CN116518940A - Strip mine step structure measuring method based on unmanned aerial vehicle image matching point cloud - Google Patents
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Abstract
The invention discloses a strip mine step structure measuring method based on unmanned aerial vehicle image matching point cloud, which comprises the following steps: step S1, obtaining open pit mine point cloud data: acquiring strip mine image data based on unmanned aerial vehicle oblique photogrammetry, importing the strip mine image data into Context Capture, and generating point cloud through image preprocessing, space three encryption processing and multi-view image dense matching processing; step S2, preprocessing strip mine point cloud data: constructing a neighborhood relation based on Kd-Tree, simplifying point cloud based on geometric sampling, and simplifying data while preserving characteristics; step S3, extracting the step elements of the strip mine: according to the neighborhood characteristics, extracting slope surfaces, selecting a plane to manually cut the slope surfaces, processing the slope surfaces through density clustering to obtain a plurality of step slope surfaces, and extracting step lines; step S4, measuring the step structure: and calculating the step height and the step slope angle according to the extracted step elements. Through the design, the invention can rapidly and accurately measure the step structure and effectively reduce the supervision cost of the strip mine.
Description
Technical Field
The invention relates to the field of mine mapping, in particular to an open pit mine step structure measuring method based on unmanned aerial vehicle image matching point cloud.
Background
Mineral resources are an important material basis for social production development, reasonable development and comprehensive utilization of mineral resources are necessary requirements for modern construction, and surface mining is widely applied to mine engineering at home and abroad as a production mode with low cost, high yield and high efficiency. Strip mines are usually mined in a top-down, step-by-step manner. The steps are the minimum exploitation units in the vertical direction, are working sites for perforation, blasting, shoveling and other tasks, and directly influence the production scale, economic benefit and slope stability of the mining area. The surface mining can form a plurality of steep slopes, and the construction operations such as blasting, mining and stripping frequently cause the damage of the step structural surface of the mine and the deformation of rock mass, finally cause geological accidents such as instability and deformation of the slopes, and seriously threaten the life and property safety of mining personnel.
Safety mining has become an extremely important part in mining engineering research, and is a precondition for orderly production in open stopes throughout the whole process of open mining. The surface mining molding step structure affects the structural stability of the strip mine and the safety of mining areas, and if effective measurement and control are not carried out, the structural unbalance is easy to be caused, and a series of geological disasters such as collapse, landslide, mud-rock flow and the like are finally caused.
The traditional method uses the equivalent measuring bench structure of the level gauge, the total station and the GNSS receiver, and has the advantages of higher cost, low efficiency and incapability of guaranteeing the safety of mapping personnel. Therefore, the stope structure is extracted and measured by a high and new mapping technology, and the continuous monitoring of the step structure in the mining process can effectively guide the production operation of a mining area, so that unbalance is avoided. As disclosed in chinese patent CN112634389a, a method, an apparatus and a server for drawing a surface map of a strip mine based on an unmanned aerial vehicle are provided, including: acquiring a scene photo of a target area; importing an orthographic image, a radar point cloud and an upper moon stage line of the current month; displaying radar point cloud connection view angles, and connecting all the connection view angles to obtain a Lei Dadian cloud view; and drawing a step line graph according to the radar point cloud view, and deriving the step line graph to obtain a current state plan of the strip mine. The current situation drawing of the mining area of the unmanned aerial vehicle aerial survey data can be completed by using professional mine software; the high-density point cloud data enables the current state plan to more accurately and comprehensively display the positions and the elevations of the slope tops, the slope bottoms and the flat plates of the mining steps. As another example, chinese patent CN112634434a discloses a mine three-dimensional model making method based on unmanned aerial vehicle, which comprises: acquiring a scene photo of a target area; preprocessing the scene photo to obtain a preprocessed scene photo; dividing the preprocessed scene photos according to the regions to obtain a plurality of groups of preprocessed scene photos, and POS data under Gaussian 3-degree belt projection; modeling processing is carried out on POS data under the projection of the Gaussian 3-degree band, and a three-dimensional model is obtained. By adopting the design scheme, the three-dimensional real model of the mine can analyze the disclosure condition of the coal seam in the mining area from various angles, can clearly and intuitively evaluate the greening degree of the mine, and can analyze the on-site step distribution and the current mining and discharging situation.
Although the above design gives a method for measuring the surface mine step structure by using the unmanned plane, the data to be processed is complex and the operation efficiency is low, so a new surface mine step structure measuring method is urgently needed to be designed so as to achieve data simplification of surface mine step structure measurement and improve the operation efficiency.
Disclosure of Invention
Aiming at the problems of complex data to be processed and low operation efficiency in the existing method for measuring the surface mine step structure, the application designs a surface mine step structure measuring method based on unmanned aerial vehicle image matching point cloud, so as to simplify the data for realizing the surface mine step structure measurement and improve the operation efficiency.
A strip mine step structure measuring method based on unmanned aerial vehicle image matching point cloud comprises the following steps:
step S1, obtaining open pit mine point cloud data: acquiring strip mine image data based on unmanned aerial vehicle oblique photogrammetry, importing the strip mine image data into ContextCapture, and generating point cloud through image preprocessing, space three encryption processing and multi-view image dense matching processing;
step S2, preprocessing strip mine point cloud data: constructing a neighborhood relation based on Kd-Tree, simplifying point cloud based on geometric sampling, and simplifying data while preserving characteristics;
step S3, extracting the step elements of the strip mine: according to the neighborhood characteristics, extracting slope surfaces, selecting a plane to manually cut the slope surfaces, processing the slope surfaces through density clustering to obtain a plurality of step slope surfaces, and extracting step lines;
step S4, measuring the step structure: and calculating the step height and the step slope angle according to the extracted step elements.
A strip mine step structure measuring method based on unmanned aerial vehicle image matching point cloud, the concrete method of the step S2 comprises the following steps:
s21, constructing a point cloud neighborhood relation in a three-dimensional space based on Kd-Tree, selecting a maximum variance dimension as a segmentation axis, setting a point with a median value in the direction of the segmentation axis as a current node, dividing the point with a smaller median value as a left subtree node, dividing the point with a larger median value as a right subtree node, sequentially updating the segmentation axis as a second largest axis with a variance and an axis with a smallest variance, repeating the steps until all data are processed, selecting a point at the moment, and taking the point as a circle center and an intra-sphere point with a radius r as a neighborhood point of the point, wherein the point can be expressed as { p } i |||p i -p|| < r }, the nearest search of data can be completed rapidly based on Kd-Tree;
step S22, realizing point cloud reduction based on geometric sampling, presetting a target sampling number U and a uniform sampling rate V, calculating a normal included angle theta from each point to a neighborhood point, wherein the larger the normal included angle theta is, the larger the curvature of the area is, setting an angle threshold eta, classifying the point with theta & gteta into a steep area, and the sampling number of the steep area is U (1-V), and the sampling number of the non-steep area is U.
Preferably, the specific method of step S3 includes:
s31, solving a normal vector, calculating an included angle theta between the normal vector and a vertical plane, setting a threshold lambda, and if theta is more than lambda, the point is a slope, otherwise, the point is a plane;
step S32, the point cloud is imported into a cloudcompare, and different steps are separated to the greatest extent based on cut-through filtering;
step S33, processing slope point clouds through density clustering, and separating the spatially discrete point clusters to obtain a plurality of step slopes S= { S 0 ,s 1 ,s 2 ,...,s n }；
Step S34, extracting a step line, and selecting a point p in the step slope i There is a neighborhood of N (p i ) N (p) i ) The points in the array are projected onto a neighborhood tangent plane, and the projected points form a set omega in p i As corner points, the points in omega are arranged in pairs and p in the clockwise direction i Form a series of angles theta = { theta 1 ,θ 2 ,...,θ m M= |n (x) i ) Selecting the maximum value theta in theta from the values of I and 1 max Setting a threshold value xi, when theta max When > ζ, p i Namely, the boundary points are used for processing all points in the step slope surface, two step lines and section boundaries exist in the step slope surface boundary B, and 3 points are randomly marked in the boundary B to fit a plane n B Calculating the midpoint of B to pi B If the distance is smaller than the distance threshold zeta, the distance is an internal point, the anti-regularization is a noise point, the plane fitting operation is repeated until no unmarked point is found in B or the iteration number is larger than the set maximum iteration number, the situation of matching the maximum internal point is the optimal selection, and the corresponding internal point set L B I.e. a step line, L is removed in B B Setting the midpoint B to be in an unmarked state, repeating the plane fitting operation, and selecting the corresponding internal point L under the most circumstance A ，L A Namely another step line in B.
Preferably, the specific method of step S4 includes:
step S41, step height calculation, L is selected A ＝{a 0 ,a 1 ,...,a n Sum L B ＝{b 0 ,b 1 ,...,b m Two adjacent slope lines, select L A One point a in i At L B Find a in i Closest point b of (2) j Calculate d= |a i -b j ||，d z I.e. the step height at this point, for L A Repeating the above operation to obtain L A Step height set D of A ＝{d 1 ,d 2 ,...,d n Corresponding L B The corresponding step height set D can also be calculated B ＝{d' 1 ,d' 2 ,...,d' m }；
Step S42, calculating a step slope angle, wherein the step slope angle at a certain point is the included angle between the normal vector of the point and the vertical plane.
The beneficial effects obtained by the invention are as follows:
1. according to the open pit step structure measurement method based on the unmanned aerial vehicle image matching point cloud, which is designed by the invention, the neighborhood relation is constructed based on Kd-Tree, so that the point cloud retrieval speed of open pit step point cloud data can be improved, the step point cloud is simplified based on geometric sampling, the data is simplified while the step characteristics are maintained, and the technical effect of improving the open pit step structure measurement efficiency is achieved.
2. According to the strip mine step structure measuring method based on the unmanned aerial vehicle image matching point cloud, the monitoring method based on the unmanned aerial vehicle image matching point cloud can conveniently acquire image data, is reasonable in method and reliable in result, and solves the problems that the traditional use of machines such as a level gauge, a total station and a GNSS receiver to measure step structures is high in cost and low in efficiency, and the safety of surveyors cannot be guaranteed.
3. According to the strip mine step structure measuring method based on the unmanned aerial vehicle image matching point cloud, the unmanned aerial vehicle image matching point cloud is processed through geometric features, strip mine step elements can be rapidly extracted, structural measurement is achieved, the step structure can be rapidly and accurately measured, the strip mine supervision cost is effectively reduced, and the availability of the unmanned aerial vehicle image matching point cloud in a strip mine scene is improved.
The foregoing description is only a summary of the technical solutions of the present application, so that the technical means of the present application may be implemented according to the content of the specification, and so that the foregoing and other objects, features and advantages of the present application may be more clearly understood, the following detailed description of the preferred embodiments of the present application is given in conjunction with the accompanying drawings.
The above and other objects, advantages and features of the present application will become more apparent to those skilled in the art from the following detailed description of the specific embodiments thereof, taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of a method for measuring a surface mine step structure based on unmanned aerial vehicle image matching point clouds;
FIG. 2 shows the slope of each step after density clustering;
fig. 3 is an extracted strip mine step line.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to facilitate a thorough understanding of embodiments of the present application. It will therefore be apparent to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the application. In addition, descriptions of well-known functions and constructions are omitted in the embodiments for clarity and conciseness.
It should be appreciated that reference throughout this specification to "one embodiment" or "the present embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the "one embodiment" or "this embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the present application may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: the terms "/and" herein describe another associative object relationship, indicating that there may be two relationships, e.g., a/and B, may indicate that: the character "/" herein generally indicates that the associated object is an "or" relationship.
The term "at least one" is herein merely an association relation describing an associated object, meaning that there may be three kinds of relations, e.g., at least one of a and B may represent: a exists alone, A and B exist together, and B exists alone.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprise," "include," or any other variation thereof, are intended to cover a non-exclusive inclusion.
Example 1
The embodiment mainly introduces a strip mine step structure measuring method based on unmanned aerial vehicle image matching point cloud, and a specific flow chart please refer to fig. 1, which includes the following steps:
step S1, obtaining open pit mine point cloud data: acquiring strip mine image data based on unmanned aerial vehicle oblique photogrammetry, importing the strip mine image data into Context Capture, and generating point cloud through image preprocessing, space three encryption processing and multi-view image dense matching processing;
step S2, preprocessing strip mine point cloud data: constructing a neighborhood relation based on Kd-Tree, simplifying point cloud based on geometric sampling, and simplifying data while preserving characteristics;
step S3, extracting the step elements of the strip mine: according to the neighborhood characteristics, extracting slope surfaces, selecting a plane to manually cut the slope surfaces, processing the slope surfaces through density clustering to obtain a plurality of step slope surfaces, and extracting step lines;
step S4, measuring the step structure: and calculating the step height and the step slope angle according to the extracted step elements.
Further, the specific method of step S2 includes:
s21, constructing a point cloud neighborhood relation in a three-dimensional space based on Kd-Tree, selecting a maximum variance dimension as a segmentation axis, setting a point with a median value in the direction of the segmentation axis as a current node, dividing the point with a smaller median value as a left subtree node, dividing the point with a larger median value as a right subtree node, sequentially updating the segmentation axis as a second largest axis with a variance and an axis with a smallest variance, repeating the steps until all data are processed, selecting a point at the moment, and taking the point as a circle center and an intra-sphere point with a radius r as a neighborhood point of the point, wherein the point can be expressed as { p } i |||p i -p|| < r }, the nearest search of data can be completed rapidly based on Kd-Tree;
step S22, realizing point cloud reduction based on geometric sampling, presetting a target sampling number U and a uniform sampling rate V, calculating a normal included angle theta from each point to a neighborhood point, wherein the larger the normal included angle theta is, the larger the curvature of the area is, setting an angle threshold eta, classifying the point with theta & gteta into a steep area, and the sampling number of the steep area is U (1-V), and the sampling number of the non-steep area is U.
Further, the specific method in step S3 includes:
s31, solving a normal vector, calculating an included angle theta between the normal vector and a vertical plane, setting a threshold lambda, and if theta is more than lambda, the point is a slope, otherwise, the point is a plane;
step S32, the point cloud is imported into a cloudcompare, and different steps are separated to the greatest extent based on cut-through filtering;
step S33, processing slope point clouds through density clustering, and separating the spatially discrete point clusters to obtain a plurality of step slopes S= { S 0 ,s 1 ,s 2 ,...,s n }；
Step S34, extracting a step line, and selecting a point p in the step slope i There is a neighborhood of N (p i ) N (p) i ) The points in the array are projected onto a neighborhood tangent plane, and the projected points form a set omega in p i As corner points, the points in omega are arranged in pairs and p in the clockwise direction i Form a series of angles theta = { theta 1 ,θ 2 ,...,θ m M= |n (x) i ) Selecting the maximum value theta in theta from the values of I and 1 max Setting a threshold value xi, when theta max When > ζ, p i Namely, the boundary points are used for processing all points in the step slope surface, two step lines and section boundaries exist in the step slope surface boundary B, and 3 points are randomly marked in the boundary B to fit a plane n B Calculating the midpoint of B to pi B If the distance is smaller than the distance threshold zeta, the distance is an internal point, the anti-regularization is a noise point, the plane fitting operation is repeated until no unmarked point is found in B or the iteration number is larger than the set maximum iteration number, the situation of matching the maximum internal point is the optimal selection, and the corresponding internal point set L B I.e. a step line, L is removed in B B Setting the midpoint B to be in an unmarked state, repeating the plane fitting operation, and selecting the corresponding internal point L under the most circumstance A ，L A Namely another step line in B.
Further, the specific method in step S4 includes:
step S41, step height calculation, L is selected A ＝{a 0 ,a 1 ,...,a n Sum L B ＝{b 0 ,b 1 ,...,b m Two adjacent slope lines, select L A One point a in i At L B Find a in i Closest point b of (2) j Calculate d= |a i -b j ||，d z I.e. the step height at this point, for L A Repeating the above operation to obtain L A Step height set D of A ＝{d 1 ,d 2 ,...,d n Corresponding L B The corresponding step height set D can also be calculated B ＝{d' 1 ,d' 2 ,...,d' m }；
Step S42, calculating a step slope angle, wherein the step slope angle at a certain point is the included angle between the normal vector of the point and the vertical plane.
According to the open pit step structure measurement method based on the unmanned aerial vehicle image matching point cloud, which is designed by the invention, the neighborhood relation is constructed based on Kd-Tree, so that the point cloud retrieval speed of open pit step point cloud data can be improved, the step point cloud is simplified based on geometric sampling, the data is simplified while the step characteristics are maintained, and the technical effect of improving the open pit step structure measurement efficiency is achieved.
Example 2
Based on the above embodiment 1, this embodiment mainly introduces an optimization method for measuring a surface mine step structure based on unmanned aerial vehicle image matching point cloud, which includes the following steps:
s1, acquiring data, namely acquiring strip mine image data based on unmanned aerial vehicle oblique photogrammetry, importing the strip mine image data into Context Capture, and then generating point cloud through steps of image preprocessing, space three encryption, multi-view image dense matching and the like.
S2, preprocessing point cloud data, constructing a neighborhood relation based on Kd-Tree, simplifying the point cloud based on geometric sampling, and simplifying the data while preserving the characteristics, wherein the process is as follows:
s21, constructing a point cloud neighborhood gateway in a three-dimensional space based on Kd-TreeSelecting a maximum variance dimension as a dividing axis, setting a point with a median value in the direction of the dividing axis as a current node, dividing a point smaller than the median value into a left subtree node, and a point larger than the median value as a right subtree node, sequentially updating the dividing axis as an axis with a second largest variance and an axis with a smallest variance, repeating the steps until all data are processed, selecting a point at the moment, taking the point as a circle center, taking an inner sphere point with a radius r as a neighborhood point of the point, and representing as { p } i |||p i -p| < r }, rapidly completing the nearest search of data;
step S22, realizing point cloud reduction based on geometric sampling, presetting a target sampling number of 100 ten thousand and a uniform sampling rate of 0.3, calculating a normal included angle theta from each point to a neighborhood point, wherein the larger the normal included angle theta is, the larger the curvature of the area is, setting an angle threshold eta, classifying the point with theta & gteta into a steep area, and the sampling number of the steep area is 70 ten thousand and the sampling number of a non-steep area is 30 ten thousand.
S3, extracting step elements of the strip mine, extracting slope surfaces according to neighborhood characteristics, cutting the slope surfaces based on direct filtering, obtaining 17 step slope surfaces through density clustering, and extracting step lines, wherein the process is as follows:
s31, solving a normal vector, calculating an included angle theta between the normal vector and a vertical plane, setting a threshold value of 16 degrees, and if theta is more than 16 degrees, the point is a slope, otherwise, the point is a plane;
step S32, importing the point cloud into a cloudcompact, and selecting planes y=520 and x=420 to manually cut the slope so as to separate different steps to the greatest extent;
step S33, processing slope point clouds through density clustering, and separating the spatially discrete point clusters to obtain a plurality of step slopes S= { S 0 ,s 1 ,s 2 ,...,s 16 }；
Fig. 2 shows the slope of each step obtained after density clustering.
Step S34, extracting a step line, and selecting a point p in the step slope i There is a neighborhood of N (p i ) N (p) i ) Projection of the points in (a) to a neighborhood cutOn the plane, the projected points form a set Ω, denoted by p i As corner points, the points in omega are arranged in pairs and p in the clockwise direction i Form a series of angles theta = { theta 1 ,θ 2 ,...,θ m M= |n (x) i ) Selecting the maximum value theta in theta from the values of I and 1 max Setting a threshold value xi, when theta max When > ζ, p i Namely, the boundary points are used for processing all points in the step slope surface, two step lines and section boundaries exist in the step slope surface boundary B, and 3 points are randomly marked in the boundary B to fit a plane n B Calculating the midpoint of B to pi B If the distance is smaller than the distance threshold zeta, the distance is an internal point, the anti-regularization is a noise point, the plane fitting operation is repeated until no unmarked point is found in B or the iteration number is larger than the set maximum iteration number, the situation of matching the maximum internal point is the optimal selection, and the corresponding internal point set L B I.e. a step line, L is removed in B B Setting the midpoint B to be in an unmarked state, repeating the plane fitting operation, and selecting the corresponding internal point L under the most circumstance A ，L A Namely another step line in B.
Fig. 3 is an extracted strip mine step line.
Step S4, measuring a step structure, and calculating the step height and the step slope angle according to the extracted step elements, wherein the step height and the step slope angle are calculated according to the following steps:
step S41, step height calculation, L is selected A ＝{a 0 ,a 1 ,...,a n Sum L B ＝{b 0 ,b 1 ,...,b m Two adjacent slope lines, select L A One point a in i At L B Find a in i Closest point b of (2) j Calculate d= |a i -b j ||，d z I.e. the step height at this point, for L A Repeating the above operation to obtain L A Step height set D of A ＝{d 1 ,d 2 ,...,d n Corresponding L B The corresponding step height set D can also be calculated B ＝{d' 1 ,d' 2 ,...,d' m }；
Step S42, calculating a step slope angle, wherein the step slope angle at a certain point is the included angle between the normal vector of the point and the vertical plane.
According to the strip mine step structure measuring method based on the unmanned aerial vehicle image matching point cloud, the monitoring method based on the unmanned aerial vehicle image matching point cloud can conveniently acquire image data, is reasonable in method and reliable in result, and solves the problems that the traditional use of machines such as a level gauge, a total station and a GNSS receiver to measure step structures is high in cost and low in efficiency, and the safety of surveyors cannot be guaranteed.
According to the strip mine step structure measuring method based on the unmanned aerial vehicle image matching point cloud, the unmanned aerial vehicle image matching point cloud is processed through geometric features, strip mine step elements can be rapidly extracted, structural measurement is achieved, the step structure can be rapidly and accurately measured, the strip mine supervision cost is effectively reduced, and the availability of the unmanned aerial vehicle image matching point cloud in a strip mine scene is improved.
The above description is only of the preferred embodiments of the present invention and it is not intended to limit the scope of the present invention, but various modifications and variations can be made by those skilled in the art. Variations, modifications, substitutions, integration and parameter changes may be made to these embodiments by conventional means or may be made to achieve the same functionality within the spirit and principles of the present invention without departing from such principles and spirit of the invention.
Claims (7)
1. The strip mine step structure measuring method based on unmanned aerial vehicle image matching point cloud is characterized by comprising the following steps of:
step S1, obtaining open pit mine point cloud data: acquiring strip mine image data based on unmanned aerial vehicle oblique photogrammetry, importing the strip mine image data into Context Capture, and generating point cloud through image preprocessing, space three encryption processing and multi-view image dense matching processing;
step S2, preprocessing strip mine point cloud data: constructing a neighborhood relation based on Kd-Tree, simplifying point cloud based on geometric sampling, and simplifying data while preserving characteristics;
step S3, extracting the step elements of the strip mine: according to the neighborhood characteristics, extracting slope surfaces, selecting a plane to manually cut the slope surfaces, processing the slope surfaces through density clustering to obtain a plurality of step slope surfaces, and extracting step lines;
step S4, measuring the step structure: and calculating the step height and the step slope angle according to the extracted step elements.
2. The method for measuring the surface mine step structure based on the unmanned aerial vehicle image matching point cloud according to claim 1, wherein the specific method of the step S2 comprises the following steps:
s21, constructing a point cloud neighborhood relation in a three-dimensional space based on Kd-Tree, selecting a maximum variance dimension as a segmentation axis, setting a point with a median value in the direction of the segmentation axis as a current node, dividing the point with a smaller median value as a left subtree node, dividing the point with a larger median value as a right subtree node, sequentially updating the segmentation axis as a second largest axis with a variance and an axis with a smallest variance, repeating the steps until all data are processed, selecting a point at the moment, and taking the point as a circle center and an intra-sphere point with a radius r as a neighborhood point of the point, wherein the point can be expressed as { p } i |||p i -p|| < r }, the nearest search of data can be completed rapidly based on Kd-Tree;
step S22, realizing point cloud reduction based on geometric sampling, presetting a target sampling number U and a uniform sampling rate V, calculating a normal included angle theta from each point to a neighborhood point, wherein the larger the normal included angle theta is, the larger the curvature of the area is, setting an angle threshold eta, classifying the point with theta & gteta into a steep area, and the sampling number of the steep area is U (1-V), and the sampling number of the non-steep area is U.
3. The method for measuring the surface mine step structure based on the unmanned aerial vehicle image matching point cloud according to claim 1, wherein the specific method of the step S3 comprises the following steps:
s31, solving a normal vector, calculating an included angle theta between the normal vector and a vertical plane, setting a threshold lambda, and if theta is more than lambda, the point is a slope, otherwise, the point is a plane;
step S32, the point cloud is imported into a closed compact, and different steps are separated to the greatest extent based on cut-through filtering;
step S33, processing slope point clouds through density clustering, and separating the spatially discrete point clusters to obtain a plurality of step slopes S= { S 0 ,s 1 ,s 2 ,...,s n }；
Step S34, extracting a step line, and selecting a point p in the step slope i There is a neighborhood of N (p i ) N (p) i ) The points in the array are projected onto a neighborhood tangent plane, and the projected points form a set omega in p i As corner points, the points in omega are arranged in pairs and p in the clockwise direction i Form a series of angles theta = { theta 1 ,θ 2 ,...,θ m M= |n (x) i ) Selecting the maximum value theta in theta from the values of I and 1 max Setting a threshold value xi, when theta max When > ζ, p i Namely, the boundary points are used for processing all points in the step slope surface, two step lines and section boundaries exist in the step slope surface boundary B, and 3 points are randomly marked in the boundary B to fit a plane n B Calculating the midpoint of B to pi B If the distance is smaller than the distance threshold zeta, the distance is an internal point, the anti-regularization is a noise point, the plane fitting operation is repeated until no unmarked point is found in B or the iteration number is larger than the set maximum iteration number, the situation of matching the maximum internal point is the optimal selection, and the corresponding internal point set L B I.e. a step line, L is removed in B B Setting the midpoint B to be in an unmarked state, repeating the plane fitting operation, and selecting the corresponding internal point L under the most circumstance A ，L A Namely another step line in B.
4. The strip mine step structure measuring method based on unmanned aerial vehicle image matching point cloud according to any one of claims 1 or 2, wherein the method is characterized by comprising the following steps: the specific method of the step S4 comprises the following steps:
step S41, step altimeterCalculating, selecting L A ＝{a 0 ,a 1 ,...,a n Sum L B ＝{b 0 ,b 1 ,...,b m Two adjacent slope lines, select L A One point a in i At L B Find a in i Closest point b of (2) j Calculate d= |a i -b j ||，d z I.e. the step height at this point, for L A Repeating the above operation to obtain L A Step height set D of A ＝{d 1 ,d 2 ,...,d n Corresponding L B The corresponding step height set D can also be calculated B ＝{d' 1 ,d' 2 ,...,d' m }；
Step S42, calculating a step slope angle, wherein the step slope angle at a certain point is the included angle between the normal vector of the point and the vertical plane.
5. The method for measuring the surface mine step structure based on the unmanned aerial vehicle image matching point cloud according to claim 2, wherein in the step S22, the preset target sampling number U is 100 ten thousand, the uniform sampling rate V is 0.3, the steep region sampling number is 70 ten thousand, and the non-steep region sampling number is 30 ten thousand.
6. The method for measuring the surface mine step structure based on the unmanned aerial vehicle image matching point cloud according to claim 3, wherein in the step S31, the threshold value is set to be 16 degrees, if θ > 16 degrees, the point is a slope, and otherwise, the point is a plane.
7. The method for measuring the surface mine step structure based on the unmanned aerial vehicle image matching point cloud according to any one of claims 3 and 6, wherein in step S32, the method based on the cut-through filter is as follows: the planes y=520 and x=420 are selected to manually cut the slope.
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