Lidar-based trolley positioning method, apparatus, device, and storage medium

Abstract

The application provides a trolley positioning method and device based on a laser radar, equipment and a storage medium, and relates to the technical field of tunnel engineering. The method comprises the following steps: acquiring a posture conversion matrix between the laser radar and the trolley, and a target center first coordinate of each target; acquiring point cloud data of the laser radar, and obtaining a target center second coordinate of each target according to the point cloud data; obtaining a first conversion matrix between a radar coordinate system and a tunnel coordinate system through singular value decomposition according to the target center first coordinate and the target center second coordinate of each target, and obtaining a second conversion matrix between a trolley coordinate system where the trolley is located and the tunnel coordinate system according to the first conversion matrix and the posture conversion matrix; and positioning the trolley according to the second conversion matrix. The method provided by the application solves the problem of improving the positioning accuracy of the trolley in the tunnel under the premise of high universality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel engineering, in particular to a trolley positioning method and device based on a laser radar, equipment and a storage medium. BACKGROUND

[0002] Tunnel construction is used to build a passageway connecting two places in mountains, underground and underwater. Tunnel construction is a complex and professional engineering activity, and accurate positioning of the construction trolley equipment in the tunnel is very important, which can ensure that the tunnel is constructed correctly according to the design requirements and avoid unnecessary resource consumption costs. With the development of wireless communication and sensor technologies, the tunnel positioning problem is solved by algorithms or fusion calculation of multiple sensor data to obtain the positioning result.

[0003] At present, the implementation mode of tunnel internal train positioning includes: pre-processing data according to optical fiber sensing network, voiceprint fingerprint sensor and geomagnetic field inductor and other equipment, and inputting the model to generate tunnel real-time monitoring data; or performing simple positioning and deviation correction based on a laser radar to obtain positioning data. However, such methods use multiple sensors for fusion calculation, which is high in cost and easy to cause cumulative error of the system, resulting in low precision; or a single point cloud feature matching positioning algorithm is used, which is difficult to ensure the stability of positioning and has the problem of adaptability range.

[0004] How to improve the positioning accuracy of the trolley in the tunnel under the premise of high universality is a problem to be solved by the present application. SUMMARY

[0005] The present application provides a trolley positioning method and device based on a laser radar, equipment and a storage medium to solve the problem of improving the positioning accuracy of the trolley in the tunnel under the premise of high universality.

[0006] In a first aspect, the present application provides a trolley positioning method based on a laser radar, which comprises:

[0007] Obtaining an attitude conversion matrix between the laser radar and the trolley, and a target center first coordinate of each target; wherein the attitude conversion matrix is obtained by calibrating the attitude relationship between the laser radar and the trolley; and each target center first coordinate is a coordinate of the corresponding target in the tunnel coordinate system measured by a total station;

[0008] Obtaining point cloud data of the laser radar, and obtaining a target center second coordinate of each target according to the point cloud data; wherein each target center second coordinate is a coordinate of the corresponding target in the radar coordinate system;

[0009] According to the first coordinate and the second coordinate of the target center of each target, a first conversion matrix between the radar coordinate system and the tunnel coordinate system is obtained by a singular value decomposition method, and a second conversion matrix between the trolley coordinate system where the trolley is located and the tunnel coordinate system is obtained according to the first conversion matrix and the attitude conversion matrix.

[0010] According to the second conversion matrix, positioning of the trolley is performed.

[0011] In a possible design, after obtaining the point cloud data of the laser radar, the method further includes:

[0012] Converting the point cloud data into text data; wherein the text data includes a plurality of three-dimensional points, a coordinate value of each three-dimensional point in the radar coordinate system, and a reflection intensity value of each three-dimensional point;

[0013] According to the point cloud data, the second coordinate of the target center of each target is obtained, including:

[0014] According to the coordinate value and the reflection intensity value of each three-dimensional point, a plurality of target point sets are determined by three-dimensional point extraction; wherein each target point set includes a plurality of target points, and each target point is used to indicate a point on the target;

[0015] According to the coordinate value and the reflection intensity value of each three-dimensional point, the second coordinate of the target center of each target is obtained.

[0016] In a possible design, according to the coordinate value and the reflection intensity value of each three-dimensional point, a plurality of target point sets are determined by three-dimensional point extraction, including:

[0017] According to the coordinate value and the reflection intensity value of each three-dimensional point, a plurality of first-type points are obtained by filtering processing.

[0018] A plurality of first point sets are obtained by performing clustering on the plurality of first-type points through a Euclidean clustering segmentation processing.

[0019] Each first point set is respectively subjected to expansion processing and fitting processing to obtain a second point set corresponding to each first point set; wherein each second point set includes a plurality of second-type points.

[0020] The plurality of target point sets are determined from the plurality of second point sets by target matching.

[0021] In a possible design, each coordinate value includes a depth value and a height value, and according to the coordinate value and the reflection intensity value of each three-dimensional point, a plurality of first-type points are obtained by filtering processing, including:

[0022] According to the respective depth value and height value of each three-dimensional point, a plurality of three-dimensional points are directionally filtered by a pass-through filtering method to obtain a plurality of filtered points; wherein the depth value of each filtered point is within a depth threshold range; and the height value of each filtered point is within a height threshold range.

[0023] According to the reflection intensity value of each filtered point, a plurality of filtered points are intensity filtered by a point cloud reflection intensity filtering to obtain a plurality of first type points; wherein the reflection intensity value of each first type point is greater than a reflection intensity first threshold.

[0024] In a possible design, the target type point is any one of the plurality of first type points, and the first target point set is a first point set in which the target type point is located.

[0025] For the target type point and the first target point set, each first point set is respectively subjected to expansion processing and fitting processing to obtain a second point set corresponding to each first point set, including:

[0026] The target type point is compared with the plurality of three-dimensional points of the text data to obtain a plurality of three-dimensional points corresponding to the target type point in the text data, an expansion range is established, and the expansion range is determined as an expansion point of the first target point set, wherein the expansion range of the target type point refers to a spherical range with the target type point as the center and a distance threshold as the radius, and the expansion range is a spherical range established for the plurality of three-dimensional points corresponding to the target type point in the text data.

[0027] The plurality of first type points and the plurality of expansion points of the first target point set are subjected to repeated point elimination to obtain a third point set corresponding to the first target point set.

[0028] The third point set corresponding to the first target point set is subjected to plane fitting by a random sample consensus algorithm to obtain the second point set corresponding to the first target point set.

[0029] In a possible design, the second target point set is any one of the plurality of second point sets.

[0030] For the second target point set, a plurality of target point sets are determined from the plurality of second point sets by target matching, including:

[0031] The first length value and the first width value of the target are obtained, and the intensity point quantity of the second target point set is obtained; wherein the intensity point quantity of the second target point set refers to the number of second type points in the second target point set whose reflection intensity value is greater than a reflection intensity second threshold.

[0032] A bounding box is made for the second target point set to obtain a second length value and a second width value of the bounding box.

[0033] According to the numerical relationship between the first length value and the second length value, the numerical relationship between the first width value and the second width value, and the numerical relationship between the number of intensity points of the second target point set and the number threshold, it is determined whether the second target point set is a target point set.

[0034] In a possible design, the target target point set is any one of a plurality of target target point sets,

[0035] For the target target point set, according to the respective coordinate value and the reflection intensity value of each three-dimensional point, a target center second coordinate of each target is obtained, including:

[0036] Obtain a point set feature vector of the target target point set; wherein the point set feature vector of the target target point set is obtained by principal component analysis;

[0037] According to the respective coordinate value and the reflection intensity value of each target point of the target target point set, and the point set feature vector of the target target point set, a target center second coordinate of a target corresponding to the target target point set is obtained.

[0038] In a second aspect, the present application provides a trolley positioning device based on a laser radar, comprising:

[0039] The acquisition module is configured to acquire a pose conversion matrix between the laser radar and the trolley, and a target center first coordinate of each target; wherein the pose conversion matrix is obtained by calibrating the pose relationship between the laser radar and the trolley; and each target center first coordinate is a coordinate of a corresponding target in a tunnel coordinate system measured by a total station;

[0040] The coordinate module is configured to acquire point cloud data of the laser radar, and obtain a target center second coordinate of each target according to the point cloud data; wherein each target center second coordinate is a coordinate of a corresponding target in a radar coordinate system;

[0041] The matrix module is configured to obtain a first conversion matrix between the radar coordinate system and the tunnel coordinate system by singular value decomposition method according to the respective target center first coordinate and the target center second coordinate of each target, and obtain a second conversion matrix between a trolley coordinate system in which the trolley is located and the tunnel coordinate system according to the first conversion matrix and the pose conversion matrix;

[0042] The positioning module is configured to position the trolley according to the second conversion matrix.

[0043] In a third aspect, the present application provides an electronic device, comprising a processor and a memory in communication connection with the processor;

[0044] The memory stores computer execution instructions;

[0045] The processor executes computer-executed instructions stored in the memory to implement the laser radar-based trolley positioning method of the first aspect.

[0046] In a fourth aspect, the present application provides a computer-readable storage medium, which stores computer-executed instructions. When the computer-executed instructions are executed by a processor, a laser radar-based trolley positioning method of the first aspect is implemented.

[0047] In a fifth aspect, the present application provides a computer program product, which comprises a computer program. When the computer program is executed by a processor, a laser radar-based trolley positioning method of the first aspect is implemented.

[0048] The present application provides a laser radar-based trolley positioning method, device, equipment and storage medium. The pose conversion matrix between the laser radar and the trolley and the target center first coordinates of each target are obtained. The pose conversion matrix is obtained by calibrating the pose relationship between the laser radar and the trolley. Each target center first coordinate is the coordinate of the corresponding target in the tunnel coordinate system obtained by measuring with a total station. The point cloud data of the laser radar is obtained, and the target center second coordinates of each target are obtained according to the point cloud data. Each target center second coordinate is the coordinate of the corresponding target in the radar coordinate system. The first conversion matrix between the radar coordinate system and the tunnel coordinate system is obtained by singular value decomposition method according to the target center first coordinates and the target center second coordinates of each target. The second conversion matrix between the trolley coordinate system where the trolley is located and the tunnel coordinate system is obtained according to the first conversion matrix and the pose conversion matrix. The positioning of the trolley is performed according to the second conversion matrix. The following technical effects are achieved: the data acquisition is realized by the laser radar, the positioning cost is reduced, and the adaptability is wide. The point cloud data calculation of the target and the coordinate conversion are realized by multiple methods, the stability of the positioning is ensured, and the positioning accuracy is improved. The positioning of the trolley is solved by multiple matrices and target center coordinates, the possibility of error is reduced, the universality is strong, the calculation is not based on the geometric feature information of the tunnel, and the positioning accuracy of the trolley is improved. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0050] Figure 1A system architecture schematic diagram of the laser radar based trolley positioning method provided by the embodiment of the present application is shown in FIG. 1.

[0051] Figure 2 A flowchart of the laser radar based trolley positioning method provided by the embodiment of the present application is shown in FIG. 2. Figure 1

[0052] Figure 3 A flowchart of the laser radar based trolley positioning method provided by the embodiment of the present application is shown in FIG. 2. Figure 2

[0053] Figure 4 A target schematic diagram of the laser radar based trolley positioning method provided by the embodiment of the present application is shown in FIG. 3.

[0054] Figure 5 A flowchart of the laser radar based trolley positioning method provided by the embodiment of the present application is shown in FIG. 2. Figure 3

[0055] Figure 6 A structure schematic diagram of the laser radar based trolley positioning device provided by the embodiment of the present application is shown in FIG. 4.

[0056] Figure 7 A structure schematic diagram of the electronic device hardware provided by the embodiment of the present application is shown in FIG. 5.

[0057] Reference signs:

[0058] 100 - trolley positioning system; 110 - target center extraction module; 120 - positioning calculation module;

[0059] 410 - reflector plate; 420 - target plate;

[0060] 600 - laser radar based trolley positioning device; 610 - acquisition module; 620 - coordinate module; 630 - matrix module; 640 - positioning module;

[0061] 700 - electronic device; 710 - processor; 720 - memory; 730 - communication component; 740 - bus. DETAILED DESCRIPTION

[0062] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The following description is made with reference to the accompanying drawings in which like reference numerals represent like elements or similar elements, unless the context dictates otherwise. The following exemplary embodiments described are not representative of all embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0063] ​​​In the embodiments of the present application, the terms "first", "second", and the like are used to distinguish between items or similar items that have substantially the same function and effect. Those skilled in the art can understand that the terms "first", "second", and the like do not limit the quantity and execution order, and the terms "first", "second", and the like do not necessarily mean different. It should be noted that in the embodiments of the present application, the words "exemplary" or "for example" are used to indicate an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the use of the words "exemplary" or "for example" is intended to present the relevant concept in a specific manner. In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more.

[0064] It should be noted that "at" in the embodiments of the present application can be at the moment when a certain condition occurs, or within a certain period of time after the occurrence of a certain condition, which is not limited in the embodiments of the present application. In addition, the laser radar based trolley positioning method provided in the embodiments of the present application is only an example, and the laser radar based trolley positioning method can include more or less content.

[0065] In order to clearly describe the technical solutions of the embodiments of the present application, the following briefly introduces some terms and technologies involved in the embodiments of the present application:

[0066] Trolley: It is a large mechanical equipment commonly used in tunnel engineering construction. Its main function is to support the tunnel wall after tunnel excavation, to ensure the safety and stability of the tunnel structure, and to perform concrete pouring and other lining operations to form a solid inner wall of the tunnel.

[0067] Point cloud feature matching: Point cloud feature matching is a key step in point cloud processing, which is used to find corresponding points or regions in two or more point cloud data sets. This process usually includes two main stages of feature extraction and feature matching. Point cloud feature matching has wide applications in three-dimensional reconstruction, object recognition, robot navigation, map construction and other fields.

[0068] Face of working: Face of working is an important term in mining and tunnel engineering, which refers to the working face or exposed rock surface. In underground engineering, the face of working is the front face that is currently being excavated or blasted.

[0069] Pose relationship calibration: Pose relationship calibration is a key technology in the fields of multi-sensor fusion, robot navigation, augmented reality, virtual reality, etc. It involves determining the relative position and attitude relationship between two or more sensors or coordinate systems. Through calibration, data between different sensors or devices can be correctly aligned and fused, improving the overall performance and accuracy of the system. In this application, the pose relationship between the LiDAR and the trolley can be calibrated.

[0070] RANdom SAmple Consensus (RANSAC): RANSAC is an iterative method for estimating the parameters of a mathematical model from a set of data containing outliers. In point cloud processing, RANSAC is often used to fit a plane, even if there are a large number of noise points or outliers in the point cloud data, it can accurately find the appropriate fitting plane.

[0071] Straight-through filtering algorithm: Straight-through filtering is a commonly used point cloud processing technique that filters out points within a certain range from point cloud data. This filtering method is particularly suitable for cutting or selecting point clouds along a certain axis (height direction, depth direction, or other direction), thereby removing unwanted points or retaining areas of interest.

[0072] Point cloud reflection intensity filtering: Point cloud reflection intensity filtering is a technique that filters and processes each point in the point cloud data based on its reflection intensity. Reflection intensity is an important attribute in point cloud data, which reflects the reflection of laser pulses emitted by the LiDAR on the target surface.

[0073] Euclidean cluster segmentation: Euclidean cluster segmentation is a commonly used technique in point cloud processing, which divides point cloud data into different clusters, each cluster representing a separate object or area. This method is based on the Euclidean distance between points to determine whether they belong to the same cluster. Euclidean cluster segmentation is particularly suitable for processing point cloud data without obvious features or textures, such as LiDAR data.

[0074] Bounding box: Bounding box is a concept commonly used in computer graphics, computer vision, three-dimensional modeling, and game development, etc., which refers to a simple geometric body that approximately represents the position and size of a more complex object or a group of objects. The main purpose of the bounding box is to simplify collision detection, view frustum culling, fast rendering, etc., thereby improving computational efficiency.

[0075] Principal Component Analysis (PCA) is a common data dimensionality reduction technique widely used in data science, machine learning, image processing, and other fields. PCA transforms the original data into a new coordinate system through linear transformation, so that the new coordinate axes (called principal components) can maximize the variance of the data while maintaining the total variation information of the data. The purpose of this is to reduce the dimensionality of the data while retaining as much information as possible, thereby simplifying data analysis and processing.

[0076] Singular Value Decomposition (SVD) is a technique widely used in matrix analysis and linear algebra, which can decompose any real or complex matrix into the product of three matrices.

[0077] Tunnel operations play an extremely important role in modern infrastructure construction, involving transportation, water conservancy, energy, and other fields. Precise operations within tunnels are particularly important, and the positioning of tunnel work equipment trolleys affects the progress and safety of tunnel work.

[0078] With the development of wireless communication and sensor technologies, tunnel positioning problems are solved by algorithms or the fusion of multiple sensor data to obtain positioning results. Existing tunnel internal positioning implementation methods include:

[0079] First, by setting up a fiber-optic sensor network, acoustic fingerprint sensors, and geomagnetic field sensors, a monitoring network layout is established. Environmental monitoring data is collected through the initialized monitoring network, and the environmental monitoring data is preprocessed.

[0080] Second, using a pre-trained first model, the environmental monitoring data is input, and the train positioning information is output.

[0081] Finally, based on the current train positioning information and environmental monitoring data, a pre-trained second model is used to predict the train position and tunnel safety state, generating real-time tunnel monitoring data.

[0082] Alternatively, laser radar is used to solve the positioning problem through point cloud matching feature algorithms.

[0083] However, while these methods achieve positioning results, they also have some technical problems:

[0084] First, the use of multiple sensors to collect data and perform fusion calculations results in high costs and accumulative errors in the inertial navigation system, affecting positioning accuracy.

[0085] In the second aspect, a simple point cloud matching feature algorithm cannot provide sufficient geometric feature information, and it is difficult to ensure the stability and accuracy of positioning.

[0086] In view of the cost problem, therefore, the present application also uses a laser radar to realize positioning of the trolley.

[0087] Therefore, how to improve the positioning accuracy of the trolley in the tunnel under the premise of high universality is a problem that needs to be solved by the present application.

[0088] Based on this, the present application provides a laser radar-based trolley positioning method, device, equipment and storage medium, which can be used in the field of tunnel engineering, and aims to solve the above technical problems of the prior art.

[0089] Figure 1 The system architecture of the laser radar-based trolley positioning method provided by the present application is shown in the figure. It should be noted that, Figure 1 The figure only shows an example of the system architecture to which the present application can be applied, to help those skilled in the art understand the technical content of the present application, but does not mean that the present application cannot be used in other devices, systems, environments or scenarios.

[0090] As Figure 1 shown, the system architecture of the laser radar-based trolley positioning method includes a trolley positioning system 100, which includes a target center extraction module 110 and a positioning calculation module 120.

[0091] The target center extraction module 110 processes the raw point cloud data input by the laser radar, including steps such as filtering, Euclidean clustering segmentation, point cloud expansion, plane fitting and target matching; and also includes calculating the feature vector of the target point cloud and calculating the target center coordinates.

[0092] The positioning calculation module 120 realizes the conversion between the radar coordinate system and the tunnel coordinate system, and the conversion between the trolley coordinate system and the tunnel coordinate system by SVD method, and obtains the positioning result of the trolley in the tunnel according to the conversion relationship.

[0093] Figure 2 The flowchart of the laser radar-based trolley positioning method provided by the present application is shown in the figure. Figure 1 As Figure 2 shown, the method includes:

[0094] S201, obtaining the attitude conversion matrix between the laser radar and the trolley, and the target center first coordinates of each target.

[0095] Wherein, the attitude conversion matrix is obtained by calibrating the attitude relationship between the laser radar and the trolley; and each target center first coordinate is the coordinate of the corresponding target in the tunnel coordinate system obtained by measuring the targets by the total station.

[0096] Specifically, since there is a position difference between the laser radar and the trolley, the attitude relationship between the laser radar and the trolley is calibrated to obtain the attitude conversion matrix between the laser radar and the trolley.

[0097] The total station measures the installed multiple targets to obtain the coordinates of the multiple targets in the tunnel coordinate system.

[0098] S202, obtain the point cloud data of the laser radar, and obtain the target center second coordinate of each target according to the point cloud data.

[0099] Wherein, each target center second coordinate is the coordinate of the corresponding target in the radar coordinate system.

[0100] Specifically, the point cloud data monitored by the laser radar is obtained, and the target center second coordinate of each target is obtained by processing the point cloud data, and each target center second coordinate is the coordinate in the radar coordinate system.

[0101] S203, according to the target center first coordinate and the target center second coordinate of each target, the first conversion matrix between the radar coordinate system and the tunnel coordinate system is obtained by singular value decomposition method, and the second conversion matrix between the trolley coordinate system where the trolley is located and the tunnel coordinate system is obtained according to the first conversion matrix and the attitude conversion matrix.

[0102] Specifically, according to the target center first coordinate and the target center second coordinate of each target, the first conversion matrix from the radar coordinate system to the tunnel coordinate system is obtained by singular value decomposition method; and the second conversion matrix from the trolley coordinate system where the trolley is located to the tunnel coordinate system is obtained according to the first conversion matrix and the attitude conversion matrix.

[0103] S204, positioning the trolley according to the second conversion matrix.

[0104] Specifically, according to the second conversion matrix and the origin of the trolley in the coordinate system, the positioning result of the trolley in the speed regulation coordinate system is obtained.

[0105] The application provides a trolley positioning method and device based on a laser radar, equipment and a storage medium. An attitude conversion matrix between the laser radar and the trolley and a target center first coordinate of each target are obtained. The attitude conversion matrix is obtained by calibrating the attitude relationship between the laser radar and the trolley. The target center first coordinate of each target is the coordinate of the corresponding target in a tunnel coordinate system obtained by measuring with a total station instrument. Point cloud data of the laser radar is obtained, and a target center second coordinate of each target is obtained according to the point cloud data. The target center second coordinate of each target is the coordinate of the corresponding target in a radar coordinate system. A first conversion matrix between the radar coordinate system and the tunnel coordinate system is obtained by singular value decomposition based on the target center first coordinate and the target center second coordinate of each target. A second conversion matrix between a trolley coordinate system in which the trolley is located and the tunnel coordinate system is obtained based on the first conversion matrix and the attitude conversion matrix. The positioning of the trolley is performed based on the second conversion matrix. The following technical effects are achieved: the laser radar is used to collect data, the cost of positioning is reduced, and the adaptability is wide; the point cloud data of the target is calculated and the coordinate conversion is performed by multiple methods, the stability of positioning is ensured, and the positioning accuracy is improved; the positioning of the trolley is solved by multiple matrices and target center coordinates, the possibility of error is reduced, the universality is high, the calculation is not based on the geometric feature information of the tunnel, and the positioning accuracy of the trolley is improved.

[0106] Figure 3 The application provides a trolley positioning method based on a laser radar Figure 2 The application provides a trolley positioning method based on a laser radar Figure 2 The application provides a trolley positioning method based on a laser radar Figure 3 The application provides a trolley positioning method based on a laser radar

[0107] S301, an attitude conversion matrix between the laser radar and the trolley and a target center first coordinate of each target are obtained.

[0108] The attitude conversion matrix is obtained by calibrating the attitude relationship between the laser radar and the trolley. The target center first coordinate of each target is the coordinate of the corresponding target in a tunnel coordinate system obtained by measuring with a total station instrument.

[0109] S301 and S201 have the same principle, and the application will not be described in detail.

[0110] S302, point cloud data of the laser radar is obtained.

[0111] Specifically, the point cloud data monitored by the laser radar is obtained.

[0112] S303, the point cloud data is converted into text data.

[0113] wherein the text data comprises a plurality of three-dimensional points, each three-dimensional point having a coordinate value in the radar coordinate system, and a reflection intensity value of each three-dimensional point.

[0114] Specifically, the point cloud data is converted into the text data. The point cloud data comprises a tunnel point cloud behind the trolley and a target point cloud, each point cloud data being composed of a coordinate value in the radar coordinate system and a reflection intensity value. The point cloud data is saved in a computer in a text data format for processing by the trolley positioning system.

[0115] S304, according to the depth value and the height value of each three-dimensional point, a plurality of three-dimensional points are directionally filtered by a straight-through filtering method to obtain a plurality of filtered points.

[0116] wherein the depth value of each filtered point is within a depth threshold range; and the height value of each filtered point is within a height threshold range.

[0117] Specifically, according to the point cloud data, a target center second coordinate of each target is obtained. Each target center second coordinate is a coordinate of a corresponding target in the radar coordinate system. According to the coordinate value and the reflection intensity value of each three-dimensional point, a plurality of target point sets are determined by three-dimensional point extraction, wherein each target point set comprises a plurality of target points, each target point being used to indicate a point on a target. According to the coordinate value and the reflection intensity value of each three-dimensional point, a plurality of first type points are obtained by filtering processing of the plurality of three-dimensional points by a filtering processing method. Each coordinate value comprises a depth value and a height value.

[0118] The plurality of three-dimensional points are filtered in the depth direction and the height direction by a straight-through filtering algorithm to obtain a plurality of filtered points.

[0119] S305, according to the reflection intensity value of each filtered point, a plurality of filtered points are intensity filtered by point cloud reflection intensity filtering to obtain a plurality of first type points.

[0120] wherein the reflection intensity value of each first type point is greater than a reflection intensity first threshold value.

[0121] Specifically, according to the reflection intensity value corresponding to each filtered point, a plurality of first type points are obtained by a point cloud reflection intensity filtering algorithm, which filters out three-dimensional points with a reflection intensity less than a reflection intensity first threshold value, and retains three-dimensional points with a reflection intensity greater than the reflection intensity first threshold value.

[0122] S306, a plurality of first point sets are obtained by clustering a plurality of first type points by Euclidean clustering segmentation processing.

[0123] Specifically, by the Euclidean clustering segmentation processing method, three-dimensional points with a Euclidean distance less than a threshold value are clustered to obtain a plurality of first point sets.

[0124] S307, compare the target type point with the plurality of three-dimensional points of the text data to obtain a plurality of three-dimensional points corresponding to the target type point in the text data, establish an expansion range, and determine the expansion range as the expansion point of the first target point set.

[0125] The expansion range of the target type point is a spherical range with the target type point as the center and the distance threshold as the radius, and the expansion range is a spherical range established for the plurality of three-dimensional points in the text data.

[0126] Specifically, each first point set is expanded and fitted to obtain a second point set corresponding to each first point set; each second point set includes a plurality of second type points; the target type point is any one of the plurality of first type points; and the first target point set is the first point set in which the target type point is located.

[0127] A sphere with the target type point as the center and the distance threshold as the radius is established. The three-dimensional points of the point cloud data within the distance threshold are added to the first point set corresponding to the points to obtain a plurality of first target point sets.

[0128] S308, repeat point removal is performed on the plurality of first type points and the plurality of expansion points of the first target point set to obtain a third point set corresponding to the first target point set.

[0129] Specifically, the plurality of first type points and the plurality of expansion points of the first target point set are subjected to repetitive removal, and points that overlap each other are removed to retain only one point to obtain a third point set corresponding to the first target point.

[0130] S309, a plane fitting is performed on the third point set corresponding to the first target point set by a random sample consensus algorithm to obtain a second point set corresponding to the first target point set.

[0131] Specifically, a plane fitting is performed on the third point set corresponding to the first target point set by a RANSAC plane fitting method to obtain a second point set corresponding to the first target point.

[0132] S310, obtain the first length value and the first width value of the target, and the intensity point quantity of the second target point set.

[0133] The second target point set is any one of the plurality of second point sets. The intensity point quantity of the second target point set refers to the number of second type points in the second target point set whose reflection intensity value is greater than the reflection intensity second threshold.

[0134] Specifically, a plurality of target point sets are determined from a plurality of second point sets by target matching. A length and a width of the target are obtained, the target length being a first length value and the target width being a first width value. A number of second type points whose reflection intensity values are greater than a second threshold value of reflection intensity is determined.

[0135] S311, a bounding box is made for the second target point set to obtain a second length value and a second width value of the bounding box.

[0136] Specifically, a bounding box is made for the second target point set, and a solution is obtained to obtain a length and a width of the bounding box, a second length value and a second width value of the bounding box.

[0137] S312, according to the numerical relationship between the first length value and the second length value, the numerical relationship between the first width value and the second width value, and the numerical relationship between the number of intensity points of the second target point set and the number threshold value, it is determined whether the second target point set is a target point set.

[0138] Specifically, according to the numerical relationship between the first length value and the second length value, the numerical relationship between the first width value and the second width value, and the numerical relationship between the number of intensity points of the second target point set and the number threshold value, it is determined whether the second target point set is a target point set.

[0139] S313, a point set feature vector of the target target point set is obtained.

[0140] The point set feature vector of the target target point set is obtained by principal component analysis; the target target point set is any one of the plurality of target point sets.

[0141] Specifically, according to the respective coordinate values and reflection intensity values of each three-dimensional point, a target center second coordinate of each target is obtained. The target point set is solved by principal component analysis (PCA) method to obtain a point set feature vector.

[0142] S314, according to the respective coordinate values and reflection intensity values of each target point of the target target point set, and the point set feature vector of the target target point set, a target center second coordinate of a target corresponding to the target target point set is obtained.

[0143] Specifically, according to the respective coordinate values, reflection intensity values, target point coordinate average values, number of target target point sets, and point set feature vector of each target point of the target target point set, the target center second coordinate corresponding to the target target point set is obtained.

[0144] S315, obtaining a first conversion matrix between the radar coordinate system and the tunnel coordinate system by singular value decomposition method according to the first coordinate and the second coordinate of the target center of each target, and obtaining a second conversion matrix between the trolley coordinate system where the trolley is located and the tunnel coordinate system according to the first conversion matrix and the attitude conversion matrix.

[0145] S316, positioning the trolley according to the second conversion matrix.

[0146] S315 and S316 are similar to S203 and S204 in principle, and the embodiment will not be described again.

[0147] For the convenience of understanding, a specific embodiment is introduced here:

[0148] Figure 4 The target schematic diagram of the trolley positioning method based on the laser radar provided in the embodiment of the application is shown in the figure. In the figure, the retroreflective plate 410 is outside the target plate 420, and the target center in the application is the retroreflective plate 410. The embodiment assumes that three targets are arranged. Figure 5 The flowchart of the trolley positioning method based on the laser radar provided in the embodiment of the application is shown in the figure. Figure 3 .

[0149] S501, obtaining an attitude conversion matrix between the laser radar and the trolley , and a first coordinate of the target center measured by the total station .

[0150] S502, obtaining point cloud data of the laser radar, and obtaining target point cloud of each target according to the point cloud data.

[0151] Through the straight-through filtering, the original point cloud is filtered in the depth direction and the height direction, and the point cloud within the range of the depth direction and the height direction is retained to obtain filtered point cloud data .

[0152] Through the point cloud intensity filtering, the point cloud with a reflection intensity less than the intensity threshold θ is filtered out, and the point cloud with a reflection intensity greater than is retained to obtain filtered point cloud data .

[0153] The is segmented by Euclidean clustering, and the point cloud with a Euclidean distance less than d is clustered into a class, and finally point cloud clusters are obtained:

[0154]

[0155] First, set the expansion threshold radius , all the original point clouds with the distance less than the radius of the sphere centered at the point are added to the current cluster , the expansion is completed; then the duplicate points are removed, and the points with overlapping positions are removed, and only one point is retained. Finally, n expanded point cloud clusters are formed as candidate point cloud clusters of the target point cloud:

[0156]

[0157] RANSAC plane fitting is performed on each expanded candidate target point cloud cluster , the plane model of each expanded cluster is obtained, and the inliers of the RANSAC fitting of each expanded cluster point cloud are retained, and finally n point cloud clusters after RANSAC plane fitting are obtained:

[0158]

[0159] First, the bounding box of is calculated, and the length and width of the bounding box are calculated; at the same time, the high-intensity point cloud number matching threshold point number is set to , the high-reflectivity intensity matching threshold is set to , and the number of point clouds in with high-reflectivity intensity greater than the intensity matching threshold is calculated.

[0160]

[0161] , wherein is the length and width of the bounding box of , Lr and Wr are the actual size of the target, i.e. the actual size of the target plate 420 of Figure 5 , and and are the length and width range thresholds, respectively.

[0162] Through the above algorithm process, three target point clouds are finally extracted, which are , , .

[0163] S503, calculate the center point coordinates of each target point cloud to obtain the second coordinates of the center of each target point cloud.

[0164] The center point coordinates of the three target point clouds are calculated:

[0165] Through PCA principal component analysis, the target point cloud​​​​ Solve the point cloud eigenvector V.

[0166]

[0167] The target point cloud center coordinates are calculated by the target center calculation formula. The formula is as follows

[0168]

[0169] Wherein, is the target point cloud center coordinates; is the average value of the target point cloud coordinates; n is the total number of target point clouds n; : the first point cloud reflection intensity value; : the first point cloud coordinates.

[0170] S404, according to the first coordinates of the target center and the second coordinates of the target point cloud center, the conversion relationship between the radar coordinate system and the tunnel coordinate system is solved, and the conversion matrix from the trolley coordinate system to the tunnel coordinate system is calculated.

[0171] Based on and coordinates, the conversion relationship between the radar coordinate system and the tunnel coordinate system is solved by SVD method, and the conversion matrix from the laser radar coordinate system to the tunnel coordinate system is obtained .

[0172] The conversion matrix from the trolley coordinate system to the tunnel coordinate system is calculated by the following calculation formula. The calculation formula is as follows

[0173]

[0174] S505, according to the conversion matrix, the positioning result of the trolley in the tunnel is calculated.

[0175] According to the following calculation formula, the positioning result of the trolley under the tunnel is calculated. The calculation formula is as follows

[0176]

[0177] Wherein, is the origin of the trolley coordinate system, is the positioning result of the trolley under the tunnel.

[0178] The application provides a trolley positioning method and device based on a laser radar, an equipment and a storage medium. A posture conversion matrix between the laser radar and the trolley and a target center first coordinate of each target are obtained. The posture conversion matrix is obtained by calibrating the posture relationship between the laser radar and the trolley. The target center first coordinate of each target is the coordinate of the corresponding target in a tunnel coordinate system and is obtained by a total station instrument.

[0179] The embodiment of the application can divide the functional modules of the electronic device or the master control device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated in one processing unit. The integrated unit can be realized in the form of hardware or in the form of a software functional module. It should be noted that the division of the modules in the embodiment of the application is illustrative, and is only a logical functional division. Another division mode can be used in actual implementation.

[0180] Figure 6 A structure diagram of the trolley positioning device based on the laser radar provided by the embodiment of the application is shown in the figure. Figure 6 As shown in the figure, the trolley positioning device based on the laser radar 600 comprises an acquisition module 610, a coordinate module 620, a matrix module 630 and a positioning module 640.

[0181] The acquisition module 610 is configured to acquire a pose conversion matrix between the laser radar and the trolley and target center first coordinates of each target; the pose conversion matrix is obtained by calibrating the pose relationship between the laser radar and the trolley; and each target center first coordinate is a coordinate of a corresponding target in a tunnel coordinate system, which is measured by a total station instrument.

[0182] The coordinate module 620 is configured to acquire point cloud data of the laser radar, and obtain target center second coordinates of each target according to the point cloud data; each target center second coordinate is a coordinate of a corresponding target in a radar coordinate system.

[0183] The matrix module 630 is configured to obtain a first conversion matrix between the radar coordinate system and the tunnel coordinate system by a singular value decomposition method according to the target center first coordinates and the target center second coordinates of each target, and obtain a second conversion matrix between a trolley coordinate system in which the trolley is located and the tunnel coordinate system according to the first conversion matrix and the pose conversion matrix.

[0184] The positioning module 640 is configured to perform positioning of the trolley according to the second conversion matrix.

[0185] In a possible design, the laser radar-based trolley positioning apparatus 600 further includes:

[0186] The conversion module is configured to convert the point cloud data into text data; the text data includes a plurality of three-dimensional points, a coordinate value of each three-dimensional point in the radar coordinate system, and a reflection intensity value of each three-dimensional point.

[0187] The coordinate module 620 includes:

[0188] The three-dimensional point extraction module is configured to determine a plurality of target point sets by three-dimensional point extraction according to the coordinate value and the reflection intensity value of each three-dimensional point; each target point set includes a plurality of target points, and each target point is used to indicate a point on a target.

[0189] The target second module is configured to obtain the target center second coordinates of each target according to the coordinate value and the reflection intensity value of each three-dimensional point.

[0190] In a possible design, the three-dimensional point extraction module includes:

[0191] The filtering processing module is configured to perform filtering processing on the plurality of three-dimensional points by a filtering processing method according to the coordinate value and the reflection intensity value of each three-dimensional point, to obtain a plurality of first type points.

[0192] The clustering segmentation module is configured to perform clustering on the plurality of first type points by a Euclidean clustering segmentation processing, to obtain a plurality of first point sets.

[0193] The expansion and fitting module is configured to perform expansion processing and fitting processing on each first point set respectively to obtain a second point set corresponding to each first point set; each second point set includes a plurality of second type points.

[0194] The target point set module is configured to determine a plurality of target point sets from the plurality of second point sets through target matching.

[0195] In a possible design, each coordinate value includes a depth value and a height value, and the filtering processing module includes:

[0196] The pass-through filtering module is configured to perform directional filtering on the plurality of three-dimensional points through a pass-through filtering method according to the respective depth value and height value of each three-dimensional point to obtain a plurality of filtered points; the depth value of each filtered point is within a depth threshold range; and the height value of each filtered point is within a height threshold range.

[0197] The reflection filtering module is configured to perform intensity filtering on the plurality of filtered points through point cloud reflection intensity filtering according to the reflection intensity value of each filtered point to obtain a plurality of first type points; the reflection intensity value of each first type point is greater than a reflection intensity first threshold.

[0198] In a possible design, the target type point is any one of the plurality of first type points, and the first target point set is the first point set in which the target type point is located.

[0199] The expansion and fitting module includes:

[0200] The expansion module is configured to compare the target type point with the plurality of three-dimensional points of the text data to obtain a plurality of three-dimensional points corresponding to the target type point in the text data, establish an expansion range, and determine the expansion range as the expansion points of the first target point set, wherein the expansion range of the target type point is a spherical range with the target type point as a spherical center and a distance threshold as a radius, and the expansion range is a spherical range established for the plurality of three-dimensional points corresponding to the target type point in the text data.

[0201] The elimination module is configured to perform repeated point elimination on the plurality of first type points and the plurality of expansion points of the first target point set to obtain a third point set corresponding to the first target point set.

[0202] The fitting module is configured to perform plane fitting on the third point set corresponding to the first target point set through a random sample consensus algorithm to obtain a second point set corresponding to the first target point set.

[0203] In a possible design, the second target point set is any one of the plurality of second point sets.

[0204] For the second target point set, the target point set module includes:

[0205] The data acquisition module is configured to acquire a first length value and a first width value of the target, and an intensity point number of a second target point set, wherein the intensity point number of the second target point set refers to a number of second type points in the second target point set, which have a reflection intensity value greater than a second threshold value of reflection intensity.

[0206] The bounding box module is configured to perform bounding box operation on the second target point set to obtain a second length value and a second width value of the bounding box.

[0207] The judgment module is configured to judge whether the second target point set is a target point set according to a numerical relationship between the first length value and the second length value, a numerical relationship between the first width value and the second width value, and a numerical relationship between the intensity point number of the second target point set and a number threshold value.

[0208] In a possible design, the target target point set is any one of a plurality of target point sets.

[0209] For the target target point set, the target second module includes:

[0210] The feature vector module is configured to acquire a point set feature vector of the target target point set, wherein the point set feature vector of the target target point set is obtained by principal component analysis.

[0211] The coordinate calculation module is configured to obtain a target center second coordinate of the target corresponding to the target target point set according to a coordinate value and a reflection intensity value of each target point of the target target point set, and the point set feature vector of the target target point set.

[0212] The trolley positioning device based on the laser radar provided in this embodiment can execute the trolley positioning method based on the laser radar in the above embodiment, and has similar implementation principles and technical effects, which will not be described here again.

[0213] In the specific implementation of the trolley positioning method based on the laser radar, each module can be implemented as a processor, and the processor can execute computer execution instructions stored in a memory, so that the processor executes the trolley positioning method based on the laser radar.

[0214] Figure 7 The electronic device hardware structure diagram provided in the embodiment of the present application is shown in FIG. 7. Figure 7 As shown in FIG. 7, the electronic device 700 includes at least one processor 710 and a memory 720. The electronic device 700 further includes a communication component 730. The processor 710, the memory 720, and the communication component 730 are connected through a bus 740.

[0215] In the implementation process, the at least one processor 710 executes the computer execution instructions stored in the memory 720, so that the at least one processor 710 performs a kind of laser radar based trolley positioning method as executed on the electronic equipment side.

[0216] The specific implementation process of the processor 710 can refer to the method embodiments described above, which has similar implementation principles and technical effects, and will not be described here.

[0217] In the above embodiments, it should be understood that the processor can be a central processing unit (English: Central Processing Unit, for short: CPU), and can also be other general-purpose processors, digital signal processors (English: Digital Signal Processor, for short: DSP), application specific integrated circuits (English: Application Specific Integrated Circuit, for short: ASIC) and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the application can be directly embodied as hardware processor execution, or executed by hardware and software modules in the processor.

[0218] The memory can include a high-speed RAM memory, and can also include a non-volatile storage NVM, such as at least one disk memory.

[0219] The bus can be an industry standard architecture (Industry Standard Architecture, ISA) bus, a peripheral component interconnect (Peripheral Component, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For the convenience of representation, the bus in the drawings of the present application does not limit only one bus or one type of bus.

[0220] The functions implemented by the electronic device and the host device are described above, and the scheme provided by the embodiments of the present application is introduced. It can be understood that the electronic device or the host device includes hardware structures and / or software modules corresponding to each function to implement the above functions. The units and algorithm steps of each example described in combination with the embodiments disclosed in the embodiments of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in hardware or computer software driven hardware 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 the technical solutions of the embodiments of the present application.

[0221] The present application also provides a computer-readable storage medium, the computer-readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the above laser radar based trolley positioning method.

[0222] The above readable storage medium can be implemented by any type of volatile or non-volatile storage device or their combination.

[0223] For example, a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0224] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium, and can write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the electronic device or the host device.

[0225] This application also provides a computer program product, which includes: a computer program stored in a readable storage medium, at least one processor of an electronic device being able to read the computer program from the readable storage medium, and when the computer program is executed by the processor, it is used to implement the above-described laser radar-based trolley positioning method.

[0226] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.

[0227] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A trolley positioning method based on lidar, characterized in that, The method is used for positioning a trolley inside a tunnel. The trolley is equipped with a lidar, and multiple targets are set up inside the tunnel. The method includes: The attitude transformation matrix between the lidar and the trolley is obtained, as well as the first coordinate of the target center of each target; wherein, the attitude transformation matrix is ​​obtained by calibrating the attitude relationship between the lidar and the trolley; the first coordinate of the center of each target is the coordinate of the corresponding target in the tunnel coordinate system obtained by measuring with a total station; The point cloud data of the lidar is acquired, and the second coordinates of the target center of each target are obtained based on the point cloud data; wherein, the second coordinates of the target center of each target are the coordinates of the corresponding target in the radar coordinate system. Based on the first and second coordinates of the target center of each target, a first transformation matrix between the radar coordinate system and the tunnel coordinate system is obtained through singular value decomposition. Based on the first transformation matrix and the attitude transformation matrix, a second transformation matrix between the trolley coordinate system and the tunnel coordinate system is obtained. The trolley is positioned according to the second transformation matrix.

2. The method according to claim 1, characterized in that, After acquiring the point cloud data of the lidar, the method further includes: The point cloud data is converted into text data; wherein the text data includes multiple three-dimensional points, the coordinate values ​​of each three-dimensional point in the radar coordinate system, and the reflection intensity value of each three-dimensional point; The step of obtaining the second coordinates of the target center of each target based on the point cloud data includes: Based on the coordinates and reflection intensity of each of the three-dimensional points, multiple target point sets are determined through three-dimensional point extraction; wherein each target point set includes multiple target points, and each target point is used to indicate a point on a target; Based on the coordinates and reflection intensity of each of the three-dimensional points, the second coordinates of the target center of each target are obtained.

3. The method according to claim 2, characterized in that, The process of determining multiple target point sets based on the coordinates and reflection intensity values ​​of each of the three-dimensional points through three-dimensional point extraction includes: Based on the coordinates and reflection intensity of each of the three-dimensional points, a filtering process is used to filter the multiple three-dimensional points to obtain multiple first-type points; By performing Euclidean clustering segmentation, multiple points of the first type are clustered to obtain multiple sets of first points; Each of the first point sets is expanded and fitted to obtain a second point set corresponding to each of the first point sets; wherein each of the second point sets includes multiple points of the second type; Multiple target point sets are determined from multiple sets of second points through target matching.

4. The method according to claim 3, characterized in that, Each coordinate value includes a depth value and a height value. Based on the coordinate value and reflection intensity value of each 3D point, a filtering process is used to filter the multiple 3D points to obtain multiple first-type points, including: Based on the depth and height values ​​of each of the three-dimensional points, a pass-through filtering method is used to perform directional filtering on the multiple three-dimensional points to obtain multiple filtered points; wherein, the depth value of each of the filtered points is within a depth threshold range; and the height value of each of the filtered points is within a height threshold range. Based on the reflection intensity value of each of the filter points, intensity filtering is performed on the plurality of filter points through point cloud reflection intensity filtering to obtain the plurality of first type points; wherein, the reflection intensity value of each of the first type points is greater than the first threshold of reflection intensity.

5. The method according to claim 3, characterized in that, The target type point is any one of the plurality of first type points, and the first target point set is the first point set in which the target type point is located; For the target type points and the first target point set, the step of performing expansion and fitting processing on each of the first point sets to obtain the second point set corresponding to each of the first point sets includes: The target type point is compared with multiple three-dimensional points of the text data to obtain multiple three-dimensional points corresponding to the target type point in the text data. An expansion range is established, and the expansion range is determined as the expansion point of the first target point set. The expansion range of the target type point refers to a spherical range with the target type point as the center and a distance threshold as the radius, and the expansion range is the spherical range established by the multiple three-dimensional points corresponding to the text data. For the first target point set, multiple first type points and multiple expansion points are removed to remove duplicate points, thereby obtaining the third point set corresponding to the first target point set; By using a random sample consensus algorithm, a plane fitting is performed on the third point set corresponding to the first target point set to obtain the second point set corresponding to the first target point set.

6. The method according to claim 3, characterized in that, The second target point set is any one of the multiple second point sets; For the second set of target points, the step of determining multiple target point sets from multiple sets of second points through target matching includes: Obtain the first length value and the first width value of the target, as well as the number of intensity points in the second target point set; wherein, the number of intensity points in the second target point set refers to the number of second type points in the second target point set whose reflection intensity value is greater than the second threshold of reflection intensity; Construct a bounding box for the second target point set to obtain the second length value and the second width value of the bounding box; Based on the numerical relationship between the first length value and the second length value, the numerical relationship between the first width value and the second width value, and the numerical relationship between the number of intensity points of the second target point set and the number threshold, it is determined whether the second target point set is the target point set.

7. The method according to claim 2, characterized in that, The target point set is any one of the multiple target point sets. For the target point set, obtaining the second coordinates of the target center of each target based on the coordinate values ​​and reflection intensity values ​​of each of the three-dimensional points includes: Obtain the feature vector of the target point set; wherein, the feature vector of the target point set is obtained by principal component analysis. Based on the coordinates and reflection intensity of each target point in the target point set, and the point set feature vector of the target point set, the second coordinates of the target center of the target corresponding to the target point set are obtained.

8. A trolley positioning device based on lidar, characterized in that, include: The acquisition module is used to acquire the attitude transformation matrix between the lidar and the trolley, and the first coordinate of the target center of each target; wherein, the attitude transformation matrix is ​​obtained by calibrating the attitude relationship between the lidar and the trolley; the first coordinate of the center of each target is the coordinate of the corresponding target in the tunnel coordinate system obtained by measuring with a total station; The coordinate module is used to acquire the point cloud data of the lidar and obtain the second coordinates of the target center of each target based on the point cloud data; wherein, the second coordinates of the target center of each target are the coordinates of the corresponding target in the radar coordinate system. The matrix module is used to obtain a first transformation matrix between the radar coordinate system and the tunnel coordinate system by using the singular value decomposition method based on the first coordinate and second coordinate of the target center of each target, and to obtain a second transformation matrix between the trolley coordinate system and the tunnel coordinate system based on the first transformation matrix and the attitude transformation matrix. The positioning module is used to position the trolley according to the second transformation matrix.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; When the processor executes the computer execution instructions stored in the memory, it is used to implement a trolley positioning method based on lidar as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement a trolley positioning method based on lidar as described in any one of claims 1 to 7.

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