A precise positioning method for the train car body

Abstract

The present invention discloses a precise positioning method for the train car body bottom. The AGV vehicle is used to collect sensing data, and the wheel hub and the axle center are analyzed according to the sensing data. Finally, the wheel hub and the axle center are matched, so as to realize the positioning of the wheels and axles at the bottom of the train, which can assist artificial intelligence for maintenance, thus solving the problem of low efficiency of manual maintenance.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and particularly to a precise positioning method for the train underbody. Background Art

[0002] In recent years, with the rapid development of China's rail transit industry, the operation safety of trains has received increasing attention, and the inspection and repair of the train underbody is the key to ensuring the safe operation of trains. The currently commonly used inspection method is manual inspection, where inspectors visually check one by one whether there are abnormalities under the train. This method has low efficiency, high labor costs, and is prone to missed inspections, resulting in train operation risks. To address the problems of the large limitations of manual inspection of the train underbody and the lack of intelligent inspection equipment to assist operations, based on technologies such as intelligent robots, image processing, deep learning, and pattern recognition, fault detection of key visible components under the train is realized to achieve the goal of intelligent inspection of the train underbody and improve the inspection efficiency. The prerequisite for all the above intelligent operations is the precise positioning of the train underbody. Summary of the Invention

[0003] The purpose of this application is to provide a precise positioning method for the train underbody, which realizes the positioning of the train underbody and assists artificial intelligence for inspection and repair, thereby solving the problem of low efficiency of manual inspection.

[0004] The present invention is realized through the following technical solutions:

[0005] A precise positioning method for the train underbody includes:

[0006] Controlling an AGV cart to travel linearly in the direction from the head to the tail of the train underbody and collecting data to obtain sensing data;

[0007] Based on the sensing data, positioning the hub center of the train wheel to obtain a hub center positioning result;

[0008] Based on the sensing data, fitting the center of the train axle to obtain an axle center positioning result, and the hub center in the hub center positioning result corresponds one-to-one with the center in the axle center positioning result;

[0009] Matching the hub center positioning result with the axle center positioning result to obtain an underbody positioning result.

[0010] In a possible implementation manner, the sensing data includes inertial navigation data, 3D lidar data, first laser ranging data, and second laser ranging data collected at consecutive time points during the process of the AGV cart traveling from the head to the tail of the train.

[0011] The first laser ranging data is the data measured by the first laser rangefinder disposed on the AGV trolley, and the second laser ranging data is the data measured by the second laser rangefinder disposed on the AGV trolley; the measuring direction of the first laser rangefinder is horizontal, and when the first laser rangefinder measures the wheel hub, the measuring direction of the first laser rangefinder is perpendicular to the wheel hub; the measuring direction of the second laser rangefinder is vertical, and when the second laser rangefinder measures the axle, the measuring direction of the second laser rangefinder is perpendicular to the axle axis.

[0012] In a possible implementation manner, based on the sensing data, positioning the hub center of the train wheel to obtain a hub center positioning result, including:

[0013] According to the inertial navigation data and the 3D lidar data, obtaining odometer data at consecutive time points;

[0014] Synchronize the odometer data and the first laser ranging data in terms of time to obtain the odometer data and the first laser ranging data corresponding to consecutive time points;

[0015] Filter the first laser ranging data to obtain the filtered first laser ranging data;

[0016] According to the odometer data and the filtered first laser ranging data, determine the hub center of the train wheel to obtain a hub center positioning result.

[0017] In a possible implementation manner, according to the inertial navigation data and the 3D lidar data, obtaining odometer data at consecutive time points, including:

[0018] According to the coordinates of the inertial navigation data in the Cartesian coordinate system and the coordinates of the 3D lidar data in the Cartesian coordinate system, and updating the extended Kalman filter according to the two coordinates to obtain the fused odometer data;

[0019] Traverse the inertial navigation data and the 3D lidar data at all time points to obtain the odometer data at consecutive time points.

[0020] In a possible implementation manner, filtering the first laser ranging data to obtain the filtered first laser ranging data, including:

[0021] Determine the target first laser ranging data among all the first laser ranging data; when the first laser ranging data corresponding to multiple consecutive time points does not change, then use the first laser ranging data corresponding to the multiple consecutive time points as the target first laser ranging data;

[0022] Determine the odometer data corresponding to the target time period of the target first laser ranging data, where the target time period represents a plurality of consecutive time points corresponding to the target first laser ranging data;

[0023] Judge whether the odometer data corresponding to the target time period is less than the pre-set hub scan line width. If so, filter out the target first laser ranging data; otherwise, retain the target first laser ranging data;

[0024] Traverse all the first laser ranging data to obtain the preliminarily filtered first laser ranging data;

[0025] Filter out the data in the preliminarily filtered first laser ranging data that is not within the first distance threshold interval R to obtain the filtered first laser ranging data.

[0026] In a possible implementation manner, according to the odometer data and the filtered first laser ranging data, determine the hub center of the train wheel to obtain the hub center positioning result, including:

[0027] According to the filtered first laser ranging data, determine the first laser ranging data corresponding to each hub;

[0028] According to the first laser ranging data corresponding to each hub, determine the first laser ranging data tof1_d_begin corresponding to when the first laser rangefinder starts scanning the hub and the first laser ranging data tof1_d_end corresponding to when the hub is finally scanned;

[0029] Determine the odometer data pose(tof1_d_begin) corresponding to the first laser ranging data tof1_d_begin and the odometer data pose(tof1_d_end) corresponding to the first laser ranging data tof1_d_end;

[0030] According to the odometer data pose(tof1_d_begin) and the odometer data pose(tof1_d_end), determine the hub center pose_wheel_center of the train wheel as: pose_wheel_center = (pose(tof1_d_end) - pose(tof1_d_begin)) / 2 to obtain the axle center positioning result.

[0031] In a possible implementation manner, based on the sensing data, fit the center of the train axle to obtain the axle center positioning result, including:

[0032] Filter the second laser ranging data to obtain the filtered second laser ranging data;

[0033] According to the filtered second laser ranging data, fit the center of the train axle to obtain the positioning result of the axle center.

[0034] In a possible implementation manner, filtering the second laser ranging data to obtain the filtered second laser ranging data includes: filtering out the second laser ranging data that is not within the second threshold interval D to obtain the filtered second laser ranging data.

[0035] In a possible implementation manner, according to the filtered second laser ranging data, fitting the center of the train axle to obtain the positioning result of the axle center includes:

[0036] A. Synchronize the odometer data and the second laser ranging data in terms of time to obtain the odometer data and the second laser ranging data corresponding to consecutive time points;

[0037] B. Combine the odometer data and the second laser ranging data corresponding to the same time point to form a data pair (pose i , tof2 i ), where pose i represents the odometer data corresponding to time point i, and tof2 i represents the second laser ranging data corresponding to time point i, i = 1, 2,..., I, and I represents the total number of time points;

[0038] C. Set counter T1 = 1 and counter T2 = 1;

[0039] D. Take three data pairs corresponding to the T1 to T1 + 2 time points, and fit the center of the circle according to the taken data pairs;

[0040] E. Increment the count value of T1 by 3 + T2, and increment the count value of T2 by one;

[0041] F. Take three data pairs corresponding to the T1 to T1 + 2 time points, and fit the center of the circle according to the taken data pairs;

[0042] G. Repeat steps E - F until all data pairs are traversed to obtain multiple fitted centers of the circle, and each said center of the circle corresponds to a radius;

[0043] H. Remove the centers of the circle whose radii do not conform to the axle radius to obtain the positioning result of the axle center.

[0044] In a possible implementation manner, matching the hub center positioning result with the axle center positioning result to obtain the vehicle bottom positioning result includes:

[0045] Draw two perpendicular lines from the hub center and the axle center corresponding to the hub center to the same horizontal plane to obtain the intersection points of the two perpendicular lines and the horizontal plane;

[0046] Determine whether the distance between two intersection points is greater than a set threshold. If so, only use the hub center as the vehicle bottom positioning result; otherwise, use both the hub center and the axle center as the vehicle bottom positioning result.

[0047] A precise positioning method for the train vehicle bottom provided by this application collects sensing data through an AGV cart, analyzes the wheel hubs and the axle centers based on the sensing data, and finally matches the wheel hubs and the axle centers, thereby realizing the positioning of the wheels and axles at the bottom of the train, which can assist artificial intelligence for maintenance, thus solving the problem of low efficiency of manual maintenance. Brief Description of the Drawings

[0048] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings. In the drawings:

[0049] Figure 1 It is a flowchart of a precise positioning method for the train vehicle bottom provided by an embodiment of this application.

[0050] Figure 2 It is a schematic diagram of an AGV cart carrying sensors provided by an embodiment of this application.

[0051] Figure 3 It is a schematic diagram of the distance between the train wheel hubs provided by an embodiment of this application.

[0052] Figure 4 It is a schematic diagram of the distance comparison between the wheel hub center and the axle center provided by an embodiment of this application.

[0053] Among them, 1 - inertial navigation measurement module, 2 - first 3D lidar, 3 - second 3D lidar, 4 - first laser rangefinder, 5 - second laser rangefinder. Detailed Embodiments

[0054] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in combination with the embodiments and the drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0055] Embodiment

[0056] As Figure 1 shown, an embodiment of this application provides a precise positioning method for the train vehicle bottom, including:

[0057] S1. Control the AGV cart to drive linearly under the train car body in the direction from the head to the tail, and collect data to obtain sensing data.

[0058] As Figure 2 shown, the AGV cart can carry an inertial navigation measurement module 1, a first 3D lidar 2, a second 3D lidar 3, a first laser rangefinder 4, and a second laser rangefinder 5. The sensing data can be collected through the inertial navigation measurement module 1, the first 3D lidar 2, the second 3D lidar 3, the first laser rangefinder 4, and the second laser rangefinder 5. It should be noted that the first laser rangefinder 4 is directed at the wheel hub, that is, the measurement direction is parallel to the axle axis, and the second laser rangefinder 5 is directed at the axle, that is, the measurement direction is perpendicular to the axle axis.

[0059] S2. Based on the sensing data, locate the hub center of the train wheel to obtain the hub center positioning result.

[0060] During the driving process of the AGV cart, the measurement data of the first laser rangefinder 4 will change continuously. When the first laser rangefinder 4 is directed at the hub and the data no longer changes or the change is within the threshold range, it can be determined that the wheel hub is scanned at this time. Therefore, according to the time period corresponding to the distance data when the data no longer changes or the change is within the threshold range, the odometer data within this time period can be determined, so as to determine the width scanned by the first laser rangefinder 4 for the hub. The central position of this width can be regarded as the hub center positioning result.

[0061] S3. Based on the sensing data, fit the center of the circle of the train axle to obtain the axle center positioning result. The hub center in the hub center positioning result corresponds one by one to the center of the circle in the axle center positioning result.

[0062] During the driving process of the AGV cart, the second laser rangefinder 5 measures real-time data and generates corresponding odometer data. Therefore, the data measured by the second laser rangefinder 5 and the odometer data can be formed into several data points, and then three points are taken from the data points to fit the center of the circle and determine the radius at the same time. The center of the circle with the corresponding radius equal to the axle radius is used as the axle center positioning result.

[0063] S4. Match the hub center positioning result with the axle center positioning result to obtain the undercarriage positioning result.

[0064] In a possible implementation manner, the sensing data includes inertial navigation data, 3D lidar data, first laser ranging data, and second laser ranging data collected at consecutive time points during the process of the AGV cart traveling from the head to the tail of the train.

[0065] Drive an AGV cart equipped with multiple sensors at a low speed (≤0.5 m / s) from the front of the train to the rear of the train at the bottom of the trench, and collect the pose data of the AGV cart under the train at a frequency greater than 50 Hz. The pose data is inertial navigation and 3D lidar. The odometry data can be obtained based on inertial navigation and 3D lidar, and is denoted as pose. Collect the data of the first laser rangefinder 4 and the second laser rangefinder 5 at the same frequency, denoted as tof1 and tof2 respectively; and synchronize the timestamps of the pose data and the data of the laser rangefinders to obtain the sensing data

[0066] The first laser ranging data is the data measured by the first laser rangefinder set on the AGV cart, and the second laser ranging data is the data measured by the second laser rangefinder set on the AGV cart. The measuring direction of the first laser rangefinder is horizontal, and when the first laser rangefinder measures the wheel hub, the measuring direction of the first laser rangefinder is perpendicular to the wheel hub. The measuring direction of the second laser rangefinder is vertical, and when the second laser rangefinder measures the axle, the measuring direction of the second laser rangefinder is perpendicular to the axle axis.

[0067] In a possible implementation, based on the sensing data, locate the center of the wheel hub of the train to obtain the wheel hub center positioning result, including:

[0068] According to the inertial navigation data and the 3D lidar data, obtain the odometry data at consecutive time points.

[0069] Synchronize the timestamps of the odometry data and the first laser ranging data to obtain the odometry data and the first laser ranging data corresponding to consecutive time points.

[0070] Filter the first laser ranging data to obtain the filtered first laser ranging data.

[0071] According to the odometry data and the filtered first laser ranging data, determine the center of the wheel hub of the train to obtain the wheel hub center positioning result.

[0072] In a possible implementation, according to the inertial navigation data and the 3D lidar data, obtain the odometry data at consecutive time points, including:

[0073] According to the coordinates of the inertial navigation data in the Cartesian coordinate system and the coordinates of the 3D lidar data in the Cartesian coordinate system, and update the extended Kalman filter based on the two coordinates to obtain the fused odometry data.

[0074] Traverse the inertial navigation data and the 3D lidar data at all time points to obtain the odometry data at consecutive time points.

[0075] In this embodiment, odometry data can be obtained only from the 3D lidar data collected by one 3D lidar.

[0076] In a possible implementation, filtering the first lidar ranging data to obtain the filtered first lidar ranging data includes:

[0077] Determine the target first lidar ranging data among all the first lidar ranging data. When the first lidar ranging data corresponding to multiple consecutive time points does not change (it can also be data fluctuating within a certain range. For example, when the ranging data is Scm, S+1cm, S+0.7cm, and S-0.7cm, it can be determined that an object such as a wheel hub or an obstacle is being scanned at this time), then the first lidar ranging data corresponding to the multiple consecutive time points is used as the target first lidar ranging data.

[0078] Determine the odometry data corresponding to the target time period of the target first lidar ranging data, where the target time period represents the multiple consecutive time points corresponding to the target first lidar ranging data.

[0079] Judge whether the odometry data corresponding to the target time period is less than the pre-set wheel hub scan line width. If so, filter out the target first lidar ranging data; otherwise, retain the target first lidar ranging data.

[0080] Traverse all the first lidar ranging data to obtain the preliminarily filtered first lidar ranging data.

[0081] Filter out the data in the preliminarily filtered first lidar ranging data that is not within the first distance threshold interval R to obtain the filtered first lidar ranging data.

[0082] During the driving of the AGV vehicle, non-wheel hub parts such as thick cables or small metal blocks may be scanned. Therefore, the wheel hub scan line width can be set. When it is determined that the width of the scanned object is less than the wheel hub scan line width, the object can be filtered. And since the AGV vehicle is driving in a straight line, the distance to the wheel hub it scans is within a certain range value. That is to say, the distance data corresponding to the scanned object that is not a wheel hub is outside the range value. Therefore, the first distance threshold interval R can be set, and when the first lidar ranging data exceeds the first distance threshold interval R, it can be filtered out. It should be noted that filtering can be to set the first lidar ranging data at the time point to zero.

[0083] In a possible implementation, according to the odometry data and the filtered first lidar ranging data, determine the hub center of the train wheel to obtain the hub center positioning result, including:

[0084] Based on the filtered first laser ranging data, determine the first laser ranging data corresponding to each wheel hub. When the first laser ranging data within a certain time period is all within a certain range, it can be determined that the first laser ranging data within this time period is data of the same wheel hub. The time period consists of multiple consecutive time points.

[0085] Based on the first laser ranging data corresponding to each wheel hub, determine the first laser ranging data tof1_d_begin corresponding to when the first laser rangefinder starts scanning the wheel hub and the first laser ranging data tof1_d_end corresponding to when it finally scans the wheel hub.

[0086] Determine the odometer data pose(tof1_d_begin) corresponding to the first laser ranging data tof1_d_begin and the odometer data pose(tof1_d_end) corresponding to the first laser ranging data tof1_d_end.

[0087] Based on the odometer data pose(tof1_d_begin) and the odometer data pose(tof1_d_end), determine the wheel hub center pose_wheel_center of the train wheel as: pose_wheel_center = (pose(tof1_d_end) - pose(tof1_d_begin)) / 2, and obtain the positioning result of the axle center.

[0088] In a possible implementation manner, based on the sensing data, fit the center of the train axle to obtain the positioning result of the axle center, including:

[0089] Filter the second laser ranging data to obtain the filtered second laser ranging data.

[0090] Based on the filtered second laser ranging data, fit the center of the train axle to obtain the positioning result of the axle center.

[0091] In a possible implementation manner, filtering the second laser ranging data to obtain the filtered second laser ranging data includes: filtering out the second laser ranging data that is not within the second threshold interval D to obtain the filtered second laser ranging data.

[0092] In a possible implementation manner, based on the filtered second laser ranging data, fit the center of the train axle to obtain the positioning result of the axle center, including:

[0093] A. Synchronize the odometer data and the second laser ranging data in time to obtain the odometer data and the second laser ranging data corresponding to consecutive time points.

[0094] B. Combine the odometer data and the second lidar ranging data corresponding to the same time point to form a data pair (pose i , tof2 i ), where pose i represents the odometer data corresponding to time point i, and tof2 i represents the second lidar ranging data corresponding to time point i, i = 1, 2, …, I, and I represents the total number of time points.

[0095] C. Set counter T1 = 1 and counter T2 = 1.

[0096] D. Take three data pairs corresponding to the T1 to T1 + 2 time points, and fit the center of the circle according to the taken data pairs.

[0097] E. Increment the count value of T1 by 3 + T2, and increment the count value of T2 by one.

[0098] F. Take three data pairs corresponding to the T1 to T1 + 2 time points, and fit the center of the circle according to the taken data pairs.

[0099] G. Repeat steps E - F until all data pairs are traversed, obtaining multiple fitted circle centers, and each circle center corresponds to a radius.

[0100] H. Remove the circle centers whose radii do not conform to the axle radius to obtain the axle center positioning result.

[0101] Optionally, the following method can be used to fit the center of the circle: The three taken data pairs are actually three points in the same plane. Therefore, the circumcircle of the three points can be obtained, that is, the cross-sectional circle of the axle can be obtained, and the center of this cross-sectional circle is the center of the axle.

[0102] By filtering out the data of non-axles, the taken data pairs are on the surface of the axle. The center of the circle obtained through these points is the center of the axle. However, in the actual filtering process, there are still some strays. Therefore, after obtaining the center of the circle, the radius corresponding to each center of the circle is compared with the set axle radius, and the center of the circle whose corresponding radius does not conform to the axle radius is filtered out. Considering that there may be some errors in actual operation, the center of the circle whose difference between the corresponding radius and the set axle radius is greater than a certain threshold can also be filtered out.

[0103] In a possible implementation manner, matching the hub center positioning result and the axle center positioning result to obtain the vehicle bottom positioning result includes:

[0104] Draw two perpendicular lines from the hub center and the axle center corresponding to the hub center to the same horizontal plane, and obtain the intersection points of the two perpendicular lines and the horizontal plane.

[0105] Determine whether the distance between two intersection points is greater than a set threshold value (such as 2 cm). If so, only use the hub center as the vehicle bottom positioning result; otherwise, use both the hub center and the axle center as the vehicle bottom positioning result.

[0106] As Figure 3 shown, record the length of the train axle radius provided by the train drawing; record the fixed distances between 4 pairs of hubs in each carriage, which are 2.5 meters, 13.2 meters, and 2.5 meters respectively; record the distance between the front and rear hub pairs at the connection of each carriage as 4.6 meters. Since the connection of the carriages is not a rigid connection, it is recorded as 4.6 ± 0.5 m. Therefore, after obtaining the axle center positioning result and the hub center positioning result, even if there is a distance between the hub of the same wheel and the axle center, it will not be too large, and it can be determined that the hub center and the center within a certain range belong to the same pair of wheels. For example: when the AGV vehicle starts from the front of the train, taking the first hub center reached as the starting point, if the distance from this hub center to the next hub center is 2.5 meters, then it can be determined that the hub center and the center within the driving mileage of 2.0 - 3.0 m belong to the same pair of wheels. The distance to the second hub center reached is 18.2 m, and it can be determined that the hub center and the center within the driving mileage of 17.7 - 18.7 m belong to the same pair of wheels, and so on. It should be noted that the range here can be set according to actual needs, that is, ranges such as 2.0 - 3.0 m and 17.7 - 18.7 m can be modified to other values.

[0107] As Figure 4 shown, when the distance between the two parallel lines where the hub center and the axle center are located is large, the concentricity is poor, and the axle center needs to be filtered out. When the distance between the two parallel lines where the hub center and the axle center are located is small, the concentricity is good, and both the hub center and the axle center are used as the vehicle bottom positioning result.

[0108] A precise positioning method for the train vehicle bottom provided by the present application collects sensing data through an AGV vehicle, analyzes the wheel hubs and the axle centers according to the sensing data, and finally matches the wheel hubs and the axle centers, so as to realize the positioning of the wheels and axles at the bottom of the train, which can assist artificial intelligence for maintenance, thus solving the problem of low efficiency of manual maintenance.

[0109] The above - described specific implementation manners have further detailed the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above - described is only the specific implementation manners of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A precise positioning method for the train car body bottom, characterized in that, Including: Controlling the AGV vehicle to travel linearly in the direction from the head to the tail under the train car body, collecting data, and obtaining sensing data; Based on the sensing data, positioning the hub center of the train wheel to obtain a hub center positioning result, where the sensing data includes inertial navigation data, 3D lidar data, first laser ranging data, and second laser ranging data collected at consecutive time points during the process of the AGV vehicle traveling from the head to the tail of the train; Based on the sensing data, positioning the hub center of the train wheel to obtain a hub center positioning result, including: According to the inertial navigation data and the 3D lidar data, obtaining odometer data at consecutive time points; Synchronizing the odometer data and the first laser ranging data in terms of time to obtain the odometer data and the first laser ranging data corresponding to consecutive time points; Filtering the first laser ranging data to obtain filtered first laser ranging data; According to the odometer data and the filtered first laser ranging data, determining the hub center of the train wheel to obtain a hub center positioning result; Based on the sensing data, fitting the center of the train axle to obtain an axle center positioning result, where the hub center in the hub center positioning result corresponds one-to-one with the center in the axle center positioning result; Based on the sensing data, fitting the center of the train axle to obtain an axle center positioning result, including: Filtering the second laser ranging data to obtain filtered second laser ranging data; According to the filtered second laser ranging data, fitting the center of the train axle to obtain an axle center positioning result; Matching the hub center positioning result and the axle center positioning result to obtain a car body bottom positioning result; Matching the hub center positioning result and the axle center positioning result to obtain a car body bottom positioning result, including: Drawing two perpendicular lines from the hub center and the corresponding axle center of the hub to the same horizontal plane to obtain the intersection points of the two perpendicular lines and the horizontal plane; Judging whether the distance between the two intersection points is greater than a set threshold. If so, only using the hub center as the car body bottom positioning result; otherwise, using both the hub center and the axle center as the car body bottom positioning result.

2. The precise positioning method for the train car body bottom according to claim 1, characterized in that, The first laser ranging data is the data measured by the first laser rangefinder set on the AGV vehicle, and the second laser ranging data is the data measured by the second laser rangefinder set on the AGV vehicle; the measuring direction of the first laser rangefinder is horizontal, and when the first laser rangefinder measures the hub, the measuring direction of the first laser rangefinder is perpendicular to the hub; the measuring direction of the second laser rangefinder is vertical, and when the second laser rangefinder measures the axle, the measuring direction of the second laser rangefinder is perpendicular to the axle axis.

3. The precise positioning method for the train car body bottom according to claim 1, characterized in that, According to the inertial navigation data and the 3D lidar data, obtaining odometer data at consecutive time points, including: According to the coordinates of the inertial navigation data in the Cartesian coordinate system and the coordinates of the 3D lidar data in the Cartesian coordinate system, and updating the extended Kalman filter based on the two coordinates to obtain fused odometer data; Traverse the inertial navigation data and 3D lidar data at all time points to obtain odometer data at consecutive time points.

4. The precise positioning method for the train car body bottom according to claim 1, characterized in that, Filter the first lidar ranging data to obtain the filtered first lidar ranging data, including: Determine the target first lidar ranging data among all the first lidar ranging data; when the first lidar ranging data corresponding to multiple consecutive time points does not change, then use the first lidar ranging data corresponding to these multiple consecutive time points as the target first lidar ranging data; Determine the odometer data corresponding to the target time period of the target first lidar ranging data, where the target time period represents the multiple consecutive time points corresponding to the target first lidar ranging data; Judge whether the odometer data corresponding to the target time period is less than the pre-set hub scan line width. If so, filter out the target first lidar ranging data; otherwise, retain the target first lidar ranging data; Traverse all the first lidar ranging data to obtain the preliminarily filtered first lidar ranging data; Filter out the data in the preliminarily filtered first lidar ranging data that is not within the first distance threshold interval R to obtain the filtered first lidar ranging data.

5. The precise positioning method for the train car body bottom according to claim 1, characterized in that, According to the odometer data and the filtered first lidar ranging data, determine the hub center of the train wheel to obtain the hub center positioning result, including: According to the filtered first lidar ranging data, determine the first lidar ranging data corresponding to each hub; According to the first lidar ranging data corresponding to each hub, determine the first lidar ranging data tof1_d_begin corresponding to when the first lidar rangefinder starts scanning the hub and the first lidar ranging data tof1_d_end corresponding to when it finally scans the hub; Determine the odometer data pose(tof1_d_begin) corresponding to the first lidar ranging data tof1_d_begin and the odometer data pose(tof1_d_end) corresponding to the first lidar ranging data tof1_d_end; According to the odometer data pose(tof1_d_begin) and the odometer data pose(tof1_d_end), determine the hub center of the train wheel pose_wheel_center as: pose_wheel_center = (pose(tof1_d_end) - pose(tof1_d_begin)) / 2 to obtain the hub center positioning result.

6. The precise positioning method for the train car body bottom according to claim 1, characterized in that, Filter the second lidar ranging data to obtain the filtered second lidar ranging data, including: filter out the second lidar ranging data that is not within the second threshold interval D to obtain the filtered second lidar ranging data.

7. The precise positioning method for the train car body bottom according to claim 1, wherein, According to the filtered second lidar ranging data, fit the center of the train axle to obtain the axle center positioning result, including: A. Synchronize the odometer data and the second lidar ranging data in time to obtain the odometer data and the second lidar ranging data corresponding to consecutive time points; B. Combine the odometer data and the second lidar ranging data corresponding to the same time point to form a data pair (pose i , tof2 i ), where pose i represents the odometer data corresponding to time point i, and tof2 i represents the second lidar ranging data corresponding to time point i, i = 1, 2,..., I, and I represents the total number of time points; C. Set the counter T1 = 1 and the counter T2 = 1; D. Take three data pairs corresponding to the time points from T1 to T1 + 2 and fit the center of the circle according to the taken data pairs; E. Increment the count value of T1 by 3 + T2, and increment the count value of T2 by one; F. Take three data pairs corresponding to the time points from T1 to T1 + 2, and fit the center of the circle based on the taken data pairs; G. Repeat steps E - F until all data pairs are traversed, obtaining multiple fitted centers of the circle, with each said center of the circle corresponding to a radius; H. Remove the centers of the circle whose radii do not conform to the axle radius, obtaining the axle center positioning result.

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