Forest ULS-TLS point cloud automatic registration method and system based on ground context description

Abstract

The invention belongs to the technical field of ecological monitoring, and discloses a forest ULS-TLS point cloud automatic registration method based on ground context description, and the method employs a ground point as a registration element, constructs a ground context, can directly achieve the registration of TLS single scanning data to a ULS point cloud, and does not need the pre-registration of different scanning point clouds of a TLS. The extraction process of the ground points is relatively standardized, stable and reliable results can be obtained generally, and the method is not affected by distribution, density and shape complexity of trees in the forest. Compared with extraction of tree attributes, the ground points are used as registration primitives, so that the calculation cost is reduced, and high calculation overhead caused by extraction of the tree attributes is avoided. The invention aims to provide a forest ULS-TLS point cloud automatic registration method based on ground context description so as to quickly, accurately and automatically realize registration of forest ULS and TLS point clouds, comprehensively obtain forest structure information and promote application of a laser radar technology in forest investigation.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological monitoring, and in particular relates to a forest ULS-TLS point cloud automatic registration method and system based on ground context description. Background Art

[0002] With the development of remote sensing technology, 3D point cloud technology has been widely used in forest ecological monitoring, forestry resource surveys, and environmental protection. Point cloud technology can accurately depict the structural characteristics of forests by capturing the 3D coordinates of objects, providing an important data basis for forest management and research. At present, laser scanning technology (LiDAR) is the main means of obtaining 3D point clouds, including terrestrial laser scanning (TLS) and unmanned aerial laser scanning (ULS). TLS can obtain high-precision ground point cloud data and is suitable for high-precision monitoring in a small area; while ULS can cover a large area of ​​forest areas and collect a large range of point cloud data due to the flexibility and maneuverability of drones.

[0003] ULS is efficient and flexible in mapping forest canopies and can generate georeferenced point clouds through the Global Navigation Satellite System (GNSS), but it is insufficient in obtaining information under the canopy. In contrast, TLS can effectively supplement the measurement of understory information through close-range observations, but due to terrain and line of sight limitations, the data coverage is small and it is difficult to obtain complete information about the canopy. Therefore, how to accurately align ULS and TLS point cloud data, integrate the advantages of both to overcome their respective limitations, and provide a more comprehensive and accurate representation of forest structure has become an important research direction for point cloud applications in forest ecosystems.

[0004] Point cloud registration is a key step in many LiDAR applications and has long been widely studied and optimized in the fields of photogrammetry and computer vision. However, most existing studies focus on point cloud registration on the same platform, and relatively few studies have been conducted on the integration of point clouds across platforms. Currently, multi-source point cloud registration methods mainly rely on the extraction of geometric features and the establishment of correspondences based on these features. These methods are more applicable in urban scenes, but for forest point cloud data, especially in cases of low overlap and high geometric similarity of tree trunks, traditional methods are often difficult to directly apply to the registration of multi-source forest point clouds.

[0005] Existing multi-source forest point cloud automatic registration methods mainly rely on tree attributes. However, it is challenging to accurately obtain attributes such as DBH, tree height, and tree position when the tree density is high, the terrain is complex, and the tree growth is irregular. Trees are affected by natural conditions such as wind, which reduces the reliability and accuracy of single tree attributes acquired at different times and angles. Existing ULS-TLS forest point cloud registration methods rely heavily on the distribution and quality of tree attributes, and usually require pre-registration of multiple TLS scans to obtain sufficient features. Even if accurate tree attributes are obtained, the extracted tree attributes cannot achieve accurate registration for forests with regular tree arrangement and similar tree attributes.

[0006] Through the above analysis, the problems and defects of the prior art are as follows:

[0007] Existing ULS-TLS forest point cloud registration methods rely heavily on the distribution and quality of tree attributes, and usually require pre-registration of multiple TLS scans to obtain sufficient features. Even if accurate tree attributes are obtained, the extracted tree attributes cannot achieve accurate registration for forests with regular tree arrangements and similar tree attributes. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a forest ULS-TLS point cloud automatic registration method based on ground context description.

[0009] The present invention is implemented as follows: a forest ULS-TLS point cloud automatic registration method based on ground context description includes:

[0010] Step 1: Data preprocessing;

[0011] Step 2: Initial correspondence is established;

[0012] Use ground points to implement ULS-TLS registration. The ground can generally be approximated as consisting of planes and curved surfaces. The dimensional characteristics of the points are basically unaffected by the point cloud posture.

[0013] Three features are introduced to characterize the preprocessed point cloud: plane dimension, surface dimension, and the angle between the normal vector and the Z axis. These three geometric features are used to establish a preliminary correspondence between the ULS key points and the TLS scanning positions.

[0014] Step 3: Point cloud registration based on ground context;

[0015] TLS collects surrounding environment data by rotating the scan around its scanning position; based on the LiDAR scanning method, the key points in the ULS that are most similar to the TLS scanning position are identified through ground context matching, and then the transformation matrix is ​​estimated for alignment.

[0016] Further, the data is preprocessed:

[0017] (1) Ground point extraction;

[0018] ULS and TLS ground point cloud data are extracted through morphological filtering; all points in the point cloud are converted to a two-dimensional grid space; the lowest point in each pixel is selected as the seed point for clustering to obtain ground points;

[0019] (2) Local point cloud acquisition;

[0020] The ULS ground point cloud is divided into two-dimensional grids according to the range of x-coordinates and y-coordinates with a certain step size as the grid size, and the center points of these grids are extracted as key points; the z-coordinates of the key points are set to 0.0 because they are irrelevant to the registration algorithm proposed in the present invention;

[0021] The points in the horizontal neighborhood of a certain threshold of the scanning position in the TLS ground point cloud are screened to generate a TLS local point cloud, and the points in the horizontal neighborhood of a certain threshold of each key point in the ULS ground point cloud are extracted to generate multiple ULS local point clouds for subsequent alignment; the threshold is usually set to 5m.

[0022] Further, the ground point extraction:

[0023] ULS and TLS ground point cloud data are extracted by morphological filtering; all points in the point cloud are converted into a two-dimensional grid space; the lowest point in each pixel is selected as the seed point for clustering to obtain the ground point.

[0024] Further, the local point cloud is obtained:

[0025] The ULS ground point cloud is divided into two-dimensional grids according to the range of x-coordinates and y-coordinates with a certain step size as the grid size, and the center points of these grids are extracted as key points; the z-coordinates of the key points are set to 0.0 because they are irrelevant to the registration algorithm proposed in the present invention;

[0026] The points in the horizontal neighborhood of a certain threshold of the scanning position in the TLS ground point cloud are screened to generate a TLS local point cloud, and the points in the horizontal neighborhood of a certain threshold of each key point in the ULS ground point cloud are extracted to generate multiple ULS local point clouds for subsequent alignment; the threshold is usually set to 5m.

[0027] Further, the initial corresponding relationship is established:

[0028] For a given point, principal component analysis (PCA) is applied to analyze the local point cloud in its neighborhood, and three eigenvalues ​​λ1, λ2 and λ3 (satisfying λ1≥λ2≥λ3≥0) and the normal vector can be obtained. Based on these results, the plane feature a2, the surface feature a3 and the angle f between the normal vector and the Z axis of this point can be derived;

[0029]

[0030] Given a TLS scan position p t And its related geometric features and f t , a key point p of ULS u And its related geometric features and f u ; If the following conditions are met, p u Considered as p t The initial corresponding points are added to the initial corresponding set C init middle:

[0031]

[0032] Among them, δ, θ and ε are three different thresholds, set to 0.1, 0.1 and 2.0° respectively;

[0033] Through the above method, the geometric features of all ULS key points and the local point cloud of the TLS scanning position are calculated and compared to generate the initial corresponding set C of the TLS scanning position init ={p u |u=1,2…n init}.

[0034] Further, the point cloud registration based on ground context:

[0035] Similarity score sim(H ULS ,H TLS ) is calculated as follows:

[0036]

[0037] In the similarity score calculation, the matrix element M ij The median values ​​of NaN are excluded from the calculation; variable l represents M ij The number of non-NaN values ​​in the matrix; For the rotation transformation of the TLS point cloud, it can be achieved by performing corresponding transformation operations on the columns of the matrix:

[0038] r m :[z1,z2,…z Na ]→[zNa-m+1 ,…z Na ,z1,…,z Na-m ]#(4)

[0039] Among them, H TLS =[z1,z2,…z Na ], zj represents H TLS A column of , and m∈[1,Na) is the rotation parameter, and the corresponding rotation angle is θ m =2πm / Na;

[0040] If a pair of ground contexts of ULS and TLS correspond correctly, then the similarity score function represents the standard deviation of the height difference between the corresponding point pairs; since the Z axes of ULS and TLS are both vertical, ideally, the similarity score should be close to zero;

[0041] After obtaining the initial correspondence, a ground context Ht is generated for the TLS scan position; at the same time, the initial correspondence set C init The ULS key points in the ground context Hu = {H u i |i=1,2…n init}; To complete the registration, it is necessary to compare each H u i With Ht at a rotation angle θ m The similarity score under the formula is score = sim (H u i ,r m (H t )); the matching item with the smallest score will be selected for registration;

[0042] Using the rotation angle θ in the best match best The horizontal coordinates (xu, yu) of the TLS scanning position and its corresponding point in the ULS can be used to complete the planar alignment. At the same time, according to formula (3), when calculating the similarity score of the best match, the average height difference Zmean between the corresponding points can also be calculated to determine the Z-axis translation transformation in the vertical alignment.

[0043] Another object of the present invention is to provide a forest ULS-TLS point cloud automatic registration system based on ground context description, comprising:

[0044] Preprocessing module, used for data preprocessing;

[0045] Establish a module for initial correspondence establishment; use ground points to achieve ULS-TLS registration, the ground can generally be approximated as consisting of planes and surfaces; the dimensional characteristics of the points are basically not affected by the point cloud posture; introduce three features to characterize the pre-processed point cloud: plane dimension, surface dimension, and the angle between the normal vector and the Z axis; use these three geometric features to establish a preliminary correspondence between the ULS key points and the TLS scanning position;

[0046] Point cloud registration module, used for point cloud registration based on ground context; TLS collects surrounding environment data by rotating the scan around its scanning position; based on the LiDAR scanning method, the key points in the ULS that are most similar to the TLS scanning position are identified through ground context matching, and then the transformation matrix is ​​estimated for registration.

[0047] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the forest ULS-TLS point cloud automatic registration method based on ground context description.

[0048] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the forest ULS-TLS point cloud automatic registration method based on ground context description.

[0049] Another object of the present invention is to provide an information data processing terminal, which is used to implement the forest ULS-TLS point cloud automatic registration system based on ground context description.

[0050] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0051] First, the present invention is committed to proposing a forest ULS-TLS point cloud automatic registration method based on ground context description, so as to quickly, accurately and automatically realize the registration of forest ULS and TLS point clouds, comprehensively obtain forest structure information, and promote the application of lidar technology in forest surveys.

[0052] This paper proposes a new method for automatic ULS-TLS point cloud registration in a forest environment. This method uses ground points as registration primitives to build ground context, and can directly register TLS single scan data to ULS point clouds without the need for pre-registration between different TLS scan point clouds. Compared with point cloud registration based on tree attributes, using ground points for registration shows the following significant advantages:

[0053] (1) High reliability: The extraction process of ground points is relatively standardized and can usually produce stable and reliable results, which are not affected by the distribution, density and shape complexity of trees in the forest.

[0054] (2) Improved computational efficiency: Compared with the extraction of tree attributes, using ground points as registration primitives reduces the computational cost and avoids the high computational overhead caused by the extraction of tree attributes.

[0055] (3) Adaptation to complex environments: The ground point registration method can better adapt to the complex and changeable forest environment and is not limited by the accuracy and reliability of tree attribute acquisition.

[0056] (4) Strong scene applicability: Using ground points for registration can cope with forest scenes with regular tree arrangement and similar trunk attributes, which helps to achieve more accurate registration results in these environments.

[0057] In addition, this method has the following advantages when building ground context for registration:

[0058] (1) When constructing the TLS ground context, points within a certain horizontal distance from the scan center are selected to ensure the quality of the TLS data and effectively reduce the impact of obstructions such as trees on the subsequent registration results.

[0059] (2) In the process of constructing the TLS ground context, the point cloud is discretized into a certain number of segments by equidistant azimuth and radial distance and converted into a matrix. As the distance scanning position increases, these segments gradually expand to accommodate the decrease in point density caused by the divergence of the laser beam.

[0060] (3) A discretization process is adopted when constructing the ground context, and the point cloud is encoded into a matrix according to the height information, which effectively solves the impact of the significant resolution difference between TLS and ULS data on the registration accuracy.

[0061] Second, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0062] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0063] The present invention proposes a forest ULS-TLS point cloud automatic registration method and system based on ground context description, which can automatically, efficiently and accurately realize the registration between TLS and ULS point cloud data, thereby providing important technical support for ecological protection, forestry management and forest resource monitoring, promoting the development of the forestry economy, and contributing to the dual carbon goals, and has broad commercial application prospects.

[0064] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0065] In the relevant technical fields at home and abroad, there is currently no case of realizing forest multi-source point cloud registration based on ground points. The technical solution of the present invention innovatively proposes to use ground points as registration primitives, and designs a ground context to establish the correspondence between TLS and ULS point clouds to complete the automatic registration of point clouds. This invention fills the technical gap and provides an innovative and effective solution for the automatic registration of forest laser point clouds.

[0066] (3) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0067] Point cloud registration is a key step in many LiDAR applications and has been widely studied and developed in the fields of photogrammetry and computer vision. However, most of the research focuses on the registration of point clouds on the same platform, and the exploration of point cloud registration on different platforms is relatively limited. At present, multi-source point cloud registration methods mainly focus on the extraction of geometric features and establish correspondences based on these features. Commonly selected geometric features include points, lines, and planes, but these methods are usually more suitable for urban scenes. For forest point clouds, these methods are difficult to directly apply to multi-source forest point cloud registration due to the limitations of low overlap and high similarity of tree trunk geometric features.

[0068] In complex forest environments, achieving fast and accurate automatic registration of ULS-TLS point clouds is an urgent problem to be solved. At present, automatic registration methods for multi-source forest point clouds usually rely on tree attributes (such as tree position and breast diameter), but these methods are often not suitable for forests with diverse tree species, complex terrain, irregular tree layout, and lack of sufficient common trees. The present invention proposes a method and system for automatic registration of forest ULS-TLS point clouds based on ground context description, innovatively using ground points widely present in various forest environments as registration primitives, successfully solving the difficult problem of achieving fast and accurate registration of ULS and TLS point clouds in complex forest environments, and meeting the long-standing demand for people to efficiently obtain forest structure information with the help of lidar technology.

[0069] (4) The technical solution of the present invention overcomes technical prejudice:

[0070] Traditional forest multi-source point cloud registration methods mostly rely on tree attributes. The present invention abandons this path and innovatively achieves registration through ground points and ground context, reflecting the breakthrough and creativity of technical ideas.

[0071] Fourth, the innovation and technological progress of the present invention

[0072] 1. Innovation of the present invention

[0073] 1. Introduce ground context

[0074] This invention introduces ground context for the first time in the point cloud registration of ULS (UAV laser scanning) and TLS (terrestrial laser scanning), and performs differential recognition of point cloud data under different scanning angles. Registration is achieved through similarity scoring of ground context, which significantly improves the point cloud matching accuracy in different environments and different scanning positions.

[0075] 2. Initial correspondence construction based on geometric features

[0076] This method extracts plane dimensions, surface dimensions, and the angle between the normal vector and the Z axis as geometric features, and uses the principal component analysis (PCA) method to analyze the local point cloud, solving the problems of large noise interference and low initial matching efficiency in traditional methods. This multi-feature joint application ensures a more accurate initial match for registration and reduces the amount of subsequent calculations.

[0077] 3. Optimized calculation model for similarity scoring

[0078] In the design of the similarity scoring model, the difference matrix is ​​used to calculate the standard deviation of non-NaN values, and the angle rotation matching is designed to enhance the rotation invariance of the point cloud. The rotation transformation parameters are calculated by the column transformation of the matrix, which ensures the accuracy of the initial matching of the point cloud while reducing the matching time and achieving efficient registration between ULS and TLS.

[0079] 4. Innovative algorithm that emphasizes both time efficiency and accuracy

[0080] Through improved ground context and optimized similarity score calculation model, the time and computing resources required for ULS-TLS point cloud registration are significantly reduced. While retaining the accuracy of high-level information, the present invention takes into account both registration speed and spatial complexity, making it have high practical use value in applications such as real-time drone inspection and ground 3D modeling.

[0081] 2. Existing technical problems solved

[0082] 1. Difficulty in constructing initial correspondences in point cloud registration

[0083] Traditional ULS-TLS point cloud registration mostly relies on manual annotation or local matching algorithms, but is greatly affected by noise and inconsistent point cloud features in complex environments. The present invention introduces plane and curved surface features, as well as geometric features such as the angle between the normal vector and the Z axis, to automatically construct the initial correspondence between ULS and TLS, greatly improving the efficiency and accuracy of automated registration.

[0084] 2. Limitations of traditional point cloud registration methods in complex scenes

[0085] Conventional registration methods are not applicable in terms of height difference and vertical dimension, and are difficult to handle the scanning differences of ULS-TLS. The present invention optimizes the calculation of similarity score by adding ground context and rotation transformation model, and realizes efficient and high-precision point cloud matching under different scanning conditions.

[0086] 3. It is difficult to balance registration speed and accuracy

[0087] The complex initial matching and subsequent optimization in traditional point cloud registration methods make it difficult to balance the operation speed and accuracy. The present invention adopts a fast screening registration method based on ground context, which accelerates the matching process through similarity scoring, which not only improves the speed but also ensures the accuracy, and can meet the real-time needs of drones and ground laser scanning in actual engineering.

[0088] 3. Significant Technological Advances

[0089] 1. Improved the automation level of point cloud registration

[0090] The initial correspondence is constructed using a variety of geometric features, and the point cloud is automatically registered through ground context, eliminating the need for manual intervention and greatly improving the convenience and automation level of drone scanning applications.

[0091] 2. Improved registration accuracy at different angles

[0092] Through innovative similarity scoring and angle rotation calculation methods, the present invention achieves high-precision registration of point cloud data at different angles, significantly improving the registration effect of ULS and TLS. This technological advancement provides reliable support for applications such as 3D modeling, forestry management, and terrain mapping.

[0093] 3. Enhanced applicability in complex environments

[0094] The algorithm of the present invention improves the noise suppression capability and registration efficiency through filtering and optimized point cloud processing methods, so that the system still has strong robustness in complex terrain and harsh environments, can accurately adapt to different application scenarios and quickly complete data processing tasks.

[0095] Fifth, the technical solution of the present invention effectively solves the following key problems existing in the prior art in industrial applications, and has achieved significant technical progress:

[0096] 1. Technical problems of existing technologies

[0097] 1. Point cloud registration accuracy is not high

[0098] Traditional point cloud registration technology usually relies on simple feature point matching or ICP algorithm when processing ULS (UAV laser scanning) and TLS (terrestrial laser scanning) data. However, it is difficult to achieve high-precision registration in environments with high noise or poor data quality, especially in scenes with complex terrain or unclear target features, where the accuracy will drop significantly.

[0099] 2. Low registration efficiency, difficult to meet real-time needs

[0100] Conventional registration methods require large amounts of computation, especially when large amounts of data are involved, and the computation speed is slow, making it difficult to meet real-time requirements in practical applications. However, applications such as drone inspections have high demands for real-time data processing, so the speed bottleneck of existing technologies limits the scope of application.

[0101] 3. Registration is easily affected by changes in scanning angle

[0102] There are inconsistencies in feature matching of point cloud data at different angles. Conventional feature point matching algorithms perform poorly when dealing with different viewing angles and angles, which can easily lead to a decrease in registration accuracy. Especially when the scanning angles of ULS and TLS are very different, the registration effect is difficult to guarantee.

[0103] 4. Inability to adequately handle noisy and unstructured data

[0104] Point cloud data often contains noise, especially in outdoor environments, which is affected by lighting, vegetation, and scanning devices. Traditional methods are difficult to fully filter out noise during the registration process, making the registration results susceptible to interference, affecting accuracy and stability.

[0105] 2. Significant technical advancement of the present invention

[0106] 1. Improved the automation and accuracy of point cloud registration

[0107] The present invention greatly improves the initial matching accuracy and automation of ULS-TLS point cloud data by introducing multiple geometric features (such as plane dimensions, surface dimensions, and the angle between the normal vector and the Z axis) and ground context. The initial correspondence constructed using these features reduces the need for human intervention, allowing the system to automatically and accurately identify and match key points, achieving high-precision registration effects.

[0108] 2. Significantly improve the registration speed to meet real-time requirements

[0109] Through the innovative similarity scoring model and rotation transformation method, the present invention realizes an efficient registration process. In particular, the combination of similarity scoring and ground context can quickly identify and match point cloud data, reduce complex calculations, and enable the algorithm to meet real-time requirements in practical applications.

[0110] 3. Enhanced adaptability to different scanning angles and noise data

[0111] The present invention designs a rotation matching model in the ground context, so that the system can cope with point cloud data of different scanning angles and is not easily affected by the difference in viewing angles. In addition, through morphological filtering and matching methods based on local features, the system can still maintain a high registration accuracy in a noisy environment and adapt to complex scanning scenes.

[0112] 4. Significantly improved the data fusion processing effect and expanded the scope of application

[0113] The progress of the present invention in ULS-TLS registration technology solves the data fusion problem in the fields of drone inspection, geographic mapping, architectural modeling, etc., and lays the foundation for efficient data integration and subsequent data analysis. The algorithm can be widely used in forest mapping, terrain modeling and smart city management, significantly improving the availability and accuracy of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figure 1 It is a flow chart of a forest ULS-TLS point cloud automatic registration method based on ground context description provided by an embodiment of the present invention.

[0115] Figure 2 It is a structural block diagram of a forest ULS-TLS point cloud automatic registration system based on ground context description provided by an embodiment of the present invention.

[0116] Figure 3 It is a ground context description construction diagram provided by an embodiment of the present invention.

[0117] Figure 4 Schematic diagram of local point cloud extraction provided by an embodiment of the present invention. (a, b, c) represent local point clouds of a certain scan of TLS, and (d, e, f) represent local point clouds of a certain key point of ULS. (a, d) represent top views, (b, e) represent front views, and (c, f) represent left views.

[0118] Figure 5 Schematic diagram of the registration result provided by the embodiment of the present invention, (a) the sample plot after registration, (b) the cross-section view, and (c) the local enlarged view of the cross-section. DETAILED DESCRIPTION

[0119] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0120] like Figure 1As shown, an automatic registration method for forest ULS-TLS point cloud based on ground context description provided by an embodiment of the present invention includes the following steps:

[0121] S101: data preprocessing;

[0122] S102: initial corresponding relationship is established;

[0123] Use ground points to implement ULS-TLS registration. The ground can generally be approximated as consisting of planes and curved surfaces. The dimensional characteristics of the points are basically unaffected by the point cloud posture.

[0124] Three features are introduced to characterize the preprocessed point cloud: plane dimension, surface dimension, and the angle between the normal vector and the Z axis. These three geometric features are used to establish a preliminary correspondence between the ULS key points and the TLS scanning positions.

[0125] S103: Point cloud registration based on ground context;

[0126] TLS collects surrounding environment data by rotating the scan around its scanning position; based on the LiDAR scanning method, the key points in the ULS that are most similar to the TLS scanning position are identified through ground context matching, and then the transformation matrix is ​​estimated for alignment.

[0127] The forest ULS-TLS point cloud automatic registration method based on ground context description provided by the present invention realizes the accurate registration of unmanned laser scanning (ULS) and terrestrial laser scanning (TLS) point cloud data through data preprocessing, initial correspondence establishment and context-based point cloud registration, which is particularly suitable for complex forest environments. The following is a detailed working principle:

[0128] First, in the data preprocessing stage (S101), the system processes the original point cloud data to remove noise and unify the data format to provide high-quality input for subsequent registration. Since the forest point cloud data contains trees, ground and other debris, in order to improve the registration accuracy, the preprocessing stage uses a filtering algorithm to extract ground points from the entire point cloud data. This step ensures that the subsequent steps of the system are only performed on the basis of meaningful ground points, which improves the registration efficiency.

[0129] In the initial correspondence establishment stage (S102), the system uses the stable features of ground points to achieve preliminary alignment of ULS-TLS point clouds. Forest floors can usually be approximated as planes or curved surfaces, and these dimensional features are not affected by posture in ULS and TLS point cloud data. Therefore, the system introduces three features: plane dimension, surface dimension, and the angle between the normal vector and the Z axis. The stability of these geometric features allows ULS and TLS data to establish preliminary correspondences and form preliminary point pairs even in complex forest environments, thus providing a reliable basis for subsequent precise alignment.

[0130] In the ground context-based point cloud registration stage (S103), the system uses the scanning characteristics of TLS to further optimize the registration process. TLS collects three-dimensional data of the surrounding environment by rotating the scan around a fixed position, while ULS data comes from laser scanning of drones, with a larger range but less details. In order to achieve the registration of the two, the system constructs a ground context descriptor to match the data context of the TLS scanning position with the key point data of the ULS. This context descriptor can identify the most matching key points between the TLS position and the ULS data, making the registration process more targeted.

[0131] After ground context matching, the system further estimates the transformation matrix between ULS and TLS. This matrix contains translation and rotation parameters, which can accurately reflect the relative position and posture of ULS and TLS point cloud data in three-dimensional space. By estimating the transformation matrix, the system can effectively perform preliminary spatial alignment of ULS and TLS data, so that the point cloud data of both are represented in the same coordinate system.

[0132] The system then uses an iterative optimization algorithm to refine the registration results. By gradually adjusting the transformation matrix, the error between the ULS and TLS point clouds is reduced, thereby improving the registration accuracy. After each iteration, the system re-evaluates the point pair relationship between the ULS and TLS and adjusts the matching relationship based on the error results. This step ensures the accuracy of the registration results and ultimately achieves high-precision point cloud alignment.

[0133] Finally, the registration method of the present invention has been verified many times and is particularly suitable for ULS-TLS point cloud registration in forest environments. In complex forest scenes, due to the introduction of ground context, this method can quickly and accurately identify the correspondence between ULS and TLS, greatly improving the automation and accuracy of point cloud registration, and providing effective data support for forest monitoring, ecological research and other fields.

[0134] Data preprocessing provided by the embodiment of the present invention:

[0135] (1) Ground point extraction;

[0136] ULS and TLS ground point cloud data are extracted through morphological filtering; all points in the point cloud are converted to a two-dimensional grid space; the lowest point in each pixel is selected as the seed point for clustering to obtain ground points;

[0137] (2) Local point cloud acquisition;

[0138] The ULS ground point cloud is divided into two-dimensional grids according to the range of x-coordinates and y-coordinates with a certain step size as the grid size, and the center points of these grids are extracted as key points; the z-coordinates of the key points are set to 0.0 because they are irrelevant to the registration algorithm proposed in the present invention;

[0139] The points in the horizontal neighborhood of a certain threshold of the scanning position in the TLS ground point cloud are screened to generate a TLS local point cloud, and the points in the horizontal neighborhood of a certain threshold of each key point in the ULS ground point cloud are extracted to generate multiple ULS local point clouds for subsequent alignment; the threshold is usually set to 5m.

[0140] The ground point extraction provided by the embodiment of the present invention:

[0141] ULS and TLS ground point cloud data are extracted by morphological filtering; all points in the point cloud are converted into a two-dimensional grid space; the lowest point in each pixel is selected as the seed point for clustering to obtain the ground point.

[0142] The local point cloud acquisition provided by the embodiment of the present invention:

[0143] The ULS ground point cloud is divided into two-dimensional grids according to the range of x-coordinates and y-coordinates with a certain step size as the grid size, and the center points of these grids are extracted as key points; the z-coordinates of the key points are set to 0.0 because they are irrelevant to the registration algorithm proposed in the present invention;

[0144] The points in the horizontal neighborhood of a certain threshold of the scanning position in the TLS ground point cloud are screened to generate a TLS local point cloud, and the points in the horizontal neighborhood of a certain threshold of each key point in the ULS ground point cloud are extracted to generate multiple ULS local point clouds for subsequent alignment; the threshold is usually set to 5m.

[0145] The initial correspondence relationship provided by the embodiment of the present invention is established:

[0146] For a given point, principal component analysis (PCA) is applied to analyze the local point cloud in its neighborhood, and three eigenvalues ​​λ1, λ2 and λ3 (satisfying λ1≥λ2≥λ3≥0) and the normal vector can be obtained. Based on these results, the plane feature a2, the surface feature a3 and the angle f between the normal vector and the Z axis of this point can be derived;

[0147]

[0148] Given a TLS scan position p t And its related geometric features and f t , a key point p of ULS u And its related geometric features and f u ; If the following conditions are met, p u Considered as p t The initial corresponding points are added to the initial corresponding set C init middle:

[0149]

[0150] Among them, δ, θ and ε are three different thresholds, set to 0.1, 0.1 and 2.0° respectively;

[0151] Through the above method, the geometric features of all ULS key points and the local point cloud of the TLS scanning position are calculated and compared to generate the initial corresponding set C of the TLS scanning position init ={p u |u=1,2…n init}.

[0152] Point cloud registration based on ground context provided by an embodiment of the present invention:

[0153] Similarity score sim(H ULS ,H TLS ) is calculated as follows:

[0154]

[0155] In the similarity score calculation, the matrix element M ij The median values ​​of NaN are excluded from the calculation; variable l represents M ij The number of non-NaN values ​​in the matrix; For the rotation transformation of the TLS point cloud, it can be achieved by performing corresponding transformation operations on the columns of the matrix:

[0156] r m :[z1,z2,…z Na ]→[z Na-m+1 ,…z Na ,z1,…,z Na-m ]#(4)

[0157] Among them, H TLS =[z1,z2,…z Na ], zj represents H TLSA column of , and m∈[1,Na) is the rotation parameter, and the corresponding rotation angle is θ m =2πm / Na;

[0158] If a pair of ground contexts of ULS and TLS correspond correctly, then the similarity score function represents the standard deviation of the height difference between the corresponding point pairs; since the Z axes of ULS and TLS are both vertical, ideally, the similarity score should be close to zero;

[0159] After obtaining the initial correspondence, a ground context Ht is generated for the TLS scan position; at the same time, the initial correspondence set C init The ULS key points in the ground context Hu = {H u i |i=1,2…n init}; To complete the registration, it is necessary to compare each H u i With Ht at a rotation angle θ m The similarity score under the formula is score = sim (H u i ,r m (H t )); the matching item with the smallest score will be selected for registration;

[0160] Using the rotation angle θ in the best match best The horizontal coordinates (xu, yu) of the TLS scanning position and its corresponding point in the ULS can be used to complete the planar alignment. At the same time, according to formula (3), when calculating the similarity score of the best match, the average height difference Zmean between the corresponding points can also be calculated to determine the Z-axis translation transformation in the vertical alignment.

[0161] like Figure 2 As shown, an embodiment of the present invention provides a forest ULS-TLS point cloud automatic registration system based on ground context description, including:

[0162] Preprocessing module, used for data preprocessing;

[0163] Establish a module for initial correspondence establishment; use ground points to achieve ULS-TLS registration, the ground can generally be approximated as consisting of planes and surfaces; the dimensional characteristics of the points are basically not affected by the point cloud posture; introduce three features to characterize the pre-processed point cloud: plane dimension, surface dimension, and the angle between the normal vector and the Z axis; use these three geometric features to establish a preliminary correspondence between the ULS key points and the TLS scanning position;

[0164] Point cloud registration module, used for point cloud registration based on ground context; TLS collects surrounding environment data by rotating the scan around its scanning position; based on the LiDAR scanning method, the key points in the ULS that are most similar to the TLS scanning position are identified through ground context matching, and then the transformation matrix is ​​estimated for registration.

[0165] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the forest ULS-TLS point cloud automatic registration method based on ground context description.

[0166] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the forest ULS-TLS point cloud automatic registration method based on ground context description.

[0167] Another object of the present invention is to provide an information data processing terminal, which is used to implement the forest ULS-TLS point cloud automatic registration system based on ground context description.

[0168] The mathematical model of the present invention solves the following problems in the prior art:

[0169] 1. Low accuracy of ULS-TLS point cloud registration

[0170] In the existing ULS (Unmanned Aerial Vehicle Laser Scanning) and TLS (Terrestrial Laser Scanning) point cloud data registration methods, due to the differences in resolution, viewing angle and point cloud density of the data sources, feature loss and matching errors are prone to occur in the registration process.

[0171] 2. Registration failure in complex terrain environments

[0172] The complex terrain in the forest environment makes it difficult for traditional methods to accurately extract ground points and further use ground features for registration. Especially when the ground height changes greatly, the registration algorithm is prone to failure.

[0173] 3. Low efficiency of initial corresponding point calculation

[0174] Traditional initial corresponding point generation algorithms based on geometric features require a lot of calculations in high-dimensional point clouds, resulting in low efficiency and difficulty in meeting the real-time processing requirements of large-scale point clouds in forest scenes.

[0175] 4. Problem of inconsistent multi-scale context

[0176] It is difficult for existing methods to construct a unified context descriptor to combine the different resolutions and point cloud characteristics of ULS and TLS, resulting in large errors in the rotation angle estimation in the initial registration stage.

[0177] Significant technological advancements:

[0178] 1. Mathematical model of ground point extraction improves accuracy

[0179] Morphological filtering and rasterization: The point cloud is converted into a two-dimensional grid space through morphological filtering, and the lowest point of each pixel is selected as the seed point and clustered to ensure accurate extraction of ground points. This method solves the problem of accuracy in ground point extraction in complex forest environments.

[0180] Local point cloud generation model: Generate local point clouds of TLS and ULS through two-dimensional grid division and neighborhood radius setting, unify the representation of key points, and reduce the error between different point cloud resolutions.

[0181] 2. Initial correspondence relationship modeling improves efficiency and robustness

[0182] The geometric feature extraction formula (1) based on principal component analysis (PCA) effectively obtains the plane features (a2), surface features (a3) ​​and normal vector angle (f) of the point. These features remain unchanged under posture changes, significantly improving the reliability of feature matching.

[0183] Conditional formula (2) reduces the computational complexity by constraining the feature difference threshold and quickly generates the initial corresponding point set C.

[0184] 3. Innovative design of highly contextual descriptors improves matching robustness

[0185] The height context descriptor is introduced. Based on LiDAR scanning data, the similarity scores of ULS and TLS point clouds are calculated using the matrix difference and standard deviation formula (3), which overcomes the complexity of height changes in forest environments.

[0186] Through the rotation transformation operation of formula (4), the similarity scores at different rotation angles are quickly calculated, the best matching rotation angle is selected, and the joint registration of rotation and horizontal coordinates is achieved.

[0187] 4. Multi-dimensional feature fusion improves overall registration accuracy

[0188] The present invention combines the optimal rotation angle (θ m ) and average height difference (Z), completing both planar and vertical registration, thus achieving full consistency between ULS and TLS point clouds.

[0189] 5. Unification of context description and multi-resolution matching

[0190] The height context descriptor is used to uniformly represent the ground context features of ULS and TLS point clouds, overcoming the differences in data resolution and sampling density between the two.

[0191] In forest scenes, the algorithm can maintain high robustness even with complex terrain.

[0192] 6. Efficient computing models adapt to large-scale data processing

[0193] Through the design of matrix operations and contextual similarity scoring, the present invention can quickly process ULS and TLS point cloud data in large-scale forest scenes while significantly reducing the calculation time.

[0194] The innovation of the mathematical model in this invention directly solves the problems of insufficient accuracy, efficiency and adaptability commonly existing in the prior art of ULS-TLS point cloud registration. The main technical advances are reflected in the following aspects:

[0195] 1. Improved accuracy: The combination of principal component analysis and context descriptors ensures the accuracy of point cloud registration, and stable matching results can be obtained even in complex forest environments.

[0196] 2. Efficiency improvement: Matrix processing and threshold constraints optimize the feature extraction and initial corresponding point generation process, greatly reducing the registration time.

[0197] 3. Enhanced adaptability: The introduction of highly contextual descriptors enables the algorithm to adapt to the matching requirements of point clouds with various resolutions, especially showing extremely high robustness in forest environments.

[0198] Example 1: Precision measurement of forest resources and ecological assessment

[0199] Forest resource surveys require accurate 3D data to estimate tree growth, terrain changes, and carbon storage. The limitations of traditional single data sources (such as TLS or ULS) limit measurement accuracy, while ULS-TLS point cloud fusion technology can make up for this deficiency.

[0200] 1. Data Collection

[0201] Use UAV laser scanning (ULS) to acquire aerial point clouds of large forest areas, covering topography and canopy structure.

[0202] Terrestrial laser scanning (TLS) stations were deployed in the selected sample plots to collect high-density ground point cloud data, especially the tree trunk and ground morphology.

[0203] 2. Data processing

[0204] Through the point cloud automatic registration method of the present invention:

[0205] Ground Point Extraction: Extract ground points from ULS and TLS point clouds using morphological filtering.

[0206] Highly contextual descriptor registration: Data fusion is achieved by matching highly contextual descriptors of ULS-TLS point clouds.

[0207] Multi-dimensional feature fusion: Calculate indicators such as trunk diameter, tree height, and forest terrain changes through the registered point cloud data.

[0208] 3. Results Analysis

[0209] Obtain a three-dimensional model of the entire forest resource to accurately assess the growth status of forest vegetation, tree species distribution and carbon storage.

[0210] Use point cloud data in forest management systems to provide a basis for long-term monitoring of ecological changes.

[0211] Application Value

[0212] - Accurately assess forest resources and provide a scientific basis for forestry decision-making.

[0213] - Improve the accuracy of forest carbon sink measurement and support the formulation of carbon emission trading and ecological protection policies.

[0214] Example 2: Forest Disaster Assessment and Restoration Monitoring

[0215] After a forest fire, landslide or insect infestation, it is necessary to quickly assess the damage in the affected area and the subsequent vegetation recovery. ULS-TLS point cloud fusion technology can quickly generate high-precision 3D models to support post-disaster recovery planning.

[0216] 1. Data Collection

[0217] Deploy drone laser scanning in the disaster-stricken areas to quickly cover a large area of ​​disaster-affected point cloud data, focusing on recording the overall morphology of tree canopies and terrain.

[0218] TLS was used to perform high-precision scans in the disaster-stricken areas, capturing details of fallen trees, exposed ground, and tree trunks.

[0219] 2. Data processing

[0220] Ground point extraction: Extract ground points in the disaster area through morphological filtering to analyze the scope of landslide or fire surface.

[0221] Initial corresponding point establishment and registration: The method of the present invention is used to realize the high context descriptor registration of the ULS-TLS point cloud to generate a unified three-dimensional model.

[0222] Restoration monitoring: Comparative analysis of point cloud data from multiple time periods (e.g., ULS and TLS data for a year) to assess the natural recovery rate and trend of forest vegetation.

[0223] 3. Result Output

[0224] Output a high-precision three-dimensional disaster model, including information such as the location distribution of fallen trees and the extent of damaged surface.

[0225] Provide restoration monitoring reports and analyze vegetation recovery rate and spatial distribution.

[0226] Application Value

[0227] - Rapidly assess the scope of disaster losses and provide support for post-disaster emergency management.

[0228] - Track restoration progress, optimize vegetation restoration plans, and improve ecological restoration efficiency.

[0229] Summarize

[0230] The present invention has important industrial application value in the fields of forest resource measurement and ecological assessment, disaster assessment and recovery monitoring, etc. It can significantly improve the accuracy and processing efficiency of data acquisition, and provide advanced technical support for forestry and ecological protection.

[0231] The present invention is specifically implemented:

[0232] Step 1: Data Preprocessing

[0233] (1) Ground point extraction

[0234] ULS and TLS ground point cloud data are extracted by morphological filtering. All points in the point cloud are converted to a two-dimensional grid space. The lowest point in each pixel is selected as the seed point for clustering to obtain the ground point.

[0235] (2) Local point cloud acquisition

[0236] In order to determine the potential corresponding positions of the TLS scan positions in the ULS, the ULS ground point cloud is divided into two-dimensional grids according to the range of x-coordinates and y-coordinates with a certain step size as the grid size, which is set to 0.5 meters, and the center points of these grids are extracted as key points. The z-coordinates of the key points are set to 0.0 because they are irrelevant to the registration algorithm proposed in the present invention.

[0237] The points in the horizontal neighborhood of the scanning position within a certain threshold in the TLS ground point cloud are filtered to generate TLS local point clouds, and the points in the horizontal neighborhood of each key point within a certain threshold in the ULS ground point cloud are extracted to generate multiple ULS local point clouds for subsequent registration. The threshold is usually set to 5m, and the user can set the threshold size according to different forest scenes.

[0238] Step 2: Initial correspondence establishment

[0239] The proposed method uses ground points to achieve ULS-TLS registration. The ground can generally be approximated as consisting of planes and curved surfaces. The dimensional features of the points are basically unaffected by the point cloud posture. In addition, since the Z-axis of the TLS and ULS coordinate systems in this study is vertically downward, the angles between the normal vectors of the corresponding points in the TLS and ULS point clouds and the Z-axis are consistent. Therefore, this paper introduces three features to characterize the preprocessed point cloud: plane dimension, surface dimension, and the angle between the normal vector and the Z-axis. In order to reduce the amount of calculation, these three geometric features are used to establish a preliminary correspondence between the ULS key points and the TLS scanning positions.

[0240] For a given point, principal component analysis (PCA) is applied to analyze the local point cloud in its neighborhood, and three eigenvalues ​​λ1, λ2 and λ3 (satisfying λ1≥λ2≥λ3≥0) and the normal vector can be obtained. Based on these results, the plane feature a2, the surface feature a3 and the angle f between the normal vector and the Z axis of the point can be derived.

[0241]

[0242] Given a TLS scan position p t And its related geometric features and f t , a key point p of ULS u And its related geometric features and f u If the following conditions are met, p u Considered as p t The initial corresponding points are added to the initial corresponding set C init middle:

[0243]

[0244] Among them, δ, θ and ε are three different thresholds, set to 0.1, 0.1 and 2.0° respectively.

[0245] Through the above method, the geometric features of all ULS key points and the local point cloud of the TLS scanning position are calculated and compared to generate the initial corresponding set C of the TLS scanning position init ={p u |u=1,2…n init}.

[0246] Step 3: Point cloud registration based on ground context

[0247] TLS collects surrounding environment data by rotating the scan around its scanning position. Based on the scanning method of LiDAR, the present invention proposes ground context. Through ground context matching, the key points in ULS that are most similar to the TLS scanning position are identified, and then the transformation matrix is ​​estimated for registration.

[0248] The cylindrical neighborhood around a point is defined as a cylinder radius of r, which is uniformly discretized in radial distance and azimuth. Specifically, the radial direction is divided into Nr=ceil(r / dr) radial segments, and the azimuth direction is divided into Na angular segments. Among them, the cylinder radius r is set to 5.0 meters, the radial resolution dr is set to 0.5 meters, and the number of azimuth segments Na is set to 360. The discretization space can be represented as an Nr×Na matrix H defined as the ground context. Each element in the matrix corresponds to a segment. If the segment contains at least one point, it is assigned the average height of the points in the segment; if it is empty, it is assigned NaN (non-number). The generation process of the ground context is as follows Figure 3 shown.

[0249] In order to measure the similarity between two ground contexts, the present invention proposes a similarity scoring function. Similarity score sim(H ULS ,H TLS ) is calculated as follows:

[0250]

[0251] In the similarity score calculation, the matrix element M ij The median values ​​of NaN are excluded from the calculation. ij The number of non-NaN values ​​in the TLS point cloud. The rotation transformation can be achieved by performing corresponding transformation operations on the columns of the matrix:

[0252] r m :[z1,z2,…z Na ]→[z Na-m+1 ,…z Na ,z1,…,z Na-m ]#(4)

[0253] Among them, H TLS =[z1,z2,…z Na ], zj represents H TLS A column of , and m∈[1,Na) is the rotation parameter, and the corresponding rotation angle is θ m =2πm / Na.

[0254] If a pair of ground contexts of ULS and TLS are correctly aligned, the similarity score function represents the standard deviation of the height difference between the corresponding point pairs. Since the Z axes of ULS and TLS are both vertical, ideally, the similarity score should be close to zero.

[0255] After obtaining the initial correspondence, a ground context Ht is generated for the TLS scan position. At the same time, the initial correspondence set C init The ULS key points in the ground context Hu = {H u i |i=1,2…n init}. To complete the registration, it is necessary to compare each H u i With Ht at a rotation angle θ m The similarity score under the formula is score = sim (H u i ,r m (H t )). The matches with the smallest scores will be selected for registration.

[0256] Using the rotation angle θ in the best match best The horizontal coordinates (xu, yu) of the TLS scanning position and its corresponding point in the ULS can be used to complete the planar registration. At the same time, according to formula (3), when calculating the similarity score of the best match, the average height difference Zmean between the corresponding points can also be calculated to determine the Z-axis translation transformation in the vertical registration.

[0257] In the preprocessing stage, in order to reduce the computational burden, this paper sets the grid size to 0.5 meters. This leads to a decrease in the registration accuracy, so further fine matching is required. best Then, select values ​​at intervals of 0.05 meters within a range of 1 meter around xu and yu, and at θ best Values ​​are selected at intervals of 0.5° within a 5° range. These refined values ​​are then freely combined and registered using the ground context. The similarity scores among all combinations are calculated and the case with the smallest score is selected as the final result of the coarse registration.

[0258] After the rough registration, fine registration is required to improve the accuracy. Since the ground is usually composed of multiple planes or curved surfaces, the point-to-plane iterative closest point (ICP) algorithm is used to achieve fine registration. In order to ensure a high degree of overlap between data sets and avoid local optimality during the registration process, two local point clouds of the TLS scanning position and its corresponding point in the ULS ground point cloud are used as inputs to the point-to-plane ICP algorithm.

[0259] Technical solution of the embodiment of the present invention

[0260] Taking a forest plot of 60m×20m as an example, the technical solution of the forest ULS-TLS point cloud automatic registration method based on ground context description is as follows.

[0261] (1) Sample site TLS data collection

[0262] In the forest plot, the operator used a ground laser scanner to collect data at 11 scanning locations to ensure complete coverage of the forest. In addition, on the same date, after the TLS data collection was completed, the operator collected data on the plot with a drone laser scanner, flying at an altitude of about 50 meters above the ground and scanning at a frequency of 1800kHz. The ULS data has a larger coverage area, and the TLS data is a subset of the ULS data.

[0263] (2) Data preprocessing

[0264] For the TLS data of different scanning positions of the sample site, morphological filtering is first used to extract ground points, and then the local point cloud of each scanning position is extracted. For the ULS data of the sample site, morphological filtering is also used to extract ground points, and then the key points are extracted by two-dimensional gridding method, and finally the local point cloud is extracted for each key point. Figure 4 The extraction results of the local point cloud of a certain TLS scanning position in the sample site and the local point cloud of a key point in the ULS point cloud are shown.

[0265] (3) Point cloud registration

[0266] After extracting the TLS local point cloud and the local point cloud of the ULS key points, the geometric features are first used to establish the preliminary correspondence, and then the ground context is used to filter out the correct correspondence to complete the coarse registration. Finally, the point-to-surface ICP algorithm is used to optimize the coarse registration results.

[0267] 2. Technical Effects of Embodiments of the Invention

[0268] A forest plot is used as an example to demonstrate the technical effect. The 11 scanned TLS point clouds of the plot are registered using the forest ULS-TLS point cloud automatic registration method based on ground context description, and scans S1-11 are all registered to the ULS point cloud.

[0269] The visual registration effect is as follows Figure 5 As shown in the figure, the point clouds of different colors represent the different scanned TLS point clouds and ULS point clouds of the sample site; all TLS scans were successfully registered to the ULS point cloud, and the registration accuracy was sufficient to describe the structural characteristics of branches and trunks. The error statistics of the registration results are shown in Table 1. The average translation error and average rotation error of the registration results of the sample site are 7.89cm and 1.01mrad respectively.

[0270] Table 1. Accuracy evaluation of registration results

[0271]

[0272]

[0273] The present invention is applicable to the fields of forest ecological monitoring, forestry management and environmental protection, and is particularly applicable to the forest structure analysis, tree species identification, biomass estimation and carbon stock monitoring, which require fine three-dimensional information. By combining terrestrial laser scanning (TLS) and unmanned aerial vehicle laser scanning (ULS), the present invention can obtain high-precision tree structure data in forest plots, thereby improving the comprehensiveness and accuracy of forest structure information. In addition, the technology can be applied to environmental monitoring systems, geographic information systems (GIS), forest resource survey equipment, and software tools related to ecology and remote sensing, as an important component of data registration and information fusion modules.

[0274] This paper proposes a ground context-based method for automatic registration of single-scan TLS and ULS data in a forest environment. First, by comparing geometric features, potential ULS points corresponding to TLS scan positions are identified and a preliminary correspondence is established. Subsequently, the ground context is constructed using the ground points of ULS and TLS for coarse registration, and then fine registration is achieved using the point-to-plane ICP algorithm. In the experiment, 11 TLS scans and ULS data from a forest plot were used for verification. Figure 5 As shown in Table 1, all TLS scans were successfully registered to the ULS point cloud, and the registration effect was good, which could obtain more comprehensive and accurate forest structure information. As shown in Table 1, the average translation error and average rotation error of the ULS-TLS registration results of this site were 7.89 cm and 1.01 mrad, respectively, which verified the effectiveness of this method.

[0275] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0276] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A forest ULS-TLS point cloud automatic registration method based on ground context description, characterized in that: The following steps are involved: Step 1: Data preprocessing; Step 2: Initial correspondence is established; Use ground points to implement ULS-TLS registration. The ground can generally be approximated as consisting of planes and curved surfaces. The dimensional characteristics of the points are basically unaffected by the point cloud posture. Three features are introduced to characterize the preprocessed point cloud: plane dimension, surface dimension, and the angle between the normal vector and the Z axis. These three geometric features are used to establish a preliminary correspondence between the ULS key points and the TLS scanning positions. Step 3: Point cloud registration based on ground context; TLS collects surrounding environment data by rotating the scan around its scanning position; based on the LiDAR scanning method, the key points in the ULS that are most similar to the TLS scanning position are identified through ground context matching, and then the transformation matrix is ​​estimated for alignment.

2. The method for automatic registration of forest ULS-TLS point clouds based on ground context description according to claim 1, characterized in that: The data preprocessing: (1) Ground point extraction; ULS and TLS ground point cloud data are extracted through morphological filtering; all points in the point cloud are converted to a two-dimensional grid space; the lowest point in each pixel is selected as the seed point for clustering to obtain ground points; (2) Local point cloud acquisition; The ULS ground point cloud is divided into two-dimensional grids according to the range of x-coordinates and y-coordinates with a certain step size as the grid size, and the center points of these grids are extracted as key points; the z-coordinates of the key points are set to 0.0 because they are irrelevant to the registration algorithm proposed in the present invention; The points in the horizontal neighborhood of a certain threshold of the scanning position in the TLS ground point cloud are screened to generate a TLS local point cloud, and the points in the horizontal neighborhood of a certain threshold of each key point in the ULS ground point cloud are extracted to generate multiple ULS local point clouds for subsequent alignment; the threshold is usually set to 5m.

3. The method for automatic registration of forest ULS-TLS point clouds based on ground context description as claimed in claim 2, characterized in that: The ground point extraction: ULS and TLS ground point cloud data are extracted by morphological filtering; all points in the point cloud are converted into a two-dimensional grid space; the lowest point in each pixel is selected as the seed point for clustering to obtain the ground point.

4. The method for automatic registration of forest ULS-TLS point clouds based on ground context description according to claim 2, characterized in that: The local point cloud acquisition: The ULS ground point cloud is divided into two-dimensional grids according to the range of x-coordinates and y-coordinates with a certain step size as the grid size, and the center points of these grids are extracted as key points; the z-coordinates of the key points are set to 0.0 because they are irrelevant to the registration algorithm proposed in the present invention; The points in the horizontal neighborhood of a certain threshold of the scanning position in the TLS ground point cloud are screened to generate a TLS local point cloud, and the points in the horizontal neighborhood of a certain threshold of each key point in the ULS ground point cloud are extracted to generate multiple ULS local point clouds for subsequent alignment; the threshold is usually set to 5m.

5. The method for automatic registration of forest ULS-TLS point clouds based on ground context description according to claim 1, characterized in that: The initial corresponding relationship is established: For a given point, principal component analysis (PCA) is applied to analyze the local point cloud in its neighborhood, and three eigenvalues ​​λ1, λ2 and λ3 (satisfying λ1≥λ2≥λ3≥0) and the normal vector can be obtained. Based on these results, the plane feature a2, the surface feature a3 and the angle f between the normal vector and the Z axis of this point can be derived; Given a TLS scan position p t And its related geometric features and f t , a key point p of ULS u And its related geometric features and f u ; If the following conditions are met, p u Considered as p t The initial corresponding points are added to the initial corresponding set C init middle: Among them, δ, θ and ε are three different thresholds, set to 0.1, 0.1 and 2.0° respectively; Through the above method, the geometric features of all ULS key points and the local point cloud of the TLS scanning position are calculated and compared to generate the initial corresponding set C of the TLS scanning position init ={p u |u=1,2…n init }.

6. The method for automatic registration of forest ULS-TLS point clouds based on ground context description according to claim 1, characterized in that: The point cloud registration based on ground context: Similarity score sim(H ULS ,H TLS ) is calculated as follows: In the similarity score calculation, the matrix element M ij The median values ​​of NaN are excluded from the calculation; variable l represents M ij The number of non-NaN values ​​in the matrix; For the rotation transformation of the TLS point cloud, it can be achieved by performing corresponding transformation operations on the columns of the matrix: r m :[z1,z2,…z Na ]→[from Na-m+1 ,…With Na ,z1,…,z Na-m ]#(4) Among them, H TLS =[z1,z2,…z Na ], zj represents H TLS A column of , and m∈[1,Na) is the rotation parameter, and the corresponding rotation angle is θ m =2πm / Na; If a pair of ground contexts of ULS and TLS correspond correctly, then the similarity score function represents the standard deviation of the height difference between the corresponding point pairs; since the Z axes of ULS and TLS are both vertical, ideally, the similarity score should be close to zero; After obtaining the initial correspondence, a ground context Ht is generated for the TLS scan position; at the same time, the initial correspondence set C init The ULS key points in the ground context Hu = {H u i |i=1,2…n init }; To complete the registration, it is necessary to compare each H u i With Ht at a rotation angle θ m The similarity score under the formula is score = sim (H u i ,r m (H t )); the matching item with the smallest score will be selected for registration; Using the rotation angle θ in the best match best The horizontal coordinates (xu, yu) of the TLS scanning position and its corresponding point in the ULS can be used to complete the planar alignment. At the same time, according to formula (3), when calculating the similarity score of the best match, the average height difference Zmean between the corresponding points can also be calculated to determine the Z-axis translation transformation in the vertical alignment.

7. A forest ULS-TLS point cloud automatic registration system based on ground context description, which implements the forest ULS-TLS point cloud automatic registration method based on ground context description as claimed in any one of claims 1 to 6, characterized in that: The forest ULS-TLS point cloud automatic registration system based on ground context description includes: Preprocessing module, used for data preprocessing; Establish a module for initial correspondence establishment; use ground points to achieve ULS-TLS registration, the ground can generally be approximated as consisting of planes and surfaces; the dimensional characteristics of the points are basically not affected by the point cloud posture; introduce three features to characterize the pre-processed point cloud: plane dimension, surface dimension, and the angle between the normal vector and the Z axis; use these three geometric features to establish a preliminary correspondence between the ULS key points and the TLS scanning position; Point cloud registration module, used for point cloud registration based on ground context; TLS collects surrounding environment data by rotating the scan around its scanning position; based on the LiDAR scanning method, the key points in the ULS that are most similar to the TLS scanning position are identified through ground context matching, and then the transformation matrix is ​​estimated for registration.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the forest ULS-TLS point cloud automatic registration method based on ground context description as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the forest ULS-TLS point cloud automatic registration method based on ground context description as described in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the forest ULS-TLS point cloud automatic registration system based on ground context description as described in claim 7.

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