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 present invention relates to noise management technique fields, in particular to a kind of noise eliminating side towards point off density cloud
Method.
Background technique
Plane characteristic, in occupation of critical role, is showed especially in building masonry wall and is become apparent, greatly in infrastructure
Measurer has the object of plane characteristic to constitute wall.Since timeliness, natural calamity, excavation of foundation pit etc. influence, building can not be kept away
Exempting from meeting, deformation occurs, and this deformation is if it exceeds certain limit will cause huge safety accident, therefore need periodically carry out it
Safety detection, excluding security risk ensures its safety.Ground laser radar technology is novel measurement fast-developing in recent years
Technology has high speed, high-precision and high-resolution advantage, is gradually applied to deformation monitoring field.However, due to instrument sheet
Body precision, illumination, the factors such as is blocked and is influenced testee Facing material, unavoidably containing making an uproar in testee surface point cloud
Sound point carries out noise eliminating to original point cloud data and has great importance to obtain reliable deformation analysis data source.Consider
The feature more to building masonry wall plane characteristic, picking for plane characteristic subject surface noise spot can effectively be rejected by seeking one kind
Except method has important practical significance.
Summary of the invention
The purpose of the present invention is to provide a kind of noise eliminating methods towards point off density cloud, can accurately reject tool
There is the point cloud noise of plane characteristic.
To achieve the goals above, technical solution used in the embodiment of the present invention is as follows：
The embodiment of the invention provides a kind of noise eliminating method towards point off density cloud, the method includes：
S1, the point cloud data collection { P with the subject surface of plane characteristic is obtained using ground laser radar systemi| i=
1,2 ... n }, wherein the data of the quantity of point cloud data centrostigma described in n, each point include the three-dimensional coordinate of subject surface point
And intensity；
S2, point cloud data collection place plane equation is calculated using Principal Component Analysis Algorithm, obtain plane normal vector；
S3, the distance d that the point cloud data concentrates each point respectively to arrive the plane equation is calculatedi, wherein i=1,
2,…,n；
S4, the distance d that the plane equation is respectively arrived according to each pointi, obtain the standard deviation sigma of the point cloud data collection；
S5, noise point judgement is carried out to the point cloud data collection, it is theoretical according to classical elimination of rough difference, select twice of mark
Quasi- difference σ is that the noise spot that threshold values concentrates the point cloud data is rejected, using remaining all the points as reservation point set Q (xj,
yj,zj)J=1,2 ..., m；
S6, K-D tree index is established to the reservation point set；
S7, input k value, traverse the reservation point set, determine that the retention point concentrates the k- neighbour of each point, and with described
The k- neighbour of each point generates neighborhood point set t (xl,yl,zl)L=1,2 ..., k；
S8, the calculating neighborhood point concentrate each point and current point distance MjAnd standard deviation sigmaj, wherein j=1,2 ...,
m；
S9, noise point judgement is carried out to the reservation point set, it is theoretical according to classical elimination of rough difference, described in twice of selection selection
Standard deviation sigma is that the noise spot that threshold values concentrates the retention point is rejected, the reservation point set R (x after obtaining cancelling noiseu,yu,
zu)U=1,2 ..., f, wherein f is the number of the retention point centrostigma after the cancelling noise；
If the retention point after S10, the cancelling noise is concentrated, there is also the noises of cluster, re-enter new k value, lay equal stress on
S7, S8 and S9 are executed again.
Further, the tool of plane equation where calculating the point cloud data collection using Principal Component Analysis Algorithm in step S2
Body step is：
The point cloud data integrates as M, constructs corresponding covariance matrix and is：
Wherein,For the barycentric coodinates of point set, to Matrix C
Principal component analysis is carried out, three eigenvalue λs can be acquired1、λ2、λ3It arranges in descending order, obtains λ1≥λ2>λ3>0, λ3Corresponding spy
Levy vector v3, and v3For normal vector, value v3(a, b, c), then, equation where point cloud plane is：
Ax+by+cz=d
Wherein,
Further, in step S3, the expression for the distance that each point respectively arrives the plane equation is：
Wherein,
Further, in step S4, the distance d of the plane equation is respectively arrived according to each pointi, obtain described cloud number
Expression formula according to the standard deviation sigma of collection is：
Further, in step S5, the concrete mode for carrying out noise point judgement to the point cloud data collection is：
Work as di>When 2 σ, which is considered as noise spot, is rejected, otherwise, as retention point；Traverse described cloud number
According to all the points of concentration, the reservation point set Q (x is obtainedj,yj,zj)J=1,2 ..., m。
Further, in step S7, determine that the retention point concentrates the concrete mode of the k- neighbour of each point to be：
For the reservation point set Q (x containing m pointj,yj,zj)J=1,2 ..., m, for current point p0∈ Q has found one and has contained
There is k point (without p0Point) subset S, and meet S ∈ Q and k<M, for any p1∈ S, p2∈ Q-S, Dist (p0-p1)≤
Dist(p0-p2), wherein Dist (pi,pj) indicate piAnd pjThe distance between, wherein piAnd pjThe distance between for it is European away from
From.
Further, it in step S8, calculates the neighborhood point and concentrates each point and current point distance MjAnd standard deviation sigmaj's
Concrete mode is：
Further, in step S9, the concrete mode for carrying out noise point judgement to the reservation point set is：
Work as Mj>2σjWhen, which is considered as noise spot, it is rejected, otherwise, as retention point；Traverse described cloud number
According to all the points of concentration, the reservation point set R (x is obtainedu,yu,zu)U=1,2 ..., f。
Compared with the existing technology, a kind of noise eliminating method towards point off density cloud provided by the embodiment of the present invention has
Following beneficial effect：Global noise is eliminated using principal component analysis fit Plane, and the small noise in part is in subsequent consideration part
It is also effectively rejected during neighborhood, the point cloud with plane characteristic can accurately be rejected by above-mentioned two-step method and made an uproar
Sound.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 shows a kind of a kind of signal of the noise eliminating method towards point off density cloud provided by the embodiment of the present invention
Property flow chart；
It disperses like the clouds an intensity map Fig. 2 shows the original point of the embodiment of the present invention；
The original point that Fig. 3 shows the embodiment of the present invention disperses like the clouds the top view of an intensity map；
Fig. 4 shows the top view using the scatterplot intensity map after principal component analysis cancelling noise of the embodiment of the present invention；
Fig. 5 shows the top view using the scatterplot intensity map after local neighborhood cancelling noise of the embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.The present invention being usually described and illustrated herein in the accompanying drawings is implemented
The component of example can be arranged and be designed with a variety of different configurations.
Therefore, the detailed description of the embodiment of the present invention provided in the accompanying drawings is not intended to limit below claimed
The scope of the present invention, but be merely representative of selected embodiment of the invention.Based on the embodiments of the present invention, this field is common
Technical staff's every other embodiment obtained without creative efforts belongs to the model that the present invention protects
It encloses.
It should be noted that：Similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.Meanwhile of the invention
In description, term " first ", " second " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
With reference to the accompanying drawing, it elaborates to some embodiments of the present invention.In the absence of conflict, following
Feature in embodiment and embodiment can be combined with each other.
Referring to Fig. 1, Fig. 1 shows a kind of noise eliminating method towards point off density cloud provided by the embodiment of the present invention
A kind of schematic flow chart, in embodiments of the present invention, should noise eliminating method towards point off density cloud include the following steps：
S1, the point cloud data collection { P with the subject surface of plane characteristic is obtained using ground laser radar systemi| i=
1,2 ... n }, wherein the data of the quantity of point cloud data centrostigma described in n, each point include the three-dimensional coordinate of subject surface point
And intensity.
S2, point cloud data collection place plane equation is calculated using Principal Component Analysis Algorithm, obtain plane normal vector.
Specifically, plane equation where calculating the point cloud data collection using Principal Component Analysis Algorithm the specific steps are：
The point cloud data integrates as M, constructs corresponding covariance matrix and is：
Wherein,For the barycentric coodinates of point set, to Matrix C
Principal component analysis is carried out, three eigenvalue λs can be acquired1、λ2、λ3It arranges in descending order, obtains λ1≥λ2>λ3>0, λ3Corresponding spy
Levy vector v3, and v3For normal vector, value v3(a, b, c), then, equation where point cloud plane is：
Ax+by+cz=d
Wherein,
S3, the distance d that the point cloud data concentrates each point respectively to arrive the plane equation is calculatedi, wherein i=1,
2,…,n。
Specifically, the expression for the distance that each point respectively arrives the plane equation is：
Wherein,
S4, the distance d that the plane equation is respectively arrived according to each point cloud datai, obtain the point cloud data collection
Standard deviation sigma.
Specifically, the distance d of the plane equation is respectively arrived according to each pointi, obtain the standard of the point cloud data collection
The expression formula of poor σ is：
S5, noise point judgement is carried out to the point cloud data collection, it is theoretical according to classical elimination of rough difference, select twice of mark
Quasi- difference σ is that the noise spot that threshold values concentrates the point cloud data is rejected, using remaining all the points as reservation point set Q (xj,
yj,zj)J=1,2 ..., m。
Specifically, it is to the concrete mode of point cloud data collection progress noise point judgement：
Work as di>When 2 σ, which is considered as noise spot, is rejected, otherwise, as retention point；Traverse described cloud number
According to all the points of concentration, the reservation point set Q (x is obtainedj,yj,zj)J=1,2 ..., m。
S6, K-D tree index is established to the reservation point set.
S7, input k value, traverse the reservation point set, determine that the retention point concentrates the k- neighbour of each point, and with described
The k- neighbour of each point generates neighborhood point set t (xl,yl,zl)L=1,2 ..., k。
Specifically, it is determined that the retention point concentrates the concrete mode of the k- neighbour of each point to be：
For the reservation point set Q (x containing m pointj,yj,zj)J=1,2 ..., m, for current point p0∈ Q has found one and has contained
There is k point (without p0Point) subset S, and meet S ∈ Q and k<M, for any p1∈ S, p2∈ Q-S, Dist (p0-p1)≤
Dist(p0-p2), wherein Dist (pi,pj) indicate piAnd pjThe distance between, wherein piAnd pjThe distance between for it is European away from
From.
S8, the calculating neighborhood point concentrate each point and current point distance MjAnd standard deviation sigmaj, wherein j=1,2 ...,
m。
Specifically, it calculates the neighborhood point and concentrates each point and current point distance MjAnd standard deviation sigmajConcrete mode be：
S9, noise point judgement is carried out to the reservation point set, it is theoretical according to classical elimination of rough difference, described in twice of selection selection
Standard deviation sigma is that the noise spot that threshold values concentrates the retention point is rejected, the reservation point set R (x after obtaining cancelling noiseu,yu,
zu)U=1,2 ..., f, wherein f is the number of the retention point centrostigma after the cancelling noise.
Specifically, it is to the concrete mode of the reservation point set progress noise point judgement：
Work as Mj>2σjWhen, which is considered as noise spot, it is rejected, otherwise, as retention point；Traverse described cloud number
According to all point cloud datas of concentration, the reservation point set R (x is obtainedu,yu,zu)U=1,2 ..., f。
If the retention point after S10, the cancelling noise is concentrated, there is also the noises of cluster, re-enter new k value, lay equal stress on
S7, S8 and S9 are executed again.
Specifically, by taking " certain field experiment masonry walls swept-volume " as an example, one kind is provided for the embodiments of the invention towards close
The fusion principal component analysis of collection point cloud and the noise eliminating method of local neighborhood are further elaborated：
(1) metope is scanned using Leica C10 laser scanner system, observation is the three of surface of wall measuring point
Coordinate and intensity are tieed up, totally 14540 points, original point cloud criterion distance difference is 2.55mm, and the original point point diagram that disperses like the clouds is coloured by intensity
As shown in Figure 2.
(2) global noise is rejected using Principal Component Analysis, it is 14133 that point set number containing point is retained after rejecting
A, the top view for rejecting front and back scatter plot is shown in Fig. 3 and Fig. 4 respectively.
(3) secondary rejecting is carried out to noise using local neighborhood method, k value is set as 20, and point set number is retained after rejecting and is
13451, criterion distance difference is 0.83mm, and the top view of scatter plot is shown in Fig. 5 after rejecting.
It is poor (before rejecting by comparing the criterion distance for rejecting front and back：2.55mm；After rejecting：0.83mm) it can be found that originally
A kind of noise eliminating method towards point off density cloud provided by inventive embodiments, being capable of effectively cancelling noise point.
In conclusion a kind of noise eliminating method towards point off density cloud provided by the embodiment of the present invention, using it is main at
Analysis fit Plane eliminates global noise, and the small noise in part is also effectively picked during subsequent consideration local neighborhood
It removes, the point cloud noise with plane characteristic can be accurately rejected by above-mentioned two-step method.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
Claims (8)
1. a kind of noise eliminating method towards point off density cloud, which is characterized in that the method includes：
S1, the point cloud data collection { P with the subject surface of plane characteristic is obtained using ground laser radar systemi| i=1,2 ...
N }, wherein the quantity of point cloud data centrostigma described in n, the data of each point include the three-dimensional coordinate and intensity of subject surface point；
S2, point cloud data collection place plane equation is calculated using Principal Component Analysis Algorithm, obtain plane normal vector；
S3, the distance d that the point cloud data concentrates each point respectively to arrive the plane equation is calculatedi, wherein i=1,2 ..., n；
S4, the distance d that the plane equation is respectively arrived according to each pointi, obtain the standard deviation sigma of the point cloud data collection；
S5, noise point judgement is carried out to the point cloud data collection, it is theoretical according to classical elimination of rough difference, select twice of standard deviation
σ is that the noise spot that threshold values concentrates the point cloud data is rejected, using remaining all the points as reservation point set Q (xj,yj,
zj)J=1,2 ..., m；
S6, K-D tree index is established to the reservation point set；
S7, input k value, traverse the reservation point set, determine that the retention point concentrates the k- neighbour of each point, and with described each
The k- neighbour of point generates neighborhood point set t (xl,yl,zl)L=1,2 ..., k；
S8, the calculating neighborhood point concentrate each point and current point distance MjAnd standard deviation sigmaj, wherein j=1,2 ..., m；
S9, noise point judgement is carried out to the reservation point set, selection selection twice standard theoretical according to classical elimination of rough difference
Poor σ is that the noise spot that threshold values concentrates the retention point is rejected, the reservation point set R (x after obtaining cancelling noiseu,yu,
zu)U=1,2 ..., f, wherein f is the number of the retention point centrostigma after the cancelling noise；
If the retention point after S10, the cancelling noise is concentrated there is also the noise of cluster, new k value is re-entered, and repeats to hold
Row S7, S8 and S9.
2. the method as described in claim 1, which is characterized in that calculate described cloud using Principal Component Analysis Algorithm in step S2
Plane equation where data set the specific steps are：
The point cloud data integrates as M, constructs corresponding covariance matrix and is：
Wherein, For the barycentric coodinates of point set, Matrix C is led
Constituent analysis can acquire three eigenvalue λs1、λ2、λ3It arranges in descending order, obtains λ1≥λ2>λ3>0, λ3Corresponding feature vector
v3, and v3For normal vector, value v3(a, b, c), then, equation where point cloud plane is：
Ax+by+cz=d
Wherein,
3. the method as described in claim 1, which is characterized in that in step S3, each point respectively arrive the plane equation away from
From expression be：
Wherein,
4. the method as described in claim 1, which is characterized in that in step S4, respectively arrive the plane equation according to each point
Distance di, the expression formula for obtaining the standard deviation sigma of the point cloud data collection is：
5. the method as described in claim 1, which is characterized in that in step S5, carry out noise point to the point cloud data collection and sentence
Disconnected concrete mode is：
Work as di>When 2 σ, which is considered as noise spot, is rejected, otherwise, as retention point；The point cloud data is traversed to concentrate
All the points, obtain the reservation point set Q (xj,yj,zj)J=1,2 ..., m。
6. the method as described in claim 1, which is characterized in that in step S7, determine that the retention point concentrates the k- of each point
The concrete mode of neighbour is：
For the reservation point set Q (x containing m pointj,yj,zj)J=1,2 ..., m, for current point p0∈ Q has found one containing k
Point (is free of p0Point) subset S, and meet S ∈ Q and k<M, for any p1∈ S, p2∈ Q-S, Dist (p0-p1)≤Dist(p0-
p2), wherein Dist (pi,pj) indicate piAnd pjThe distance between, wherein piAnd pjThe distance between be Euclidean distance.
7. the method as described in claim 1, which is characterized in that in step S8, calculate the neighborhood point and concentrate each point and work as
The distance M of preceding pointjAnd standard deviation sigmajConcrete mode be：
8. the method as described in claim 1, which is characterized in that in step S9, carry out noise point judgement to the reservation point set
Concrete mode be：
Work as Mj>2σjWhen, which is considered as noise spot, it is rejected, otherwise, as retention point；Traverse the point cloud data collection
In all the points, obtain the reservation point set R (xu,yu,zu)U=1,2 ..., f。
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| WO2021189375A1 (en) * | 2020-03-26 | 2021-09-30 | Baidu.Com Times Technology (Beijing) Co., Ltd. | A point cloud feature-based obstacle filter system |
| US11609333B2 (en) | 2020-03-26 | 2023-03-21 | Baidu Usa Llc | Point cloud feature-based obstacle filter system |
| CN112102178A (en) * | 2020-07-29 | 2020-12-18 | 深圳市菲森科技有限公司 | Point cloud feature-preserving 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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