odometer reaches the current element in the predetermined mileage sequence, the synchronization controller outputs a trigger pulse to the line structured light 3D camera, laser inertial navigation system, and GNSS positioning module, so that the line structured light 3D camera, laser inertial navigation system, and GNSS positioning module can synchronously acquire data.

[0106] For example, the odometer outputs a trigger pulse every time the coded mileage reaches 1 millimeter of travel.

[0107] S2: Data is collected synchronously via a line structured light 3D camera, laser inertial navigation, and GNSS positioning module, including:

[0108] S21: The original profile data of the rail to be measured is synchronously acquired by the line structured light 3D camera. Since the line structured light 3D camera is installed facing the rail, the original profile data is actually the data of the inside of the rail.

[0109] S22: The motion attitude of the vehicle frame body is acquired through the laser inertial navigation system.

[0110] The motion posture of the frame body is the motion state of the frame body on the track.

[0111] S23: Collect the relative mileage data of the vehicle frame body at the current moment and the previous moment through the odometer.

[0112] The laser inertial navigation system outputs data at a frequency of 200Hz relative to the previous moment. The odometer outputs a pulse for every millimeter the vehicle body travels, and the mileage is accumulated by the synchronous controller.

[0113] S24: The absolute spatial position of the vehicle frame body is acquired through the GNSS positioning module.

[0114] S3: Based on the inherent three-dimensional geometry of the rail profile calibration plate, the processing computer aligns the camera coordinate systems of the first line structured light 3D camera 11 and the second line structured light 3D camera 12 using an iterative nearest-point method. The first line structured light 3D camera 11 is placed on one side of the vehicle frame body, and the second line structured light 3D camera 12 is placed on the other side, with the two cameras symmetrically arranged. By establishing the transformation relationship between the camera coordinate system and the inertial navigation coordinate system, the data collected by the first line structured light 3D camera 11 and the second line structured light 3D camera 12 are converted to the inertial navigation coordinate system.

[0115] For a specific rail profile calibration plate, its three-dimensional geometry is known and fixed based on its mechanical design. Step S3, based on the rail profile calibration plate, calculates the positional relationship between the same feature point on the rail and the inertial navigation coordinate system, thereby obtaining the transformation relationship between the first line structured light 3D camera 11 and the second line structured light 3D coordinate system and the inertial navigation coordinate system.

[0116] S4: The GNSS positioning module outputs a timestamp to the laser inertial navigation system and multiple line structured light 3D cameras. The laser inertial navigation system marks the acquired data with a timestamp after acquiring the data.

[0117] The laser inertial navigation system and the multiple line structured light 3D cameras all use the timestamp of the GNSS positioning module to unify the time of data acquisition by each device.

[0118] S5: The processing computer uses the timestamp of the data collected by the GNSS positioning module as the reference time, and performs data interpolation on the reference time using the data corresponding to the timestamp to obtain the data collected by the laser inertial navigation system at the reference time, and aligns the data collected by the laser inertial navigation system with the data collected by the GNSS positioning module.

[0119] By using the above data interpolation method, minor deviations in the data collection time of different devices can be corrected.

[0120] Steps S1 and S5 synchronize the data collected by each device in time and space. The odometer pulse signal, with each pulse corresponding to a 1 mm travel distance, serves as the trigger source. The synchronization controller drives each device to sample, with time deviation controlled within ±50 μs. Data is aligned using timestamps. Minor deviations are corrected using linear interpolation.

[0121] For example, the process of performing S5 to correct small deviations using linear interpolation includes:

[0122] Assume the laser inertial navigation at two moments , The data are as follows: , The interpolated data for the laser inertial navigation system at time t is calculated as follows:

[0123] (2)

[0124] Since different devices collect data at different frequencies, the above method is used to correct the slight deviations between the data collected by different devices due to the different collection frequencies.

[0125] S6: The processing computer is also used to perform serpentine correction on the original profile data. The process of performing serpentine correction on the original profile data includes: using the RANSAC algorithm combined with the least squares method to perform circular arc fitting on the specific three-dimensional point cloud data corresponding to the rail base, fitting the optimal circular arc model, and extracting the center coordinates of the optimal circular arc model; calculating the angle of the serpentine movement of the chassis body based on the angle between the line connecting the center coordinates of the optimal circular arc model and the initial angle, obtaining the serpentine movement trajectory based on the mileage information obtained from the odometer count, and compensating for the spatial displacement caused by the serpentine movement of the chassis body in the original profile data; at the same time, constructing a standard reference plane aligned with the track design benchmark, projecting the compensated profile point cloud onto the standard reference plane, and aligning it with the standard profile data of the rail to obtain the geometrically corrected data, which is the corrected profile data.

[0126] More specifically, the process of calculating the angle and trajectory of the frame body's serpentine movement, generating a standard projection plane, and projecting the compensated profile data back to the standard profile dimensions to obtain the corrected profile data includes:

[0127] The lateral sway angle of the trolley is detected by the first line structured light 3D camera 11 and the third line structured light 3D camera 13 installed in parallel, dynamically compensating for the deviation of the profile data and correcting the measurement error caused by the serpentine motion (amplitude ±5mm).

[0128] refer to Figure 2 Let the distance between the first-line structured light 3D camera 11 and the third-line structured light 3D camera 13 in front of and behind the crossbeam where the laser inertial navigation and GNSS positioning modules are located be L = 500 mm, and the coordinates of the detection point of the first-line structured light 3D camera 11 be... The coordinates of detection point 13 of the third-line structured light 3D camera are: Then the lateral deviation Lateral swing angle The calculation is as follows:

[0129] (3)

[0130] (4)

[0131] (5)

[0132] in, To correct the coordinates of the point, The original point coordinates, To correct the rotation matrix. For example, to... Substitute into the above formulas (3), (4), and (5). = After being corrected .

[0133] By rotation matrix Profile data can be corrected.

[0134] S7: The processing computer is used to preprocess the corrected profile data. Furthermore, since there may be debris (such as gravel, weeds, leaves, etc.) interfering with the track bottom, during the preprocessing process, a clustering algorithm is used to distinguish the track bottom area, and the track bottom area is smoothed or adjacent unobstructed contours are used to remove debris interference. The missing obstructed areas are filled with adjacent frame data.

[0135] Data for the corresponding rail web, rail base, and rail head regions are extracted through clustering and segmentation. Different weighting coefficients are then applied to the rail web, rail base, and rail head region data to obtain the matching results, which are the matched profile data. The processing computer is also used to calculate the vertical wear and lateral wear of the rail to be measured based on the matched profile data.

[0136] The processing computer is used to preprocess the corrected profile data, extract the corresponding rail waist, rail bottom, and rail head region data through clustering and segmentation, and further match different weight coefficients to the rail waist, rail bottom, and rail head region data to obtain the matching results. The specific process is as follows:

[0137] Create a collection containing the revised profile data. The set of data points obtained by the line structured light 3D camera scanning the rail profile calibration plate is obtained. The following formula is used to iteratively calculate the nearest point of the corrected profile data;

[0138] (1);

[0139] in, Let be a rotation matrix. It is a translation vector. Here, P is the weight of data point i, and P is the set of sampling points in the rail profile calibration plate. It is the first in the rail profile calibration plate During calibration, Q is the set of scan points obtained by the line-structured light 3D camera scanning the rail profile calibration plate. During measurement, Q is the set of scan points obtained by the line-structured light 3D camera scanning the rail, and N is the number of sampling points or scan points in the set. It is the first in the set obtained after scanning the steel rail with a line structured light 3D camera. One scan point.

[0140] S8: The processing computer calculates the vertical wear and side wear of the rail to be measured based on the matched profile data.

[0141] Vertical and lateral wear can be calculated based on the matching results. Vertical wear is measured at the vertical centerline of the rail, and lateral wear is measured 14mm below the top of the rail. The total wear is vertical wear + 1 / 2 × lateral wear.

[0142] S9: Obtain the relative and absolute motion trajectories;

[0143] S91: Obtain the relative motion trajectory. The processing computer combines the relative mileage data and the motion attitude using a Kalman filter algorithm for navigation, generating the absolute motion trajectory of the vehicle body in the geodetic coordinate system. The processing computer is used to solve the relative mileage data and the motion information using a combined navigation algorithm to generate the relative motion trajectory of the vehicle body.

[0144] S92: Obtain the absolute motion trajectory. The processing computer combines the relative mileage data, the absolute spatial position, and the motion attitude using a Kalman filter algorithm to generate the absolute motion trajectory of the vehicle body in the geodetic coordinate system. The processing computer is used to solve the relative mileage data and the motion information using a combined navigation algorithm to generate the absolute motion trajectory of the vehicle body.

[0145] Specifically, the integrated navigation algorithm is as follows:

[0146] System state vector Defined as:

[0147]

[0148] The system state vector is the input to the Kalman filter and represents the current state of the chassis.

[0149] Among them, the position of the vehicle frame body in the geodetic coordinate system , It refers to the value of the horizontal axis of the vehicle frame in the east-west direction in the geodetic coordinate system. It refers to the value of the horizontal axis of the vehicle frame in the north-south direction of the geodetic coordinate system. It refers to the value of the vertical axis of the chassis body in the geodetic coordinate system, and the speed of the chassis body. , It is the velocity component of the vehicle frame body in the east-west horizontal axis direction of the geodetic coordinate system. It is the velocity component of the vehicle frame body in the horizontal axis direction (north-south) of the geodetic coordinate system. It refers to the velocity component of the chassis body along the vertical axis in the Earth coordinate system, and the attitude of the chassis body. It is the rolling Euler angle, It is pitch Euler angle, Orientation to Euler angle, zero gyro deviation of the chassis body The accelerometer of the chassis body is zero biased. Equations of state for:

[0150]

[0151] in, Here is the state transition matrix. For noise driving matrix, This is process noise, which occurs during actual data acquisition. and It is coupled into the acquired data.

[0152] Specifically, the Kalman filter iterative formula set is as follows:

[0153] Step 1: Predict the current system state based on the system state at the previous moment.

[0154]

[0155]

[0156] in, The prior state estimate includes the current position, velocity, attitude angle, and sensor bias (gyroscope, accelerometer). The prior covariance matrix reflects the uncertainty of the predicted state; It is the noise covariance matrix of the Kalman filter iteration process, corresponding to random disturbances in the physical processes not precisely described by the system model. Its core function is to quantify the uncertainty of the model prediction. Indicates the number of iterations.

[0157] Step 2: Observation (acquiring sensor data and building an observation model)

[0158]

[0159] in, For the observation matrix, To observe noise; The observation vector is the GNSS position. / speed Relative mileage to odometer Fusion value:

[0160]

[0161] Step 3: Update (used to correct the predicted trajectory based on the observed values)

[0162]

[0163]

[0164]

[0165] in, Kalman gain, representing the predicted value. Compared with observed values Feasibility. This is the posterior state estimate, i.e., the corrected trajectory parameters. The posterior covariance matrix reflects the uncertainty of the corrected state. It is the measurement noise covariance matrix.

[0166] Furthermore, Figure 4 This is a schematic diagram of the trajectory orientation of one example of the trajectory in this application, such as... Figure 4 As shown, put Figure 4 Replace the coordinates in () ), ( ), ( This is an example of a trajectory elevation diagram. The process of calculating the elevation and direction parameters of the left and right tracks using the midpoint chord method includes: extracting the top coordinates of the actual trajectory and mapping them to the inertial navigation coordinate system to obtain p1( p2( p3 p2 is the midpoint between p1 and p3 along the y-axis. (Track direction) The calculation formula is:

[0167]

[0168] High and low The calculation formula is:

[0169]

[0170] Calculate the horizontal and superelevation H using the elevation difference between the left and right rail tops:

[0171]

[0172] in, The standard rail top midpoint spacing is 1500mm, and θ is the rail surface inclination angle, i.e., the trolley roll attitude angle.

[0173] Calculation of the triangular pit T based on the algebraic difference of the horizontal difference within the base length:

[0174]

[0175] Furthermore, Figure 5 This is a schematic diagram of the rail cross-section in one example of this application, such as... Figure 5 As shown, This is the horizontal (superelevation) measurement value at section 1 of the rail track; The value is the horizontal (superelevation) measurement at point 2 on the rail section.

[0176] The line connecting the vertices of the left and right rails at the same cross section (tangent) is shifted downwards by 16mm. The coordinates of the intersection point under the left and right profiles are calculated, and the Euclidean distance is calculated, which is the rail gauge value. The coordinates of the first-line structured light 3D camera 11 in the inertial navigation coordinate system are: The coordinates of the second-line structured light 3D camera 12 in the inertial navigation coordinate system are: Track gauge value for:

[0177]

[0178] The track parameter measurement system based on multi-source data fusion in this embodiment of the invention includes a chassis body, which is a lightweight mobile trolley that can be positioned in the middle of the rail to be measured. It collects multi-source data through multiple line structured light 3D cameras, a synchronous controller, a processing computer, and an odometer. The multiple line structured light 3D cameras collect the original profile data of the rail to be measured, the laser inertial navigation system collects the motion attitude of the chassis body, the odometer collects the relative mileage data of the chassis body compared to the previous moment, and the GNSS positioning module collects the absolute spatial position of the chassis body. After processing the data collected by the above devices, the data is converted to a unified coordinate system, and the multi-source data collected by each device is aligned with the timestamp of the GNSS positioning module as a reference.

[0179] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computing software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0180] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0181] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A track parameter measurement system based on multi-source data fusion, characterized in that, The system includes an inertial navigation measurement unit, an odometer, a visual measurement unit, a synchronization controller, and a processing computer; wherein, The inertial navigation measurement unit is used to collect motion information of the vehicle frame body traveling in the area of ​​the rail to be measured. The motion information includes motion attitude and acceleration. The odometer is used to collect relative mileage data of the chassis body traveling within the area of ​​the rail to be measured; the relative mileage data is the distance traveled by the chassis body from the starting point to the corresponding position at the current time. The visual measurement unit is used to collect attitude change information; the attitude change information is the position change information of the corresponding steel rail to be measured during the movement of the vehicle frame body. The synchronization controller is used for system synchronization control to determine the time synchronization of data collected by the odometer, the vision measurement unit, and the inertial navigation measurement unit. The processing computer is used to solve the relative mileage data and the motion information through a combined navigation algorithm to generate the relative motion trajectory of the vehicle body; the combined navigation algorithm is based on the Kalman filter algorithm. The processing computer is also used to perform serpentine correction on the relative motion trajectory; the serpentine correction of the relative motion trajectory is achieved by using attitude change information to compensate for the spatial displacement caused by the serpentine motion of the chassis body; The processing computer calculates the relative track parameters of the rail to be measured based on the corrected relative motion trajectory, which is used to realize the relative measurement of track geometric parameters; The visual measurement unit consists of multiple line structured light 3D cameras, which are positioned on both sides of the vehicle frame. The optical axes of the line structured light 3D cameras are set perpendicular to the axis of the rail to be measured. The synchronization controller, processing computer, and odometer are integrated at the bottom of the vehicle frame. The synchronization controller is connected to the line structured light 3D cameras via cables. The line structured light 3D cameras are used to synchronously acquire the original profile data of the rail to be measured. The processing computer is also used to perform serpentine correction on the original profile data. The process of serpentine correction includes: The RANSAC algorithm combined with the least squares method is used to fit the circular arc to the specific three-dimensional point cloud data corresponding to the track bottom, to fit the optimal circular arc model, and to extract the center coordinates of the optimal circular arc model. The angle of the serpentine movement of the chassis body is calculated based on the angle between the line connecting the center coordinates of the optimal circular arc model and the initial angle, and the serpentine movement trajectory is obtained by integrating the mileage information obtained from the odometer count. The spatial displacement caused by the serpentine motion of the chassis body is compensated in the original profile data; at the same time, a standard reference plane aligned with the track design benchmark is constructed, the compensated profile point cloud is projected onto the standard reference plane and aligned with the standard profile data of the rail to obtain the geometrically corrected data, which is the corrected profile data. The processing computer is used to preprocess the corrected profile data, extract the corresponding rail waist, rail bottom and rail head area data through clustering and segmentation, and continue to match different weight coefficients on the rail waist, rail bottom and rail head area data to obtain the matching result, which is the matched profile data. The processing computer is also used to calculate the vertical wear and side wear of the rail to be measured based on the matched profile data. The processing computer is used to preprocess the corrected profile data, extract the corresponding rail waist, rail bottom, and rail head region data through clustering and segmentation, and further match different weight coefficients to the rail waist, rail bottom, and rail head region data to obtain the matching results. The specific process is as follows: Create a collection containing the revised profile data. The data point set obtained by scanning the rail profile calibration plate with a line structured light 3D camera was acquired. The following formula is used to iteratively calculate the nearest point of the corrected profile data; （1）； in, For rotation matrix, It is a translation vector. Here, P is the weight of data point i, and P is the set of sampling points in the rail profile calibration plate. It is the first in the rail profile calibration plate During calibration, Q is the set of scan points obtained by the line-structured light 3D camera scanning the rail profile calibration plate. During measurement, Q is the set of scan points obtained by the line-structured light 3D camera scanning the rail, and N is the number of sampling points or scan points in the set. It is the first in the set obtained after scanning the steel rail with a line structured light 3D camera. One scan point.

2. The multi-source data fusion based orbit parameter measurement system of claim 1, wherein, The relative track parameters include the elevation parameters of the left and right tracks, the direction parameters of the left and right tracks, the horizontal parameters of the tracks, the parameters of the track triangular pits, and the track gauge value.

3. The orbital parameter measurement system based on multi-source data fusion according to claim 2, characterized in that, The extraction process of the left and right track elevation parameters, left and right track direction parameters, track horizontal parameters, and track triangular pit parameters includes: The processing computer is also used to obtain the rotation matrix and translation vector between the line structured light 3D camera and the laser inertial navigation system; and to extract the left and right track vertices from the matched profile data. By using rotation matrices and translation vectors, the left and right rail vertices are mapped onto the inertial navigation coordinate system to obtain the true left and right rail vertices. The true left and right rail vertices are then fused with their relative motion trajectories to obtain the true trajectory of the left rail vertex and the true trajectory of the right rail top vertex of the rail to be measured. The true trajectories of the left and right rail vertices are then measured using a sliding window method combined with the midpoint chord measurement method to calculate the left and right rail elevation parameters and the left and right rail direction parameters. The true trajectories of the left and right rail apexes are calculated to obtain the elevation difference between the left and right rail apexes; the horizontal parameters of the track are then obtained; and the triangular pit is calculated based on the algebraic difference of the horizontal difference within the sliding window size. The process of extracting the track gauge value includes: Extract the profile data of the left and right rails of the rail to be measured from the matched profile data, and draw tangents to the vertices of the left and right rail profile data respectively to obtain the first and second tangents. Shift the first and second tangents downward by 16mm at the same cross section, and calculate the coordinates of the intersection point of the left and right rail profile data. Calculate the Euclidean distance based on the coordinates of the intersection point of the left and right rail profile data to obtain the rail gauge value.

4. The multi-source data fusion based orbit parameter measurement system of claim 1, wherein, The orbital parameter measurement system based on multi-source data fusion also includes a GNSS positioning module; The GNSS positioning module is used to collect the absolute spatial position of the vehicle frame body; the absolute spatial position includes the longitude, latitude and altitude information of the vehicle frame body in the geodetic coordinate system; The processing computer calculates the relative mileage data, the motion information, and the absolute spatial position using a combined navigation algorithm to generate the absolute motion trajectory of the vehicle body in the geodetic coordinate system. The processing computer is also used to process the attitude change information, to compensate for the spatial displacement caused by the serpentine movement of the chassis body, and to correct the absolute motion trajectory; The processing computer calculates the absolute track parameters of the rail to be measured based on the corrected absolute motion trajectory, which is used to realize the absolute measurement of track geometric parameters.

5. The multi-source data fusion based orbit parameter measurement system of claim 4, wherein, The process by which the synchronization controller performs synchronization control on the system includes: The synchronization controller is used to output a single trigger pulse to the odometer, the visual measurement unit, the inertial navigation measurement unit, and the GNSS positioning module when the coded mileage output by the odometer first reaches or exceeds the next untriggered trigger time point in the predetermined mileage sequence, so that the odometer, the visual measurement unit, the inertial navigation measurement unit, and the GNSS positioning module can synchronously collect data; the interval between adjacent trigger time points is set according to a fixed spatial interval.

6. The multi-source data fusion based orbit parameter measurement system of claim 4, wherein, The GNSS positioning module is also used to output timestamps to the inertial navigation measurement unit and the synchronization controller.

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