CN108986024B - Grid-based laser point cloud rule arrangement processing method - Google Patents
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Abstract
The invention relates to a method for rearranging irregularly distributed laser point clouds based on background grids, which comprises the following steps: establishing an auxiliary background grid; establishing a projection relationship; projecting a three-dimensional laser point cloud to a grid; establishing an interpolation algorithm; interpolation rule point cloud; fill in point clouds, etc. The invention is realized by the following steps: (1) establishing an auxiliary background grid cell; (2) Establishing a projection relation, and projecting the three-dimensional laser point cloud into a grid unit; (3) Establishing an interpolation algorithm, wherein interpolation contents comprise point cloud coordinates, reflection intensity, RGB values and the like; (4) The point grids interpolate according to the point clouds in the grid units, and new point clouds with regular arrangement are reconstructed; (5) The new point cloud of the non-valued grid is realized by interpolation of the new point cloud of the adjacent grid cells. The method provided by the invention is suitable for rearranging the irregularly arranged point clouds and improving the quality of the point clouds.
Description
Technical Field
The invention relates to the field of three-dimensional laser point cloud processing, and relates to the field of laser modeling and laser monitoring.
Background
The three-dimensional laser scanning technology comprises airborne laser scanning, vehicle-mounted laser scanning, ground laser scanning, handheld laser scanning and the like. With the continuous promotion of laser scanning instrument, computer hardware, software technology, the fall-back of all kinds of laser scanning instrument prices, three-dimensional laser scanning technology obtains promoting, and wide application is in fields such as complicated handicraft manufacturing, complicated part manufacturing, relic reduction, relic preservation, city sand table construction, dangerous rock body monitoring, dam monitoring, structural monitoring. The three-dimensional laser point cloud is original data collected by various laser scanners, and for objects with complex surfaces (such as ground surfaces and complex parts), untreated point clouds usually show irregular arrangement, uneven density and other phenomena due to the factors of different reflection capability of targets on laser, different inclination angles of laser beams, shielding and the like, and the original laser point cloud data generally needs to be processed to improve the quality of the point clouds.
The rearrangement of the irregularly distributed laser point clouds is a means of point cloud processing, and the conventional rearrangement method is mainly based on the rearrangement of window units. The method is characterized in that a window unit is given in advance, the lowest point in the window can be used as a terrain point, the lowest point is found, the window is moved to the next area, the lowest point is found again, and the method can be used for filtering and rearranging the clutter point cloud until all data are traversed. In addition to the nadir method, there are generally methods based on curvature, median or mean and gaussian methods, etc. The Gaussian method regards the weight in the appointed area as Gaussian distribution, and the average effect is smaller, so that the appearance of the original data can be well kept. The averaging method averages individual data points in a window. The median method takes the statistical median of the individual data points within the window.
The point cloud processing using the above method generally has a certain effect, but has many disadvantages. On the one hand, the method only considers the coordinate information of the point clouds, and does not consider other information such as reflection intensity, color RGB value and the like, and the attribute values of the point clouds are also indispensable in some post-processing; on the other hand, most point cloud arrangement methods and interpolation methods are simpler, such as an average method and a median method, cannot properly represent laser points, or are too complex, such as a Gaussian method, and have low processing efficiency on disordered mass point cloud data. For this purpose, a new method is used in the present invention to rearrange the three-dimensional, chaotic, irregularly distributed point clouds. By a method based on a background grid, a simple distance inverse proportion interpolation algorithm is established to interpolate the elevation coordinates, the reflection intensity and the color RGB. The invention aims at improving the quality of disordered, irregularly distributed and uneven-density three-dimensional laser point clouds, and lays a foundation for subsequent cultural relics preservation, sand table construction, structure monitoring and other works.
Disclosure of Invention
First, the technical problem to be solved
Because of the characteristics of different reflection capability of the target surface on laser, different inclination angles of laser beams, shielding and the like, for some objects with complex surfaces, the untreated point clouds usually show irregular distribution, uneven density and the like, and the rearrangement of the disordered and irregularly distributed laser point clouds is a necessary means for carrying out point cloud processing in certain modeling and monitoring works. The invention mainly aims at: based on background grids, the disordered laser point clouds are regularly arranged, so that a foundation is laid for subsequent cultural relics preservation, sand table construction, structure monitoring and other works, and reasonable modeling and monitoring effects are achieved.
(II) technical scheme
In order to achieve the above purpose, the invention adopts the following technical scheme:
the method comprises the following steps of regularly arranging disordered and irregularly distributed point cloud data acquired by a three-dimensional laser scanner:
(1) Establishing an auxiliary background grid unit;
(2) Establishing a projection relation, and projecting the three-dimensional laser point cloud into a grid unit;
(3) Establishing an interpolation algorithm, wherein interpolation contents comprise point cloud coordinates, reflection intensity, RGB values and the like;
(4) Interpolating the origin cloud by the dot grid according to the grid unit, and reconstructing a new dot cloud with a regular arrangement;
(5) The new point cloud of the non-point grid is realized by interpolation of the new point cloud of the adjacent grid cells.
In the above scheme, establishing the auxiliary background grid unit specifically includes: determining a planar rectangular range covering all object point cloudsR（X 1 ，X 2 ；Y 1 ，Y 2 ) I.e. the point cloud is distributed in%X 1 ，X 2 ) The range of [ (]Y 1 ，Y 2 ) A range; determining the grid unit side length according to the scanning density of the original point cloud, if the scanning density isdThen the grid cell side size is takenl=dIf the scanning density is not detailed, the scanning density can be analyzed and judged according to the point cloud data, and the specific judging method comprises the following steps: measuring the laser point spacing of different areas at 10 by adopting corresponding point cloud checking software, and marking as @d 1 ，d 2 ，…，d 9 ，d 10 ) Grid cell side size extractionl=（d 1 +d 2 +…+d 9 +d 10 ) 10, in addition, can be set according to the requirement of the userlThe method comprises the steps of carrying out a first treatment on the surface of the Then fromX 1 To the point ofX 2 From the slaveY 1 To the point ofY 2 According to the side lengthlArranging grid cells, using C ij （a 1 ，a 2 ；b 1 ，b 2 ) The meaning is: first, theiLine 1jGrid cells of columns, X-coordinate froma 1 To the point ofa 2 Y-coordinate slaveb 1 To the point ofb 2 。
In the above scheme, a projection relation is established, and the three-dimensional laser point cloud is projected into the grid unit. The method specifically comprises the following steps: for any laser pointP（X，Y，Z) Find the grid cell C where it is located ij （a 1 ，a 2 ；b 1 ，b 2 ) At the same time satisfya 1 ≤X≤a 2 Andb 1 ≤Y≤b 2 calculating the distance from the point to the centroid of the grid unit to which the point belongsrThe formula is as follows, noted:P ij （r，Z，RA，RGB）。
in the above scheme, the step of establishing the interpolation algorithm mainly includes: for the dotted grid, interpolating the origin cloud in the grid by adopting a distance inverse proportion interpolation method according to grid cells, and reconstructing a new point cloud with regular arrangement, wherein the interpolation content comprises point cloud coordinates, reflection intensity, RGB values and the like; according toP ij （r，Z，RA，RGB) Extracting allP ij If there isnPersonal (S)P ij Pressing downrSize ordering {P ij （r 0 ，Z 0 ， RA 0 ，RGB 0 ）、P ij （r 1 ，Z 1 ，RA 1 ，RGB 1 ）、…、P ij （r n ，Z n ，RA n ，RGB n ) }, whereinr 0 ≤r 1 ≤…≤r n The method comprises the steps of carrying out a first treatment on the surface of the The interpolation formula is:
in the above scheme, the step of interpolating the origin cloud by the grid unit by the point grid to reconstruct the new point cloud of the arrangement rule includes: creating new points at the centroid of the grid, such that the new points contain attribute values {Z ij ，RA ij ，RGB ij Because of the regular arrangement of the grids, new points located at the centroids of the grids are also regularly arranged.
In the above scheme, the step of realizing the new point cloud of the non-point grid through the new point cloud interpolation of the adjacent grid units mainly comprises the following steps: eliminating the non-point grid cells outside the point cloud boundary, and reserving the non-point grid cells contained in the point cloud; the user sets the direction of the interpolation of the point-free grid point cloud, the direction is divided into an X direction and a Y direction, new points are obtained through interpolation of adjacent point-free grids and are positioned at the centroid of a point-free grid unit, and interpolation contents comprise point cloud coordinates, reflection intensity, RGB values and the like; if the X direction is interpolated, fromiStarting at line 1, pressjColumn=1 until the last column starts searching until a non-point grid cell is foundC ij Then interpolate the new pointP ij0 （Z ij ，RA ij ，RGB ij ) The average value of adjacent grid cells is calculated as shown in fig. 2, and expressed as:
similar calculationRA ij ，RGB ij The method comprises the steps of carrying out a first treatment on the surface of the Y-direction interpolation computation is vice versa; new points for a non-point grid cellP ij0 Attached with elevation (Z) ij ) Intensity of reflectionRA ij ) Color value [ ]RGB ij ) Equal attributes; and (3) cycling until all the non-point grid cells are interpolated to establish new points.
(III) beneficial effects
1. The invention provides a grid-based disordered laser point cloud rule arrangement method, which enables point clouds to be arranged in a rule by means of arranging based on a background grid, and the processing means improves the quality of the point clouds and is beneficial to modeling and monitoring of later point clouds. 2. According to the grid-based disordered laser point cloud regular arrangement method, according to the distance inverse proportion interpolation method, complex formula iterative computation is not needed, the processing efficiency and the effect are considered, the regular arrangement effect is obvious, and the interpolation quality is good;
3. the grid-based disordered laser point cloud rule arrangement method provided by the invention comprises the steps of firstly carrying out rule arrangement (with a point grid) on the point cloud overall, then checking the vulnerability (without a point grid) for further processing, and providing a simple point cloud rule interpolation method for the vulnerability (without a point grid), which is simple and effective.
Drawings
FIG. 1 is a flow chart of a grid-based method for ordering a random laser point cloud.
Fig. 2 is a schematic diagram of interpolation of a new point cloud of adjacent grid cells.
Claims (2)
1. A grid-based method for regularly arranging clutter laser point clouds is characterized in that:
the method comprises the following specific steps:
(1) For the object point clouds which need to be regularly arranged, an auxiliary background grid unit is established according to an engineering coordinate system of the object point clouds, and the object point clouds need to be covered in a background grid range;
(2) Establishing a projection relation, and projecting the three-dimensional laser point cloud into a grid;
according to the inclusion relation of the point cloud point coordinates and the grid unit under the engineering coordinate system, the three-dimensional laser point cloud is projected into the grid unit according to the inclusion relation of X, Y coordinates; for laser points just at the boundary of adjacent grids, the adjacent grids are counted at the same time, and the characteristics are as follows:
for any laser point P (X, Y, Z), if X, Y coordinates are contained in grid cell C ij (a 1 ，a 2 ；b 1 ，b 2 ) The following is noted: p (P) ij (r, Z, RA, RGB), wherein r is the formula:
the point P (X, Y, Z) coordinates are contained in grid cell C ij (a 1 ，a 2 ；b 1 ，b 2 ) Is defined as: simultaneously satisfies that a1 is more than or equal to X is more than or equal to a2 and b1 is more than or equal to Y is more than or equal to b2;
(3) Establishing an interpolation algorithm, wherein interpolation contents comprise point cloud elevation coordinates, reflection intensity and RGB values, and taking a grid centroid as an interpolation center;
(4) For a grid cell containing at least one laser point, namely a point grid, the point grid interpolates the laser points in the cell grid to the grid centroid, a new point cloud with regular arrangement is reconstructed, and a new point is established at the grid centroid; the method comprises the following steps:
a) P established according to the method in (2) ij (r, Z, RA, RGB), extracting all P ij If there are n P ij And ordered by r, P ij Comprises { P ] ij (r 0 ，Z 0 ，RA 0 ，RGB 0 )、P ij (r 1 ，Z 1 ，RA 1 ，RGB 1 )、…、P ij (r n ，Z n ，RA n ，RGB n ) -where r 0 ≤r 1 ≤…≤r n ；
b) Will { P in a) ij (r 0 ，Z 0 ，RA 0 ，RGB 0 )、P ij (r 1 ，Z 1 ，RA 1 ，RGB 1 )、…、P ij (r n ，Z n ，RA n ，RGB n ) Interpolation, comprising: the point cloud elevation coordinates, the reflection intensity and the RGB value are interpolated by the following formula:
for a grid cell containing at least one laser spot, i.e., a dotted grid, the dotted grid interpolates the laser spot in the grid cell to the grid cell centroid, the method of reconstructing the regular new spot cloud includes creating a new spot at the grid cell centroid such that the new spot contains the attribute value { Z } ij ，RA ij ，RGB ij }；
(5) If the original three-dimensional point cloud is sparse and uneven, grid units which do not contain laser points appear at the non-point cloud boundary, namely, non-point grids, and the non-point grid value points are realized through interpolation of new point clouds of adjacent grids until all the non-point grids finish interpolation; the method comprises the following steps:
a) Eliminating the non-point grid cells outside the point cloud boundary, and reserving the non-point grid cells contained in the point cloud;
b) Setting a point cloud interpolation direction of a non-point grid unit by a user, dividing the interpolation direction into an X direction and a Y direction, filling new point clouds by interpolation of new point clouds of adjacent grid units, and establishing new points at the centroid of the grid units in the same step (4), wherein the interpolation content comprises point cloud elevation coordinates, reflection intensity and RGB values;
c) The interpolation process in step b) is specifically: if interpolation is performed in the X direction, starting searching from row i=1, and starting searching from column j=1 to the last column until no point grid cell C is found ij Then interpolate a new point P0 ij (Z ij ，RA ij ，RGB ij ) Calculated from the average of adjacent dotted grid cells, expressed as:
similarly, calculate RA ij ，RGB ij The method comprises the steps of carrying out a first treatment on the surface of the Y-direction interpolation computation is vice versa;
d) New Point P0 being a non-Point grid cell ij The method comprises the steps of carrying out a first treatment on the surface of the Attached with the cloud heightDistance coordinate Z ij Reflection intensity RA ij 、RGB ij An attribute;
e) Steps c) through d) are cycled until all the non-point grid cells interpolate to create new points.
2. A method of grid-based clutter cloud alignment according to claim 1, wherein the grid plane rectangle range is R (X 1 ，X 2 ；Y 1 ，Y 2 ) The length of the grid cell side is l; the grid cells are denoted as C ij (a 1 ，a 2 ；b 1 ，b 2 ) Their determination method is as follows:
a) Plane rectangular range R (X) 1 ，X 2 ；Y 1 ，Y 2 ) To cover the whole object point cloud, i.e. the object point cloud is distributed in the X direction (X 1 ，X 2 ) The range is distributed along the Y direction in (Y 1 ，Y 2 ) A range;
b) Determining the grid cell side length according to the scanning density of the original point cloud, if the scanning density is d, taking l=d, and if the scanning density is not detailed, performing simplified analysis and judgment according to the point cloud data, wherein the specific judgment method comprises the following steps: the laser spot spacing of the different areas at 10 was measured using corresponding spot cloud viewing software and noted as (d 1 ，d 2 ，…，d 9 ，d 10 ) Then the grid cell side size takes l= (d 1 +d 2 +…+d 9 +d 10 ) And/10, setting l according to the requirement of the user;
c) After determining the grid cell side length l, the point cloud range is within R (X 1 ，X 2 ；Y 1 ，Y 2 ) In the range of X-direction from small to large, i.e. from X 1 To X 2 From Y 1 To Y 2 Grid cells arranged according to side length l, C ij (a 1 ，a 2 ；b 1 ，b 2 ) The representation is: grid cell of ith row and jth column, X coordinate from a 1 To a 2 Y-coordinate from b 1 To b 2 。
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| CN111435551B (en) * | 2019-01-15 | 2023-01-13 | 华为技术有限公司 | Point cloud filtering method and device and storage medium |
| WO2020237663A1 (en) * | 2019-05-31 | 2020-12-03 | 深圳市大疆创新科技有限公司 | Multi-channel lidar point cloud interpolation method and ranging apparatus |
| CN112184900B (en) * | 2019-07-04 | 2024-03-19 | 北京四维图新科技股份有限公司 | Method, device and storage medium for determining elevation data |
| CN112816993B (en) * | 2020-12-25 | 2022-11-08 | 北京一径科技有限公司 | Laser radar point cloud processing method and device |
| CN114528730B (en) * | 2022-01-25 | 2022-11-29 | 水利部交通运输部国家能源局南京水利科学研究院 | Construction method of real coral sand particle discrete element model |
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