A ground-UAV collaborative positioning method and device suitable for forest environments

Abstract

The present invention discloses a ground-based, unmanned aerial vehicle (UAV) collaborative positioning method and device suitable for forest environments. The method first acquires lidar data and combined navigation data obtained from drone scanning to obtain a forest point cloud. This point cloud is then preprocessed and subjected to vertical feature extraction, trunk point clustering, trunk point fitting, and trunk cluster center point clustering to obtain the tree locations for the drone's prior map. Lidar data from a ground-based mobile device is then acquired, and a local point cloud map is constructed using a SLAM algorithm. The same operations are performed on the local point cloud map to obtain the tree locations for the local map. The local map is then fused with the UAV's georeferenced three-dimensional point cloud to address the positioning issues of ground mobile mapping systems. This method utilizes the natural distribution of trees in a forest environment for positioning, eliminating the need for deploying artificial reference targets in the field. The method also provides information on the availability and uncertainty of the positioning results, facilitating integration with other positioning technologies.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, specifically to the field of GNSS positioning in a stationary environment and lidar mobile mapping technology, more specifically to a mobile mapping equipment and intelligent mechanical high-precision positioning technology and method suitable for forest environments, and especially to a ground-UAV collaborative positioning method and device suitable for forest environments. Background Art

[0002] Forests are a vital natural ecosystem. The sustainable development of forest resources requires accurate and efficient forest inventory, including the precise acquisition of three-dimensional spatial data and the extraction of forest structural information, such as diameter at breast height (DBH), tree height, and canopy dimensions. Due to its high accuracy and efficiency, LiDAR technology has become a popular choice for the development of various mobile mapping systems, including unmanned aerial vehicles (UAVs) and ground-based mobile platforms (such as backpacks, handheld systems, and intelligent forestry machinery). These mobile mapping systems can be used to collect three-dimensional point clouds of forests above and below the canopy.

[0003] When operating above the tree canopy, drone-based mobile mapping systems typically use Global Navigation Satellite System (GNSS) positioning as a georeferencing solution. They have been widely used in forestry resource surveys, such as segmenting individual trees and estimating crown dimensions and tree height. However, mobile mapping above the tree canopy is not sufficient for detailed mapping of the structure below the canopy, such as estimating DBH and aboveground biomass (AGB). Therefore, ground mobile platforms such as backpack mobile mapping and intelligent machinery are needed to integrate lidar with ground platforms to map and perceive the structure below the canopy. Such ground systems operate below the forest canopy, and the GNSS signal attenuates severely, resulting in reduced positioning accuracy and insufficient position reference for 3D point clouds.

[0004] The navigation and mobile mapping community has long been researching precise positioning technology in GNSS-degraded environments. GNSS and inertial measurement unit (GNSS / INS) integrated positioning is the most widely used solution. The integration of GNSS and inertial sensors can effectively solve the problem of short-term interruptions in GNSS signals and alleviate GNSS positioning anomalies caused by signal interference. However, when the GNSS interruption time continues to increase, the positioning error will continue to accumulate, resulting in its inability to provide accurate position for a long time. LiDAR-based simultaneous localization and mapping (SLAM) is a self-positioning method, but the SLAM method is a relative positioning method without a geographic coordinate reference, and the positioning accuracy of SLAM decreases with the increase of the moving distance, and needs to be calibrated by an absolute positioning method. At present, obtaining accurate positioning information in the environment under the forest canopy is still a challenge faced by ground mobile mapping and intelligent machinery. Summary of the Invention

[0005] In order to overcome the problem of inaccurate positioning of mobile surveying and mapping equipment and intelligent machinery in forests, the present invention proposes a forest environment ground-UAV collaborative positioning method based on lidar map matching.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A first aspect provides a ground-based UAV collaborative positioning method suitable for a forest environment, comprising:

[0008] Obtain the lidar data and combined navigation data obtained from the drone scan to obtain the overall forest point cloud as the drone's prior map;

[0009] The overall forest point cloud is preprocessed and vertical feature extraction, trunk point clustering, trunk point fitting and trunk cluster center point clustering are performed to obtain the tree locations of the UAV prior map;

[0010] Obtain lidar data from a ground-based mobile device, construct a local point cloud map using the SLAM algorithm, perform preprocessing and vertical feature extraction on the local point cloud map, perform trunk point clustering, trunk point fitting, and trunk cluster center point clustering on the local point cloud map, and obtain the tree locations on the local map;

[0011] Based on the tree positions in the UAV prior map and the tree positions in the local map, a triangulated network is constructed, and corresponding triangles and corresponding points are found based on the similarity between the triangles contained in the two triangulated networks;

[0012] The coordinate transformation matrix is ​​calculated based on the corresponding triangles found, and according to the coordinate transformation matrix and the distance between the corresponding points, the coordinate transformation matrix with the smallest error is selected as the position of the local map in the drone's prior map.

[0013] In one embodiment, preprocessing and vertical feature extraction, trunk point clustering, trunk point fitting, and trunk cluster center point clustering are performed on the entire forest point cloud to obtain the tree locations of the drone prior map, including:

[0014] Downsampling and ground filtering are performed on the entire forest point cloud. The verticality characteristics of all points in the entire forest point cloud are calculated, and points whose verticality meets the preset conditions are selected to obtain the tree trunk point cloud.

[0015] The tree trunk point cloud is sliced ​​vertically at a preset thickness and clustered based on distance to obtain clusters of tree trunk points of different heights and different trees;

[0016] Fit each trunk point cluster and determine whether each trunk point cluster is part of the trunk;

[0017] The center points of the trunk clusters obtained in the trunk point fitting step are projected onto the XOY plane, and the center points are clustered by distance to obtain the tree centers and generate the tree positions of the UAV prior map.

[0018] In one embodiment, each trunk point cluster is fitted to determine whether each trunk point cluster is part of a trunk, including: using a three-point method to determine whether each trunk point cluster is a circle, and determining whether the trunk point cluster is a trunk based on the distance between the points in the trunk point cluster and the circle.

[0019] In one embodiment, finding corresponding triangles and corresponding points based on similarities between triangles contained in two triangulated meshes includes:

[0020] Calculate the similarity between a triangle in the local map triangulation network and all triangles in the drone's prior map triangulation network;

[0021] According to the similarity, the corresponding triangles of the triangles in the local map triangulation network are selected from the UAV prior map triangulation network;

[0022] Find corresponding points based on corresponding triangles.

[0023] In one embodiment, after selecting corresponding triangles of triangles in the local map triangulation network from the drone prior map triangulation network based on the size of the similarity, the method further includes: using a nearest triangle similarity ratio method to determine whether there are two or more corresponding triangles, wherein the nearest triangle similarity ratio method determines whether there are two or more corresponding triangles based on the relationship between the ratio of the minimum similarity to the second minimum similarity and the ratio threshold.

[0024] In one embodiment, a coordinate transformation matrix is ​​calculated based on the corresponding triangles found, and a coordinate transformation matrix with the smallest error is selected as the position of the local map in the drone's prior map according to the coordinate transformation matrix and the distance between the corresponding points, including:

[0025] Based on the three points in each corresponding triangle found in the triangulated network of the UAV prior map, a coordinate transformation matrix between the UAV prior map and the local map is calculated using the singular value decomposition algorithm;

[0026] Filter out target corresponding points based on whether the distance between the corresponding points meets the distance threshold;

[0027] The coordinate transformation matrix with the largest number of corresponding points is selected as the optimal solution. When there are multiple triangle pairs with the same number of corresponding points, the coordinate transformation matrix corresponding to the triangle pair with the smallest error is selected as the optimal solution. The optimal solution is the position of the local map in the drone's prior map.

[0028] In one embodiment, after calculating a coordinate transformation matrix based on the corresponding triangles found, and selecting a coordinate transformation matrix with the smallest error as the position of the local map in the drone's prior map based on the coordinate transformation matrix and the distance between the corresponding points, the method further includes:

[0029] Calculate the average distance between all corresponding points according to the selected coordinate transformation matrix;

[0030] Whether the positioning is successful is determined based on the relationship between the calculated average distance between the corresponding points and the average distance threshold.

[0031] Based on the same inventive concept, the second aspect of the present invention provides a ground-UAV collaborative positioning device suitable for a forest environment, characterized by comprising:

[0032] The UAV prior map construction module is used to obtain the lidar data and combined navigation data obtained by the UAV scanning to obtain the overall forest point cloud as the UAV prior map;

[0033] The tree location generation module of the UAV prior map is used to preprocess the forest point cloud and extract vertical features, cluster tree points, fit tree points, and cluster the center points of tree clusters to obtain the tree locations of the UAV prior map;

[0034] The local map tree location generation module is used to obtain lidar data from ground mobile devices, construct a local point cloud map through the SLAM algorithm, and perform preprocessing and vertical feature extraction, trunk point clustering, trunk point fitting, and trunk cluster center point clustering on the local point cloud map to obtain the tree locations of the local map;

[0035] The feature matching module is used to construct a triangulated network based on the tree positions in the drone's prior map and the tree positions in the local map, and to find corresponding triangles and corresponding points based on the similarity between the triangles contained in the two triangulated networks;

[0036] The positioning module is used to calculate the coordinate transformation matrix based on the corresponding triangles found, and select the coordinate transformation matrix with the smallest error as the position of the local map in the drone's prior map based on the coordinate transformation matrix and the distance between the corresponding points.

[0037] Based on the same inventive concept, the third aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the ground-UAV collaborative positioning method for forest environments described in the first aspect.

[0038] Based on the same inventive concept, the fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the ground-UAV collaborative positioning method suitable for forest environments described in the first aspect is implemented.

[0039] Compared with the prior art, the advantages and beneficial technical effects of the present invention are as follows:

[0040] This paper proposes a ground-based, unmanned aerial vehicle (UAV) collaborative positioning method for forest environments. This method, based on LiDAR (LiDAR) global positioning, is implemented by collaboratively fusing georeferenced LiDAR point clouds collected by UAVs / helicopters with ground-based mobile scanning LiDAR point clouds. This method addresses the absolute positioning challenges faced by backpack-based mobile mapping ground systems or intelligent machines. First, a georeferenced three-dimensional point cloud (the entire forest point cloud) is acquired using GNSS positioning. This point cloud is then preprocessed and subjected to vertical feature extraction, trunk point clustering, trunk point fitting, and trunk cluster center point clustering to obtain the tree locations for the UAV's prior map. LiDAR data from a ground-based mobile device is then used to construct a local point cloud map using a SLAM (Simultaneous Localization and Mapping) algorithm. This local point cloud map is then preprocessed and subjected to vertical feature extraction, trunk point clustering, trunk point fitting, and trunk cluster center point clustering to obtain the tree locations for the local map. Finally, the local map is fused with the UAV's georeferenced three-dimensional point cloud. This involves constructing triangulated meshes (TMNs) separately, finding corresponding triangles and points based on the similarity between the triangles in the two TMNs, and selecting a target coordinate transformation matrix, thereby addressing the positioning challenges faced by ground-based mobile mapping systems. The present invention utilizes the natural distribution of trees in a forest environment to achieve positioning, eliminating the need to deploy artificial reference targets in the field, and provides information on the availability and uncertainty of positioning results, facilitating integration with other positioning technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is an overall flow chart of a ground-UAV collaborative positioning method applicable to a forest environment according to an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the process of fitting tree trunk points in the positioning method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention proposes a global positioning method based on LiDAR, which is achieved by collaboratively fusing the geo-referenced LiDAR point cloud collected by UAVs / helicopters and the ground mobile scanning LiDAR point cloud, solving the absolute positioning problem of backpack-type mobile mapping ground systems or intelligent machinery. The UAV-based LiDAR mobile mapping system first uses GNSS positioning to obtain a three-dimensional point cloud with geo-reference; then, a ground mobile scanning system equipped with one or more LiDAR sensors builds a local map. Finally, the local map is fused with the UAV's geo-referenced three-dimensional point cloud to solve the positioning problem of the ground mobile mapping system. The present invention utilizes the natural tree distribution in a forest environment to achieve positioning, eliminating the need to deploy artificial reference targets in the field, and provides information on the availability and uncertainty of the positioning results, making it easy to integrate with other positioning technologies.

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0046] Example 1

[0047] The present invention discloses a ground-based unmanned aerial vehicle (UAV) collaborative positioning method applicable to a forest environment, comprising:

[0048] S1: Obtain the lidar data and combined navigation data obtained by the drone scanning to obtain the overall forest point cloud as the drone's prior map;

[0049] S2: Preprocessing and vertical feature extraction of the entire forest point cloud, trunk point clustering, trunk point fitting and trunk cluster center point clustering are performed to obtain the tree locations of the UAV prior map;

[0050] S3: Obtain lidar data from a ground mobile device, construct a local point cloud map using the SLAM algorithm, perform preprocessing and vertical feature extraction on the local point cloud map, perform trunk point clustering, trunk point fitting, and trunk cluster center point clustering on the local point cloud map, and obtain the tree locations in the local map;

[0051] S4: Construct triangulated networks based on the tree positions in the UAV prior map and the tree positions in the local map, and find corresponding triangles and corresponding points based on the similarity between the triangles contained in the two triangulated networks;

[0052] S5: Calculate the coordinate transformation matrix based on the corresponding triangles found, and select the coordinate transformation matrix with the smallest error as the position of the local map in the drone's prior map based on the coordinate transformation matrix and the distance between the corresponding points.

[0053] Specifically, S1 uses the lidar data and combined navigation data obtained from drone scanning to obtain the drone prior map. S2 performs feature extraction and clustering on the point cloud map in S1 to obtain the tree positions in the drone prior map. S3 uses the lidar data of ground mobile equipment to construct a local point cloud map and performs the same operations on the drone prior map in S2 to obtain the tree positions in the local map. S4 performs feature matching based on the tree positions of the two maps to find the corresponding triangles and corresponding points in the two maps. S5 finds the target coordinate transformation matrix based on the corresponding triangles and corresponding points.

[0054] The positioning method of the present invention is described below by using specific examples. Figure 1 The present invention discloses a mobile surveying and mapping device and a real-time high-precision positioning method for intelligent machinery suitable for forest environments. The specific process is as follows:

[0055] 101, take the lidar data and combined navigation data obtained by the drone scanning to obtain the overall forest point cloud as the drone prior map.

[0056] During the specific implementation process, the drone is equipped with a lidar scanning system and a GNSS / IMU combined navigation system. The combination of the two can obtain the overall point cloud of the forest with geographic coordinates, which serves as a priori map for the positioning of ground mobile equipment.

[0057] 102. Downsample and ground filter the point cloud map; calculate the verticality features of all points in the point cloud map, select points with high verticality, and obtain the tree trunk point cloud.

[0058] In specific implementation, the point cloud map is first uniformly downsampled, dividing the entire point cloud into voxels of a certain side length. Only the centroid point is retained in each voxel to reduce the point cloud density and number of points. Then, a cloth simulation algorithm is used to segment and identify ground points, retaining only non-ground points at a certain height above the ground. The verticality feature is calculated for all non-ground points:

[0059] v=1-|n z |

[0060] Among them, v represents the verticality of the point, n z Represents the third component of the normal vector for a point. The closer v is to 1, the closer the local plane on which the point lies is vertical. Based on the assumption that real tree trunks grow vertically, points with v greater than a threshold γ are considered trunk points. Points that meet this condition are then filtered to form a trunk point cloud.

[0061] 103, the trunk point cloud is sliced ​​at a certain thickness in the vertical direction and clustered according to the distance to obtain clusters of trunk points of different heights and different trees.

[0062] Specifically, the trunk point cloud is segmented vertically at a certain thickness and interval to obtain a series of slices with a thickness of h. Euclidean clustering is used for each slice at each height to divide it into multiple clusters, each cluster containing partial trunks of different trees at the same height.

[0063] 104, fitting each trunk point cluster, and further determining whether each cluster is part of the trunk.

[0064] Specifically, the clusters obtained in 103 may not be tree trunks due to various reasons such as different tree species and inaccurate normal vector calculation. Therefore, fitting is required to determine. Traditional fitting methods such as RANSAC are time-consuming and not suitable for real-time positioning. The present invention proposes a three-point method to determine whether each cluster is circular, and then determine whether the cluster is a tree trunk. Figure 2 , which is a schematic diagram of the process of fitting tree trunk points in the positioning method provided by an embodiment of the present invention, including:

[0065] 201, calculate the distance between all points in the cluster, and obtain the distance set d = {d 12 ,d 23 ,…d ij ,…}, and select the two points p corresponding to the largest value i ,p j ;

[0066] 202, calculate the other points in the cluster and point p i ,p j The distance between them is denoted as d i and d j ;

[0067] 203, calculate d i and d j The difference between them and take the absolute value, we get the set Δd={Δd1,Δd2,…Δd k}, select the point p corresponding to the smallest element in the set Δd k , p k is the third point needed to calculate the circle parameters;

[0068] 204, through point p i 、p j 、p k Compute the parameters of a circle:

[0069] x 2 +y 2 +ax+by+c=0

[0070] 205. Calculate the distance between other points and the circle:

[0071]

[0072] Where Δ is the distance from the point to the circle, and a, b, and c are the parameters of the circle calculated in 204. Points with a distance less than the threshold are marked as inliers. If the number of inliers is greater than 80% of the number of cluster points, the cluster is considered part of the trunk and participates in subsequent calculations (clusters that meet the conditions are calculated in 105, and those that do not meet the conditions are discarded).

[0073] 105, project the center points of the tree trunk clusters onto the XOY plane, and perform distance clustering on the center points to obtain the tree centers and generate the tree locations on the drone map.

[0074] Specifically, the cluster centers selected in 104 are projected onto the XOY plane, and Euclidean clustering is performed on these center points on the XOY plane to obtain the centroid of the cluster as the position of the tree.

[0075] 106, read the laser radar data of the ground mobile scanning device, and build a local point cloud map through the SLAM (Simultaneous Localization and Mapping) algorithm, and perform steps 102-105 to obtain the tree positions on the local map.

[0076] Specifically, the ground mobile scanning data is used to construct a local map using a SLAM algorithm. A local map is a point cloud composed of the relative poses of a certain number of LiDAR scan frames calculated using the SLAM algorithm. Compared to single-frame LiDAR scans, a local map contains richer information for tree location extraction. Steps 102-105 are repeated for the local map to obtain the tree location distribution on the local map.

[0077] 107. In the tree locations on the drone map and the local map, a triangulated network is constructed, and corresponding triangles and corresponding points are found based on triangle similarity.

[0078] Specifically, any three points in the tree location map can form a triangle. By traversing all tree location points, a triangulated network (TMN) can be constructed for both the drone map and the local map. To ensure computational efficiency, each TMN is constructed only with points within a certain distance range. This range is set to 50 meters, based on the scanning range of the LiDAR on mobile ground scanning equipment.

[0079] The local map triangulation is defined as T s , including all constructed triangles; the drone map triangulation network is defined as T t, including all constructed triangles. For the two triangulated networks obtained, corresponding points are found by triangle similarity.

[0080] Specifically, define triangle T i and T j They are local map triangulation network T s and UAV map triangulation network T t The similarity between them is calculated as follows:

[0081] Sort the three sides of the triangle by length and take the shortest side as the first side to find the corresponding side. Calculate the similarity according to the following formula:

[0082]

[0083] Δ1, Δ2, and Δ3 are the length differences between the three corresponding sides of the triangle, and S is the similarity.

[0084] According to the above formula, calculate triangle T i With drone map triangulation network T t The similarity of all triangles in , and sort the similarity in ascending order to get the set If the minimum similarity S first Satisfy the maximum threshold S th , then T t Zhong and S first The corresponding triangle is triangle T i The potential corresponding triangle of . The threshold S th It is usually determined by the noise level of the point cloud and is generally set to 10 cm.

[0085] Due to the errors between the tree center position and the LiDAR scan, it is possible that t Find multiple T i To ensure the uniqueness of the correspondence and reduce the impact of mismatching, the nearest triangle similarity ratio method is used to determine whether there are two or more corresponding triangles. The correct correspondence is determined by the following formula:

[0086]

[0087] Among them, S first and S second are the minimum similarity and the second minimum similarity, SR th Is a threshold greater than 1. If the conditions described in the above formula are not met, then the triangle T i In T t There is no corresponding triangle in ; if the conditions are met, then S first The corresponding triangle is triangle T iIn the UAV map triangulation network T t After determining the corresponding triangle, the corresponding points in the drone map and the local map can be determined.

[0088] 108, calculate the coordinate transformation matrix based on all corresponding triangles, calculate the distance between corresponding points, and select the coordinate transformation matrix with the smallest error as the position of the local map in the UAV's prior map.

[0089] Specifically, after finding all corresponding triangles between the drone map and the local map, the singular value decomposition (SVD) algorithm can be used to calculate a transformation matrix between the drone map and the local map based on the three points in each corresponding triangle. The optimal solution is selected through geometric consistency testing, and mismatched corresponding triangles can be excluded. For each pair of triangles in {(Tsi, Tti), i = 1, 2…N}, a transformation matrix is ​​calculated by SVD. Using the transformation matrix, the points in the local map are transformed into the drone map coordinate system. The transformed points are iteratively calculated to check whether there are corresponding points in the drone map that are close enough, that is,

[0090]

[0091] in, and where Δd is the distance threshold between the transformed point and the corresponding point in the drone map. Points that meet the distance threshold are considered corresponding points; otherwise, there are no corresponding points, and this triangle pair is considered a mismatch. The transformation matrix with the largest number of corresponding points is selected as the optimal solution. If many triangle pairs produce the same number of corresponding points, the transformation matrix corresponding to the triangle pair with the smallest error is selected. This transformation matrix represents the device's current position in the geographic coordinate system.

[0092] 109, based on the number of corresponding points in the local map and the UAV map, it is determined whether the positioning is successful.

[0093] Specifically, the number of corresponding points found in step 108 is N orresponding The number of tree locations extracted from the local map in step 106 is N extracted , the completeness index Ig can be written as:

[0094]

[0095]

[0096] Where CS is the confidence score; Ig is the completeness of the global positioning; and Thr is the CS threshold. After calculating the current position in step 108, the completeness is calculated using the above formula, which determines whether the positioning result is usable. If Ig is 1, the positioning result is usable; conversely, if Ig is 0, the positioning result is unusable. The confidence score Thr is generally set to 0.75.

[0097] Prior art (publication number CN115480257A) discloses a rod-based matching and positioning method. This method uses a vehicle to scan a laser point cloud map and extract rod-shaped features. The vehicle then uses the real-time laser point cloud scanned by the vehicle to extract rod-shaped features and determine the vehicle's position and posture through feature matching. This inventive concept differs from the present invention in the following aspects.

[0098] (1) The laser point cloud maps used are different. The laser point cloud maps and real-time positioning acquisition maps used in the prior art are both collected on-board. The laser point cloud map used in the present invention is the ALS point cloud scanned by the drone, which is different from the real-time laser point cloud obtained during positioning in terms of scanning angle, accuracy, density, etc. From the perspective of practical application, ALS point cloud acquisition is more convenient and has higher accuracy (POS system position and posture can be used).

[0099] (2) The feature extraction methods used are different. The prior art uses the laser radar line number characteristic to filter the point cloud (paragraph

[0013] of the specification). This line number feature is only available for rotating laser radars and cannot be used for solid-state laser radars. The feature extraction method used in the present invention does not require the type of laser radar. The prior art uses the z-axis component of the feature vector to judge and extract the point cloud cluster of the trunk part (paragraphs

[0019] -

[0020] ), while the present invention uses the cylindrical fitting method to judge, which has high computational efficiency; and the center of the trunk is extracted at multiple heights, which can eliminate the influence of bifurcated branches and is more accurate. The rod-shaped feature described in the prior art is a point cloud cluster that meets the feature vector requirements (paragraph

[0023] ). The present invention directly uses the fitted trunk center as the feature point.

[0100] (3) The feature matching method used is different. The existing technology requires an initial pose (paragraph

[0094] of the specification) and requires continuous operation to achieve positioning. The present invention performs each matching and positioning process independently and does not require an initial pose. The pose calculation method used in the existing technology is point-to-point residual optimization, which is relatively time-consuming. The present invention uses a triangulated network matching method, which is more efficient and introduces a false match elimination step to improve accuracy.

[0101] In summary, this invention provides a high-precision positioning technology and method for mobile surveying and mapping equipment and intelligent machinery suitable for forest environments. This technology leverages prior maps efficiently collected by drones to provide a positioning reference for ground-based equipment. This proposed method for ground-based equipment positioning in forest environments can address issues such as low positioning accuracy and robustness caused by insufficient GNSS signals in forests. It provides a stable, high-precision position reference in GNSS-free environments and provides confidence in each positioning result.

[0102] Example 2

[0103] Based on the same inventive concept, this embodiment discloses a ground-UAV collaborative positioning device suitable for a forest environment, comprising:

[0104] The UAV prior map construction module is used to obtain the lidar data and combined navigation data obtained by the UAV scanning to obtain the overall forest point cloud as the UAV prior map;

[0105] The tree location generation module of the UAV prior map is used to preprocess the forest point cloud and extract vertical features, cluster tree points, fit tree points, and cluster the center points of tree clusters to obtain the tree locations of the UAV prior map;

[0106] The local map tree location generation module is used to obtain lidar data from ground mobile devices, construct a local point cloud map through the SLAM algorithm, and perform preprocessing and vertical feature extraction, trunk point clustering, trunk point fitting, and trunk cluster center point clustering on the local point cloud map to obtain the tree locations of the local map;

[0107] The feature matching module is used to construct a triangulated network based on the tree positions in the drone's prior map and the tree positions in the local map, and to find corresponding triangles and corresponding points based on the similarity between the triangles contained in the two triangulated networks;

[0108] The positioning module is used to calculate the coordinate transformation matrix based on the corresponding triangles found, and select the coordinate transformation matrix with the smallest error as the position of the local map in the drone's prior map based on the coordinate transformation matrix and the distance between the corresponding points.

[0109] Since the device described in Example 2 of the present invention is used to implement the ground-based and unmanned aerial vehicle collaborative positioning method for forest environments described in Example 1 of the present invention, those skilled in the art will be able to understand the specific structure and variations of the device based on the method described in Example 1 of the present invention, and therefore, no further description is given here. All devices used in the method described in Example 1 of the present invention fall within the scope of protection of the present invention.

[0110] Example 3

[0111] Based on the same inventive concept, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0112] Since the computer-readable storage medium described in Example 3 of the present invention is the computer-readable storage medium used to implement the ground-based and unmanned aerial vehicle collaborative positioning method for forest environments described in Example 1 of the present invention, those skilled in the art will be able to understand the specific structure and variations of the computer-readable storage medium based on the method described in Example 1 of the present invention, and therefore will not be described in detail here. All computer-readable storage media used in the method of Example 1 of the present invention fall within the scope of protection of the present invention.

[0113] Example 4

[0114] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first embodiment when executing the program.

[0115] Since the computer device described in Example 4 of the present invention is the computer device used to implement the ground-based and unmanned aerial vehicle collaborative positioning method for forest environments described in Example 1 of the present invention, those skilled in the art will be able to understand the specific structure and variations of the computer device based on the method described in Example 1 of the present invention, and therefore will not be described in detail here. All computer devices used in the method of Example 1 of the present invention fall within the scope of protection of the present invention.

[0116] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, the present invention is intended to include such changes and modifications to the embodiments of the present invention if they fall within the scope of the claims and their equivalents.

Claims

1. A ground-UAV collaborative positioning method suitable for forest environments, characterized in that: include: Obtain the lidar data and combined navigation data obtained from the drone scan to obtain the overall forest point cloud as the drone's prior map; The overall forest point cloud is preprocessed and vertical feature extraction, trunk point clustering, trunk point fitting and trunk cluster center point clustering are performed to obtain the tree positions of the UAV prior map; Obtain lidar data from a ground-based mobile device, construct a local point cloud map using the SLAM algorithm, perform preprocessing and vertical feature extraction on the local point cloud map, perform trunk point clustering, trunk point fitting, and trunk cluster center point clustering on the local point cloud map, and obtain the tree locations on the local map; Based on the tree positions in the UAV prior map and the tree positions in the local map, a triangulated network is constructed, and corresponding triangles and corresponding points are found based on the similarity between the triangles contained in the two triangulated networks; The coordinate transformation matrix is ​​calculated based on the corresponding triangles found, and according to the coordinate transformation matrix and the distance between the corresponding points, the coordinate transformation matrix with the smallest error is selected as the position of the local map in the drone's prior map.

2. The ground-based UAV collaborative positioning method for forest environments according to claim 1, wherein: The forest point cloud is pre-processed and vertical feature extraction, trunk point clustering, trunk point fitting and trunk cluster center point clustering are performed to obtain the tree locations of the UAV prior map, including: Downsampling and ground filtering are performed on the entire forest point cloud. The verticality characteristics of all points in the entire forest point cloud are calculated, and points whose verticality meets the preset conditions are selected to obtain the tree trunk point cloud. The tree trunk point cloud is sliced ​​vertically at a preset thickness and clustered based on distance to obtain clusters of tree trunk points of different heights and different trees; Fit each trunk point cluster and determine whether each trunk point cluster is part of the trunk; The center points of the trunk clusters obtained in the trunk point fitting step are projected onto the XOY plane, and the center points are clustered by distance to obtain the tree centers and generate the tree positions of the UAV prior map.

3. The ground-based UAV collaborative positioning method for forest environments according to claim 2, wherein: Fitting each trunk point cluster to determine whether each trunk point cluster is part of a trunk includes: using a three-point method to determine whether each trunk point cluster is a circle, and determining whether the trunk point cluster is a trunk based on the distance between a point in the trunk point cluster and the circle.

4. The ground-based UAV collaborative positioning method for forest environments according to claim 1, wherein: Find corresponding triangles and corresponding points based on the similarity between the triangles contained in two triangulated networks, including: Calculate the similarity between a triangle in the local map triangulation network and all triangles in the drone's prior map triangulation network; According to the similarity, the corresponding triangles of the triangles in the local map triangulation network are selected from the UAV prior map triangulation network; Find corresponding points based on corresponding triangles.

5. The ground-based UAV collaborative positioning method applicable to forest environments according to claim 4, characterized in that: After selecting corresponding triangles of triangles in the local map triangulation network from the UAV prior map triangulation network based on the size of the similarity, the method further includes: using a nearest triangle similarity ratio method to determine whether there are two or more corresponding triangles, wherein the nearest triangle similarity ratio method determines whether there are two or more corresponding triangles based on the relationship between the ratio of the minimum similarity to the second minimum similarity and a ratio threshold.

6. The ground-based UAV collaborative positioning method for a forest environment according to claim 1, wherein: The coordinate transformation matrix is ​​calculated based on the corresponding triangles found, and the coordinate transformation matrix with the smallest error is selected as the position of the local map in the drone's prior map according to the coordinate transformation matrix and the distance between the corresponding points, including: Based on the three points in each corresponding triangle found in the triangulated network of the UAV prior map, a coordinate transformation matrix between the UAV prior map and the local map is calculated using the singular value decomposition algorithm; Filter out target corresponding points based on whether the distance between the corresponding points meets the distance threshold; The coordinate transformation matrix with the largest number of corresponding points is selected as the optimal solution. When there are multiple triangle pairs with the same number of corresponding points, the coordinate transformation matrix corresponding to the triangle pair with the smallest error is selected as the optimal solution. The optimal solution is the position of the local map in the drone's prior map.

7. The ground-based UAV collaborative positioning method for a forest environment according to claim 1, wherein: After calculating a coordinate transformation matrix based on the corresponding triangles found, and selecting a coordinate transformation matrix with the smallest error as the position of the local map in the drone's prior map based on the coordinate transformation matrix and the distances between the corresponding points, the method further includes: Calculate the average distance between all corresponding points according to the selected coordinate transformation matrix; Whether the positioning is successful is determined based on the relationship between the calculated average distance between the corresponding points and the average distance threshold.

8. A ground-UAV collaborative positioning device suitable for forest environments, characterized in that: include: The UAV prior map construction module is used to obtain the lidar data and combined navigation data obtained by the UAV scanning to obtain the overall forest point cloud as the UAV prior map; The tree location generation module of the UAV prior map is used to preprocess the forest point cloud and extract vertical features, cluster tree points, fit tree points, and cluster the center points of tree clusters to obtain the tree locations of the UAV prior map; The local map tree location generation module is used to obtain lidar data from ground mobile devices, construct a local point cloud map through the SLAM algorithm, and perform preprocessing and vertical feature extraction, trunk point clustering, trunk point fitting, and trunk cluster center point clustering on the local point cloud map to obtain the tree locations of the local map; The feature matching module is used to construct a triangulated network based on the tree positions in the drone's prior map and the tree positions in the local map, and to find corresponding triangles and corresponding points based on the similarity between the triangles contained in the two triangulated networks; The positioning module is used to calculate the coordinate transformation matrix based on the corresponding triangles found, and select the coordinate transformation matrix with the smallest error as the position of the local map in the drone's prior map based on the coordinate transformation matrix and the distance between the corresponding points.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the ground-UAV collaborative positioning method applicable to a forest environment as described in any one of claims 1 to 7 is implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the ground-UAV collaborative positioning method applicable to a forest environment is implemented as described in any one of claims 1 to 7.

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