CN108846809A - A kind of noise eliminating method towards point off density cloud - Google Patents
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Abstract
The embodiment of the present invention proposes a kind of noise eliminating method towards point off density cloud, is related to noise management technique field, this method includes：Principal component analysis fit Plane equation；Distance and standard deviation of the calculating point cloud midpoint to plane；Noise spot judgement, point set after being handled；Establish K-D tree index；K value is inputted, point set is traversed, determines each vertex neighborhood；Calculate the average value and standard deviation of the distance (point number) put in each vertex neighborhood；Noise spot judgement and rejecting.A kind of noise eliminating method towards point off density cloud provided by the embodiment of the present invention, can accurately reject the point cloud noise with plane characteristic.
Description
Technical Field
The invention relates to the technical field of noise processing, in particular to a noise elimination method for dense point cloud.
Background
The plane features occupy an important position in infrastructure, particularly show more obviously in building walls, and a large number of objects with the plane features form the walls. Because influence such as ageing, natural disasters, foundation ditch excavation, the building inevitable takes place deformation, and this kind of deformation can cause huge incident if surpassing certain limit, consequently need regularly carry out safety inspection to it, gets rid of the potential safety hazard and ensures its security. The ground laser radar technology is a novel measurement technology which is rapidly developed in recent years, has the advantages of high speed, high precision and high resolution, and is gradually applied to the field of deformation monitoring. However, due to the influence of factors such as the precision of the instrument, the surface material of the object to be measured, illumination, shielding and the like, the point cloud on the surface of the object to be measured inevitably contains noise points, and the noise elimination of the original point cloud data is of great significance for obtaining a reliable deformation analysis data source. In consideration of the characteristic that the plane features of the building wall are more, the method for removing the surface noise points of the plane feature objects has important practical significance.
Disclosure of Invention
The invention aims to provide a noise elimination method facing dense point clouds, which can eliminate point cloud noise with plane characteristics at high precision.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
the embodiment of the invention provides a noise elimination method facing dense point cloud, which comprises the following steps:
s1, acquiring point cloud data set { P ] of object surface with plane features by adopting ground laser radar systemi1,2, … n, where n is the number of point cloud data concentration points, and the data of each point includes the three-dimensional coordinates and intensity of the object surface point;
s2, calculating a plane equation where the point cloud data set is located by utilizing a principal component analysis algorithm to obtain a plane normal vector;
s3, calculating the distance d between each point in the point cloud data set and the plane equationiWherein i is 1,2, …, n;
s4, distance d from each point to the plane equationiObtaining a standard deviation sigma of the point cloud data set;
s5, judging noise points of the point cloud data set, selecting two times of standard deviation sigma as a threshold value to remove the noise points of the point cloud data set according to a classical gross error removal theory, and taking all the remaining points as a reserved point set Q (x)j,yj,zj)j＝1,2,…,m；
S6, establishing a K-D tree index for the reserved point set;
s7, inputting k value, traversing the reserved point set, and determining each point in the reserved point setAnd generating a neighborhood point set t (x) with the k-neighbors of each pointl,yl,zl)l＝1,2,…,k；
S8, calculating the distance M between each point in the neighborhood point set and the current pointjAnd standard deviation sigmajWherein j is 1,2, …, m;
s9, judging noise points of the reserved point set, selecting two times of standard deviation sigma as a threshold value according to a classical gross error rejection theory to reject the noise points in the reserved point set, and obtaining a reserved point set R (x) after noise rejectionu,yu,zu)u＝1,2,…,fF is the number of the concentrated points of the reserved points after the noise is eliminated;
s10, if cluster noise exists in the reserved point set after the noise is removed, re-inputting a new k value, and repeatedly executing S7, S8 and S9.
Further, the specific steps of calculating the plane equation where the point cloud data set is located by using the principal component analysis algorithm in step S2 are as follows:
the point cloud data set is M, and the corresponding covariance matrix is constructed as follows:
wherein,for the barycentric coordinates of the point set, the principal component analysis is carried out on the matrix C to obtain three characteristic values lambda1、λ2、λ3In descending order to obtain lambda1≥λ2>λ3>0，λ3Corresponding feature vector v3And v is3Is a normal vector with a value v3(a, b, c), then the equation for the point cloud plane is:
ax+by+cz＝d
wherein,
further, in step S3, the specific expression of the distance from each point to the plane equation is:
wherein,
further, in step S4, the distance d from each point to the plane equation is determined according to the distance diObtaining an expression of the standard deviation sigma of the point cloud data set as follows:
further, in step S5, the specific manner of determining the noise point of the point cloud data set is as follows:
when d isi>When the distance is 2 sigma, the point is considered as a noise point and is removed, otherwise, the point is a reserved point; traversing all the points in the point cloud data set to obtain the reserved point set Q (x)j,yj,zj)j＝1,2,…,m。
Further, in step S7, the specific way of determining the k-nearest neighbor of each point in the reserved point set is as follows:
for a set of reservation points Q (x) containing m pointsj,yj,zj)j＝1,2,…,mFor the current point p0E.g. Q, find a new one containing k points (no p)0Point), and satisfies S e Q and k<m for any p1∈S，p2∈Q-S，Dist(p0-p1)≤Dist(p0-p2) Wherein, Dist (p)i,pj) Represents piAnd pjA distance between p, whereiniAnd pjThe distance between them is the euclidean distance.
Further, in step S8, a distance M between each point in the neighborhood point set and the current point is calculatedjAnd standard deviation sigmajThe specific mode is as follows:
further, in step S9, the specific manner of determining the noise point for the reserved point set is as follows:
when M isj>2σjIf so, the point is regarded as a noise point and is removed, otherwise, the point is a reserved point; traversing all the points in the point cloud data set to obtain the reserved point set R (x)u,yu,zu)u＝1,2,…,f。
Compared with the prior art, the noise elimination method for dense point clouds provided by the embodiment of the invention has the following beneficial effects: the global noise is eliminated by utilizing the principal component analysis fitting plane, the local small noise is effectively eliminated in the subsequent process of considering the local neighborhood, and the point cloud noise with the plane characteristic can be eliminated with high precision through the two-step method. .
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 shows a schematic flow chart of a noise elimination method for dense point cloud according to an embodiment of the present invention;
FIG. 2 illustrates a raw point cloud scatter intensity map of an embodiment of the invention;
FIG. 3 illustrates a top view of an original point cloud scatter intensity map of an embodiment of the present invention;
FIG. 4 illustrates a top view of a plot of the intensity of the scatter plot after noise rejection using principal component analysis in accordance with an embodiment of the present invention;
FIG. 5 illustrates a top view of a scatter plot after noise culling using local neighborhoods, in accordance with an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Some embodiments of the invention are described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a noise rejection method for dense point clouds according to an embodiment of the present invention, where the noise rejection method for dense point clouds includes the following steps:
s1 acquisition tool adopting ground laser radar systemPoint cloud dataset { P) of an object surface with planar featuresi1,2, … n, where n is the number of points in the point cloud data set, and the data for each point includes the three-dimensional coordinates and intensity of the object surface point.
And S2, calculating a plane equation where the point cloud data set is located by utilizing a principal component analysis algorithm to obtain a plane normal vector.
Specifically, the specific steps of calculating the plane equation where the point cloud data set is located by using a principal component analysis algorithm are as follows:
the point cloud data set is M, and the corresponding covariance matrix is constructed as follows:
wherein,for the barycentric coordinates of the point set, the principal component analysis is carried out on the matrix C to obtain three characteristic values lambda1、λ2、λ3In descending order to obtain lambda1≥λ2>λ3>0，λ3Corresponding feature vector v3And v is3Is a normal vector with a value v3(a, b, c), then the equation for the point cloud plane is:
ax+by+cz＝d
wherein,
s3, calculating the distance d between each point in the point cloud data set and the plane equationiWherein i is 1,2, …, n.
Specifically, the specific expression of the distance from each point to the plane equation is:
wherein,
s4, according to the distance d between each point cloud data and the plane equationiAnd obtaining the standard deviation sigma of the point cloud data set.
In particular, the distance d from each point to the plane equationiObtaining an expression of the standard deviation sigma of the point cloud data set as follows:
s5, judging noise points of the point cloud data set, selecting two times of standard deviation sigma as a threshold value to remove the noise points of the point cloud data set according to a classical gross error removal theory, and taking all the remaining points as a reserved point set Q (x)j,yj,zj)j＝1,2,…,m。
Specifically, the specific way of judging the noise point of the point cloud data set is as follows:
when d isi>When the distance is 2 sigma, the point is considered as a noise point and is removed, otherwise, the point is a reserved point; traversing all the points in the point cloud data set to obtain the reserved point set Q (x)j,yj,zj)j＝1,2,…,m。
And S6, establishing a K-D tree index for the reserved point set.
S7, inputting k value, traversing the reserved point set, determining k-neighbor of each point in the reserved point set, and generating neighborhood point set t (x) by using the k-neighbor of each pointl,yl,zl)l＝1,2,…,k。
Specifically, the specific way of determining the k-nearest neighbor of each point in the reserved point set is as follows:
for a set of reservation points Q (x) containing m pointsj,yj,zj)j＝1,2,…,mFor the current point p0E.g. Q, find a new one containing k points (no p)0Point), and satisfies S e Q and k<m for any p1∈S，p2∈Q-S，Dist(p0-p1)≤Dist(p0-p2) Wherein, Dist (p)i,pj) Represents piAnd pjA distance between p, whereiniAnd pjThe distance between them is the euclidean distance.
S8, calculating the distance M between each point in the neighborhood point set and the current pointjAnd standard deviation sigmajWherein j is 1,2, …, m.
Specifically, calculating the distance M between each point in the neighborhood point set and the current pointjAnd standard deviation sigmajThe specific mode is as follows:
s9, judging noise points of the reserved point set, selecting two times of standard deviation sigma as a threshold value according to a classical gross error rejection theory to reject the noise points in the reserved point set, and obtaining a reserved point set R (x) after noise rejectionu,yu,zu)u＝1,2,…,fAnd f is the number of the concentrated points of the reserved points after the noise is eliminated.
Specifically, the specific way of performing noise point judgment on the reserved point set is as follows:
when M isj>2σjIf so, the point is regarded as a noise point and is removed, otherwise, the point is a reserved point; traversing all point cloud data in the point cloud data set to obtain the reserved point set R (x)u,yu,zu)u＝1,2,…,f。
S10, if cluster noise exists in the reserved point set after the noise is removed, re-inputting a new k value, and repeatedly executing S7, S8 and S9.
Specifically, taking "brick and stone wall scanning in a certain experimental field" as an example, the method for dense point cloud-oriented fusion principal component analysis and local neighborhood noise elimination provided by the embodiment of the present invention is further described:
(1) a Leica C10 laser scanner system is used for scanning the wall surface, observed values are three-dimensional coordinates and intensity of measuring points on the surface of the wall body, 14540 points are counted, the distance standard deviation of the original point cloud is 2.55mm, and the scatter diagram of the original point cloud is colored according to the intensity and is shown in figure 2.
(2) And (3) removing the global noise by using a principal component analysis method, wherein the number of points contained in the removed reserved point set is 14133, and top views of the scatter diagrams before and after removal are respectively shown in fig. 3 and fig. 4.
(3) And (3) performing secondary elimination on the noise by using a local neighborhood method, setting the k value as 20, keeping the number of the point sets after the elimination as 13451, setting the distance standard deviation as 0.83mm, and showing a top view of the scatter diagram after the elimination as shown in FIG. 5.
By comparing the distance standard deviation (2.55 mm before the elimination and 0.83mm after the elimination) before and after the elimination, the noise elimination method for the dense point cloud can effectively eliminate the noise points.
In summary, according to the noise elimination method for dense point clouds provided by the embodiment of the invention, the principal component analysis fitting plane is utilized to eliminate the global noise, the local small noise is effectively eliminated in the subsequent process of considering the local neighborhood, and the point cloud noise with plane characteristics can be eliminated with high precision through the two-step method.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (8)
1. A noise elimination method facing dense point cloud is characterized by comprising the following steps:
s1, acquiring point cloud data set { P ] of object surface with plane features by adopting ground laser radar systemi1,2, … n, where n is the number of point cloud data concentration points, and the data of each point includes the three-dimensional coordinates and intensity of the object surface point;
s2, calculating a plane equation where the point cloud data set is located by utilizing a principal component analysis algorithm to obtain a plane normal vector;
s3, computing stationThe distance d from each point in the point cloud data set to the plane equationiWherein i is 1,2, …, n;
s4, distance d from each point to the plane equationiObtaining a standard deviation sigma of the point cloud data set;
s5, judging noise points of the point cloud data set, selecting two times of standard deviation sigma as a threshold value to remove the noise points of the point cloud data set according to a classical gross error removal theory, and taking all the remaining points as a reserved point set Q (x)j,yj,zj)j＝1,2,…,m；
S6, establishing a K-D tree index for the reserved point set;
s7, inputting k value, traversing the reserved point set, determining k-neighbor of each point in the reserved point set, and generating neighborhood point set t (x) by using the k-neighbor of each pointl,yl,zl)l＝1,2,…,k；
S8, calculating the distance M between each point in the neighborhood point set and the current pointjAnd standard deviation sigmajWherein j is 1,2, …, m;
s9, judging noise points of the reserved point set, selecting two times of standard deviation sigma as a threshold value according to a classical gross error rejection theory to reject the noise points in the reserved point set, and obtaining a reserved point set R (x) after noise rejectionu,yu,zu)u＝1,2,…,fF is the number of the concentrated points of the reserved points after the noise is eliminated;
s10, if cluster noise exists in the reserved point set after the noise is removed, re-inputting a new k value, and repeatedly executing S7, S8 and S9.
2. The method of claim 1, wherein the step of calculating the plane equation of the point cloud data set by using the principal component analysis algorithm in the step S2 comprises the following specific steps:
the point cloud data set is M, and the corresponding covariance matrix is constructed as follows:
wherein, for the barycentric coordinates of the point set, the principal component analysis is carried out on the matrix C to obtain three characteristic values lambda1、λ2、λ3In descending order to obtain lambda1≥λ2>λ3>0，λ3Corresponding feature vector v3And v is3Is a normal vector with a value v3(a, b, c), then the equation for the point cloud plane is:
ax+by+cz＝d
wherein,
3. the method of claim 1, wherein in step S3, the specific expression for the distance of each point from the plane equation is:
wherein,
4. the method according to claim 1, wherein in step S4, the distance d from each point to the plane equation is determined according to the distance d between each point and the plane equationiObtaining an expression of the standard deviation sigma of the point cloud data set as follows:
5. the method of claim 1, wherein in step S5, the noise point determination for the point cloud data set is performed by:
when d isi>When the distance is 2 sigma, the point is considered as a noise point and is removed, otherwise, the point is a reserved point; traversing all the points in the point cloud data set to obtain the reserved point set Q (x)j,yj,zj)j＝1,2,…,m。
6. The method of claim 1, wherein in step S7, the k-neighbors of each point in the set of reserved points are determined by:
for a set of reservation points Q (x) containing m pointsj,yj,zj)j＝1,2,…,mFor the current point p0E.g. Q, find a new one containing k points (no p)0Point), and satisfies S e Q and k<m for any p1∈S，p2∈Q-S，Dist(p0-p1)≤Dist(p0-p2) Wherein, Dist (p)i,pj) Represents piAnd pjA distance between p, whereiniAnd pjThe distance between them is the euclidean distance.
7. The method of claim 1, wherein in step S8, the distance M between each point in the neighborhood point set and the current point is calculatedjAnd standard deviation sigmajThe specific mode is as follows:
8. the method according to claim 1, wherein in step S9, the noise point determination for the reserved point set is performed by:
when M isj>2σjIf so, the point is regarded as a noise point and is removed, otherwise, the point is a reserved point; traversing all the points in the point cloud data set to obtain the reserved point set R (x)u,yu,zu)u＝1,2,…,f。
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| CN112102178B (en) * | 2020-07-29 | 2024-09-06 | 深圳市菲森科技有限公司 | Point cloud feature denoising method and device, electronic equipment and storage medium |
| CN112233039A (en) * | 2020-10-29 | 2021-01-15 | 中电建路桥集团有限公司 | Three-dimensional laser point cloud denoising method based on domain point space characteristics |
| CN112233039B (en) * | 2020-10-29 | 2022-09-13 | 中电建路桥集团有限公司 | Three-dimensional laser point cloud denoising method based on domain point space characteristics |
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