Single tree segmentation method and device based on laser point cloud data, equipment and medium

Abstract

The invention provides a single tree segmentation method and device based on laser point cloud data, equipment and a medium. The method comprises the steps that laser radar point cloud data of a forest tree is collected, a point cloud bounding box is calculated, and the point cloud bounding box is segmented into multiple grid blocks; and performing individual tree segmentation operation on the point cloud of each grid block to obtain the individual tree point cloud of the corresponding grid block. In the process of single tree segmentation operation, seed points of each tree are generated, and growth is carried out based on the seed points, so that point clouds belonging to each tree are determined. According to the technical scheme disclosed by the invention, the forest data can be quickly acquired by utilizing the laser scanning technology, and then the single tree is automatically identified and segmented, so that the working efficiency is greatly improved, personal errors are reduced through automatic processing, the objectivity of the data is improved, and meanwhile, the safety coefficient and the detection precision are also improved. In addition, according to the technical scheme, a block segmentation strategy is adopted, the memory requirement is effectively reduced, and error segmentation of a single tree is avoided while the large-scene point cloud segmentation effect is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of laser data processing and single tree segmentation, and discloses a single tree segmentation method based on laser point cloud data and a device, equipment and medium. Background Art

[0002] The forest census is an important forestry task, which aims to find out the types, quantity, distribution and growth of forest germplasm resources in a certain area, and provide a scientific basis for forestry management, protection, evaluation and utilization. Traditional census and detection methods mainly rely on manual field measurement and visual inspection, which are inefficient, time-consuming and labor-intensive, especially in large forest areas, and it is difficult to achieve large-scale and high-frequency detection. In addition, manual measurement is prone to errors, and it is difficult to accurately obtain various parameters such as single tree height, breast diameter, crown width, etc.

[0003] The purpose of single tree segmentation is to obtain accurate three-dimensional location information of individual trees and provide data basis for various applications. These accurate three-dimensional location information can be widely used in many industries such as forest resource survey, precision forestry management, urban greening management, agriculture, etc. At present, single tree segmentation technology has the defects of poor accuracy, low efficiency and poor safety factor. Summary of the invention

[0004] The present disclosure at least provides a single tree segmentation method and device, equipment, and medium based on laser point cloud data to solve at least one of the above technical problems.

[0005] According to one aspect of the present disclosure, a single tree segmentation method based on laser point cloud data is provided, comprising:

[0006] Acquire a three-dimensional point cloud obtained by scanning a forestry scene with a laser radar, and calculate a point cloud bounding box of the three-dimensional point cloud, and divide the point cloud bounding box into a plurality of grid blocks; wherein adjacent grid blocks have a preset degree of overlap;

[0007] The point cloud data in the first grid block is used as the segmentation point cloud; for each grid block other than the first grid block, the intersection of the point cloud of the grid block and the single tree bounding box of the single tree point cloud of all grid blocks that have been subjected to the single tree segmentation operation is calculated, and the single tree point cloud corresponding to the single tree bounding box whose intersection is not empty is used as the first supplementary point cloud; the intersection of the point cloud of the grid block and the unsegmented point cloud bounding box of all grid blocks that have been subjected to the single tree segmentation operation is calculated, and the point cloud corresponding to the unsegmented point cloud bounding box whose intersection is not empty is used as the second supplementary point cloud; the point cloud of the grid block, the first supplementary point cloud and the second supplementary point cloud are used as the segmentation point cloud of the grid block;

[0008] Perform the following single tree segmentation operation on the segmented point cloud of each grid block to obtain the single tree point cloud of the corresponding grid block:

[0009] Preprocessing the segmented point cloud to obtain a ground point cloud and a high vegetation point cloud; normalizing the elevation value of the high vegetation point cloud based on the input ground point category; extracting point cloud segments of the normalized high vegetation point cloud within a preset height range, clustering the point cloud segments to obtain a trunk point cloud of each tree; determining the center point of the trunk of each tree based on the trunk point cloud of each tree, and using the center point of each trunk as a seed point;

[0010] The normalized high vegetation point cloud is sliced ​​along the elevation to obtain multiple point cloud slices; the point cloud in each point cloud slice is matched to the nearest seed point; the points in each point cloud slice are sorted in ascending order of the distance to the matched seed point; for each seed point, traverse from the first point that matches the seed point, search for the neighboring points of the current point with the growth distance as the search radius, determine the first seed point corresponding to the nearest neighboring point searched, search for the second seed point closest to the current point, and if the first seed point and the second seed point belong to the same tree, determine the target single tree to which the current point belongs based on the distance between the first seed point and the second seed point and the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point.

[0011] In a possible implementation, determining the target tree to which the current point belongs based on the distance between the first seed point and the second seed point and the length of a projection of a line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point includes:

[0012] Use the following formula to determine the target tree to which the current point p belongs:

[0013]

[0014] Among them, T P represents the target tree to which the current point p belongs, T2 represents the tree corresponding to the second seed point, T1 represents the tree corresponding to the first seed point, D represents the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point, and S represents the distance between the first seed point and the second seed point.

[0015] In a possible implementation manner, after searching for neighboring points of the current point with the growth distance as the search radius, the above-mentioned single tree segmentation method based on laser point cloud data further includes:

[0016] If no neighboring point that matches the seed point is found, the current point is skipped.

[0017] In a possible implementation, it further includes:

[0018] When the preset ID of the first seed point is the same as the preset ID of the second seed point, it is determined that the first seed point and the second seed point belong to the same tree.

[0019] In a possible implementation, the preprocessing operation on the segmented point cloud to obtain a ground point cloud and a high vegetation point cloud includes:

[0020] The segmented point cloud is filtered, and the filtered point cloud is classified to obtain a ground point cloud and a high vegetation point cloud.

[0021] In a possible implementation, determining the trunk center point of each tree based on the trunk point cloud of each tree includes:

[0022] The least squares circle fitting algorithm is used to process the trunk point cloud of each tree to obtain the center point of the trunk of each tree.

[0023] In a possible implementation, the laser radar is a vehicle-mounted laser radar or a ground-based laser radar.

[0024] According to another aspect of the present disclosure, a single wood segmentation device based on laser point cloud data is provided, comprising:

[0025] A data acquisition processing module is used to acquire a three-dimensional point cloud obtained by scanning a forestry scene with a laser radar, and to calculate a point cloud bounding box of the three-dimensional point cloud, and to divide the point cloud bounding box into a plurality of grid blocks; wherein adjacent grid blocks have a preset degree of overlap;

[0026] The point cloud determination module is used to use the point cloud data in the first grid block as the segmentation point cloud; for each grid block other than the first grid block, calculate the intersection of the point cloud of the grid block and the single tree bounding box of the single tree point cloud of all grid blocks that have been subjected to the single tree segmentation operation, and use the single tree point cloud corresponding to the single tree bounding box whose intersection is not empty as the first supplementary point cloud; calculate the intersection of the point cloud of the grid block and the unsegmented point cloud bounding box of all grid blocks that have been subjected to the single tree segmentation operation, and use the point cloud corresponding to the unsegmented point cloud bounding box whose intersection is not empty as the second supplementary point cloud; use the point cloud of the grid block, the first supplementary point cloud and the second supplementary point cloud as the segmentation point cloud of the grid block;

[0027] The segmentation processing module is used to perform the following single tree segmentation operation on the segmentation point cloud of each grid block to obtain the single tree point cloud of the corresponding grid block:

[0028] Preprocessing the segmented point cloud to obtain a ground point cloud and a high vegetation point cloud; normalizing the elevation value of the high vegetation point cloud based on the input ground point category; extracting point cloud segments of the normalized high vegetation point cloud within a preset height range, clustering the point cloud segments to obtain a trunk point cloud of each tree; determining the center point of the trunk of each tree based on the trunk point cloud of each tree, and using the center point of each trunk as a seed point;

[0029] The normalized high vegetation point cloud is sliced ​​along the elevation to obtain multiple point cloud slices; the point cloud in each point cloud slice is matched to the nearest seed point; the points in each point cloud slice are sorted in ascending order of the distance to the matched seed point; for each seed point, traverse from the first point that matches the seed point, search for the neighboring points of the current point with the growth distance as the search radius, determine the first seed point corresponding to the nearest neighboring point searched, search for the second seed point closest to the current point, and if the first seed point and the second seed point belong to the same tree, determine the target single tree to which the current point belongs based on the distance between the first seed point and the second seed point and the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point.

[0030] According to another aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements any of the above methods when executing the computer program.

[0031] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any of the above-mentioned methods is implemented.

[0032] The disclosed single tree segmentation method, device, equipment, and medium based on laser point cloud data collects the laser radar point cloud data of the forest, calculates the point cloud bounding box, and divides the point cloud bounding box into multiple grid blocks; the single tree segmentation operation is performed on the point cloud of each grid block respectively to obtain the single tree point cloud of the corresponding grid block. In the process of performing the single tree segmentation operation, the seed point of each tree is generated, and growth is performed based on the seed point to determine the point cloud belonging to each tree. The technical solution disclosed in the present invention can quickly collect forest data using laser scanning technology, and then automatically identify and segment individual trees, which greatly improves the operation efficiency. The automated processing also reduces human errors, improves the objectivity of the data, and also improves the safety factor and detection accuracy. In addition, the amount of laser point cloud data is huge and the space occupied is large. Simple single processing will seriously test the device memory and processing power. The technical solution disclosed in the present invention adopts a block segmentation strategy to effectively reduce the memory demand, while ensuring the point cloud segmentation effect of large scenes, avoiding the single tree from being incorrectly segmented.

[0033] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0035] Figure 1 is a flow chart of a single tree segmentation method based on laser point cloud data according to the present disclosure;

[0036] Figure 2 is a schematic structural diagram of a single wood segmentation device based on laser point cloud data according to the present disclosure;

[0037] Figure 3 is a schematic structural diagram of an electronic device according to the present disclosure. DETAILED DESCRIPTION

[0038] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0039] The present invention is to solve the problems of low efficiency, poor precision and high equipment requirements in the current single tree segmentation, and provides a single tree segmentation method, device, equipment and medium based on laser point cloud data. The present invention collects the laser radar point cloud data of forest trees, calculates the point cloud bounding box, and divides the point cloud bounding box into multiple grid blocks; the single tree segmentation operation is performed on the point cloud of each grid block respectively to obtain the single tree point cloud of the corresponding grid block. In the process of performing the single tree segmentation operation, the seed point of each tree is generated, and growth is performed based on the seed point, so as to determine the point cloud belonging to each tree. The technical solution of the present invention can quickly collect forest data by using laser scanning technology, and then automatically identify and segment individual trees, which greatly improves the operation efficiency, and the automated processing also reduces human errors, improves the objectivity of the data, and also improves the safety factor and detection accuracy. In addition, the amount of laser point cloud data is huge, and the space occupied is large. Simple single processing will seriously test the device memory and processing capacity. The technical solution of the present invention adopts a block segmentation strategy to effectively reduce the memory demand, and avoids the single tree from being wrongly segmented while ensuring the point cloud segmentation effect of large scenes.

[0040] The technical solution of the present disclosure is described below through specific embodiments.

[0041] like Figure 1 As shown, it is a flowchart of the single tree segmentation method based on laser point cloud data of this embodiment. The execution subject of this embodiment is a computing device or component with data processing capability. The specific method of this embodiment may include the following steps:

[0042] S110, obtaining a three-dimensional point cloud obtained by scanning a forestry scene with a laser radar, and calculating a point cloud bounding box of the three-dimensional point cloud, and dividing the point cloud bounding box into a plurality of grid blocks; wherein adjacent grid blocks have a preset degree of overlap.

[0043] The laser radar is a vehicle-mounted laser radar or a ground-based laser radar. Adjacent grid blocks are provided with an overlap to ensure the integrity of the single tree segmentation.

[0044] S120. Use the point cloud data in the first grid block as the segmentation point cloud; for each grid block other than the first grid block, calculate the intersection of the point cloud of the grid block with the single tree bounding boxes of the single tree point clouds of all grid blocks that have undergone single tree segmentation operations, and use the single tree point cloud corresponding to the single tree bounding boxes whose intersection is not empty as the first supplementary point cloud; calculate the intersection of the point cloud of the grid block with the unsegmented point cloud bounding boxes of all grid blocks that have undergone single tree segmentation operations, and use the point cloud corresponding to the unsegmented point cloud bounding boxes whose intersection is not empty as the second supplementary point cloud; use the point cloud of the grid block, the first supplementary point cloud, and the second supplementary point cloud as the segmentation point cloud of the grid block.

[0045] S130, performing the following single tree segmentation operation on the segmentation point cloud of each grid block to obtain the single tree point cloud of the corresponding grid block:

[0046] 1) Preprocessing the segmented point cloud to obtain a ground point cloud and a high vegetation point cloud; normalizing the elevation value of the high vegetation point cloud based on the input ground point category; extracting point cloud segments of the normalized high vegetation point cloud within a preset height range, clustering the point cloud segments to obtain a trunk point cloud of each tree; determining the trunk center point of each tree based on the trunk point cloud of each tree, and using each trunk center point as a seed point.

[0047] The elevation of high vegetation points is normalized based on the ground point category provided by the input, making the subsequent segmentation process more convenient.

[0048] The preset height range may be 1.3 meters to 1.5 meters. The trunk point cloud of each tree is separated using Euclidean clustering, and then the center point of the trunk is obtained based on the least squares circle fitting algorithm as the seed point position.

[0049] 2) Slice the normalized high vegetation point cloud along the elevation to obtain multiple point cloud slices; match the point cloud in each point cloud slice to the nearest seed point; sort the points in each point cloud slice in ascending order of distance from the matched seed point; for each seed point, traverse from the first point that matches the seed point, search the neighborhood points of the current point with the growth distance as the search radius, determine the first seed point corresponding to the nearest neighborhood point searched, search for the second seed point closest to the current point, if the first seed point and the second seed point belong to the same tree, then determine the target single tree to which the current point belongs based on the distance between the first seed point and the second seed point and the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point; if the first seed point and the second seed point belong to the same tree, then the current point belongs to the single tree corresponding to the first seed point or the second seed point.

[0050] This step implements the growth inside each point cloud slice to determine the single tree to which each point belongs. In order to ensure the order of growth from the inside to the outside, for each slice, the points are first assigned to the nearest seed point according to the distance from the seed point, and then sorted from small to large by distance.

[0051] Traverse all points in order of arrangement, set the current point to P, and use the growth distance as the search radius to search for neighboring points. If no neighboring point is found, skip the point. Assume that the ID of the tree or seed point to which the nearest neighboring point belongs is T1, and then search for the nearest seed point, whose ID is T2. In order to avoid overgrowth, perform the following restriction processing. If T1 and T2 are not the same tree, set the distance between T1 and T2 to S, and the projection length on the line connecting T2P to T1T2 to D, then determine the target tree to which P belongs based on the following rules.

[0052]

[0053] Among them, k∈(0,0.5] is used to control the segmentation boundary.

[0054] The present invention uses laser scanning technology to quickly obtain three-dimensional point cloud data of a large forest, reducing field work time; the single tree segmentation algorithm in the present invention can automatically identify and segment individual trees, greatly improving efficiency; at the same time, automated processing also reduces human errors and improves the objectivity of data. In addition, the present invention supports the segmentation processing of large scene point clouds, has low requirements on equipment performance, and avoids the phenomenon of wrong segmentation at the edges of blocks.

[0055] In some embodiments, an ID may be assigned to each seed point. One seed point corresponds to one tree. The ID may uniquely identify both the seed point and the tree.

[0056] When judging whether the first seed point and the second seed point are a tree, the IDs of the two can be compared. If they are the same, the first seed point and the second seed point belong to the same tree; if they are different, the first seed point and the second seed point do not belong to the same tree.

[0057] In some embodiments, after searching for neighboring points of the current point with the growth distance as the search radius, the single tree segmentation method based on laser point cloud data may further include the following steps:

[0058] If no neighboring point that matches the seed point is found, the current point is skipped.

[0059] The above-mentioned preprocessing operation on the segmented point cloud to obtain the ground point cloud and the high vegetation point cloud may specifically include: filtering the segmented point cloud, and classifying the filtered point cloud to obtain the ground point cloud and the high vegetation point cloud.

[0060] The above-mentioned method of determining the center point of the trunk of each tree based on the trunk point cloud of each tree can be implemented by the following steps: the trunk point cloud of each tree is processed by the least squares circle fitting algorithm to obtain the center point of the trunk of each tree.

[0061] In some embodiments, determining the target tree to which the current point belongs based on the distance between the first seed point and the second seed point and the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point includes:

[0062] Use the following formula to determine the target tree to which the current point p belongs:

[0063]

[0064] Among them, T P represents the target tree to which the current point p belongs, T2 represents the tree corresponding to the second seed point, T1 represents the tree corresponding to the first seed point, D represents the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point, and S represents the distance between the first seed point and the second seed point. In the specific implementation, T1, T2, T p It can be the ID of a single tree corresponding to the seed point.

[0065] K can be flexibly set according to the actual scenario, for example, it can be set to k∈(0,0.5].

[0066] The single tree segmentation technology provided by the embodiments of the present disclosure requires the input of ground point categories and high vegetation point categories, and provides two processing modes: "Growth based on seed points" and "Segmentation". "Growth based on seed points" is suitable for situations where the user already has single tree seed points, and the segmented single trees will completely match the seed points. The "Segmentation" mode directly segments single trees and generates corresponding seed points. The single tree segmentation method disclosed in the present disclosure, based on the ground-based three-dimensional laser point cloud, realizes the automatic extraction of single trees by combining automatic collection of mobile laser measurement and post-analysis processing, providing data support for subsequent single tree parameter measurement.

[0067] Based on the same inventive concept, the present disclosure provides a single wood splitting device based on laser point cloud data, and the steps performed by the components of the device are the same or similar to those of the above method, so similar parts will not be repeated. Figure 2 As shown, the single wood segmentation device based on laser point cloud data of this embodiment includes:

[0068] The data acquisition processing module 210 is used to acquire the three-dimensional point cloud obtained by scanning the forestry scene with the laser radar, and to calculate the point cloud bounding box of the three-dimensional point cloud, and divide the point cloud bounding box into multiple grid blocks; wherein adjacent grid blocks have a preset overlap.

[0069] The point cloud determination module 220 is used to use the point cloud data in the first grid block as the segmentation point cloud; for each grid block other than the first grid block, calculate the intersection of the point cloud of the grid block with the single tree bounding box of the single tree point cloud of all grid blocks that have been subjected to the single tree segmentation operation, and use the single tree point cloud corresponding to the single tree bounding box whose intersection is not empty as the first supplementary point cloud; calculate the intersection of the point cloud of the grid block with the unsegmented point cloud bounding box of all grid blocks that have been subjected to the single tree segmentation operation, and use the point cloud corresponding to the unsegmented point cloud bounding box whose intersection is not empty as the second supplementary point cloud; use the point cloud of the grid block, the first supplementary point cloud and the second supplementary point cloud as the segmentation point cloud of the grid block.

[0070] The segmentation processing module 230 is used to perform the following single tree segmentation operation on the segmentation point cloud of each grid block to obtain the single tree point cloud of the corresponding grid block:

[0071] Preprocessing the segmented point cloud to obtain a ground point cloud and a high vegetation point cloud; normalizing the elevation value of the high vegetation point cloud based on the input ground point category; extracting point cloud segments of the normalized high vegetation point cloud within a preset height range, clustering the point cloud segments to obtain a trunk point cloud of each tree; determining the center point of the trunk of each tree based on the trunk point cloud of each tree, and using the center point of each trunk as a seed point;

[0072] The normalized high vegetation point cloud is sliced ​​along the elevation to obtain multiple point cloud slices; the point cloud in each point cloud slice is matched to the nearest seed point; the points in each point cloud slice are sorted in ascending order of the distance to the matched seed point; for each seed point, traverse from the first point that matches the seed point, search for the neighboring points of the current point with the growth distance as the search radius, determine the first seed point corresponding to the nearest neighboring point searched, search for the second seed point closest to the current point, and if the first seed point and the second seed point belong to the same tree, determine the target single tree to which the current point belongs based on the distance between the first seed point and the second seed point and the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point.

[0073] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a computer-readable storage medium.

[0074] Figure 3A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0075] like Figure 3 As shown, the device 300 includes a computing unit 310, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 320 or a computer program loaded from a storage unit 380 into a random access memory (RAM) 330. In the RAM 330, various programs and data required for the operation of the device 300 can also be stored. The computing unit 310, the ROM 320, and the RAM 330 are connected to each other via a bus 340. An input / output (I / O) interface 350 is also connected to the bus 340.

[0076] A number of components in the device 300 are connected to the I / O interface 350, including: an input unit 360, such as a keyboard, a mouse, etc.; an output unit 370, such as various types of displays, speakers, etc.; a storage unit 380, such as a disk, an optical disk, etc.; and a communication unit 390, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 390 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0077] The computing unit 310 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 310 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 310 performs the various methods and processes described above. For example, in some embodiments, any of the above methods may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 380. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via the ROM 320 and / or the communication unit 390. When the computer program is loaded into the RAM 330 and executed by the computing unit 310, one or more steps of any of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 310 may be configured to perform any of the methods described above in any other appropriate manner (e.g., by means of firmware).

[0078] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0079] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0080] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0081] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0082] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0083] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0084] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0085] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A single tree segmentation method based on laser point cloud data, characterized in that: include: Acquire a three-dimensional point cloud obtained by scanning a forestry scene with a laser radar, and calculate a point cloud bounding box of the three-dimensional point cloud, and divide the point cloud bounding box into a plurality of grid blocks; wherein adjacent grid blocks have a preset degree of overlap; The point cloud data in the first grid block is used as the segmented point cloud; For each grid block other than the first grid block, calculate the intersection of the point cloud of the grid block and the single tree bounding boxes of the single tree point clouds of all grid blocks that have been subjected to the single tree segmentation operation, and use the single tree point cloud corresponding to the single tree bounding box whose intersection is not empty as the first supplementary point cloud; calculate the intersection of the point cloud of the grid block and the unsegmented point cloud bounding boxes of all grid blocks that have been subjected to the single tree segmentation operation, and use the point cloud corresponding to the unsegmented point cloud bounding box whose intersection is not empty as the second supplementary point cloud; use the point cloud of the grid block, the first supplementary point cloud and the second supplementary point cloud as the segmentation point cloud of the grid block; Perform the following single tree segmentation operation on the segmented point cloud of each grid block to obtain the single tree point cloud of the corresponding grid block: Preprocessing the segmented point cloud to obtain a ground point cloud and a high vegetation point cloud; normalizing the elevation value of the high vegetation point cloud based on the input ground point category; Extracting point cloud segments within a preset height range from the normalized high vegetation point cloud, performing clustering operations on the point cloud segments to obtain a trunk point cloud of each tree; determining the center point of the trunk of each tree based on the trunk point cloud of each tree, and using the center point of each trunk as a seed point; The normalized high vegetation point cloud is sliced ​​along the elevation to obtain multiple point cloud slices; the point cloud in each point cloud slice is matched to the nearest seed point; the points in each point cloud slice are sorted in ascending order of the distance to the matched seed point; for each seed point, traverse from the first point that matches the seed point, search for the neighboring points of the current point with the growth distance as the search radius, determine the first seed point corresponding to the nearest neighboring point searched, search for the second seed point closest to the current point, and if the first seed point and the second seed point belong to the same tree, determine the target single tree to which the current point belongs based on the distance between the first seed point and the second seed point and the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point.

2. The method according to claim 1, characterized in that The method of determining the target tree to which the current point belongs based on the distance between the first seed point and the second seed point and the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point comprises: Use the following formula to determine the target tree to which the current point p belongs: Among them, T P represents the target tree to which the current point p belongs, T2 represents the tree corresponding to the second seed point, T1 represents the tree corresponding to the first seed point, D represents the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point, and S represents the distance between the first seed point and the second seed point.

3. The method according to claim 1, characterized in that: After searching the neighboring points of the current point with the growth distance as the search radius, the method further includes: If no neighboring point that matches the seed point is found, the current point is skipped.

4. The method according to claim 1, characterized in that: Also includes: When the preset ID of the first seed point is the same as the preset ID of the second seed point, it is determined that the first seed point and the second seed point belong to the same tree.

5. The method according to claim 1, characterized in that The preprocessing operation is performed on the segmented point cloud to obtain a ground point cloud and a high vegetation point cloud, including: The segmented point cloud is filtered, and the filtered point cloud is classified to obtain a ground point cloud and a high vegetation point cloud.

6. The method according to claim 1, characterized in that The method of determining the center point of the trunk of each tree based on the trunk point cloud of each tree comprises: The least squares circle fitting algorithm is used to process the trunk point cloud of each tree to obtain the center point of the trunk of each tree.

7. The method according to claim 1, characterized in that The laser radar is a vehicle-mounted laser radar or a ground-based laser radar.

8. A single wood segmentation device based on laser point cloud data, characterized in that: include: A data acquisition processing module is used to acquire a three-dimensional point cloud obtained by scanning a forestry scene with a laser radar, and to calculate a point cloud bounding box of the three-dimensional point cloud, and to divide the point cloud bounding box into a plurality of grid blocks; wherein adjacent grid blocks have a preset degree of overlap; A point cloud determination module is used to use the point cloud data in the first grid block as a segmented point cloud; For each grid block other than the first grid block, calculate the intersection of the point cloud of the grid block and the single tree bounding boxes of the single tree point clouds of all grid blocks that have been subjected to the single tree segmentation operation, and use the single tree point cloud corresponding to the single tree bounding box whose intersection is not empty as the first supplementary point cloud; calculate the intersection of the point cloud of the grid block and the unsegmented point cloud bounding boxes of all grid blocks that have been subjected to the single tree segmentation operation, and use the point cloud corresponding to the unsegmented point cloud bounding box whose intersection is not empty as the second supplementary point cloud; use the point cloud of the grid block, the first supplementary point cloud and the second supplementary point cloud as the segmentation point cloud of the grid block; The segmentation processing module is used to perform the following single tree segmentation operation on the segmentation point cloud of each grid block to obtain the single tree point cloud of the corresponding grid block: Preprocessing the segmented point cloud to obtain a ground point cloud and a high vegetation point cloud; normalizing the elevation value of the high vegetation point cloud based on the input ground point category; Extracting point cloud segments within a preset height range from the normalized high vegetation point cloud, performing clustering operations on the point cloud segments to obtain a trunk point cloud of each tree; determining the center point of the trunk of each tree based on the trunk point cloud of each tree, and using the center point of each trunk as a seed point; The normalized high vegetation point cloud is sliced ​​along the elevation to obtain multiple point cloud slices; the point cloud in each point cloud slice is matched to the nearest seed point; the points in each point cloud slice are sorted in ascending order of the distance to the matched seed point; for each seed point, traverse from the first point that matches the seed point, search for the neighboring points of the current point with the growth distance as the search radius, determine the first seed point corresponding to the nearest neighboring point searched, search for the second seed point closest to the current point, and if the first seed point and the second seed point belong to the same tree, determine the target single tree to which the current point belongs based on the distance between the first seed point and the second seed point and the length of the projection of the line connecting the current point and the second seed point on the line connecting the first seed point and the second seed point.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images