Vein boundary extraction method, computer equipment and storage medium

Abstract

The invention discloses a vein boundary extraction method, computer equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring point cloud data of a mine roadway through a three-dimensional laser scanner to obtain initial point cloud data; segmenting according to the initial point cloud data to obtain non-ground point cloud data; performing grid division on the non-ground point cloud data to obtain an initial point cloud grid map; extracting an elevation point cloud image and a reflection intensity point cloud image according to the initial point cloud grid map; based on the maximum entropy threshold, performing point cloud elimination on the initial point cloud grid map according to the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map; and based on an Otsu threshold method, performing threshold segmentation on the target point cloud grid map according to the reflection intensity point cloud image to obtain a target vein boundary. In this way, the boundary difference between objects can be identified in an auxiliary mode through the reflection intensity information, and the accuracy and stability of vein boundary extraction can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for extracting mineral vein boundaries, a computer device and a storage medium. Background Art

[0002] When extracting mineral vein boundaries, the existing technical solution first collects the road pothole point cloud and projects it in the road plane to obtain the pothole point cloud data set; then extracts sparse boundary points and dense boundary points in sequence. This will result in inaccurate extraction of mineral vein boundaries due to the limitations of mineral identification. Summary of the invention

[0003] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method for extracting ore vein boundaries, a computer device and a storage medium, which are used to utilize the difference in laser reflectivity between ore veins and waste rock, and adopt filtering algorithms and threshold segmentation to extract reflection intensity to identify ore vein boundaries.

[0004] The present invention provides the following technical solutions: In a first aspect, the present invention provides a method for extracting a mineral vein boundary, comprising: The point cloud data of the mine tunnel is collected by a 3D laser scanner to obtain initial point cloud data; Segmenting the initial point cloud data to obtain non-ground point cloud data; Performing grid division on the non-ground point cloud data to obtain an initial point cloud grid map; Extracting an elevation point cloud image and a reflection intensity point cloud image according to the initial point cloud grid map; Based on the maximum entropy threshold, point cloud removal is performed on the initial point cloud grid map according to the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map; Based on the Otsu threshold method, the target point cloud grid map is threshold segmented according to the reflection intensity point cloud image to obtain the target vein boundary.

[0005] In one embodiment, segmenting the initial point cloud data to obtain non-ground point cloud data includes: De-noising the initial point cloud data to obtain target point cloud data; Based on a progressive morphological filtering method, the target point cloud data is segmented according to a height difference threshold to obtain the non-ground point cloud data; The expression of the height difference threshold is:

[0006] in, is the height difference threshold under the current window size, is the initial elevation threshold, is the slope parameter, is the maximum height difference threshold, For the Secondary filter window size, The size of the grid.

[0007] In one embodiment, the denoising of the initial point cloud data to obtain target point cloud data includes: Calibrating the initial point cloud data to obtain horizontal initial point cloud data in the x and y directions; The initial point cloud data horizontally in the x and y directions is denoised to obtain the target point cloud data.

[0008] In one embodiment, calibrating the initial point cloud data to obtain the initial point cloud data horizontally in the x and y directions includes: Obtaining measurement values ​​in a stationary state within a preset time period through an inertial measurement unit; determining a direction of gravity based on the measurements; Based on Schmidt orthogonalization, construct an initial pose according to the gravity direction; The initial point cloud data is registered according to the initial position and posture to obtain the initial point cloud data horizontal in the x and y directions.

[0009] In one embodiment, the grid division of the non-ground point cloud data to obtain an initial point cloud grid map includes: Traversing the position information of each point cloud in the non-ground point cloud data to obtain a coordinate threshold in each axis; and calculating grid dimension information according to the coordinate threshold; Based on a preset step size, the non-ground point cloud data is grid-divided according to the grid dimension information to obtain the initial point cloud grid map.

[0010] In one embodiment, extracting the elevation point cloud image and the reflection intensity point cloud image according to the initial point cloud grid map includes: Calculating the average value of the point cloud height values ​​in each grid in the initial point cloud grid map, and constructing the elevation point cloud image according to the average value of the point cloud height values; The average value of the point cloud intensity values ​​in each grid in the initial point cloud grid map is calculated, and the reflection intensity point cloud image is constructed according to the average value of the point cloud intensity values.

[0011] In one embodiment, the maximum entropy threshold includes a first maximum entropy threshold and a second maximum entropy threshold, and the method based on the maximum entropy threshold, performing point cloud removal on the initial point cloud grid map according to the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map, includes: The elevation point cloud image and the reflection intensity point cloud image are processed according to a second-order differential operator to obtain an elevation gradient image and a reflection intensity gradient image; Calculating the entropy of the elevation gradient image to obtain the first maximum entropy threshold; calculating the entropy of the reflection intensity gradient image to obtain the second maximum entropy threshold; Point cloud culling is performed on the initial point cloud grid map according to the first maximum entropy threshold and the second maximum entropy threshold to obtain the target point cloud grid map.

[0012] In one embodiment, the Otsu threshold method is used to perform threshold segmentation on the target point cloud grid map according to the reflection intensity point cloud image to obtain the target vein boundary, including: Calculating a global threshold of the reflection intensity gradient image based on the Otsu threshold method, and performing threshold segmentation on the target point cloud grid map according to the global threshold to obtain an initial vein boundary; Filling the initial vein boundary based on a region growing algorithm to obtain a candidate vein boundary; The candidate vein boundaries are processed based on the Euclidean clustering algorithm to obtain the target vein boundaries.

[0013] In a second aspect, the present invention proposes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for extracting mineral vein boundaries as described in the first aspect is implemented.

[0014] In a third aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for extracting mineral vein boundaries as described in the first aspect.

[0015] The vein boundary extraction method, computer equipment and storage medium disclosed in the present invention collect point cloud data of mine tunnels through a three-dimensional laser scanner to obtain initial point cloud data; segment the initial point cloud data to obtain non-ground point cloud data; grid the non-ground point cloud data to obtain an initial point cloud grid map; extract elevation point cloud images and reflection intensity point cloud images according to the initial point cloud grid map; based on the maximum entropy threshold, perform point cloud elimination on the initial point cloud grid map according to the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map; based on the Otsu threshold method, perform threshold segmentation on the target point cloud grid map according to the reflection intensity point cloud image to obtain the target vein boundary. In this way, while the elevation information provides the elevation basis, the reflection intensity information can assist in identifying the boundary differences between objects, which helps to improve the accuracy and stability of vein boundary extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope of protection of the present invention. In each of the drawings, similar components are numbered similarly.

[0017] Figure 1 A schematic diagram of a process of extracting the mineral vein boundary proposed in this embodiment is shown; Figure 2 Another schematic diagram of the process of extracting the mineral vein boundary proposed in this embodiment is shown; Figure 3 Another schematic diagram of the process of extracting the mineral vein boundary proposed in this embodiment is shown; Figure 4 Another schematic diagram of the process of extracting the mineral vein boundary proposed in this embodiment is shown; Figure 5 Another schematic diagram of the process of extracting the mineral vein boundary proposed in this embodiment is shown; Figure 6 A structural schematic diagram of the vein boundary extraction system proposed in this embodiment is shown.

[0018] Description of the accompanying drawings: 600 - mineral vein boundary extraction system; 601 - acquisition module; 602 - segmentation module; 603 - division module; 604 - extraction module; 605 - elimination module; 606 - determination module. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0020] The components of the embodiments of the present invention generally described and shown in the drawings herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0021] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present invention, are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0022] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0023] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present invention belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present invention.

[0024] Example 1 The disclosed embodiment provides a vein boundary extraction method for collecting mine point cloud data, utilizing the difference in laser reflectivity between veins and waste rocks, and adopting filtering algorithm and threshold segmentation to extract reflection intensity to identify vein boundaries.

[0025] See also Figure 1 A method for extracting a mineral vein boundary includes steps S101 to S106, and each step is described in detail below.

[0026] Step S101, collecting point cloud data of the mine tunnel by using a 3D laser scanner to obtain initial point cloud data.

[0027] In this embodiment, the tunnel point cloud data is collected by a handheld three-dimensional laser scanner, so that a large amount of tunnel point cloud data can be collected in a short time to obtain initial point cloud data.

[0028] Step S102, performing segmentation according to the initial point cloud data to obtain non-ground point cloud data.

[0029] In this embodiment, non-ground point cloud data can be obtained by segmenting the initial point cloud data. The non-ground point cloud data includes the ore vein, so that the non-ground point cloud data can be used to extract the boundary of the ore vein later.

[0030] See also Figure 2 In a specific embodiment, step S102 includes steps S1021 to S1022, and each step is described in detail below.

[0031] Step S1021, denoising the initial point cloud data to obtain target point cloud data.

[0032] In this embodiment, due to the complex underground environment, the presence of water mist, dust and other problems, the laser radar will generate noise points in the process of collecting point cloud data, which will inevitably affect the extraction of the vein boundary. Therefore, it is necessary to use a deep learning network to remove noise points and outliers in the initial point cloud data to obtain the target point cloud data.

[0033] See also Figure 3 In a specific embodiment, step S1021 includes steps S301 to S302, and each step is described in detail below.

[0034] Step S301, calibrating the initial point cloud data to obtain horizontal initial point cloud data in the x and y directions.

[0035] In this embodiment, since the initial point cloud data of the mine tunnel is not horizontal in the x and y directions, this will inevitably lead to poor segmentation quality of the ground point cloud and the non-ground point cloud. Therefore, it is necessary to calibrate the initial point cloud data to obtain initial point cloud data that is horizontal in the x and y directions.

[0036] In a specific embodiment, step S301 includes: obtaining measurement values ​​in a stationary state within a preset time period through an inertial measurement unit; determining the direction of gravity based on the measurement values; constructing an initial posture based on the direction of gravity based on Schmidt orthogonalization; and aligning the initial point cloud data according to the initial posture to obtain the initial point cloud data horizontal in the x and y directions.

[0037] In this embodiment, the measurement values ​​of the inertial measurement unit (IMU) within a preset static time period are obtained, which generally include the data of the three-axis accelerometer and the three-axis gyroscope. Since the IMU is in a static state, the accelerometer mainly measures the acceleration of gravity (assuming that there is no other external force), so these measurement values ​​can be used to determine the direction of gravity.

[0038] Align the measurements with the local gravity direction. Once the gravity direction is determined and aligned with the world coordinate system, the Schmidt orthogonalization process can be used to construct an orthogonal basis, which can be used to define the initial attitude of the IMU. Schmidt orthogonalization is a method of orthogonalizing a set of linearly independent vectors, which can be used to construct two horizontal direction vectors (usually the x-axis and y-axis) that are orthogonal to the gravity direction.

[0039] For example, taking the preset time period of 2s as an example, let is the acceleration information measured by IMU. The measured value on is the gravitational acceleration, which is equal to the gravitational acceleration , direction and On the contrary, the average value of the accelerometer within 2s is calculated to obtain ,take out Axis direction values ​​construct non-zero vectors ,in Represents the normalized result, any non-zero vector , , construct a set of orthogonal bases through Schmidt orthogonalization:

[0040]

[0041]

[0042] pass , ,and Construct the initial pose.

[0043] Furthermore, the first frame of point cloud data is converted into the global coordinate system using the initial pose to construct the first frame of point cloud map. In this way, the first frame of point cloud serves as the starting point of the point cloud map.

[0044] The second frame of point cloud data in the initial point cloud data is combined with the IMU data at the corresponding time to construct the second frame of point cloud map. The IMU data can provide information about the acceleration and angular velocity of the sensor, which is used to estimate the relative pose change between consecutive frames.

[0045] The point cloud map of the first frame is registered with the point cloud map of the second frame, that is, the estimated pose of the point cloud of the second frame is applied to its point cloud data and converted into the global coordinate system.

[0046] A point cloud registration algorithm (such as ICP, NDT, etc.) is used to further align the second frame point cloud with the first frame point cloud map, thereby reducing the pose estimation error caused by IMU integration error or other factors, and finally obtaining the initial point cloud data horizontal in the x and y directions. The initial point cloud data horizontal in the x and y directions is a set of point cloud data distributed in the x and y directions and with small changes in the z direction.

[0047] Step S302: De-noising the initial point cloud data horizontally in the x and y directions to obtain the target point cloud data.

[0048] In this embodiment, based on the initial point cloud data horizontally in the x and y directions, different degrees of Gaussian noise are added to construct a data set; a deep learning network structure is constructed, PointCleanNet is used as the network structure, a pre-trained model is loaded, and the pre-trained model is re-trained using the initial point cloud data horizontally in the x and y directions to prevent the PointCleanNet network from overfitting. The loss function expression used in the training process is expressed as follows:

[0049] In the formula is the noise point, is the estimated denoised point , is the estimated neighborhood of the denoised point, It is a parameter and is generally set to 0.99.

[0050] The collected mine tunnel point cloud data is sent to the trained PointCleanNet network to obtain the denoised target point cloud data.

[0051] Please see again Figure 2 , step S1022, based on the progressive morphological filtering method, the target point cloud data is segmented according to the height difference threshold to obtain the non-ground point cloud data.

[0052] In this embodiment, a progressive morphological filtering method is used to continuously increase the filtering window, set the height difference threshold, retain points less than the height difference threshold, and segment the target point cloud data into ground point cloud data and non-ground point cloud data, where the height difference threshold can be expressed as:

[0053] In the formula, is the height difference threshold under the current window size, is the initial elevation threshold, is the slope parameter, is the maximum height difference threshold, For the Secondary filter window size, The size of the grid, that is, the size of the grid when loading irregular point clouds into a regular grid structure.

[0054] Please see again Figure 1 , step S103, gridding the non-ground point cloud data to obtain an initial point cloud grid map.

[0055] In this embodiment, the non-ground point cloud data is divided into grids, each grid only contains a limited amount of point cloud data, thereby reducing the overall data volume.

[0056] In a specific embodiment, step S103 includes: traversing the position information of each point cloud in the non-ground point cloud data to obtain the coordinate threshold in each axis; and calculating the grid dimension information according to the coordinate threshold; based on a preset step size, grid-dividing the non-ground point cloud data according to the grid dimension information to obtain the initial point cloud grid map.

[0057] In this embodiment, the location information of each point cloud in the non-ground point cloud data is traversed. , get the coordinate thresholds on each axis, the coordinate thresholds are included in direction, Direction and Maximum value in a direction , , and minimum value , , .

[0058] The grid dimension information is calculated according to the coordinate threshold; and based on the preset step size, the non-ground point cloud data is grid-divided according to the grid dimension information to obtain an initial point cloud grid map.

[0059] Exemplarily, the preset step size is set to , then any point cloud The index in the grid can be expressed as ,in It can be calculated by the following formula:

[0060] Please see again Figure 1 , step S104, extracting the elevation point cloud image and the reflection intensity point cloud image according to the initial point cloud grid map.

[0061] In this embodiment, the elevation point cloud image and the reflection intensity point cloud image are extracted according to the initial point cloud grid map. The elevation point cloud image mainly reflects the height information of the object surface, and the reflection intensity point cloud image mainly reflects the reflection intensity information of the object surface to the laser. By taking advantage of the difference in laser reflectivity between the ore vein and waste rock, the inaccurate extraction of the ore vein boundary caused by the limitation of mineral identification can be reduced by extracting the reflection intensity.

[0062] In a specific embodiment, step S104 includes: calculating the average value of the point cloud height values ​​in each grid in the initial point cloud grid map, and constructing the elevation point cloud image based on the average value of the point cloud height values; calculating the average value of the point cloud intensity values ​​in each grid in the initial point cloud grid map, and constructing the reflection intensity point cloud image based on the average value of the point cloud intensity values.

[0063] In this embodiment, each grid in the initial point cloud grid map is traversed to calculate the average value of all point cloud height values ​​in each grid. , and use the average value of the point cloud height Instead of the height values ​​of all point clouds in the grid, the elevation point cloud image is formed. Accordingly, the average value of all point cloud intensity values ​​in each grid is calculated. , and use the average value of the point cloud intensity The intensity values ​​of all point clouds in the grid are replaced to form a reflection intensity point cloud image.

[0064] in, , .

[0065] In the formula, is the number of point clouds in the grid, , is the intensity value corresponding to each point cloud in the grid.

[0066] Please see again Figure 1 , step S105, based on the maximum entropy threshold, point cloud removal is performed on the initial point cloud grid map according to the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map.

[0067] In this embodiment, the maximum entropy threshold is calculated based on the elevation point cloud image and the reflection intensity point cloud image, and the point cloud data with smaller slope and smaller intensity are eliminated from the initial point cloud grid map based on the maximum entropy threshold to obtain the target point cloud grid map.

[0068] The maximum entropy threshold of the elevation point cloud image can accurately identify ground points and non-ground points, thereby eliminating ground points that are not related to the vein and improving the accuracy of the point cloud data. The reflection intensity point cloud image provides the reflection intensity information of the point cloud. The maximum entropy threshold of the reflection intensity point cloud image helps to further distinguish point clouds of different materials and attributes, thereby eliminating interference points such as vegetation and soil.

[0069] See also Figure 4 In a specific embodiment, the maximum entropy threshold includes a first maximum entropy threshold and a second maximum entropy threshold, and step S105 includes steps S1051 to S1053. Each step is described in detail below.

[0070] Step S1051, processing the elevation point cloud image and the reflection intensity point cloud image respectively according to a second-order differential operator to obtain an elevation gradient image and a reflection intensity gradient image.

[0071] In this embodiment, the target point cloud grid map is traversed, and the elevation point cloud image and the reflection intensity point cloud image are processed respectively using the second-order differential (Laplacian) operator to obtain the elevation gradient image and the reflection intensity gradient image.

[0072] Step S1052, calculating the entropy of the elevation gradient image to obtain the first maximum entropy threshold; calculating the entropy of the reflection intensity gradient image to obtain the second maximum entropy threshold.

[0073] In this embodiment, the entropy of the elevation gradient image is calculated to obtain the maximum entropy threshold of the elevation gradient image, that is, the first maximum entropy threshold; the entropy of the reflection intensity gradient image is calculated to obtain the maximum entropy threshold of the reflection intensity gradient image, that is, the second maximum entropy threshold.

[0074] Step S1053: performing point cloud culling on the initial point cloud grid map according to the first maximum entropy threshold and the second maximum entropy threshold to obtain the target point cloud grid map.

[0075] In this embodiment, the initial point cloud raster map is binarized according to the first maximum entropy threshold, pixels greater than the first maximum entropy threshold are assigned a value of 1, pixels less than the first maximum entropy threshold are assigned a value of 0, and point cloud data with a smaller slope (i.e., a pixel value of 0) in the initial point cloud raster map is eliminated.

[0076] Furthermore, the initial point cloud raster map after elimination is binarized according to the second maximum entropy threshold, pixels greater than the second maximum entropy threshold are assigned a value of 1, pixels less than the second maximum entropy threshold are assigned a value of 0, and point cloud data with low intensity (i.e., pixel value of 0) in the initial point cloud raster map is eliminated, and finally the target point cloud raster map is obtained.

[0077] Please see again Figure 1 , step S106, based on the Otsu threshold method, threshold segmentation is performed on the target point cloud grid map according to the reflection intensity point cloud image to obtain the target vein boundary.

[0078] In this embodiment, a global threshold is calculated according to the reflection intensity point cloud image based on the Otsu threshold method, and the target point cloud grid map is segmented according to the global threshold, so as to obtain a more accurate target vein boundary.

[0079] It should be noted that the processed point cloud data and the target vein boundary can be displayed in three dimensions for easy analysis of the vein structure and boundary information by users.

[0080] See also Figure 5 In a specific embodiment, step S106 includes steps S1061 to S1063, and each step is described in detail below.

[0081] Step S1061, calculating the global threshold of the reflection intensity gradient image based on the Otsu threshold method, and performing threshold segmentation on the target point cloud grid map according to the global threshold to obtain an initial vein boundary.

[0082] In this embodiment, the Otsu threshold method is used to calculate the global threshold of the reflection intensity gradient image, and the target point cloud grid map is threshold segmented according to the global threshold, that is, the pixel points greater than the global threshold are identified as the initial vein boundary, and the initial vein boundary is the rough vein boundary. Through the optimized threshold division method, the detailed information of the vein boundary is retained to the maximum extent, while effectively reducing the interference of background noise.

[0083] Step S1062, filling the initial vein boundary based on the region growing algorithm to obtain a candidate vein boundary.

[0084] In this embodiment, the region growing algorithm is used to fill the initial vein boundary, thereby generating a continuous candidate vein boundary. The spatial connectivity of the boundary pixels is used to effectively eliminate the broken and discontinuous edge phenomena, providing more complete boundary information for subsequent processing.

[0085] Step S1063, processing the candidate vein boundary based on the Euclidean clustering algorithm to obtain the target vein boundary.

[0086] In this embodiment, the point cloud data within the candidate vein boundary is processed by applying the Euclidean clustering algorithm, and the point cloud data can be grouped according to its geometric structure, and the spatial distribution characteristics of the vein area can be identified, thereby obtaining the target vein boundary. This clustering-based processing method can improve the accuracy and reliability of vein data extraction, and lay a solid foundation for vein morphology analysis and subsequent three-dimensional modeling.

[0087] The method for extracting the boundary of a mineral vein proposed in this embodiment collects point cloud data of a mine tunnel through a three-dimensional laser scanner to obtain initial point cloud data; performs segmentation based on the initial point cloud data to obtain non-ground point cloud data; performs grid division on the non-ground point cloud data to obtain an initial point cloud grid map; extracts an elevation point cloud image and a reflection intensity point cloud image based on the initial point cloud grid map; based on the maximum entropy threshold, performs point cloud elimination on the initial point cloud grid map based on the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map; based on the Otsu threshold method, performs threshold segmentation on the target point cloud grid map based on the reflection intensity point cloud image to obtain the target mineral vein boundary. In this way, while the elevation information provides an elevation basis, the reflection intensity information can assist in identifying boundary differences between objects, which helps to improve the accuracy and stability of mineral vein boundary extraction.

[0088] Example 2 In addition, the present disclosure provides a vein boundary extraction system 600, see Figure 6 The system comprises: The acquisition module 601 is used to acquire point cloud data of the mine tunnel by using a 3D laser scanner to obtain initial point cloud data; A segmentation module 602 is used to segment the initial point cloud data to obtain non-ground point cloud data; A division module 603 is used to perform grid division on the non-ground point cloud data to obtain an initial point cloud grid map; An extraction module 604 is used to extract an elevation point cloud image and a reflection intensity point cloud image according to the initial point cloud grid map; A removal module 605 is used to remove point clouds from the initial point cloud grid map based on the maximum entropy threshold and the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map; The determination module 606 is used to perform threshold segmentation on the target point cloud grid map based on the reflection intensity point cloud image based on the Otsu threshold method to obtain the target vein boundary.

[0089] Optionally, the segmentation module 602 is used to denoise the initial point cloud data to obtain target point cloud data; based on the progressive morphological filtering method, the target point cloud data is segmented according to the height difference threshold to obtain the non-ground point cloud data; the expression of the height difference threshold is: ;in, is the height difference threshold under the current window size, is the initial elevation threshold, is the slope parameter, is the maximum height difference threshold, For the Secondary filter window size, The size of the grid.

[0090] Optionally, the segmentation module 602 is used to calibrate the initial point cloud data to obtain initial point cloud data horizontally in the x and y directions; and denoise the initial point cloud data horizontally in the x and y directions to obtain the target point cloud data.

[0091] Optionally, the segmentation module 602 is used to obtain measurement values ​​in a static state within a preset time period through an inertial measurement unit; determine the direction of gravity based on the measurement values; construct an initial posture according to the direction of gravity based on Schmidt orthogonalization; align the initial point cloud data according to the initial posture to obtain the initial point cloud data horizontal in the x and y directions.

[0092] Optionally, the division module 603 is used to traverse the position information of each point cloud in the non-ground point cloud data to obtain the coordinate threshold in each axis; and calculate the grid dimension information according to the coordinate threshold; based on a preset step size, the non-ground point cloud data is grid-divided according to the grid dimension information to obtain the initial point cloud grid map.

[0093] Optionally, the extraction module 604 is used to calculate the average value of the point cloud height values ​​in each grid in the initial point cloud grid map, and construct the elevation point cloud image based on the average value of the point cloud height values; calculate the average value of the point cloud intensity values ​​in each grid in the initial point cloud grid map, and construct the reflection intensity point cloud image based on the average value of the point cloud intensity values.

[0094] Optionally, the maximum entropy threshold includes a first maximum entropy threshold and a second maximum entropy threshold, and a elimination module 605 is used to process the elevation point cloud image and the reflection intensity point cloud image according to a second-order differential operator, respectively, to obtain an elevation gradient image and a reflection intensity gradient image; calculate the entropy of the elevation gradient image to obtain the first maximum entropy threshold; calculate the entropy of the reflection intensity gradient image to obtain the second maximum entropy threshold; perform point cloud elimination on the initial point cloud grid map according to the first maximum entropy threshold and the second maximum entropy threshold to obtain the target point cloud grid map.

[0095] Optionally, a determination module 606 is used to calculate a global threshold of the reflection intensity gradient image based on the Otsu threshold method, and perform threshold segmentation on the target point cloud grid map according to the global threshold to obtain an initial vein boundary; fill the initial vein boundary based on a region growing algorithm to obtain a candidate vein boundary; and process the candidate vein boundary based on a Euclidean clustering algorithm to obtain the target vein boundary.

[0096] The system provided in the embodiment of the present disclosure can execute the steps of the vein boundary extraction method provided in Example 1, which will not be described again to avoid repetition.

[0097] The vein boundary extraction system proposed in this embodiment collects point cloud data of mine tunnels through a three-dimensional laser scanner to obtain initial point cloud data; performs segmentation based on the initial point cloud data to obtain non-ground point cloud data; performs grid division on the non-ground point cloud data to obtain an initial point cloud grid map; extracts elevation point cloud images and reflection intensity point cloud images based on the initial point cloud grid map; based on the maximum entropy threshold, performs point cloud elimination on the initial point cloud grid map based on the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map; based on the Otsu threshold method, performs threshold segmentation on the target point cloud grid map based on the reflection intensity point cloud image to obtain the target vein boundary. In this way, while the elevation information provides the elevation basis, the reflection intensity information can assist in identifying the boundary differences between objects, which helps to improve the accuracy and stability of vein boundary extraction.

[0098] Example 3 In addition, an embodiment of the present disclosure provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for extracting the mineral vein boundary described in Example 1 is implemented.

[0099] The device provided in the embodiment of the present disclosure can execute the steps of the vein boundary extraction method provided in Example 1, which will not be described again to avoid repetition.

[0100] Example 4 The embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, which implements the vein boundary extraction method described in the embodiment 1 when executed by a processor.

[0101] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0102] The computer-readable storage medium provided in this embodiment can implement the vein boundary extraction method provided in Example 1, and will not be described again here to avoid repetition.

[0103] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limiting, and thus other examples of the exemplary embodiments may have different values.

[0104] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0105] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for extracting a mineral vein boundary, characterized in that: include: The point cloud data of the mine tunnel is collected by a 3D laser scanner to obtain initial point cloud data; Segmenting the initial point cloud data to obtain non-ground point cloud data; Performing grid division on the non-ground point cloud data to obtain an initial point cloud grid map; Extracting an elevation point cloud image and a reflection intensity point cloud image according to the initial point cloud grid map; Based on the maximum entropy threshold, point cloud removal is performed on the initial point cloud grid map according to the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map; Based on the Otsu threshold method, the target point cloud grid map is threshold segmented according to the reflection intensity point cloud image to obtain the target mineral vein boundary.

2. The method for extracting mineral vein boundaries according to claim 1, characterized in that: The step of segmenting the initial point cloud data to obtain non-ground point cloud data includes: De-noising the initial point cloud data to obtain target point cloud data; Based on a progressive morphological filtering method, the target point cloud data is segmented according to a height difference threshold to obtain the non-ground point cloud data; The expression of the height difference threshold is: in, is the height difference threshold under the current window size, is the initial elevation threshold, is the slope parameter, is the maximum height difference threshold, For the Secondary filter window size, The size of the grid.

3. The method for extracting the mineral vein boundary according to claim 2, characterized in that: The denoising of the initial point cloud data to obtain target point cloud data includes: Calibrating the initial point cloud data to obtain horizontal initial point cloud data in the x and y directions; The initial point cloud data horizontally in the x and y directions is denoised to obtain the target point cloud data.

4. The method for extracting mineral vein boundaries according to claim 3, characterized in that: The step of calibrating the initial point cloud data to obtain horizontal initial point cloud data in the x and y directions includes: Obtaining measurement values ​​in a stationary state within a preset time period through an inertial measurement unit; determining a direction of gravity based on the measurements; Based on Schmidt orthogonalization, construct an initial pose according to the gravity direction; The initial point cloud data is registered according to the initial position and posture to obtain the initial point cloud data horizontal in the x and y directions.

5. The method for extracting mineral vein boundaries according to claim 1, characterized in that: The grid division of the non-ground point cloud data to obtain an initial point cloud grid map includes: Traversing the position information of each point cloud in the non-ground point cloud data to obtain a coordinate threshold in each axis; and calculating grid dimension information according to the coordinate threshold; Based on a preset step size, the non-ground point cloud data is grid-divided according to the grid dimension information to obtain the initial point cloud grid map.

6. The method for extracting mineral vein boundaries according to claim 1, characterized in that: The step of extracting an elevation point cloud image and a reflection intensity point cloud image according to the initial point cloud grid map comprises: Calculating the average value of the point cloud height values ​​in each grid in the initial point cloud grid map, and constructing the elevation point cloud image according to the average value of the point cloud height values; The average value of the point cloud intensity values ​​in each grid in the initial point cloud grid map is calculated, and the reflection intensity point cloud image is constructed according to the average value of the point cloud intensity values.

7. The method for extracting mineral vein boundaries according to claim 1, characterized in that: The maximum entropy threshold includes a first maximum entropy threshold and a second maximum entropy threshold. The method, based on the maximum entropy threshold, performs point cloud elimination on the initial point cloud grid map according to the elevation point cloud image and the reflection intensity point cloud image to obtain a target point cloud grid map, includes: The elevation point cloud image and the reflection intensity point cloud image are processed according to a second-order differential operator to obtain an elevation gradient image and a reflection intensity gradient image; Calculating the entropy of the elevation gradient image to obtain the first maximum entropy threshold; calculating the entropy of the reflection intensity gradient image to obtain the second maximum entropy threshold; Point cloud culling is performed on the initial point cloud grid map according to the first maximum entropy threshold and the second maximum entropy threshold to obtain the target point cloud grid map.

8. The method for extracting mineral vein boundaries according to claim 7, characterized in that: The method of performing threshold segmentation on the target point cloud grid map based on the reflection intensity point cloud image to obtain the target mineral vein boundary includes: Calculating a global threshold of the reflection intensity gradient image based on the Otsu threshold method, and performing threshold segmentation on the target point cloud grid map according to the global threshold to obtain an initial vein boundary; Filling the initial vein boundary based on a region growing algorithm to obtain a candidate vein boundary; The candidate vein boundaries are processed based on the Euclidean clustering algorithm to obtain the target vein boundaries.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for extracting the mineral vein boundary as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the method for extracting the mineral vein boundary as described in any one of claims 1 to 8.

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