WO2024094118A1 - Point cloud data processing method and apparatus, electronic device, and storage medium - Google Patents

Abstract

The present application provides a point cloud data processing method and apparatus, an electronic device, and a computer readable storage medium. The point cloud data processing method comprises: projecting original point cloud data of an area to be processed to a preset gridded plane to obtain a projected point cloud; obtaining target structure data of the original point cloud data on the basis of the height value and plane coordinates of each projected point, the target structure data comprising structure data of each plane grid, and the structure data comprising at least one of the plane coordinates, the number of projected points, point cloud curvature, a point cloud height maximum value, a point cloud height minimum value, a point cloud height average value and a point cloud height variance value; performing point cloud processing on the basis of the target structure data to obtain processed structure data of the original point cloud data; and restoring the processed structure data to obtain processed point cloud data of the original point cloud data. The present application reduces the degree of loss of an original data volume and improves the processing efficiency of point cloud data.

Description

Point cloud data processing method, device, electronic device and storage medium Technical Field

The present application relates to the technical field of three-dimensional point cloud processing, and specifically to a point cloud data processing method, device, electronic device and storage medium.

Background of the Invention

3D point cloud data is of great guiding significance for object detection such as volume measurement. However, the amount of 3D point cloud data obtained based on sensor measurement or 3D imaging equipment is usually large. If filtering and 3D reconstruction are performed based on the full amount of 3D point cloud data, the processing efficiency will be low.

In the prior art, in order to improve data processing efficiency, the full amount of collected point cloud data is usually downsampled, and then point cloud data filtering and reconstruction processing such as interference object recognition and boundary recognition are performed. However, the inventors of the present application found in the actual application process that: on the one hand, downsampling the point cloud data will cause a large amount of original information to be lost; on the other hand, if downsampling is performed under the premise of ensuring that a certain amount of original information is not lost, the amount of data cannot be effectively reduced, and a large amount of downsampled point cloud data still needs to be processed, and the processing efficiency is difficult to effectively improve.

Summary of the invention

The present application provides a point cloud data processing method, device, electronic device and computer-readable storage medium, which can improve the processing efficiency of point cloud data on the basis of reducing the degree of loss of original data volume.

In a first aspect, the present application provides a point cloud data processing method, comprising: projecting the original point cloud data of the area to be processed onto a preset gridded plane to obtain a projected point cloud of the original point cloud data; based on the height value and plane coordinates of each projection point in the projected point cloud, obtaining target structural data of the original point cloud data, wherein the target structural data includes the structural data of each plane grid into which the original point cloud data falls, the structural data of each plane grid includes the plane coordinates and the number of projection points of each plane grid, and the structural data of each plane grid also includes the point cloud curvature of each plane grid, the maximum point cloud height, the minimum point cloud height, the average point cloud height and at least one of the point cloud height variance values; performing point cloud processing based on the target structural data to obtain processed structural data of the original point cloud data; and restoring the processed structural data to obtain processed point cloud data of the original point cloud data.

In a second aspect, the present application provides a point cloud data processing device, which includes: a first acquisition unit, which is used to project the original point cloud data of the area to be processed onto a preset gridded plane to obtain a projected point cloud of the original point cloud data; a second acquisition unit, which is used to acquire target structure data of the original point cloud data based on the height value and plane coordinates of each projection point in the projected point cloud, wherein the target structure data includes the structure data of each plane grid into which the original point cloud data falls, and the structure data of each plane grid includes the plane coordinates of each plane grid, the number of projection points, and the structure of each plane grid. The data also includes at least one of the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height and the point cloud height variance value of each plane grid; a processing unit is used to perform point cloud processing based on the target structure data to obtain processed structure data of the original point cloud data; and a restoration unit is used to restore the processed structure data to obtain processed point cloud data of the original point cloud data.

In a third aspect, the present application further provides an electronic device, comprising a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, any point cloud data processing method provided in the first aspect of the present application is executed.

In a fourth aspect, the present application further provides a computer-readable storage medium on which a computer program is stored, and the computer program is loaded by a processor to execute the point cloud data processing method provided in the first aspect of the present application.

The present application processes the plane coordinates, the number of projection points, and at least one of the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance of each plane grid into the structural data of each plane grid. On the one hand, it can compress multiple point clouds in each plane grid to obtain target structural data, so that the amount of data processing can be reduced when the target structural data is used for subsequent processing, thereby improving the processing efficiency of the point cloud processing process; on the other hand, since the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance of each plane grid can reflect the point cloud distribution in each plane grid, when the processed structural data is restored, a point cloud that matches the distribution of the original point cloud data can be accurately and completely obtained. Therefore, the embodiments of the present application can improve the processing efficiency of point cloud data on the basis of reducing the degree of loss of the original data volume.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

FIG1 is a schematic diagram of a scene of a point cloud data processing system provided in an embodiment of the present application;

FIG2 is a flow chart of an implementation of a point cloud data processing method provided in an embodiment of the present application;

FIG3 is a schematic diagram of a preset grid plane provided in an embodiment of the present application;

FIG4 is an exemplary flowchart of step 202 provided in an embodiment of the present application;

FIG5 is an exemplary flowchart of step 203 provided in an embodiment of the present application;

FIG6 is a schematic structural diagram of an implementation of a point cloud data processing device provided in an embodiment of the present application;

FIG. 7 is a schematic diagram of the structure of an implementation method of an electronic device provided in an embodiment of the present application.

Mode for Carrying Out the Invention

The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

In the description of the embodiments of the present application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

In order to enable any person skilled in the art to implement and use the present application, the following description is provided. In the following description, details are listed for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present application can also be implemented without using these specific details. In other examples, the known process will not be elaborated in detail to avoid unnecessary details that make the description of the present application embodiment obscure. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest range of principles and features disclosed in accordance with the embodiments of the present application.

The present application provides a point cloud data processing method, device, electronic device and computer readable storage medium. The point cloud data processing device can be integrated in an electronic device, which can be a server or a terminal.

The execution subject of the point cloud data processing method of the embodiment of the present application may be the point cloud data processing device provided in the embodiment of the present application, or various types of electronic devices such as a server device, a physical host or a user equipment (UE) integrated with the point cloud data processing device. The point cloud data processing device may be implemented in hardware or software, and the UE may specifically be a terminal device such as a smart phone, a tablet computer, a laptop computer, a PDA, a handheld computer, a desktop computer or a personal digital assistant (PDA). The electronic device may work in a single operation mode, or in a device cluster mode.

Exemplarily, the point cloud data processing method provided in the embodiment of the present application can be applied to the point cloud data processing system shown in FIG1 . The point cloud data processing system includes a terminal 101 and a server 102. The terminal 101 can be a device having both receiving and transmitting functions, that is, a device capable of performing two-way communication (receiving and transmitting) on a two-way communication link. The terminal 101 can be, for example, one of the electronic devices such as a mobile phone, a tablet computer, a laptop computer, or a camera installed at a monitoring site for raw point cloud data collection, storage, and transmission. The terminal 101 and the server 102 can communicate bidirectionally via a network. The server 102 can be an independent server, or a server network or server cluster composed of servers, which includes but is not limited to computers, network hosts, a single network server, a set of multiple network servers, or multiple A cloud server is composed of a large number of computers or network servers based on cloud computing.

Those skilled in the art will appreciate that the application environment shown in FIG. 1 is only an application scenario applicable to the embodiment of the present application, and does not constitute a limitation on the application scenario of the embodiment of the present application. Other application environments applicable to the embodiment of the present application may include more or fewer computer devices than those shown in FIG. 1. For example, FIG. 1 shows only one server 102, while in other application environments, the point cloud data processing system may also include one or more other servers, which are not specifically limited here. In addition, as shown in FIG. 1, the point cloud data processing system may also include a memory for storing data, such as storing original point cloud data.

It should also be noted that the scenario diagram of the point cloud data processing system shown in Figure 1 is merely an example. The point cloud data processing system and scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not constitute a limitation on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the point cloud data processing system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

The point cloud data processing method provided in the embodiment of the present application is introduced below. In the embodiment of the present application, an electronic device is used as an execution subject for illustration. For the sake of simplicity and ease of description, the execution subject will be omitted in the subsequent method embodiments.

FIG2 is a flowchart of an implementation of a point cloud data processing method provided in an embodiment of the present application. It should be noted that although a logical order is shown in the flowchart shown in FIG2 or other figures, in some cases, the steps shown or described may be performed in a different order than that shown here.

As shown in FIG. 2 , the point cloud data processing method provided in the embodiment of the present application may include steps 201 to 204 , which are specifically as follows.

Step 201 : Project the original point cloud data of the area to be processed onto a preset grid plane to obtain a projected point cloud of the original point cloud data.

Among them, the area to be processed refers to the area that needs to be processed by point cloud data, which can be a geographical area (such as a community, a business district, etc.), a pre-planned site area (such as a logistics transfer site, a warehouse, etc.), a local area of an object (for example, the compartment area on a truck, the deck area on a ship, etc.), etc.

The original point cloud data refers to the collection of points formed by collecting point cloud data of the area to be processed through a three-dimensional imaging device. Furthermore, when the area to be processed is too large to collect all the point cloud data at one time, the original point cloud data of the area to be processed can be formed by multi-angle data collection and point cloud splicing.

The preset plane coordinate system is a coordinate system used to record the position of each projection point and each plane grid position. Specifically, the preset plane coordinate system can be a coordinate system composed of the X-axis and the Y-axis in the three-dimensional coordinate system of the original point cloud data, and a coordinate system composed of the Y-axis and the The coordinate system formed by the Z axis and the coordinate system formed by the X axis and the Z axis. For ease of understanding, the coordinate system formed by the X axis and the Y axis (abbreviated as the XY coordinate system) is exemplarily used as the preset plane coordinate system in this embodiment.

The preset gridded plane refers to a plane obtained by gridding the plane where the preset plane coordinate system is located.

Furthermore, in order to reduce the number of plane grids that need to be traversed for subsequent processing, the size of the preset gridded plane can be limited. As shown in FIG3, illustratively, assuming that the original point cloud data is located in the range of the preset plane coordinate system "X = (Xmin, Xmax) = (-30 meters, 50 meters), Y = (Ymin, Ymax) = (-20 meters, 100 meters)", the plane with the range X = (-30 meters, 50 meters) and Y = (-20 meters, 100 meters) can be gridded according to the preset size of each grid (such as the X-axis direction size Xsize = the axis direction size Ysize = 0.1 meters), and a preset gridded plane containing 800 grids in the X-axis direction and 1200 grids in the Y-axis direction is obtained.

The projected point cloud refers to a set of projection points obtained after each point in the original point cloud data is projected onto a preset grid plane.

Exemplarily, as shown in FIG3 , by projecting the original point cloud data onto a preset gridding plane, the projection point of each point in the original point cloud data in the preset gridding plane can be obtained, thereby obtaining the projected point cloud of the original point cloud data (as represented by the curve area in FIG3 ).

For ease of description, this article takes "using the coordinates of the upper left corner point of each plane grid to represent the plane coordinates of each plane grid" as an example for explanation.

Step 202: Based on the height value and plane coordinates of each projection point in the projected point cloud, obtain the target structure data of the original point cloud data.

Among them, the target structural data includes the structural data of each plane grid into which the original point cloud data falls, and the structural data of each plane grid includes the plane coordinates and the number of projection points of each plane grid, and also includes at least one of the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance value of each plane grid.

Among them, each point in the original point cloud data has obtained its coordinate value in the three-dimensional coordinate system when it is collected, so the height of the original point corresponding to each projection point (that is, the coordinate value on the Z axis) can be directly used as the height value of each projection point.

Here, the set of plane grids that the original point cloud data falls into is defined as the falling grid set, which specifically refers to the set of grids where the projected point cloud is located in each grid of the preset grid plane after the original point cloud data is projected onto the preset grid plane. In other words, each plane grid that falls into the grid set is each plane grid that the original point cloud data falls into.

It can be understood that the target structural data is a set including structural data of each plane grid falling into the grid set. Therefore, the target structural data can be obtained by acquiring the structural data of each plane grid falling into the grid set.

The following is an example of an implementation in which the structural data of each plane grid includes the plane coordinates of each plane grid, the number of projection points, the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance value, to illustrate the method of obtaining the structural data of each plane grid. At this time, as shown in FIG. 4, step 202 can specifically include the following steps 2021A to 2022A:

Step 2021A, based on the height value and plane coordinates of each projection point in the projected point cloud, obtain the plane coordinates, number of projection points, point cloud curvature, maximum point cloud height, minimum point cloud height, average point cloud height and point cloud height variance value of each plane grid.

The following is a description of the specific implementation methods for obtaining the plane coordinates, the number of projection points, the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance of each plane grid in step 2021A. It should be understood that the implementation methods described below are only exemplary solutions for obtaining these data, and those skilled in the art may also use other methods to obtain these data, and the embodiments of the present application are not limited to this.

1. The plane coordinates of each plane grid.

In order to record the position of each plane grid in the preset gridded plane, the plane coordinates of each plane grid in the preset plane coordinate system can be recorded. Since each plane grid includes multiple points, for all plane grids, it is necessary to uniformly record the coordinates of each plane grid at the same position (such as the upper left corner point, lower left corner point, upper right corner point, or lower right corner point of the grid, etc.) as the plane coordinates of each plane grid, so as to accurately record the plane coordinates of each plane grid. For example, taking the recording of the coordinates of the upper left corner point of each plane grid as the plane coordinates of each plane grid as an example, the plane coordinates of each plane grid can be obtained by the following steps A1 to A3:

A1. Obtain the minimum value of the first coordinate axis of each plane grid in the preset plane coordinate system.

A2. Obtain the minimum value of the second coordinate axis of each plane grid in the preset plane coordinate system.

A3. Based on the minimum value of the first coordinate axis and the minimum value of the second coordinate axis, the plane coordinates of each plane grid are obtained.

For example, taking the preset plane coordinate system as a coordinate system composed of the X-axis and the Y-axis in the three-dimensional coordinate system where the original point cloud data is located, the first coordinate axis and the second coordinate axis can be the X-axis and the Y-axis respectively. At this time, the minimum value of the X-axis of a plane grid in the preset plane coordinate system is obtained as (x=1), and the minimum value of the Y-axis is obtained as (y=1), thereby determining that the coordinates of the upper left corner point of the plane grid are (1,1), and use them as the plane coordinates of the plane grid.

2. The number of projection points of each plane grid.

The preset grid coordinate system is a coordinate system used to indicate the position of each plane grid. As shown in Figure 3, for ease of understanding, the preset grid coordinate system is represented by a coordinate system consisting of the h-axis and the w-axis, and the directions of the h-axis and the w-axis are respectively the same as the directions of the X-axis and the Y-axis of the preset plane coordinate system. For example, as shown in Figure 3, for the first position in the h-axis direction of the preset grid plane, The grid of the 11th row and the 13th column in the w-axis direction (as shown in the rectangular box in Figure 3) has grid coordinates (h=11, w=13), and its plane coordinates are (x=-30+10*0.1m, y=-20+12*0.1m)=(x=-29m, y=-18.8m).

It is understandable that, as mentioned above, "using the coordinates of the upper left corner of each plane grid to represent the plane coordinates of each plane grid" is mainly for the convenience of recording the plane coordinates of each plane grid. In fact, the projection points within the plane coordinate range: x = (-30 + 10 * 0.1 meters, -30 + 11 * 0.1 meters) = (-29 meters, -28.9 meters), y = (-20 + 12 * 0.1 meters, -20 + 13 * 0.1 meters) = (-18.8 meters, -18.7 meters) all fall into the plane grid with the plane coordinates of (x = -29 meters, y = -18.8 meters).

Exemplarily, first, the plane coordinate range of each plane grid is determined according to the grid coordinates of each plane grid, such as: x = (-29 meters, -28.9 meters), y = (-18.8 meters, -18.7 meters); and the plane coordinates of each projection point in the preset plane coordinate system are obtained. Then, each projection point in the projection point cloud is traversed. If the plane coordinates of the current projection point (such as x = -28.93, y = -18.71) are in the plane coordinate range: x = (-29 meters, -28.9 meters), y = (-18.8 meters, -18.7 meters), the current projection point is determined as the target projection point located in the plane grid until all projection points in the projection point cloud are traversed. Finally, the number of target projection points in each plane grid is counted to obtain the number of projection points of each plane grid.

3. The point cloud curvature of each plane mesh.

In some embodiments, the point cloud curvature of each plane grid can be determined directly based on the target projection points in each plane grid. For example, first, referring to the aforementioned method, according to the grid coordinates of each plane grid and the plane coordinates of each projection point in the preset plane coordinate system, the set composed of all target projection points located in each plane grid is determined as the target projection point cloud of the plane grid. Then, in combination with the three-dimensional coordinates corresponding to the target projection point cloud of each plane grid, the surface composed of the target projection point cloud in each plane is calculated. Finally, based on the surface composed of the target projection point cloud in each plane, the curvature at the same position (such as the upper left corner point) in each plane grid is calculated as the point cloud curvature of each plane grid.

The projection point is the point obtained by projecting the original point cloud data onto the preset grid plane, so the 3D coordinates corresponding to the projection point are the 3D coordinates of the point corresponding to the target projection point in the original point cloud data. For example, if the point with 3D coordinates (2,3,4) in the original point cloud data is projected onto the preset grid plane, the plane coordinates of the projection point are (2,3), and the 3D coordinates corresponding to the projection point are also (2,3,4).

In some embodiments, the curvature of each plane mesh may also be determined based on the projected point cloud. For example, first, the three-dimensional coordinates of each projection point in the projected point cloud are combined to calculate the surface formed by the projected point cloud. Then, based on the surface formed by the projected point cloud, the curvature at the same position (such as the upper left corner point) in each plane mesh is calculated as the point cloud curvature of each plane mesh.

4. The maximum point cloud height of each plane grid.

For example, first, referring to the above method, the target projection point cloud of each plane grid is determined according to the grid coordinates of each plane grid and the plane coordinates of each projection point in the preset plane coordinate system. Then, according to the height values of each target projection point in the target projection point cloud in each plane grid, the maximum height value is found as the maximum height value of the point cloud of the plane grid. For example, the target projection points in the first plane grid are point 1, point 2, point 3, point 4, and point 5, and the corresponding height values are 0.1m, 0.2m, 0.3m, 0.4m, and 0.5m, respectively. The maximum point cloud height in the first plane grid is 0.5m.

5. Minimum point cloud height of each plane grid.

Exemplarily, first, referring to the aforementioned method, the target projection point cloud of each plane grid is determined according to the grid coordinates of each one-sided grid and the plane coordinates of each projection point in the preset plane coordinate system. Then, according to the height values of each target projection point in the target projection point cloud in each plane grid, the minimum height value is found as the minimum point cloud height value of the plane grid. For example, the target projection point clouds in the first plane grid are: point 1, point 2, point 3, point 4, point 5, and the corresponding height values are 0.1 meters, 0.2 meters, 0.3 meters, 0.4 meters, and 0.5 meters, respectively. Then the minimum point cloud height value in the first plane grid is 0.1 meters.

6. The average point cloud height of each plane grid.

Exemplarily, first, referring to the aforementioned method, the target projection point cloud of each plane grid is determined according to the grid coordinates of each one-sided grid and the plane coordinates of each projection point in the preset plane coordinate system. Then, according to the height values of each target projection point in the target projection point cloud in each plane grid, the average height of the target projection points in each plane grid is counted as the average height of the point cloud of each plane grid. For example, the target projection points in the first plane grid are: point 1, point 2, point 3, point 4, point 5, and the corresponding height values are 0.1 meters, 0.2 meters, 0.3 meters, 0.4 meters, and 0.5 meters, respectively. Then, the average height of the point cloud in the first plane grid is 0.3 meters.

7. The point cloud height variance value of each plane grid.

Exemplarily, first, referring to the aforementioned method, the target projection point cloud of each plane grid is determined according to the grid coordinates of each one-sided grid and the plane coordinates of each projection point in the preset plane coordinate system. Then, according to the height values of each target projection point in the target projection point cloud in each plane grid, the height value variance of the target projection point in the plane grid is calculated as the point cloud height variance value of the plane grid. For example, the target projection points in the first plane grid are: point 1, point 2, point 3, point 4, point 5, and the corresponding height values are 0.1 meters, 0.2 meters, 0.3 meters, 0.4 meters, and 0.5 meters, respectively. Then, the point cloud height variance value in the first plane grid is 0.22.

Step 2022A, according to the preset data storage format, the plane coordinates, the number of projection points, the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance of each plane grid are stored to obtain the structural data of each plane grid.

For example, the plane coordinates, number of projection points, point cloud curvature, maximum point cloud height, minimum point cloud height, average point cloud height, and point cloud height variance of each plane grid can be stored in the preset data format [X, Y, Zmin, Zmax, Zmean, Zstd, Num, Cur], that is, [X-axis coordinate value of plane coordinates, Y-axis coordinate value of plane coordinates, minimum point cloud height The point cloud height value, the maximum value of the point cloud height, the average value of the point cloud height, the variance value of the point cloud height, the number of projection points, and the point cloud curvature are stored as the structural data of each plane grid.

Thus, for the grid set, an M*N*K data matrix can be obtained as the target structure data. Among them, M and N are the number of grids in the X-axis direction and the number of grids in the Y-axis direction in the grid set, respectively. K represents the number of structural data of each plane grid. For example, if the data storage format is [X, Y, Zmin, Zmax, Zmean, Zstd, Num, Cur], then K = 8.

Similarly, the structural data of each plane grid can be obtained based on at least one of the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance of each plane grid.

For example, when the structural data of each plane grid includes the point cloud curvature and the point cloud height variance value of each plane grid, step 202 may specifically include the following steps: based on the plane coordinates of each projection point, obtaining the target projection point of each plane grid; based on the height value of the target projection point, obtaining the point cloud height variance value of each plane grid; based on the height value of the target projection point, obtaining the point cloud curvature of each plane grid; based on the point cloud height variance value and the point cloud curvature of each plane grid, forming the structural data of each plane grid to obtain the target structural data.

For the specific implementation of each step, please refer to the relevant descriptions in the "Number of Projected Point Clouds", "Point Cloud Height Variance" and "Point Cloud Height Value" sections in the previous article, which will not be repeated here.

The plane coordinates, number of projection points, point cloud curvature and point cloud height variance of each plane grid can be stored in a preset data storage format [X, Y, Zstd, Num, Cur], i.e. [X-axis coordinate value of plane coordinates, Y-axis coordinate value of plane coordinates, point cloud height variance value, number of projection points, point cloud curvature] as the structural data of each plane grid. Thus, a data matrix of M*N*K=M*N*5 can be obtained as the target structural data.

Step 203: Perform point cloud processing based on the target structure data to obtain processed structure data of the original point cloud data.

The processed structural data refers to the structural data obtained after using the target structural data to perform point cloud processing such as boundary recognition, interference recognition, and interference filtering.

Since a plane grid is stored as a structural data, and a plane grid contains multiple projection points, the point cloud data including multiple points that originally need to be processed separately can be compressed into one structural data. It can be understood that this embodiment can realize batch processing of multiple points in the point cloud by processing one structural data, thereby improving the processing efficiency of point cloud processing processes such as boundary recognition, interference recognition, and interference filtering, and realizing efficient processing of point clouds. Based on this, in order to perform point cloud processing processes such as boundary recognition, interference recognition, and interference filtering on the original point cloud data, the original point cloud data can be first stored as target structural data, and then the above processing is performed based on the target structural data, thereby reducing the amount of data processing.

There are many specific implementation methods for obtaining the recognized structure data by processing the point cloud in step 203. Three implementation methods are described below as examples. It should be understood that the implementation methods described below are only examples of obtaining the recognized structure data. Those skilled in the art may also select other methods according to actual needs, and the embodiments of the present application are not limited to this.

(1) The processed structural data is interference point structural data. At this time, the structural data of each plane grid at least includes the point cloud curvature and point cloud height variance value of each plane grid. Step 203 may specifically include: traversing each plane grid where the original point cloud data falls, detecting the point cloud curvature of the current plane grid; detecting the point cloud height variance value of the current plane grid; if the point cloud curvature of the current plane grid is greater than a preset curvature threshold and the point cloud height variance value is greater than a preset variance threshold, then determining the structural data of the current plane grid as the interference point structural data of the original point cloud data.

For example, the target structural data includes the structural data of grid 1, the structural data of grid 2, ..., the structural data of grid 100. In the above steps, grid 1, grid 2, ..., grid 100 can be traversed to detect the point cloud curvature and point cloud height variance value of the current plane grid (such as grid 1). If the point cloud curvature of the current plane grid (such as 0.5) is greater than the preset curvature threshold (such as 0.3) and the point cloud height variance value of the current plane grid (such as 1) is greater than the preset variance threshold (such as 0.5), the current plane grid (such as grid 1) can be used as the interference point structural data of the original point cloud data. By analogy, when the traversal of grid 1, grid 2, ..., grid 100 is completed, all the interference point structural data of the original point cloud data can be obtained.

Therefore, on the one hand, since the point cloud height variance value and the point cloud curvature can reflect the location of the interference object to a certain extent, the interference point is identified by setting a preset curvature threshold and a preset variance threshold, which can improve the recognition accuracy of the interference point. On the other hand, since the interference point is identified in units of structural data of each plane grid, there is no need to process each point in the original point cloud data separately, which can improve the recognition speed of interference points (such as walls, vehicles, columns, etc. in the scene of man-made interference objects for non-bulk goods in the positioning warehouse).

(2) The processed structural data is the boundary structural data. At this time, the structural data of each plane grid at least includes the average point cloud height of each plane grid. Step 203 may specifically include the following steps: traverse each plane grid where the original point cloud data falls, and obtain the difference between the average point cloud height of the current plane grid and the average point cloud height of the adjacent plane grids of the current plane grid; if the difference is greater than a preset difference threshold, the structural data of the current plane grid is used as the boundary structural data of the original point cloud data.

Among them, the adjacent plane grid can be a plane grid that falls into the grid set and is adjacent to the current plane grid in the h-axis direction; or a plane grid that falls into the grid set and is adjacent to the current plane grid in the w-axis direction; or a plane grid that falls into the grid set and is adjacent to the current plane grid in one of the h-axis and w-axis directions.

For example, the target structure data includes the structure data of grid 1, the structure data of grid 2, ..., the structure data of grid 100. In the above steps, grid 1, grid 2, ..., grid 100 can be traversed to detect the average point cloud height of the current plane grid (such as grid 1) and the average point cloud height of the adjacent plane grid (such as grid 2). If the difference (such as 0.5) between the average point cloud height of the current plane grid (such as grid 1) and the adjacent plane grid (such as grid 2) is greater than the preset difference threshold If the value (such as 0.3) is set, the current plane grid (such as grid 1) can be used as the boundary structure data of the original point cloud data. Similarly, when the traversal of grid 1, grid 2, ..., grid 100 is completed, all the boundary structure data of the original point cloud data can be obtained.

Among them, since there may be multiple adjacent plane grids, in the case of multiple adjacent plane grids, as long as the difference between the average point cloud height of the current plane grid and the average point cloud height of one of the adjacent plane grids is greater than the preset difference threshold, the structural data of the current plane grid can be used as the boundary structural data of the original point cloud data.

Therefore, on the one hand, since the position where the difference between the average value of the point cloud height and the average value of the point cloud height of the adjacent plane grid is large can reflect the point where the boundary between the interference object and the target object is located to a certain extent, the recognition accuracy of the boundary point can be improved by setting a preset difference threshold between the average value of the point cloud height and the average value of the point cloud height of the adjacent plane grid to identify the boundary point. On the other hand, since the identification of the boundary point is performed in units of the structural data of each plane grid, it is not necessary to process each point in the original point cloud data separately, and the recognition speed of the boundary point (such as the boundary point between the interference object such as the wall, vehicle, column and the target object (i.e., bulk cargo) in the scene of the artificial interference object of non-bulk cargo in the positioning warehouse) can be improved.

(3) The processed structural data is the filtered structural data.

In some embodiments, the entire interfering object may be filtered. At this time, as shown in FIG5 , step 203 may specifically include the following steps 2031C to 2034C:

Step 2031C: identify interference points based on the target structure data to obtain interference point structure data of the original point cloud data.

Step 2032C: perform boundary point recognition based on the target structure data to obtain boundary structure data of the original point cloud data.

The implementation of steps 2031C to 2032C may refer to the relevant descriptions in the above-mentioned implementation methods, and will not be repeated here.

Step 2033C: Based on the interference point structure data and the boundary structure data, interference area identification is performed to obtain interference structure data of the original point cloud data.

For example, the interference point structure data and boundary structure data can be used as seed points, and point cloud processing methods such as clustering and region generation can be used to identify interference objects (such as man-made objects such as vehicles, walls, and columns in warehouses other than goods) to determine the interference object structure data of the original point cloud data.

Step 2034C: filter out the interference structure data from the target structure data to obtain filtered structure data of the original point cloud data.

For example, the target structural data includes the structural data of grid 1, the structural data of grid 2, ..., the structural data of grid 100. Through steps 2031C to 2033C, the structural data of grid 1, grid 2, grid 5, and grid 6 are identified as interference structural data. Then, the structural data of grid 1, grid 2, grid 5, and grid 6 can be filtered out from the target structural data to obtain the filtered structural data of the original point cloud data.

In some embodiments, the interference points may be filtered. In this case, the interference point structure data obtained in the above steps may be filtered out from the target structure data to obtain filtered structure data of the original point cloud data.

In some embodiments, the interference points may be filtered. At this time, the boundary structure data obtained in the above steps may be filtered out from the target structure data to obtain filtered structure data of the original point cloud data.

Therefore, since the boundary points are identified based on the structural data of each plane grid, there is no need to process each point in the original point cloud data separately, which can improve the filtering speed of interference objects (such as walls, vehicles, columns and other interference objects in the scene of non-bulk man-made interference objects in the positioning warehouse), interference points, and boundary points.

Step 204: restore the processed structural data to obtain processed point cloud data of the original point cloud data.

The processed point cloud data refers to the point cloud data obtained after performing point cloud processing processes such as boundary recognition, interference recognition, and interference filtering on the original point cloud data based on the target structure data.

Exemplarily, the structural data of each plane grid in the processed structural data can be regenerated under the condition that the newly generated point cloud of each plane grid conforms to the point cloud curvature, the maximum value of the point cloud height, the minimum value of the point cloud height, the average value of the point cloud height, and the variance value of the point cloud height of the grid, and the number of newly generated points of each plane grid is equal to the number of projection points of the grid. Thus, the newly generated point cloud of each plane grid in the processed structural data constitutes the processed point cloud data of the original point cloud data.

From the above content, it can be seen that by processing the plane coordinates, the number of projection points, and at least one of the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance value of each plane grid into the structural data of each plane grid, on the one hand, it is possible to compress multiple point clouds in each plane grid to obtain target structural data, so that the amount of data processing can be reduced when the target structural data is used for subsequent processing, thereby improving the processing efficiency of the point cloud processing process; on the other hand, since the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance value of each plane grid can reflect the point cloud distribution in each plane grid, when restoring the processed structural data, the point cloud that matches the distribution of the original point cloud data can be accurately and completely restored. Therefore, the embodiment of the present application can improve the processing efficiency of point cloud data on the basis of reducing the degree of loss of the original data volume.

In order to better implement the point cloud data processing method in the embodiment of the present application, the embodiment of the present application also provides a point cloud data processing device. FIG6 is a schematic diagram of a structure of an implementation method of the point cloud data processing device provided in the embodiment of the present application. As shown in FIG6, the point cloud data processing device 600 includes:

The first acquisition unit 601 is used to project the original point cloud data of the area to be processed onto a preset grid plane to obtain a projected point cloud of the original point cloud data;

A second acquisition unit 602 is used to acquire target structural data of the original point cloud data based on the height value and plane coordinates of each projection point in the projected point cloud, wherein the target structural data includes structural data of each plane grid into which the original point cloud data falls, the structural data of each plane grid includes the plane coordinates of each plane grid and the number of projection points, and the structural data of each plane grid also includes at least one of the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance of each plane grid;

A processing unit 603 is used to perform point cloud processing based on the target structure data to obtain processed structure data of the original point cloud data;

The restoration unit 604 is used to restore the processed structural data to obtain processed point cloud data of the original point cloud data.

In some embodiments, the processed structural data includes interference point structural data, and the structural data of each plane grid includes the point cloud curvature and point cloud height variance value of each plane grid. The processing unit 603 is specifically used to: traverse each plane grid where the original point cloud data falls, detect the point cloud curvature of the current plane grid; detect the point cloud height variance value of the current plane grid; if the point cloud curvature of the current plane grid is greater than a preset curvature threshold and the point cloud height variance value is greater than a preset variance threshold, then determine the structural data of the current plane grid as the interference point structural data of the original point cloud data.

In some embodiments, the processed structural data includes boundary structural data, and the structural data of each plane grid includes the average point cloud height of each plane grid. The processing unit 603 is specifically used to: traverse each plane grid where the original point cloud data falls, obtain the difference between the average point cloud height of the current plane grid and the average point cloud height of the adjacent plane grids of the current plane grid; if the difference is greater than a preset difference threshold, use the structural data of the current plane grid as the boundary structural data of the original point cloud data.

In some embodiments, the processed structural data includes filtered structural data. The processing unit 603 is specifically used to: identify interference points based on the target structural data to obtain interference point structural data of the original point cloud data; identify boundary points based on the target structural data to obtain boundary structural data of the original point cloud data; identify interference object regions based on the interference point structural data and boundary structural data to obtain interference object structural data of the original point cloud data; and filter out the interference object structural data from the target structural data to obtain filtered structural data of the original point cloud data.

In some embodiments, the processed structural data includes filtered structural data. The processing unit 603 is specifically used to: identify interference points based on the target structural data to obtain interference point structural data of the original point cloud data; and filter the interference point structural data from the target structural data to obtain filtered structural data of the original point cloud data;

Alternatively, boundary point recognition is performed based on the target structure data to obtain boundary structure data of the original point cloud data; and the boundary structure data is filtered out from the target structure data to obtain filtered structure data of the original point cloud data.

In some embodiments, the second acquisition unit 602 is specifically used to: acquire the target projection point of each plane grid based on the plane coordinates of each projection point; count the number of target projection points of each plane grid as the projection of each plane grid; The number of points; based on the height value of the target projection point, the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height and the point cloud height variance value of each plane grid are obtained; according to the preset data storage format, the plane coordinates, the number of projection points, the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height and the point cloud height variance value of each plane grid are stored to form the structural data of each plane grid and obtain the target structural data.

In some embodiments, the second acquisition unit 602 is specifically used to: obtain the minimum value of the first coordinate axis of each plane grid in the preset plane coordinate system; obtain the minimum value of the second coordinate axis of each plane grid in the preset plane coordinate system; based on the minimum value of the first coordinate axis and the minimum value of the second coordinate axis, obtain the plane coordinates of each plane grid.

Therefore, the point cloud data processing device 600 provided in the embodiment of the present application can bring the following technical effects: by processing the plane coordinates, the number of projection points, and at least one of the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance value of each plane grid into the structural data of each plane grid, on the one hand, it is possible to compress multiple point clouds in each plane grid to obtain target structural data, so that the amount of data processing can be reduced when the target structural data is used for subsequent processing, thereby improving the processing efficiency of the point cloud processing process; on the other hand, since the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height, and the point cloud height variance value of each plane grid can reflect the point cloud distribution in each plane grid, when restoring the processed structural data, a point cloud that matches the distribution of the original point cloud data can be accurately and completely obtained. Therefore, the processing efficiency of point cloud data can be improved on the basis of reducing the degree of loss of the original data volume.

In specific implementation, the above units can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation methods and effects of the above units can refer to the corresponding contents in the previous method embodiments, which will not be repeated here.

Furthermore, an embodiment of the present application also provides an electronic device, which may be a terminal device such as a smart phone, a tablet computer, a laptop computer, a touch screen, a personal computer (PC, Personal Computer), a personal digital assistant (Personal Digital Assistant, PDA), etc.

FIG7 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. As shown in FIG7, the electronic device 700 includes a processor 701 having one or more processing cores, a memory 702 having one or more computer-readable storage media, and a computer program stored in the memory 702 and executable on the processor. The processor 701 is electrically connected to the memory 702. Those skilled in the art will appreciate that the electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or arrange components differently.

The processor 701 is the control center of the electronic device 700. It uses various interfaces and lines to connect various parts of the entire electronic device 700, executes various functions of the electronic device 700 and processes data by running or loading software programs and/or modules stored in the memory 702, and calling data stored in the memory 702, thereby monitoring the electronic device 700 as a whole.

In an embodiment of the present application, the processor 701 in the electronic device 700 will load instructions corresponding to the processes of one or more applications into the memory 702 in accordance with the steps of any point cloud data processing method provided in the present application, and the processor 701 will run the application stored in the memory 702, thereby implementing the specific process of the above-mentioned point cloud data processing method.

Optionally, as shown in FIG7 , the electronic device 700 further includes: a touch screen 703, a radio frequency circuit 704, an audio circuit 705, an input unit 706, and a power supply 707. The processor 701 is electrically connected to the touch screen 703, the radio frequency circuit 704, the audio circuit 705, the input unit 706, and the power supply 707. Those skilled in the art will appreciate that the electronic device structure shown in FIG7 does not limit the electronic device, and may include more or fewer components than shown, or combine certain components, or arrange components differently.

The touch display screen 703 can be used to display a graphical user interface and receive operation instructions generated by the user acting on the graphical user interface. The touch display screen 703 may include a display panel and a touch panel. Among them, the display panel can be used to display information input by the user or information provided to the user and various graphical user interfaces of the electronic device, which can be composed of graphics, text, icons, videos and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD, Liquid Crystal Display), an organic light-emitting diode (OLED, Organic Light-Emitting Diode), etc. The touch panel can be used to collect user touch operations on or near it (such as operations performed by the user using any suitable object or accessory such as a finger, stylus, etc. on the touch panel or near the touch panel), and generate corresponding operation instructions, and the operation instructions execute the corresponding program. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 701, and can receive the command sent by the processor 701 and execute it. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 701 to determine the type of touch event, and then the processor 701 provides a corresponding visual output on the display panel according to the type of touch event. In an embodiment of the present application, the touch panel and the display panel can be integrated into the touch display screen 703 to realize the input and output functions. However, in some embodiments, the touch panel and the touch panel can be used as two independent components to realize the input and output functions. That is, the touch display screen 703 can also be used as a part of the input unit 706 to realize the input function.

The radio frequency circuit 704 may be used to send and receive radio frequency signals, so as to establish wireless communication with a network device or other electronic devices through wireless communication, and to send and receive signals with the network device or other electronic devices.

The audio circuit 705 can be used to provide an audio interface between the user and the electronic device through a speaker and a microphone. The audio circuit 705 can convert the received audio data into an electrical signal and transmit it to the speaker, which converts it into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 705 and converted into audio data. The audio data is then processed by the processor 701 and sent to another electronic device, or to another electronic device, through the radio frequency circuit 704. The audio circuit 705 may also include an earplug jack to provide communication between an external headset and the electronic device.

The input unit 706 may be used to receive input numbers, character information or user feature information (such as fingerprint, iris, facial information, etc.), and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

The power supply 707 is used to supply power to various components of the electronic device 700. Optionally, the power supply 707 can be logically connected to the processor 701 through a power management system, so that the power management system can manage charging, discharging, and power consumption. The power supply 707 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

Although not shown in FIG. 7 , the electronic device 700 may further include a camera, a sensor, a wireless fidelity module, a Bluetooth module, etc., which will not be described in detail herein.

A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

To this end, an embodiment of the present application provides a computer-readable storage medium, in which multiple computer programs are stored. The computer programs can be loaded by a processor to execute the steps in any point cloud data processing method provided in the embodiment of the present application.

Among them, the computer readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or CD, etc.

In the above-mentioned point cloud data processing device, computer-readable storage medium, and electronic device embodiments, the description of each embodiment has its own emphasis. For parts not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and beneficial effects of the above-mentioned point cloud data processing device, computer-readable storage medium, electronic device and its corresponding units can refer to the description of the point cloud data processing method in the above embodiment, and will not be repeated here.

The above is a detailed introduction to a point cloud data processing method, device, electronic device and computer-readable storage medium provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core ideas of the present application, and is not used to limit the scope of protection of the present application. In addition, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and scope of application, and these changes should also be included in the scope of protection of the present application. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims (10)

A point cloud data processing method, characterized in that the method comprises: Projecting the original point cloud data of the area to be processed onto a preset grid plane to obtain a projected point cloud of the original point cloud data; Based on the height value and plane coordinates of each projection point in the projected point cloud, the target structure data of the original point cloud data is obtained, wherein the target structure data includes the structure data of each plane grid into which the original point cloud data falls, the structure data of each plane grid includes the plane coordinates of each plane grid and the number of projection points, and the structure data of each plane grid also includes at least one of the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height and the point cloud height variance value of each plane grid; Performing point cloud processing based on the target structure data to obtain processed structure data of the original point cloud data; The processed structural data is restored to obtain processed point cloud data of the original point cloud data. The point cloud data processing method according to claim 1, characterized in that the processed structural data includes interference point structural data, and the structural data of each plane grid includes the point cloud curvature and point cloud height variance value of each plane grid; The step of performing point cloud processing based on the target structure data to obtain processed structure data of the original point cloud data includes: Traversing each plane grid into which the original point cloud data falls, and detecting the point cloud curvature of the current plane grid; Detecting the point cloud height variance value of the current plane grid; If the point cloud curvature of the current plane mesh is greater than a preset curvature threshold and the point cloud height variance value is greater than a preset variance threshold, the structural data of the current plane mesh is determined as the interference point structural data of the original point cloud data. The point cloud data processing method according to claim 1, characterized in that the processed structural data includes boundary structural data, and the structural data of each plane grid includes the average value of the point cloud height of each plane grid; The step of performing point cloud processing based on the target structure data to obtain processed structure data of the original point cloud data includes: Traversing each plane grid into which the original point cloud data falls, obtaining a difference between an average point cloud height of a current plane grid and an average point cloud height of adjacent plane grids of the current plane grid; If the difference is greater than a preset difference threshold, the structural data of the current plane grid is used as the boundary structural data of the original point cloud data. The point cloud data processing method according to claim 1, characterized in that the processed structural data includes filtered structural data, and the point cloud processing based on the target structural data to obtain the processed structural data of the original point cloud data includes: Perform interference point identification based on the target structure data to obtain interference point structure data of the original point cloud data; Perform boundary point recognition based on the target structure data to obtain boundary structure data of the original point cloud data; Based on the interference point structure data and the boundary structure data, interference area recognition is performed to obtain interference structure data of the original point cloud data; The interference structure data is filtered out from the target structure data to obtain filtered structure data of the original point cloud data. The point cloud data processing method according to claim 1, characterized in that the processed structural data includes filtered structural data, and the point cloud processing based on the target structural data to obtain the processed structural data of the original point cloud data includes: Identifying interference points based on the target structure data to obtain interference point structure data of the original point cloud data; and filtering the interference point structure data from the target structure data to obtain filtered structure data of the original point cloud data; or, Boundary point recognition is performed based on the target structure data to obtain boundary structure data of the original point cloud data; and the boundary structure data is filtered out from the target structure data to obtain filtered structure data of the original point cloud data. The point cloud data processing method according to any one of claims 1 to 5, characterized in that the step of acquiring target structure data of the original point cloud data based on the point cloud height value and plane coordinates of each projection point in the projected point cloud comprises: Based on the plane coordinates of each projection point, obtaining the target projection point of each plane grid; Counting the number of target projection points of each plane grid as the number of projection points of each plane grid; Based on the height value of the target projection point, obtain the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height and the point cloud height variance value of each plane grid; According to a preset data storage format, the plane coordinates, the number of projection points, the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height and the point cloud height variance value of each plane grid are stored to form the structural data of each plane grid and obtain the target structural data. The point cloud data processing method according to claim 1 is characterized in that the plane coordinates of each plane grid are obtained by the following steps: Obtaining the minimum value of the first coordinate axis of each plane grid in a preset plane coordinate system; Obtaining the minimum value of the second coordinate axis of each plane grid in the preset plane coordinate system; The plane coordinates of each plane grid are obtained based on the minimum value of the first coordinate axis and the minimum value of the second coordinate axis. A point cloud data processing device, characterized in that the point cloud data processing device comprises: A first acquisition unit is used to project the original point cloud data of the area to be processed onto a preset grid plane to obtain a projected point cloud of the original point cloud data; A second acquisition unit is used to acquire target structural data of the original point cloud data based on the height value and plane coordinates of each projection point in the projected point cloud, wherein the target structural data includes structural data of each plane grid into which the original point cloud data falls, the structural data of each plane grid includes the plane coordinates and the number of projection points of each plane grid, and the structural data of each plane grid also includes at least one of the point cloud curvature, the maximum point cloud height, the minimum point cloud height, the average point cloud height and the point cloud height variance value of each plane grid; A processing unit, configured to perform point cloud processing based on the target structure data to obtain processed structure data of the original point cloud data; The restoration unit is used to restore the processed structural data to obtain processed point cloud data of the original point cloud data. An electronic device, characterized in that it includes a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the point cloud data processing method according to any one of claims 1 to 7 is executed. A computer-readable storage medium, characterized in that a computer program is stored thereon, and the computer program is loaded by a processor to execute the point cloud data processing method according to any one of claims 1 to 7.