CN110349092B - Point cloud filtering method and device - Google Patents
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Abstract
The invention is suitable for the technical field of data analysis, and provides a point cloud filtering method and a point cloud filtering device, wherein the point cloud filtering method comprises the following steps: processing the point cloud data by adopting a preset cloth simulation algorithm, and determining first ground point information in the point cloud data; processing the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm, and determining second ground point information in the point cloud data; generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information; determining a first area corresponding to the height difference value meeting a first preset condition, and marking the ground points out of the first area in the first ground points as third ground points; and determining ground point information and non-ground point information of the target area based on the point cloud data, the second ground point and the third ground point. The method does not need to set specific parameters or limit terrain scenes, and can distinguish ground points and non-ground points of various terrains.
Description
Technical Field
The invention belongs to the technical field of data analysis, and particularly relates to a point cloud filtering method and point cloud filtering equipment.
Background
Point cloud filtering is a basic step of point cloud processing, and is also a key step of distinguishing ground points from non-ground points and generating an accurate digital ground model. The existing point cloud filtering methods mainly comprise the following methods: the method comprises a surface fitting filtering method, a topological filtering method, an irregular triangular net asymptotic filtering method, a classification and segmentation filtering method, a statistical analysis filtering algorithm, a multi-scale comparison filtering method and a filtering method based on machine learning.
However, the existing methods have respective disadvantages, and the filtering method of surface fitting cannot well retain some terrain details and can wrongly divide some smaller non-ground objects; due to the limitation of the size of a filtering window, the topological filtering method is difficult to deal with ground features and terrains with variable sizes; due to the limitation of parameter setting, the irregular triangulation network asymptotic filtering cannot acquire dense ground point cloud; the classification and segmentation filtering method may fail in dense vegetation areas, and the uncertainty of classification will increase with the change of the set parameters; the statistical analysis filtering method cannot obtain a good result in a complicated and variable terrain area; the multi-scale comparison filtering method may be limited by the size of the filtering window, and the computational complexity of the algorithm is increased; although the method based on machine learning can obtain a good filtering effect, the method is based on a large amount of training data with different characteristics, and needs to consume a large amount of effort to label training samples, and has high calculation cost, so that the method does not have good applicability. That is to say, the existing point cloud filtering methods cannot obtain dense ground point clouds to accurately represent the changes of different types of landforms under the condition of low cost.
Disclosure of Invention
In view of this, embodiments of the present invention provide a point cloud filtering method and device, so as to solve the problem that none of the point cloud filtering methods in the prior art can obtain dense ground point clouds to accurately represent changes of different types of landforms at a low cost.
A first aspect of an embodiment of the present invention provides a point cloud filtering method, including:
acquiring point cloud data of a target area to be detected;
processing the point cloud data by adopting a preset cloth simulation algorithm, and screening out first ground point information in the point cloud data; wherein the first ground point information comprises identification and location information of the first ground point;
processing the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm, and screening out second ground point information in the point cloud data; the second ground point information comprises identification and position information of the second ground point;
generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information; wherein the height difference is the difference between the point cloud data and the second ground point in the gravity direction;
determining a first area corresponding to the height difference value meeting a first preset condition, and marking the ground points out of the first area in the first ground points as third ground points;
and determining ground point information and non-ground point information of the target area based on the point cloud data, the second ground point and the third ground point.
A second aspect of an embodiment of the present invention provides a point cloud filtering apparatus, including:
the acquisition unit is used for acquiring point cloud data of a target area to be detected;
the first screening unit is used for processing the point cloud data by adopting a preset cloth simulation algorithm and screening out first ground point information in the point cloud data; wherein the first ground point information comprises identification and location information of the first ground point;
the second screening unit is used for processing the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm and screening out second ground point information in the point cloud data; the second ground point information comprises identification and position information of the second ground point;
the generating unit is used for generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information; wherein the height difference is the difference between the point cloud data and the second ground point in the gravity direction;
the first determining unit is used for determining a first area corresponding to the height difference value meeting a first preset condition and marking the ground points out of the first area in the first ground points as third ground points;
and the second determining unit is used for determining the ground point information and the non-ground point information of the target area based on the point cloud data, the second ground point and the third ground point.
A third aspect of embodiments of the present invention provides a point cloud filtering apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the point cloud filtering method according to the first aspect when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the point cloud filtering method according to the first aspect.
According to the embodiment of the invention, point cloud data of a target area to be detected is obtained; processing the point cloud data by adopting a preset cloth simulation algorithm, and determining first ground point information in the point cloud data; wherein the first ground point information comprises identification and location information of the first ground point; processing the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm, and determining second ground point information in the point cloud data; the second ground point information comprises identification and position information of the second ground point; generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information; wherein the height difference is the difference between the point cloud data and the second ground point in the gravity direction; determining a first area corresponding to the height difference value meeting a first preset condition, and marking the ground points out of the first area in the first ground points as third ground points; and determining ground point information and non-ground point information of the target area based on the point cloud data, the second ground point and the third ground point. According to the method, the ground points and the non-ground points of various terrains can be well distinguished without setting specific parameters and limiting terrain scenes.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a point cloud filtering method according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of S101 refinement in a point cloud filtering method according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of S104 refinement in a point cloud filtering method according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of another point cloud filtering method provided by the embodiment of the invention;
FIG. 5 is a schematic flow chart of a refinement at S208 in another point cloud filtering method provided by the embodiment of the invention;
FIG. 6 is a schematic diagram of a point cloud filtering apparatus according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a point cloud filtering apparatus according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Referring to fig. 1, fig. 1 is a schematic flow chart of a point cloud filtering method according to an embodiment of the present invention. The main execution subject of the point cloud filtering method in the present embodiment is a point cloud filtering apparatus, for example, a point cloud filtering server. The point cloud filtering method as shown in fig. 1 may include:
s101: and acquiring point cloud data of a target area to be detected.
When a laser beam irradiates the surface of an object, the reflected laser beam carries information such as direction, distance and the like. When a laser beam is scanned along a certain trajectory, reflected laser point information is recorded while scanning, and since scanning is extremely fine, a large number of laser points can be obtained, and laser point cloud, that is, point cloud data can be formed. In the operation of the airborne laser radar equipment, the laser scanning process is all-regional, namely laser pulses can be applied to the ground and artificial ground objects or vegetation such as buildings, bridges, power lines, lighthouses, vehicles and the like. Therefore, the acquired point cloud data of the airborne laser radar has ground points and ground object points. The process of separating the subset of the terrain surface laser foot point data from the airborne laser radar point cloud data is called filtering.
The point cloud filtering device acquires point cloud data of a target area to be monitored, wherein the target area to be monitored is an area needing to be distinguished from ground points and non-ground points, and the point cloud data of the target area to be monitored comprises the ground points and the ground-flying points of the target area to be monitored.
Further, to remove the erroneous measurement values in the point cloud data, S101 may include S1011 to S1012, as shown in fig. 2, S1011 to S1012 specifically include the following steps:
s1011: and acquiring original point cloud data of the target area.
The point cloud filtering device obtains original point cloud data of the target area, and the details are the same as those in S101, please refer to S101 specifically, which is not described herein again, and the original point cloud data of the target area is point cloud data of the target area that is not screened and processed.
S1012: screening outliers in the original point cloud data to obtain point cloud data of the target area; wherein the outliers are erroneous measurements in the raw point cloud data.
The raw point cloud data may contain erroneous measurements that are neither ground points nor non-ground points, referred to as outliers in this embodiment. In order to remove outliers from the original point cloud data, the point cloud filtering device may preset a screening condition to screen out outliers in the original point cloud data, remove outliers in the original point cloud data, and obtain point cloud data of the target area.
S102: processing the point cloud data by adopting a preset cloth simulation algorithm, and determining first ground point information in the point cloud data; wherein the first ground point information includes an identification and location information of the first ground point.
A cloth simulation algorithm is preset in the point cloud filtering equipment, and is based on simple physical process simulation, wherein the Cloth Simulation Filtering (CSF) algorithm assumes that a piece of virtual cloth falls on the surface of a terrain under the action of gravity, if the piece of cloth is soft enough, the cloth can be attached to the terrain, and the shape of the cloth is DSM. When the terrain is turned over, the shape of the cloth falling on the surface is DEM, and the principle of the cloth simulation algorithm is as follows:
(1) firstly, mirror surface turning is carried out on the point cloud with the outliers removed.
(2) And generating calculation points of the simulated cloth according to the grid resolution set by the user.
(3) The point cloud data and the calculation points of the simulated cloth are projected to a two-dimensional plane, and in the plane, the corresponding points closest to the calculation points of the simulated cloth in the point cloud data are found.
(4) The height value of the corresponding point is determined by the height value of the intersection of the simulated cloth and the point cloud data, and represents the approximate height value of the lowest calculated point.
(5) And comparing the current height value of the calculation point with the size of the intersected height value, and when the current height value is less than or equal to the intersected height, moving the calculation point to the intersected position and setting the calculation point as a fixed point.
(6) And (4) performing multiple simulated cloth circulation until the maximum value of the height change of all the calculation points is smaller than a user-set threshold or the simulation times exceed the user-set threshold.
(7) And calculating the distance between the point cloud data and the calculation points of the simulated cloth, and distinguishing ground points from non-ground points according to a distance threshold. Cloth simulation filtering methods have fewer parameters and are easier to set, but cannot remove lower building point clouds and may fail in data boundaries, sparse and complex terrain. In the invention, the resolution parameter of the cloth simulation filtering is set to be the same as the approximate resolution of the original point cloud data, and the distance parameter is set to be twice the resolution parameter.
The point cloud filtering device processes the point cloud data by adopting a preset cloth simulation algorithm, determines ground points and non-ground points in the point cloud data, marks the ground points in the point cloud data as first ground points, acquires identification and position information of the first ground points, and determines first ground point information, wherein the first ground point information comprises the identification and the position information of the first ground points.
S103: processing the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm, and determining second ground point information in the point cloud data; and the second ground point information comprises the identification and the position information of the second ground point.
An irregular triangulation network asymptotic filtering algorithm is preset in the point cloud filtering equipment, and the algorithm flow of the irregular triangulation network asymptotic filtering is as follows:
(1) and projecting the point cloud data without the outliers onto a two-dimensional plane, then carrying out grid meshing according to the set grid size, and selecting the lowest point in the grid as a seed point.
(2) And constructing an irregular triangulation network by using the seed points, and selecting ground points according to the set angle and the distance threshold.
(3) And (3) continuously circulating the process (2) until no new ground point is detected in the data. Although ground points generated by the method can better cover most of the area of the point cloud data, the smaller point cloud density cannot accurately describe all terrain features.
The point cloud filtering device processes the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm, determines ground points and non-ground points in the point cloud data, acquires the mark of the ground point in the point cloud data as a second ground point through the irregular triangulation network asymptotic filtering algorithm, acquires the mark and the position information of the second ground point, and determines second ground point information, wherein the second ground point information comprises the mark and the position information of the second ground point.
S104: generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information; and the height difference value is the difference value between the point cloud data and the second ground point in the gravity direction.
The second ground points are obtained by adopting the irregular triangulation asymptotic filtering algorithm, so the second ground points are sparse, but are more accurate compared with the first ground points obtained by the cloth simulation algorithm. In order to remove non-ground points which are wrongly divided into ground points in the first ground points, the point cloud filtering device obtains a height difference value between the original point cloud data and the second ground points in the gravity direction, namely a height difference value corresponding to the target area, based on the point cloud data and the identification and position information of the second ground points.
Further, in order to further accurately acquire the height difference corresponding to the target area, S104 may include S1041 to S1042, as shown in fig. 3, S1041 to S1042 specifically include the following:
s1041: and performing two-dimensional rasterization processing on the point cloud data and the second ground point information to acquire coordinate information of the point cloud data and coordinate information of the second ground point.
The point cloud filtering equipment performs two-dimensional rasterization processing on the point cloud data and the second ground point, wherein rasterization is to convert a vector graph into a bitmap (raster image), and a basic rasterization algorithm renders a three-dimensional scene represented by a polygon to a two-dimensional surface. And after the size of the grid is fixed, dividing all the point cloud data and the second ground points into grids, and acquiring coordinate information of the point cloud data and coordinate information of the second ground points.
S1042: and determining a height difference value corresponding to the target area based on the coordinate information of the point cloud data and the coordinate information of the second ground point.
The point cloud filtering device calculates an average value Z of coordinate Z values in the gravity direction of the point cloud data in each grid based on the coordinate information of the point cloud dataRAWCalculating an average value Z of the Z values of the coordinates in the gravity direction of the second ground point based on the coordinate information of the second ground pointTINCalculating the difference between the average value of the coordinate Z values in the gravity direction of the point cloud data and the average value of the coordinate Z values in the gravity direction of the second ground point, wherein the difference is expressed as delta Z, and the height difference corresponding to the target area can also be determined by the delta Z, wherein the calculation formula is as follows:
ΔZ＝ZRAW-ZTIN
s105: determining a first area corresponding to the height difference value meeting a first preset condition, and marking the ground points out of the first area in the first ground points as third ground points.
The point cloud filtering device is preset with conditions for screening out non-ground points, the height difference value can be processed in a statistical analysis mode, the mean value and the standard deviation of the height difference value are calculated, the preset conditions can be that the threshold range is set to be the standard deviation which is larger than the mean value minus three times and smaller than the standard deviation which is larger than the mean value plus three times, the area represented by the height difference value outside the range is marked as a first area which is a vegetation or building area, and the area is the non-ground point area.
If the first ground points exist in the first area, the points are the non-ground points extracted by mistake in the first ground points, the point cloud filtering equipment obtains the ground points out of the first area in the first ground points, namely the non-ground points extracted by mistake in the first ground points are abandoned, and the ground points out of the first area in the first ground points are marked as third ground points.
S106: and determining ground point information and non-ground point information of the target area based on the point cloud data, the second ground point and the third ground point.
Since the third ground point is a relatively accurate ground point obtained after screening, the ground point and the non-ground point of the target area can be distinguished based on the point cloud data, the second ground point and the third ground point. In one embodiment, simple differentiation can be performed, that is, after the ground is confirmed based on the second ground point and the third ground point, the non-ground point is confirmed after the ground point is removed from the point cloud data; in one embodiment, a fitting plane corresponding to point cloud data obtained by combining the second ground point and the third ground point may be obtained, then a difference between a coordinate Z of the point cloud data in the central grid in the gravity direction and a Z value of a corresponding key point of the fitting plane may be obtained, and the ground point information and the non-ground point information of the target area may be determined based on the difference.
According to the embodiment of the invention, point cloud data of a target area to be detected is obtained; processing the point cloud data by adopting a preset cloth simulation algorithm, and determining first ground point information in the point cloud data; wherein the first ground point information comprises identification and location information of the first ground point; processing the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm, and determining second ground point information in the point cloud data; the second ground point information comprises identification and position information of the second ground point; generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information; wherein the height difference is the difference between the point cloud data and the second ground point in the gravity direction; determining a first area corresponding to the height difference value meeting a first preset condition, and marking the ground points out of the first area in the first ground points as third ground points; and determining ground point information and non-ground point information of the target area based on the point cloud data, the second ground point and the third ground point. According to the method, the ground points and the non-ground points of various terrains can be well distinguished without setting specific parameters and limiting terrain scenes.
Referring to fig. 4, fig. 4 is a schematic flow chart of another point cloud filtering method according to an embodiment of the present invention. The main execution subject of the point cloud filtering method in the present embodiment is a point cloud filtering apparatus, for example, a point cloud filtering server. In order to accurately distinguish the ground points from the non-ground points, the present embodiment is different from the previous embodiment in that S206 to S209, and S201 to S205 are the same as S101 to S105 in the previous embodiment, and are not described herein again, S206 to S209 are refinements of S106 in the previous embodiment, S206 to S209 are executed after S201 to S205, and S206 to S209 specifically include the following steps:
s206: and performing two-dimensional rasterization processing on the point cloud data to acquire coordinate information of the point cloud data in a target grid.
The point cloud filtering equipment carries out two-dimensional rasterization processing on the point cloud data, rasterization is to convert vector graphics into bitmaps (raster images), and the most basic rasterization algorithm renders three-dimensional scenes represented by polygons to two-dimensional surfaces. And after the size of the grid is fixed, dividing all point cloud data into each grid to obtain coordinate information of the point cloud data.
S207: and acquiring a first coordinate value of the point cloud data in the gravity direction based on the coordinate information of the point cloud data in the target grid.
And the point cloud filtering equipment acquires a first coordinate value of the point cloud data in the gravity direction based on the coordinate information of the point cloud data in the target grid.
S208: and determining a fitting plane based on the second ground point, the third ground point and the target grid, and acquiring a second coordinate value of the key point of the fitting plane in the gravity direction.
The fitting of the plane refers to generating a smooth plane based on the target point, so that the target point is all located on the generated plane, the point cloud filtering device determines a fitting plane based on the second ground point, the third ground point and the target grid to obtain key points of the fitting plane, and the method for selecting the key points can determine the number of the key points and the distance between each key point according to the size of the fitting plane. And acquiring a second coordinate value of the key point in the gravity direction.
Further, for generating the fitting plane accurately, S208 may include S2081 to S2082, and as shown in fig. 5, S2081 to S2082 are specifically as follows:
s2081: and determining a plane fitting window based on a preset geometric precision factor, and determining a fitting plane in the plane fitting window based on the second ground point and the third ground point by taking a central grid in the target grid as a center.
In this embodiment, the point cloud filtering device presets a geometric accuracy factor to ensure reasonable distribution of the selection of the plane fitting points. When the geometric accuracy factor is set to be larger, the plane fitting points are distributed around the grid in a more dispersed manner, so that the situation that the fitting accuracy is not high due to the fact that the fitting points are distributed in a more concentrated area can be avoided. Specifically, a threshold of geometric accuracy factor may be set, and it is determined whether the geometric accuracy factor of the distribution of the plane fitting points in the current plane fitting window is greater than the threshold, and if the geometric accuracy factor is less than the threshold, the size of the plane fitting window is gradually enlarged to obtain the plane fitting points whose distribution is more dispersed.
The point cloud filtering equipment determines the size of a plane fitting window through a geometric precision factor, and determines a fitting plane in the plane fitting window based on the second ground point and the third ground point by taking a central grid in the target grid as a center.
S2082: and acquiring a second coordinate value of the fitting plane in the gravity direction.
For details in S2082, reference may be made to S208, which is not described herein again.
S209: and classifying the point cloud data based on the first coordinate value and the second coordinate value, and determining the ground point information and the non-ground point information of the target area.
The point cloud filtering equipment acquires a difference value between the first coordinate value and the second coordinate value, reserves negative values in all the difference values, carries out absolute value operation on all the negative values to obtain an operation result, sets a classification threshold value based on the operation result, and divides the difference value between the first coordinate value and the second coordinate value into two types based on the classification threshold value, wherein points corresponding to the difference value in the two types are ground points and non-ground points.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Referring to fig. 6, fig. 6 is a schematic diagram of a point cloud filtering apparatus according to an embodiment of the present invention. The included units are used for executing steps in the embodiments corresponding to fig. 1 to fig. 5, and refer to the related descriptions in the embodiments corresponding to fig. 1 to fig. 5. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 6, the point cloud filtering device 6 includes:
an obtaining unit 610, configured to obtain point cloud data of a target area to be detected;
a first screening unit 620, configured to process the point cloud data by using a preset cloth simulation algorithm, and screen out first ground point information in the point cloud data; wherein the first ground point information comprises identification and location information of the first ground point;
a second screening unit 630, configured to process the point cloud data by using a preset irregular triangulation asymptotic filtering algorithm, and screen out second ground point information in the point cloud data; the second ground point information comprises identification and position information of the second ground point;
a generating unit 640, configured to generate a height difference value corresponding to the target area based on the point cloud data and the second ground point information; wherein the height difference is the difference between the point cloud data and the second ground point in the gravity direction;
a first determining unit 650, configured to determine a first region corresponding to the height difference meeting a first preset condition, and mark, as a third ground point, a ground point, which is located outside the first region, in the first ground points;
a second determining unit 660, configured to determine ground point information and non-ground point information of the target area based on the point cloud data, the second ground point, and the third ground point.
Further, the second determining unit 660 includes:
the first processing unit is used for performing two-dimensional rasterization processing on the point cloud data to acquire coordinate information of the point cloud data in a target grid;
the second processing unit is used for acquiring a first coordinate value of the point cloud data in the gravity direction based on the coordinate information of the point cloud data in the target grid;
the third processing unit is used for determining a fitting plane based on the second ground point, the third ground point and the target grid, and acquiring a second coordinate value of a key point of the fitting plane in the gravity direction;
and the fourth processing unit is used for classifying the point cloud data based on the first coordinate value and the second coordinate value and determining the ground point information and the non-ground point information of the target area.
Further, the third processing unit is specifically configured to:
determining a plane fitting window based on a preset geometric precision factor, and determining a fitting plane in the plane fitting window based on the second ground point and the third ground point by taking a central grid in the target grid as a center;
and acquiring a second coordinate value of the fitting plane in the gravity direction.
Further, the generating unit 640 is specifically configured to:
performing two-dimensional rasterization processing on the point cloud data and the second ground point information to acquire coordinate information of the point cloud data and coordinate information of the second ground point;
and determining a height difference value corresponding to the target area based on the coordinate information of the point cloud data and the coordinate information of the second ground point.
Further, the obtaining unit 610 is specifically configured to:
acquiring original point cloud data of a target area;
screening outliers in the original point cloud data to obtain point cloud data of the target area; wherein the outliers are erroneous measurements in the raw point cloud data.
Fig. 7 is a schematic diagram of a point cloud filtering apparatus according to an embodiment of the present invention. As shown in fig. 7, the point cloud filtering apparatus 7 of this embodiment includes: a processor 70, a memory 71 and a computer program 72, such as a point cloud filtering program, stored in the memory 71 and executable on the processor 70. The processor 70, when executing the computer program 72, implements the steps in the various point cloud filtering method embodiments described above, such as the steps 101-106 shown in fig. 1. Alternatively, the processor 70, when executing the computer program 72, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 610 to 660 shown in fig. 6.
Illustratively, the computer program 72 may be partitioned into one or more modules/units that are stored in the memory 71 and executed by the processor 70 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 72 in the point cloud filtering device 7. For example, the computer program 72 may be divided into an acquisition unit, a first filtering unit, a second filtering unit, a generation unit, a first determination unit, and a second determination unit, and each unit has the following specific functions:
the acquisition unit is used for acquiring point cloud data of a target area to be detected;
the first screening unit is used for processing the point cloud data by adopting a preset cloth simulation algorithm and screening out first ground point information in the point cloud data; wherein the first ground point information comprises identification and location information of the first ground point;
the second screening unit is used for processing the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm and screening out second ground point information in the point cloud data; the second ground point information comprises identification and position information of the second ground point;
the generating unit is used for generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information; wherein the height difference is the difference between the point cloud data and the second ground point in the gravity direction;
the first determining unit is used for determining a first area corresponding to the height difference value meeting a first preset condition and marking the ground points out of the first area in the first ground points as third ground points;
and the second determining unit is used for determining the ground point information and the non-ground point information of the target area based on the point cloud data, the second ground point and the third ground point.
The point cloud filtering device may include, but is not limited to, a processor 70, a memory 71. Those skilled in the art will appreciate that fig. 7 is merely an example of the point cloud filtering device 7, and does not constitute a limitation of the point cloud filtering device 7, and may include more or less components than those shown, or combine some components, or different components, for example, the point cloud filtering device may also include an input-output device, a network access device, a bus, etc.
The Processor 70 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the point cloud filtering device 7, such as a hard disk or a memory of the point cloud filtering device 7. The memory 71 may also be an external storage device of the point cloud filtering device 7, such as a plug-in hard disk provided on the point cloud filtering device 7, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 71 may also include both an internal storage unit and an external storage device of the point cloud filtering device 7. The memory 71 is used to store the computer program and other programs and data required by the point cloud filtering device. The memory 71 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A point cloud filtering method, comprising:
acquiring point cloud data of a target area to be detected;
processing the point cloud data by adopting a preset cloth simulation algorithm, and determining first ground point information in the point cloud data; wherein the first ground point information comprises identification and location information of the first ground point;
processing the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm, and determining second ground point information in the point cloud data; the second ground point information comprises identification and position information of the second ground point;
generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information; wherein the height difference is the difference between the point cloud data and the second ground point in the gravity direction;
determining a first area corresponding to the height difference value meeting a first preset condition, and marking the ground points out of the first area in the first ground points as third ground points; the first preset condition is used for screening a non-ground area, and the first area is a non-ground area;
and determining ground point information and non-ground point information of the target area based on the point cloud data, the second ground point and the third ground point.
2. The point cloud filtering method of claim 1, wherein said determining ground point information and non-ground point information for the target area based on the point cloud data, the second ground points, and the third ground points comprises:
performing two-dimensional rasterization processing on the point cloud data to acquire coordinate information of the point cloud data in a target grid;
acquiring a first coordinate value of the point cloud data in the gravity direction based on the coordinate information of the point cloud data in the target grid;
determining a fitting plane based on the second ground point, the third ground point and the target grid, and acquiring a second coordinate value of a key point of the fitting plane in the gravity direction;
and classifying the point cloud data based on the first coordinate value and the second coordinate value, and determining the ground point information and the non-ground point information of the target area.
3. The point cloud filtering method of claim 2, wherein said determining a fitting plane based on said second ground points, said third ground points and said target grid, obtaining a second coordinate value of a key point of said fitting plane in a direction of gravity, comprises:
determining a plane fitting window based on a preset geometric precision factor, and determining a fitting plane in the plane fitting window based on the second ground point and the third ground point by taking a central grid in the target grid as a center;
and acquiring a second coordinate value of the fitting plane in the gravity direction.
4. The point cloud filtering method of claim 1, wherein the generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information comprises:
performing two-dimensional rasterization processing on the point cloud data and the second ground point information to acquire coordinate information of the point cloud data and coordinate information of the second ground point;
and determining a height difference value corresponding to the target area based on the coordinate information of the point cloud data and the coordinate information of the second ground point.
5. The point cloud filtering method according to any one of claims 1 to 4, wherein the acquiring point cloud data of the target area to be detected comprises:
acquiring original point cloud data of a target area;
screening outliers in the original point cloud data to obtain point cloud data of the target area; wherein the outliers are erroneous measurements in the raw point cloud data.
6. A point cloud filtering device, comprising:
the acquisition unit is used for acquiring point cloud data of a target area to be detected;
the first screening unit is used for processing the point cloud data by adopting a preset cloth simulation algorithm and screening out first ground point information in the point cloud data; wherein the first ground point information comprises identification and location information of the first ground point;
the second screening unit is used for processing the point cloud data by adopting a preset irregular triangulation network asymptotic filtering algorithm and screening out second ground point information in the point cloud data; the second ground point information comprises identification and position information of the second ground point;
the generating unit is used for generating a height difference value corresponding to the target area based on the point cloud data and the second ground point information; wherein the height difference is the difference between the point cloud data and the second ground point in the gravity direction;
the first determining unit is used for determining a first area corresponding to the height difference value meeting a first preset condition and marking the ground points out of the first area in the first ground points as third ground points; the first preset condition is used for screening a non-ground area, and the first area is a non-ground area;
and the second determining unit is used for determining the ground point information and the non-ground point information of the target area based on the point cloud data, the second ground point and the third ground point.
7. The point cloud filtering device of claim 6, the second determining unit, comprising:
the first processing unit is used for performing two-dimensional rasterization processing on the point cloud data to acquire coordinate information of the point cloud data in a target grid;
the second processing unit is used for acquiring a first coordinate value of the point cloud data in the gravity direction based on the coordinate information of the point cloud data in the target grid;
the third processing unit is used for determining a fitting plane based on the second ground point, the third ground point and the target grid, and acquiring a second coordinate value of a key point of the fitting plane in the gravity direction;
and the fourth processing unit is used for classifying the point cloud data based on the first coordinate value and the second coordinate value and determining the ground point information and the non-ground point information of the target area.
8. The point cloud filtering device of claim 7, the third processing unit being specifically configured to:
determining a plane fitting window based on a preset geometric precision factor, and determining a fitting plane in the plane fitting window based on the second ground point and the third ground point by taking a central grid in the target grid as a center;
and acquiring a second coordinate value of the fitting plane in the gravity direction.
9. A point cloud filtering device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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| CN111160328B (en) * | 2020-04-03 | 2023-07-07 | 速度科技股份有限公司 | Automatic extraction method of traffic marking based on semantic segmentation technology |
| CN111859772B (en) * | 2020-07-07 | 2023-11-17 | 河南工程学院 | Power line extraction method and system based on cloth simulation algorithm |
| CN112129266B (en) * | 2020-09-28 | 2022-12-16 | 北京嘀嘀无限科技发展有限公司 | Method, apparatus, device and computer readable storage medium for processing map |
| CN113534193A (en) * | 2021-07-19 | 2021-10-22 | 京东鲲鹏(江苏)科技有限公司 | Method and device for determining target reflection point, electronic equipment and storage medium |
| CN114283090A (en) * | 2021-12-27 | 2022-04-05 | 深圳朗道智通科技有限公司 | Ground filtering method, equipment, storage medium and computer program product |
| CN114255325B (en) * | 2021-12-31 | 2023-07-18 | 广州极飞科技股份有限公司 | Ground model generation, obstacle data determination and operation control method and related device |
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| CN106408604A (en) * | 2016-09-22 | 2017-02-15 | 北京数字绿土科技有限公司 | Filtering method and device for point cloud data |
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