CN114565735A - Filtering method and system based on laser point cloud data - Google Patents
Filtering method and system based on laser point cloud data Download PDFInfo
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Abstract
The invention discloses a filtering method and a system based on laser point cloud data, wherein the method comprises the following steps: preprocessing, initializing a grid, determining the nearest neighbor point, calculating external factors, calculating weight coefficients and calculating internal factors; and repeatedly executing the external factor calculation step, the weight coefficient calculation step and the internal factor calculation step until the elevation change of all the grid particles is smaller than a preset value or the iteration times are larger than the preset value. The method aims at the area with complicated change of landform, the scheme can improve the calculation precision of the cloth simulation algorithm, the generated DEM or DSM data is closer to the real landform, and the divided ground or non-ground points are more accurate.
Description
Technical Field
The invention belongs to the field of point cloud processing, and particularly relates to a point cloud filtering method and system.
Background
The geographic environment of China is complex, and the data collected by the laser radar comprises various complex terrains such as plains, cities, mountains, hills and the like. According to requirements, the laser point cloud data can be used for establishing:
digital Terrain Model (DTM): the method is a simple statistical representation of continuous ground by utilizing a large number of selected known x, y and z coordinate points in an arbitrary coordinate system, or DTM is a digital representation of topographic surface form attribute information and is a digital description with spatial position characteristics and topographic attribute characteristics. The attribute information of the topography generally includes elevation, slope, heading, and the like.
Digital elevation model (DEM for short): the data set is a data set of plane coordinates (X, Y) and elevation (Z) of regular grid points in a certain range, mainly describes spatial distribution of regional landform forms, and is formed by performing data acquisition (including sampling and measurement) through contour lines or similar three-dimensional models and then performing data interpolation. The DEM is a virtual representation of the morphology of the terrain from which information such as contour lines, gradient maps, etc. can be derived.
Digital Surface Model (DSM): the ground elevation model comprises the heights of ground surface buildings, bridges, trees and the like. Which represents the most realistic representation of the ground relief situation. Compared with the DEM, the DEM only contains the elevation information of the terrain and does not contain other land surface information, and the DSM further contains the elevation of other land surface information except the ground on the basis of the DEM.
For DTM and DEM, the existing point cloud filtering method is difficult to distinguish ground points from non-ground points such as buildings, trees and the like; and it is difficult to obtain a relatively accurate calculation result for the DSM existing location cloud filtering method.
Disclosure of Invention
In view of this, the present invention provides a filtering method and system based on laser point cloud data, which performs filtering processing on an original point cloud, so that a point cloud calculation result is more accurate.
In order to solve the technical problems, the technical scheme of the invention is as follows: a filtering method based on laser point cloud data comprises the following steps:
a pretreatment step: preprocessing the original point cloud to eliminate isolated points; if a digital terrain model or a digital elevation model needs to be acquired, inverting the point cloud;
mesh initialization step: generating a grid, setting the mesh size, and arranging the grid above the point cloud;
determining the nearest neighbor point: searching the nearest point in the point cloud for each grid particle;
calculating external factors: calculating the displacement of the grid particles caused by the natural falling of the grid particles only under the gravity;
calculating a weight coefficient: determining a weight coefficient between adjacent grid particles according to the reference value of the topographic relief parameter and the topographic relief parameter;
internal factor calculation step: calculating displacement generated by the action force between the grid particles on the grid particles through the weight coefficient;
and repeatedly executing the external factor calculation step, the weight coefficient calculation step and the internal factor calculation step until the elevation change of all the grid particles is smaller than a preset value or the iteration times are larger than the preset value.
As an improvement, in the preprocessing step, the point cloud is inverted to turn the point cloud by taking the xoy plane as a symmetrical plane.
As an improvement, in the step of initializing the mesh, the mesh is composed of a plurality of quadrilateral meshes, and each vertex of each quadrilateral mesh is a mesh particle.
As a preference, the step of determining the nearest neighbor point comprises: projecting the grid particles and the point cloud to the same plane, calculating the distance between the grid particles and the surrounding point cloud, and selecting a point with the closest distance as a nearest point.
As an improvement, the external factor calculating step: and calculating the displacement of the grid particles caused by the natural falling of the gravity only.
As an improvement, in the external factor calculating step, after calculating the displacement amount generated by all the grid particles falling naturally only under the action of gravity, comparing the elevation of each grid particle after falling with the elevation of the nearest point in the point cloud, if the elevation of the grid particle is less than or equal to the elevation of the nearest point, adjusting the elevation of the grid particle to the elevation of the nearest point and marking the grid particle as a non-movable particle; if the elevation of a grid particle is greater than the elevation of its nearest neighbor, then the grid particle is labeled as a movable particle.
As an improvement, for any two adjacent grid particles in the internal factor calculation step, if both grid particles are immobile particles, the movement is not performed; if the two grid particles are both movable particles, the two grid particles move in opposite directions for the same distance; if one of the two grid particles is an immovable particle and the other is a movable particle, the movable particle is moved.
In the internal factor calculating step, when two adjacent grid particles are immovable particles and movable particles, the displacement intervals of the movable particles are (0, H) and H is the height difference of the two adjacent grid particles and are adjusted within the interval range by the weight coefficient, and when two adjacent grid particles are movable particles, the displacement intervals of the two grid particles are (0, 0.5H) and H is the height difference of the two adjacent grid particles and are adjusted within the interval range by the weight coefficient.
As an improvement, if a digital terrain model or a digital elevation model needs to be acquired, the height difference between the point cloud and the nearest point in the grid particles is calculated, if the height difference is larger than or equal to a threshold value, the point cloud is considered as a non-ground point and is eliminated, and if the height difference is smaller than the threshold value, the point cloud is considered as a ground point and is reserved.
The invention also provides a filtering system based on the laser point cloud data, which comprises the following components:
the point cloud preprocessing module is used for preprocessing the original point cloud and eliminating isolated points; for digital terrain model or digital elevation model output, the point cloud can be inverted;
the grid generating module is used for generating a grid, defining the mesh size and arranging the grid above the point cloud;
a nearest neighbor searching module for searching nearest neighbors in the point cloud for each mesh particle;
the external factor calculation module is used for calculating the displacement of the grid particles falling naturally only under the action of gravity;
the weight coefficient calculation module is used for determining the weight coefficient between adjacent grid particles by utilizing the terrain fluctuation parameter reference value and the terrain fluctuation parameter;
and the internal factor calculation module is used for calculating displacement generated by the action force between the grid particles on the grid particles through the weight coefficient.
The system comprises a point cloud segmentation module, a data acquisition module and a data processing module, wherein the point cloud segmentation module is used for segmenting the ground point cloud and the non-ground point cloud when outputting the digital terrain model or the digital elevation model.
The invention also provides an electronic device which comprises a memory and a processor, wherein the memory stores the point cloud filtering program, and the processor executes the point cloud filtering program to realize the filtering method based on the laser point cloud data.
The invention also provides a storage device, wherein the storage device stores a point cloud filtering program, and the point cloud filtering program can realize the filtering method based on the laser point cloud data when being executed by the processor.
The invention has the advantages that: the filtering method with the steps can improve the calculation precision of a cloth simulation algorithm aiming at the region with complicated change of the landform, so that the generated DEM or DSM data is closer to the real landform, and the divided ground or non-ground points are more accurate.
Drawings
Fig. 1 is a schematic diagram of the principle of the present invention when outputting DTM and DEM (grid falls on the surface of a point cloud).
Fig. 2 is a schematic diagram of the output DSM of the present invention (the trellis is initialized).
FIG. 3 is a flow chart of the present invention.
FIG. 4 is a graph of weight coefficient function characteristics.
FIG. 5 shows a difference drefAnd (5) taking a weight curve graph.
Fig. 6 is a schematic diagram of the structure of the present invention.
The labels in the figure are: 1 point cloud and 2 grids.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present invention, the present invention will be further described in detail with reference to the following embodiments.
As shown in fig. 1 and 2, the principle of the invention is that a mesh naturally falls on a point cloud, and a model consistent with the point cloud appearance is obtained by covering the outline of the point cloud appearance with the mesh, so as to perform filtering: (1) the influence of abnormal data (such as air noise, underground noise and relatively sparse point cloud data) on the result is reduced, and the stability of the calculation result is improved; (2) the calculation result is continuous. In addition, for DTM and DEM output, non-ground points are filtered by turning over the point cloud and utilizing the mutual dragging action among grid particles.
In order to achieve the above object, the present invention provides a filtering method based on laser point cloud data, as shown in fig. 3, including:
s1 preprocessing step: preprocessing the original point cloud to eliminate isolated points; if a digital terrain model or a digital elevation model needs to be acquired, inverting the point cloud; the point cloud inversion refers to turning over the point cloud by taking the xoy plane as a symmetric plane, and actually, the point cloud is turned over with the bottom upwards.
S2 mesh initialization step: generating a grid, setting the mesh size, and arranging the grid above the point cloud; in this embodiment, the mesh is composed of a plurality of quadrilateral meshes, each vertex of each mesh is a mesh particle, and each mesh particle has four adjacent points. During initialization, the whole grid is located on the same plane, and if the coordinates of a grid particle are (x, y), the coordinates of four adjacent points of the grid particle are (x +1, y +1), (x +1, y-1), (x-1, y +1), and (x-1, y-1), respectively. In addition, the mesh particles need to be higher than all the point clouds when initializing.
Step S3 determining nearest neighbor: searching the nearest point in the point cloud for each grid particle; in this embodiment, the method for finding the closest point includes projecting the mesh particles and the point cloud onto the same plane, calculating a distance between the mesh particles and the surrounding point cloud, and selecting a point with the closest distance as the closest point. After the grid particles and the point cloud are projected to the same plane, only the plane distance between the grid particles and the point cloud needs to be calculated, and the point with the shortest distance is selected, wherein the grid particles and the point cloud are the closest points to each other.
S4 external factor calculating step: calculating the displacement of the grid particles caused by the natural falling of the grid particles only under the gravity; assuming that the grid particles are all particles with constant mass and no size, this way of grid is also referred to as particle spring model. To simulate a mesh as a shape at a certain moment in a free-fall, the spatial position of each mesh particle needs to be calculated. It is assumed that each mesh particle can only move in the vertical direction and is only subject to the interaction force of gravity and neighboring points. When only the action of gravity is considered, the relationship between the spatial position of the grid particle and the force it is subjected to can be determined from newton's second law:
X(t+Δt)＝2X(t)-X(t-Δt)+GΔt2/m
where m represents the mass of the grid particles, G represents the gravitational acceleration constant, x (t) represents the grid particle position at a certain time, and Δ t represents the time step. When the time step and the initial position of the mesh particle are known, the current position of the mesh particle can be calculated.
After calculating the displacement generated by the natural falling of all the grid particles only under the action of gravity, comparing the elevation of each grid particle after falling with the elevation of the nearest point in the point cloud, if the elevation of the grid particle is less than or equal to the elevation of the nearest point, adjusting the elevation of the grid particle to the elevation of the nearest point and marking the grid particle as a non-movable particle; if the elevation of a grid particle is greater than the elevation of its nearest neighbor, then the grid particle is labeled as a movable particle.
S5 weight coefficient calculation step: determining a weight coefficient between adjacent grid particles through a terrain fluctuation parameter reference value and a terrain fluctuation parameter;
in order to improve the calculation precision of the filtering under different terrains, the invention introduces a weight coefficient function between adjacent grid particles
Regulation is carried out, wherein piIs pjThe x is a terrain relief parameter (the larger the x is, the larger the terrain relief is, the smaller the x is, the smaller the terrain relief is; the curvature, the gradient, the characteristic entropy, the anisotropy, the linearity, the height difference among grid particles and the like are included but not limited), and the terrain relief is directly converted into a weight coefficient among adjacent particles so as to improve the adaptability of the filter to complex terrain.
As shown in fig. 4, the weight coefficient function has the following characteristics:
1. when x tends to 0, the weight coefficient tends to 1, and with the increase of x, the weight coefficient tends to 0;
2. the function is in a decreasing trend as a whole, but the descending speed is gradually increased, and when the descending speed is increased to a certain speed, the descending speed is gradually reduced.
In this embodiment, the topographic relief parameter is a function of height difference between grid particles and weight coefficient
The calculation formula of (2) is as follows:
wherein x is an adjacent particle piAnd pjHeight difference of (d)refIs the height of reference, entered by the user. FIG. 5 shows a difference drefAnd (5) taking a weight curve graph.
S6 internal factor calculation step: calculating displacement generated by the action force between the grid particles on the grid particles through the weight coefficient; for any two adjacent grid particles, if the two grid particles are both immovable particles, the two grid particles do not move; if the two grid particles are both movable particles, the two grid particles move in opposite directions for the same distance; if one of the two grid particles is an immovable particle and the other is a movable particle, the movable particle is moved.
When two adjacent grid particles are one immovableWhen the other movable particle is a movable particle, the displacement interval of the movable particle is (0, Δ H)]，ΔH＝pi-pjThe height difference of two adjacent grid particles is adjusted in an interval range through a weight coefficient, and the formula is as follows:
when two adjacent grid particles are movable particles, the displacement interval of the two grid particles is (0, 0.5 delta H)],ΔH＝pi-pjThe height difference of two adjacent grid particles is adjusted in an interval range through a weight coefficient, and the formula is as follows:
in the above two equations, d is the displacement of the particle, b is equal to 1 when the particle is movable, b is 0 when the particle is not movable, and p isiIs pjN is a unit vector (0, 0, 1) normalized to the vertical direction of the dotT。
S7, repeatedly executing the step of calculating the external factors of S4, the step of calculating the weight coefficient of S5 and the step of calculating the internal factors of S6 until the elevation change of all the grid particles is smaller than a preset value or the iteration times is larger than the preset value.
S8, if it is needed to obtain digital terrain model or digital elevation model, calculating the height difference between the point cloud and its nearest neighbor point in the grid particles, if the height difference is greater than or equal to the threshold value, it is considered as non-ground point and removed, if the height difference is less than the threshold value, it is considered as ground point and retained.
As shown in fig. 6, the present invention further provides a filtering system based on laser point cloud data, including:
the point cloud preprocessing module is used for preprocessing the original point cloud and eliminating isolated points; for digital terrain model or digital elevation model output, the point cloud can be inverted;
the grid generating module is used for generating a grid, defining the mesh size and arranging the grid above the point cloud;
a nearest point searching module for searching nearest points in the point cloud for each mesh particle;
the external factor calculation module is used for calculating the displacement of the grid particles falling naturally only under the action of gravity;
the weight coefficient calculation module is used for determining the weight coefficient between adjacent grid particles by utilizing the terrain fluctuation parameter reference value and the terrain fluctuation parameter;
the internal factor calculation module is used for calculating displacement generated by the action force between the grid particles on the grid particles through the weight coefficient;
and the point cloud segmentation module is used for segmenting the ground point cloud and the non-ground point cloud according to a result of comparing the height difference of the point cloud and the nearest point in the grid particles with a threshold value when the digital terrain model or the digital elevation model is output.
The invention also provides an electronic device which comprises a memory and a processor, wherein the memory stores the point cloud filtering program, and the processor executes the point cloud filtering program to realize the filtering method based on the laser point cloud data.
The invention also provides a storage device, wherein the storage device stores a point cloud filtering program, and the point cloud filtering program can realize the filtering method based on the laser point cloud data when being executed by the processor.
The above is only a preferred embodiment of the present invention, and it should be noted that the above preferred embodiment should not be considered as limiting the present invention, and the protection scope of the present invention should be subject to the scope defined by the claims. It will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the spirit and scope of the invention, and these modifications and adaptations should be considered within the scope of the invention.
Claims (12)
1. A filtering method based on laser point cloud data is characterized by comprising the following steps:
a pretreatment step: preprocessing the original point cloud to eliminate isolated points; if a digital terrain model or a digital elevation model needs to be acquired, inverting the point cloud;
a mesh initializing step: generating a grid, setting the mesh size, and arranging the grid above the point cloud;
determining the nearest neighbor point: searching a nearest point in the point cloud for each grid particle;
calculating external factors: calculating the displacement of the grid particles caused by the natural falling of the grid particles only under the gravity;
calculating a weight coefficient: determining a weight coefficient between adjacent grid particles according to the reference value of the topographic relief parameter and the topographic relief parameter;
internal factor calculation step: calculating displacement generated by the action force between the grid particles on the grid particles through the weight coefficient;
and repeatedly executing the external factor calculation step, the weight coefficient calculation step and the internal factor calculation step until the elevation change of all the grid particles is smaller than a preset value or the iteration times are larger than the preset value.
2. The method for filtering based on laser point cloud data according to claim 1, wherein the step of preprocessing comprises inverting the point cloud by using the xoy plane as a symmetry plane.
3. The method as claimed in claim 1, wherein the step of initializing mesh comprises a plurality of quadrilateral meshes, and each vertex of the mesh is a mesh particle.
4. The method of claim 1, wherein the step of determining the nearest neighbor comprises:
projecting the grid particles and the point cloud to the same plane, calculating the distance between the grid particles and the surrounding point cloud, and selecting a point with the closest distance as a nearest point.
5. The filtering method based on the laser point cloud data as claimed in claim 1, wherein:
in the external factor calculation step, after calculating the displacement amount generated by the natural falling of the grid particles only under the action of gravity, comparing the height of each grid particle after falling with the height of the nearest point in the point cloud, and if the height of the grid particle is less than or equal to the height of the nearest point, adjusting the height of the grid particle to the height of the nearest point and marking the grid particle as a non-movable particle; if the elevation of a grid particle is greater than the elevation of its nearest neighbor, then the grid particle is labeled as a movable particle.
6. The filtering method based on the laser point cloud data of claim 1, wherein in the internal factor calculating step, for any two adjacent grid particles, if both grid particles are immobile particles, no movement is performed; if the two grid particles are both movable particles, the two grid particles move in opposite directions for the same distance; if one of the two grid particles is an immovable particle and the other is a movable particle, the movable particle is moved.
7. The method as claimed in claim 6, wherein in the step of calculating the internal factors, when two adjacent grid particles are immovable particles and movable particles, the displacement interval of the movable particles is (0, H), and H is the height difference between the two adjacent grid particles and is adjusted within the interval range by the weight coefficient, and when two adjacent grid particles are movable particles, the displacement interval of the two grid particles is (0, 0.5H), and H is the height difference between the two adjacent grid particles and is adjusted within the interval range by the weight coefficient.
8. The method of claim 1, wherein if a digital terrain model or a digital elevation model is required, the height difference between the point cloud and its nearest neighboring point in the grid particles is calculated, if the height difference is greater than or equal to a threshold, it is considered as a non-ground point and removed, and if the height difference is less than the threshold, it is considered as a ground point and retained.
9. A filtering system based on laser point cloud data, comprising:
the point cloud preprocessing module is used for preprocessing the original point cloud and eliminating isolated points; for digital terrain model or digital elevation model output, the point cloud can be inverted;
the grid generating module is used for generating a grid, defining the mesh size and arranging the grid above the point cloud;
a nearest point searching module for searching nearest points in the point cloud for each mesh particle;
the external factor calculation module is used for calculating the displacement of the grid particles which naturally fall only under the action of gravity;
the weight coefficient calculation module is used for determining the weight coefficient between adjacent grid particles by using the topographic relief parameter reference value and the topographic relief parameter;
and the internal factor calculation module is used for calculating displacement generated by the action force between the grid particles on the grid particles through the weight coefficient.
10. The laser point cloud data-based filtering system of claim 9, further comprising a point cloud segmentation module for segmenting the ground point cloud and the non-ground point cloud while outputting the digital terrain model or the digital elevation model.
11. An electronic device, comprising a memory and a processor, wherein the memory stores a point cloud filtering program, and the processor executes the point cloud filtering program to implement the filtering method based on the laser point cloud data according to any one of claims 1 to 8.
12. A memory, wherein the memory stores a point cloud filtering program, and the point cloud filtering program, when executed by the processor, can implement the filtering method based on laser point cloud data according to any one of claims 1 to 8.
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| CN114820400A (en) * | 2022-07-01 | 2022-07-29 | 湖南盛鼎科技发展有限责任公司 | Airborne LiDAR point cloud ground point filtering method |
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| CN114820400A (en) * | 2022-07-01 | 2022-07-29 | 湖南盛鼎科技发展有限责任公司 | Airborne LiDAR point cloud ground point filtering method |
| CN114820400B (en) * | 2022-07-01 | 2022-09-23 | 湖南盛鼎科技发展有限责任公司 | Airborne LiDAR point cloud ground point filtering method |
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