CN117994461A - Method for constructing earth surface three-dimensional model based on laser point cloud data - Google Patents
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Abstract
The invention discloses a method for constructing a three-dimensional model of an earth surface based on laser point cloud data, which relates to the technical field of point cloud data processing, and can improve the precision and the authenticity of the construction of the earth surface model, optimize the registration process, improve the data processing efficiency, enhance the stability of the model, provide more reliable data support for the application and analysis of data in a geographic information system and related fields, promote the further development and the application of the construction technology of the earth surface model, adopt different data processing methods and parameters according to the attribute evaluation coefficients of different data layers, enable the processing process to be more adaptive and flexible, improve the data processing efficiency and the expression capability of earth surface characteristics, and dynamically select proper registration algorithms and parameters so as to solve the problem of difficult registration.
Description
Technical Field
The invention relates to the technical field of point cloud data processing, in particular to a method for constructing a three-dimensional model of the earth surface based on laser point cloud data.
Background
In building and city planning, three-dimensional models of the building and city environment need to be acquired for design, visualization, and decision support. Accurate geometric information of buildings and urban environments can be obtained by utilizing laser point cloud data, and a real three-dimensional model is built, so that a method for building the earth surface three-dimensional model based on the laser point cloud data is generated.
In the prior art, accurate basis and method may be lacking in adjusting registration parameters, and parameter adjustment is often performed in an empirical or trial-and-error mode, which may lead to unstable registration results and difficult to achieve an optimal registration effect, and obviously, the construction method has at least the following problems: 1. in the prior art, the quality and importance of point cloud data may not be accurately estimated in the modeling process, so that the precision of a three-dimensional model of the earth surface is not high, and meanwhile, all the point cloud data can not be flexibly processed according to the characteristics of different data layers by only adopting one data processing method.
In the prior art, the most suitable registration method and parameters are difficult to select according to the attributes and characteristics of the point cloud, so that the registration effect is not ideal, only single factors, such as the density or the reflection intensity of the point cloud, are possibly considered when the point cloud data are processed, and the capability of comprehensively considering a plurality of factors is lacking.
The prior art may require manual intervention and adjustment of parameters during data processing, increasing processing complexity and time consumption, and may lack accurate basis and methods for adjusting registration parameters, and may rely on empirical or trial-and-error means for parameter adjustment. This may lead to unstable registration results, which makes it difficult to achieve optimal registration.
Disclosure of Invention
Aiming at the technical defects, the invention aims to provide a method for constructing a three-dimensional model of the earth surface based on laser point cloud data.
In order to solve the technical problems, the invention adopts the following technical scheme: the invention provides a method for constructing a three-dimensional model of the earth surface based on laser point cloud data, which comprises the following steps: step one, acquiring point cloud attribute data: and acquiring point cloud attribute data corresponding to the point cloud data in the target original laser point cloud database, wherein the point cloud attribute data comprises reflection intensity, elevation value and normal vector value, so that the reflection intensity, elevation value and normal vector value corresponding to the point cloud data are analyzed, and an attribute evaluation coefficient corresponding to the point cloud data is obtained.
Step two, data hierarchy segmentation: and evaluating the coefficients according to the attributes corresponding to the cloud data of each point, so as to analyze the data hierarchy corresponding to the cloud data of each point, and dividing the cloud data of each point according to the corresponding data hierarchy.
Step three, acquiring a point cloud characteristic influence factor: and acquiring point cloud characteristic data corresponding to each data level, wherein the point cloud characteristic data comprises point cloud density, voxel rate and average distance value corresponding to the point cloud data, and analyzing to obtain point cloud characteristic influence factors corresponding to each data level.
Step four, acquiring a comprehensive attribute evaluation coefficient: and analyzing and obtaining comprehensive attribute evaluation coefficients corresponding to all the data layers according to the point cloud characteristic influence factors corresponding to all the data layers and the attribute evaluation coefficients corresponding to all the point cloud data in all the data layers.
Fifth, analysis of the most suitable registration method: and analyzing the most suitable registration method corresponding to each data level according to the comprehensive attribute evaluation coefficient corresponding to each data level, and registering each data level according to the corresponding most suitable registration method.
Step six, analysis of optimal registration parameters: and according to the comprehensive attribute evaluation coefficient corresponding to each data level, further analyzing the optimal registration parameters corresponding to the optimal registration method in each data level, and adjusting the optimal registration method in each data level according to the corresponding optimal registration parameters.
Preferably, the acquiring the point cloud attribute data corresponding to the point cloud data in the target original laser point cloud database specifically includes the following steps: a1, scanning a target area by using a laser scanning device, transmitting laser pulses through the laser scanning device and recording the return time of the laser pulses, thereby obtaining the distance information of the ground or the object surface, and then obtaining the point cloud attribute information corresponding to the point cloud data by processing the data acquired by the laser scanning device.
And A2, extracting point cloud attribute data of the point cloud attribute information, and further obtaining reflection intensity, elevation value and normal vector value corresponding to the point cloud data in the target original laser point cloud database.
Preferably, the obtaining the attribute evaluation coefficient corresponding to the cloud data of each point specifically includes the following steps: respectively marking the reflection intensity, elevation value and normal vector value corresponding to each point cloud data as、/>And/>Wherein/>Representing the number corresponding to each point cloud data,/>N is any integer greater than 2, and is substituted into a calculation formulaObtaining attribute evaluation coefficients/>, corresponding to each point cloud dataWherein/>、/>、/>Respectively, the standard reflection intensity, the standard elevation value and the standard normal vector value corresponding to the set point cloud data,/>、/>、/>Respectively setting weight factors corresponding to the reflection intensity of the point cloud data, the elevation value and the normal vector value,/>、/>、/>And respectively setting the difference of reflection intensity of the set allowable point cloud data, the difference of elevation values of the allowable point cloud data and the difference of normal vector values of the allowable point cloud data.
Preferably, the analyzing the data hierarchy corresponding to the cloud data of each point includes the following specific analysis process: comparing the attribute evaluation coefficient corresponding to each point cloud data with the attribute evaluation coefficient interval corresponding to each data level in the database, and if the attribute evaluation coefficient corresponding to a certain point cloud data is positioned in the attribute evaluation coefficient interval corresponding to a certain data level in the database, dividing the point cloud data into the data level in the database, so that the data level corresponding to each point cloud data is analyzed in the mode.
Preferably, the analyzing obtains the point cloud characteristic influence factors corresponding to each data level, and the specific analyzing process is as follows: respectively marking the point cloud density, the voxelization rate and the average distance value corresponding to the point cloud data corresponding to each data hierarchy as、、/>Wherein/>Representing the number corresponding to each data hierarchy,/>，/>Representing the number corresponding to each point cloud data,/>U is any integer greater than 2, n is any integer greater than 2, and the integer is substituted into a calculation formulaObtaining point cloud characteristic influence factors/>, corresponding to each data levelWherein/>、/>、/>Respectively setting standard point cloud density, standard voxel rate and standard average distance value corresponding to point cloud data corresponding to the data hierarchy,/>、/>、/>Respectively setting a weight factor corresponding to the point cloud density of the data hierarchy, a weight factor corresponding to the voxelization rate and a weight factor corresponding to the average distance value of the point cloud data,/>、/>、/>The set allowable data hierarchy point cloud density difference, allowable data hierarchy voxel difference and Xu Kedian cloud data average distance value difference are respectively.
Preferably, the analysis obtains comprehensive attribute evaluation coefficients corresponding to each data layer, and the specific analysis process is as follows: the point cloud characteristic influence factors corresponding to all the data layers are obtainedAttribute evaluation coefficient/>, corresponding to each point cloud data in each data hierarchySubstituting the formula/>Obtaining the comprehensive attribute evaluation coefficients corresponding to each data levelWherein e represents a natural constant.
Preferably, the most suitable registration method corresponding to each data layer is analyzed, and the specific analysis process is as follows: and comparing the comprehensive attribute evaluation coefficient corresponding to each data level with the comprehensive attribute evaluation coefficient corresponding to each registration method in the database, and if the comprehensive attribute evaluation coefficient corresponding to a certain data level is the same as the comprehensive attribute evaluation coefficient corresponding to a certain registration method in the database, using the registration method in the database as the most suitable registration method corresponding to the data level, and analyzing the most suitable registration method corresponding to each data level in this way.
Preferably, the analyzing the optimal registration parameters corresponding to the most suitable registration method in each data hierarchy includes the following specific analysis process: comparing the comprehensive attribute evaluation coefficients corresponding to the data levels with the comprehensive attribute evaluation coefficients corresponding to the registration parameters in the most suitable registration methods corresponding to the data levels, and if the comprehensive attribute evaluation coefficients corresponding to the data levels are the same as the comprehensive attribute evaluation coefficients corresponding to the registration parameters in the most suitable registration methods corresponding to the data levels, using the registration parameters in the most suitable registration methods corresponding to the data levels as the best registration parameters corresponding to the most suitable registration methods in the data levels, and analyzing the best registration parameters corresponding to the most suitable registration methods in the data levels in this way.
The invention has the beneficial effects that: 1. the invention provides a method for constructing a three-dimensional model of the earth surface based on laser point cloud data, which can improve the precision and the authenticity of the construction of the earth surface model, optimize the registration process, improve the data processing efficiency, enhance the stability of the model, provide more reliable data support for the application and analysis of data in a geographic information system and related fields and promote the further development and the application of the construction technology of the earth surface model through the application of high-precision attribute analysis, a self-adaptive registration method, data hierarchy segmentation and processing and an optimal registration parameter optimization technology.
2. According to the embodiment of the invention, the data hierarchy segmentation is performed according to the attribute evaluation coefficients of the cloud data of each point. The point cloud data are grouped according to the difference of the attribute evaluation coefficients to form different data layers, and different data processing methods and parameters are adopted according to the attribute evaluation coefficients of the different data layers, so that the processing process is more self-adaptive and flexible, the data processing efficiency and the expression capability of the surface features are improved, and the proper registration algorithm and parameters are dynamically selected to solve the problem of difficult registration.
3. According to the embodiment of the invention, the comprehensive attribute evaluation coefficients of all data layers are obtained by combining the point cloud characteristic influence factors and the attribute evaluation coefficients of the point cloud data in all data layers. This can be used to evaluate the importance and quality of each data hierarchy and analyze the best registration parameters for the most appropriate registration method. This can be used to adjust the parameters of the most appropriate registration method in each data hierarchy to optimize the registration results, and to adjust the registration parameters to improve the stability and accuracy of the model.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention is shown in fig. 1, and the method for constructing the earth surface three-dimensional model based on laser point cloud data comprises the following steps: step one, acquiring point cloud attribute data: and acquiring point cloud attribute data corresponding to the point cloud data in the target original laser point cloud database, wherein the point cloud attribute data comprises reflection intensity, elevation value and normal vector value, so that the reflection intensity, elevation value and normal vector value corresponding to the point cloud data are analyzed, and an attribute evaluation coefficient corresponding to the point cloud data is obtained.
In a specific embodiment, the acquiring the point cloud attribute data corresponding to the point cloud data in the target original laser point cloud database specifically includes the following steps: a1, scanning a target area by using a laser scanning device, transmitting laser pulses through the laser scanning device and recording the return time of the laser pulses, thereby obtaining the distance information of the ground or the object surface, and then obtaining the point cloud attribute information corresponding to the point cloud data by processing the data acquired by the laser scanning device.
And A2, extracting point cloud attribute data of the point cloud attribute information, and further obtaining reflection intensity, elevation value and normal vector value corresponding to the point cloud data in the target original laser point cloud database.
It should be noted that, the data collected by the processing laser scanning device is stored in the target original laser point cloud database.
It should be further noted that the laser scanning device may provide corresponding software to process and analyze the laser scanning data, which includes the extraction function of the point cloud attribute data. For example, veloView software of Velodyne, cyclene software of Leica, scene software of Faro and the like, and further, the laser scanning equipment provides corresponding software to process and analyze the laser scanning data so as to extract cloud attribute data in the cloud attribute information of each point.
In another specific embodiment, the obtaining the attribute evaluation coefficient corresponding to the cloud data of each point specifically includes the following steps: respectively marking the reflection intensity, elevation value and normal vector value corresponding to each point cloud data as、/>And/>Wherein, the method comprises the steps of, wherein,Representing the number corresponding to each point cloud data,/>N is any integer greater than 2, and is substituted into a calculation formulaObtaining attribute evaluation coefficients/>, corresponding to each point cloud dataWherein/>、/>、/>Respectively, the standard reflection intensity, the standard elevation value and the standard normal vector value corresponding to the set point cloud data,/>、/>、/>Respectively setting weight factors corresponding to the reflection intensity of the point cloud data, the elevation value and the normal vector value,/>、/>、/>And respectively setting the difference of reflection intensity of the set allowable point cloud data, the difference of elevation values of the allowable point cloud data and the difference of normal vector values of the allowable point cloud data.
It should be noted that the number of the substrates,、/>、/>Are all greater than 0 and less than 1.
It should be further noted that, according to the application requirement and the target scene, a proper standard reflection intensity, standard elevation value and standard normal vector value are set. According to data statistical analysis or priori knowledge, the method is usually required to be adjusted according to actual application scenes and experience, the method is set according to the importance degree of the reflection intensity, the elevation value and the normal vector value in attribute evaluation, the setting of the weight factors can be determined according to experience or domain knowledge, and the set allowable point cloud data reflection intensity difference, allowable point cloud data elevation value difference and allowable point cloud data normal vector value difference refer to the maximum difference between allowable point cloud attribute data and standard point cloud attribute data. And setting a proper difference range according to the application requirements and the data characteristics. For example, the allowable point cloud data difference may be set according to an error tolerance or accuracy requirement.
Step two, data hierarchy segmentation: and evaluating the coefficients according to the attributes corresponding to the cloud data of each point, so as to analyze the data hierarchy corresponding to the cloud data of each point, and dividing the cloud data of each point according to the corresponding data hierarchy.
In a specific embodiment, the analyzing the data hierarchy corresponding to the cloud data of each point specifically includes the following steps: comparing the attribute evaluation coefficient corresponding to each point cloud data with the attribute evaluation coefficient interval corresponding to each data level in the database, and if the attribute evaluation coefficient corresponding to a certain point cloud data is positioned in the attribute evaluation coefficient interval corresponding to a certain data level in the database, dividing the point cloud data into the data level in the database, so that the data level corresponding to each point cloud data is analyzed in the mode.
In the database, the section of the attribute evaluation coefficient corresponding to each data hierarchy is determined. This may be determined by historical data, experience, or expert knowledge, partitioning point cloud data meeting the conditions into corresponding data hierarchies in the database. Therefore, the point cloud data can be classified and organized according to the results of the attribute evaluation coefficients of the point cloud data to form different data layers.
Step three, acquiring a point cloud characteristic influence factor: and acquiring point cloud characteristic data corresponding to each data level, wherein the point cloud characteristic data comprises point cloud density, voxel rate and average distance value corresponding to the point cloud data, and analyzing to obtain point cloud characteristic influence factors corresponding to each data level.
In a specific embodiment, the analyzing obtains the point cloud feature influence factors corresponding to each data hierarchy, and the specific analyzing process is as follows: respectively marking the point cloud density, the voxelization rate and the average distance value corresponding to the point cloud data corresponding to each data hierarchy as、/>And/>Wherein/>Representing the number corresponding to each data hierarchy,/>，/>Representing the number corresponding to each point cloud data,/>U is any integer greater than 2, n is any integer greater than 2, and the integer is substituted into a calculation formulaObtaining point cloud characteristic influence factors/>, corresponding to each data levelWherein/>、/>、/>Respectively setting standard point cloud density, standard voxel rate and standard average distance value corresponding to point cloud data corresponding to the data hierarchy,/>、/>、/>Respectively setting a weight factor corresponding to the point cloud density of the data hierarchy, a weight factor corresponding to the voxelization rate and a weight factor corresponding to the average distance value of the point cloud data,/>、/>、/>The set allowable data hierarchy point cloud density difference, allowable data hierarchy voxel difference and Xu Kedian cloud data average distance value difference are respectively.
It should be noted that the number of the substrates,、/>、/>Are all greater than 0 and less than 1.
It should be noted that, according to the application requirement and the target scene, a suitable standard point cloud density, standard voxel rate, and standard average distance value corresponding to the point cloud data are set. According to data statistical analysis or priori knowledge, the data statistical analysis or priori knowledge is usually required to be adjusted according to actual application scenes and experiences, the importance degree of the point cloud density, the voxelization rate and the average distance value corresponding to the point cloud data in the influence of the point cloud features is set, the setting of the weight factors can be determined according to experience or field knowledge, and the set allowable data level point cloud density difference, allowable data level voxelization difference and Xu Kedian cloud data average distance value difference refer to the maximum difference between allowable point cloud feature data and standard point cloud feature data. And setting a proper difference range according to the application requirements and the data characteristics. For example, the allowable point cloud characteristic data difference may be set according to an error tolerance or an accuracy requirement.
Step four, acquiring a comprehensive attribute evaluation coefficient: and analyzing and obtaining comprehensive attribute evaluation coefficients corresponding to all the data layers according to the point cloud characteristic influence factors corresponding to all the data layers and the attribute evaluation coefficients corresponding to all the point cloud data in all the data layers.
In a specific embodiment, the analysis obtains the comprehensive attribute evaluation coefficients corresponding to each data hierarchy, and the specific analysis process is as follows: the point cloud characteristic influence factors corresponding to all the data layers are obtainedAttribute evaluation coefficient/>, corresponding to each point cloud data in each data hierarchySubstituting the formula/>In the method, the comprehensive attribute evaluation coefficient/>, corresponding to each data level, is obtainedWherein e represents a natural constant.
Fifth, analysis of the most suitable registration method: and analyzing the most suitable registration method corresponding to each data level according to the comprehensive attribute evaluation coefficient corresponding to each data level, and registering each data level according to the corresponding most suitable registration method.
In a specific embodiment, the most suitable registration method corresponding to each data hierarchy is analyzed, and the specific analysis process is as follows: and comparing the comprehensive attribute evaluation coefficient corresponding to each data level with the comprehensive attribute evaluation coefficient corresponding to each registration method in the database, and if the comprehensive attribute evaluation coefficient corresponding to a certain data level is the same as the comprehensive attribute evaluation coefficient corresponding to a certain registration method in the database, using the registration method in the database as the most suitable registration method corresponding to the data level, and analyzing the most suitable registration method corresponding to each data level in this way.
According to the embodiment of the invention, the data hierarchy segmentation is performed according to the attribute evaluation coefficients of the cloud data of each point. The point cloud data are grouped according to the difference of the attribute evaluation coefficients to form different data layers, and different data processing methods and parameters are adopted according to the attribute evaluation coefficients of the different data layers, so that the processing process is more self-adaptive and flexible, the data processing efficiency and the expression capability of the surface features are improved, and the proper registration algorithm and parameters are dynamically selected to solve the problem of difficult registration.
Step six, analysis of optimal registration parameters: and according to the comprehensive attribute evaluation coefficient corresponding to each data level, further analyzing the optimal registration parameters corresponding to the optimal registration method in each data level, and adjusting the optimal registration method in each data level according to the corresponding optimal registration parameters.
In a specific embodiment, the analyzing the optimal registration parameters corresponding to the most suitable registration method in each data hierarchy includes the following specific analysis process: comparing the comprehensive attribute evaluation coefficients corresponding to the data levels with the comprehensive attribute evaluation coefficients corresponding to the registration parameters in the most suitable registration methods corresponding to the data levels, and if the comprehensive attribute evaluation coefficients corresponding to the data levels are the same as the comprehensive attribute evaluation coefficients corresponding to the registration parameters in the most suitable registration methods corresponding to the data levels, using the registration parameters in the most suitable registration methods corresponding to the data levels as the best registration parameters corresponding to the most suitable registration methods in the data levels, and analyzing the best registration parameters corresponding to the most suitable registration methods in the data levels in this way.
According to the embodiment of the invention, the comprehensive attribute evaluation coefficients of all data layers are obtained by combining the point cloud characteristic influence factors and the attribute evaluation coefficients of the point cloud data in all data layers. This can be used to evaluate the importance and quality of each data hierarchy and analyze the best registration parameters for the most appropriate registration method. This can be used to adjust the parameters of the most appropriate registration method in each data hierarchy to optimize the registration results, and to adjust the registration parameters to improve the stability and accuracy of the model.
The invention provides a method for constructing a three-dimensional model of the earth surface based on laser point cloud data, which can improve the precision and the authenticity of the construction of the earth surface model, optimize the registration process, improve the data processing efficiency, enhance the stability of the model, provide more reliable data support for the application and analysis of data in a geographic information system and related fields and promote the further development and the application of the construction technology of the earth surface model through the application of high-precision attribute analysis, a self-adaptive registration method, data hierarchy segmentation and processing and an optimal registration parameter optimization technology.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of the invention or beyond the scope of the invention as defined in the description.
Claims (8)
1. A method for constructing a three-dimensional model of the earth surface based on laser point cloud data is characterized by comprising the following steps:
Step one, acquiring point cloud attribute data: acquiring point cloud attribute data corresponding to each point cloud data in a target original laser point cloud database, wherein the point cloud attribute data comprises reflection intensity, elevation value and normal vector value, so that the reflection intensity, elevation value and normal vector value corresponding to each point cloud data are analyzed, and attribute evaluation coefficients corresponding to each point cloud data are obtained;
Step two, data hierarchy segmentation: according to the attribute evaluation coefficient corresponding to each point cloud data, analyzing the data hierarchy corresponding to each point cloud data, and carrying out hierarchy segmentation on each point cloud data according to the corresponding data hierarchy;
step three, acquiring a point cloud characteristic influence factor: acquiring point cloud characteristic data corresponding to each data level, wherein the point cloud characteristic data comprises point cloud density, voxel rate and average distance value corresponding to the point cloud data, and analyzing to obtain point cloud characteristic influence factors corresponding to each data level;
Step four, acquiring a comprehensive attribute evaluation coefficient: analyzing and obtaining comprehensive attribute evaluation coefficients corresponding to all data layers according to point cloud characteristic influence factors corresponding to all data layers and attribute evaluation coefficients corresponding to all point cloud data in all data layers;
Fifth, analysis of the most suitable registration method: analyzing the most suitable registration method corresponding to each data level according to the comprehensive attribute evaluation coefficient corresponding to each data level, and registering each data level according to the corresponding most suitable registration method;
Step six, analysis of optimal registration parameters: and according to the comprehensive attribute evaluation coefficient corresponding to each data level, further analyzing the optimal registration parameters corresponding to the optimal registration method in each data level, and adjusting the optimal registration method in each data level according to the corresponding optimal registration parameters.
2. The method for constructing the earth surface three-dimensional model based on the laser point cloud data according to claim 1, wherein the acquiring the point cloud attribute data corresponding to the point cloud data in the target original laser point cloud database comprises the following specific acquiring process:
a1, scanning a target area by using a laser scanning device, transmitting laser pulses through the laser scanning device and recording the return time of the laser pulses, thereby obtaining the distance information of the surface of the ground or the object, and then processing the data acquired by the laser scanning device to obtain point cloud attribute information corresponding to the point cloud data;
And A2, extracting point cloud attribute data of the point cloud attribute information, and further obtaining reflection intensity, elevation value and normal vector value corresponding to the point cloud data in the target original laser point cloud database.
3. The method for constructing the earth surface three-dimensional model based on the laser point cloud data according to claim 2, wherein the obtaining of the attribute evaluation coefficient corresponding to the point cloud data comprises the following specific obtaining process:
Respectively marking the reflection intensity, elevation value and normal vector value corresponding to each point cloud data as 、/>And/>Wherein/>Representing the number corresponding to each point cloud data,/>N is any integer greater than 2, and is substituted into a calculation formulaObtaining attribute evaluation coefficients/>, corresponding to each point cloud dataWherein/>、/>、/>Respectively, the standard reflection intensity, the standard elevation value and the standard normal vector value corresponding to the set point cloud data,/>、/>、/>Respectively setting weight factors corresponding to the reflection intensity of the point cloud data, the elevation value and the normal vector value,/>、/>、/>And respectively setting the difference of reflection intensity of the set allowable point cloud data, the difference of elevation values of the allowable point cloud data and the difference of normal vector values of the allowable point cloud data.
4. The method for constructing the surface three-dimensional model based on the laser point cloud data according to claim 3, wherein the data hierarchy corresponding to each point cloud data is analyzed, and the specific analysis process is as follows:
Comparing the attribute evaluation coefficient corresponding to each point cloud data with the attribute evaluation coefficient interval corresponding to each data level in the database, and if the attribute evaluation coefficient corresponding to a certain point cloud data is positioned in the attribute evaluation coefficient interval corresponding to a certain data level in the database, dividing the point cloud data into the data level in the database, so that the data level corresponding to each point cloud data is analyzed in the mode.
5. The method for constructing the earth surface three-dimensional model based on the laser point cloud data according to claim 1, wherein the analysis obtains point cloud characteristic influence factors corresponding to all data layers, and the specific analysis process is as follows:
Respectively marking the point cloud density, the voxelization rate and the average distance value corresponding to the point cloud data corresponding to each data hierarchy as 、/>And/>Wherein/>Representing the number corresponding to each data hierarchy,/>，/>Representing the number corresponding to each point cloud data,/>U is any integer greater than 2, n is any integer greater than 2, and the integer is substituted into a calculation formulaObtaining point cloud characteristic influence factors/>, corresponding to each data levelWherein/>、/>、/>Respectively setting standard point cloud density, standard voxel rate and standard average distance value corresponding to point cloud data corresponding to the data hierarchy,/>、/>、/>Respectively setting a weight factor corresponding to the point cloud density of the data hierarchy, a weight factor corresponding to the voxelization rate and a weight factor corresponding to the average distance value of the point cloud data,/>、/>、/>The set allowable data hierarchy point cloud density difference, allowable data hierarchy voxel difference and Xu Kedian cloud data average distance value difference are respectively.
6. The method for constructing the surface three-dimensional model based on the laser point cloud data as set forth in claim 5, wherein the analysis obtains comprehensive attribute evaluation coefficients corresponding to each data level, and the specific analysis process is as follows:
the point cloud characteristic influence factors corresponding to all the data layers are obtained Attribute evaluation coefficient/>, corresponding to each point cloud data in each data hierarchySubstituting the formula/>In the method, the comprehensive attribute evaluation coefficient/>, corresponding to each data level, is obtainedWherein e represents a natural constant.
7. The method for constructing the earth surface three-dimensional model based on the laser point cloud data as set forth in claim 6, wherein the analysis of the most suitable registration method corresponding to each data layer is performed by the following specific analysis process:
and comparing the comprehensive attribute evaluation coefficient corresponding to each data level with the comprehensive attribute evaluation coefficient corresponding to each registration method in the database, and if the comprehensive attribute evaluation coefficient corresponding to a certain data level is the same as the comprehensive attribute evaluation coefficient corresponding to a certain registration method in the database, using the registration method in the database as the most suitable registration method corresponding to the data level, and analyzing the most suitable registration method corresponding to each data level in this way.
8. The method for constructing the surface three-dimensional model based on the laser point cloud data as set forth in claim 7, wherein the analyzing the optimal registration parameters corresponding to the most suitable registration method in each data layer comprises the following specific analysis process:
Comparing the comprehensive attribute evaluation coefficients corresponding to the data levels with the comprehensive attribute evaluation coefficients corresponding to the registration parameters in the most suitable registration methods corresponding to the data levels, and if the comprehensive attribute evaluation coefficients corresponding to the data levels are the same as the comprehensive attribute evaluation coefficients corresponding to the registration parameters in the most suitable registration methods corresponding to the data levels, using the registration parameters in the most suitable registration methods corresponding to the data levels as the best registration parameters corresponding to the most suitable registration methods in the data levels, and analyzing the best registration parameters corresponding to the most suitable registration methods in the data levels in this way.
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