Complex roadway network center line calculation method based on skeleton shrinkage

Abstract

The invention discloses a complex roadway network center line calculation method based on skeleton contraction, belongs to the technical field of underground mine three-dimensional modeling and point cloud data processing, is used for complex roadway network center line calculation, and comprises the following steps: establishing a neighborhood set of KDTree acquisition points for underground mine roadway point cloud data; calculating geometric features and neighborhood distances of the points according to the neighborhood point set; iteratively shrinking the underground mine roadway point cloud data; skeleton refinement is carried out according to geometric features of each shrinkage skeleton point, and discrete noise points are removed by combining an octree filtering method; processing the intersection area point set by using a principal component method and a DBScan algorithm, judging the type of the intersection, and performing center point refined calculation on each intersection; and the disordered center points are fitted into an ordered center line. According to the method, the integral roadway center line of underground mine well and roadway engineering can be accurately calculated, complex structural characteristics of crossing and staggering are overcome, and refined calculation is achieved for different roadway intersection types.

Description

Technical Field

[0001] The invention discloses a method for calculating the center line of a complex tunnel network based on skeleton contraction, and belongs to the technical field of underground mine three-dimensional modeling and point cloud data processing. Background Art

[0002] As an important foundation and main component of mining resources, the accurate grasp of the spatial geometric information of underground mine tunnels is the key to realizing the digital and intelligent management of mines. As the skeleton structure of the spatial geometry of the tunnel, the tunnel centerline is not only an important data basis for tunnel modeling, navigation, monitoring and maintenance, but also directly related to the production efficiency and safety management level of the mine. Due to the complex tunnel network structure in underground mine shaft and tunnel engineering, its diversity and intersection bring great difficulties and challenges to the acquisition of the centerline. In recent years, with the development of 3D laser scanning technology and point cloud data processing technology, the acquisition of high-precision point cloud data has become more convenient, providing strong support for the calculation of the tunnel centerline. Therefore, the efficient calculation of the centerline of the complex tunnel network of underground mine shaft and tunnel engineering based on 3D laser point cloud data is an important means to improve the level of mine management.

[0003] The present invention proposes a method for calculating the center line of a complex tunnel network based on skeleton shrinkage. The method adopts a coarse-to-fine calculation process, performs coarse skeleton shrinkage and refinement based on the geometric features of the tunnel point cloud and a filtering method, and designs different processing methods according to the morphological features of the Y-shaped and T-shaped intersections in the tunnel to achieve precise calculation of the center line. The method can solve the problem of difficult calculation of the center line in complex locations such as tunnel intersections and staggered levels, and provide effective spatial geometric information for realizing digital and intelligent management of mines. Summary of the invention

[0004] The purpose of the present invention is to provide a method for calculating the center line of a complex tunnel network based on skeleton shrinkage, so as to solve the problem of difficulty in calculating the center line of a tunnel network with a complex structure in underground mine shaft and tunnel engineering in the prior art.

[0005] A method for calculating the centerline of a complex laneway network based on skeleton shrinkage, comprising:

[0006] S1. Point cloud data of underground mine tunnels Establish KDTree for initial contraction to obtain neighborhood distance, read point cloud data point by point and calculate the neighborhood point set of each point, and calculate the surface feature and line feature of each point from the feature value of the neighborhood point set;

[0007] S2. Iteratively shrink the underground mine tunnel point cloud data, continuously update the coordinate value, plane feature, linear feature and neighborhood range of each point, and obtain the roughly shrunk skeleton point;

[0008] S3. Refine the skeleton according to the geometric features of each contracted skeleton point, and remove discrete noise points by combining the octree filtering method to obtain the center points of the straight lanes and curved lanes and the point set of the intersection area;

[0009] S4. By analyzing the morphological characteristics of underground mine tunnels, the Y-shaped and T-shaped tunnel intersections are defined, the intersection area point set is processed using the principal component method and DBScan algorithm, and the intersection type is determined, and the center point of each intersection is refined.

[0010] S5. Smoothly connect the refined calculated center points with other center points, and use the minimum spanning tree to organize the disordered center points, and fit the disordered center points into an ordered center line.

[0011] S1 includes, S1.1. For the Point cloud data , , is the total number of point cloud data, based on the distance The distance to the second closest point Set the distance neighborhood D for field search, D is:

[0012] ;

[0013] Get the neighborhood point set with D as radius , , , and For the Neighborhood points , and value, is the total number of neighbors.

[0014] S1 includes, S1.2. Calculate the distance mean of the neighborhood range based on the neighborhood point set :

[0015] ;

[0016] In the formula, , and They are exist , and The range of directions, and They are exist , and Maximum and minimum values ​​in direction;

[0017] by Re-search the neighborhood point set for the neighborhood radius and calculate the eigenvalues ​​based on the covariance matrix of the neighborhood point set , and , get the line feature Features of dough :

[0018] .

[0019] S2 includes, S2.1. After the initial contraction of the neighborhood-based skeleton, the point cloud data is updated to points with 6 attribute values ;

[0020] S2.2. Perform iterative contraction. If Close to 0, If it is close to 1, it means that the current point presents a surface feature rather than a skeleton line. The neighborhood point set requeried with the current point The coordinate mean of the point is updated , and value, , , and Respectively The neighborhood points of the requeried , and value, is the total number of neighborhood points to be requeried:

[0021] ;

[0022] In the formula, , and The updated , and value;

[0023] Update the neighborhood distance:

[0024] ;

[0025] In the formula, is the mean distance of the updated neighborhood range;

[0026] Keep updating , the number of shrinking rounds is 4.

[0027] S3 includes, S3.1. analyzing the skeleton points after coarse shrinkage, and establishing KDTree for further refinement of the coarse skeleton line;

[0028] S3.2. Re-update of , and Value, according to the point cloud data value, find twice The neighborhood point set of the range, find the distance between the points The farthest point ;

[0029] S3.3. If of If the value is greater than 0.2, it means Points that are not on the skeleton line need to be refined and calculated and of , and The mean of the values, update The coordinate value of of The value is updated to The distance to the closest point;

[0030] S3.4. Perform octree filtering on the refined data to remove discrete points and obtain a roughly calculated center line.

[0031] S4 includes, S4.1. performing principal component analysis on the neighborhood of each point in the roughly calculated center line, calculating the points where the difference between the first principal component and the second principal component is less than a threshold value of 13 to obtain a set of intersection area points;

[0032] S4.2. Use the DBScan algorithm to cluster the intersection area point set, and each intersection is clustered into one category.

[0033] S4 includes, S4.3. According to the structural characteristics of the mine tunnel, the tunnel intersection is defined as Y-type and T-type, the intersection area point set presents a triangular feature, and the three vertices are obtained. , and ;

[0034] Connect the three vertices obtained to form the three sides of a triangle, and obtain the center points of the three sides, lmean_01, lmean_12, and lmean_20;

[0035] Extend the vertices to their respective connected center lines to obtain their respective far end points max_distance0, max_distance1, max_distance2;

[0036] Connect the far end point and the center point to get three angles angle_01, angle_12, angle_02 that can reflect the type of intersection;

[0037] Calculate the difference between the smallest angle and the middle angle. If the difference is less than 10°, it is a T-type intersection, otherwise it is a Y-type intersection.

[0038] S4 includes, S4.4. For the Y-type intersection, the side corresponding to the maximum angle is the center line of the straight lane. The two far end points corresponding to the maximum angle are directly connected to generate a straight line, and the straight line points are evenly generated as the points of the center line according to the point cloud density. The side corresponding to the middle angle is the branch lane of the large lane, showing the characteristics of arc-shaped projection. The cubic B-spline curve fitting method is used to calculate the center point of the lane. The side corresponding to the minimum angle is not the center line, and the side corresponding to the minimum angle is directly deleted.

[0039] S4 includes, S4.5. For a T-intersection, the side corresponding to the maximum angle is the center line of the straight lane. The two far end points corresponding to the maximum angle are directly connected to generate a straight line, and straight line points are uniformly generated as center line points according to the point cloud density, and a new skeleton line is generated between the third point and the center point. After processing different intersection area point sets respectively, the accurately calculated lane center line is obtained.

[0040] S5 includes, S5.1. marking and sorting the acquired center points, and storing the generated center points in an array In the example above, create a new array And initialized to -1, express The corresponding center points have not been sorted yet. express The corresponding center point enters the sorting sequence, Indicates the first elements;

[0041] S5.2. Use the minimum spanning tree to connect the center points of the straight lanes and the center points of the cross lanes respectively. Each segment constitutes the shortest connected line. Finally, the center line extracted from the cross lane is connected to the center line of the straight lane by endpoints to generate a complete center line of the lane.

[0042] Compared with the prior art, the present invention has the following beneficial effects: the present invention can calculate the center line of underground mine shaft and tunnel engineering with complex features such as crossed and staggered layers; the problem of overlapping of tunnels with different heights is avoided by skeleton contraction and refinement; at the same time, different types of tunnel intersections are defined, the calculation of the center line is more precise, and the spatial geometric characteristics of the shaft and tunnel engineering can be better reflected, providing an effective geometric feature data foundation for realizing digital and intelligent management of mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a technical flow chart of the present invention;

[0044] Figure 2 This is the rough calculation result diagram of the center line;

[0045] Figure 3 Calculate and cluster graph for intersection area point set;

[0046] Figure 4 This is a schematic diagram of a Y-shaped intersection;

[0047] Figure 5 This is a schematic diagram of a T-intersection;

[0048] Figure 6 This is the result diagram of the precise calculation of the Y-type intersection;

[0049] Figure 7 This is the T-intersection calculation result diagram;

[0050] Figure 8 This is a graph of centerline calculation results. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] A method for calculating the centerline of complex laneway networks based on skeleton shrinkage, such as Figure 1 ,include:

[0053] S1. Point cloud data of underground mine tunnels Establish KDTree for initial contraction to obtain neighborhood distance, read point cloud data point by point and calculate the neighborhood point set of each point, and calculate the surface feature and line feature of each point from the feature value of the neighborhood point set;

[0054] S2. Iteratively shrink the underground mine tunnel point cloud data, continuously update the coordinate value, plane feature, linear feature and neighborhood range of each point, and obtain the roughly shrunk skeleton point such as Figure 2 As shown;

[0055] S3. Refine the skeleton according to the geometric features of each contracted skeleton point, and remove discrete noise points by combining the octree filtering method to obtain the center points of the straight lanes and curved lanes and the point set of the intersection area;

[0056] S4. By analyzing the morphological characteristics of underground mine tunnels, the intersections of Y-shaped and T-shaped tunnels are defined, and the point sets of the intersection areas are processed using the principal component method and DBScan algorithm. Figure 3 As shown, the intersection type is determined and the center point of each intersection is refined.

[0057] S5. Smoothly connect the refined calculated center points with other center points, and use the minimum spanning tree to organize the disordered center points, and fit the disordered center points into an ordered center line.

[0058] S1 includes, S1.1. For the Point cloud data , , is the total number of point cloud data, based on the distance The distance to the second closest point Set the distance neighborhood D for field search, D is:

[0059] ;

[0060] Get the neighborhood point set with D as radius , , , and For the Neighborhood points , and value, is the total number of neighbors.

[0061] S1 includes, S1.2. Calculate the distance mean of the neighborhood range based on the neighborhood point set :

[0062] ;

[0063] In the formula, , and They are exist , and The range of directions, and They are exist , and Maximum and minimum values ​​in direction;

[0064] by Re-search the neighborhood point set for the neighborhood radius and calculate the eigenvalues ​​based on the covariance matrix of the neighborhood point set , and , get the line feature Features of dough :

[0065] .

[0066] S2 includes, S2.1. After the initial contraction of the neighborhood-based skeleton, the point cloud data is updated to points with 6 attribute values ;

[0067] S2.2. Perform iterative contraction. If Close to 0, If it is close to 1, it means that the current point presents a surface feature rather than a skeleton line. The neighborhood point set requeried with the current point The coordinate mean of the point is updated , and value, , , and Respectively The neighborhood points of the requeried , and value, is the total number of neighborhood points to be requeried:

[0068] ;

[0069] In the formula, , and The updated , and value;

[0070] Update the neighborhood distance:

[0071] ;

[0072] In the formula, is the mean distance of the updated neighborhood range;

[0073] Keep updating , the number of shrinking rounds is 4.

[0074] S3 includes, S3.1. analyzing the skeleton points after coarse shrinkage, and establishing KDTree for further refinement of the coarse skeleton line;

[0075] S3.2. Re-update of , and Value, according to the point cloud data value, find twice The neighborhood point set of the range, find the distance between the points The farthest point ;

[0076] S3.3. If of If the value is greater than 0.2, it means Points that are not on the skeleton line need to be refined and calculated and of , and The mean of the values, update The coordinate value of of The value is updated to The distance to the closest point;

[0077] S3.4. Perform octree filtering on the refined data to remove discrete points and obtain a roughly calculated center line.

[0078] S4 includes, S4.1. performing principal component analysis on the neighborhood of each point in the roughly calculated center line, calculating the points where the difference between the first principal component and the second principal component is less than a threshold value of 13 to obtain a set of intersection area points;

[0079] S4.2. Use the DBScan algorithm to cluster the intersection area point set, and each intersection is clustered into one category.

[0080] S4 includes, S4.3. According to the structural characteristics of the mine tunnel, such as Figure 4 and Figure 5 As shown, the roadway intersection is defined as Y-type and T-type, and the intersection point set presents a triangular feature, and the three vertices are obtained. , and ;

[0081] Connect the three vertices obtained to form the three sides of a triangle, and obtain the center points of the three sides, lmean_01, lmean_12, and lmean_20;

[0082] Extend the vertices to their respective connected center lines to obtain their respective far end points max_distance0, max_distance1, max_distance2;

[0083] Connect the far end point and the center point to get three angles angle_01, angle_12, angle_02 that can reflect the type of intersection;

[0084] Calculate the difference between the smallest angle and the middle angle. If the difference is less than 10°, it is a T-type intersection, otherwise it is a Y-type intersection.

[0085] S4 includes, S4.4. For the Y-type intersection, the side corresponding to the maximum angle is the center line of the straight lane. The two far end points corresponding to the maximum angle are directly connected to generate a straight line, and the straight line points are evenly generated as the center line points according to the point cloud density. The side corresponding to the middle angle is the bifurcation lane of the large lane, showing the characteristics of arc-shaped projection. The cubic B-spline curve fitting method is used to calculate the center point of the lane. The side corresponding to the minimum angle is not the center line. The side corresponding to the minimum angle is directly deleted. The refined extraction results are as follows: Figure 6 shown.

[0086] S4 includes, S4.5. For a T-shaped intersection, the side corresponding to the maximum angle is the center line of the straight lane. The two far end points corresponding to the maximum angle are directly connected to generate a straight line, and the straight line points are uniformly generated as the points of the center line according to the point cloud density, and a new skeleton line is generated between the third point and the center point. The refined extraction result is as follows Figure 7 As shown in the figure, after processing different intersection area point sets respectively, the accurately calculated lane center line is obtained as follows Figure 8 shown.

[0087] S5 includes, S5.1. marking and sorting the acquired center points, and storing the generated center points in an array In the example above, create a new array And initialized to -1, express The corresponding center points have not been sorted yet. express The corresponding center point enters the sorting sequence, Indicates the first elements;

[0088] S5.2. Use the minimum spanning tree to connect the center points of the straight lanes and the center points of the cross lanes respectively. Each segment constitutes the shortest connected line. Finally, the center line extracted from the cross lane is connected to the center line of the straight lane by endpoints to generate a complete center line of the lane.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features may be replaced by equivalents, and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating the centerline of a complex laneway network based on skeleton shrinkage, characterized in that: include: S1. Point cloud data of underground mine tunnels Establish KDTree for initial contraction to obtain neighborhood distance, read point cloud data point by point and calculate the neighborhood point set of each point, and calculate the surface feature and line feature of each point from the feature value of the neighborhood point set; S2. Iteratively shrink the underground mine tunnel point cloud data, continuously update the coordinate value, plane feature, linear feature and neighborhood range of each point, and obtain the roughly shrunk skeleton point; S3. Refine the skeleton according to the geometric features of each contracted skeleton point, and remove discrete noise points by combining the octree filtering method to obtain the center points of the straight lanes and curved lanes and the point set of the intersection area; S4. By analyzing the morphological characteristics of underground mine tunnels, the Y-shaped and T-shaped tunnel intersections are defined, the principal component method and DBScan algorithm are used to process the intersection area point set, and the intersection type is determined, and the center point of each intersection is refined. S5. Smoothly connect the refined calculated center points with other center points, and use the minimum spanning tree to organize the disordered center points, and fit the disordered center points into an ordered center line.

2. The method for calculating the centerline of a complex laneway network based on skeleton shrinkage according to claim 1 is characterized in that: S1 includes, S1.

1. For the Point cloud data , , is the total number of point cloud data, based on the distance The distance to the second nearest point Set the distance neighborhood D for field search, D is: ； Get the neighborhood point set with D as radius , , , and For the Neighborhood points , and value, is the total number of neighbors.

3. The method for calculating the centerline of a complex laneway network based on skeleton shrinkage according to claim 2 is characterized in that: S1 includes, S1.

2. Calculate the distance mean of the neighborhood range based on the neighborhood point set : ； In the formula, , and They are exist , and The range of directions, and They are exist , and Maximum and minimum values ​​in direction; by Re-search the neighborhood point set for the neighborhood radius and calculate the eigenvalues ​​based on the covariance matrix of the neighborhood point set , and , get the line feature Features of dough : 。 4. The method for calculating the centerline of a complex laneway network based on skeleton shrinkage according to claim 3 is characterized in that: S2 includes, S2.

1. After the initial contraction of the neighborhood-based skeleton, the point cloud data is updated to points with 6 attribute values ; S2.

2. Perform iterative contraction. If Close to 0, If it is close to 1, it means that the current point presents a surface feature rather than a skeleton line. The neighborhood point set requeried with the current point The coordinate mean of the point is updated , and value, , , and Respectively The neighborhood points of the requeried , and value, is the total number of neighborhood points to be requeried: ； In the formula, , and The updated , and value; Update the neighborhood distance: ； In the formula, is the mean distance of the updated neighborhood range; Keep updating , the number of shrinking rounds is 4.

5. The method for calculating the centerline of a complex laneway network based on skeleton shrinkage according to claim 4, characterized in that: S3 includes, S3.

1. analyzing the skeleton points after coarse shrinkage, and establishing KDTree for further refinement of the coarse skeleton line; S3.

2. Re-update of , and Value, according to the point cloud data value, find twice The neighborhood point set of the range, find the distance between the points The farthest point ; S3.

3. If of If the value is greater than 0.2, it means Points that are not on the skeleton line need to be refined and calculated and of , and The mean of the values, update The coordinate value of of The value is updated to The distance to the closest point; S3.

4. Perform octree filtering on the refined data to remove discrete points and obtain a roughly calculated center line.

6. The method for calculating the centerline of a complex laneway network based on skeleton shrinkage according to claim 5, characterized in that: S4 includes, S4.

1. performing principal component analysis on the neighborhood of each point in the roughly calculated center line, calculating the points where the difference between the first principal component and the second principal component is less than a threshold value of 13 to obtain a set of intersection area points; S4.

2. Use the DBScan algorithm to cluster the intersection area point set, and each intersection is clustered into one category.

7. The method for calculating the centerline of a complex laneway network based on skeleton shrinkage according to claim 6, characterized in that: S4 includes, S4.

3. According to the structural characteristics of the mine tunnel, the tunnel intersection is defined as Y-type and T-type, the intersection area point set presents a triangular feature, and the three vertices are obtained. , and ; Connect the three vertices obtained to form the three sides of a triangle, and obtain the center points of the three sides, lmean_01, lmean_12, and lmean_20; Extend the vertices to their respective connected center lines to obtain their respective far end points max_distance0, max_distance1, max_distance2; Connect the far end point and the center point to get three angles angle_01, angle_12, angle_02 that can reflect the type of intersection; Calculate the difference between the smallest angle and the middle angle. If the difference is less than 10°, it is a T-type intersection, otherwise it is a Y-type intersection.

8. The method for calculating the centerline of a complex laneway network based on skeleton shrinkage according to claim 7, characterized in that: S4 includes, S4.

4. For the Y-type intersection, the side corresponding to the maximum angle is the center line of the straight lane. The two far end points corresponding to the maximum angle are directly connected to generate a straight line, and the straight line points are evenly generated as the points of the center line according to the point cloud density. The side corresponding to the middle angle is the branch lane of the large lane, showing the characteristics of arc-shaped projection. The cubic B-spline curve fitting method is used to calculate the center point of the lane. The side corresponding to the minimum angle is not the center line, and the side corresponding to the minimum angle is directly deleted.

9. The method for calculating the centerline of a complex laneway network based on skeleton shrinkage according to claim 8, characterized in that: S4 includes, S4.

5. For a T-intersection, the side corresponding to the maximum angle is the center line of the straight lane. The two far end points corresponding to the maximum angle are directly connected to generate a straight line, and straight line points are uniformly generated as center line points according to the point cloud density, and a new skeleton line is generated between the third point and the center point. After processing different intersection area point sets respectively, the accurately calculated lane center line is obtained.

10. The method for calculating the centerline of a complex laneway network based on skeleton shrinkage according to claim 9, characterized in that: S5 includes, S5.

1. marking and sorting the acquired center points, and storing the generated center points in an array In the example above, create a new array And initialized to -1, express The corresponding center points have not been sorted yet. express The corresponding center point enters the sorting sequence, Indicates the first elements; S5.

2. Use the minimum spanning tree to connect the center points of the straight lanes and the center points of the cross lanes respectively. Each segment constitutes the shortest connected line. Finally, the center line extracted from the cross lane is connected to the center line of the straight lane by endpoints to generate a complete center line of the lane.

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