A method, device, medium and product for reconstructing the breast height cross-section of a tree trunk and determining the diameter at breast height

Abstract

The present application discloses a method, device, medium and product for trunk breast height cross-section reconstruction and breast diameter determination, relating to the field of point cloud data processing. The method includes: normalizing the point cloud height information; extracting trunk slice point cloud based on the normalized point cloud by using the height threshold method; performing individual segmentation on the trunk slice point cloud by using the connected component analysis method to obtain individual trunk slice point cloud; extracting the point with the maximum local point cloud density and the trunk center point based on the individual trunk slice point cloud to construct a reference direction, and performing polar coordinate transformation on the trunk slice point cloud to obtain polar coordinate trunk slice point cloud; using the Kalman filtering algorithm to perform point cloud reconstruction based on the polar coordinate trunk slice point cloud and performing Cartesian coordinate transformation to obtain the breast height cross-section reconstruction result; constructing a convex hull for the breast height cross-section reconstruction result and determining the equivalent diameter to obtain the breast diameter. The present application can improve the accuracy and efficiency of trunk breast height cross-section reconstruction and breast diameter determination, and at the same time improve the reliability of trunk breast height cross-section reconstruction.

Description

Technical Field

[0001] The present application relates to the field of point cloud data processing, and in particular, to a method, device, medium and product for reconstructing a tree trunk breast height cross-section and determining the breast diameter. Background Art

[0002] The tree trunk breast height cross-section is an important indicator for evaluating the tree condition. In forestry surveys, technicians usually use a diameter tape or caliper to measure the breast diameter (i.e., the breast height diameter) to quantitatively describe the tree trunk breast height cross-section. Although this method is accurate, it is time-consuming and costly in terms of human resources, which restricts the monitoring and management efficiency of forest resources. Therefore, there is an urgent need to develop an efficient and accurate tree trunk cross-section characterization technology to improve the efficiency and accuracy of forest resource management. Handheld lidar, as a revolutionary mobile three-dimensional digitization technology, has received extensive attention due to its high efficiency brought by portability and flexibility. However, due to the lightweight design of handheld lidar, its accuracy is limited, resulting in a point cloud noise thickness of about 3 cm to 8 cm on the tree trunk surface, thereby reducing the accuracy of cross-section characterization. Based on this, existing algorithms mainly use the regular primitive fitting method to reconstruct the tree trunk breast height cross-section to eliminate the influence of point cloud noise. However, such algorithms are only applicable to tree species with regular trunk shapes and are sensitive to point cloud density, and the reliability of reconstructing the tree trunk breast height cross-section with uneven point cloud density distribution is insufficient. Summary of the Invention

[0003] The purpose of the present application is to provide a method, device, medium and product for reconstructing a tree trunk breast height cross-section and determining the breast diameter, which can improve the accuracy and efficiency of tree trunk breast height cross-section reconstruction and breast diameter determination, and at the same time improve the reliability of tree trunk breast height cross-section reconstruction.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In a first aspect, the present application provides a method for reconstructing a tree trunk breast height cross-section and determining the breast diameter, including:

[0006] Obtaining the height information of the point cloud data, and performing normalization processing on the height information to obtain normalized point cloud;

[0007] Extracting the tree trunk slice point cloud based on the normalized point cloud by using the height threshold method;

[0008] Performing individual segmentation on the tree trunk slice point cloud by using the connected component analysis method to obtain individual tree trunk slice point clouds;

[0009] Extracting the local point cloud density maximum point and the tree trunk center point based on the individual tree trunk slice point clouds;

[0010] Construct a reference direction based on the local point cloud density maximum point and the tree trunk center point, and perform point cloud polar coordinate transformation on the tree trunk slice point cloud based on the reference direction to obtain a polar coordinate tree trunk slice point cloud;

[0011] Use the Kalman filtering algorithm to perform point cloud reconstruction based on the polar coordinate tree trunk slice point cloud and perform Cartesian coordinate transformation to obtain a breast height cross-section reconstruction result;

[0012] Construct a convex hull for the breast height cross-section reconstruction result and determine an equivalent diameter based on the convex hull to obtain the breast diameter.

[0013] Optionally, the point cloud data is collected using a handheld lidar.

[0014] Optionally, using the Kalman filtering algorithm to perform point cloud reconstruction based on the polar coordinate tree trunk slice point cloud and perform Cartesian coordinate transformation to obtain a breast height cross-section reconstruction result, including:

[0015] Obtain the position of each point in the polar coordinate tree trunk slice point cloud;

[0016] Arrange all the points in the polar coordinate tree trunk slice point cloud in ascending order according to the abscissa value of each point position to obtain a point cloud sequence;

[0017] Use the Kalman filtering algorithm to reconstruct each point in the point cloud sequence in turn and perform Cartesian coordinate transformation to obtain the breast height cross-section reconstruction result.

[0018] Optionally, the process of reconstructing each point in the point cloud sequence using the Kalman filtering algorithm includes:

[0019] Initialize the predicted value and prediction uncertainty based on the distance between the starting point in the point cloud sequence and the tree trunk center point;

[0020] Determine the estimated value of the current state based on the predicted value and the measured value, and determine the state estimation uncertainty based on the prediction uncertainty;

[0021] Update the predicted value using the estimated value, and update the prediction uncertainty using the state estimation uncertainty and the process uncertainty.

[0022] Optionally, the initialized predicted value and prediction uncertainty are expressed as:

[0023]

[0024] In the formula, r p represents the predicted value, u p represents the prediction uncertainty, P represents the initial uncertainty, Q represents the process uncertainty, (x ir ,yir ) represents a point in the point cloud sequence, (x o , y o ) represents the center point of the tree trunk.

[0025] Optionally, the estimated value and the state estimation uncertainty are expressed as:

[0026]

[0027] In the formula, r e represents the estimated value, r p represents the predicted value, u e represents the state estimation uncertainty, u p represents the prediction uncertainty, K represents the Kalman gain, and r represents the measured value.

[0028] Optionally, the updated predicted value and the prediction uncertainty are expressed as:

[0029]

[0030] In the formula, r e represents the estimated value, r p represents the predicted value, u e represents the state estimation uncertainty, u p represents the prediction uncertainty, and Q represents the process uncertainty.

[0031] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the trunk breast height cross-section reconstruction and breast diameter determination method described in any one of the above.

[0032] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the trunk breast height cross-section reconstruction and breast diameter determination method described in any one of the above are implemented.

[0033] In a fourth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the trunk breast height cross-section reconstruction and breast diameter determination method described in any one of the above are implemented.

[0034] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0035] The present application provides a method, device, medium and product for trunk breast height cross-section reconstruction and breast diameter determination. By applying the Kalman filtering algorithm to the trunk breast height cross-section reconstruction, accurate and stable expression of the trunk breast height cross-section can be achieved. Moreover, the point with the maximum local point cloud density is close to the real trunk surface. Using the point with the maximum local point cloud density to determine the reference direction can provide an accurate initial state estimate for the Kalman filtering algorithm, which can improve the convergence speed while enhancing the accuracy and reliability of the trunk breast height cross-section reconstruction. Furthermore, based on the reconstruction result of the breast height cross-section, the breast diameter can be calculated, improving the accuracy and efficiency of breast diameter determination. In addition, the Kalman filtering algorithm has no restrictions on the trunk shape, enabling the present application to perform well in data of irregular trunks. Therefore, the method provided by the present application can not only effectively reconstruct the breast height cross-section of trunks with any shape, but also provide fast and stable reconstruction results for data with uneven point cloud density, having strong practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0037] Figure 1 It is a schematic flowchart of a method for trunk breast height cross-section reconstruction and breast diameter determination provided by an embodiment of the present application;

[0038] Figure 2 It is an effect diagram of key steps provided by an embodiment of the present application;

[0039] Figure 3 It is a reconstruction result diagram of the trunk breast height cross-section of sample 1 provided by an embodiment of the present application;

[0040] Figure 4 It is a reconstruction result diagram of the trunk breast height cross-section of sample 2 provided by an embodiment of the present application;

[0041] Figure 5 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0043] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] In an exemplary embodiment, the present application provides a method for trunk breast height cross-section reconstruction and breast diameter determination. When executed by a computer device, this method can be executed independently by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, this method is described by taking its application to a server as an example. As Figure 1 shown, the method for trunk breast height cross-section reconstruction and breast diameter determination provided by the present application includes:

[0045] Step 100: Obtain the height information of the point cloud data and perform normalization processing on the height information to obtain normalized point cloud.

[0046] For example, after collecting point cloud data using a handheld lidar, the cloth simulation filtering algorithm is used to extract ground points from the point cloud data. The ground points are rasterized to generate a terrain model. Calculate the height difference between the handheld lidar point cloud and the terrain model, and use the height difference as the elevation value of the point cloud to achieve the normalization of the height information of the point cloud data.

[0047] Step 101: Extract the trunk slice point cloud based on the normalized point cloud using the height threshold method.

[0048] For example, the trunk breast height is defined as 1.3 m. Therefore, extract the point cloud with a height between 1.25 m and 1.35 m from the normalized point cloud, and project the extracted slice point cloud onto the X-Y horizontal plane to obtain the trunk slice point cloud.

[0049] Step 102: Perform individual segmentation on the trunk slice point cloud using the connected component analysis method to obtain individual trunk slice point clouds. Among them, considering that different individual trunks in the trunk slice point cloud are separated in the X-Y space, the connected component analysis method is used to segment the trunk slice point cloud in this step to obtain individual trunk slice point clouds.

[0050] Step 103: Extract the point with the maximum local point cloud density and the trunk center point based on the individual trunk slice point cloud.

[0051] In this step, the purpose of the point with the maximum local point cloud density is to determine the starting point of the Kalman filtering algorithm. The convergence rate and overall filtering accuracy of the Kalman filtering algorithm are related to the estimation of the initial state. Therefore, the starting point should be as close as possible to the real tree trunk surface. Considering that the point cloud density is higher near the real tree trunk surface, the point with the maximum local point cloud density is selected as the starting point of the Kalman filtering algorithm. Among them, the local point cloud density of each point can be obtained by counting the number of points within a circular area with a radius of 0.2m. The tree trunk center point is obtained by calculating the mean value of the x and y ranges of the point cloud of the individual tree trunk slices.

[0052] Step 104: Construct a reference direction based on the point with the maximum local point cloud density and the tree trunk center point, and perform point cloud polar coordinate transformation on the tree trunk slice point cloud based on the reference direction to obtain the polar coordinate tree trunk slice point cloud.

[0053] Step 105: Use the Kalman filtering algorithm to perform point cloud reconstruction based on the polar coordinate tree trunk slice point cloud, and perform Cartesian coordinate transformation to obtain the reconstructed result of the breast height cross-section.

[0054] Step 106: Construct a convex hull based on the reconstructed result of the breast height cross-section, and determine the equivalent diameter based on the convex hull to obtain the breast diameter.

[0055] In another exemplary embodiment of the present application, the Kalman filtering algorithm is used to extract effective information from the point cloud of the tree trunk breast height cross-section represented in polar coordinates. The Kalman filtering algorithm utilizes a dynamic model and measurement data to optimally estimate the state in the presence of noisy data. The Kalman filtering algorithm includes two stages, namely the prediction stage and the update stage. In the prediction stage, the Kalman filtering algorithm predicts the current state based on the previous state estimation information. In the update stage, the Kalman filtering algorithm uses the measurement data to update the prediction to achieve a more accurate state estimation. By estimating the error variance to balance the uncertainty between prediction and measurement, the Kalman filtering algorithm can effectively extract the effective signal under different noise conditions. Based on this, the implementation process of step 105 in the present application may include:

[0056] (1) Obtain the position of each point in the polar coordinate tree trunk slice point cloud.

[0057] (2) Sort all the points in the polar coordinate tree trunk slice point cloud in ascending order according to the abscissa value of each point position to obtain a point cloud sequence.

[0058] (3) Use the Kalman filtering algorithm to reconstruct each point in the point cloud sequence in turn, and perform Cartesian coordinate transformation to obtain the reconstructed result of the breast height cross-section, as follows:

[0059] (3-1) Initialize the prediction value and prediction uncertainty based on the distance between the points in the point cloud sequence and the tree trunk center point. The initialized prediction value and prediction uncertainty are expressed as:

[0060]

[0061] Wherein, r p represents the predicted value, u p represents the prediction uncertainty, P represents the initial uncertainty, Q represents the process uncertainty, (x ir , y ir ) represents a point in the point cloud sequence, (x o , y o ) represents the center point of the tree trunk.

[0062] In this step, the initialized predicted value and prediction uncertainty are the starting points of the Kalman filter and affect the first estimate. As the filtering process progresses, the predicted value and prediction uncertainty are continuously updated, gradually reducing the influence of initialization and getting closer to the combination of the true measurement and the system state.

[0063] (3-2) Determine the estimated value of the current state based on the predicted value and the measured value, and determine the state estimation uncertainty based on the prediction uncertainty. Among them, the estimated value and the state estimation uncertainty are expressed as:

[0064]

[0065] Wherein, r e represents the estimated value, u e represents the state estimation uncertainty, K represents the Kalman gain, and r represents the measured value.

[0066] Furthermore, the Kalman gain K can be calculated based on the prediction uncertainty u p and the uncertainty R of the state measurement, and is expressed as:

[0067]

[0068] (3-3) Update the predicted value using the estimated value, and update the prediction uncertainty using the state estimation uncertainty and the process uncertainty. Among them, the updated predicted value and prediction uncertainty are expressed as:

[0069]

[0070] Based on the above description, the implementation process of step 105 is to iteratively execute steps (3-1) to (3-3) on the point cloud of the tree trunk breast height section represented in polar coordinates, from the point with the smallest abscissa value to the largest point.

[0071] In another exemplary embodiment of the present application, from Figure 2 it can be seen that the present application can thin the thickness of the point cloud of the tree trunk breast height section. From Figure 3 and Figure 4It can be seen that, compared with the traditional method based on fitting of regular geometric primitives, the present application can accurately depict the cross-section of the tree trunk. In the Cartesian coordinate system, Figure 3 and Figure 4 X in represents the horizontal axis, and Y represents the vertical axis. The gray points are the point clouds of the handheld lidar, the red points are the reconstruction results of the tree trunk breast height section obtained by the present application, the blue points are the reconstruction results of the circle-fitted point clouds, and the black points are the point clouds of the ground-based lidar.

[0072] Based on the above description, the method for reconstructing the tree trunk breast height section and determining the breast diameter provided by the present application can accurately obtain the reconstruction result of the tree trunk breast height section. By way of example and not limitation, the final reconstruction result of the tree trunk breast height section obtained by the present application can be output as a point cloud file (*.las) for easy use.

[0073] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as shown in Figure 5 . The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data for reconstructing the tree trunk breast height section and determining the breast diameter. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for reconstructing the tree trunk breast height section and determining the breast diameter.

[0074] Those skilled in the art can understand that Figure 5 The structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0075] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which when executed by a processor, implements the steps in the above method embodiments.

[0076] In an exemplary embodiment, a computer program product is provided, including a computer program, which when executed by a processor, implements the steps in the above method embodiments.

[0077] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0078] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0079] In each of the embodiments provided in the present application, the databases involved may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on a blockchain, etc., without limitation. In each of the embodiments provided in the present application, the processor may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without limitation.

[0080] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0081] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for reconstructing the breast-height cross-section of a tree trunk and determining the diameter at breast height, characterized in that, The method for reconstructing the breast-height cross-section of the tree trunk and determining the diameter at breast height includes: Obtain the height information of the point cloud data, and perform normalization processing on the height information to obtain normalized point cloud; Extract the tree trunk slice point cloud based on the normalized point cloud by using the height threshold method; Perform individual segmentation on the tree trunk slice point cloud by using the connected component analysis method to obtain individual tree trunk slice point cloud; Extract the point with the maximum local point cloud density and the center point of the tree trunk based on the individual tree trunk slice point cloud; Construct a reference direction based on the point with the maximum local point cloud density and the center point of the tree trunk, and perform point cloud polar coordinate transformation on the tree trunk slice point cloud based on the reference direction to obtain polar coordinate tree trunk slice point cloud; Use the Kalman filtering algorithm to perform point cloud reconstruction based on the polar coordinate tree trunk slice point cloud, and perform Cartesian coordinate transformation to obtain the reconstruction result of the breast-height cross-section; Construct a convex hull for the reconstruction result of the breast-height cross-section, and determine the equivalent diameter based on the convex hull to obtain the diameter at breast height.

2. The method for reconstructing the cross-section at breast height of a tree trunk and determining the diameter at breast height according to claim 1, wherein The tree trunk point cloud data is collected by using a handheld lidar.

3. The method for reconstructing the cross-section at breast height of the tree trunk and determining the diameter at breast height according to claim 1, wherein, Using the Kalman filtering algorithm to perform point cloud reconstruction based on the polar coordinate tree trunk slice point cloud, and perform Cartesian coordinate transformation to obtain the reconstruction result of the breast-height cross-section, including: Obtain the position of each point in the polar coordinate tree trunk slice point cloud; Arrange all the points in the polar coordinate tree trunk slice point cloud in ascending order according to the abscissa value of each point position to obtain a point cloud sequence; Use the Kalman filtering algorithm to reconstruct each point in the point cloud sequence in turn, and perform Cartesian coordinate transformation to obtain the reconstruction result of the breast-height cross-section.

4. The method for trunk breast height cross-section reconstruction and diameter at breast height determination according to claim 3, characterized in that, The process of using the Kalman filtering algorithm to reconstruct each point in the point cloud sequence in turn includes: Initialize the predicted value and prediction uncertainty based on the distance between the starting point in the point cloud sequence and the center point of the tree trunk; Determine the estimated value of the current state based on the predicted value and the measured value, and determine the state estimation uncertainty based on the prediction uncertainty; Update the predicted value with the estimated value, and update the prediction uncertainty with the state estimation uncertainty and the process uncertainty.

5. The method for trunk breast height cross-section reconstruction and diameter at breast height determination according to claim 4, wherein The initialized predicted value and prediction uncertainty are expressed as: where r p represents the predicted value, u p represents the prediction uncertainty, P represents the initial uncertainty, Q represents the process uncertainty, (x ir , y ir ) represents a point in the point cloud sequence, (x o , y o ) represents the center point of the tree trunk.

6. The method for reconstructing the cross-section at breast height of a tree trunk and determining the diameter at breast height according to claim 4, wherein The estimated value and the state estimation uncertainty are expressed as: where r e represents the estimated value, r p represents the predicted value, u e represents the state estimation uncertainty, u p represents the prediction uncertainty, K represents the Kalman gain, and r represents the measured value.

7. The method for reconstructing the cross-section at breast height of a tree trunk and determining the diameter at breast height according to claim 4, characterized in that, The updated predicted value and prediction uncertainty are expressed as: where r e represents the estimated value, r p represents the predicted value, u e represents the state estimation uncertainty, u p represents the prediction uncertainty, and Q represents the process uncertainty.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for reconstructing the breast-height cross-section of the tree trunk and determining the diameter at breast height according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for reconstructing the breast-height cross-section of the tree trunk and determining the diameter at breast height according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for reconstructing the breast-height cross-section of the tree trunk and determining the diameter at breast height according to any one of claims 1-7.

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