Trunk breast height section reconstruction and breast diameter determination method, equipment, medium and product

Abstract

The invention discloses a trunk breast height section reconstruction and breast diameter determination method and device, a medium and a product, and relates to the field of point cloud data processing, and the method comprises the steps: carrying out the normalization of point cloud height information; adopting a height threshold method to extract a trunk slice point cloud based on the normalized point cloud; performing individual segmentation on the trunk slice point clouds by adopting a connected component analysis method to obtain individual trunk slice point clouds; extracting a point with the maximum local point cloud density and a trunk center point based on the individual trunk slice point cloud to construct a reference direction, and performing polar coordinate conversion on the trunk slice point cloud to obtain a polar coordinate trunk slice point cloud; performing point cloud reconstruction based on the polar coordinate trunk slice point cloud by using a Kalman filtering algorithm, and performing Cartesian coordinate conversion to obtain a chest height section reconstruction result; and constructing a convex hull for a chest height section reconstruction result, and determining an equivalent diameter to obtain the diameter at breast height. According to the method, the trunk breast height section reconstruction reliability can be improved while the accuracy and efficiency of trunk breast height section reconstruction and breast diameter determination are improved.

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 trunk breast-height section and determining a breast-height diameter. Background Art

[0002] The trunk breast height section is an important indicator for assessing the condition of trees. In forestry surveys, technicians usually use a breast diameter ruler or caliper to measure the breast diameter (i.e., breast height diameter) to quantitatively describe the trunk breast height section. Although this method is accurate, it is time-consuming and labor-intensive, which restricts the efficiency of forest resource monitoring and management. Therefore, it is urgent to develop efficient and accurate trunk cross-section characterization technology to improve the efficiency and accuracy of forest resource management. As a revolutionary mobile three-dimensional digital technology, handheld laser radar has attracted widespread attention due to its high efficiency brought by portability and flexibility. However, due to its lightweight design and limited accuracy, the handheld laser radar has a point cloud noise thickness of about 3 cm to 8 cm on the trunk surface, which reduces the accuracy of cross-section characterization. Based on this, the existing algorithms mainly use regular primitive fitting methods to reconstruct the trunk breast height section to eliminate the influence of point cloud noise. However, this type of algorithm is only applicable to tree species with regular trunk shapes and is sensitive to point cloud density. The reliability of reconstructing the trunk breast height section with uneven point cloud density distribution is insufficient. Summary of the invention

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

[0004] To achieve the above objectives, this application provides the following solutions:

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

[0006] Acquiring height information of the point cloud data, and normalizing the height information to obtain a normalized point cloud;

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

[0008] Using connected component analysis to segment the tree trunk slice point cloud into individual segments, so as to obtain individual tree trunk slice point clouds;

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

[0010] Constructing a reference direction based on the local point cloud with the maximum density and the trunk center point, and performing point cloud polar coordinate conversion on the trunk slice point cloud based on the reference direction to obtain a polar coordinate trunk slice point cloud;

[0011] Using a Kalman filter algorithm to reconstruct a point cloud based on the polar coordinate tree trunk slice point cloud, and performing a Cartesian coordinate transformation to obtain a chest height section reconstruction result;

[0012] A convex hull is constructed for the reconstruction result of the chest height section, and the equivalent diameter is determined based on the convex hull to obtain the chest diameter.

[0013] Optionally, the point cloud data is collected by a handheld laser radar.

[0014] Optionally, a Kalman filter algorithm is used to reconstruct a point cloud based on the polar coordinate tree trunk slice point cloud, and a Cartesian coordinate conversion is performed to obtain a chest height section reconstruction result, including:

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

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

[0017] The Kalman filter algorithm is used to reconstruct each point in the point cloud sequence in turn, and a Cartesian coordinate transformation is performed to obtain the chest height section reconstruction result.

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

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

[0020] determining an estimate of a current state based on the predicted value and the measured value, and determining a state estimate uncertainty based on the predicted uncertainty;

[0021] The estimated value is used to update the predicted value, and the predicted uncertainty is updated using the state estimation uncertainty and the process uncertainty.

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

[0023]

[0024] In the formula, r p represents the predicted value, u p represents prediction uncertainty, P represents initial uncertainty, Q represents process uncertainty, (x ir ,yir ) represents a point in the point cloud sequence, (x o ,y o ) indicates 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 uncertainty of state estimation, u p represents prediction uncertainty, K represents the Kalman gain, and r represents the measurement value.

[0028] Optionally, the updated predicted value and the predicted 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 uncertainty of state estimation, u p represents prediction uncertainty and Q represents process uncertainty.

[0031] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for reconstructing a trunk breast height section and determining a breast diameter.

[0032] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for reconstructing a trunk breast height section and determining a breast diameter.

[0033] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for reconstructing a trunk breast height section and determining a breast diameter.

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

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

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A schematic flow chart of a method for reconstructing a trunk breast height section and determining a breast height diameter provided in one embodiment of the present application;

[0038] Figure 2 A key step effect diagram provided for an embodiment of the present application;

[0039] Figure 3 A diagram showing the reconstruction result of the chest-high cross section of the trunk of sample 1 provided in one embodiment of the present application;

[0040] Figure 4 A diagram showing the reconstruction result of the chest-high cross section of the trunk of sample 2 provided in one embodiment of the present application;

[0041] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0044] In an exemplary embodiment, the present application provides a method for reconstructing a trunk breast height section and determining a breast diameter. When the method is executed by a computer device, it can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the present embodiment, the method is applied to a server as an example for explanation. Figure 1 As shown, the method for reconstructing the trunk breast height section and determining the breast diameter provided in this application includes:

[0045] Step 100: Acquire height information of point cloud data, and normalize the height information to obtain a normalized point cloud.

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

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

[0048] For example, the trunk chest height is defined as 1.3m, so the point cloud with height between 1.25m and 1.35m is extracted from the normalized point cloud, and the extracted slice point cloud is projected to the XY horizontal plane to obtain the trunk slice point cloud.

[0049] Step 102: Use the connected component analysis method to segment the trunk slice point cloud into individual parts to obtain the individual trunk slice point cloud. Considering that different individual trunks in the trunk slice point cloud are separated in the XY space, the connected component analysis method is used in this step to segment the trunk slice point cloud to obtain the individual trunk slice point cloud.

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

[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 filter algorithm. The convergence speed and overall filtering accuracy of the Kalman filter algorithm are related to the estimation of the initial state, so the starting point should be as close to the real trunk surface as possible. Considering that the point cloud density near the real trunk surface is higher, the point with the maximum local point cloud density is selected as the starting point of the Kalman filter algorithm. Among them, the local point cloud density of each point can be obtained by counting the number of points in a circular area with a radius of 0.2m. The center point of the trunk is obtained by calculating the average value of the x and y ranges of the point cloud of the individual trunk slice.

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

[0053] Step 105: Use the Kalman filter algorithm to reconstruct the point cloud based on the polar coordinate tree trunk slice point cloud, and perform Cartesian coordinate conversion to obtain the chest height section reconstruction result.

[0054] Step 106: construct a convex hull based on the chest height section reconstruction result, and determine the equivalent diameter based on the convex hull to obtain the chest diameter.

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

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

[0057] (2) According to the horizontal coordinate value of each point position, all points in the polar coordinate tree trunk slice point cloud are arranged in ascending order to obtain a point cloud sequence.

[0058] (3) The Kalman filter algorithm is used to reconstruct each point in the point cloud sequence in turn, and the Cartesian coordinate transformation is performed to obtain the chest height section reconstruction result, which is:

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

[0060]

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

[0062] In this step, the initial predicted value and prediction uncertainty are the starting point of the Kalman filter, affecting the first estimate. As the filtering process progresses, the predicted value and prediction uncertainty are continuously updated, gradually reducing the initialization impact and getting closer to the combination of the actual measurement and 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 predicted uncertainty. The estimated value and state estimation uncertainty are expressed as:

[0064]

[0065] In the formula, r e represents the estimated value, u e represents the uncertainty of state estimation, K represents the Kalman gain, and r represents the measurement value.

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

[0067]

[0068] (3-3) The estimated value is used to update the predicted value, and the state estimation uncertainty and process uncertainty are used to update the predicted uncertainty. The updated predicted value and predicted 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) from the point with the smallest horizontal coordinate value to the point with the largest horizontal coordinate value for the trunk chest-height cross-section point cloud represented by polar coordinates.

[0071] In another exemplary embodiment of the present application, Figure 2 It can be seen that the present application can reduce the thickness of the cross-section point cloud at chest height of the tree trunk. Figure 3 and Figure 4It can be seen that compared with the traditional method based on regular geometric primitive fitting, the present application can accurately characterize the cross section of the tree trunk. In Cartesian coordinates, Figure 3 and Figure 4 The X in the figure represents the horizontal axis, and the Y represents the vertical axis. The gray dots are handheld laser radar point clouds, the red dots are the reconstruction results of the chest-high cross-section of the trunk obtained in this application, the blue dots are the circle fitting point cloud reconstruction results, and the black dots are ground-based laser radar point clouds.

[0072] Based on the above description, the trunk chest height section reconstruction and breast diameter determination method provided in this application can accurately obtain the trunk chest height section reconstruction result. As an example but not limitation, the trunk chest height section reconstruction result finally obtained by this 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 may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. 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. 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 cross-section of the trunk at breast height and determining the diameter at breast height. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for reconstructing the cross-section of the trunk at breast height and determining the diameter at breast height is implemented.

[0074] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure 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 certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

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

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

[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 used 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 must comply with relevant regulations.

[0078] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. 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), magnetic 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0079] The database involved in each embodiment provided in this application 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 blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0080] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0081] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for reconstructing the cross section of a tree trunk at breast height and determining the diameter at breast height, characterized in that: The method for reconstructing the trunk breast height section and determining the breast diameter comprises: Acquiring height information of the point cloud data, and normalizing the height information to obtain a normalized point cloud; Extracting a trunk slice point cloud based on the normalized point cloud using a height threshold method; Using connected component analysis to segment the tree trunk slice point cloud into individual segments, so as to obtain individual tree trunk slice point clouds; Extracting the point with the maximum local point cloud density and the center point of the trunk based on the point cloud of the individual trunk slice; Constructing a reference direction based on the local point cloud density maximum point and the trunk center point, and performing point cloud polar coordinate conversion on the trunk slice point cloud based on the reference direction to obtain a polar coordinate trunk slice point cloud; Using a Kalman filter algorithm to reconstruct a point cloud based on the polar coordinate tree trunk slice point cloud, and performing a Cartesian coordinate transformation to obtain a chest height section reconstruction result; A convex hull is constructed for the reconstruction result of the chest height section, and the equivalent diameter is determined based on the convex hull to obtain the chest diameter.

2. The method for reconstructing the trunk breast height section and determining the breast diameter according to claim 1, characterized in that: The tree trunk point cloud data is collected by using a handheld laser radar.

3. The method for reconstructing the trunk breast height section and determining the breast diameter according to claim 1, characterized in that: The Kalman filter algorithm is used to reconstruct the point cloud based on the polar coordinate tree trunk slice point cloud, and the Cartesian coordinate conversion is performed to obtain the chest height section reconstruction result, including: Obtaining the position of each point in the polar coordinate tree trunk slice point cloud; Arrange all points in the polar coordinate tree trunk slice point cloud in ascending order according to the horizontal coordinate value of each point position to obtain a point cloud sequence; The Kalman filter algorithm is used to reconstruct each point in the point cloud sequence in turn, and a Cartesian coordinate transformation is performed to obtain the chest height section reconstruction result.

4. The method for reconstructing the trunk breast height section and determining the breast diameter according to claim 3, characterized in that: The process of sequentially reconstructing each point in the point cloud sequence using the Kalman filter algorithm includes: Initializing a prediction value and a prediction uncertainty based on a distance between a starting point in the point cloud sequence and a center point of the tree trunk; determining an estimate of a current state based on the predicted value and the measured value, and determining a state estimate uncertainty based on the predicted uncertainty; The estimated value is used to update the predicted value, and the predicted uncertainty is updated using the state estimation uncertainty and the process uncertainty.

5. The method for reconstructing the trunk breast height section and determining the breast diameter according to claim 4, characterized in that: The initial predicted value and prediction uncertainty are expressed as: In the formula, r p represents the predicted value, u p represents prediction uncertainty, P represents initial uncertainty, Q represents process uncertainty, (x ir ,y ir ) represents a point in the point cloud sequence, (x o ,y o ) indicates the center point of the tree trunk.

6. The method for reconstructing the trunk breast height section and determining the breast diameter according to claim 4, characterized in that: The estimated value and the state estimation uncertainty are expressed as: In the formula, r e represents the estimated value, r p represents the predicted value, u e represents the uncertainty of state estimation, u p represents prediction uncertainty, K represents the Kalman gain, and r represents the measurement value.

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

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

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

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

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