A distance-based point cloud slicing processing method and system

Abstract

The present invention discloses a distance-based point cloud slicing processing method and system, and the method of the present invention is performed in the following steps: S1, obtaining the original point cloud data A to be processed, and removing the ground point cloud; S2, calculating the average point B (X1, Y1, Z1); S3, sorting the values ​​of each axis from large to small, calculating the interval distance between each point in sequence, and calculating the average interval distance dx, dy, dz; S4, slicing according to the average interval distance at the average point B according to the number N of point cloud slices. The present invention reduces the number of point clouds to be processed and has strong point cloud adaptability.

Description

Technical Field

[0001] The present invention belongs to the technical field of point cloud data processing, and more specifically relates to a distance-based point cloud slicing processing method and system. Background Art

[0002] At present, laser point cloud data processing includes point cloud filtering, point cloud registration, point cloud segmentation and classification, point cloud 3D reconstruction and other related technologies, but its application in point cloud data size information processing is relatively small.

[0003] The use of 3D laser scanning technology to collect point cloud data has the advantages of high speed, high precision, and measurability, which makes it a popular means of obtaining 3D data. However, the unorganized, unstructured, and high-density characteristics of point clouds make the extraction of point cloud data size information a hot topic and difficulty in research.

[0004] Point cloud slicing technology can slice point clouds to reduce the number of point clouds to be processed. However, most of the current point cloud slicing processes are fixed thickness. When the point cloud density is particularly high, the fixed thickness slices still have a lot of data to process and the processing speed is also very slow.

[0005] Therefore, based on the above situation, this field needs to develop a more efficient point cloud slicing technology. Summary of the invention

[0006] In view of the above problems existing in the prior art, the present invention provides a distance-based point cloud slicing processing method and system. The present invention can slice laser point cloud data to adapt to different types of point clouds, has strong adaptability, and fast processing speed.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A distance-based point cloud slicing processing method is performed in the following steps:

[0009] S1. Obtain the original point cloud data A to be processed and remove the ground point cloud;

[0010] S2, calculate the average point B (X1, Y1, Z1);

[0011] S3, sort the values ​​of each axis from large to small, calculate the interval distance between each point in order, and calculate the average interval distance dx, dy, dz;

[0012] S4. Determine the number N of point cloud slices, and slice at the average value point B according to the average interval distance.

[0013] Preferably, in step S2, since the point cloud is generally published more densely in the Z-axis direction, it is necessary to add 10 to 30 times dz to the value of Z1 to finally obtain the average value point B (X1, Y1, Z1).

[0014] Preferably, step S3 includes:

[0015] S31. For the X axis, sort each point cloud data from large to small according to the size of the X value, calculate the distance between each adjacent point by subtracting the next digit from the previous digit, and calculate the average value dx;

[0016] S32, for the Y axis, sort each point cloud data from large to small according to the size of the Y value, calculate the distance between each adjacent point by subtracting the next digit from the previous digit, and calculate the average value dy;

[0017] S33. For the Z axis, sort each point cloud data from large to small according to the size of the Z value, calculate the distance between each adjacent point by subtracting the next digit from the previous digit, and calculate the average value dz.

[0018] Preferably, step S4 comprises:

[0019] S41, determining the plane to be sliced;

[0020] S42, the starting point of the point cloud slice is the average value point B;

[0021] S43. The thickness of the point cloud slice is related to the number N and the average interval distance. Taking the vertical Z-axis slice as an example, determine whether the Z value of each point is in the interval (z1-1 / 2×N×dz,z1+1 / 2×N×dz). These points are the results of the slice.

[0022] The present invention also discloses a distance-based point cloud slicing processing system, which includes the following modules:

[0023] Obtaining point cloud raw data module: Obtaining the original point cloud data A to be processed and removing the ground point cloud;

[0024] Calculate average point module: calculate average point B (X1, Y1, Z1);

[0025] Module for calculating average interval distance: sort the values ​​of each axis from large to small, calculate the interval distance between each point in order, and calculate the average interval distance dx, dy, dz;

[0026] Slicing module: According to the number of point cloud slices N, slices are performed at the average point B according to the average interval distance.

[0027] Preferably, in the module for calculating the average point, the value of Z1 is added with 10 to 30 times of dz, and finally the average point B is obtained.

[0028] Preferably, the module for calculating the average interval distance is specifically as follows:

[0029] For the X axis, sort each point cloud data from large to small according to the size of the X value, calculate the distance between each adjacent point by subtracting the previous digit from the next digit, and calculate the average value dx;

[0030] For the Y axis, sort each point cloud data from large to small according to the size of the Y value, calculate the distance between each adjacent point by subtracting the previous digit from the next digit, and calculate the average value dy;

[0031] For the Z axis, sort each point cloud data from large to small according to the size of the Z value, calculate the distance between each adjacent point by subtracting the previous digit from the next digit, and calculate the average value dz.

[0032] Preferably, the slicing module is as follows:

[0033] Determine the plane to be sliced; the starting point of the point cloud slice is the average value point B; the thickness of the point cloud slice is related to the number N and the average interval distance. In the vertical Z-axis slice, if the Z value of each point is in the range of (z1-1 / 2×N×dz,z1+1 / 2×N×dz), then these points are the results of the slice.

[0034] Compared with the prior art, the present invention discloses a distance-based point cloud slicing processing method, which solves the problem of low efficiency in processing massive point cloud data, is adaptable to point clouds of different densities, and has strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a schematic diagram of the method flow of Example 1 of the present invention;

[0036] Figure 2 It is a point cloud image processed by the present invention;

[0037] Figure 3 This is the XYZ axis slice result diagram of the present invention;

[0038] Figure 4 It is a block diagram of a distance-based point cloud slicing processing system of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in combination with specific implementation methods and with reference to the accompanying drawings. 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 work are within the scope of protection of the present invention.

[0040] Example 1

[0041] like Figure 1 As shown, the embodiment of the present invention discloses a distance-based point cloud slicing processing method, including:

[0042] S1: Obtain the original point cloud data to be processed, and remove the ground point cloud to obtain the point cloud set Q1;

[0043] Assume the installation height of the laser radar is H, and remove the point cloud with Z value H in the point cloud;

[0044] S2: Calculate the average value point B (X1, Y1, Z1) of the point cloud set Q1. The calculation formula is:

[0045]

[0046] (x i ,y i ,z i ) is the point of the point cloud, n is the number of point clouds;

[0047] Add 10 to 30 times dz to the value of Z1, and finally get the average value point B;

[0048] S3: Sort the values ​​of each axis in descending order, and sort the points in the point cloud set Q1 according to the size of the x, y, and z values, respectively, from large to small, calculate the distance between each adjacent point by subtracting the previous digit from the next digit, and calculate the interval of each point and the average value dx, dy, dz. The calculation formula is:

[0049]

[0050] (x i ,y i ,z i ) is the point of the point cloud, n is the number of point clouds;

[0051] S4: Determine the plane to be sliced. The starting point of the point cloud slice is the average point B. The thickness of the point cloud slice is related to the number N and the average interval distance. After determining the number N of point cloud slices, slice at the average point B according to the average interval distance. Taking the vertical Z-axis slice as an example, determine whether the Z value of each point is in the interval (z1-1 / 2×N×dz, z1+1 / 2×N×dz). These points are the results of the slice, such as Figure 2-3 shown.

[0052] Different point cloud data densities are different, and when point clouds are sliced ​​at fixed distances, there are still many remaining points. The present invention uses a distance-based point cloud segmentation method to greatly reduce the number of point clouds to be processed, and has strong point cloud adaptability.

[0053] Example 2

[0054] like Figure 4 As shown, this embodiment discloses a distance-based point cloud slicing processing system, including the following modules:

[0055] Obtaining original point cloud data module: Obtain the original point cloud data to be processed, and remove the ground point cloud to obtain the point cloud set Q1; Assume that the installation height of the laser radar is H, and remove the point cloud with a Z value of H in the point cloud;

[0056] Calculate the average point module: Calculate the average point B (X1, Y1, Z1) of the point cloud set Q1. The calculation formula is:

[0057]

[0058] (x i ,y i ,z i ) is the point of the point cloud, n is the number of point clouds;

[0059] Add 10 to 30 times dz to the value of Z1, and finally get the average value point B;

[0060] Module for calculating the average interval distance: sort the values ​​of each axis from small to large, sort the points in the point cloud set Q1 from large to small according to the size of the x, y, and z values, calculate the distance of each adjacent point by subtracting the previous digit from the next digit, and calculate the interval of each point and the average value dx, dy, dz. The calculation formula is:

[0061]

[0062] (x i ,y i ,z i ) is the point of the point cloud, n is the number of point clouds;

[0063] Slicing module: Determine the plane to be sliced. The starting point of the point cloud slice is the average point B. The thickness of the point cloud slice is related to the number N and the average interval distance. After determining the number N of point cloud slices, slice at the average point B according to the average interval distance. Taking the vertical Z-axis slice as an example, determine whether the Z value of each point is in the range of (z1-1 / 2×N×dz,z1+1 / 2×N×dz). These points are the results of the slice.

[0064] The contents described in the embodiments of this specification are merely an enumeration of implementation forms of the inventive concept. The protection scope of the present invention should not be regarded as limited to the specific forms described in the embodiments. The protection scope of the present invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A distance-based point cloud slicing processing method, characterized by Proceed as follows: S1. Obtain the original point cloud data A to be processed and remove the ground point cloud; S2, calculate the average value point B (X1, Y1, Z1) of the point cloud set Q1, the calculation formula is: Among them, (x i ,y i ,z i ) is a point of the point cloud, and n is the number of point clouds; S3, sort the values ​​of the X-axis, Y-axis, and Z-axis from large to small, calculate the interval distances between adjacent points in order, and calculate the average interval distances dx, dy, and dz; Step S3 specifically includes: S31. For the X-axis, sort each point cloud data from large to small according to the size of the X-axis coordinate value, calculate the distance between adjacent points by subtracting the next digit from the previous digit, and calculate the average value dx; S32, for the Y axis, sort each point cloud data from large to small according to the size of the Y axis coordinate value, calculate the distance between adjacent points by subtracting the next digit from the previous digit, and calculate the average value dy; S33, for the Z axis, sort each point cloud data from large to small according to the size of the Z axis coordinate value, calculate the distance between adjacent points by subtracting the next digit from the previous digit, and calculate the average value dz; S4, according to the number N of point cloud slices, slice at the average value point B according to the average interval distance; Step S4 specifically includes: S41, determining the plane to be sliced; S42, the starting point of the point cloud slice is the average value point B; S43. The thickness of the point cloud slice is related to the number N and the average interval distance. In the vertical Z-axis slice, if the Z-axis coordinate value of each point is in the range of (Z1-1 / 2×N×dz, Z1+1 / 2×N×dz), then these points are the results of the slice.

2. A distance-based point cloud slicing processing method as claimed in claim 1, characterized in that: In step S2, 10 to 30 times dz is added to the value of Z1, and finally the average value point B is obtained.

3. A distance-based point cloud slicing processing system, characterized by Includes the following modules: Obtaining point cloud raw data module: Obtaining the original point cloud data A to be processed and removing the ground point cloud; Calculate the average point module: Calculate the average point B (X1, Y1, Z1) of the point cloud set Q1. The calculation formula is: Among them, (x i ,y i ,z i ) is the point of the point cloud, n is the number of point clouds; Module for calculating average interval distance: sort the values ​​of the X-axis, Y-axis, and Z-axis from large to small, calculate the interval distance between adjacent points in order, and calculate the average interval distance dx, dy, and dz; the details are as follows: For the X-axis, sort each point cloud data from large to small according to the size of the X-axis coordinate value, calculate the distance between adjacent points by subtracting the next digit from the previous digit, and calculate the average value dx; For the Y axis, sort each point cloud data from large to small according to the Y axis coordinate value, calculate the distance between adjacent points by subtracting the previous digit from the next digit, and calculate the average value dy; For the Z axis, sort each point cloud data from large to small according to the size of the Z axis coordinate value, calculate the distance between adjacent points by subtracting the next digit from the previous digit, and calculate the average value dz; Slicing module: According to the number N of point cloud slices, slices are performed at the average value point B according to the average interval distance; specifically as follows: determine the plane to be sliced; the starting point of the point cloud slice is the average value point B; the thickness of the point cloud slice is related to the number N and the average interval distance. In the vertical Z-axis slice, if the Z-axis coordinate value of each point is in the range of (Z1-1 / 2×N×dz, Z1+1 / 2×N×dz), then these points are the results of the slice.

4. A distance-based point cloud slicing processing system as claimed in claim 3, characterized in that: In the module for calculating the average point, add 10 to 30 times dz to the value of Z1 to finally obtain the average point B.

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