A semi-automatic collection method for road elevation discrete points based on laser point cloud data

Abstract

The application relates to a kind of road elevation discrete point semi-automatic acquisition methods based on laser point cloud data, comprising the following steps: S1, obtaining ground point cloud data and several auxiliary lines, while setting the interval between discrete points, sequentially executing S2 and S3 for each auxiliary line;S2, according to the position and direction of auxiliary line, adjacent ground point cloud is screened out, and the ground contour line close to the road surface is calculated based on the adjacent ground point cloud;S3, on the ground contour line, a point is taken every interval distance between discrete points, the elevation value of each point obtained is saved in the corresponding field in the table, and the elevation discrete point on the auxiliary line is obtained.Compared with the prior art, the application has the advantages of ensuring precision and improving the degree of automation of terrain data production.

Description

Technical Field

[0001] This invention relates to the field of terrain data processing technology, and in particular to a semi-automatic method for collecting discrete road elevation points based on laser point cloud data. Background Technology

[0002] In new-type basic surveying and mapping, geographic information is acquired through multi-source sensing technologies such as oblique photography and laser scanning. Three-dimensional laser point cloud data, as one of the main data sources, contains a wealth of information including three-dimensional coordinates, color, intensity values, and time. It boasts high accuracy, fast acquisition speed, and rich content, making it suitable for terrain data production. Currently, numerous algorithms exist for identifying and extracting various terrain features from point cloud data, automatically identifying and rapidly extracting features based on their shape, reflection intensity, and other information, thereby improving the efficiency of terrain data production.

[0003] In traditional topographic mapping, scattered elevation points are typically measured using geometric leveling, electromagnetic ranging, trigonometric leveling, or GPS RTK height measurement, which involves a significant workload. While using 3D laser point cloud data to collect discrete elevation points can reduce fieldwork, manual collection remains tedious, requiring repeated distance measurement, data collection, and elevation recording. Unlike ordinary features, discrete elevation points lack fixed shape or intensity characteristics and must be collected manually based on site conditions and operational requirements; they cannot be automatically identified and obtained through algorithms. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a semi-automatic method for collecting discrete road elevation points from laser point cloud data. This method can collect discrete elevation points conveniently and quickly according to user requirements, improving the automation level of terrain data production while ensuring accuracy.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A semi-automatic method for collecting discrete road elevation points based on laser point cloud data includes the following steps:

[0007] S1. Acquire ground point cloud data and several auxiliary lines, and set the interval between discrete points. Perform S2 and S3 sequentially for each auxiliary line. The auxiliary line is located at the position of the discrete points of road elevation to be collected and is collected on the ground point cloud.

[0008] S2. Filter out the nearby ground point cloud based on the position and direction of the auxiliary line, and draw the ground outline line that closely follows the road surface based on the nearby ground point cloud.

[0009] S3. On the ground contour line obtained in S2, take a point at a distance between the discrete points set in S1, and save the elevation value of each point in the corresponding field of the table to obtain the elevation discrete points on the auxiliary line.

[0010] Furthermore, the specific steps for filtering out neighboring ground point clouds are as follows:

[0011] S2.1 On the xy plane, with the auxiliary line as the center line, a buffer zone is established with a preset buffer radius. Points in the ground point cloud data whose planar positions fall within the buffer zone are stored in a new point cloud. The new point cloud is used as the neighboring ground point cloud.

[0012] Furthermore, the specific steps for calculating the ground contour line closely following the road surface based on nearby ground points are as follows:

[0013] S2.2 Project the points in the neighboring ground point cloud onto a plane, calculate the planar coordinates of each point, and store the set of points composed of the planar coordinates of each point into an array. At the same time, record the corresponding index of each point in the neighboring ground point cloud.

[0014] S2.3 Sort the points in the array;

[0015] S2.4 Extract the contour lines from the points in the sorted array to obtain an array of contour line nodes;

[0016] S2.5. Based on the sequence number of each point recorded in S2.2 in the neighboring ground point cloud, restore the contour line node array to the three-dimensional coordinate points in the neighboring ground point cloud and connect them into line elements to obtain the contour line of the ground.

[0017] Furthermore, the origin of the coordinate system of plane S2.2 is the starting point of the auxiliary line, the x-axis of the plane is the cumulative projection distance L from a point in the nearby ground point cloud to the starting point of the auxiliary line, and the y-axis of the plane is the elevation value of that point.

[0018] Furthermore, in S2.3, the sorting principle is to sort the points from smallest to largest according to their x-axis coordinates, and when the x-axis coordinates are the same, to sort them from smallest to largest according to their y-axis coordinates.

[0019] Furthermore, in S2.4, the specific steps for extracting the contour lines are as follows:

[0020] Traverse the sorted array in reverse order, adding each point to the contour node array sequentially. Filter the points as you add them. Once all points in the sorted array have been filtered, use this current contour node array as S2.4 to obtain the final contour node array. The specific filtering steps are as follows:

[0021] Determine the number of points in the outline node array.

[0022] When the number of points in the outline node array is less than 2, the newly added point is directly added to the outline node array, and then the next point is added and filtered.

[0023] When the number of points in the contour node array is greater than or equal to 2, determine the relationship between the last point and the second-to-last point in the contour node array and the newly added point. If both conditions a and b are satisfied, add the newly added point to the contour node array. If neither condition a nor condition b is satisfied, then:

[0024] Remove the second-to-last point from the outline node array, update the second-to-last point, and repeat the above filtering steps until the number of points in the outline node array is less than 2, or the number of points in the outline node array is greater than or equal to 2 and conditions a and b are met simultaneously. Add the newly added point to the outline node array, and then perform the next point addition filtering.

[0025] Furthermore, condition a is that the angle ∠ABP formed by the last point A, the second-to-last point B, and the newly added point P satisfies: 120° ≥ ∠ABP ≥ 10°, and condition b is the vector formed by the last point A, the second-to-last point B, and the newly added point P. with vector satisfy:

[0026] Furthermore, suppose the auxiliary line has n+1 nodes {(x0, y0), (x1, y1), ..., (xn, yn)}, and the auxiliary line consists of n line segments. Point p lies between nodes Pm(xm-1, ym-1) and Pm+1(xm, ym). The cumulative projection distance L represents the sum of the lengths of the first m-1 line segments and the projection length of point p onto the m-th line segment. The expression for the cumulative projection distance L is:

[0027]

[0028] Where m is the index of the line segment where point p is located, l i Let be the length of the i-th segment in the auxiliary line, and s be the projection length of point p onto the m-th segment.

[0029] Furthermore, the characteristic is that the expression for the projection length of point p on the m-th line segment is:

[0030]

[0031] Among them, P m Let P be the m-th node of the auxiliary line. m+1 Let P be the (m+1)th node of the auxiliary line, and let P be the node on the auxiliary line at node P. mand node P m+1 The point between.

[0032] Furthermore, the buffer radius is 3cm.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] (1) The present invention calculates the ground contour line close to the road surface based on the nearby ground point cloud, obtains the elevation value of each point on the ground contour line, and obtains the elevation discrete points on the auxiliary line. It can be applied to various terrains, whether flat or undulating, to prevent elevation calculation errors when the ground is uneven and to ensure that the accuracy meets the requirements.

[0035] (2) This invention realizes semi-automatic acquisition of elevation discrete points, and realizes rapid acquisition of elevation discrete points at specified intervals at specified locations in the point cloud. Compared with manual acquisition, it is simple to operate and highly efficient. Attached Figure Description

[0036] Figure 1 This is a flowchart of the present invention;

[0037] Figure 2 This is a schematic diagram showing the auxiliary lines selected and the set interval distances in this invention;

[0038] Figure 3 This is a partial top view of the point cloud adjacent to both sides of the auxiliary line in this invention;

[0039] Figure 4 This is a partial front view of the point cloud adjacent to the auxiliary line on both sides in this invention;

[0040] Figure 5 This is a schematic diagram of the cumulative projection distance in this invention;

[0041] Figure 6 This is a diagram illustrating the effect of projecting the dimension reduction of the point cloud adjacent to both sides of the auxiliary line onto a two-dimensional plane in this invention.

[0042] Figure 7 This is a top view of the ground contour line extracted in this invention;

[0043] Figure 8 This is a front view of the ground contour line extracted in this invention;

[0044] Figure 9 These are the elevation points extracted in this invention. Detailed Implementation

[0045] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0046] This invention proposes a semi-automatic method for acquiring discrete road elevation points based on laser point cloud data, which improves the automation level of terrain data production while ensuring accuracy and meeting user needs. The flowchart of the method is as follows: Figure 1 As shown. The method includes the following steps:

[0047] Step 1: The user collects an auxiliary line on the ground point cloud. The auxiliary line falls on the location of the discrete points where the user wants to collect road elevation data, with each node falling on the ground of the point cloud during collection. Simultaneously, a distance d is set between the discrete points; in this example, it is set to 5 meters. Steps 2 and 3 are then executed on the auxiliary line. A schematic diagram of the selected auxiliary line and the set distance is shown below. Figure 2 As shown.

[0048] Step 2: Filter out the nearby ground point cloud based on the position and direction of the auxiliary line, and use the nearby point cloud to draw the ground outline line that is close to the road surface.

[0049] The steps in step 2 for calculating the neighboring point cloud and extracting the ground contour line for an auxiliary line are as follows:

[0050] Step 2.1: On the xy plane only, establish a buffer zone with the auxiliary line as the center line and a certain buffer radius (in this invention, the buffer radius is set to 3cm). Store the points in the point cloud data whose planar positions fall within the buffer zone into a new point cloud cloudA. cloudA is the neighboring point cloud on both sides of the auxiliary line. See Figure 3 , Figure 4 . Figure 3 This is a partial top view of the point cloud adjacent to the auxiliary lines on both sides; Figure 4 This is a partial front view of the point cloud adjacent to the auxiliary line on both sides.

[0051] Step 2.2: Project the points in cloudA onto a plane and calculate the planar coordinates of each point. This plane has the origin at the starting point P0(x0, y0) of the auxiliary line. The x-axis represents the cumulative projection distance L from point p(x, y) in the point cloud to the starting point P0 of the auxiliary line, and the y-axis represents the elevation z value of point p. Assume the auxiliary line has n+1 nodes {(x0, y0), (x1, y1), ..., (xn, yn)}, i.e., it consists of n line segments. Point p is located at node P0. m (xm-1, ym-1) and P m+1 Between (xm, ym), the cumulative projection distance L represents the sum of the lengths of the first m-1 line segments and the projection length of point p on the m-th line segment. A schematic diagram of the cumulative projection distance is shown below. Figure 5 The calculation formula is as follows:

[0052]

[0053] Where m is the index of the line segment where point p is located, l i Let be the length of the i-th segment in the auxiliary line, and s be the projection length of point p onto the m-th segment. The formula for calculating the projection length s of point p onto the m-th segment is as follows:

[0054]

[0055] Among them, P m Let P be the m-th node of the auxiliary line. m+1 Let P be the (m+1)th node of the auxiliary line, and let P be the node on the auxiliary line at node P. m and node P m+1 The point between.

[0056] The projected coordinate set is stored in an array `points`, recording both the projected coordinates and the corresponding index of each point in the original point cloud. The effect of projecting the neighboring point clouds on both sides of the auxiliary line onto a 2D plane in a dimension-reduced manner is shown in the image below. Figure 6 As shown.

[0057] Step 2.3: Sort the points in the projected array points according to the sorting principle of x from small to large, and y from small to large when x is the same.

[0058] Step 2.4: Extract the outlines of the points in the array points, as follows:

[0059] ① Traverse the points in points in reverse order and add them to the contour node array contour in turn. Each time a new point is added, perform a point filtering, and the filtering rules are as follows ② and ③.

[0060] ②When the number of points in the contour is less than 2, add a new point Q directly and repeat ①;

[0061] ③ When the number of points is greater than or equal to 2, determine the relationship between the last point A and the second-to-last point B in the contour and the new point Q to be added. If conditions a and b are satisfied at the same time, add point Q to the contour and repeat ①; otherwise, delete B from the contour and repeat ③ until the conditions are satisfied or the number of points in the contour is less than 2, then add point Q to the contour and repeat ①.

[0062] Conditions a and b in step 2.4 are as follows:

[0063] Condition a: The angle ∠ABP formed by points A, B, and P satisfies: 120° ≥ ∠ABP ≥ 10°

[0064] Condition b: The vector formed by points A, B, and P with vector satisfy:

[0065] Step 2.5: Based on the sequence number of each point recorded in Step 2.2, restore the points in the contour to their original 3D coordinates in the point cloud and connect them to form line features, thus obtaining the ground contour. A top view of the ground contour is shown below. Figure 7 As shown, the extracted ground contour line front view is as follows: Figure 8 As shown.

[0066] Step 3: Based on the interval distance d set in Step 1, take a point every d meters on the ground contour line generated in Step 2, and store its elevation value in the corresponding field of the attribute table to obtain the discrete elevation points on this auxiliary line. The extracted elevation points are shown below. Figure 9 As shown.

[0067] This invention significantly improves automation and reduces the time spent on manual distance measurement, point collection, and recording by fitting the ground contour line to the ground point cloud near manually specified auxiliary lines and calculating a number of discrete elevation points at certain intervals. Simultaneously, the fitting of the ground contour line ensures accuracy even when encountering situations such as... Figure 4 When the ground surface is undulating, the obtained discrete elevation points are located on the ground surface, ensuring the accuracy of the results.

[0068] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A semi-automatic method for acquiring discrete road elevation points based on laser point cloud data, characterized in that, Includes the following steps: S1. Acquire ground point cloud data and several auxiliary lines, and set the interval between discrete points. Perform S2 and S3 sequentially for each auxiliary line. The auxiliary line is located at the position of the discrete points of road elevation to be collected and is collected on the ground point cloud. S2. Filter out the nearby ground point cloud based on the position and direction of the auxiliary line, and draw the ground outline line that closely follows the road surface based on the nearby ground point cloud. S3. On the ground contour line obtained in S2, take a point at a distance between the discrete points set in S1, and save the elevation value of each point in the corresponding field of the table to obtain the elevation discrete points on the auxiliary line. The specific steps for calculating the ground contour line that closely follows the road surface based on nearby ground points are as follows: S2.2 Project the points in the neighboring ground point cloud onto a plane, calculate the planar coordinates of each point, and store the set of points composed of the planar coordinates of each point into an array. At the same time, record the corresponding index of each point in the neighboring ground point cloud. S2.3 Sort the points in the array; S2.4 Extract the contour lines from the points in the sorted array to obtain an array of contour line nodes; S2.

5. Based on the sequence number of each point recorded in S2.2 in the neighboring ground point cloud, restore the contour line node array to the three-dimensional coordinate points in the neighboring ground point cloud and connect them into line elements to obtain the contour line of the ground. In S2.4, the specific steps for extracting the contour lines are as follows: Traverse the sorted array in reverse order, adding each point to the contour node array sequentially. Filter the points as you add them. Once all points in the sorted array have been filtered, use this current contour node array as S2.4 to obtain the final contour node array. The specific filtering steps are as follows: Determine the number of points in the outline node array. When the number of points in the outline node array is less than 2, the newly added point is directly added to the outline node array, and then the next point is added and filtered. When the number of points in the contour node array is greater than or equal to 2, determine the relationship between the last point and the second-to-last point in the contour node array and the newly added point. If both conditions a and b are satisfied, add the newly added point to the contour node array. If neither condition a nor condition b is satisfied, then: Remove the second-to-last point from the outline node array, update the second-to-last point, and repeat the above filtering steps until the number of points in the outline node array is less than 2, or the number of points in the outline node array is greater than or equal to 2 and conditions a and b are met simultaneously. Add the newly added point to the outline node array, and then perform the next point addition filtering.

2. The semi-automatic method for acquiring discrete road elevation points based on laser point cloud data according to claim 1, characterized in that, The specific steps for filtering out nearby ground point clouds are as follows: S2.1 On the xy plane, with the auxiliary line as the center line, a buffer zone is established with a preset buffer radius. Points in the ground point cloud data whose planar positions fall within the buffer zone are stored in a new point cloud. The new point cloud is used as the neighboring ground point cloud.

3. The semi-automatic method for acquiring discrete road elevation points based on laser point cloud data according to claim 1, characterized in that, The origin of the coordinate system of plane S2.2 is the starting point of the auxiliary line. The x-axis of the plane is the cumulative projection distance L from a point in the nearby ground point cloud to the starting point of the auxiliary line. The y-axis of the plane is the elevation value of that point.

4. The semi-automatic method for acquiring discrete road elevation points based on laser point cloud data according to claim 3, characterized in that, In S2.3, the sorting principle is to sort the points from smallest to largest according to their x-axis coordinates, and when the x-axis coordinates are the same, sort them from smallest to largest according to their y-axis coordinates.

5. The semi-automatic method for acquiring discrete road elevation points based on laser point cloud data according to claim 1, characterized in that, Condition a is that the angle ∠ABP formed by the last point A, the second-to-last point B, and the newly added point P satisfies: 120° ≥ ∠ABP ≥ 10°. Condition b is the vector formed by the last point A, the second-to-last point B, and the newly added point P. with vector satisfy:

6. The semi-automatic method for acquiring discrete road elevation points based on laser point cloud data according to claim 3, characterized in that, Let the auxiliary line have n+1 nodes {(x0,y0),(x1,y1),...,(xn,yn)}, and the auxiliary line consists of n line segments. Point p lies between nodes Pm(xm-1,ym-1) and Pm+1(xm,ym). The cumulative projection distance L represents the sum of the lengths of the first m-1 line segments and the projection length of point p onto the m-th line segment. The expression for the cumulative projection distance L is: Where m is the index of the line segment where point p is located, l i Let be the length of the i-th segment in the auxiliary line, and s be the projection length of point p onto the m-th segment.

7. A semi-automatic method for acquiring discrete road elevation points based on laser point cloud data according to claim 6, characterized in that, The expression for the projection length of point p onto the m-th line segment is: Among them, P m Let P be the m-th node of the auxiliary line. m+1 Let P be the (m+1)th node of the auxiliary line, and let P be the node on the auxiliary line at node P. m and node P m+1 The point between.

8. A semi-automatic method for acquiring discrete road elevation points based on laser point cloud data according to claim 2, characterized in that, The buffer radius is 3cm.

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