CN117876465A - Volume calculation method, system, equipment and medium based on point cloud data in water falling hole - Google Patents
Volume calculation method, system, equipment and medium based on point cloud data in water falling hole Download PDFInfo
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Abstract
The invention discloses a volume calculation method, a system, equipment and a medium of point cloud data in a water falling hole, which belong to the technical field of geological disaster investigation and evaluation, wherein the point cloud data are acquired by scanning the internal structure of the water falling hole, and a three-dimensional space of the water falling hole is constructed; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull; gridding a plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole. The method can improve the accuracy of volume calculation.
Description
Technical Field
The invention relates to the technical field of geological disaster investigation and evaluation, in particular to a method, a system, equipment and a medium for acquiring loess downhill internal point cloud based on a handheld laser scanner and calculating the volume thereof.
Background
Loess plateau develops tens of thousands of loess downspouts, which tend to cause collapse of road surfaces, damage of buildings, damage of farmlands, piping of dams, and damage of roadbeds. In some cases, these holes may have a significant impact on the stability of the slope, even causing landslides and other geological disasters. Moreover, loess downspouts are also an important manifestation of erosion of underground soil. The water falling hole is a complex three-dimensional structure entity occupying the underground space, and the related methods for acquiring three-dimensional water falling hole data in the prior art are all water falling hole detection and identification performed from a two-dimensional view. Therefore, the method has a very important scientific problem of acquiring three-dimensional water falling hole data and calculating the soil erosion amount, and has important significance for deeply knowing the form, structure, scale, connectivity and development rule of the water falling hole.
At present, a fine investigation method of a loess water falling hole three-dimensional structure and a calculation method of a water falling hole volume are not established, but are exactly key technical methods for evaluating the soil erosion amount caused by the loess water falling hole. Therefore, how much the amount of erosion of the underground soil on the loess plateau due to the loess downspout is a pending scientific problem.
The prior art mainly estimates the volume of the water falling hole through a digital elevation model DEM, and is a method which is used for research at home and abroad, but the estimation method has obvious defects: although the DEM is a solid ground model which uses a group of ordered value array to represent the ground elevation, the DEM is not a real three-dimensional model, so that the water falling hole with complex morphology and internal structure cannot be accurately depicted; the DEM is simpler in algorithm for estimating the volume of the falling water hole, namely a rough method for obtaining the total volume by adding the multiplied areas of each grid and the depth is adopted, and because the traditional method for calculating the volume of the falling water hole does not consider the real and complex three-dimensional structure of the falling water hole, the calculated volume error of the falling water hole is large, and when the resolution ratio of the DEM is large, the calculated volume error of the falling water hole is larger.
Disclosure of Invention
Aiming at the problems in the field, the invention provides a volume calculation method, a system, equipment and a medium based on the cloud data of the internal point of a water falling hole, which can replace the technical problem that the calculated volume error of the water falling hole is large because the traditional water falling hole volume calculation method does not consider the real and complex three-dimensional structure of the water falling hole.
In order to solve the technical problems, the invention discloses a volume calculation method based on point cloud data in a water falling hole, which comprises the following steps:
acquiring point cloud data by scanning the internal structure of the falling water hole;
constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
gridding a plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
Preferably, the acquiring the point cloud data includes the following steps:
a handheld laser scanner with a scanning visual angle of 270 degrees multiplied by 360 degrees, a scanning speed of 300,000 points/second, a relative precision of 1.5-3cm and a maximum scanning distance of 100m is used for collecting field data of the water falling hole;
carrying out 360-degree surrounding scanning on each falling hole by using the handheld laser scanner to obtain three-dimensional laser point cloud data of each falling hole;
and (3) completing data splicing and preprocessing work on the three-dimensional laser point cloud data of each water falling hole, taking one water falling hole as a research object, and performing volume calculation.
Preferably, the obtaining the two-dimensional point cloud data on the xy plane includes the following steps:
sorting the point cloud data on XYZ axes of three-dimensional space coordinates, and determining the maximum and minimum values in three dimensions;
subtracting the minimum value from the maximum value to correspondingly obtain the space span in the three dimensions;
taking the largest span as the side length of the cube to construct a three-dimensional space of the water falling hole;
uniformly dividing the whole three-dimensional space into 20 parts according to the Z-axis direction, traversing all the point cloud data, and distributing each point cloud data to a corresponding section according to the Z-coordinate value of each point cloud data;
and projecting the three-dimensional point cloud data onto an xy plane, deleting the z value in the (x, y, z) coordinates of each point, and reserving the rest (x, y) to form two-dimensional point cloud coordinates of the point cloud data.
Preferably, the performing the GiftWrapping algorithm on each two-dimensional point cloud data includes the following steps:
step1: inputting two-dimensional point cloud data in a slice, and selecting a point with the smallest y value in all point sets as a starting point P 0 ；
Step2: with P 0 For ray-counter-clockwise rotation of the apex, the first point appearing on the ray serves as the next starting point P 1 And P 0 A first edge of the convex hull;
step3: with P 1 For ray-counter-clockwise rotation of the apex, the first point appearing on the ray serves as the next starting point P 2 And P 1 Forming a second edge of the convex hull, and judging P 2 Whether or not to sum with P 0 Overlapping; if the two paths coincide, ending the GiftWrapping algorithm; if not, continuing to execute the following steps;
step4: step3 is performed a plurality of times until P i As a new starting point, judge P i Whether or not to sum with P 0 Overlapping; if not coincide, use P i For ray-counter-clockwise rotation of the apex, the first point appearing on the ray serves as the next starting point P i+1 And the last starting point P i The i+1th edge forming the convex hull, and the value of i+1 is given to i; if the two paths coincide, ending the GiftWrapping algorithm;
step5: multiple runs of Step4 up to P i+1 And P 0 The coincidence, giftWrapping algorithm ends.
Preferably, the classifying the grids includes the steps of:
dividing the square where the two-dimensional point cloud convex hull is positioned into 20 multiplied by 20, and 400 grids with the same size in total;
dividing the divided grids into three types of type 0, type 1 and type 2, wherein:
the type 0 is that the middle part of the water falling hole can completely cover the grid, and the volume is calculated directly by using a column volume formula; the type 1 is a grid where the convex hull point of the water falling hole is located, the water falling hole in the grid does not occupy the whole grid space, and the volume of the grid determined as the type 1 is calculated through a Monte Carlo algorithm; type 2 is a mesh that does not include a drop hole portion.
Preferably, the determining the type of each grid includes the following steps:
traversing each row of grids, and judging the type of each grid, wherein:
each row of leftmost grids serves as a starting grid, and a first grid with the type 1 is searched from left to right: obtaining the boundary information of the upper, lower, left and right sides of the grid, traversing the convex hull point set, judging whether points fall in the grid, and if so, judging that the grid is 1; if not, judging as type 2;
when the row is traversed and no grid of the second type 1 is found, the grids after the first type 1 are all of the type 2; if the first type 1 grid is found and then the traversal to the right is continued, if the second type 1 grid is found, the grid in the middle of the two type 1 grids is judged to be type 0; if the second type 1 is not found until the line is traversed, the grids after the first type 1 are of type 2;
when the second type 1 is found, the lower grid is divided into type 1 or type 2 according to whether the convex hull points are contained, and the judgment of the grid type on the whole plane is completed through the traversal of the XY two dimensions.
Preferably, the calculation process of the total volume of the water falling hole comprises the following steps:
all convex hull points in the grid are numbered in sequence and are connected in sequence, and a closed polygon S is formed together with the left edge line of the grid with the similar head and tail points;
randomly sampling N points in the grid, assuming that X points finally fall into S, wherein the grid side length is L, and obtaining the area of S as follows according to the probability that all the points fall into S being equal to the proportion of S to the area of the grid:
adding the areas of the water holes in all grids and multiplying the areas by the grid side length L to obtain the volume of the water holes in one slice;
adding the volumes of the water falling holes in all the slices to obtain the volume of the whole water falling hole:
wherein i represents the number of layers of the whole space of the water falling hole, j represents the grid corresponding to the i layers, S ij The area of the jth grid in the ith slice is shown.
Preferably, the volume computing system based on the cloud data of the inner point of the water falling hole further comprises:
the data acquisition module is used for acquiring point cloud data by scanning the internal structure of the falling water hole;
the convex hull generation module is used for constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; constructing a three-dimensional space of the water falling hole by taking the maximum span as a reference; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
the volume calculation module is used for gridding the plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
Preferably, the computer device further comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
acquiring point cloud data by scanning the internal structure of the falling water hole;
constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
gridding a plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
Preferably, a computer readable storage medium is further included, the computer readable storage medium storing a computer program, which when executed by a processor, causes the processor to perform the steps of:
acquiring point cloud data by scanning the internal structure of the falling water hole;
constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
gridding a plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, accurate spatial information of the water falling hole is obtained in a three-dimensional point cloud data form, so that not only is the stereoscopic impression of the data improved, but also a solid foundation is laid for subsequent volume calculation. The acquired point cloud data can be subjected to slicing and gridding treatment, the form of the water falling hole can be captured more accurately, the convex hull of the two-dimensional point cloud is obtained by adopting the GiftWrapping algorithm, the bottleneck problem of the traditional method in the process of processing the convex hull and calculating the grid volume is effectively solved, the three-dimensional structure of the water falling hole can be comprehensively acquired through the point cloud data, and the accuracy of the water falling hole volume calculation result is greatly improved. By slicing and meshing the cloud data of the water falling hole, accurate calculation of the water falling hole with a complex three-dimensional structure is possible, the calculation accuracy is improved, and an accurate data basis is provided for subsequent volume calculation.
Drawings
FIG. 1 is a flow chart of a method for calculating a volume based on point cloud data in a water falling hole according to the present invention;
FIG. 2 is a view of the source point cloud data of the water falling hole obtained by the present invention;
FIG. 3 is a schematic view of two-dimensional point cloud data obtained by slicing according to the present invention;
FIG. 4 is a flowchart illustrating steps of a GiftWrapping algorithm according to an embodiment of the present invention;
FIG. 5 is a schematic view of the present invention traversing each grid;
FIG. 6 is a schematic illustration of an incomplete mesh obtained in accordance with the present invention;
FIG. 7 is a view of a hand-held lidar of the present invention versus a scanned drop hole.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 7 in the embodiments of the present invention. It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
As shown in fig. 1, an embodiment of the present invention provides a volume calculation method based on point cloud data inside a water falling hole, including the following steps:
s1: acquiring point cloud data by scanning the internal structure of the falling water hole;
s2: constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; constructing a three-dimensional space of the water falling hole by taking the maximum span as a reference; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
s3: gridding a plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice;
s4: and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
In the step S1, a handheld laser scanner with a scanning visual angle of 270 degrees multiplied by 360 degrees, a scanning speed of 300,000 points/second, a relative precision of 1.5-3cm and a maximum scanning distance of 100m is used for collecting field data of the water falling hole;
carrying out 360-degree surrounding scanning on each falling hole by using the handheld laser scanner to obtain three-dimensional laser point cloud data of each falling hole;
and (3) using GeoSLAM Connect and CloudCompare v2.12.4 software to finish data splicing and preprocessing work on the obtained three-dimensional laser point cloud data of each falling hole, taking one falling hole as a study object, and carrying out volume calculation.
In step S2, sorting the point cloud data on XYZ axes of three-dimensional space coordinates, and determining maximum and minimum values in three dimensions;
subtracting the minimum value from the maximum value to correspondingly obtain the space span in the three dimensions;
taking the largest span as the side length of the cube to construct a three-dimensional space of the water falling hole;
uniformly dividing the whole three-dimensional space into 20 parts according to the Z-axis direction, traversing all the point cloud data, and distributing each point cloud data to a corresponding section according to the Z-coordinate value of each point cloud data;
projecting the three-dimensional point cloud data onto an xy plane, deleting a z value in the (x, y, z) coordinates of each point, and reserving the rest (x, y) to form two-dimensional point cloud coordinates of the point cloud data;
and executing a GiftWrapping algorithm on the two-dimensional point cloud coordinates of the point cloud data to obtain a convex hull of the two-dimensional point cloud.
Specifically, a GiftWrapping algorithm is executed on two-dimensional point cloud coordinates of each point cloud data, including the following steps:
step1: inputting two-dimensional point cloud data in a slice, and selecting a point with the smallest y value in all point sets as a starting point P 0 ；
Step2: with P 0 For ray-counter-clockwise rotation of the apex, the first point appearing on the ray serves as the next starting point P 1 And P 0 A first edge of the convex hull;
step3: with P 1 For ray-counter-clockwise rotation of the apex, the first point appearing on the ray is taken as the nextStart point P 2 And P 1 Forming a second edge of the convex hull, and judging P 2 Whether or not to sum with P 0 Overlapping; if the two paths coincide, ending the GiftWrapping algorithm; if not, continuing to execute the following steps;
step4: step3 is performed a plurality of times until P i As a new starting point, judge P i Whether or not to sum with P 0 Overlapping; if not coincide, use P i For ray-counter-clockwise rotation of the apex, the first point appearing on the ray serves as the next starting point P i+1 And the last starting point P i The i+1th edge forming the convex hull, and the value of i+1 is given to i; if the two paths coincide, ending the GiftWrapping algorithm;
step5: multiple runs of Step4 up to P i+1 And P 0 The coincidence, giftWrapping algorithm ends.
In step S3, dividing the square where the two-dimensional point cloud convex hull is located into 20 multiplied by 20, and 400 grids with the same size in total;
dividing the divided grids into three types of type 0, type 1 and type 2, wherein:
the type 0 is that the middle part of the water falling hole can completely cover the grid, and the volume is calculated directly by using a column volume formula; the type 1 is a grid where the convex hull point of the water falling hole is located, the water falling hole in the grid does not occupy the whole grid space, and the volume of the grid determined as the type 1 is calculated through a Monte Carlo algorithm; type 2 is a mesh that does not include a drop hole portion.
Traversing each row of grids, and judging the type of each grid, wherein:
each row of leftmost grids serves as a starting grid, and a first grid with the type 1 is searched from left to right: obtaining the boundary information of the upper, lower, left and right sides of the grid, traversing the convex hull point set, judging whether points fall in the grid, and if so, judging that the grid is 1; if not, judging as type 2;
when the row is traversed and no grid of the second type 1 is found, the grids after the first type 1 are all of the type 2; if the first type 1 grid is found and then the traversal to the right is continued, if the second type 1 grid is found, the grid in the middle of the two type 1 grids is judged to be type 0; if the second type 1 is not found until the line is traversed, the grids after the first type 1 are of type 2;
when the second type 1 is found, the lower grid is divided into type 1 or type 2 according to whether the convex hull points are contained, and the judgment of the grid type on the whole plane is completed through the traversal of the XY two dimensions.
The calculation process of the total volume of the water falling hole comprises the following steps:
all convex hull points in the grid are numbered in sequence and are connected in sequence, and a closed polygon S is formed together with the left edge line of the grid with the similar head and tail points;
randomly sampling N points in the grid, assuming that X points finally fall into S, wherein the grid side length is L, and obtaining the area of S as follows according to the probability that all the points fall into S being equal to the proportion of S to the area of the grid:
adding the areas of the water holes in all grids and multiplying the areas by the grid side length L to obtain the volume of the water holes in one slice;
adding the volumes of the water falling holes in all the slices to obtain the volume of the whole water falling hole:
wherein i represents the number of layers of the whole space of the water falling hole, j represents the grid corresponding to the i layers, S ij The area of the jth grid in the ith slice is shown.
The application also provides a volume computing system based on the cloud data of the internal point of the falling water hole, which comprises:
the data acquisition module is used for acquiring point cloud data by scanning the internal structure of the falling water hole;
the convex hull generation module is used for constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; constructing a three-dimensional space of the water falling hole by taking the maximum span as a reference; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
the volume calculation module is used for gridding the plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
By the volume calculation method and the system based on the cloud data of the internal point of the falling hole, the soil erosion amount of the falling hole can be accurately determined, so that geological disaster risk assessment and scientific formulation of corresponding preventive measures are facilitated.
Examples
As shown in fig. 4, an embodiment of the present invention provides a volume calculation method based on point cloud data inside a water falling hole, including the following steps:
step (1): scanning the internal structure of the falling water hole by using a handheld laser scanner to obtain point cloud data and preprocessing the point cloud data;
step (2): slicing the point cloud data in a three-dimensional space;
step (3): projecting the point cloud of each slice onto a two-dimensional plane to obtain xy two-dimensional point cloud data;
step (4): executing a GiftWrapping algorithm on each two-dimensional point cloud to obtain a convex hull with a closed point cloud;
step (5): dividing a plane into 20 multiplied by 20 grids, and traversing the grids of each row in parallel;
step (6): judging the grid type to be 0, 1 or 2;
step (7): calculating the volume of the incomplete grid by using the convex hull points;
step (8): and calculating the total volume of the water falling hole.
Specifically, in the step (1), the laser scanner used in the application is a mobile handheld scanner model ZEB-HORIZON developed by GeoSLAM company (https:// gelam. Com /) based on Simultaneous Localization and Mapping (SLAM) algorithm, the scanning view angle of the device is 270 degrees multiplied by 360 degrees, the scanning speed is 300,000 points/second, the relative precision is 1.5-3cm, and the maximum scanning distance is 100m.
And carrying out 360-degree surrounding scanning on each falling hole by using a handheld laser scanner to obtain a three-dimensional laser point cloud of each falling hole, as shown in fig. 7.
In order to ensure the scanning precision and the data splicing precision of the whole area, 61 scanning operations are performed in a research area, control point measurement (X/Y error + -5 cm, Z error + -10 cm) is performed by using RTK, the original point cloud data of 20GB of the research area are obtained, the data splicing and preprocessing work is completed in GeoSLAM Connect and CloudCompare v2.12.4 software, one of the water falling holes is taken as a research object, and the volume calculation is performed.
The invention adopts the advanced laser radar technology to acquire the accurate spatial information of the water falling hole in the form of three-dimensional point cloud data, thereby not only improving the third dimension of the data, but also laying a solid foundation for the subsequent volume calculation.
In the step (2), the point cloud data are firstly ordered on XYZ axes, the maximum value and the minimum value in the three dimensions are determined, the space spans in the three dimensions are respectively obtained by subtracting the minimum value from the maximum value, and the largest span is taken as the side length of a cube to construct the three-dimensional space of the water falling hole. The whole three-dimensional space is uniformly divided into 20 parts according to the Z-axis direction, the point cloud data is traversed, and each point is distributed to a corresponding section according to the Z-coordinate value of the point, as shown in fig. 3.
In step (3), in order to facilitate the subsequent GiftWrapping algorithm, three-dimensional point cloud data is projected onto the xy plane, i.e., the Z value in the (x, y, Z) coordinates of each point is deleted, and the remaining (x, y) is retained, forming two-dimensional point cloud coordinates.
In the step (4), a GiftWrapping algorithm is adopted to obtain a convex hull of the two-dimensional point cloud.
The GiftWrapping algorithm, also called the Jarvis March algorithm, is an algorithm that searches for a convex hull of a point set. Convex hulls can be seen as a collection of vertices that can contain the smallest convex polygon of all point clouds, and these vertices also belong to the point cloud data. The basic idea of the application, which uses a two-dimensional GiftWrapping algorithm that is faster than the three-dimensional GiftWrapping algorithm, is to select a starting point from a set of points, starting from the current point, and selecting the next point that forms the smallest polar angle with the current point, and looping until returning to the starting point.
The following are specific steps of the GiftWrapping algorithm employed in the present application, as shown in fig. 4:
step (4.1): selecting a starting point 0 : one of the two-dimensional point sets is selected as a starting point, and the point with the smallest y value in the point set is generally selected to ensure that the starting point of the convex hull is at the bottom of the whole convex hull.
Step (4.2): traversing the point set, and comparing the point set with the pole rotation angle: one point is selected from the set of points such that the angle between the line connecting the current point to the point and the horizontal line is minimized. If there are a plurality of points having the same minimum included angle, the point farthest from the start point is taken as the next point.
Step (4.3): updating the current point: and (3) taking the point found in the step (4.2) as a new current point.
Step (4.4): repeating the step (4.2) and the step (4.3) until point 0 And the current point is formed again, the algorithm is ended, and the convex hull construction is completed.
Step (4.5): the points constituting the convex hull are stored in order.
In step (4) a two-dimensional point cloud convex hull is obtained, which is a closed envelope made up of edge points, as shown in fig. 5.
In the step (5), dividing the square where the convex hull is located into 20 multiplied by 20, and 400 grids with the same size in total; these grids are classified into three types, wherein:
the type 0 is that the middle part of the water falling hole can completely cover the grid, and the volume can be calculated directly by using a column volume formula; the type 1 is a grid where the convex hull points of the water falling holes are located, the water falling holes in the grids do not occupy the whole grid space, and the volume needs to be further calculated; type 2 is a mesh that does not include a drop hole portion.
In step (6), traversing each row of grids, and judging the type of each grid, wherein the specific steps are as follows:
each row of leftmost grids serves as a starting grid, and a first grid with the type 1 is searched from left to right: firstly, obtaining upper, lower, left and right boundary information of a grid, traversing a convex hull point set, judging whether points fall in the grid, and if so, judging that the grid is 1; if not, then it is determined to be type 2.
If the second type 1 grid is not found until the line is traversed, the grids after the first type 1 are of type 2; if the first type 1 grid is found then go on to the right. If a second type 1 grid is found, then the grid in between the two type 1 grids is determined to be type 0. If the second type 1 is not found until the line is traversed, then the grids after the first type 1 are type 2.
If a second type 1 is found, the underlying mesh is classified as either type 1 or type 2 depending on whether it contains convex hull points.
The specific steps of traversing each row of grids are shown in fig. 1, and the determination of the grid type on the whole plane can be completed through the traversal of XY two dimensions, and the determination result is shown in fig. 6, wherein the red grid represents type 0, the green represents type 1 and the white represents type 2.
According to the invention, a calculus idea is innovatively introduced, and the accurate calculation of the water falling hole with a complex three-dimensional structure is possible through the slicing and gridding treatment of the water falling hole point cloud data, so that the calculation accuracy is improved, and an accurate data basis is provided for the subsequent volume calculation.
Step (7) uses a Monte Carlo algorithm to finely calculate the volume of the mesh determined to be 1, and specifically comprises the following steps:
as shown in the enlarged small grid on the right side in FIG. 6, all convex hull points in the grid are numbered 0-6 in sequence and are connected in sequence, and a closed polygon S is formed together with the left edge line of the grid with the points 0 at the head and the tail and the point 6 being similar.
Randomly taking points in the grid, taking rays, judging the number of intersection points with S, and taking points P 1 、P 2 、P 3 And P 4 Wherein P is 1 And P 2 In S, the number of intersection points with S is 1 and 3 respectively; p (P) 3 And P 4 Outside S, the number of S-intersections is 2 and 4, respectively. By enumeration, we do not have difficulty in concluding that the number of points inside S and S intersection points is odd and the number of points outside S is even.
Randomly sampling N points in the grid, assuming that X points finally fall into S, wherein the grid side length is L, and obtaining the area of S as follows according to the probability that all the points fall into S being equal to the proportion of S to the area of the grid:
the step of calculating the total volume in step (8) is specifically as follows:
adding the areas of the water holes in all grids in the step (7), and multiplying the areas by the grid side length L to obtain the volume of the water holes in one slice;
adding the volumes of the water falling holes in all the slices to obtain the volume of the whole water falling hole:
in the whole calculation process, the invention innovatively adopts GiftWrapping and Monte Carlo algorithm to obtain the convex hull of the two-dimensional point cloud, and simultaneously introduces the Monte Carlo algorithm to finely calculate the volume of the grid judged to be 1. The organic combination of the two algorithms effectively solves the bottleneck problem of the traditional method in processing convex hulls and grid volume calculation, and improves the calculation accuracy.
The method utilizes a novel laser radar mapping technology (SLAM) to acquire point cloud data, uses related software to preprocess the point cloud data, slices and grids the point cloud data, calculates the volume through GiftWrapping and Monte Carlo algorithm, and has the following advantages compared with the traditional method:
1. the laser radar measurement accuracy is high: the handheld laser scanner can acquire centimeter-level high-precision three-dimensional point cloud data of the falling hole, and can more accurately capture the internal morphological characteristics of the falling hole.
2. The volume of the water falling hole is calculated accurately: the method abandons the method for calculating the volume by using the traditional two-dimensional DEM, directly adopts the three-dimensional laser point cloud data of the falling hole, and greatly improves the accuracy of the calculation result of the falling hole volume.
3. The data processing speed is high: the volume is calculated by utilizing the Monte Carlo algorithm, and the volume of the water falling hole can be calculated more efficiently by combining a numerical calculation method and a probability method. The method can obviously improve the calculation speed and efficiency especially when processing a large amount of point cloud data.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. All documents mentioned in this specification are incorporated by reference for the purpose of disclosing and describing the methodologies associated with the documents. In case of conflict with any incorporated document, the present specification will control.
Claims (10)
1. The volume calculation method based on the cloud data of the internal point of the water falling hole is characterized by comprising the following steps of:
acquiring point cloud data by scanning the internal structure of the falling water hole;
constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
gridding a plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
2. The method for calculating the volume based on the point cloud data in the water falling hole according to claim 1, wherein the step of acquiring the point cloud data comprises the following steps:
a handheld laser scanner with a scanning visual angle of 270 degrees multiplied by 360 degrees, a scanning speed of 300,000 points/second, a relative precision of 1.5-3cm and a maximum scanning distance of 100m is used for collecting field data of the water falling hole;
carrying out 360-degree surrounding scanning on each falling hole by using the handheld laser scanner to obtain three-dimensional laser point cloud data of each falling hole;
and (3) completing data splicing and preprocessing work on the three-dimensional laser point cloud data of each water falling hole, taking one water falling hole as a research object, and performing volume calculation.
3. The method for calculating the volume based on the point cloud data in the water falling hole according to claim 2, wherein the obtaining the two-dimensional point cloud data on the xy plane comprises the following steps:
sorting the point cloud data on XYZ axes of three-dimensional space coordinates, and determining the maximum and minimum values in three dimensions;
subtracting the minimum value from the maximum value to correspondingly obtain the space span in the three dimensions;
taking the largest span as the side length of the cube to construct a three-dimensional space of the water falling hole;
uniformly dividing the whole three-dimensional space into 20 parts according to the Z-axis direction, traversing all the point cloud data, and distributing each point cloud data to a corresponding section according to the Z-coordinate value of each point cloud data;
and projecting the three-dimensional point cloud data onto an xy plane, deleting the z value in the (x, y, z) coordinates of each point, and reserving the rest (x, y) to form two-dimensional point cloud coordinates of the point cloud data.
4. A method for calculating a volume based on point cloud data inside a water fall hole according to claim 3, wherein the step of performing a giftwraping algorithm on each two-dimensional point cloud data comprises the steps of:
step1: inputting two-dimensional point cloud data in a slice, and selecting a point with the smallest y value in all point sets as a starting point P 0 ；
Step2: with P 0 For ray-counter-clockwise rotation of the apex, the first point appearing on the ray serves as the next starting point P 1 And P 0 A first edge of the convex hull;
step3: with P 1 For ray-counter-clockwise rotation of the apex, the first point appearing on the ray serves as the next starting point P 2 And P 1 Forming a second edge of the convex hull, and judging P 2 Whether or not to sum with P 0 Overlapping; if the two paths coincide, ending the GiftWrapping algorithm; if not, continuing to execute the following steps;
step4: step3 is performed a plurality of times until P i As a new starting point, judge P i Whether or not to sum with P 0 Overlapping; if not coincide, use P i For ray-counter-clockwise rotation of the apex, the first point appearing on the ray serves as the next starting point P i+1 And the last starting point P i The i+1th edge forming the convex hull, and the value of i+1 is given to i; if the two paths coincide, ending the GiftWrapping algorithm;
step5: multiple runs of Step4 up to P i+1 And P 0 The coincidence, giftWrapping algorithm ends.
5. The method for computing the volume based on the cloud data of the inside of the water falling hole according to claim 4, wherein the classifying the mesh comprises the steps of:
dividing the square where the two-dimensional point cloud convex hull is positioned into 20 multiplied by 20, and 400 grids with the same size in total;
dividing the divided grids into three types of type 0, type 1 and type 2, wherein:
the type 0 is that the middle part of the water falling hole can completely cover the grid, and the volume is calculated directly by using a column volume formula; the type 1 is a grid where the convex hull point of the water falling hole is located, the water falling hole in the grid does not occupy the whole grid space, and the volume of the grid determined as the type 1 is calculated through a Monte Carlo algorithm; type 2 is a mesh that does not include a drop hole portion.
6. The method for calculating the volume based on the cloud data of the inside of the water falling hole according to claim 5, wherein the determining the type of each grid comprises the following steps:
traversing each row of grids, and judging the type of each grid, wherein:
each row of leftmost grids serves as a starting grid, and a first grid with the type 1 is searched from left to right: obtaining the boundary information of the upper, lower, left and right sides of the grid, traversing the convex hull point set, judging whether points fall in the grid, and if so, judging that the grid is 1; if not, judging as type 2;
when the row is traversed and no grid of the second type 1 is found, the grids after the first type 1 are all of the type 2; if the first type 1 grid is found and then the traversal to the right is continued, if the second type 1 grid is found, the grid in the middle of the two type 1 grids is judged to be type 0; if the second type 1 is not found until the line is traversed, the grids after the first type 1 are of type 2;
when the second type 1 is found, the lower grid is divided into type 1 or type 2 according to whether the convex hull points are contained, and the judgment of the grid type on the whole plane is completed through the traversal of the XY two dimensions.
7. The method for calculating the volume based on the cloud data of the inner point of the water falling hole according to claim 6, wherein the calculation process of the total volume of the water falling hole comprises the following steps:
all convex hull points in the grid are numbered in sequence and are connected in sequence, and a closed polygon S is formed together with the left edge line of the grid with the similar head and tail points;
randomly sampling N points in the grid, assuming that X points finally fall into S, wherein the grid side length is L, and obtaining the area of S as follows according to the probability that all the points fall into S being equal to the proportion of S to the area of the grid:
adding the areas of the water holes in all grids and multiplying the areas by the grid side length L to obtain the volume of the water holes in one slice;
adding the volumes of the water falling holes in all the slices to obtain the volume of the whole water falling hole:
wherein i represents the number of layers of the whole space of the water falling hole, j represents the grid corresponding to the i layers, S ij The area of the jth grid in the ith slice is shown.
8. A volume computing system based on point cloud data inside a drop hole, comprising:
the data acquisition module is used for acquiring point cloud data by scanning the internal structure of the falling water hole;
the convex hull generation module is used for constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; constructing a three-dimensional space of the water falling hole by taking the maximum span as a reference; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
the volume calculation module is used for gridding the plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
9. A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
acquiring point cloud data by scanning the internal structure of the falling water hole;
constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
gridding a plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
10. A computer readable storage medium, wherein the computer readable storage medium stores a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring point cloud data by scanning the internal structure of the falling water hole;
constructing a three-dimensional space of the water falling hole according to the acquired point cloud data; slicing the point cloud data in a three-dimensional space, and projecting the sliced point cloud data onto a two-dimensional plane to obtain two-dimensional point cloud data on an xy plane; executing a GiftWrapping algorithm on each two-dimensional point cloud data to obtain a two-dimensional point cloud convex hull;
gridding a plane where the two-dimensional point cloud convex hull is located, judging whether the plane is a complete grid, and classifying the grid if the plane is the complete grid; traversing grids of each row in parallel, calculating the volumes of grids of corresponding types according to different grid types by judging the type of each grid, and adding to obtain the volumes of all grids in the slice; and adding the volumes of the grids of all the slices to obtain the total volume of the water falling hole.
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