A train positioning correction method and device based on high-precision map trajectory prior

Abstract

The present invention discloses a method and device for correcting train positioning based on a priori high-precision map trajectory. The train is equipped with a positioning sensor. The method comprises obtaining the train trajectory in the sensor coordinate system based on the positioning data of the positioning sensor; marking the actual track centerline in the map coordinate system on a high-precision map of the train operating environment; converting the first pose of the positioning point of the train trajectory into the second pose of the corresponding point of the actual track centerline based on the spatial conversion relationship between the calibrated train trajectory and the actual track centerline; obtaining a second optimized pose based on the second pose and the actual track centerline using a nearest neighbor search algorithm; and correcting the train trajectory based on the second optimized pose and the spatial conversion relationship. The present invention corrects the train positioning by combining the high-precision map with the priori train trajectory, thereby eliminating train positioning jitter and improving train positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of train positioning technology, and in particular to a train positioning correction method and device based on high-precision map trajectory prior. Background Art

[0002] Rail transit trains usually run on fixed routes. The use of train autonomous positioning technology can provide trains with real-time location information, helping to improve the accuracy of the train's forward environmental perception. At present, the acquisition of train location information mainly relies on the signal system. As a public transportation tool, rail transit trains need to reduce the coupling between the perception system and the signal system in order to further improve the reliability of the perception system. Among the perception system technology solutions, Simultaneous Localization And Mapping (SLAM) technology has received widespread attention. It enables objects with perception capabilities to move from an unknown position in an unknown environment, and locate themselves according to the position and map during the movement. At the same time, based on their own positioning, they build incremental maps to achieve autonomous positioning and navigation. However, SLAM positioning has jitter, and positioning errors seriously restrict its application in the field of train positioning.

[0003] Therefore, how to overcome the above problems and propose a train positioning correction solution to eliminate jitter and improve positioning accuracy is a technical problem that needs to be solved. Summary of the Invention

[0004] In response to the deficiencies in the prior art, the present invention provides a train positioning correction method, device, electronic device and storage medium based on high-precision map trajectory prior.

[0005] In a first aspect, a train positioning correction method based on high-precision map trajectory prior is proposed, wherein the train is equipped with a positioning sensor; the method comprises:

[0006] Based on the positioning data of the positioning sensor, the train running trajectory in the sensor coordinate system is obtained;

[0007] Mark the actual track centerline in the map coordinate system on a high-precision map of the train operating environment;

[0008] According to the calibrated spatial conversion relationship between the train running track and the actual track centerline, converting the first pose of the positioning point of the train running track into the second pose of the corresponding point of the actual track centerline;

[0009] Obtaining a second optimized posture using a nearest neighbor search algorithm based on the second posture and the actual track centerline;

[0010] The train running trajectory is corrected according to the second optimized posture and the spatial transformation relationship.

[0011] Furthermore, in a straight-line running scenario, calibrating the spatial conversion relationship between the train running trajectory and the actual track centerline includes:

[0012] Matching the predicted train posture obtained by the positioning sensor with the actual track centerline posture on the high-precision map of the train operating environment;

[0013] Based on the matching results, multiple sets of continuous posture deviations between the predicted train posture and the actual track centerline posture are calculated;

[0014] The average of multiple groups of posture deviations is taken as the spatial transformation relationship between the train running trajectory and the actual track centerline.

[0015] Furthermore, before calibrating the spatial conversion relationship between the train running trajectory and the actual track centerline, the train running trajectory is smoothed using the mean method; and / or, the train running trajectory and the actual track centerline are smoothed using the least squares method.

[0016] Furthermore, the train running trajectory is smoothed using a mean method, including:

[0017] Get the current position, N positions before the current position, and N positions after the current position; each position includes three degrees of freedom displacement and three degrees of freedom rotation;

[0018] The displacement mean and rotation mean of the obtained 2N+1 postures are calculated respectively as the posture of the smoothed train trajectory.

[0019] Further, according to the second posture and the actual track centerline, a nearest neighbor search algorithm is used to obtain a second optimized posture, including:

[0020] Obtaining the pose of evenly distributed discrete points of the actual track centerline, wherein the position of the pose is the spatial coordinate of the discrete point and the posture of the pose is the tangent direction of the current discrete point;

[0021] Calculating the position distance between the second pose and each discrete point pose, and selecting the discrete point with the minimum position distance as the matching point;

[0022] The pose of the matching point is used as the second optimized pose of the train in the map coordinate system.

[0023] Furthermore, based on the second optimized posture and the spatial conversion relationship, the train running trajectory is corrected, including converting the second optimized posture in the map coordinate system into the train posture in the sensor coordinate system based on the spatial conversion relationship to obtain the corrected train running trajectory.

[0024] Furthermore, the positioning sensor includes a positioning sensor based on a SLAM algorithm.

[0025] In the second aspect, a train positioning correction device based on high-precision map trajectory prior is proposed, wherein the train is equipped with a positioning sensor; the device comprises:

[0026] The first module is configured to obtain a train running track in a sensor coordinate system based on positioning data of the positioning sensor;

[0027] The second module is configured to mark the actual track centerline in the map coordinate system on the high-precision map of the train operation environment;

[0028] A third module is configured to convert a first pose of a positioning point on the train running track into a second pose of a corresponding point on the actual track centerline based on a spatial conversion relationship between the calibrated train running track and the actual track centerline;

[0029] A fourth module is configured to obtain a second optimized posture using a nearest neighbor search algorithm based on the second posture and the actual track centerline;

[0030] The fifth module is configured to correct the train running trajectory based on the second optimized posture and the spatial transformation relationship.

[0031] In a third aspect, an electronic device is proposed, comprising: a processor; and a memory arranged to store computer-executable instructions, wherein when the executable instructions are executed, the processor executes the method described in the first aspect.

[0032] In a fourth aspect, a computer-readable storage medium is proposed, characterized in that the computer-readable storage medium stores one or more programs, which, when executed by an electronic device including multiple applications, enable the electronic device to execute the method described in the first aspect.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention configures a positioning sensor on the train, obtains the train's running track in the sensor coordinate system based on the positioning data of the positioning sensor; marks the actual track centerline in the map coordinate system on a high-precision map of the train's running environment; converts the first pose of the positioning point of the train's running track into the second pose of the corresponding point of the actual track centerline based on the spatial conversion relationship between the calibrated train running track and the actual track centerline; obtains a second optimized pose based on the second pose and the actual track centerline using a nearest neighbor search algorithm; and corrects the train's running track based on the second optimized pose and the spatial conversion relationship. The present invention corrects the train's positioning by combining a high-precision map with the train's prior trajectory, thereby eliminating train positioning jitter and improving train positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. By referring to the drawings, the features and advantages of the present invention can be more clearly understood. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0036] Figure 1 This is one of the schematic diagrams of the train positioning correction method based on high-precision map trajectory prior provided by the present invention.

[0037] Figure 2 This is the second schematic diagram of the train positioning correction method based on high-precision map trajectory prior provided by the present invention.

[0038] Figure 3 This is the third schematic diagram of the train positioning correction method based on high-precision map trajectory prior provided by the present invention.

[0039] Figure 4 This is the fourth schematic diagram of the train positioning correction method based on high-precision map trajectory prior provided by the present invention.

[0040] Figure 5 This is the fifth schematic diagram of the train positioning correction method based on high-precision map trajectory prior provided by the present invention.

[0041] Figure 6 Schematic diagram six of the train positioning correction method based on high-precision map trajectory prior provided by the present invention.

[0042] Figure 7 Schematic diagram of the train positioning correction device based on high-precision map trajectory prior provided by the present invention.

[0043] Figure 8 A schematic structural diagram of an electronic device provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features therein can be combined with each other without conflict.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0046] As described in the background technology, rail transit trains usually run on fixed routes. The use of train autonomous positioning technology can provide real-time location information for the train, helping to improve the accuracy of the train's forward environmental perception. At this stage, the acquisition of train location information mainly depends on the signal system. As a public transportation tool, rail transit trains need to reduce the coupling degree between the perception system and the signal system in order to further improve the reliability of the perception system. Among the perception system technical solutions, Simultaneous Localization And Mapping (SLAM) technology has received widespread attention. This technology enables objects with perception capabilities to move from an unknown position in an unknown environment, and position themselves according to the position and map during the movement. At the same time, based on their own positioning, they build incremental maps to achieve autonomous positioning and navigation.

[0047] SLAM is a research field focused on positioning and mapping technologies. Currently, the mainstream SLAM technologies are laser SLAM (based on lidar) and visual SLAM (based on monocular or binocular cameras). Implementations are primarily categorized into filter-based SLAM and graph-based SLAM. SLAM is currently supported by two main sensor types: lidar, which comes in single-line and multi-line versions; and cameras, which come in monocular, binocular, monocular structured light, and binocular structured light versions.

[0048] However, SLAM positioning suffers from jitter, and positioning errors severely restrict its application in train positioning. The inventors identified prior art, including CN115900553A and CN112977548B, which achieve autonomous train positioning by matching current frame data with a constructed 3D model template of the operating environment and a local map. However, this positioning process fails to account for the fact that trains can only travel on tracks and on fixed routes, resulting in jitter and limited positioning accuracy.

[0049] The following is a detailed description of a train positioning correction solution based on high-precision map trajectory priors involved in the present invention through specific examples.

[0050] Example 1

[0051] like Figure 1 As shown in FIG. 1 , a train positioning correction method based on high-precision map trajectory prior is provided in an embodiment of the present application. The train is equipped with a positioning sensor. The method includes:

[0052] S101: Obtaining a train running track in a sensor coordinate system based on positioning data of a positioning sensor;

[0053] S102: Marking the actual track centerline in the map coordinate system on the high-precision map of the train operation environment;

[0054] S103: According to the calibrated spatial conversion relationship between the train running track and the actual track center line, convert the first pose of the positioning point of the train running track into the second pose of the corresponding point of the actual track center line.

[0055] S104: Obtain a second optimized posture using a nearest neighbor search algorithm based on the second posture and the actual track centerline.

[0056] S105: Correcting the train trajectory according to the second optimized posture and the spatial transformation relationship.

[0057] In some embodiments, the positioning sensor includes a positioning sensor based on a SLAM algorithm.

[0058] In some embodiments, as Figure 2 As shown, in the straight line running scenario, combined with Figure 3 The method of calibrating the spatial conversion relationship between the train running track and the actual track centerline includes:

[0059] S211: Matching the predicted train posture obtained based on the positioning sensor with the actual track centerline posture on the high-precision map of the train operating environment.

[0060] S212: Based on the matching results, multiple sets of continuous posture deviations between the predicted train posture and the actual track centerline posture are calculated.

[0061] S213: Taking an average of the multiple groups of posture deviations as the spatial transformation relationship between the train running trajectory and the actual track centerline.

[0062] In some embodiments, before calibrating the spatial transformation relationship between the train running trajectory and the actual track centerline, the train running trajectory is smoothed using a mean method.

[0063] In some embodiments, the least squares method is used to smooth the train running trajectory and the actual track centerline.

[0064] In some implementations, the train trajectory is smoothed using a mean value method, such as Figure 4 Shown, including:

[0065] S411: Acquire the posture of the current positioning point, N postures before the current positioning point, and N postures after the current positioning point; wherein each posture includes a three-degree-of-freedom displacement and a three-degree-of-freedom rotation.

[0066] S412: Calculate the displacement mean and rotation mean of the obtained 2N+1 postures respectively as the posture of the smoothed train running trajectory.

[0067] In some embodiments, as Figure 5 As shown, according to the second posture and the actual track center line, the nearest neighbor search algorithm is used to obtain the second optimized posture, combined with Figure 6 Shown, including:

[0068] S511: Obtaining the pose of evenly distributed discrete points of the actual track center line, wherein the position of the pose is the spatial coordinate of the discrete point, and the posture of the pose is the tangent direction of the current discrete point.

[0069] S512: Calculate the position distance between the second posture and each discrete point posture, and select the discrete point with the minimum position distance as the matching point.

[0070] S513: Using the posture of the matching point as the second optimized posture of the train in the map coordinate system.

[0071] In some embodiments, the train trajectory is corrected based on the second optimized posture and the spatial conversion relationship, including converting the second optimized posture in the map coordinate system into the train posture in the sensor coordinate system based on the spatial conversion relationship to obtain the corrected train trajectory.

[0072] In the embodiment of the present application, a positioning sensor is configured on the train, and the train running track in the sensor coordinate system is obtained based on the positioning data of the positioning sensor; the actual track center line in the map coordinate system is marked on the high-precision map of the train running environment; based on the spatial conversion relationship between the calibrated train running track and the actual track center line, the first pose of the positioning point of the train running track is converted to the second pose of the corresponding point of the actual track center line; based on the second pose and the actual track center line, a nearest neighbor search algorithm is used to obtain a second optimized pose; based on the second optimized pose and the spatial conversion relationship, the train running track is corrected. The present invention corrects the train positioning by combining the high-precision map with the train prior trajectory, which can eliminate the train positioning jitter and improve the train positioning accuracy.

[0073] Example 2

[0074] Figure 7 This is a schematic diagram of a high-precision map trajectory prior train positioning correction device 700 provided by an embodiment of the present invention. Figure 7 In one embodiment, a high-precision map trajectory prior train positioning correction device 700 is provided, wherein the train is equipped with a positioning sensor; the device comprises:

[0075] The first module 701 is configured to obtain a train running track in a sensor coordinate system based on positioning data of the positioning sensor.

[0076] The second module 702 is configured to mark the actual track center line in the map coordinate system on the high-precision map of the train operation environment.

[0077] The third module 703 is configured to convert the first posture of the positioning point of the train running track into the second posture of the corresponding point of the actual track center line according to the calibrated spatial conversion relationship between the train running track and the actual track center line.

[0078] The fourth module 704 is configured to obtain a second optimized posture using a nearest neighbor search algorithm based on the second posture and the actual track centerline.

[0079] The fifth module 705 is configured to correct the train running trajectory based on the second optimized posture and the spatial transformation relationship.

[0080] It should be understood that the high-precision map trajectory prior train positioning correction device 700 of the embodiment of the present invention can also perform Figures 1 to 6 Method for executing a train positioning correction device (or apparatus) based on a priori high-precision map trajectory, and realizing a train positioning correction device (or apparatus) based on a priori high-precision map trajectory 700 Figures 1 to 6 The functions of the examples shown will not be described in detail here.

[0081] Example 3

[0082] Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 8 At the hardware level, the electronic device includes a processor and, optionally, an internal bus, a network interface, and memory. The memory may include internal memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for its services.

[0083] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0084] The memory is used to store programs. Specifically, the program may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0085] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, forming a shared resource access control device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0086] The train is equipped with a positioning sensor; the method comprises:

[0087] Based on the positioning data of the positioning sensor, the train running trajectory in the sensor coordinate system is obtained;

[0088] Mark the actual track centerline in the map coordinate system on a high-precision map of the train operating environment;

[0089] According to the calibrated spatial conversion relationship between the train running track and the actual track centerline, converting the first pose of the positioning point of the train running track into the second pose of the corresponding point of the actual track centerline;

[0090] Obtaining a second optimized posture using a nearest neighbor search algorithm based on the second posture and the actual track centerline;

[0091] The train running trajectory is corrected according to the second optimized posture and the spatial transformation relationship.

[0092] The above Figures 1 to 6 The train positioning correction method based on high-precision map trajectory priors disclosed in the illustrated embodiments can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor or by software instructions. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0093] Of course, in addition to software implementation, the electronic device of the embodiment of the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0094] Example 4

[0095] The embodiment of the present invention further provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to execute Figures 1 to 6 The train positioning correction method based on high-precision map trajectory prior in the illustrated embodiment is specifically used to perform the following steps:

[0096] The train is equipped with a positioning sensor; the method comprises:

[0097] Based on the positioning data of the positioning sensor, the train running trajectory in the sensor coordinate system is obtained;

[0098] Mark the actual track centerline in the map coordinate system on a high-precision map of the train operating environment;

[0099] According to the calibrated spatial conversion relationship between the train running track and the actual track centerline, converting the first pose of the positioning point of the train running track into the second pose of the corresponding point of the actual track centerline;

[0100] Obtaining a second optimized posture using a nearest neighbor search algorithm based on the second posture and the actual track centerline;

[0101] The train running trajectory is corrected according to the second optimized posture and the spatial transformation relationship.

[0102] In short, the above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0103] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an electronic data carrier device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0104] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0106] The various embodiments of the present invention are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so its description is relatively simple. For relevant portions, refer to the description of the method embodiment.

Claims

1. A train positioning correction method based on high-precision map trajectory prior, characterized in that: The train is equipped with a positioning sensor; the method comprises: Based on the positioning data of the positioning sensor, the train running trajectory in the sensor coordinate system is obtained; Mark the actual track centerline in the map coordinate system on a high-precision map of the train operating environment; According to the calibrated spatial conversion relationship between the train running track and the actual track centerline, converting the first pose of the positioning point of the train running track into the second pose of the corresponding point of the actual track centerline; Obtaining a second optimized posture using a nearest neighbor search algorithm based on the second posture and the actual track centerline; Correcting the train trajectory according to the second optimized posture and the spatial transformation relationship; Obtaining a second optimized posture using a nearest neighbor search algorithm based on the second posture and the actual track centerline includes: Obtaining the pose of evenly distributed discrete points of the actual track centerline, wherein the position of the pose is the spatial coordinate of the discrete point and the posture of the pose is the tangent direction of the current discrete point; Calculating the position distance between the second pose and each discrete point pose, and selecting the discrete point with the minimum position distance as the matching point; The pose of the matching point is used as the second optimized pose of the train in the map coordinate system.

2. The train positioning correction method based on high-precision map trajectory prior according to claim 1 is characterized in that: In a straight-line running scenario, calibrating the spatial conversion relationship between the train running trajectory and the actual track centerline includes: Matching the predicted train posture obtained by the positioning sensor with the actual track centerline posture on the high-precision map of the train operating environment; Based on the matching results, multiple sets of continuous posture deviations between the predicted train posture and the actual track centerline posture are calculated; The average of multiple groups of posture deviations is taken as the spatial transformation relationship between the train running trajectory and the actual track centerline.

3. The train positioning correction method based on high-precision map trajectory prior according to claim 2 is characterized in that: Before calibrating the spatial conversion relationship between the train running track and the actual track center line, the train running track is smoothed using a mean method; or, the train running track and the actual track center line are smoothed using a least squares method.

4. The train positioning correction method based on high-precision map trajectory prior according to claim 3 is characterized in that: The train running trajectory is smoothed using a mean method, including: Get the current position, N positions before the current position, and N positions after the current position; each position includes three degrees of freedom displacement and three degrees of freedom rotation; The displacement mean and rotation mean of the obtained 2N+1 postures are calculated respectively as the posture of the smoothed train trajectory.

5. The train positioning correction method based on high-precision map trajectory prior according to claim 1 is characterized in that: According to the second optimized posture and the spatial conversion relationship, the train running trajectory is corrected, including converting the second optimized posture in the map coordinate system into the train posture in the sensor coordinate system according to the spatial conversion relationship to obtain the corrected train running trajectory.

6. The train positioning correction method based on high-precision map trajectory prior according to any one of claims 1 to 5, characterized in that: The positioning sensor includes a positioning sensor based on a SLAM algorithm.

7. A train positioning correction device based on high-precision map trajectory prior, characterized in that: The train is equipped with a positioning sensor; the device includes: The first module is configured to obtain a train running track in a sensor coordinate system based on positioning data of the positioning sensor; The second module is configured to mark the actual track centerline in the map coordinate system on the high-precision map of the train operation environment; A third module is configured to convert a first pose of a positioning point on the train running track into a second pose of a corresponding point on the actual track centerline based on a spatial conversion relationship between the calibrated train running track and the actual track centerline; A fourth module is configured to obtain a second optimized posture using a nearest neighbor search algorithm based on the second posture and the actual track centerline; The fifth module is configured to correct the train running trajectory based on the second optimized posture and the spatial transformation relationship.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the processor implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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