A method for extracting verticality and taper of single tree based on laser point cloud

Abstract

The present invention discloses a method for extracting the verticality and taper of a single tree based on laser point cloud, and relates to the field of forestry and three-dimensional point cloud data processing. Based on the dense transverse slicing of the three-dimensional point cloud data of a single tree, the method projects the sliced ​​point cloud data onto a two-dimensional plane, performs two-dimensional point cloud segmentation based on a density clustering method, and generates the best fitting circle of the distribution form of the point cloud data using a random sample consistency algorithm. The centers of the best fitting circles at different slices are extracted, and the skeleton of a single tree is constructed by connecting the centers, and the simple spline method is used to calculate the verticality of a single tree. The diameters at different positions of the trunk of a single tree are extracted, and the calculation of the taper of a single tree is realized by using STM. The technical solution of the present invention can solve the problem that it is difficult to perform efficient and accurate calculations of the verticality and taper of a single tree through point cloud data in the prior art.

Description

Technical Field

[0001] The invention relates to the field of forestry and three-dimensional point cloud data processing, and mainly relates to a method for extracting the verticality and taper of a single tree based on laser point cloud. Background Art

[0002] Forests are one of the most important ecosystems on Earth, playing an irreplaceable role in maintaining biodiversity, regulating climate, and conserving water resources. Accurate forest survey data is crucial for developing climate change mitigation strategies and implementing carbon trading mechanisms. Traditional forest survey methods are time-consuming and labor-intensive, and are difficult to meet the needs of obtaining forest structural parameters quickly and with high precision over a large area. This not only affects the precise management of forest resources, but also limits the accurate assessment of the forest carbon sink function.

[0003] The application of point cloud technology in the forestry field provides new means and perspectives for forest resource management and ecological monitoring. In particular, 3D laser scanning technology has the characteristics of high precision, high density and easy operation, and has become an important means of forest survey data collection. It can not only obtain high-precision and high-density understory point cloud data and provide detailed forest structure information, but also provide more reliable data support for carbon sink measurement through high-precision biomass estimation.

[0004] The acquired raw point cloud data usually needs to go through a series of preprocessing such as filtering, registration, and segmentation before it can be used for subsequent analysis. These steps have an important impact on the accuracy of the final results. At the scale of a single tree, point cloud data can be used to extract key parameters such as tree height, diameter at breast height, and crown width. Currently commonly used methods include cylinder fitting method, least squares circle fitting method, etc. At the forest stand scale, point cloud data can be used to estimate indicators such as average forest stand height, canopy density, and biomass. Common methods include canopy height model method, voxel method, etc. However, there is currently no method that can be used to extract collective parameters such as tree taper and verticality. Summary of the invention

[0005] The present invention aims to solve the problem that the existing single-tree parameter calculation method cannot calculate the verticality and taper of a single tree, and provides a single-tree verticality and taper extraction method based on laser point cloud. The method uses three-dimensional laser scanning data as input, extracts skeleton points through slicing processing and RANSAC algorithm fitting circle, and then calculates taper and verticality, which provides a new technical means for single-tree parameter calculation and improves the accuracy and efficiency of single-tree data measurement.

[0006] A method for extracting verticality and taper of a single tree based on laser point cloud, comprising:

[0007] S1. Acquire 3D laser point cloud data, perform single-tree segmentation processing on the 3D laser point cloud data, and obtain single-tree 3D laser point cloud data;

[0008] S2. Filtering the single tree 3D laser point cloud data to obtain single tree 3D filtered point cloud data;

[0009] S3. Slice the single-wood three-dimensional filtered point cloud data to obtain a set of single-wood slice data;

[0010] S4. performing point cloud clustering on each single wood slice data based on density to obtain a set of point cloud cluster data;

[0011] S5. Based on the random sample consensus algorithm RANSAC, a fitting circle is generated for each point cloud cluster data;

[0012] S6. Calculate the center coordinates and radius of each fitting circle, select the best fitting circle, and construct a single wood skeleton;

[0013] S7. Based on the single-tree skeleton, the simple spline method is used to calculate the verticality of the single tree, and the segmented taper method STM is used to calculate the taper of the single tree.

[0014] In S2, the three-dimensional filtered point cloud data of a single tree is projected onto the yoz plane, and the direction D of the trunk of the single tree on the yoz plane is obtained by calculating the difference between the y-axis coordinate and the z-axis coordinate:

[0015] ;

[0016] In the formula, and are the maximum and minimum values ​​of the single tree filter data in the y direction coordinate, and They are the maximum and minimum values ​​of the single tree filtering data in the z direction coordinate.

[0017] In S3, the single tree filtered point cloud data is densely sliced ​​along the vertical direction of the single tree center line, with a slice thickness of 5 cm, to obtain a set of single tree slice data, and the center point of each single tree slice data is extracted to obtain the initial center line.

[0018] In S5, based on the random sample consensus algorithm RANSAC, the data distribution shape is fitted to the initial center line of the single tree, and the noise points in the point cloud cluster data are removed. The center line after fitting is:

[0019] ;

[0020] In the formula, , , They are the x-axis coordinate, y-axis coordinate, and z-axis coordinate of the observation point of the 3D laser point cloud data. are the direction vectors of the trunks of individual trees The corresponding x-axis coordinates, y-axis coordinates, and z-axis coordinates, is the coordinates of the center point of the single wood slice data;

[0021] A 3D point cloud data outside the fitted center line The expression projected onto the line is:

[0022] ;

[0023] In the formula, is the projected coordinate, is the projection coefficient, and the calculation formula is:

[0024] ;

[0025] Three points are selected from each point cloud cluster data as the minimum subset, and RANSAC is used to calculate the initial fitting circle parameters based on the minimum subset.

[0026] In S5, the number of iterations, distance threshold, and proportion of inliers of the RANSAC calculation model are adjusted. , generate a fitting circle for the point cloud cluster data, bring all the data points in each point cloud cluster data into the generated fitting circle, calculate the distance from each data point to the circumference, the points with a distance less than the distance threshold are inner points, and the points with a distance greater than the distance threshold are outer points. The number of inner points accounts for for:

[0027] ;

[0028] In the formula, is the number of interior points, is the number of external points;

[0029] The model uses each calculation data points, the probability that at least one of the selected points is an outlier is , in progress After iterations, the expected probability of the model is:

[0030] ;

[0031] The number of iterations can be obtained by the expected probability of the model for:

[0032] ;

[0033] The distance threshold is set at 3-5cm, and the minimum number of inliers is set at Set at 50%.

[0034] In S6, the model parameters of each fitting circle are calculated, including the coordinates of the center and the diameter. The fitting circle with the largest number of inner points, i.e., the largest radius, is selected as the best fitting circle. The point cloud data contained in the best fitting circle is the single tree skeleton data in the single tree three-dimensional filtered point cloud data. The RANSAC calculation is repeated until The best fitting circle is:

[0035] ;

[0036] In the formula, is the radius of the best fitting circle, is the center of the best fitting circle, that is, the projection of the skeleton point of the single wooden skeleton on the xoy plane.

[0037] The skeleton points of a single wood skeleton are fitted using the least squares method to construct a single wood skeleton. :

[0038] ;

[0039] In the formula, , , They are the x-axis coordinate, y-axis coordinate, and z-axis coordinate of the skeleton point respectively. For the sum operation, , , is the summation coefficient;

[0040] ;

[0041] ;

[0042] ;

[0043] The straight line of a single tree trunk is fitted by summing the coefficients, and the equation of the straight line is:

[0044] ;

[0045] They are the x-axis coordinate, y-axis coordinate and z-axis coordinate corresponding to the single-wood trunk straight line.

[0046] Convert a single tree trunk straight line into vector form :

[0047] ;

[0048] The direction vector of the single-tree trunk straight line is,

[0049] ;

[0050] ;

[0051] is the direction vector along the y-axis, is the direction vector of the x-axis, the angle between the single wood trunk and the xoy plane The tangent value of is:

[0052] ;

[0053] The angle between the single wood trunk and the xoy plane for:

[0054] ;

[0055] The angle between the single wood trunk and the xoy plane Indicates the verticality of a single tree.

[0056] In S7, the taper of a single log is calculated using the segmented taper method (STM). :

[0057] ;

[0058] In the formula, Indicates the trunk radius of a single tree; It is a dimensionless parameter, which indicates the relative cutting amount, that is, the ratio of the cutting amount of a single tree to the trunk diameter, and its value range is between 0 and 1; , , , It represents the model coefficient, which determines the linear relationship between the amount of single wood cut and the relative amount of cut; , Represents the segmentation threshold, which determines the relative amount of cutting The segment interval of , It is an indicator function that controls the segmentation condition. It takes the value 1 when the condition is met, otherwise it takes the value 0.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The present invention uses three-dimensional laser point cloud data as input, obtains point cloud cluster data by performing single-tree segmentation, point cloud filtering, density-based horizontal dense slicing and point cloud clustering on the point cloud data, and generates a fitting circle for the point cloud cluster data based on the RANSAC random sample consistency algorithm. By calculating the center coordinates and radius of the fitting circle, a single tree skeleton is constructed, the three-dimensional laser point cloud data is projected onto a two-dimensional plane, and the verticality and taper of a single tree are calculated, providing a new management method for forestry and solving the problem that the existing technology cannot calculate the verticality and taper of a single tree. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flowchart of a method for extracting verticality and taper of a single tree based on laser point cloud provided by an embodiment of the present invention;

[0062] Figure 2 A point cloud data diagram of a single tree after segmentation according to a method for extracting verticality and taper of a single tree based on laser point cloud provided in an embodiment of the present invention;

[0063] Figure 3 A single tree three-dimensional filtered point cloud data diagram of a single tree verticality and taper extraction method based on laser point cloud provided in an embodiment of the present invention;

[0064] Figure 4 A schematic diagram of a fitting circle containing a single tree trunk and branches provided in an embodiment of the present invention;

[0065] Figure 5 A schematic diagram of constructing a single wood skeleton based on a best-fit circle provided by an embodiment of the present invention:

[0066] Figure 6 A schematic diagram of the skeleton points at the position of a tree trunk provided by an embodiment of the present invention;

[0067] Figure 7 A schematic diagram of calculating the verticality of a single tree based on point cloud data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] 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, 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.

[0069] A method for extracting verticality and taper of a single tree based on laser point cloud, comprising:

[0070] S1. Acquire 3D laser point cloud data, perform single-tree segmentation processing on the 3D laser point cloud data, and obtain single-tree 3D laser point cloud data;

[0071] S2. Filtering the single tree 3D laser point cloud data to obtain single tree 3D filtered point cloud data;

[0072] S3. Slice the single-wood three-dimensional filtered point cloud data to obtain a set of single-wood slice data;

[0073] S4. performing point cloud clustering on each single wood slice data based on density to obtain a set of point cloud cluster data;

[0074] S5. Based on the random sample consensus algorithm RANSAC, a fitting circle is generated for each point cloud cluster data;

[0075] S6. Calculate the center coordinates and radius of each fitting circle, select the best fitting circle, and construct a single wood skeleton;

[0076] S7. Based on the single-tree skeleton, the simple spline method is used to calculate the verticality of the single tree, and the segmented taper method STM is used to calculate the taper of the single tree.

[0077] In S2, the three-dimensional filtered point cloud data of a single tree is projected onto the yoz plane, and the direction D of the trunk of the single tree on the yoz plane is obtained by calculating the difference between the y-axis coordinate and the z-axis coordinate:

[0078] ;

[0079] In the formula, and are the maximum and minimum values ​​of the single tree filter data in the y direction coordinate, and They are the maximum and minimum values ​​of the single tree filtering data in the z direction coordinate.

[0080] In S3, the single tree filtered point cloud data is densely sliced ​​along the vertical direction of the single tree center line, with a slice thickness of 5 cm, to obtain a set of single tree slice data, and the center point of each single tree slice data is extracted to obtain the initial center line.

[0081] In S5, based on the random sample consensus algorithm RANSAC, the data distribution shape is fitted to the initial center line of the single tree, and the noise points in the point cloud cluster data are removed. The center line after fitting is:

[0082] ;

[0083] In the formula, , , are the x-axis coordinate, y-axis coordinate, and z-axis coordinate of the observation point of the three-dimensional laser point cloud data, respectively. , are the direction vectors of the trunks of individual trees The corresponding x-axis coordinates, y-axis coordinates, and z-axis coordinates, is the coordinates of the center point of the single wood slice data;

[0084] A 3D point cloud data outside the fitted center line The expression projected onto the line is:

[0085] ;

[0086] In the formula, is the projected coordinate, is the projection coefficient, and the calculation formula is:

[0087] ;

[0088] Three points are selected from each point cloud cluster data as the minimum subset, and RANSAC is used to calculate the initial fitting circle parameters based on the minimum subset.

[0089] In S5, the number of iterations, distance threshold, and proportion of inliers of the RANSAC calculation model are adjusted. , generate a fitting circle for the point cloud cluster data, bring all the data points in each point cloud cluster data into the generated fitting circle, calculate the distance from each data point to the circumference, the points with a distance less than the distance threshold are inner points, and the points with a distance greater than the distance threshold are outer points. The number of inner points accounts for for:

[0090] ;

[0091] In the formula, is the number of interior points, is the number of external points;

[0092] The model uses each calculation data points, the probability that at least one of the selected points is an outlier is , in progress After iterations, the expected probability of the model is:

[0093] ;

[0094] The number of iterations can be obtained by the expected probability of the model for:

[0095] ;

[0096] The distance threshold is set at 3-5cm, and the minimum number of inliers is set at Set at 50%.

[0097] In S6, the model parameters of each fitting circle are calculated, including the coordinates of the center and the diameter. The fitting circle with the largest number of inner points, i.e., the largest radius, is selected as the best fitting circle. The point cloud data contained in the best fitting circle is the single tree skeleton data in the single tree three-dimensional filtered point cloud data. The RANSAC calculation is repeated until The best fitting circle is:

[0098] ;

[0099] In the formula, is the radius of the best fitting circle, is the center of the best fitting circle, that is, the projection of the skeleton point of the single wooden skeleton on the xoy plane.

[0100] The skeleton points of a single wood skeleton are fitted using the least squares method to construct a single wood skeleton. :

[0101] ;

[0102] In the formula, , , They are the x-axis coordinate, y-axis coordinate, and z-axis coordinate of the skeleton point respectively. For the sum operation, , , is the summation coefficient;

[0103] ;

[0104] ;

[0105] ;

[0106] The straight line of a single tree trunk is fitted by summing the coefficients, and the equation of the straight line is:

[0107] ;

[0108] , , They are the x-axis coordinate, y-axis coordinate and z-axis coordinate corresponding to the single-wood trunk straight line.

[0109] Convert a single tree trunk straight line into vector form :

[0110] ;

[0111] The direction vector of the straight line of a single tree trunk is:

[0112] ;

[0113] ;

[0114] is the direction vector along the y-axis, is the direction vector of the x-axis, the angle between the single wood trunk and the xoy plane The tangent value of is:

[0115] ;

[0116] The angle between the single wood trunk and the xoy plane for:

[0117] ;

[0118] The angle between the single wood trunk and the xoy plane Indicates the verticality of a single tree.

[0119] In S7, the taper of a single log is calculated using the segmented taper method (STM). :

[0120] ;

[0121] In the formula, Indicates the trunk radius of a single tree; It is a dimensionless parameter, which indicates the relative cutting amount, that is, the ratio of the cutting amount of a single tree to the trunk diameter, and its value range is between 0 and 1; , , , It represents the model coefficient, which determines the linear relationship between the amount of single wood cut and the relative amount of cut; , Represents the segmentation threshold, which determines the relative amount of cutting The segment interval of , It is an indicator function that controls the segmentation condition. It takes the value 1 when the condition is met, otherwise it takes the value 0.

[0122] The embodiment of the present invention constructs a single tree skeleton through three-dimensional laser point cloud data, calculates the verticality and taper of a single tree, solves the problem that the prior art cannot calculate the verticality and taper of a single tree, and provides a new technical means for the development of forestry. The flowchart of the present invention is as follows Figure 1 As shown in the figure, this method uses three-dimensional laser point cloud data as input, and obtains point cloud cluster data by performing data preprocessing such as single tree segmentation, point cloud filtering, point cloud slicing and point cloud clustering on the three-dimensional laser point cloud data. The point cloud cluster data is fitted with circles using the random sample consistency algorithm RANSAC, and the center coordinates and radius of the generated fitting circle are extracted. The fitting circle with the largest radius is taken as the best fitting circle, and the skeleton of a single tree is constructed by connecting the centers of the best fitting circles. The verticality of a single tree is calculated based on the skeleton of a single tree using the simple spline method, and the taper of a single tree tip is calculated using the segmented taper method, providing a new technical means for the development of forestry. The three-dimensional laser point cloud data after single tree segmentation is shown in the figure. Figure 2As shown, it can basically reflect the morphology, branches and other information of a single tree, but there are many types of data points and noise points. The intensity of the data point in the figure is the laser reflection intensity corresponding to the single tree data point. Due to different surface characteristics (such as roughness, color, etc.), the reflection intensity of the single tree trunk is relatively strong, and the reflection intensity of the single tree branch is relatively weak. By analyzing the intensity information of the data points, we can further understand the growth status and health status of the trees and their interaction with other environmental factors. Figure 3 The three-dimensional laser point cloud data after point cloud filtering was used to remove noise points in the three-dimensional laser point cloud data, and a preliminary fitting was performed on the data distribution shape of a single tree, highlighting the single tree branch characteristics of the three-dimensional laser point cloud data.

[0123] Figure 4 The schematic diagram of the fitting circle containing the trunk and branches of a single tree provided in the embodiment of the present invention is based on the point cloud cluster data of the current slice, and the fitting circle of the single tree trunk and branches is generated according to the intensity of the data points and the distribution of the data points. It is common sense that a single tree trunk is usually thicker than a branch, and there is only one single tree trunk, so the fitting circle with the largest radius is selected as the best fitting circle to construct the single tree skeleton.

[0124] Figure 5 This is a schematic diagram of constructing a single wood skeleton based on the best fitting circle. The center of the best fitting circle is the skeleton point of the single wood skeleton. The single wood skeleton is constructed by connecting the centers of the best fitting circles. The line connecting the centers of the best fitting circles is the center line of the single wood skeleton, and the circumference is the periphery of the single wood skeleton where the current slice is located.

[0125] Figure 6 A schematic diagram of the skeleton points at the tree trunk position provided by an embodiment of the present invention, Figure 7 A schematic diagram of calculating the verticality of a single tree based on point cloud data provided by an embodiment of the present invention, wherein the center line of the single tree is fitted by skeleton points, and the angle between the center line of the single tree and the x-axis is the verticality of the single tree.

[0126] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for extracting the verticality and taper of a single tree based on laser point cloud, characterized in that: include: S1. Acquire 3D laser point cloud data, perform single-tree segmentation processing on the 3D laser point cloud data, and obtain single-tree 3D laser point cloud data; S2. Filtering the single tree 3D laser point cloud data to obtain single tree 3D filtered point cloud data; S3. Slice the single-wood three-dimensional filtered point cloud data to obtain a set of single-wood slice data; S4. performing point cloud clustering on each single wood slice data based on density to obtain a set of point cloud cluster data; S5. Based on the random sample consensus algorithm RANSAC, a fitting circle is generated for each point cloud cluster data; S6. Calculate the center coordinates and radius of each fitting circle, select the best fitting circle, and construct a single wood skeleton; S7. Based on the single tree skeleton, the simple spline method is used to calculate the verticality of the single tree, and the segmented taper method STM is used to calculate the taper of the single tree; In S5, based on the random sample consensus algorithm RANSAC, the data distribution shape is fitted to the initial center line of the single tree, and the noise points in the point cloud cluster data are removed. The center line after fitting is: ； In the formula, , , They are the x-axis coordinate, y-axis coordinate, and z-axis coordinate of the observation point of the 3D laser point cloud data. are the direction vectors of the trunks of individual trees The corresponding x-axis coordinates, y-axis coordinates, and z-axis coordinates, is the coordinates of the center point of the single wood slice data; A 3D point cloud data outside the fitted center line The expression projected onto the line is: ； In the formula, is the projected coordinate, is the projection coefficient, and the calculation formula is: ； Select 3 points from each point cloud cluster data as the minimum subset, and use RANSAC to calculate the initial fitting circle parameters based on the minimum subset; In S6, the model parameters of each fitting circle are calculated, including the coordinates of the center and the diameter. The fitting circle with the largest number of inner points, i.e., the largest radius, is selected as the best fitting circle. The point cloud data contained in the best fitting circle is the single tree skeleton data in the single tree three-dimensional filtered point cloud data. The RANSAC calculation is repeated until The best fitting circle is: ； In the formula, is the radius of the best fitting circle, is the center of the best fitting circle, i.e., the projection of the skeleton point of the single wood skeleton on the xoy plane; The skeleton points of a single wood skeleton are fitted using the least squares method to construct a single wood skeleton. : ； In the formula, , , They are the x-axis coordinate, y-axis coordinate, and z-axis coordinate of the skeleton point respectively. For the sum operation, , , is the summation coefficient; ； ； ； The straight line of a single tree trunk is fitted by summing the coefficients, and the equation of the straight line is: ； , , are the x-axis coordinate, y-axis coordinate and z-axis coordinate corresponding to the straight line of the single tree trunk respectively; Convert a single tree trunk straight line into vector form : ； The direction vector of the straight line of a single tree trunk is: ； ； is the direction vector along the y-axis, is the direction vector of the x-axis, the angle between the single wood trunk and the xoy plane The tangent value of is: ； The angle between the single wood trunk and the xoy plane for: ； The angle between the single wood trunk and the xoy plane is the verticality of a single tree.

2. The method for extracting verticality and taper of a single tree based on laser point cloud according to claim 1, characterized in that: In S2, the three-dimensional filtered point cloud data of a single tree is projected onto the yoz plane, and the direction D of the trunk of the single tree on the yoz plane is obtained by calculating the difference between the y-axis coordinate and the z-axis coordinate: ； In the formula, and are the maximum and minimum values ​​of the single tree filter data in the y direction coordinate, and They are the maximum and minimum values ​​of the single tree filtering data in the z direction coordinate.

3. The method for extracting the verticality and taper of a single tree based on laser point cloud according to claim 2, characterized in that: In S3, the single tree filtered point cloud data is densely sliced ​​along the vertical direction of the single tree center line, with a slice thickness of 5 cm, to obtain a set of single tree slice data, and the center point of each single tree slice data is extracted to obtain the initial center line.

4. The method for extracting verticality and taper of a single tree based on laser point cloud according to claim 3, characterized in that: In S5, the number of iterations, distance threshold, and proportion of inliers of the RANSAC calculation model are adjusted. , generate a fitting circle for the point cloud cluster data, bring all the data points in each point cloud cluster data into the generated fitting circle, calculate the distance from each data point to the circumference, the points with a distance less than the distance threshold are inner points, and the points with a distance greater than the distance threshold are outer points. The number of inner points accounts for for: ； In the formula, is the number of interior points, is the number of external points; The model uses each calculation data points, the probability that at least one of the selected points is an outlier is , in progress After iterations, the expected probability of the model is: ； The number of iterations can be obtained by the expected probability of the model for: ； The distance threshold is set at 3-5cm, and the minimum number of inliers is set at Set at 50%.

5. The method for extracting verticality and taper of a single tree based on laser point cloud according to claim 4, characterized in that: In S7, the taper of a single log is calculated using the segmented taper method (STM). : ； In the formula, Indicates the trunk radius of a single tree; It is a dimensionless parameter, which indicates the relative cutting amount, that is, the ratio of the cutting amount of a single tree to the trunk diameter, and its value range is between 0 and 1; , , , It represents the model coefficient, which determines the linear relationship between the amount of single wood cut and the relative amount of cut; , Represents the segmentation threshold, which determines the relative amount of cutting The segment interval of , It is an indicator function that controls the segmentation condition. It takes the value 1 when the condition is met, otherwise it takes the value 0.

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