A multi-scale terrain feature extraction method based on terrestrial point cloud

Abstract

A multi-scale terrain feature extraction method based on ground point cloud includes the following steps: first, selecting the extraction range of terrain feature points, then constructing a triangular face, then establishing a spatial triangular face equation, and then traversing the distance from the point to the triangular face within the triangular face projection ground plane range, finding the point cloud point with the largest distance, which is the most significant terrain feature point in the current triangular face, and then distinguishing the precision of terrain feature extraction according to the scale factor set by the terrain feature to extract terrain feature points. Finally, terrain feature points are added to perform fission decomposition and iterative extraction on the triangular face. The algorithm of the present invention is reasonable and simple to operate. It cleverly solves the technical drawbacks of multi-scale terrain feature extraction of ground point clouds, not only improves the efficiency of distinguishing and extracting feature points, but also can accurately complete the selection and integration of terrain data features and complexity according to consistent standards, and accurately obtain shape feature points used to express terrain.

Description

Technical Field

[0001] The present invention relates to a method for extracting multi-scale terrain feature points, and in particular to a method for extracting multi-scale terrain features based on ground point clouds, belonging to the technical field of terrain surveying and mapping. Background Art

[0002] Terrain refers to various undulating states presented jointly by fixed objects distributed above the earth's surface. Usually, the ground features and landforms are projected onto a horizontal plane along the plumb line direction by the method of horizontal projection and drawn on a drawing according to a certain scale for expression. The description of any landform and ground feature on the ground can be reflected by the lines composed of its transformation points. The landform can be reflected by contour lines to show its height and morphological changes; ground features such as houses, roads, rivers, etc. can all be represented by the lines formed by their transformation feature points; there are also special legend symbols for many special landforms and ground features. Therefore, the work of mapping topographic maps is actually to measure and represent the feature points of all landforms and ground features on the ground, and according to topographic maps of different scales, it is necessary to select, discard, simplify and synthesize terrain features.

[0003] When drawing, the most representative line reflecting the terrain features is the ground line, also known as the landform structure line, which is the ridge line of the terrain morphological change, such as ridge lines, valley lines, inclination transformation lines, direction transformation lines, etc., as shown in the appendix. Figure 1 The process of selecting, discarding, simplifying and synthesizing terrain features is to extract the feature points that make up these ground feature lines. The previous methods for obtaining terrain feature data were mainly through manual point-by-point collection. During the collection process, surveyors selected and discarded, simplified and synthesized terrain features according to the terrain morphology at a specific scale. This method requires a large amount of manpower and has low efficiency on the one hand, and has certain requirements for the ability of surveyors to select feature points on the other hand. Nowadays, with the increasingly widespread application of non-contact remote sensing mapping methods, the data sets of various types including ground point clouds, images, etc. can be obtained due to their high data collection efficiency, good integrity and fineness, and there has been a qualitative leap in the fineness of terrain expression. Among them, the ground point clouds after filtering and classification can complete multi-scale terrain mapping. However, the work of selecting, discarding, simplifying and synthesizing terrain data features still exists. Different from the field manual on-site discrimination and collection, the new data collection method transfers this process to the massive ground point cloud data.

[0004] The new data collection mode has led to a huge change in the way of topographic mapping. At present, the multi-scale terrain feature extraction method for ground point cloud data is mainly to manually identify and extract points from massive point cloud data, which has the disadvantages of low efficiency, easy to produce errors, difficult to unify the selection and integration standards of terrain features at the same scale, and difficulty in extracting multi-scale terrain features. There has always been a lack of a method that can select and integrate the fine terrain expressed by ground point clouds at different geographic spatial scales to efficiently realize multi-scale terrain data feature extraction, and finally obtain measured terrain feature points for terrain mapping to solve the above technical problems. Summary of the invention

[0005] The purpose of the present invention is to solve the defects and shortcomings of the existing multi-scale terrain feature extraction of ground point cloud data relying on manual judgment, such as the traditional manual judgment and extraction point method is not only inefficient, prone to errors and omissions, but also difficult to unify the selection and simplification of terrain features at the same scale, and difficult to extract multi-scale terrain features. It is impossible to efficiently obtain the measured terrain feature points at different geographic spatial scales. A method with reasonable algorithm and simple operation is now provided, which cleverly solves the technical drawbacks of manually extracting terrain feature points from massive point cloud data, not only improves the judgment processing efficiency of the extracted points, but also can efficiently complete the selection and simplification of terrain data features at different scales, and can accurately obtain terrain feature points for terrain mapping. A multi-scale terrain feature extraction method based on ground point cloud.

[0006] To achieve the above-mentioned purpose, the technical solution of the present invention is: a multi-scale terrain feature extraction method based on ground point cloud, comprising the following steps:

[0007] A. First, select the extraction range of terrain feature points. Select a closed space polygon to enclose the feature point extraction range as needed, and use the range of this space polygon projected onto the horizontal plane to mark it, that is, it is displayed as a closed polygon surface on a two-dimensional plane. Usually, the area enclosed by this range line is not larger than the coverage of all point cloud projections and is a convex polygon;

[0008] B. Then construct the initial triangular face, using any edge of the range polygon as the starting edge, and construct the triangular face according to the "empty circumscribed circle" principle;

[0009] C. Then establish the spatial triangular surface equation. In three-dimensional space, three points that are not on a straight line can form a plane, and its equation can be expressed as αx+βy+γz+λ=0. Assuming that the coordinates of the three points are represented by P1(x1, y1, z1), P2(x2, y2, z2), and P3(x3, y3, z3), respectively, then:

[0010] α = (y3 - y1) * (z3 - z1) - (z2 - z1) * (y3 - y1)

[0011] β = (x3 - x1) * (z2 - z1) - (x2 - x1) * (z3 - z1)

[0012] γ = (x2 - x1) * (y3 - y1) - (x3 - x1) * (y2 - y1)

[0013] Then λ = -(α * x1 + β * y1 + γ * z1)

[0014] The plane equation can be obtained according to the three - dimensional space coordinates of the three corner points of each triangular face;

[0015] D. Then, set the scale factor according to the relevant specification requirements and actual needs. The size of the set scale factor can distinguish the fineness of terrain feature extraction. The fineness of terrain feature point extraction is set by the distance d from the point cloud point to the triangular face, that is, the maximum distance d max When the distance is less than the scale factor φ, terrain feature points are not extracted, that is, it is considered that the terrain feature selection and complexity comprehensively meet the requirements;

[0016] E. Then extract the terrain feature points. The terrain feature points are extracted by using the points with the maximum distance from the point cloud points to the triangular face. Traverse the distance from the points within the plane range of the triangular face projection to the triangle, and find the point cloud point with the maximum distance, which is the most significant terrain feature point within the current triangular projection plane;

[0017] F. Finally, iteratively extract the terrain feature points through triangular face fission and dissection. Add the terrain feature points extracted from each triangular face to the original triangular face and connect them to the three vertices of the triangular face to fission and dissect into three new triangular faces. Then, continue to iteratively extract the terrain feature points for the new triangular faces according to the method in step E until the distance from all point cloud points to the triangular face where the last fission and dissection is located is less than φ, that is, the extraction operation of terrain feature points is completed.

[0018] Furthermore, in step B, any side of the range polygon is used as the starting side, and triangular faces are constructed according to the "empty circumcircle" principle. The "empty circumcircle" means that no other polygon corner points are included within the circumcircle of the triangle. When there are multiple points on the same circle, the triangle with the largest minimum angle is selected. If the minimum angles are the same, the triangle with the larger second - smallest angle is selected, and so on for construction.

[0019] Furthermore, the calculation method and process of extracting terrain feature points in step E are as follows:

[0020] a. First, judge the boundary line of the triangular face and the point cloud points within the range of the triangular face. Use the coordinates of the triangular face and the point cloud points in two horizontal directions for judgment. Let the judgment point be point Q. To judge whether point Q is inside the triangular face MNO, first calculate the vector Then calculate respectively using the vector cross product method If the vector cross products of all three groups are positive, negative, or zero, then point Q is within the MNO plane or on the boundary line of the triangular plane;

[0021] b. Secondly, obtain the feature points within the triangular plane. Solve the spatial distance between the point cloud points within the range of all triangular planes in the three-dimensional coordinate system and the triangular plane. Assume the point coordinates are T(x0, y0, z0), and for the triangular plane α1x + β1y + γ1z + λ1 = 0, then the distance d from the point to the triangular plane is:

[0022] d = |α1x + β1y + γ1z + λ1| / (α1 + β1 + γ1);

[0023] The inclination angle θ of the triangular plane is:

[0024]

[0025] Then traverse the distances between all the point cloud points within the range of the triangular plane and the triangular plane, and find the maximum distance d max Find the corresponding point and calculate the scale factor corresponding to this triangular plane according to the method in step D using the inclination angle θ of the triangular plane for comparison; if it is greater than or equal to the scale factor, then extract this point as the terrain feature point within the range of this triangular plane; if it is less than the scale factor, then do not extract the terrain feature point, and no more terrain feature point extraction is performed for this triangular plane;

[0026] c. Finally, find all the initial feature points, and traverse all the triangular planes to find all the terrain feature points within the triangular planes according to the method in step b.

[0027] The beneficial effects of the present invention are:

[0028] 1. The present invention constructs triangular planes using the "empty circumcircle" principle, and the exact segmentation method adopted makes the sizes and shapes of the finally obtained triangular planes as uniform as possible, so that the extracted terrain feature points are more accurate and efficient, and the spatial distribution is relatively uniform and reasonable.

[0029] 2. The present invention can set the scale factor according to actual needs. The size of the set scale factor can distinguish the fineness of terrain feature extraction, enable the terrain feature extraction result to perfectly meet the regulations on the elevation mean square error under different terrain types in relevant specifications, and can also avoid the problem of too large differences in the density of feature points caused by using a unified scale factor for different ground inclination angles. Moreover, it can directly use the point cloud data to "adjust measures to local conditions" for the local ground inclination angle to extract more refined terrain feature points, making the terrain feature selection and the combination of complexity and simplicity more rigorous and reasonable.

[0030] 3. The terrain feature points of the present invention are extracted by using the point with the maximum distance from the point cloud point to the triangular surface, and the feature point extraction is performed through the iterative fission and decomposition of the triangular surface. The terrain feature points extracted in each triangular surface and the three vertices of the original triangular surface are fissioned and decomposed into new triangular surfaces, and the terrain feature points in all triangular surfaces are found. Finally, the feature point set extracted by multiple fission and decomposition iterations becomes the required terrain feature points.

[0031] 4. The algorithm of the present invention is reasonable and simple to operate. It cleverly solves the technical drawbacks of manual identification of extraction points. It not only improves the efficiency of identification and processing of extraction points, but also can efficiently complete the selection and integration of terrain data features, and can accurately obtain terrain feature points for topographic mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of selecting feature points in a topographic map in the prior art.

[0033] Figure 2 It is an example of common existing terrain feature points.

[0034] Figure 3 It is a principle block diagram of the present invention.

[0035] Figure 4 It is an example of the top view and side view of the extraction range of the present invention.

[0036] Figure 5 It is a schematic diagram of constructing the initial triangular surface of the present invention.

[0037] Figure 6 This is the triangulated surface construction process 1 of the present invention.

[0038] Figure 7 This is the triangulated surface construction process 2 of the present invention.

[0039] Figure 8 It is a schematic diagram of the triangular surface construction result of the present invention.

[0040] Figure 9 It is a schematic diagram of the distance from a point cloud point to a triangle surface of the present invention.

[0041] Figure 10 It is a schematic elevation diagram of the distance from a point cloud point to a triangle surface according to the present invention.

[0042] Figure 11 It is a schematic diagram of point cloud point determination within the triangular surface range of the present invention.

[0043] Figure 12 It is a schematic diagram of constructing a new triangular face according to the present invention.

[0044] Figure 13 It is a topographic map of the first embodiment of the present invention and a position comparison diagram of topographic feature points in a real scene image.

[0045] Figure 14 It is the top view of the terrain feature points of the present invention in the real scene image.

[0046] Figure 15 It is the side view of the terrain feature points of the present invention in the real scene 3D model. Detailed implementation manners

[0047] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0048] See Figures 1 to 12 , a multi-scale terrain feature extraction method based on ground point cloud of the present invention includes the following steps:

[0049] A. First, select the extraction range of terrain feature points. According to the need, a closed space polygon is selected to enclose the extraction range of feature points, and it is marked by the range of the projection of this space polygon onto the horizontal plane, that is, it is shown as a closed polygon surface in the two-dimensional plane. Usually, the area surrounded by this range line is not larger than the coverage range of the projection of all point clouds and is a convex polygon;

[0050] B. Then, initially construct triangular faces. Any side of the range polygon is used as the starting side, and triangular faces are constructed according to the "empty circumcircle" principle;

[0051] C. Subsequently, establish the spatial triangular face equation. In three-dimensional space, three points not on the same straight line can form a plane, and its equation can be expressed as αx + βy + γz + λ = 0. Assuming the coordinates of the three points are represented by P1(x1, y1, z1), P2(x2, y2, z2), and P3(x3, y3, z3) respectively, then:

[0052] α = (y3 - y1)*(z3 - z1) - (z2 - z1)*(y3 - y1)

[0053] β = (x3 - x1)*(z2 - z1) - (x2 - x1)*(z3 - z1)

[0054] γ = (x2 - x1)*(y3 - y1) - (x3 - x1)*(y2 - y1)

[0055] Then λ = -(α * x1 + β * y1 + γ * z1)

[0056] The plane equation can be obtained according to the three-dimensional spatial coordinates of the three corner points of each triangular face;

[0057] D. Then, set the scale factor according to relevant specification requirements and actual needs. The size of the set scale factor can distinguish the fineness of terrain feature extraction. The fineness of terrain feature point extraction is set by the distance d from the point cloud point to the triangular face, that is, the maximum distance dmax When it is less than the scale factor φ, topographic feature points are not extracted, that is, it is considered that the selection and simplification of topographic features meet the requirements;

[0058] E. Then extract the topographic feature points. The topographic feature points are extracted by using the points with the maximum distance from the point cloud points to the triangular surface. Traverse the distances from the points within the plane range of the triangular surface projection to the triangle, and find the point cloud point with the maximum distance, which is the most prominent topographic feature point within the current triangular projection surface;

[0059] F. Finally, iteratively extract the topographic feature points through triangular surface fission dissection. Add the topographic feature points extracted from each triangular surface to the original triangular surface and connect them to the three vertices of the triangular surface to fission dissect into three new triangular surfaces. Then continue to iteratively extract the topographic feature points for the new triangular surfaces according to the method in step E until the distance from all point cloud points to the triangular surface where the last fission dissection is located is less than φ, that is, the extraction operation of the topographic feature points is completed.

[0060] In step B, any side of the range polygon is used as the starting side, and triangular surfaces are constructed according to the "empty circumcircle" principle. The "empty circumcircle" means that no other polygon corner points are included within the circumcircle of the triangle. When there are multiple points on the same circle, the triangle with the largest minimum angle is selected. If the minimum angles are the same, the triangle with the second largest second smallest angle is selected, and so on for construction.

[0061] The calculation method and process for extracting topographic feature points in step E are as follows:

[0062] a. First, judge the boundary line of the triangular surface and the point cloud points within the triangular surface range. Use the coordinates in two directions on the horizontal plane of the triangular surface and the point cloud points for judgment. Let the judgment point be point Q. To judge whether point Q is within the triangular surface MNO, first calculate the vector Then use the cross product method of vectors to calculate If the cross products of all three groups of vectors are positive, negative, or 0, then point Q is within the MNO surface or on the triangular surface boundary;

[0063] b. Secondly, obtain the feature points within the triangular surface. Solve the spatial distance from all point cloud points within the triangular surface range in the three-dimensional coordinate system to the triangular surface. Assume the point coordinates are T(x0, y0, z0), and the triangular surface is α1x + β1y + γ1z + λ1 = 0. Then the distance d from the point to the triangular surface is:

[0064] d = |α1x + β1y + γ1z + λ1| / (α1 + β1 + γ1);

[0065] The inclination angle θ of the triangular surface is:

[0066]

[0067] Then traverse the distances between all the point cloud points within the triangular surface range to the triangular surface, and find the maximum distance d max Find the corresponding point and calculate the scale factor corresponding to this triangular surface according to the method in step D using the inclination angle θ of the triangular surface for comparison; if it is greater than or equal to the scale factor, extract this point as the terrain feature point within the range of this triangular surface; if it is less than the scale factor, do not extract the terrain feature point, and no terrain feature point extraction will be performed on this triangular surface;

[0068] c. Finally, find all the initial feature points, and traverse all the triangular surfaces to find all the terrain feature points within all the triangular surfaces according to the method in step b.

[0069] See Figures 1 to 5 , in order to efficiently and accurately extract the terrain feature points of the ground point cloud data, the present invention avoids the working method of extracting points through manual discrimination adopted in the prior art. The traditional method of extracting points through manual discrimination not only has low efficiency, but also has disadvantages such as difficult to unify the trade-off and comprehensive scale of features and easy to miss. The present invention ingeniously adopts the principle of "empty circumcircle" to construct triangular surfaces, establishes a spatial triangular surface equation, sets a scale factor to select and comprehensively simplify the terrain feature points, and gradually narrows the geographical range of feature extraction during the iterative process of triangular surface fission and dissection until all the terrain feature points that meet the requirements are obtained. The specific method steps and principles are as follows:

[0070] First, select the range for extracting terrain feature points. According to the need, select a closed space polygon to enclose the range for extracting feature points, and use the range of the projection of this space polygon onto the horizontal plane to indicate, that is, it is displayed as a closed polygon surface in the two-dimensional plane. Usually, the area surrounded by this range line is not larger than the coverage range of the projection of all point clouds and is a convex polygon. As shown in the attached Figure 4 The gray area shown, the space polygon ABCDEFG in the figure.

[0071] Next, triangular faces are constructed. This process is carried out in a two-dimensional horizontal plane. Since the division method of decomposing a polygon into multiple triangular faces is not unique, an exact division method is required to make the sizes and shapes of the finally obtained triangular faces as uniform as possible, so that the extracted terrain feature points are more accurate and efficient, and evenly and reasonably distributed. The method is as follows: Use any side of the range polygon as the starting side and construct triangular faces according to the "empty circumcircle" principle. The "empty circumcircle" means that no other polygon corner points are included inside the circumcircle of the triangle. When multiple points are concyclic, select the triangle with the largest minimum angle. If the minimum angles are the same, select the triangle with the larger second smallest angle, and so on for construction. The implementation method is to select any side as the initial side, find the circumcircle of the remaining vertices and this side, and select the points that do not contain other vertices inside the circumcircle to form the first triangular face. This way can solve the problem of non-unique initial triangular face division method and obtain triangular faces with as uniform sizes and shapes as possible, laying a foundation for more accurate, efficient and evenly distributed extraction of subsequent terrain feature points.

[0072] Take the polygon ABCDEFG in the appendix Figure 4 as an example. Select side AB as the initial side and construct triangular faces in a clockwise direction. As shown in the appendix Figure 5 , the endpoints A, B of the initial side are concyclic with E, G, and the circumcircle is an "empty circumcircle". At this time, the minimum angles of the two triangles ∠AGB = ∠AEB. Therefore, select the triangle ABE with the larger second smallest angle to construct the initial triangular face, as shown in the appendix Figure 5 .

[0073] Then, successively find the adjacent points that have not participated in the construction of triangular faces and find their corresponding triangular faces until all points are at least the vertices of one triangular face. On the basis of the previous step, with CD as the fixed side, the third vertex of the triangular face needs to be found. As shown in the appendix Figure 6 , the new triangular face is CDE. At this time, A, B, C, D, E are all vertices of triangular faces, and G, F have not yet formed triangular faces. Therefore, a triangular face needs to be constructed with GB as the side, as shown in the appendix Figure 7 . The final combination of triangular faces is as shown in the appendix Figure 8 . At this time, the shapes of all the triangles in the triangular face combination are relatively uniform as a whole.

[0074] Subsequently, establish the spatial triangular face equation. In three-dimensional space, three points not on the same straight line can form a plane, and its equation can be expressed as αx + βy + γz + λ = 0. Assuming the coordinates of the three points are represented by P1(x1, y1, z1), P2(x2, y2, z2), P3(x3, y3, z3) respectively, then:

[0075] α = (y3 - y1)*(z3 - z1) - (z2 - z1)*(y3 - y1)

[0076] β = (x3 - x1) * (z2 - z1) - (x2 - x1) * (z3 - z1)

[0077] γ = (x2 - x1) * (y3 - y1) - (x3 - x1) * (y2 - y1)

[0078] Then λ = -(α * x1 + β * y1 + γ * z1)

[0079] The plane equation can be obtained according to the three - dimensional spatial coordinates of the three corner points of each triangular face.

[0080] Then set the scale factor. The size of the set scale factor can distinguish the fineness of terrain feature extraction. The fineness of terrain feature point extraction is set by the distance d from the point cloud point to the triangular face, that is, when the maximum distance dmax is less than the scale factor φ, terrain feature points are not extracted, which means that the trade - off and complexity synthesis of terrain features meet the requirements.

[0081] The requirements for the trade - off and complexity synthesis of terrain features in topographic maps of different scales are inconsistent. Therefore, a scale factor needs to be set, and its size can distinguish the fineness of terrain feature extraction. Commonly used topographic map scales are 1:5000, 1:2000, 1:1000, 1:500, etc.

[0082] The fineness of terrain feature point extraction is set by the distance d from the point cloud point to the triangular face, that is, when the maximum distance dmax is less than the scale factor φ, terrain feature points are not extracted, which means that the trade - off and complexity synthesis of terrain features meet the requirements. The distance d is as shown in the appendix Figure 9 as follows.

[0083] The setting of the scale factor φ is converted with reference to the relevant regulations of the digital elevation model grid point elevation mean square error in Table 5.1.7 of the "Engineering Surveying Standard" (GB 50026 - 2020). The conversion method is to convert the elevation mean square error h specified in the standard into the limit of the distance d from the point to the plane according to the ground dip angle. Among them, the relationship between d and h is as follows

[0084] d = h * cosθ, where θ is the ground dip angle, as shown in the appendix Figure 10 as follows.

[0085] The relevant regulations in the specification are as follows in the table

[0086] Table 1 Digital elevation model grid point elevation mean square error

[0087]

[0088] Since for each specific triangular face, cosα is a constant, according to the error propagation law, m d = m l ×cosα, where m dis the distance mean error, m l is the elevation mean error, and the scale factor is set as follows according to the conversion:

[0089] Table 2 Scale factor after conversion

[0090]

[0091] For the triangular surface with an inclination angle less than 6°, which belongs to flat ground and hilly areas, the scale factor is relatively close to the elevation mean error in the specification. In practical applications, the elevation mean error can be directly adopted. Therefore, the scale factor setting can be simplified as the following table:

[0092] Table 2 Simplified scale factor

[0093]

[0094] This method independently sets the scale factor according to both the topographic scale and the inclination angle of each triangular surface. Compared with the single fixed scale factor setting, it not only takes into account the influence of different triangular surface inclination angles but also avoids the problem of inconsistent density of feature points caused by using a unified scale factor for different ground inclination angles. It can "adjust measures to local conditions" to extract more refined topographic data for different ground inclination angles, that is, it reduces the redundancy of feature points in regular topographic areas and ensures the complete expression of topographic features in complex topographic areas, ensuring more reasonable topographic selection and complexity synthesis. This scale factor setting method perfectly fits the regulations on elevation mean error for different topographic types in relevant specifications and has great practical value.

[0095] Then extract the topographic feature points, and the topographic feature points are extracted by the points with the maximum distance from the point cloud points to the triangular surface. Traverse the distances from the points within the projection plane of the triangular surface to the triangle, and find the point cloud point with the maximum distance as the topographic feature point.

[0096] The calculation method and process for extracting topographic feature points are as follows:

[0097] a. First, judge the points on the boundary line of the triangular surface and within the triangular surface range. Use the coordinates in two directions on the horizontal plane of the triangular surface and the point cloud points for judgment. As shown in the attachment Figure 11 to judge whether point Q is within the triangular surface MNO. First, calculate the vector Then use the vector cross product method to calculate If the cross products of the three groups of vectors are all positive, negative, or 0, then point Q is within the MNO plane or on the boundary line of the triangular surface;

[0098] b. Secondly, obtain the feature points within the triangular surface. Solve the spatial distance from all the point cloud points within the triangular surface range in the three-dimensional coordinate system to the triangular surface. Assume the point coordinates are T(x0, y0, z0), and the triangular surface is α1x + β1y + γ1z + λ1 = 0, then the distance d from the point to the triangular surface is:

[0099] d = |α1x + β1y + γ1z + λ1| / (α1 + β1 + γ1);

[0100] The inclination angle θ of the triangular surface is:

[0101]

[0102] Then traverse the distances from all the point cloud points within the range to the triangular surface, and find the maximum distance d max The corresponding points and use the inclination angle θ of the triangular surface to calculate the scale factor corresponding to this triangular surface according to the method in step D for comparison; if it is greater than or equal to the scale factor, extract this point as the terrain feature point within the range of this triangular surface; if it is less than the scale factor, do not extract the terrain feature point, and no more terrain feature points are extracted for this triangular surface;

[0103] c. Finally, find all the initial feature points, and traverse all the triangular surfaces to find all the terrain feature points within the triangular surfaces according to the method in step b.

[0104] Finally, perform feature point extraction through fission dissection iteration, that is, add the terrain feature points extracted from each triangular surface to the triangular surface and connect them to the three vertices of the original triangular surface to form a new triangular surface, as shown in the appendix Figure 11 As shown, then continue to iteratively extract terrain feature points for the new triangular surface according to the method in step E until the distance from all the point cloud points to the triangular surface where they are located is less than φ, that is, the extraction operation of the terrain feature points is completed.

[0105] Specific Example 1: In the topographic survey data of a certain section of river dike at a scale of 1:5000, according to this method, the terrain feature points required for the topographic map such as the dike top, dike foot, slope top, slope foot, inclination angle transformation line feature points, and direction transformation line feature points can be accurately extracted. In the appendix Figure 13 The left side is the topographic map, in the appendix Figure 13 The right side is the position comparison map of the terrain feature points extracted from the point cloud in the real scene image. It can be seen from the test results of the example data that the extraction of terrain feature points based on ground point cloud in this invention can meet the requirements of topographic map drawing.

[0106] The above content is a further detailed description of the present invention in combination with specific implementation manners. It cannot be considered that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, simple modifications and substitutions made should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for extracting multi-scale terrain features based on ground point clouds, comprising the following steps: A. First, select the extraction range of terrain feature points. According to the need, use a closed - space polygon to enclose the extraction range of feature points, and mark it with the range of the projection of this spatial polygon onto the horizontal plane, that is, it is displayed as a closed polygon surface in the two - dimensional plane. Usually, the area enclosed by this range line is not larger than the coverage range of the projection of all point clouds and is a convex polygon; B. Then, initially construct triangular faces. Use any side of the range polygon as the starting side and construct triangular faces according to the "empty circum - circle" principle; C. Subsequently, establish the spatial triangular - face equation. In three - dimensional space, three non - collinear points can form a plane, and its equation can be expressed as αx + βy+γz + λ = 0. Assume that the coordinates of the three points are represented by P1(x1,y1,z1), P2(x2,y2,z2), and P3(x3,y3,z3) respectively, then: α=(y3 - y1)*(z3 - z1)-(z2 - z1)*(y3 - y1) β=(x3 - x1)*(z2 - z1)-(x2 - x1)*(z3 - z1) γ=(x2 - x1)*(y3 - y1)-(x3 - x1)*(y2 - y1) Then λ=-(α*x1 + β*y1 + γ*z1) According to the three - dimensional spatial coordinates of the three corner points of each triangular face, the plane equation can be obtained; D. Then, set the scale factor according to relevant specification requirements and actual needs. The size of the set scale factor can distinguish the fineness of terrain feature extraction. The fineness of terrain feature point extraction is set by the distance d from the point cloud point to the triangular surface, that is, the maximum distance d max When it is less than the scale factor φ, terrain feature points are not extracted, that is, it is considered that the trade-off and complexity of terrain features meet the requirements; E. Then, extract terrain feature points. The terrain feature points are extracted by using the points with the maximum distance from the point cloud points to the triangular face. Traverse the distances from the points within the plane range of the triangular - face projection to the triangular face, and find the point cloud point with the maximum distance, which is the most prominent terrain feature point within the current triangular - face projection; The calculation method and process for extracting terrain feature points are as follows: a. First, judge the boundary lines of the triangular surface and the point cloud points within the triangular surface. Use the coordinates in two directions on the horizontal plane of the triangular surface and the point cloud points for judgment. Let the judgment point be point Q. To judge whether point Q is within the triangular surface MNO, first calculate the vector Then, use the cross product method of vectors to calculate respectively If the cross products of all three groups of vectors are positive, negative, or 0, then point Q is within the MNO surface or on the boundary line of the triangular surface; b. Secondly, obtain the feature points within the triangular face. Solve the spatial distance between the point cloud points within the range of all triangular faces in the three - dimensional coordinate system. Assume the point coordinates are T(x0,y0,z0), and the triangular - face equation is α1x + β1y + γ1z + λ1 = 0, then the distance d from the point to the triangular face is: d = |α1x + β1y + γ1z + λ1| / (α1 + β1 + γ1); The inclination angle θ of the triangular face is: Then traverse the distances between all the point cloud points within the triangular surface range and the triangular surface to find the maximum distance d max Find the corresponding points and use the inclination angle θ of the triangular surface to calculate the scale factor corresponding to this triangular surface according to the method in step D for comparison; if it is greater than or equal to the scale factor, extract this point as the terrain feature point within the range of this triangular surface; If it is less than the scale factor, no terrain feature points are extracted, and no terrain feature points are extracted from this triangular face anymore; c. Finally, find all initial feature points. Traverse all triangular faces and find all terrain feature points within the triangular faces according to the method in step b; F. Finally, iteratively extract terrain feature points through triangular - face fission and dissection. Add the terrain feature points extracted from each triangular face to the original triangular face and connect them to the three vertices of the triangular face to fission and dissect into three new triangular faces. Subsequently, continue to iteratively extract terrain feature points from the new triangular faces according to the method in step E until the distance from all point cloud points to the triangular face where the last fission and dissection is located is less than φ, that is, the extraction operation of terrain feature points is completed.

2. The method for extracting multi-scale terrain features based on ground point clouds according to claim 1, wherein: In step B, any side of the range polygon is used as the starting side, and triangular faces are constructed according to the "empty circum - circle" principle; the "empty circum - circle" means that no other polygon corner points are included within the circum - circle of the triangle. When multiple points are co - circular, select the triangle with the largest minimum angle. If the minimum angles are the same, select the triangle with the second - largest second - smallest angle, and so on for construction.

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