CN114299235A - DOM (document object model) manufacturing method based on color point cloud - Google Patents
DOM (document object model) manufacturing method based on color point cloud Download PDFInfo
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Abstract
The invention provides a DOM making method based on color point cloud, which can obtain more accurate color information. The method comprises the steps of S1, determining the range of the DOM to be manufactured, and constructing an external rectangular area covering point cloud data; s2, gridding the rectangular area in the step S1 to ensure that the length value and the width value of each grid are equal to the resolution value of the DOM to be generated; s3, calculating the plane coordinates of the central point of each grid; s4, obtaining RGB values of all point clouds in the rectangular area, and averaging the RGB values of the point clouds in each grid; calculating the elevation of the central point of each grid; and generating raster data to obtain a DOM file. The LiDAR point cloud data utilization efficiency is improved, meanwhile, the cost is effectively saved, and the working efficiency is improved.
Description
Technical Field
The invention relates to the technical field of surveying and mapping, in particular to a DOM (document object model) manufacturing method based on color point cloud.
Background
Point cloud data is typically obtained by laser LiDAR scanning. The three-dimensional laser point cloud is essentially a set of three-dimensional coordinate values and attribute information, and comprises attribute information such as a spatial position, RGB color values and the like. Dom (digital ortho map) is a short term for digital Orthophoto map, which is a raster image map containing coordinate information. The existing method for acquiring the DOM is mainly manufactured by digital differential correction and the like by utilizing a digital remote sensing image and an aerial photo image.
Patent document No. 201710970058.2 discloses a surface three-dimensional modeling method based on laser point cloud data. The method mainly comprises the steps of constructing a surface DOM by using point cloud color information, obtaining color information of a cavity by using high-density point cloud data through technologies such as fitting and interpolation, and finally forming the complete surface DOM. The method for constructing the surface DOM comprises a step B2 of reserving at most one point in each grid of the surface DOM to be constructed to extract the color; step B3, acquiring a concave-envelope contour line of the laser point cloud data through boundary analysis, filling colors into grids positioned in the contour line, and not processing colors positioned outside the contour line; and if the grid to be filled with the color exists in the grid, the point processed in the step B2 is filled with the color information of the point, and if the grid does not exist in the grid, the point processed in the step B2 is used, and the color information of the point closest to the grid in the original point cloud is obtained and filled.
Since there may be a plurality of points closest to the mesh, i.e. a plurality of points are arranged along the periphery of the mesh, and a plurality of points are all adjacent to the mesh. If the processed point in step B2 does not exist in the grid during the operation in step B3, the nearest neighbor interpolation method is used, which is likely to cause an error, and the acquired color information is inaccurate.
Disclosure of Invention
The invention aims to provide a DOM (document object model) making method based on color point cloud, which can obtain more accurate color information.
The technical scheme adopted by the invention for solving the technical problems is as follows: the DOM making method based on the color point cloud comprises the following steps,
s1, determining the range of the DOM to be manufactured, and constructing an external rectangular area covering the point cloud data;
s2, gridding the rectangular area in the step S1 to ensure that the length value and the width value of each grid are equal to the resolution value of the DOM to be generated;
s3, calculating the plane coordinates of the central point of each grid;
s4, obtaining RGB values of all point clouds in the rectangular area, and averaging the RGB values of the point clouds in each grid; calculating the elevation of the central point of each grid; and generating raster data to obtain a DOM file.
Further, step S5 is further included before step S1, the feature points are searched in the point cloud to obtain point cloud coordinates of the feature points, the corresponding feature points are actually measured on site to obtain actually measured coordinates of the feature points, a conversion relationship between the point cloud coordinates and the actually measured coordinates is established, and parameter conversion is performed on the point cloud.
Further, after the gridding of the rectangular area is finished, the original point cloud data in each grid is filtered, the filtering process comprises the steps,
s61, sorting the points in the grid according to the elevation;
and S62, calculating the height difference of two adjacent points: h isi,j＝hi-hj(i＝1......)
Wherein h isi(i＝1，2……)，hj(j ═ 1, 2 … …) represents the elevation of the point in meters;
hi，jrepresenting the height difference of two adjacent point clouds, and the unit is meter;
s63, finding the maximum value of the difference between adjacent points: h is MAX (h)i,j)
S64, will be lower than hjThe point cloud is marked as ground points, all the ground points are removed, and the rest points are ground object points.
Further, after filtering, the points in each grid are effective ground object point clouds, in step S4, the elevation h of the effective ground object point clouds is calculated as the elevation of the grid center point, the average value of RGB is calculated as the RGB attribute value,
and n represents the number of point clouds after the noise points in each grid are eliminated.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a DOM making method based on color point cloud, which can obtain more accurate color information. The LiDAR point cloud data utilization efficiency is improved, meanwhile, the cost is effectively saved, and the working efficiency is improved.
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FIG. 1 is a block flow diagram of the present invention;
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The DOM making method based on the color point cloud comprises the following steps,
and S1, converting the coordinate system of the original point cloud data into an engineering independent coordinate system, determining the range of the DOM to be manufactured, and constructing a rectangular area covering the point cloud data.
X, Y maximum value and minimum value of the converted point cloud coordinate are obtained, and X is respectivelyMAX、XMIN、YMAXAnd YMINTo obtain XMAX、XMIN、YMAXAnd YMINAnd constructing a circumscribed rectangular area which completely covers the point cloud data.
And S2, performing grid formation on the rectangular area constructed in the step S1, and enabling the length value and the width value of each grid to be equal to the resolution value of the DOM to be generated.
The size of the mesh is determined according to the resolution of the DOM to be generated. The grids are squares, the side lengths of the squares are marked as L, and the size of each grid is L x L. For example, if the resolution of the DOM to be generated is 5cm, the size of each grid is 5cm × 5 cm. Constructing a square grid of M x N from the rectangular region according to the method, wherein:
wherein, XMAX、XMIN、YMAXAnd YMINCoordinate values representing the boundary of the point cloud data, wherein the unit is meter;
l represents the resolution value of the DOM to be generated, and the unit is meter;
m, N represents the number of grids.
And S3, traversing each grid, and calculating the plane coordinates of the central point of each grid.
Calculating the coordinates (X) of the center point of each gridi,Yi) In meters;
the coordinate of each respective grid point is (X'i、Y'i) In meters;
X'i＝XMIN+L*i(i＝1......M)
Y'i＝YMIN+L*i(i＝1......N)
s4, obtaining RGB values of all point clouds in the rectangular area, and averaging the RGB values of the point clouds in each grid; calculating the elevation of the central point of each grid; and generating raster data to obtain a DOM file.
Preferably, step S5 is further included before step S1, the feature points are searched in the point cloud to obtain point cloud coordinates of the feature points, corresponding feature points are actually measured on site to obtain actually measured coordinates of the feature points, a conversion relationship between the point cloud coordinates and the actually measured coordinates is established, the point cloud is subjected to parameter conversion, and the precision of the point cloud coordinates is improved.
In order to further improve the precision, preferably, after the gridding of the rectangular region is completed, the original point cloud data is divided into a plurality of blocks, one block corresponds to one grid, then the original point cloud data in each grid is filtered, and noise points including ground points penetrating through vegetation are filtered out.
The filtering process includes the steps of:
s61, sorting the points in the grid according to the elevation;
and S62, calculating the height difference of two adjacent points: h isi,j＝hi-hj(i＝1......)
Wherein h isi(i＝1，2……)，hj(j ═ 1, 2 … …) represents the elevation of the point in meters;
hi，jrepresenting the height difference of two adjacent point clouds, and the unit is meter;
s63, finding the maximum value of the difference between adjacent points: h is MAX (h)i,j)
S64, will be lower than hjMarking the point cloud as ground points, and removing all the ground pointsAnd the rest points are the ground object points.
For example, in a vegetation covered area, only the position of the vegetation itself need be indicated, but not for the land covered by the vegetation. The laser penetrates through the vegetation, so that a part of the point cloud data represents the ground, and the laser point cloud reflected by the ground is removed, and only the laser point cloud reflected by the ground object is reserved.
After filtering, the points in each grid are effective ground object point clouds, in step S4, the elevation h of the effective ground object point clouds is calculated as the elevation of the grid center point, the average value of RGB is calculated as the RGB attribute value,
and n represents the number of point clouds after the noise points in each grid are eliminated.
The spatial position and the RGB value of the central point of the grid represent the attribute information of the point cloud in the grid. The spatial position of the central point of the grid is determined by the plane coordinates and the elevation h of the effective ground object point cloud.
The above is a specific implementation manner of the present invention, and it can be seen from the implementation process that the present invention provides a DOM manufacturing method based on color point cloud, which can obtain more accurate color information. The LiDAR point cloud data utilization efficiency is improved, meanwhile, the cost is effectively saved, and the working efficiency is improved.
Claims (4)
1. The DOM manufacturing method based on the color point cloud is characterized by comprising the following steps: comprises the steps of (a) carrying out,
s1, determining the range of the DOM to be manufactured, and constructing an external rectangular area covering the point cloud data;
s2, gridding the rectangular area in the step S1 to ensure that the length value and the width value of each grid are equal to the resolution value of the DOM to be generated;
s3, calculating the plane coordinates of the central point of each grid;
s4, obtaining RGB values of all point clouds in the rectangular area, and averaging the RGB values of the point clouds in each grid; calculating the elevation of the central point of each grid; and generating raster data to obtain a DOM file.
2. A method of making a DOM based on a color point cloud as claimed in claim 1, wherein: step S5 is further included before step S1, the feature points are searched in the point cloud to obtain point cloud coordinates of the feature points, corresponding feature points are actually measured on site to obtain actually measured coordinates of the feature points, a conversion relation between the point cloud coordinates and the actually measured coordinates is established, and parameter conversion is performed on the point cloud.
3. A method of making a DOM based on a color point cloud as claimed in claim 1, wherein: after the gridding of the rectangular area is finished, the original point cloud data in each grid is filtered, the filtering process comprises the steps,
s61, sorting the points in the grid according to the elevation;
and S62, calculating the height difference of two adjacent points: h isi,j＝hi-hj(i＝1......)
Wherein h isi(i＝1，2……)，hj(j ═ 1, 2 … …) represents the elevation of the point in meters;
hi，jrepresenting the height difference of two adjacent point clouds, and the unit is meter;
s63, finding the maximum value of the difference between adjacent points: h is MAX (h)i,j)
S64, will be lower than hjIs marked as a ground pointAll ground points are removed, and the rest points are ground object points.
4. A method of making a DOM based on a color point cloud as claimed in claim 3, wherein: after filtering, the points in each grid are effective ground object point clouds, in step S4, the elevation h of the effective ground object point clouds is calculated as the elevation of the grid center point, the average value of RGB is calculated as the RGB attribute value,
and n represents the number of point clouds after the noise points in each grid are eliminated.
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- 2021-12-31 CN CN202111670385.9A patent/CN114299235A/en active Pending
Patent Citations (7)
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| CN103606188A (en) * | 2013-11-15 | 2014-02-26 | 南京师范大学 | Geographical information on-demand acquisition method based on image point cloud |
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