CN111080536A - Self-adaptive filtering method for airborne laser radar point cloud - Google Patents
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Abstract
The invention discloses a self-adaptive filtering method of airborne laser radar point cloud, which comprises the steps of debugging and installing the airborne laser radar, collecting data of the airborne laser radar, eliminating noise points of the data generated by the airborne laser radar, identifying and eliminating a few points far above or far below surrounding points in the point cloud, conducting down-sampling treatment on the point cloud after the noise elimination, calculating a gradient value according to the down-sampling data, finally conducting up-sampling on a gradient calculation result by utilizing Krigin interpolation, setting parameters, filtering all the point cloud by combining the gradient data, and marking ground points in the point cloud; according to the invention, parameters in filtering are continuously selected in a self-adaptive manner through the gradient, so that the purpose of improving the filtering precision of the airborne laser radar is achieved.
Description
Technical Field
The invention belongs to the technical field of airborne laser radar data processing, particularly relates to processing and issuing of airborne laser radar data, and particularly relates to a method for improving filtering precision of airborne laser radar data.
Background
An airborne laser radar (LiDAR) combines a high-precision laser range finder and a positioning and orientation system, and three-dimensional information covering the earth surface, namely point cloud data (point cloud), is automatically acquired by measuring the propagation time of a laser pulse signal. Compared with traditional field measurement and photogrammetry, the LiDAR can quickly and accurately acquire the three-dimensional information of the earth surface under difficult terrain conditions such as forest zones, mountain zones, deserts and the like, so that the method is widely applied to high-precision DEM generation.
However, the raw point cloud data does not carry classification information, and may include non-ground points such as vegetation, buildings, bridges and the like, so that automatic ground point extraction, namely filtering processing, is required in the generation of the DEM based on the LiDAR point cloud.
Although the existing filtering algorithm achieves ideal effects in relevant data testing and practical application, some defects exist. Firstly, the accuracy of the existing algorithm is easily influenced by the filtering parameters. If a better filtering effect is required, filtering parameters are manually set according to the gradient and the terrain features. In the filtering process, non-ground points are filtered only by using one group of filtering parameters, and the differences of the slopes and the terrain features in the filtering area are ignored. Therefore, after filtering, the local area needs to be processed more specifically so as to achieve the best effect.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a self-adaptive filtering method for airborne laser radar point cloud.
In order to achieve the purpose, the technical scheme adopted by the invention for solving the technical problem is as follows: an adaptive filtering method for airborne laser radar point cloud comprises the following steps:
step 1, debugging and installing an airborne laser radar and acquiring data of the airborne laser radar;
step 2, carrying out data acquisition by using the equipment in the step 1, carrying out noise point elimination processing on data generated by the airborne laser radar, and identifying and eliminating a few points far higher or far lower than surrounding points in point cloud data;
step 3, performing down-sampling processing on the point cloud data after the noise is removed, calculating a gradient value according to the down-sampled data, and finally performing up-sampling on a gradient calculation result by utilizing Krigin interpolation;
step 4, performing gridding processing by using the point cloud data processed in the step 2, judging that the lowest point in each grid is a ground point, and constructing an irregular triangular net by using the points; dividing the whole point cloud data according to the size of the maximum grid of the point cloud data after the noise is removed, selecting the lowest point in each grid as a ground point, constructing an irregular triangular net by using the ground points, and then judging the category of the rest points which are not judged; and sequentially bringing other points into the triangulation network for judgment, determining a triangle corresponding to the point to be judged, and calculating a threshold corresponding to the point to be judged by combining the gradient and three-dimensional coordinates of three vertexes of the triangle: calculating two thresholds of a maximum iteration distance and a maximum iteration angle according to three-dimensional coordinates and gradient values of three vertexes of a triangle corresponding to a point to be determined in the irregular triangular network;
and 5, comparing the calculated threshold result with the actual maximum iteration distance and the maximum iteration angle calculated by the three-dimensional coordinates of the point to be determined and the three vertexes of the corresponding triangle in the irregular triangular network, marking the point as a ground point and adding the ground point into the irregular triangular network if the actual values are smaller than the threshold, repeating the iteration process until no point is determined as the ground point, and obtaining the data which is the airborne laser radar point cloud data subjected to self-adaptive filtering.
Further, the debugging and installation of the airborne laser radar and the generation of the airborne laser radar data acquisition in the step 1 comprise the following steps:
step 1.1, a complete LiDAR system is carried on a flight carrier, namely an airplane, and comprises a laser scanning ranging system, a Differential GPS (DGPS), an Inertial Measurement Unit (IMU) and imaging equipment;
step 1.2, making a complete flight scheme, and carrying out aviation flight on the survey area according to the flight scheme;
and step 1.3, generating a theoretical model according to the laser propagation time and the airborne laser radar data to obtain the point cloud data of the survey area.
Further, the elimination processing in step 2 includes the following steps:
step 2.1, counting all point cloud data according to elevation information, and sequencing the point cloud data from low to high;
and 2.2, taking all point clouds with the maximum elevation and the minimum elevation in the statistical result as noise rejection.
Further, the step 3 specifically comprises the following steps:
step 3.1, the acquired point cloud data is subjected to downsampling according to the size of a maximum grid, the number of points corresponding to each grid is N, and the elevation of each point is hiCalculating the corresponding elevation of each grid according to the following formula:
step 3.2, selecting 3 x 3 grid space from the processing result of step 3.1, according to Z1To Z9Numbering the grids, setting the size of a maximum grid in 3.1 as S, and calculating the gradient according to the following formula:
fx＝(Z8-Z2)/2S
fy＝(Z6-Z4)/2S
and 3.3, interpolating the gradient data by using the gradient value calculated in the step 3.2 by using kriging interpolation.
Further, the step 4 specifically includes the following steps:
step 4.1, determining the three-dimensional coordinates of the point to be judged and the triangle corresponding to the point in the irregular triangulation network;
step 4.2, according to the point P to be judged1Three-dimensional coordinate X of1,Y1,Z1And three vertices P of a triangle2,P3,P4Coordinate X of2,Y2,Z2、X3,Y3,Z3、X4,Y4,Z4Calculate P1Distances to three vertices, according toArranged from low to high, assuming that the order is P2,P3,P4；
Step 4.3, P1Along the Z axis to plane P2P3P4Has a focal point of PprojectThe focus is to a straight line P2P3Is PlineWherein the threshold values of the maximum iteration distance and the maximum iteration angle are calculated by the following formula:
lmax＝Pproject×tan(slope)
compared with the prior art, the invention has the following advantages:
the invention can self-adaptively select the filter parameters which are not intuitively and empirically selected in the traditional algorithm, thereby greatly reducing the difficulty of manual parameter selection and improving the adaptability of the filter algorithm; the invention effectively reduces the misjudgment rate of the ground point misjudged as the non-ground point and the misjudgment rate of the non-ground point misjudged as the ground point in the filtering simultaneously, and greatly improves the integral filtering precision.
The invention carries out filtering processing on data in different areas, and compares the processing result with the standard result of manual classification at the same time, and the result shows that the invention can obtain good point cloud filtering effect.
Drawings
FIG. 1 is a flow chart of an adaptive filtering method according to the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Examples
Referring to fig. 1, the invention discloses an all-terrain adaptive filtering method for airborne laser radar point cloud data, which comprises the following steps.
Step 1, a complete LiDAR system is carried on a flight carrier, namely an airplane, and comprises a laser scanning ranging system, a Differential GPS (DGPS), an Inertial Measurement Unit (IMU) and imaging equipment. And making a complete flight scheme, carrying out flight on the survey area according to the flight scheme, and generating a theoretical model according to the laser propagation time and airborne laser radar data of an airborne laser radar system to obtain the point cloud data of the survey area.
And 2, carrying out noise point elimination processing on data generated by the airborne laser radar, and identifying and eliminating a few points far above or far below surrounding points in the point cloud. The method comprises the following specific steps:
step 2.1, counting all point cloud data according to elevation information, and sequencing the point cloud data from low to high;
and 2.2, taking all point clouds with the maximum elevation and the minimum elevation in the statistical result as noise rejection.
And 3, performing down-sampling processing on the point cloud after the noise is removed, calculating a gradient value according to down-sampled data, and finally performing up-sampling on a gradient calculation result by using kriging interpolation. The method comprises the following specific steps:
step 3.1, the acquired point cloud data is subjected to downsampling according to the size of a maximum grid, the number of points corresponding to each grid is N, and the elevation of each point is hiAnd calculating the corresponding elevation of each grid according to the formula (1):
step 3.2, selecting 3 x 3 grid space from the processing result of step 3.1, according to Z1To Z9Numbering the grids, and calculating the gradient according to formulas (2) and (3) under the assumption that the size of the maximum grid in 3.1 is S:
and 3.3, interpolating the gradient data by using the gradient value calculated in the step 3.2 by using kriging interpolation.
And 4, carrying out category judgment on the rest points which are not judged, and calculating two thresholds of a maximum iteration distance and a maximum iteration angle by using the gradient value calculated in the step 3.2 and three-dimensional coordinates of three vertexes of a triangle corresponding to the point to be judged in the irregular triangulation network in each judgment. Wherein the step of terrain adaptive threshold calculation is:
step 4.1, determining the three-dimensional coordinates of the point to be judged and the triangle corresponding to the point in the irregular triangulation network;
step 4.2, according to the point P to be judged1Three-dimensional coordinate X of1,Y1,Z1And three vertices P of a triangle2,P3,P4Coordinate X of2,Y2,Z2、X3,Y3,Z3、X4,Y4,Z4Calculate P1Distances to the three vertices are arranged from low to high, assuming that the order is P2,P3,P4；
Step 4.3, P1Along the Z axis to plane P2P3P4Has a focal point of PprojectThe focus is to a straight line P2P3Is PlineWherein the threshold values for the maximum iteration distance and the maximum iteration angle are calculated by equation (4):
lmax＝Pproject×tan(slope)
and 5, comparing the calculated threshold with the actual maximum iteration distance and the maximum iteration angle calculated by the three-dimensional coordinates of the point to be determined and three vertexes of a corresponding triangle in the irregular triangulation network, marking the point as a ground point and adding the ground point into the irregular triangulation network if the actual values are smaller than the threshold, and repeating the iteration process until no point is determined as the ground point.
In the present invention, the point cloud filtering is performed at all research levels limited to the present stage and the judgment between the ground point and the non-ground point in the point cloud. The method and the device aim at solving the problems that filtering precision is reduced due to the fact that ground points are wrongly divided into non-ground points in point cloud filtering and the non-ground points are wrongly divided into the ground points, and the filtering precision of the airborne laser radar is automatically improved. The invention carries out filtering processing on data in different areas, and compares the processing result with the standard result of manual classification at the same time, and the result shows that the invention can obtain good point cloud filtering effect.
The method disclosed by the invention can be used for directly processing and estimating the original airborne laser radar point cloud data and counting the basic gradient parameters, adaptively filtering the point clouds in different terrain environments according to the gradient parameters, and continuously adaptively selecting the parameters in the filtering through the gradient, thereby achieving the purpose of improving the filtering precision of the airborne laser radar.
The following is a precision evaluation table when the invention is applied to airborne laser radar data filtering.
The above-described embodiments are merely illustrative of the principles and effects of the present invention, and some embodiments may be applied, and it will be apparent to those skilled in the art that various changes and modifications may be made without departing from the inventive concept of the present invention, and these embodiments are within the scope of the present invention.
Claims (5)
1. A self-adaptive filtering method for airborne laser radar point cloud is characterized by comprising the following steps: comprises the following steps
Step 1, installing and debugging an airborne laser radar, and then collecting point cloud data;
step 2, identifying and eliminating a few points far above or far below surrounding points in the point cloud data;
step 3, performing down-sampling processing on the point cloud data after the noise is removed, calculating a gradient value according to the down-sampled data, and performing up-sampling on a gradient calculation result by using kriging interpolation;
step 4, dividing the whole point cloud data according to the size of the maximum grid, selecting the lowest point in each grid as a ground point, constructing an irregular triangular net by using the ground points, then bringing other points into the triangular net in sequence for judgment, and calculating two thresholds of a maximum iteration distance and a maximum iteration angle by combining three-dimensional coordinates and gradient values of three vertexes of a triangle corresponding to a point to be judged in the irregular triangular net;
and 5, comparing the calculated threshold with the actual maximum iteration distance and the maximum iteration angle calculated by three-dimensional coordinates of three vertexes of a triangle corresponding to the point to be determined in the irregular triangular network, marking the point as a ground point and adding the ground point into the irregular triangular network if the actual values are smaller than the threshold, otherwise, repeating the iteration process until no point is determined as the ground point, and obtaining point cloud data subjected to self-adaptive filtering.
2. The method for adaptively filtering the point cloud of the airborne lidar according to claim 1, wherein the step 1 specifically comprises the following steps:
step 1.1, a set of complete LiDAR system is carried on an airplane;
step 1.2, making a flight scheme and carrying out aviation flight on a measuring area;
and step 1.3, generating a theoretical model according to the laser propagation time and the airborne laser radar data to obtain the point cloud data of the survey area.
3. The method for adaptively filtering the point cloud of the airborne lidar according to claim 1, wherein the step 2 specifically comprises the following steps:
step 2.1, counting point cloud data according to the elevation information, and sequencing the point cloud data from low to high;
and 2.2, taking all point clouds with the maximum elevation and the minimum elevation in the statistical result as noise rejection.
4. The method for adaptively filtering the point cloud of the airborne lidar according to claim 1, wherein the step 3 specifically comprises the following steps:
step 3.1, the acquired point cloud data is subjected to downsampling according to the size of a maximum grid, the number of points corresponding to each grid is N, and the elevation of each point is hiCalculating the corresponding elevation of each grid according to the following formula:
step 3.2, selecting 3 multiplied by 3 grid space from the processing result according to Z1To Z9Numbering the grid, setting the size of the maximum grid as S, and calculating the gradient according to the following formula:
and 3.3, interpolating the gradient data by using the gradient value through Kergin interpolation.
5. The method according to claim 1, wherein the step 4 specifically comprises the following steps:
step 4.1, determining a point P to be judged1Three-dimensional coordinate X of1,Y1,Z1And the point corresponds to a triangle in an irregular triangulation network;
step 4.2, according to three vertexes P of the triangle2,P3,P4Coordinate X of2,Y2,Z2、X3,Y3,Z3、X4,Y4,Z4Calculate P1To the vertex P2,P3,P4Is arranged from low to high in the order of P2,P3,P4；
Step 4.3, P1Along the Z axis to plane P2P3P4Has a focal point of PprojectThe focus is to a straight line P2P3Is PlineThen, the threshold values of the maximum iteration distance and the maximum iteration angle are calculated by the following formulas:
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