CN116977580A - Method for manufacturing mountain area large scale DEM based on airborne LiDAR - Google Patents
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Abstract
The invention discloses a method for manufacturing a mountain area large scale DEM based on an onboard LiDAR, which comprises the following steps: step 1, collecting operation related data according to a topographic mapping task; step 2, checking a laser radar system to improve the accuracy of laser radar measurement; step 3, designing an image control point layout scheme and measuring according to the topography situation of the area and the precision requirement of the aerial survey task; step 4, designing a route and collecting data according to the designed route; step 5, denoising the collected point cloud and roughly classifying the collected point cloud to provide preliminary ground point cloud data for the ground profile map; step 6, filtering the ground point cloud data which are preliminarily extracted; step 7, carrying out data reconstruction on the filtered ground point cloud, and regenerating a DEM; the technical problems that DEM precision is low due to vegetation point influence, sparse ground points and the like in complex topography mapping of a mountain area in the prior art are solved.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle topographic mapping, and particularly relates to a mountain area large scale DEM manufacturing method based on an onboard LiDAR.
Background
The mountain terrain fluctuation is great, vegetation coverage is thick, and traditional method that adopts total powerstation, RTK etc. to survey and draw the topography often need consume a large amount of time, manpower and material resources etc. can't satisfy the high efficiency of current topography data, scale technical requirement. Along with the development of novel mapping technology, unmanned aerial vehicle aerial survey is applied to topography mapping work gradually, but to the geographical environment of mountain area complicacy, unmanned aerial vehicle aerial photogrammetry is difficult to reject thick vegetation, leads to ground precision unable assurance. In recent years, the laser radar technology is rapidly developed, and can rapidly acquire three-dimensional information, so that the laser radar technology is widely applied to the fields of electric power, mapping and the like.
Although airborne LiDAR has the advantages of high efficiency, high resolution, high performance, penetrating vegetation gaps and the like, the following problems still exist in the complex topography mapping of mountain areas at present: (1) The fall of the mountain area is large, the vegetation coverage is thick, and the vegetation layer is difficult to completely filter out, so that the ground points contain non-ground points, and the manufactured DEM has large elevation precision error; (2) Because the laser radar penetrates to the ground through the vegetation gap, the ground point cloud of the vegetation coverage area is sparse, and the DEM constructed by using the ground point cloud is low in precision.
Disclosure of Invention
The invention aims to solve the technical problems that: the method for manufacturing the high-scale DEM in the mountain area based on the airborne LiDAR is provided, so that the technical problems that the accuracy of the DEM is low due to vegetation point influence, sparse ground points and the like in the complex topography mapping of the mountain area in the prior art are solved.
The technical scheme of the invention is as follows:
a method for manufacturing a mountain area large scale DEM based on airborne LiDAR, the method comprising:
step 1, collecting operation related data according to a topographic mapping task;
step 2, checking a laser radar system to improve the accuracy of laser radar measurement;
step 3, designing an image control point layout scheme and measuring according to the topography situation of the area and the precision requirement of the aerial survey task;
step 4, designing a route and collecting data according to the designed route;
step 5, denoising the collected point cloud and roughly classifying the collected point cloud to provide preliminary ground point cloud data for the ground profile map;
step 6, filtering the ground point cloud data which are preliminarily extracted;
and 7, carrying out data reconstruction on the filtered ground point cloud, and regenerating the DEM.
The collected operation related data comprises 1:10000DEM, satellite images and traffic conditions; and selecting a proper unmanned aerial vehicle and laser radar equipment.
The method for checking the laser radar system comprises the following steps: before the operation starts, selecting a flat and open field as a checking field, arranging control points on the ground, obtaining a ground point cloud by flying the checking field, establishing an error model by the coordinates of the control points and the point cloud operation, and solving system arrangement angles alpha, beta and gamma; the method specifically comprises the following steps:
obtaining the coordinates of a laser foot point WGS-84 by scanning a calibration field through a laser radar:
wherein: r is R N A transformation matrix from inertial coordinates to WGS-84 coordinates; r is R M Scanning a conversion matrix from a reference coordinate to an inertial coordinate for laser; r is R L A conversion matrix for instantaneous laser beam coordinates and scanning reference coordinates; (x) G ，y G ，z G ) The coordinates of the GPS antenna phase center in a WGS-84 coordinate system;the eccentricity of the laser emission reference point and the GPS antenna phase center in the inertial coordinate reference system; (0, ρ) is the spatial vector of the laser foot point in the instantaneous laser beam coordinate system; (x) 84 ，y 84 ，z 84 ) WGS-84 coordinates of the laser foot point;
the control point coordinates were measured using a total station as (x q ，y q ，z q ) As a true value of the control point, the observation equation is:
wherein: r is R M The transformation matrix from the reference coordinates to the inertial coordinates for the laser scan is a function of the placement angles α, β, γ;
calculating an error equation, constructing a normal equation according to a least square method principle, and solving the normal equation to obtain three setting angle parameters alpha, beta and gamma; the laser radar system is checked by designing a checking field in advance, the azimuth and position parameters of the system are calculated reversely, and correction is carried out according to the parameters, so that the measurement accuracy of the laser radar is improved.
The method for designing the image control point layout scheme comprises the following steps:
step 3.1, determining the distribution density of the image control points: the density of the image control points measured by the topographic map 1:500 and the topographic map 1:1000 is respectively 200-300 meters/m and 300-500 meters/m, and the number of the image control points is respectively increased by 10% -20%;
step 3.2, determining a drawing mode of the image control point: drawing by combining target image control and spray-coating image control points;
and 3.3, carrying out coordinate measurement according to the distributed image control points.
The method for designing the route in the step 4 comprises the following steps: when the mapping scale of the aerial survey task is known, calculating the aerial height H according to the scale, the focal length of the camera and the laser ranging
Wherein: h is the altitude; f is the focal length of the lens; GSD is ground resolution; a is the pixel size;
and generating a contour line according to the collected DEM, processing the contour line through a Douglas-Peucker algorithm to obtain a simplified contour line, performing contour line offset processing according to the scene investigation condition, setting a contour line offset distance by comprehensively considering the altitude, the contour line interval and the altitude overlapping degree to obtain a final contour line, generating a flying route according to the final contour line and the altitude, and finally acquiring laser radar and visible light data.
The method for denoising the acquired point cloud and roughly classifying the acquired point cloud into the ground point cloud data for providing the ground map comprises the following steps of:
step 5.1, removing noise points: the method comprises the steps of ground low points, ground high points, isolated points and other noise points; removing abnormal points such as ground low points and ground high points by using a region searching algorithm; for isolated points and other noise points, calculating the distance between the points and peripheral adjacent points by establishing K-D tree point cloud data management and adopting a K-near field algorithm, performing cyclic traversal, screening all the point cloud data, and eliminating the noise points;
step 5.2, after noise removal, performing coarse classification on the point cloud based on the existing DEM data, and determining a maximum elevation value H according to the denoised point cloud data max And a minimum elevation value H min Dividing point cloud data, and setting h 1 ，h 2 ，h 3 ，…，h n To the extent ofThe elevation of the point cloud data in the enclosure is set to be a high Cheng Fa value D h ，D h Taking 1, and then based on 1:10000DEM elevation value H j Will h n ＞H j The +1 point is divided into non-ground points, the rest points are saved as ground points, and the extraction of the point cloud ground points is primarily completed to obtain primary ground point cloud data.
The method for filtering the ground point cloud data which is primarily extracted comprises the following steps:
step 6.1, dividing the grid attribute, wherein the space ratio is defined as F:
wherein: h is a i The elevation value of the ith point in the grid; h is a min The elevation value of the secondary low point in the grid; h is the height of the grid; n is the number of point clouds in the grid;
step 6.2, overturning the point cloud data;
step 6.3, scene classification: setting a threshold according to the terrain classification and the gradient, wherein the gradient of a mountain area is 10-25 degrees, and the gradient of a high mountain is more than 25 degrees;
step 6.4, starting the Bragg, and determining the number of protons according to the self-defined grid resolution GR;
step 6.5, projecting protons and all point clouds to a horizontal plane, and finding out each grid proton CP on the plane, wherein the height is recorded as IHV;
step 6.6, calculating the size and the position of the protons in each grid, which are influenced by gravity when moving, comparing the height of the cloth particles in the grid with IHV, and if the height of the cloth particles is not greater than IHV, setting the particles at the height of IHV to be immovable protons;
step 6.7, iterating the gradient information by using the grid protons;
step 6.8, calculating displacement of protons influenced by internal force;
step 6.9, repeating the steps 6.4-6.5, and terminating the simulation when the variation generated by the maximum height of all protons is very small or exceeds the established iteration times;
and 6.10, calculating the distance between the protons and the point cloud.
And 6.11, distinguishing the ground point cloud from the non-ground point cloud.
The method for reconstructing and regenerating the DEM by the filtered ground point cloud comprises the following steps: constructing triangles by utilizing a series of points through an irregular triangular network algorithm, and then interpolating; and constructing point cloud surface areas according to the terrain features, constructing boundaries according to each type of terrain, dividing each surface area into boundary points and non-boundary points, generating a grid by using a front edge propulsion method for the non-boundary points in the surface area, extracting the non-surface area points and the boundary points in the area, constructing the grid by using the extracted data, repeatedly extracting the non-surface area points and the boundary points, constructing grids, and generating a final grid after all point clouds are traversed.
The invention has the beneficial effects that:
according to the method for manufacturing the mountain area large scale DEM based on the airborne LiDAR, aiming at the problem that the precision of a digital elevation model is low due to thicker vegetation coverage in complex mountain area airborne LiDAR mapping, the method is mainly used for starting from two aspects of point cloud filtering and point cloud interpolation, fully considers the characteristics of complex mountain area terrains, solves the problems that the mountain area fall is large, the vegetation coverage is thick, non-ground points are difficult to completely filter out and the ground point cloud is sparse, and accordingly builds a high-precision digital elevation model.
The technical problems that DEM precision is low due to vegetation point influence, sparse ground points and the like in complex topography mapping of a mountain area in the prior art are solved.
Detailed Description
A method for manufacturing a mountain area large scale DEM based on an onboard LiDAR comprises the following steps:
step 1, collecting operation related data, such as 1:10000DEM, satellite images, traffic conditions and the like, according to a topographic mapping task book, and additionally selecting a proper unmanned aerial vehicle and laser radar equipment.
Step 2, checking a laser radar system, and acquiring system setting angles alpha, beta and gamma: before the operation starts, a flat and open field is selected as a checking field, control points are distributed on the ground, the ground point cloud is obtained by flying the checking field, an error model is built by the coordinates of the control points and the operation of the point cloud, and the system arrangement angles alpha, beta and gamma are obtained.
Scanning the calibration field by a laser radar to obtain laser foot point WGS-84 coordinates:
wherein: r is R N A transformation matrix from inertial coordinates to WGS-84 coordinates; r is R M Scanning a conversion matrix from a reference coordinate to an inertial coordinate for laser; r is R L A conversion matrix for instantaneous laser beam coordinates and scanning reference coordinates; (x) G ，y G ，z G ) The coordinates of the GPS antenna phase center in a WGS-84 coordinate system;the eccentricity of the laser emission reference point and the GPS antenna phase center in the inertial coordinate reference system; (0, ρ) is the spatial vector of the laser foot point in the instantaneous laser beam coordinate system; (x) 84 ，y 84 ，z 84 ) Is the WGS-84 coordinates of the laser foot point.
The control point coordinates were measured using a total station as (x q ，y q ，z q ) As a true value of the control point, the observation equation is:
wherein: r is R M The transformation matrix of the reference coordinates into inertial coordinates for laser scanning is a function of the placement angles α, β, γ.
And calculating an error equation, constructing a normal equation according to the least square method principle, and solving the normal equation to obtain three setting angle parameters alpha, beta and gamma. The laser radar system is checked by designing a checking field in advance, so that the system azimuth and position parameters can be calculated reversely, correction is carried out according to the parameters, and the laser radar measurement accuracy is improved.
Step 3, arranging field image control points according to the aerial survey task range: according to the topography situation of the area and the precision requirement of the aerial survey task, an image control point layout scheme is designed and measurement is carried out. Firstly, determining the distribution density of image control points, wherein the distribution density of the image control points is 200-300 m/m and 300-500 m/m respectively in general cases of 1:500 and 1:1000 topographic map measurement, and considering the reason that the topography of a mountain area is complex, the number of the image control points needs to be properly increased by 10% -20% during mountain area navigation so as to improve the measurement accuracy; then determining the drawing mode of the image control points, wherein the mountain terrain has larger fluctuation, more vegetation and fewer terrain characteristic points, so that the target image control and the spraying type image control points are combined for drawing, the spraying type image control points can be drawn on a hard ground, the storage time is longer, the positions are fixed and flexible, and the target image control drawing is adopted in the areas which have more vegetation and cannot be sprayed, so that the defects of no characteristic points and image control points in the areas which cannot be sprayed are overcome; and finally, carrying out coordinate measurement according to the distributed image control points. By adopting the scheme to carry out field image control point layout, the uniform distribution of the image control points in the area can be effectively ensured, the problem that the image control points of the complex topography in the mountain area are difficult to layout is solved, and the precision of aerial survey results is improved.
Step 4, designing a route, and acquiring data by field aviation: and reasonably planning a route by utilizing the collected 1:10000DEM and on-site survey conditions and combining the requirements of a land shape survey map scale, and then carrying out data acquisition according to the designed route.
When the mapping scale of the aerial survey task is known, the aerial altitude H can be calculated according to the scale, the focal length of the camera, the laser ranging and the like.
Wherein: h is the altitude; f is the focal length of the lens; GSD is ground resolution; a is the pixel size.
And the collected DEM is used for generating the contour line, and the direct generated contour line has more folding points and is intricate because the DEM is smaller in scale and relatively low in precision, so that the processing is further simplified. And processing the contour lines through a Douglas-Peucker algorithm to obtain simplified contour lines, performing contour line offset processing according to the on-site survey condition, setting contour line offset distances by comprehensively considering the altitude, the contour line interval and the altitude overlapping degree by taking the direction of the contour line value reduction as an offset direction, generating an altitude flight line according to the contour lines and the altitude, and finally acquiring laser radar and visible light data. Based on the existing method for generating the route after the low-proportion DEM extracts the contour lines, simplifies the contour lines, shifts the contour lines and the like, frequent height changes can be effectively avoided, the aerial survey operation efficiency of the unmanned aerial vehicle is improved, and the method is an effective and used route design method.
Step 5, denoising the point cloud and roughly classifying, namely separating the ground points from the non-ground points: when the DEM is manufactured through topographic mapping, only ground point data are needed, and point cloud data acquired through field aviation comprise various ground objects such as noise points, vegetation, buildings and the like, so that denoising is needed first, coarse classification is then carried out, and the ground point cloud data are initially extracted. Firstly, removing noise points, including ground low points, ground high points, isolated points, other noise points and the like, wherein the ground low points and the ground high points belong to obvious high Cheng Yi constant points, and removing the obvious abnormal points by using an area search algorithm. For other noise points, the K-D tree point cloud data management is established, the distance between the point and the peripheral adjacent point is calculated by adopting a K near field algorithm, the cyclic traversal is carried out, all the point cloud data are screened, and the noise points are removed. After noise is removed, carrying out coarse classification on point cloud based on the existing DEM data, and determining the maximum elevation value H according to the denoised point cloud data max And a minimum elevation value H min Dividing point cloud data, and setting h 1 ，h 2 ，h 3 ，…，h n Setting a high Cheng Fa value D for the point cloud data elevation in the range h D is because the accuracy of the topographic map is 1 meter at 1:10000 h Taking 1, and then based on 1:10000DEM elevation value H j Will h n ＞H j The +1 point is divided into non-ground points, the rest points are saved as ground points, and the extraction of the point cloud ground points is finished preliminarily. Through the above point cloud denoising and coarse classification, the point cloud can be improvedAnd the denoising and rough classification efficiency and effect provide preliminary ground point cloud data for the landform map.
Step 6, performing point cloud filtering based on the preliminarily extracted ground point cloud data: the coarse classified ground point cloud data is already obtained in step 5, and further filtering is required to obtain accurate ground point cloud data.
(1) Grid properties are first partitioned. The space ratio is defined as F:
wherein: h is a i The elevation value of the ith point in the grid; h is a min The elevation value of the secondary low point in the grid; h is the height of the grid; n is the number of point clouds in the grid.
(2) And turning over the point cloud data.
(3) And (5) scene classification. According to the terrain classification, the adaptive threshold is set according to the gradient, wherein the gradient of the mountain area is 10-25 DEG, and the gradient of the mountain is more than 25 deg.
(4) And starting the Bragg, and determining the proton number according to the self-defined grid resolution GR.
(5) The protons and all point clouds are projected onto a horizontal plane and each grid proton CP is found on the plane, highly noted as IHV.
(6) The size and position of the protons in each grid under the influence of gravity when moving are calculated, and the height of the cloth particles in the grid is compared with IHV, if not more than IHV, the particles are placed at the height of IHV, and are set as immovable protons.
(7) And iterating the gradient information by using the grid protons, so that the gradient information is more accurate.
(8) Calculating displacement of protons influenced by internal force;
(9) Repeating (4) - (5), and terminating the simulation when the maximum height of all protons changes by less than 1cm or more than the established number of iterations.
(10) The distance between the proton and the point cloud is calculated.
(11) The ground point cloud is distinguished from the non-ground point cloud.
The algorithm considers the characteristic of large elevation change of complex terrain conditions, identifies scene information, and then adopts self-adaptive adjustment of filtering parameters.
Step 7, interpolating ground point cloud, constructing an irregular triangular net to generate DEM: the ground point cloud data obtained through the point cloud filtering has data holes, and the ground point cloud of the vegetation coverage area is sparse, so that the filtered ground point cloud needs to be subjected to data reconstruction, and then the DEM is generated. And constructing triangles by using a series of points through an irregular triangular network algorithm, and then interpolating. According to the terrain features, constructing point cloud areas, constructing boundaries of the point cloud areas according to each type of terrain, dividing each area into boundary points and non-boundary points, generating a grid by using a front propulsion method for the non-boundary points in the area, extracting the non-area points and the boundary points in the area, constructing the grid by using the extracted data, repeatedly extracting the non-area points and the boundary points, constructing grids, and generating a final grid after all point clouds are traversed. The method fully considers morphological characteristics of the landform, reserves surface details and effectively improves the precision of the digital elevation model.
Claims (8)
1. A method for manufacturing a mountain area large scale DEM based on an onboard LiDAR is characterized by comprising the following steps: the method comprises the following steps:
step 1, collecting operation related data according to a topographic mapping task;
step 2, checking a laser radar system to improve the accuracy of laser radar measurement;
step 3, designing an image control point layout scheme and measuring according to the topography situation of the area and the precision requirement of the aerial survey task;
step 4, designing a route and collecting data according to the designed route;
step 5, denoising the collected point cloud and roughly classifying the collected point cloud to provide preliminary ground point cloud data for the ground profile map;
step 6, filtering the ground point cloud data which are preliminarily extracted;
and 7, carrying out data reconstruction on the filtered ground point cloud, and regenerating the DEM.
2. The method for manufacturing the mountain area large scale DEM based on the airborne LiDAR, which is characterized by comprising the following steps of: the collected operation related data comprises 1:10000DEM, satellite images and traffic conditions; and selecting a proper unmanned aerial vehicle and laser radar equipment.
3. The method for manufacturing the mountain area large scale DEM based on the airborne LiDAR, which is characterized by comprising the following steps of: the method for checking the laser radar system comprises the following steps: before the operation starts, selecting a flat and open field as a checking field, arranging control points on the ground, obtaining a ground point cloud by flying the checking field, establishing an error model by the coordinates of the control points and the point cloud operation, and solving system arrangement angles alpha, beta and gamma; the method specifically comprises the following steps:
obtaining the coordinates of a laser foot point WGS-84 by scanning a calibration field through a laser radar:
wherein: r is R N A transformation matrix from inertial coordinates to WGS-84 coordinates; r is R M Scanning a conversion matrix from a reference coordinate to an inertial coordinate for laser; r is R L A conversion matrix for instantaneous laser beam coordinates and scanning reference coordinates; (x) G ，y G ，z G ) The coordinates of the GPS antenna phase center in a WGS-84 coordinate system;the eccentricity of the laser emission reference point and the GPS antenna phase center in the inertial coordinate reference system; (0, ρ) is the spatial vector of the laser foot point in the instantaneous laser beam coordinate system; (x) 84 ，y 84 ，z 84 ) WGS-84 coordinates of the laser foot point;
the control point coordinates were measured using a total station as (x q ，y q ，z q ) As a true value of the control point, the observation equation is:
wherein: r is R M The transformation matrix from the reference coordinates to the inertial coordinates for the laser scan is a function of the placement angles α, β, γ;
calculating an error equation, constructing a normal equation according to a least square method principle, and solving the normal equation to obtain three setting angle parameters alpha, beta and gamma; the laser radar system is checked by designing a checking field in advance, the azimuth and position parameters of the system are calculated reversely, and correction is carried out according to the parameters, so that the measurement accuracy of the laser radar is improved.
4. The method for manufacturing the mountain area large scale DEM based on the airborne LiDAR, which is characterized by comprising the following steps of: the method for designing the image control point layout scheme comprises the following steps:
step 3.1, determining the distribution density of the image control points: the density of the image control points measured by the topographic map 1:500 and the topographic map 1:1000 is respectively 200-300 meters/m and 300-500 meters/m, and the number of the image control points is respectively increased by 10% -20%;
step 3.2, determining a drawing mode of the image control point: drawing by combining target image control and spray-coating image control points;
and 3.3, carrying out coordinate measurement according to the distributed image control points.
5. The method for manufacturing the mountain area large scale DEM based on the airborne LiDAR, which is characterized by comprising the following steps of: the method for designing the route in the step 4 comprises the following steps: when the mapping scale of the aerial survey task is known, calculating the aerial height H according to the scale, the focal length of the camera and the laser ranging
Wherein: h is the altitude; f is the focal length of the lens; GSD is ground resolution; a is the pixel size;
and generating a contour line according to the collected DEM, processing the contour line through a Douglas-Peucker algorithm to obtain a simplified contour line, performing contour line offset processing according to the scene investigation condition, setting a contour line offset distance by comprehensively considering the altitude, the contour line interval and the altitude overlapping degree to obtain a final contour line, generating a flying route according to the final contour line and the altitude, and finally acquiring laser radar and visible light data.
6. The method for manufacturing the mountain area large scale DEM based on the airborne LiDAR, which is characterized by comprising the following steps of: the method for denoising the acquired point cloud and roughly classifying the acquired point cloud into the ground point cloud data for providing the ground map comprises the following steps of:
step 5.1, removing noise points: the method comprises the steps of ground low points, ground high points, isolated points and other noise points; removing abnormal points such as ground low points and ground high points by using a region searching algorithm; for isolated points and other noise points, calculating the distance between the points and peripheral adjacent points by establishing K-D tree point cloud data management and adopting a K-near field algorithm, performing cyclic traversal, screening all the point cloud data, and eliminating the noise points;
step 5.2, after noise removal, performing coarse classification on the point cloud based on the existing DEM data, and determining a maximum elevation value H according to the denoised point cloud data max And a minimum elevation value H min Dividing point cloud data, and setting h 1 ，h 2 ，h 3 ，…，h n Setting a high Cheng Fa value D for the point cloud data elevation in the range h ，D h Taking 1, and then based on 1:10000DEM elevation value H j Will h n ＞H j The +1 point is divided into non-ground points, the rest points are saved as ground points, and the extraction of the point cloud ground points is primarily completed to obtain primary ground point cloud data.
7. The method for manufacturing the mountain area large scale DEM based on the airborne LiDAR, which is characterized by comprising the following steps of: the method for filtering the ground point cloud data which is primarily extracted comprises the following steps:
step 6.1, dividing the grid attribute, wherein the space ratio is defined as F:
wherein: h is a i The elevation value of the ith point in the grid; h is a min The elevation value of the secondary low point in the grid; h is the height of the grid; n is the number of point clouds in the grid;
step 6.2, overturning the point cloud data;
step 6.3, scene classification: setting a threshold according to the terrain classification and the gradient, wherein the gradient of a mountain area is 10-25 degrees, and the gradient of a high mountain is more than 25 degrees;
step 6.4, starting the Bragg, and determining the number of protons according to the self-defined grid resolution GR;
step 6.5, projecting protons and all point clouds to a horizontal plane, and finding out each grid proton CP on the plane, wherein the height is recorded as IHV;
step 6.6, calculating the size and the position of the protons in each grid, which are influenced by gravity when moving, comparing the height of the cloth particles in the grid with IHV, and if the height of the cloth particles is not greater than IHV, setting the particles at the height of IHV to be immovable protons;
step 6.7, iterating the gradient information by using the grid protons;
step 6.8, calculating displacement of protons influenced by internal force;
step 6.9, repeating the steps 6.4-6.5, and terminating the simulation when the variation generated by the maximum height of all protons is very small or exceeds the established iteration times;
and 6.10, calculating the distance between the protons and the point cloud.
And 6.11, distinguishing the ground point cloud from the non-ground point cloud.
8. The method for manufacturing the mountain area large scale DEM based on the airborne LiDAR, which is characterized by comprising the following steps of: the method for reconstructing and regenerating the DEM by the filtered ground point cloud comprises the following steps: constructing triangles by utilizing a series of points through an irregular triangular network algorithm, and then interpolating; and constructing point cloud surface areas according to the terrain features, constructing boundaries according to each type of terrain, dividing each surface area into boundary points and non-boundary points, generating a grid by using a front edge propulsion method for the non-boundary points in the surface area, extracting the non-surface area points and the boundary points in the area, constructing the grid by using the extracted data, repeatedly extracting the non-surface area points and the boundary points, constructing grids, and generating a final grid after all point clouds are traversed.
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