CN116358527A

CN116358527A - Point cloud data processing and elevation determining method, equipment and storage medium

Info

Publication number: CN116358527A
Application number: CN202310341816.XA
Authority: CN
Inventors: 杨宽; 陈时远
Original assignee: Autonavi Software Co Ltd
Current assignee: Autonavi Software Co Ltd
Priority date: 2023-03-31
Filing date: 2023-03-31
Publication date: 2023-06-30

Abstract

The application provides a point cloud data processing and elevation determining method, equipment and a storage medium, wherein the method comprises the following steps: acquiring point cloud data of an acquired target area; performing geospatial coding on longitude and latitude of point cloud data to obtain an address after the point cloud data coding; the geospatial coding enables the addresses of the coded point cloud data in the space within a preset range to be the same; dividing point cloud data with the same address into a grid to obtain at least one grid of a target area; and acquiring a grid object of the grid according to the point cloud data in the grid, wherein the grid object at least comprises the elevation of the grid. The grid division accuracy can be improved, and the accuracy of the elevation of the grid is further improved.

Description

Point cloud data processing and elevation determining method, equipment and storage medium

Technical Field

The present disclosure relates to the field of electronic maps, and in particular, to a method and apparatus for processing point cloud data and determining elevation, and a storage medium.

Background

In the map data processing process, the coordinates of map elements only comprise longitude and latitude, and elevation information can be added to the coordinates of the map elements in an elevation restoration mode. At present, the existing elevation reduction method mainly comprises the following steps: dividing the target area where the map element is located into a plurality of grids, and taking the elevation of the grid where the map element is located as the elevation of the map element.

The current mesh division mode of the target area is as follows: and extracting point cloud data in the target area as vertexes of a triangle, and randomly generating a plurality of non-fixed triangular grids covering the target area. However, the accuracy of the elevation of the mesh determined based on the existing mesh generation method is poor.

Disclosure of Invention

The application provides a point cloud data processing and elevation determining method, equipment and a storage medium, which can improve the accuracy of the elevation of a grid.

In a first aspect, the present application provides a method for processing point cloud data, where the method includes:

acquiring point cloud data of an acquired target area;

performing geospatial coding on the longitude and latitude of the point cloud data to obtain an address after the point cloud data coding; the geographic space codes enable the addresses after the point cloud data in the space and in the preset range are coded to be the same;

dividing point cloud data with the same address into a grid to obtain at least one grid of the target area;

and acquiring a grid object of the grid according to the point cloud data in the grid, wherein the grid object at least comprises the elevation of the grid.

In a second aspect, the present application provides a method of elevation determination, the method comprising:

Acquiring a grid query tree of a target area; the grid query tree comprises grid objects of at least one grid of the target area, wherein the grid objects are obtained by adopting the method of any one of the first aspect;

acquiring longitude and latitude of a target point of a map element positioned in the target area in a map;

performing geospatial coding on the longitude and latitude of the target point to obtain an address coded by the target point;

according to the address of the target point after encoding, acquiring a grid object of at least one target grid corresponding to the target point by utilizing the grid query tree;

and determining the elevation of the target point according to the grid object of the at least one target grid.

In a third aspect, the present application provides a point cloud data processing apparatus, the apparatus comprising:

the acquisition module is used for acquiring the point cloud data of the acquired target area;

the encoding module is used for performing geospatial encoding on the longitude and latitude of the point cloud data to obtain an address after the point cloud data is encoded; the geographic space codes enable the addresses after the point cloud data in the space and in the preset range are coded to be the same;

the dividing module is used for dividing the point cloud data with the same address into one grid to obtain at least one grid of the target area;

And the processing module is used for acquiring a grid object of the grid according to the point cloud data in the grid, wherein the grid object at least comprises the elevation of the grid.

In a fourth aspect, the present application provides an elevation determining apparatus, the apparatus comprising:

the first acquisition module is used for acquiring a grid query tree of the target area; the grid query tree comprises grid objects of at least one grid of the target area, wherein the grid objects are obtained by adopting the method of any one of the first aspect;

the second acquisition module is used for acquiring the longitude and latitude of a target point of a map element positioned in the target area in the map;

the coding module is used for carrying out geospatial coding on the longitude and latitude of the target point to obtain an address after the target point is coded;

the third acquisition module is used for acquiring a grid object of at least one target grid corresponding to the target point by utilizing the grid query tree according to the address of the target point after being coded;

and the processing module is used for determining the elevation of the target point according to the grid object of the at least one target grid.

In a fifth aspect, the present application provides an electronic device, comprising: a processor and a memory; the processor is in communication with the memory;

The memory stores computer instructions;

the processor executes computer instructions stored by the memory to implement the method of any one of the first or second aspects.

In a sixth aspect, the present application provides a computer readable storage medium having stored thereon computer executable instructions which, when executed by a processor, implement the method according to any one of the first or second aspects.

In a seventh aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method of any one of the first or second aspects.

According to the point cloud data processing and elevation determining method, device and storage medium, the longitude and latitude of the point cloud data of the target area are subjected to geospatial coding, so that the address after the point cloud data coding can be obtained, the longitude and latitude of the point cloud data are coded, and the target area can be subjected to grid division by using the coded address. By dividing the point cloud data with the same address into one grid, the point cloud data adjacent to the geographic space position is divided into one grid, and at least one grid of the target area can be obtained. Then, through the point cloud data in the mesh, a mesh object of the mesh "including at least the elevation of the mesh" can be acquired. By the method, the address after the geospatial coding of the point cloud data based on the target area is realized, at least one grid of the target area is determined, the point cloud data does not need to be extracted as grid vertices, the problem that the grid is large due to the fact that a road surface is shielded is avoided, and the problem of grid inclination is avoided, so that grid division accuracy is improved, and accuracy of determining the height of the grid based on the grid division result is improved. By increasing the accuracy of the elevation of the grid, the accuracy of the subsequent determination of the elevation of the target point based on the grid object may be increased.

Drawings

For a clearer description of the technical solutions of the present application or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flow chart of a point cloud data processing method provided in an embodiment of the present application;

FIG. 2 is a schematic illustration of a grid fission process provided herein;

FIG. 3 is a schematic flow chart of an elevation determining method provided in the present application;

fig. 4 is a flow chart of another method for processing point cloud data provided in the present application;

FIG. 5 is a schematic diagram of a meshing result provided in the present application;

FIG. 6 is a flow chart of another elevation determination method provided in the present application;

FIG. 7 is a schematic diagram of a double-array result and an AC automaton in an AC automaton double-array dictionary tree;

FIG. 8 is a schematic diagram of an array indexing of an ac automaton double array dictionary tree;

FIG. 9 is a schematic diagram of at least one target grid corresponding to a target point provided in the present application;

Fig. 10 is a schematic structural diagram of a point cloud data processing device provided in the present application;

FIG. 11 is a schematic structural view of an elevation determining apparatus provided in the present application;

fig. 12 is a schematic hardware structure of an electronic device provided in the present application.

Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

When map data acquisition is performed on a target area, point cloud data of the target area can be acquired. The map data processing platform may then identify map elements (e.g., lane lines, ground arrow identifications, etc.) from the point cloud data and determine three-dimensional coordinates of the map elements. Generally, after determining the three-dimensional coordinates of the map element, the three-dimensional coordinates of the map element may be further rectified to improve accuracy of the three-dimensional coordinates of the map element.

At present, the common correction method for the three-dimensional coordinates of the map elements mainly comprises the steps of placing the three-dimensional map elements in point cloud data of the target area, and performing manual intervention correction through manual observation. Taking the map element as a lane line as an example, the three-dimensional lane line may be placed in the point cloud data of the target area, and the coordinates of the three-dimensional lane line may be changed by manually observing and attaching the three-dimensional lane line to the point cloud data. However, the process of operating the above-described procedure in three-dimensional space is complicated and inefficient.

In order to improve the correction efficiency of the map elements, the three-dimensional map elements and the three-dimensional point cloud data may be removed, that is, the three-dimensional map elements and the three-dimensional point cloud data may be projected into a two-dimensional plane (for example, projection based on a pyramid slicing technology), and the longitude and latitude of the two-dimensional map elements may be corrected in the two-dimensional plane. Still take the map element as a lane line for example, the lane line is manually operated in the two-dimensional plane, and the lane line is attached to the two-dimensional point cloud data, so that the deviation correction of the longitude and latitude of the lane line in the two-dimensional plane can be realized. However, the map elements obtained by correcting the deviation by the above method are map elements having lost the elevation.

In addition, in the correction process, the map elements that are not recognized by the automatic recognition process may be manually added to the two-dimensional plane, or the two-dimensional plane may be complemented with the map elements that are not recognized by the automatic recognition process. However, the newly added map elements are map elements having only two-dimensional coordinates and no elevation.

Therefore, how to perform high Cheng Haiyuan on the map elements in the two-dimensional plane is a problem to be solved.

At present, the existing elevation reduction method mainly comprises the following steps: the target area is divided into a plurality of grids, and the elevation of the grid is used as the elevation of the map element positioned in the grid. The current mesh division mode of the target area is as follows: and extracting point cloud data in the target area as vertexes of a triangle, and randomly generating a plurality of non-fixed triangular grids covering the target area.

However, when the grid is generated by the above-described conventional method, if an object is present to block the road surface (for example, there may be other vehicles to block the road surface) during the point cloud data acquisition, the triangular grid corresponding to the blocked area will be large. That is, the entire occluded area corresponds to only one triangular mesh. However, there may be elevation changes in the entire shielded area, so that the accuracy of the elevation representing the entire shielded area by the one triangular mesh Gao Chenglai is poor. In addition, there are locations such as shoulders (or curbs, etc.) on the road surface. The triangular mesh may have one vertex on the ground and the other vertices on the road shoulder, resulting in the triangular mesh being an inclined mesh, and thus the elevation of the triangular mesh may be neither on the ground nor on the road shoulder, thus resulting in poor accuracy of the elevation of the mesh obtained based on the mesh generation method.

The reason why the existing grid generation method has the problem of poor accuracy is that point cloud data is extracted to serve as triangular grid vertices to construct a non-fixed triangular grid, so that the application provides a point cloud data processing method for generating fixed grids based on geographic positions of the point cloud data in a target area and determining heights of the grids. Alternatively, the execution body of the point cloud data processing method may be any data processing platform (for example, a map data processing platform), or any electronic device (for example, a terminal, or a server) with a processing function.

It should be understood that the grid object obtained by the point cloud data processing method provided by the application can be used for carrying out elevation restoration of two-dimensional map elements and can be used for other scenes. That is, the application scenario of the mesh object is not limited in this application. For example, the map data processing platform may further use the elevation of the grid as a road surface reference, and determine the elevation of other map elements (such as a street lamp, a signboard, etc.) on the road surface based on the road surface reference, which is not described herein.

The following takes the main execution body of the point cloud data processing method as an example of an electronic device, and the technical scheme of the application is described in detail with reference to specific embodiments. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.

Fig. 1 is a flow chart of a point cloud data processing method according to an embodiment of the present application. As shown in fig. 1, the method may include the steps of:

s101, acquiring point cloud data of an acquired target area.

Alternatively, the target area may be an area with any position or any shape, which is not limited in this application. For example, the target area may include at least one item target segment. In addition, the device type for collecting the point cloud data and the number of the point cloud data in the target area are not limited. For example, the point cloud data may be collected by any type of lidar.

Alternatively, the electronic device may receive the point cloud data of the target area input by the user through a graphical user interface (Graphical User Interface, GUI) or an application program interface (Application Programming Interface, API), for example. Or, the electronic device may also receive, for example, the point cloud data of the target area sent to the electronic device by the map data collection vehicle after the point cloud data set is collected. Or, for example, the electronic device may receive the identifier of the target area input by the user, and obtain, according to the identifier of the target area, point cloud data of the target area from the point cloud data set.

S102, performing geospatial encoding on longitude and latitude of the point cloud data to obtain an address after the encoding of the point cloud data.

The address encoded by the point cloud data can be used for representing the spatial position of the point cloud data.

The geospatial coding may enable the addresses of the coded point cloud data located in a preset range in space to be the same. The electronic device may, for example, receive an identifier for characterizing the preset range input by a user, and encode the identifier by using the above-mentioned geographic space coding, so that the addresses of the encoded point cloud data located in the preset range in the space are the same.

For example, the electronic device may receive a geohash (a name of a geospatial coding algorithm) code class identifier (the class identifier may be used to characterize the preset range) input by the user, and input the longitude and latitude of the point cloud data to the geohash coding algorithm. Then, the electronic device may perform geohash encoding on the longitude and latitude of the point cloud data based on the level identifier, obtain a geohash encoding corresponding to the longitude and latitude of the point cloud data, and use the geohash encoding as an address of the encoded point cloud data. Taking the longitude and latitude of the point cloud data as [31.1932993, 121.43960190000007] as an example, the electronic device may perform geospatial encoding on the longitude and latitude through geohash encoding to obtain an encoded address wtw37q with a character string length of 6 (assumed preset value).

S103, dividing the point cloud data with the same address into one grid to obtain at least one grid of the target area.

If the coded addresses of the plurality of point cloud data are the same, the plurality of point cloud data are adjacent in geographic space positions, so that the electronic equipment can divide the point cloud data with the same address into one grid, and the target area is divided into at least one grid. Taking the example that the target area corresponds to a plurality of grids, the sizes of the different grids can be the same or different.

It should be understood that the present application does not limit the number of meshes, the shape of meshes, and the size of mesh of the target area. Alternatively, the electronic device may receive a user input of a parameter characterizing the size of the grid dimensions. Then, the electronic device may perform geospatial encoding on longitude and latitude of the point cloud data according to the parameter to obtain an address after encoding the point cloud data, and divide the point cloud data with the same address into one grid to obtain at least one grid of the target area.

S104, acquiring a grid object of the grid according to the point cloud data in the grid.

Wherein the grid object includes at least an elevation of the grid. In some embodiments, for any grid, the grid object of the grid may further comprise, for example, at least one of: the size of the grid, the amount of point cloud data employed in determining the elevation of the grid, the confidence of the grid, etc. Wherein the confidence of the grid may be used, for example, to characterize the flatness of the grid.

Alternatively, for any grid, the electronic device may directly calculate, for example, an average value of the elevations of all the point cloud data in the grid as the elevation of the grid, and add the elevation of the grid to the grid object of the grid. Or, for any grid, the electronic device may delete the noise point cloud data in the grid by using any existing noise point cloud data removing method, so as to obtain the point cloud data after removing the noise point. The electronic device may then take the average of the elevations of the denoised point cloud data as the elevation of the grid and add the elevation of the grid to the grid object of the grid.

In this embodiment, by performing geospatial encoding on the longitude and latitude of the point cloud data of the target area, an address after encoding the point cloud data can be obtained, so that encoding of the longitude and latitude of the point cloud data is achieved, and the target area can be subsequently grid-divided by using the encoded address. By dividing the point cloud data with the same address into one grid, the point cloud data adjacent to the geographic space position is divided into one grid, and at least one grid of the target area can be obtained. Then, through the point cloud data in the mesh, a mesh object of the mesh "including at least the elevation of the mesh" can be acquired. By the method, the address after the geospatial coding of the point cloud data based on the target area is realized, at least one grid of the target area is determined, the point cloud data does not need to be extracted as grid vertices, the problem that the grid is large due to the fact that a road surface is shielded is avoided, and the problem of grid inclination is avoided, so that grid division accuracy is improved, and accuracy of determining the height of the grid based on the grid division result is improved. In addition, compared with the way of representing the target area by the point cloud data (the point cloud data is scattered and has thickness generally), the way of representing the target area by using grids, which can be regarded as a plane, improves the smoothness of the target area in visual effect.

As a possible implementation manner, for any grid, the grid object of the grid may further include at least one of the following: confidence of the grid, size of the grid, and the number of point cloud data employed in determining the elevation of the grid.

The confidence level of the grid may be used to characterize the accuracy of the elevation of the grid. The size of the grid may be characterized by the grade of the grid. The smaller the size of the mesh with higher rank, the larger the size of the mesh with lower rank. The amount of point cloud data employed in determining the elevation of the grid described above may also be used to characterize the accuracy of the elevation of the grid. The more the number of point cloud data adopted in determining the elevation of the grid, the higher the accuracy of the elevation of the grid is; the smaller the amount of point cloud data employed in determining the elevation of the grid, the less accurate the elevation of the grid.

Taking the example of the confidence that the grid object also includes a grid, in some embodiments, the electronic device may determine the confidence of the grid based on the elevation flatness of the grid.

For example, for any grid, the electronic device may first obtain the elevation flatness of the grid according to the elevation of the point cloud data in the grid. Alternatively, the electronic device may, for example, subtract the difference obtained by subtracting the minimum value of the elevation of the point cloud data in the grid from the maximum value of the elevation of the point cloud data in the grid, as the elevation flatness of the grid. Alternatively, the elevation flatness of the grid may also be positively correlated with the maximum minus the minimum as described above. That is, the larger the difference obtained by subtracting the minimum from the maximum value, the worse the flatness of the coverage area of the grid, the larger the elevation flatness of the grid. The smaller the difference obtained by subtracting the minimum from the maximum value, the better the flatness of the coverage area of the grid is, and the smaller the elevation flatness of the grid is.

After the electronic device obtains the elevation flatness of the grid, the confidence coefficient of the grid can be obtained according to the elevation flatness of the grid. Wherein the elevation flatness of the grid is inversely related to the confidence level of the grid. That is, the greater the elevation flatness (which is indicative of the poorer flatness of the grid coverage area), the less confidence the grid. The smaller the height Cheng Pingzheng degrees (which means the better the flatness of the grid coverage area), the greater the confidence of the grid.

In this embodiment, because the flatter (less flat) the elevation changes within the grid, the higher the accuracy of characterizing the elevation of any point in the grid by Gao Chenglai of the grid, and therefore the higher the confidence of the grid. By the method, the elevation flatness of the grid is obtained based on the elevation of the point cloud data in the grid, and the confidence level of the grid is determined based on the flatness, so that the accuracy of determining the confidence level of the grid is improved.

Alternatively, the electronic device may obtain the confidence level of the grid, for example, according to the elevation flatness of the grid and the gradient of the grid. Or, the electronic device may further obtain the confidence level of the grid according to the elevation flatness of the grid and the number of point cloud data in the grid. Or, the electronic device may further obtain the confidence level of the grid according to the elevation flatness of the grid, the gradient of the grid, and the number of cloud data in the grid. By the method, the confidence coefficient of the grid can be determined based on the gradient of the grid and/or the number of point cloud data in the grid on the basis of the elevation flatness of the grid, and the accuracy of determining the confidence coefficient of the grid is further improved.

Taking the example that the electronic device obtains the confidence coefficient of the grid according to the altitude flatness of the grid, the gradient of the grid and the quantity of point cloud data in the grid, the electronic device can obtain the gradient of the grid according to any one existing gradient calculation method. The electronic device may then calculate, for example, a weighted sum of the elevation flatness of the grid, the slope of the grid, and the number of point cloud data in the grid, and take the weighted sum as the confidence of the grid.

Alternatively, the electronic device may also store a mapping relationship between the elevation flatness range and the confidence of the grid, for example. In this implementation, the electronic device may determine a range of Cheng Pingzheng degrees at which the height Cheng Pingzheng degrees is based on the elevation flatness of the grid. Then, the electronic device may determine the confidence level of the grid according to the range of the altitude flatness of Cheng Pingzheng degrees and the mapping relationship between the range of the altitude flatness and the confidence level of the grid.

For example, the mapping relationship between the altitude flatness range and the confidence of the grid may be as shown in the following table 1:

TABLE 1

Elevation flatness range	Confidence of grid
		Less than 1 centimeter (cm)	1
Greater than or equal to 1cm and less than 2.5cm	0.9
		Greater than or equal to 2.5cm and less than 5cm	0.6
Greater than or equal to 5cm	0

Taking table 1 as an example, assuming that the range of the height Cheng Pingzheng degrees where the height Cheng Pingzheng degrees is "greater than or equal to 1cm and less than 2.5cm", the electronic device can determine that the confidence of the grid is 0.9 according to the mapping relationship shown in table 1.

In some embodiments, the electronic device may determine the confidence of the grid based on the grade of the grid and/or the amount of point cloud data in the grid. Alternatively, the confidence of the grid may also be related to the rank of the grid. Illustratively, table 2 is a confidence level of a grid, a grade of the grid, and a flatness example of the grid:

TABLE 2

Grade of grid	Flatness of grid	Confidence of grid	Gradient of grid
				10-level grid	Flatness of<1cm	1	Gradient of slope<3％
10-level grid	Flatness of<2.5cm	0.9	Gradient of slope<5％
				10-level grid	Flatness of<5cm	0.6	Gradient of slope<10％
10-level grid	Flatness of not less than 5cm	0	Gradient of slope>15％
				11-level grid	Flatness of<0.5cm	1	Slope of about 3%
11-level grid	Flatness of<1cm	0.7	Slope of about 5%
				11-level grid	Flatness of<2cm	0.5	Slope of about 10%
11-level grid	Flatness of not less than 2cm	0	Slope is about 15%
				11.5 grid	Flatness of<0.25cm	1	Slope of about 3%
11.5 grid	Flatness of<1cm	0.2	Slope of about 10%
				11.5 grid	Flatness of not less than 1cm	0	Gradient of slope>15％

Taking the example that the electronic device determines the confidence coefficient of the grid according to the gradient of the grid, the confidence coefficient of the grid and the gradient of the grid can be inversely related. The greater the slope of the grid, the greater the elevation change of the grid coverage area, and thus the lower the confidence of the grid. The smaller the slope of the grid, the less elevation change that the grid covers, and thus the higher the confidence of the grid can be. For example, the electronic device may also store a mapping relationship between the gradient range and the confidence of the grid. In this implementation, the electronic device may determine, according to the gradient of the grid, a gradient range in which the gradient of the grid is located. Then, the electronic device may determine the confidence level of the grid according to the gradient range in which the gradient of the grid is located and the mapping relationship between the gradient range and the confidence level of the grid.

For example, the mapping relationship between the gradient range and the confidence of the grid may be as shown in the following table 3:

TABLE 3 Table 3

Gradient range	Confidence of grid
		Less than 3%	1
More than or equal to 3 percent and less than 5 percent	0.9
		Greater than or equal to 5 percent and less than 10 percent	0.6
Greater than or equal to 10%	0

Taking table 3 as an example, assuming that the gradient of the grid is in a gradient range of "greater than or equal to 3% and less than 5%", the electronic device may determine that the confidence of the grid is 0.9 according to the mapping relationship shown in table 3.

Taking the example that the electronic device determines the confidence coefficient of the grid according to the number of the point cloud data in the grid, the confidence coefficient of the grid and the number of the point cloud data in the grid can be in positive correlation. The greater the number of point cloud data in a grid, the greater the accuracy of the elevation of the grid determined based on the point cloud data in the grid, and thus, the greater the confidence of the grid. The smaller the number of point cloud data in a grid, the lower the accuracy of the elevation of the grid determined based on the point cloud data in the grid, and thus, the lower the confidence of the grid.

How the electronic device obtains the grid object of the grid according to the point cloud data in the grid is described in detail below:

as a possible implementation manner, the electronic device may perform weighted average processing on the elevation of the point cloud data in the grid to obtain the elevation of the grid.

Optionally, the electronic device may first obtain a weight corresponding to an elevation of each point cloud data in the grid, and then perform weighted average processing on the elevation of the point cloud data in the grid by using the weight corresponding to the elevation of each point cloud data. For example, the electronic device may first calculate a normal distribution of the elevation of the cloud data of each point in the grid, and determine an elevation dense distribution range according to the normal distribution. Then, the electronic device may determine the weight corresponding to the elevation of the point cloud data according to whether the elevation of the point cloud data is within the high Cheng Miji distribution range. For example, the weight of the elevation of the point cloud data located within the high Cheng Miji distribution range is greater than the weight of the elevation of the point cloud data located outside the high Cheng Miji distribution range.

Under the implementation mode, the elevation of the grid is obtained by carrying out weighted average processing on the elevation of the point cloud data in the grid, so that the influence degree of different elevations on the elevation of the grid is different, the flexibility of determining the elevation of the grid is improved, and the accuracy of obtaining the grid object is further improved.

As another possible implementation manner, the electronic device may further sort the elevations of the point cloud data in the grid in order from low to high, and determine the elevations of the grid according to the elevations of the point cloud data in the preset positions in the sorting order.

The preset position may be stored in the electronic device in advance. For example, the preset location may be determined for a user based on a service usage scenario of the mesh object and stored in the electronic device.

For example, it is assumed that the mesh object is used for elevation restoration of a map element (e.g., lane line, etc.) on a road surface of a traveling road, because the elevation of the road surface is generally lower than the elevation of a pedestrian passageway on a road shoulder, and thus the above-described preset position may be a position near the elevation of lower point cloud data, for example, in the lower third of the above-described sort order. In this example, the electronic device may order the elevations of the point cloud data in the grid in order from low to high, for example, and locate at the elevation of the point cloud data of the next third according to the order of ordering as the elevations of the grid.

In the implementation manner, the elevation of the grid is determined through the elevation of the point cloud data which are sequentially arranged at the preset position, so that the elevation of the grid accords with the service scene applied by the grid object, and the accuracy of the subsequent use of the grid object is improved.

As yet another possible implementation manner, the electronic device may further perform denoising processing on the point cloud data in the grid before acquiring the grid object of the grid according to the point cloud data in the grid. Abnormal point cloud data in the grid can be removed by denoising the point cloud data in the grid, so that the accuracy of the point cloud data in the grid is improved, and the accuracy of the grid object determined based on the denoised point cloud data is further improved. In this implementation manner, optionally, the electronic device may further use the number of the point cloud data after the denoising process in the grid as the number of the point cloud data used when determining the elevation of the grid.

Optionally, the electronic device may, for example, perform denoising processing on the point cloud data in the grid based on the elevation of the point cloud data in the grid. For example, the electronic device may first calculate a normal distribution of elevations of the point cloud data in the grid, and determine an elevation dense distribution range according to the normal distribution. Then, the electronic device may use the point cloud data whose elevation is out of the high Cheng Miji distribution range as noise point cloud data, and delete the noise point cloud data, so as to implement denoising processing on the point cloud data in the grid.

After denoising the point cloud data in the grid, the electronic device can acquire the grid object of the grid according to the point cloud data after denoising in the grid. Optionally, the specific implementation manner of obtaining, by the electronic device, the grid object of the grid according to the point cloud data after the denoising process in the grid may refer to the method for obtaining, by using the point cloud data in the grid, the grid object of the grid described in the foregoing embodiment, which is not described herein.

As yet another possible implementation, before acquiring the grid object of the grid according to the point cloud data in the grid, the electronic device may further determine whether to perform fission processing on the grid based on an elevation of the point cloud data in the grid.

For example, the electronic device may obtain an elevation difference of the grid according to an elevation of the point cloud data in the grid, and then determine whether the elevation difference of the grid is greater than a preset threshold. Alternatively, the preset threshold may be, for example, stored in the electronic device in advance.

Alternatively, the electronic device may subtract the minimum elevation from the maximum elevation among the elevations of all the point cloud data in the grid as the elevation difference of the grid. Alternatively, the electronic device may also use, for example, a difference obtained by subtracting the minimum elevation from the maximum elevation in the elevations of the point cloud data after the denoising process in the grid as the elevation difference of the grid, so as to improve the accuracy of determining the elevation difference of the grid.

If the elevation difference of the grid is less than or equal to the preset threshold, the grid of the grade (or the grid of the size, in the grade concept of the grid related to the application, the larger the grade, the smaller the size of the grid, and the larger the size of the grid with the smaller grade) can be used for expressing the constant-elevation surface of a pavement. Therefore, optionally, the electronic device may not perform fission processing on the grid, and directly obtain the grid object of the grid according to the point cloud data in the grid.

If the elevation difference of the grid is greater than the preset threshold value, the grid of the grade is insufficient to express the constant elevation surface of a pavement. Thus, optionally, the electronic device may perform a fission process on the grid until the height difference of the fissile grid is less than or equal to the preset threshold. Wherein the size of the fissionable mesh is smaller than the size of the pre-fissionable mesh.

Alternatively, the electronic device may also have a maximum level threshold of the grid stored in advance. In the implementation manner, the electronic device performs fission processing on the grid with the elevation difference being greater than the preset threshold value until the elevation difference of the grid after the fission is less than or equal to the preset threshold value, and stops grid fission when the level of the grid after the fission is equal to the maximum level threshold value.

Illustratively, FIG. 2 is a schematic diagram of a grid fission process provided herein. As shown in fig. 2, the non-fissile grid is assumed to be an N-level grid, where each N-level grid is the same size. As shown in fig. 2, if the elevation difference of the N-level grid 2 is greater than the preset threshold, the electronic device may perform fission processing on the N-level grid 2 to obtain a plurality of n+1-level grids. Then, for the n+1-level grid with the elevation difference greater than the preset threshold, the electronic device may perform fission processing on the n+1-level grid to obtain a plurality of n+2-level grids.

It should be understood that the present application is not limited to the fission of the above-mentioned grid having a height difference greater than the above-mentioned preset threshold value into several grids. In addition, the number of the fissionable cells obtained by fissionally splitting the cells may be the same as or different from the number of the re-fissionable cells obtained by re-fissionally splitting any of the fissionable cells. For example, still taking the N-level grid shown in fig. 2 as an example, the electronic device may split one N-level grid into 32 n+1-level grids, and then split an n+1-level grid having a height difference greater than the above-described preset threshold into 4 n+2-level grids.

Optionally, in this implementation, taking an example that the foregoing mesh object further includes the size of the mesh, the electronic device may determine the size of the mesh, for example, according to whether the mesh is subjected to fission processing. For example, if the grid is not subjected to fission processing, the electronic device may determine that the size of the grid may be a size corresponding to the preset range. If the grid is subject to fission, the electronic device may determine that the size of the grid may be the size of the grid after the grid is subject to fission. According to the method, whether the grid is fissile or not is based on the fact that the grid object is determined according to the size of the grid after the grid is fissile, accuracy of determining the size of the grid object is improved, and further accuracy of data processing based on the grid object is improved.

In the implementation mode, the grids with the height Cheng Chada being higher than the preset threshold are fissile, so that each grid of the target area is ensured to sufficiently express the equal-height surface of a pavement, the flatness of each grid is ensured, the accuracy of grid division of the target area is further improved, and the accuracy of data processing by using the grid objects of the target area is further improved.

As described above, the application scenario of the mesh object is not limited. It should be understood that the execution body for performing the service processing based on the above mesh object may be any electronic device or data processing platform having a processing function. It should be understood that the execution subject of the business process based on the mesh object may be the same as or different from the execution subject of the mesh object for acquiring the mesh based on the point cloud data of the target area.

The following describes an application scenario of the mesh object by taking the above mesh object for determining an elevation of a map element of a target area, and taking an execution subject as an electronic device as an example.

As a possible implementation manner, fig. 3 is a schematic flow chart of an elevation determining method provided in the present application. As shown in fig. 3, the method may include the steps of:

S201, acquiring a grid query tree of the target area.

Wherein the grid query tree may comprise grid objects of at least one grid of the target area. The grid object may be obtained by using the point cloud data processing method according to any of the foregoing embodiments.

Alternatively, the grid query tree of the target area may be pre-stored in the electronic device. That is, the electronic device may obtain the grid query tree from its own stored data. Alternatively, the electronic device may also generate a grid query tree for the target area based on the grid object of the grid.

S202, acquiring longitude and latitude of a target point of a map element positioned in a target area in the map.

Alternatively, the map element of the target area may be any map element in the target area. A map element may include at least one target point.

Alternatively, the electronic device may receive, for example, the longitude and latitude of the target point input by the user through a GUI or an API. Alternatively, the electronic device may also receive, for example, the three-dimensional coordinates of the target point input by the user, and read the longitude and latitude of the target point from the three-dimensional coordinates. Alternatively, the electronic device may acquire the latitude and longitude of a plurality of points of the map element, and extract the latitude and longitude of the target point of the map element from the latitude and longitude of the plurality of points.

S203, performing geospatial coding on longitude and latitude of the target point to obtain an address after the target point coding.

Optionally, the specific implementation manner of the electronic device "performing geospatial encoding on the longitude and latitude of the target point to obtain the address after encoding the target point" may refer to the foregoing step S102, which is not described herein again.

S204, according to the address of the target point after encoding, acquiring a grid object of at least one target grid corresponding to the target point by using the grid query tree.

Taking the example that the target point corresponds to a target grid, optionally, the target grid may be: and the grid where the point cloud data with the same address as the address coded by the target point is located. Taking the example that the target point corresponds to a plurality of target grids, optionally, the plurality of target grids may include: a grid in which the point cloud data whose address is the same as the address encoded by the target point is located, another grid in the vicinity of the grid, and the like.

S205, determining the elevation of the target point according to the grid object of at least one target grid.

For example, the electronic device may determine the elevation of the target point based on the elevation of the at least one target grid. For example, the electronic device may take as the elevation of the target point an average of the elevations of the at least one target grid.

In this embodiment, the address of the target point after the target point encoding can be obtained by performing geospatial encoding on the longitude and latitude of the target point of the map element of the target area. The method comprises the steps of obtaining the address of the target point after encoding, enabling the subsequent step to obtain the grid object of at least one target grid corresponding to the target point by utilizing the grid query tree of the target area based on the address of the target point after encoding, and determining the elevation of the target point based on the grid object of the at least one target grid. The grid object of at least one target grid is obtained by utilizing the grid query tree of the target area, so that the efficiency of obtaining the grid object of the at least one target grid is improved, and the efficiency of determining the elevation of the target point is further improved. The method of any embodiment improves the accuracy of the grid object, and further improves the accuracy of the elevation of the target point determined based on the grid object with higher accuracy.

How the electronic device obtains the grid query tree of the target area is described in detail below:

as a possible implementation manner, after the electronic device obtains the grid object of the grid according to the point cloud data in the grid, the electronic device may also generate a grid query tree of the target area according to the grid object of the grid, so that the electronic device in each subsequent service scenario may perform service processing based on the grid query tree. Through the grid query tree of the target area, the efficiency of querying the grid objects required by the service processing from a plurality of grid objects corresponding to the target area in the subsequent service processing process can be improved.

Alternatively, the electronic device may generate a grid query tree for the target area using an ac automaton double-array dictionary tree (collectively Aho Corasick Double Array Trie), for example, from the grid object of the grid. For example, the electronic device may input the grid object of at least one grid of the target area to an ac automaton double-array dictionary tree generation algorithm to obtain a grid query tree of the target area. In this implementation, the edges of the grid query tree that connect nodes may be used to characterize a character of the identification of the grid object.

The grid query tree of the target area is generated through the ac automaton double-array dictionary tree, so that the grid query tree can be stored in an array mode, and therefore the storage space required for storing the grid query tree is reduced. When the subsequent service processing process queries the grid query tree, the required grid object can be obtained by querying the array, so that the efficiency of the subsequent service processing is further improved.

Or the electronic equipment can also obtain the grid query tree of the target area according to the grid object of the grid by any one of the existing grid query tree construction modes. For example, the electronic device may also input the mesh object of the mesh into a trie (a name of a query tree construction algorithm) query tree construction algorithm to obtain a mesh query tree of the target area.

Still alternatively, the electronic device may also store the mesh object of the target area directly in the form of key-value pairs (k, v). In this implementation, k in the key-value pair may be used to characterize the identity of the mesh object, and v in the key-value pair may include information about the elevation of the mesh, the size of the mesh, etc. that the mesh object includes.

The following details how the electronic device obtains the grid object of at least one target grid corresponding to the target point by using the grid query tree of the target area according to the address after the target point is coded:

as a possible implementation manner, the electronic device may determine the grid object of the at least one target grid based on the address encoded by the target point and at least one auxiliary point located within the preset range of the target point.

For example, the electronic device may first obtain, from the map, the latitude and longitude of at least one auxiliary point located within a preset range of the target point.

Alternatively, the preset range may be, for example, stored in the electronic device in advance. Alternatively, the number of the at least one auxiliary point may be stored in the electronic device in advance. For example, assuming that the longitude and latitude of the target point is (x, y), the electronic device may consider four points of longitude and latitude of (x-i, y), (x, y-i), (x+i, y), and (x, y+i) as auxiliary points of the target point.

After the electronic device obtains the longitude and latitude of the auxiliary point, the electronic device can perform geospatial coding on the longitude and latitude of the auxiliary point to obtain the address after the auxiliary point is coded. Optionally, the specific implementation manner of the electronic device "performing geospatial encoding on the latitude and longitude of the auxiliary point to obtain the address after the auxiliary point encoding" may refer to the foregoing step S102, which is not described herein again.

The electronic device may then obtain, from the above-described grid query tree of the target area, a grid object of at least one target grid having the same prefix as the "target point encoded address and the auxiliary point encoded address".

For example, for the address after the target point is encoded, and for the address after any auxiliary point is encoded, assuming that the geospatial encoding is geohash encoding and the address is wx4gd8g4wbp, the electronic device may use the address to match, in order of characters from the grid query tree, to obtain a grid object of at least one target grid having the same prefix as the address. The mesh object of the at least one target mesh having the same prefix as the address may include, for example: grid objects of a target grid addressed to wx4gd8g4, wx4gd8g4w, wx4gd8g4wb, or wx4gd8g4 wbp.

In this embodiment, the electronic device may obtain, according to the encoded address corresponding to the at least one auxiliary point within the preset range of the target point and the address encoded by the target point, a grid object of at least one target grid with the same prefix from the grid query tree. By the method, the target grid plane where the query target point and the auxiliary point are located is converted into the index prefix matching of grids, so that the index result (i.e. the grid object of the at least one target grid) can comprise grids of at least one level (the prefixes of grids of different levels can be the same). Therefore, by the method, the richness of the grid object for determining the at least one target grid is improved, and the accuracy of determining the elevation of the target point based on the grid object of the at least one target grid is further improved.

As another possible implementation manner, the electronic device may also obtain, from the above-mentioned grid query tree of the target area, a grid object of the target grid that is the same as the address after the target point is encoded. Then, the electronic device may obtain, from the grid query tree, a grid object of at least one grid geographically adjacent to the target grid, and use the grid object of the at least one grid, and the grid object of the target grid as the grid object of the at least one target grid corresponding to the target point. It should be understood that the present application is not limited to how the electronic device obtains the grid object of at least one grid geographically adjacent to the target grid from the grid query tree.

In this implementation, the grid object of the target grid that is the same as the target point encoded address may be obtained from the grid query tree. The grid objects of other target grids of the target point can be obtained through the grid objects of the target grid, the purpose of obtaining the grid object of at least one target grid corresponding to the target point by utilizing the grid query tree of the target area based on the address of the target point after encoding is achieved, and a foundation is laid for the follow-up determination of the elevation of the target point.

How the electronic device determines the elevation of the target point according to the grid object of at least one target grid is described in detail below:

as a possible implementation manner, the electronic device may determine whether the grid objects of the at least one target grid have grids with the same identifier, and determine the elevation of the target point according to the determination result. Optionally, for any grid, the identification of the grid may be an address encoded by the point cloud data in the grid.

And if the grids with the same identification do not exist in the at least one target grid, indicating that the areas covered by the at least one target grid are all areas in which the point cloud data are acquired once. Alternatively, the electronic device may determine the elevation of the target point based on a weighted sum of elevations of the mesh objects of the at least one target mesh. Wherein the weighted sum may be associated with a confidence level of the mesh object of the target mesh. For example, the higher the confidence, the greater the weight of the elevation to which the mesh object corresponds. The lower the confidence, the less the weight of the elevation corresponding to the mesh object may be. For example, the electronic device may directly take as the elevation of the target point a weighted sum of elevations of the mesh objects of the at least one target mesh.

If the grid with the same identification exists in the at least one target grid, the method indicates that the area covered by the grid with the same identification may be acquired for multiple times at different times. Thus, alternatively, the electronic device may determine the elevation and the weight corresponding to the grid with the same identification, and determine the elevation of the target point according to a weighted sum of the "elevation corresponding to the grid with the same identification" and the "elevation of the grid object of the other target grid".

In some embodiments, the electronic device may determine the elevation and the weight corresponding to the grid with the same identifier according to whether the grid with the same identifier includes grids with different levels. For example:

if the grids with the same identity are grids with the same level, optionally, the electronic device may use the elevation of the grid with high confidence as the elevation corresponding to the grid with the same identity, and determine the weight corresponding to the grid with the same identity according to the confidence of the grid. For example, the higher the confidence of the grid, the greater the weight that the grid with the same identity corresponds to. The lower the confidence of the grid, the less the corresponding weight of the grid with the same identity can be.

By the method, the elevation of the grid with high confidence is used as the elevation corresponding to the grid with the same identification, and the weight corresponding to the grid with the same identification is related to the confidence of the grid, so that the accuracy of determining the elevation and the weight corresponding to the grid with the same identification is improved, and the accuracy of determining the elevation of the target point based on the elevation and the weight corresponding to the grid with the same identification is further improved.

If the grids with the same identity comprise grids with different levels, optionally, the electronic device may use the elevation of the grid with the lowest level as the elevation corresponding to the grid with the same identity, and determine the weight corresponding to the grid with the same identity according to the confidence level of the grid.

Since the grid with the same identifier has the largest size, the grid with the lowest level indicates that the number of the point cloud data in the grid is larger and the flatness is better, that is, the reliability of the grid is higher. Therefore, by taking the elevation of the grid with the lowest level as the elevation corresponding to the grid with the same identification, the accuracy of determining the elevation corresponding to the grid with the same identification is improved. Optionally, the implementation manner and the technical effect of determining, by the electronic device, the weights corresponding to the grids with the same identifier according to the confidence level of the grid may refer to the above embodiment, which is not described herein again.

In some embodiments, the electronic device may further use, for example, an average value of the heights of the grids in the grids with the same identifier as the heights corresponding to the grids with the same identifier, and determine the weights corresponding to the grids with the same identifier according to the average value of the confidence degrees of the grids. For example, the higher the average value of the confidence of each grid, the greater the weight corresponding to the grid with the same identity may be. The lower the average value of the confidence of each grid, the smaller the weight corresponding to the grid with the same identity may be.

For example, after determining the elevation and the weight corresponding to the grid with the same identification, the electronic device may directly use, as the elevation of the target point, a weighted sum of the "elevation corresponding to the grid with the same identification" and the "elevation of the grid object of the other target grid", for example.

In this embodiment, when there is no grid with the same identifier in the at least one target grid, the elevation of the target point may be determined directly according to the weighted sum of the elevations of the grid objects of the at least one target grid, so that the efficiency of determining the elevation of the target point is improved. When grids with the same identification exist in the at least one target grid, the elevation and the weight corresponding to the grid with the same identification can be determined first, and then the elevation of the target point can be determined according to the weighted sum of the elevation corresponding to the grid with the same identification and the elevations of the grid objects of other target grids. According to the method, different situations that the grid coverage area is subjected to single acquisition of point cloud data and is subjected to multiple acquisition of point cloud data are considered, the elevation of the target point is acquired through different processing procedures according to the different situations, the efficiency of acquiring the elevation of the target point is ensured, and meanwhile, the accuracy of acquiring the elevation of the target point is improved.

As another possible implementation, the electronic device may further determine an elevation of the target point according to the grid object of the at least one target grid and the historical candidate elevation of the target point.

For example, the electronic device may first obtain the candidate elevation of the target point according to the grid object of the at least one target grid. Optionally, the implementation manner of the electronic device to obtain the candidate elevation of the target point may refer to the method for determining the elevation of the target point according to the mesh object of at least one target mesh in the foregoing embodiment, which is not described herein.

The electronic device may then determine the elevation of the target point using the candidate elevation of the target point and the historical candidate elevations of the target point. Wherein, the history candidate elevation of the target point is: and (3) obtaining the elevation by using the grid query tree of the target area constructed by the point cloud data of the target area acquired in a history mode. Optionally, the method for constructing the grid query tree of the target area by using the historically collected point cloud data of the target area may refer to the method for constructing the grid fork genus described in the foregoing embodiment, which is not described herein again. Alternatively, the target point may, for example, have at least one historical candidate elevation. For example, each time point cloud data acquisition is performed on the target area, a grid query tree may be constructed based on the point cloud data of the target area acquired at the time, and the elevation of the target point may be determined as a historical candidate elevation of the target point required for determining the target point next time.

In some embodiments, the electronic device may determine the elevation of the target point based on a weighted sum of the candidate elevations of the target point and the historical candidate elevations of the target point.

Alternatively, the electronic device may determine the weight corresponding to the candidate elevation of the target point and the weight corresponding to the history candidate elevation of the target point according to, for example, "the confidence of the grid object of the at least one target grid for determining the candidate elevation of the target point" and "the confidence of the grid object of the at least one target grid for determining the history candidate elevation of the target point".

Alternatively, the electronic device may also calculate, for example, a normal distribution of candidate elevations of the target point and historical candidate elevations of the target point, and determine the dense distribution range of elevations according to the normal distribution. The electronic device may then determine the weight of candidate elevations within the high Cheng Miji distribution range and calculate a weighted sum of candidate elevations within the high Cheng Miji distribution range as elevations of the target point based on the weight.

In some embodiments, the electronic device may also directly calculate, for example, an average value of the candidate elevation of the target point and the historical candidate elevation of the target point as the elevation of the target point.

In this embodiment, the candidate elevation of the target point may be acquired by the mesh object of the at least one target mesh. The elevation of the target point may then be determined based on the candidate elevation of the target point, and the historical candidates Gao Chenglai of the target point. By the method, when the point cloud data are acquired for a plurality of times in the target area, the influence of the historical candidate elevation determined according to the historically acquired point cloud data on the elevation of the target point is considered, and the accuracy of determining the elevation of the target point is further improved.

The point cloud data processing method provided by the application is exemplified by geohash coding as the geospatial coding. The point cloud data processing method can be mainly divided into two parts of grid generation and grid use. First, fig. 4 is a schematic flow chart of another point cloud data processing method provided in the present application, corresponding to a grid generating process. As shown in fig. 4, as a possible implementation manner, the method may include the following steps:

and step 1, acquiring the point cloud data of the acquired target area.

And 2, performing geohash coding on the longitude and latitude of the point cloud data to obtain an address after the point cloud data coding, and performing grid division according to the address to obtain at least one grid of the target area.

geohash is a Z-order curve, and has the advantages of local order retention, common prefix in index strings near points, and the like. The following describes an exemplary encoding process of geohash encoding by taking latitude and longitude coordinates [31.1932993, 121.43960190000007] as an example:

1. and the user determines the initial size of the grid, then determines the number of stages of the geohash codes corresponding to the initial size by querying a geohash code related table, and inputs the stages to the electronic equipment.

2. And processing the longitude and latitude. Taking latitude as an example, the latitude interval is [ -90,90]. The interval is divided into 2 parts, namely [ -90,0), [0,90]. The latitude coordinate 31.1932993 is located in the (0,90) section, i.e., the right section, and is marked as 1. The (0,90) section is then divided into two sections [0,45 ], [45,90], and the latitude coordinate 31.1932993 is located in the [0,45 ] section, i.e., the left section, and is marked as 0. And so on, the electronic device is always divided according to the above-mentioned number of stages (as shown in table 4 below):

TABLE 4 Table 4

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As shown in table 4, the electronic device may obtain a binary 101011000101110 corresponding to the latitude.

Taking longitude as an example, the longitude interval is [ -180,180], and the processing manner is the same as the latitude processing manner, and will not be described here again. As shown in table 5 below, the electronic device may obtain a binary value 110101100101101 corresponding to longitude.

TABLE 5

Left interval	Median value	Right interval	Binary result
				-180	0	180	1
0	90	180	1
				90	135	180	0
0	112.5	135	1
				112.5	123.75	135	0
112.5	118.125	123.75	1
				118.125	120.9375	123.75	1
120.9375	122.34375	123.75	0
				120.9375	121.640625	122.34375	0
120.9375	121.289062	121.640625	1
				121.289062	121.464844	121.640625	0
121.289062	121.376953	121.464844	1
				121.376953	121.420898	121.464844	1
121.420898	121.442871	121.464844	0
				121.420898	121.431885	121.442871	1

3. The binary strings are reassembled. According to the rule of 'even bit longitude and odd bit latitude', the binary strings of longitude and latitude are recombined to generate a new binary string: 111001100111100000110011110110.

4. converted into character strings. The electronic device may convert the binary string into characters by looking up a Base32 table corresponding to the geohash code. 111001100111100000110011110110 is converted to decimal 28 25 28 37 22. The Base32 table may be shown in the following tables 6 and 7:

TABLE 6

Decimal number	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
																		Base32	0	1	2	3	4	5	6	7	8	9	b	c	d	e	f	g	h

TABLE 7

Decimal number	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31
																Base32	j	k	m	n	p	q	r	s	t	u	v	w	x	y	z

From tables 6 and 7, the character string wtw q can be determined. The electronic device may use the character string as a geohash encoding result of the longitude and latitude coordinates.

The electronic equipment can realize grid division by dividing the point cloud data with the same geohash coding result into the same grid. For any grid, the electronic device can use the geohash code corresponding to the point cloud data in the grid as the identification of the grid.

And step 3, judging whether the elevation difference of the grids is larger than a preset threshold value, and performing fission treatment on the grids with the elevation difference larger than the preset threshold value.

The operation of determining whether the elevation difference of the grid is greater than the preset threshold may also be referred to as gradient checking.

Illustratively, table 8 below is an example of the size and geohash encoding of different level grids:

TABLE 8

Grid grade	Mesh size (m, m)	Geohash encoding of point cloud data in a grid
			Level
7	≤153m×153m	For example: wx4gd8g
			Level
8	≤38.2m×19.1m	For example: wx4gd8g4
			Grade
9	≤4.77m×4.77m	For example: wx4gd8g4w
			Grade
10	≤1.19m×0.596m	For example: wx4gd8g4wc
			11 grade	≤0.149m×0.149m	For example: wx4gd8g4wcb
11.5 grade	≤0.0745m×0.0745m	For example: wx4gd8g wcb _00
			12 grade	≤0.0372m×0.0186m	For example: wx4gd8g4wbp7

As shown in table 8, where a 11-level grid may be split into 4 11.5-level grids, or 32 12-level grids.

Taking the map element to be processed with the height Cheng Haiyuan as the lane line, because the interval between the target points on the lane line is about 1-2 meters, the range of selecting the point cloud candidate set (auxiliary point) for each target point is 1 square meter, and if the range of selecting the grade 9, 4.77m by 4.77m is relatively large, the target points on a plurality of lane lines can be covered. Thus, the pre-fission grid may be determined to be a 10-level grid. Taking 10-level grids as examples before fission, the electronic device can perform elevation sequencing on point cloud data in each 10-level grid, and take a range with relatively dense main body elevation distribution (namely the altitude dense distribution range) and treat point cloud data outside the altitude dense distribution range as noise points. For the elevations in the dense distribution range of the elevations, if the difference obtained by subtracting the maximum elevation from the minimum elevation is larger than 5 cm, the gradient of the 10-level grid does not meet the precision requirement, and grid splitting is needed. Thus, the electronic device may split the grid into a 11-level grid. A grade check is then continued for the point cloud elevation of each 11-level grid to determine whether the fission process is continued. That is, if a certain level of mesh is generated that is insufficient to express a constant-altitude surface of a road surface, particularly a slope scene, it is necessary to perform mesh fission on the level of mesh to generate a multi-level mesh.

Fig. 5 is a schematic diagram of a meshing result provided in the present application. As shown in fig. 5, there may be a plurality of different levels (i.e., different sizes) of grids, such as a size of an N-level grid, greater than a size of an n+1-level grid, at the road segment.

The Google s2 geospatial coding method requires 5 levels of grids 22-26 to represent 10-11 levels of grids of the geohash coding, so that the grid division of a target area is realized based on the geohash coding, the grid division efficiency is improved, and the data volume of grid objects is reduced.

And 4, acquiring the elevation of the grid, the confidence coefficient of the grid and the like, obtaining a grid object of the grid, and realizing grid generation.

The elevation of the grid may be used to represent the elevation of the road surface in the target area. Optionally, the grid object may include an identifier of the grid (which may be a geohash index), a confidence level of the grid, a size of the grid (because the sizes of different levels of the grid are different, the size of the grid may be represented by a level of the grid), and a number of point cloud data used in determining an elevation of the grid.

By the method, in the service scene of road surface calculation of the high-precision map, the grids of the target area have the following characteristics: 1. the amount of mesh is small. Compared with the method for encoding the geospatial codes of google s2, the method has more broken files with 30 levels of codes, and the same path can generate a large number of grid files, and the geohash codes can have 12 levels of grids, so that the number of the grid files is smaller than that of google s2, and the efficiency of service processing based on the grids of the target area is improved. 2. The ability to multi-level coverage. Compared with the multi-level coverage of the uber h3 coding mode, the multi-level coverage method has the advantages that the problem of the condition of edge joint multi-level coding jump can occur, the multi-level coverage of the geohash coding is good, and the accuracy of grid division of a target area is improved. 3. The versatility of geohash coding.

For the grid use process, fig. 6 is a schematic flow chart of another elevation determining method provided in the present application. As shown in fig. 6, as a possible implementation manner, the method may include the following steps:

and step 1, generating a grid query tree of the target area by utilizing the ac automaton double-array dictionary tree according to the grid object of the grid.

The AC automaton double-array dictionary tree is an implementation mode of dictionary tree, double-array and AC automaton. Based on the grid query tree constructed by the ac automaton double-array dictionary tree, grid matching recall (namely, determining the grid object of at least one target grid corresponding to the target point) can be realized by utilizing the characteristic that the coordinates are similar and the prefixes are the same.

Illustratively, FIG. 7 is a schematic diagram of a double-array result and an AC automaton in an AC automaton double-array dictionary tree. Fig. 8 is a schematic diagram of an array indexing of an ac automaton double array dictionary tree. As shown in fig. 7 and 8, in the mesh query tree constructed by the above-described ac automaton double-array (base array and check array) dictionary tree, the edges of the tree=characters, characters=j-base [ i ], and characters=j-check [ j ], the characters being stored in the corresponding ASCII codes. The base as shown in fig. 7 refers to a base array storing information for finding child node positions; the check refers to a check array, which is used to store the parent index of the child node; index refers to an index pointer used to characterize the index in the base array; root represents the root node. As shown in fig. 7, taking BACEZQ as an input as an example, AC, ACE, ZQ and other results can be obtained by matching through the ac automaton double-array dictionary tree. Fig. 8 is an exemplary illustration of dictionary trees, base array calculations, and state calculations (i.e., array indexes), taking string AC, ACE, ACEF, AD, CD, CF, ZQ as an example. The meanings of root, base, index, edges and nodes of the tree are the same as those shown in fig. 7, and will not be described again.

The values in the nodes in the tree represent array indices corresponding to the double arrays, and the relationship value corresponding to each index. The calculation rule of the relation is as follows: base [0] =1, check [0] =0, base [ i ] =check [ j ], where j is the child node (child) of i. From the double-array reduction dictionary tree, the sub-node of each layer can be determined according to the base [ i ] =check [ j ], for example, when base=1, all the nodes of the check=1 are the sub-nodes of the upper stage.

The double-array dictionary tree is combined with the AC automaton, so that the space occupied by the grid query tree can be compressed, two arrays represent one tree, single mode matching and single string matching are advantageous. Furthermore, combining the dual-array dictionary tree with the AC automaton may reduce matching complexity.

The single pattern matching process of the AC automaton to solve the dual array dictionary tree can be as follows: success (success): the transition to the other state is successful as shown by the solid line in fig. 7. Failure (failure): if it is not possible to jump along the string, it jumps to a particular node, and the path from the root node to this particular node is just part of the text before failure (as shown by the dashed line in fig. 7). Hit (emits): hit one pattern string (as shown by the darker nodes in fig. 7).

And 2, acquiring the longitude and latitude of a target point of a map element positioned in the target area in the map, and performing geohash coding on the longitude and latitude of the target point to obtain the address after the target point coding.

And step 3, acquiring a grid object of at least one target grid corresponding to the target point by utilizing the grid query tree according to the address of the target point after encoding.

The implementation manner of obtaining the grid object of at least one target grid corresponding to the target point according to the address coded by the target point according to any one of the foregoing embodiments by using the grid query tree may obtain the at least one target grid. Taking around 4 target grids as an example, this process may be referred to as a four Gong Gewang grid recall. Taking an example of about 9 target grids, this process may be referred to as nine Gong Gewang grid recalls. The geohash code is used as a coding mode of geospatial coding, and the problem of corner mutation can be solved by filtering grids.

Fig. 9 is a schematic diagram of at least one target grid corresponding to a target point provided in the present application. As shown in fig. 9, assuming that the target point is an address after encoding, at least one target grid corresponding to the target point is obtained by using the grid query tree, and the target grids are respectively four target grids of upper left, upper right, lower left and lower right. Illustratively, the latitude and longitude coordinates of the four target grids and the geohash encoded address may be as shown in table 9 below, for example:

TABLE 9

Target grid	Longitude and latitude coordinates	Coded address
			Upper left target grid	(39.994783046100004,116.481471366)	wx4gd8g4rp
Right upper target grid	(39.9947850461,116.481471366)	wx4gd8g4x0
			Lower left target grid	(39.994783046100004,116.481469366)	wx4gd8g4qz
Lower right target grid	(39.9947850461,116.481469366)	wx4gd8g4wb

By the method, the target grid plane where the query target point and the auxiliary point are located is converted into index prefix matching of grids (the mode of calculating the elevation by the point cloud is converted into space query), so that an index result (namely, the grid object of the at least one target grid) can comprise grids of at least one grade (the prefixes of grids of different grades can be the same), and the index result can also be called multi-mode prefix matching.

As shown in fig. 9, where multiple observations refer to an area covered by the target grid, point cloud data may be acquired multiple times at different times, and thus, a multi-layer grid of the area may be obtained according to the grid query tree query described above. Wherein 11, 10, 00, 0111, 0110, 0100, 0101 are used to characterize the identity of each target grid.

And 4, carrying out grid fusion on the grid objects of the at least one target grid, and determining the elevation of the target point.

Optionally, the mesh object of the at least one target mesh is fused to determine a specific implementation manner of the elevation of the target point, which may refer to the method described in the foregoing embodiment and will not be described herein. Taking wx4gd8g4wbp as an example, the address after the target point is encoded, for example, the following four mesh objects of the target mesh may be determined according to the address after the target point is encoded:

Grid object of target grid 1: id=12361, tag=1, index name= 'wx4gd8g4', index level=8, zvalue=26.668, score=0, where id represents the sequence number of the grid in the target area. tag=1 represents the traffic scenario for which the mesh object is used. The index name represents the identification of the grid, that is, the address of the grid after the cloud data is encoded. The index level represents the level of the grid. zValue represents the elevation of the grid. Score represents the confidence level of the grid.

Grid object of target grid 2: id=12347, tag=1, index name= 'wx4gd8g4w', index level=9, zvalue=26.633, score=0;

grid object of target grid 3: id=12346, tag=1, index name= 'wx4gd8g4wb', index level=10, zvalue=26.659, score=1.0;

grid object of target grid 4: id=12345, tag=1, index name= 'wx4gd8g4wbp', index level=11, zvalue=26.637, score=0.5.

By comparing the grid objects of the four target grids, the confidence of the target grid 3 is highest, and optionally, the electronic device may determine that the altitude of the target point is zvalue= 26.659, for example.

In this embodiment, geohash encoding is used to perform geospatial encoding on longitude and latitude of point cloud data of a target area, divide the point cloud data with the same address into a grid, calculate elevation of the grid, and package the grid into grid objects. And (5) establishing an index tree (grid query tree) for the grid object by adopting an ac automaton double-array dictionary tree. And carrying out geohash coding on the longitude and latitude of the target point to be calculated, and recalling at least one target grid of the area where the target point is located through the index tree. By the method, when the reasons such as road surface shielding exist, grids near the area where the target point is located can be recalled, and the grids are used for fusion calculation on the elevation, so that the elevation of the target point is finally obtained. By the method, the packaged grid object can be precipitated into the reusable pavement information, so that the method is applicable to various service scenes, and the reusability of the grid object is improved. By storing mesh objects instead of storing point cloud data (the point cloud data becomes a mesh with a reduced space size of 99%, which can be understood as a kind of thinning). In addition, the grids of the target area determined by the method are fixed grids, the calculated index values of the coordinates in the same grid are the same, the acquired grids are the same grid, and the grids recalled by the index values calculated by the same coordinates are the same. Compared with the prior art that the triangular grids are non-fixed grids, the same area is acquired for multiple times, the formed triangular grid expression ranges are all non-fixed in size, and the obtained triangular grids are different grids, so that accuracy and stability of grid division of the target area are improved.

Fig. 10 is a schematic structural diagram of a point cloud data processing device provided in the present application. As shown in fig. 10, the apparatus includes: an acquisition module 31, an encoding module 32, a dividing module 33, and a processing module 34. Wherein, the liquid crystal display device comprises a liquid crystal display device,

the acquiring module 31 is configured to acquire point cloud data of the acquired target area.

The encoding module 32 is configured to perform geospatial encoding on the longitude and latitude of the point cloud data, so as to obtain an address encoded by the point cloud data. The geographic space codes enable the addresses after the point cloud data in the preset range are coded in the space to be the same.

The dividing module 33 is configured to divide the point cloud data with the same address into one grid, and obtain at least one grid of the target area.

And the processing module 34 is used for acquiring the grid object of the grid according to the point cloud data in the grid. Wherein the mesh object comprises at least an elevation of the mesh.

Optionally, the processing module 34 is specifically configured to perform weighted average processing on the elevation of the point cloud data in the grid, so as to obtain the elevation of the grid. Or, the processing module 34 is specifically configured to sort the elevations of the point cloud data in the grid in the order from low to high, and determine the elevations of the grid according to the elevations of the point cloud data in the preset positions in the sorting order.

Optionally, the mesh object further includes at least one of: the confidence of the grid, the size of the grid, and the amount of point cloud data employed in determining the elevation of the grid.

Taking the example that the grid object further includes the confidence coefficient of the grid, optionally, the processing module 34 is specifically configured to obtain the elevation flatness of the grid according to the elevation of the point cloud data in the grid; and acquiring the confidence coefficient of the grid according to the elevation flatness of the grid. Wherein the elevation flatness is inversely related to the confidence level.

Optionally, the processing module 34 is specifically configured to obtain the confidence level of the grid according to the elevation flatness of the grid, the gradient of the grid, and/or the number of point cloud data in the grid.

Optionally, the processing module 34 is further configured to perform denoising processing on the point cloud data in the grid before acquiring the grid object of the grid according to the point cloud data in the grid.

Optionally, the processing module 34 is further configured to obtain an elevation difference of the grid according to an elevation of the point cloud data in the grid before obtaining the grid object of the grid according to the point cloud data in the grid; and when the elevation difference of the grid is larger than a preset threshold value, performing fission treatment on the grid until the elevation difference of the grid after the fission is smaller than or equal to the preset threshold value. Wherein the size of the grid after fission is smaller than the size of the grid before fission.

Taking the example that the grid object further includes the size of the grid, optionally, the processing module 34 is further configured to, when the grid is not subjected to fission processing, set the size of the grid to be the size corresponding to the preset range; and when the grid is subjected to fission treatment, the size of the grid is the size of the grid after the grid is subjected to fission.

The map point cloud data processing device provided by the application is used for executing the map point cloud data processing method embodiment, and the implementation principle and the technical effect are similar, and are not repeated.

Fig. 11 is a schematic structural diagram of an elevation determining apparatus provided in the present application. As shown in fig. 11, the apparatus includes: a first acquisition module 41, a second acquisition module 42, an encoding module 43, a third acquisition module 44, and a processing module 45. Wherein, the liquid crystal display device comprises a liquid crystal display device,

a first obtaining module 41, configured to obtain a grid query tree of the target area. The grid query tree comprises grid objects of at least one grid of the target area, wherein the grid objects are obtained by adopting the point cloud data processing method according to any one of the previous embodiments.

A second acquiring module 42, configured to acquire latitude and longitude of a target point of a map element located in the target area in the map.

And the encoding module 43 is configured to perform geospatial encoding on the longitude and latitude of the target point, so as to obtain an address after encoding the target point.

And a third obtaining module 44, configured to obtain, according to the address encoded by the target point, a grid object of at least one target grid corresponding to the target point by using the grid query tree.

A processing module 45, configured to determine an elevation of the target point according to the grid object of the at least one target grid.

Optionally, the third obtaining module 44 is specifically configured to obtain, from the map, a latitude and a longitude of at least one auxiliary point located within a preset range of the target point; performing geospatial coding on the longitude and latitude of the auxiliary point to obtain an address after the auxiliary point is coded; and acquiring a grid object of at least one target grid with the same prefix as the address coded by the target point and the address coded by the auxiliary point from the grid query tree of the target area.

Optionally, the processing module 45 is specifically configured to determine, when a grid with the same identifier does not exist in the at least one target grid, an elevation of the target point according to a weighted sum of elevations of grid objects of the at least one target grid; and when grids with the same identification exist in the at least one target grid, determining the elevation and the weight corresponding to the grids with the same identification, and determining the elevation of the target point according to the weighted sum of the elevation corresponding to the grid with the same identification and the elevations of the grid objects of other target grids. Wherein the weighted sum employs weights that are related to the confidence of the mesh object of the target mesh.

Optionally, the processing module 45 is specifically configured to, when the grids with the same identifier are grids with the same level, adopt an elevation of the grid with high confidence as an elevation corresponding to the grid with the same identifier, and determine a weight corresponding to the grid with the same identifier according to the confidence of the grid; or when the grids with the same identity comprise grids with different levels, adopting the elevation of the grid with the lowest level as the elevation corresponding to the grid with the same identity, and determining the weight corresponding to the grid with the same identity according to the confidence level of the grid.

Optionally, the processing module 45 is specifically configured to obtain, according to the mesh object of the at least one target mesh, a candidate elevation of the target point; and determining the elevation of the target point by utilizing the candidate elevation of the target point and the historical candidate elevation of the target point. The historical candidate elevation of the target point is an elevation obtained by utilizing a grid query tree of a target area constructed by the point cloud data of the target area collected in a historical mode.

The elevation determining apparatus provided in the present application is configured to execute the foregoing elevation determining method embodiment, and its implementation principle is similar to that of the technical effect, and will not be described again.

Fig. 12 is a schematic hardware structure of an electronic device provided in the present application, and the electronic device 50 shown in fig. 12 includes a memory 51, a processor 52, and a communication interface 53. The memory 51, the processor 52 and the communication interface 53 are communicatively connected to each other. For example, the memory 51, the processor 52 and the communication interface 53 may be connected by a network. Alternatively, the electronic device 50 may also include a bus 54. The memory 51, the processor 52, and the communication interface 53 are communicatively connected to each other via a bus 54. Fig. 12 shows an electronic device 50 in which a memory 51, a processor 52, and a communication interface 53 are communicatively connected to each other via a bus 54.

The Memory 51 may be a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a random access Memory (Random Access Memory, RAM). The memory 51 may store a program, and the processor 52 and the communication interface 53 are configured to perform the map point cloud data processing method and/or the elevation determination method described in any one of the foregoing embodiments when the program stored in the memory 51 is executed by the processor 52. The memory may also store data required for the map point cloud data processing method and/or the elevation determination method.

The processor 52 may employ a general purpose CPU, microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), graphics processor (graphics processing unit, GPU) or one or more integrated circuits.

Processor 52 may also be an integrated circuit chip with signal processing capabilities. In implementation, the data processing functions of the present application may be performed by integrated logic circuits in hardware in processor 52 or by instructions in the form of software. The processor 52 may also be a general purpose processor, a digital signal processor (digital signal processing, DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (field programmable gate array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or may implement or perform the methods, steps, and logic blocks disclosed in the embodiments herein below. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments disclosed below may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 51 and the processor 52 reads the information in the memory 51 and combines with its hardware to perform the data processing functions of the present application.

The communication interface 53 enables communication between the electronic device 50 and other devices or communication networks using a transceiver module such as, but not limited to, a transceiver. For example, the data set may be acquired through the communication interface 53.

When the electronic device 50 includes a bus 54, the bus 54 may include a path to transfer information between the various components of the electronic device 50 (e.g., memory 51, processor 52, communication interface 53).

The present application also provides a computer-readable storage medium, which may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, etc., in which program codes may be stored, and in particular, the computer-readable storage medium stores program instructions for the methods in the above embodiments.

The present application also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the electronic device may read the execution instructions from the readable storage medium, and execution of the execution instructions by the at least one processor causes the electronic device to implement the map point cloud data processing method and/or the elevation determination method provided by the various embodiments described above.

The term "plurality" herein refers to two or more. The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship; in the formula, the character "/" indicates that the front and rear associated objects are a "division" relationship. In addition, it should be understood that in the description of this application, the words "first," "second," and the like are used merely for distinguishing between the descriptions and not for indicating or implying any relative importance or order.

It will be appreciated that the various numerical numbers referred to in the embodiments of the present application are merely for ease of description and are not intended to limit the scope of the embodiments of the present application.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for processing point cloud data, the method comprising:

acquiring point cloud data of an acquired target area;

2. The method of claim 1, wherein the acquiring the mesh object of the mesh from the point cloud data in the mesh comprises:

carrying out weighted average processing on the elevation of the point cloud data in the grid to obtain the elevation of the grid;

or ordering the elevations of the point cloud data in the grid according to the order from low to high, and determining the elevations of the grid according to the elevations of the point cloud data positioned at the preset positions in the ordering order.

3. The method of claim 2, wherein the mesh object further comprises at least one of:

The confidence of the grid, the size of the grid, and the amount of point cloud data employed in determining the elevation of the grid.

4. The method of claim 3, wherein the grid object further comprises a confidence level of the grid; the obtaining the grid object of the grid according to the point cloud data in the grid includes:

acquiring the elevation flatness of the grid according to the elevation of the point cloud data in the grid;

acquiring the confidence coefficient of the grid according to the elevation flatness of the grid; the elevation flatness is inversely related to the confidence level.

5. The method of claim 4, wherein the obtaining the confidence level of the grid according to the elevation flatness of the grid comprises:

and acquiring the confidence coefficient of the grid according to the elevation flatness of the grid and the gradient of the grid and/or the quantity of point cloud data in the grid.

6. The method of any of claims 3-5, wherein prior to the acquiring the mesh object of the mesh from the point cloud data in the mesh, the method further comprises:

acquiring the elevation difference of the grid according to the elevation of the point cloud data in the grid;

If the elevation difference of the grid is larger than a preset threshold value, performing fission treatment on the grid until the elevation difference of the grid after the fission is smaller than or equal to the preset threshold value; the size of the grid after fission is smaller than the size of the grid before fission.

7. The method of claim 6, wherein the mesh object further comprises a size of the mesh; the obtaining the grid object of the grid according to the point cloud data in the grid includes:

if the grid is not subjected to fission treatment, the size of the grid is the size corresponding to the preset range;

and if the grid is subjected to fission treatment, the size of the grid is the size of the grid after the grid is subjected to fission.

8. A method of elevation determination, the method comprising:

acquiring a grid query tree of a target area; the grid query tree comprising grid objects of at least one grid of the target area, the grid objects being obtained using the method of any one of claims 1-7;

9. The method according to claim 8, wherein the obtaining, according to the address encoded by the target point, the mesh object of the at least one target mesh corresponding to the target point using the mesh query tree includes:

acquiring longitude and latitude of at least one auxiliary point located in a preset range of the target point from the map;

performing geospatial coding on the longitude and latitude of the auxiliary point to obtain an address after the auxiliary point is coded;

and acquiring a grid object of at least one target grid with the same prefix as the address coded by the target point and the address coded by the auxiliary point from the grid query tree of the target area.

10. The method of claim 8, wherein determining the elevation of the target point from the mesh object of the at least one target mesh comprises:

if the grids with the same identification do not exist in the at least one target grid, determining the elevation of the target point according to the weighted sum of the elevations of the grid objects of the at least one target grid; wherein the weight employed by the weighted sum is related to the confidence level of the mesh object of the target mesh;

And if the grids with the same identification exist in the at least one target grid, determining the elevation and the weight corresponding to the grids with the same identification, and determining the elevation of the target point according to the weighted sum of the elevation corresponding to the grid with the same identification and the elevations of the grid objects of other target grids.

11. The method of claim 10, wherein the determining the elevation and weight of the grid correspondence having the same identity comprises:

if the grids with the same identity are grids with the same level, adopting the elevation of the grid with high confidence as the elevation corresponding to the grid with the same identity, and determining the weight corresponding to the grid with the same identity according to the confidence of the grid; or alternatively, the process may be performed,

if the grids with the same identity comprise grids with different levels, adopting the elevation of the grid with the lowest level as the elevation corresponding to the grid with the same identity, and determining the weight corresponding to the grid with the same identity according to the confidence level of the grid.

12. The method of claim 8, wherein determining the elevation of the target point from the mesh object of the at least one target mesh comprises:

Acquiring candidate elevations of the target points according to the grid objects of the at least one target grid;

determining the elevation of the target point by utilizing the candidate elevation of the target point and the historical candidate elevation of the target point; the historical candidate elevation of the target point is an elevation obtained by utilizing a grid query tree of the target area constructed by the point cloud data of the target area collected in a historical manner.

13. An electronic device, comprising: a processor and a memory; the processor is in communication with the memory;

the memory stores computer instructions;

the processor executes the computer instructions stored by the memory to implement the method of any one of claims 1-12.

14. A computer readable storage medium having stored thereon computer executable instructions which, when executed by a processor, implement the method of any of claims 1-12.