CN117808703B - Multi-scale large-scale component assembly gap point cloud filtering method - Google Patents

Multi-scale large-scale component assembly gap point cloud filtering method Download PDF

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CN117808703B
CN117808703B CN202410224098.2A CN202410224098A CN117808703B CN 117808703 B CN117808703 B CN 117808703B CN 202410224098 A CN202410224098 A CN 202410224098A CN 117808703 B CN117808703 B CN 117808703B
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CN117808703A (en
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汪俊
吴翔
濮宬涵
李子宽
张沅
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention relates to a multi-scale large-scale component assembly gap point cloud filtering method, which comprises the following steps: the ROI region is segmented for the initial point cloud P of the assembly gap of the large-scale component, and the point cloud density is obtained through extraction of a point cloud density sensorMinimum ROI region point cloudThereby preserving effective point cloud data; for ROI region point cloudFiltering the invalid points and the large-scale outlier points to obtain filtered point clouds; Extraction by region growing clustering methodClustering all the point clouds to form a continuous point cloud cluster, and dividing and filtering the gap irrelevant point clouds based on a weight voting method to obtain an effective point cloudThe accuracy of the gap-related point cloud is improved; by aiming at the effective point cloudPerforming small-scale point cloud fairing to solve the problem of rough point cloud in local area and obtain final filtered point cloud. The invention realizes rapid and comprehensive filtering treatment of the point cloud by utilizing a multi-scale filtering method.

Description

Multi-scale large-scale component assembly gap point cloud filtering method
Technical Field
The invention relates to an algorithm for three-dimensional data processing, in particular to a multi-scale large-scale component assembly gap point cloud filtering method.
Background
The gap is an important index for large-scale component assembly, so that the gap of a vehicle body is very necessary to measure, the point cloud of a large-scale component assembly gap structure can be completely obtained through a three-dimensional scanning technology, and the size of the large-scale component assembly gap can be measured by further carrying out feature analysis according to the obtained point cloud, so that the process of large-scale component assembly is guided.
However, due to environmental interference, vibration and other problems, the obtained point cloud may have outliers, invalid points, mixing of point clouds in irrelevant areas, rough point clouds and other conditions, which all cause interference to subsequent feature extraction and calculation processes. Because the cloud states of noise points are different, the noise points cannot be well filtered by using a single traditional filtering algorithm. In addition, the existence of invalid point clouds and irrelevant area point clouds can influence the result and also need to be filtered.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a multi-scale large-scale component assembly gap point cloud filtering method, which solves the problems that a single traditional filtering algorithm cannot accurately and effectively filter point clouds with outlier points, invalid points, irrelevant area point cloud mixing and rough point clouds, and the like. The segmentation process can identify areas with higher density in the point cloud, so that effective point cloud data can be reserved. And then, filtering invalid points and large-scale outliers from the segmented ROI region point cloud to improve the accuracy of subsequent processing. And then, clustering the filtered point cloud by adopting a region growing method, so as to form a point cloud cluster with continuity. Secondly, dividing and filtering the clustered point cloud data based on a weight voting method; judging whether the point belongs to the point cloud irrelevant to the gap or not by analyzing the weight of each point, and further segmenting and filtering the operation so as to improve the accuracy of the point cloud relevant to the gap; finally, aiming at the situation of rough point cloud of a local area, small-scale point cloud fairing processing is carried out on the point cloud; the processing can smooth the point cloud data, so that the point cloud data is more continuous and finer, and the quality of the point cloud data is improved; the invention utilizes a multi-scale filtering method to rapidly filter the point cloud of the assembly gap of the large-scale component, thereby realizing rapid and comprehensive filtering treatment of the point cloud.
In order to solve the technical problems, the invention provides the following technical scheme: a multi-scale large-scale component assembly clearance point cloud filtering method comprises the following steps:
s1, segmenting an ROI (region of interest) region of an original point cloud P of a large component assembly gap, and extracting by a point cloud density sensor to obtain a point cloud density Minimum ROI region Point cloud/>；
S2, for the ROI region point cloudFiltering the invalid points and the large-scale outlier points to obtain filtered point clouds；
S3, extracting by using a region growing clustering methodClustering all point clouds, and dividing and filtering the gap irrelevant point clouds based on a weight voting method to obtain effective point clouds/>；
S4, through effective point cloudPerforming small-scale point cloud fairing to solve the problem of rough point cloud in a local area and obtain final filtered point cloud/>。
Further, in the step S1, the initial point cloud P of the assembly gap of the large-scale component is segmented into ROI areas, and the point cloud density is obtained through extraction of a point cloud density sensorMinimum ROI region Point cloud/>The specific process comprises the following steps:
S11, initializing a minimum coordinate set of three dimensions of the point cloud X, Y, Z The middle element is infinite and maximum coordinate set/>The medium elements are infinitely small, all point clouds are traversed through a point cloud iterator, and new/>' is finally obtained through continuous iteration updatingAnd/>Constructing an integral bounding box B of the point cloud according to the two sets of parameters;
s12, constructing a point cloud density sensor, wherein 、/>、/>Is the length, width and height of the point cloud density sensor,，/>Is 2 times of the gap width based on prior；
S13, in the point cloud integral bounding box B, performing iterative search through a point cloud density sensor to perform density calculation, and performing point cloud density calculation，/>For the number of points in the current density sensor range, the point cloud density is determined by searchingMinimum ROI region Point cloud/>。
Further, in the step S2, the ROI region point cloud is obtainedFiltering the invalid points and the large-scale outlier points to obtain filtered point cloud/>The specific process comprises the following steps:
S21, performing constraint on the object by a multi-condition constraint method Filtering the invalid point cloud to make constraint conditionsCo/>Judging whether the point cloud is invalid or not according to the individual conditions, and judging the conditions，/>For a point in the current point cloud, when/>When the point cloud is an invalid point cloud, the point cloud after filtering the invalid point cloud is/>；
S22, point cloudEach point/>Defining a local neighborhood/>For each point/>Curvature/>And based on local neighborhood/>Curvature characteristics/>Based on the dot/>Curvature/>And its local curvature characteristics/>Filtering outliers by a standard deviation statistical method to obtain filtered point cloud/>。
Further, the step S3 is extracted by a region growing clustering methodClustering all point clouds, and dividing and filtering the gap irrelevant point clouds based on a weight voting method to obtain effective point clouds/>The specific process comprises the following steps:
s31, clustering point clouds in a mode based on region growth, and selecting The inner single point is used as a seed point to be used as a clustering starting point to construct k-near neighborhood/>Based on cosine similarity/>The manner of measurement determines the similarity between points,/>The value of the product is within the range of [ -1,1 ]/>Representing the current seed point,/>Representing a current neighborhoodThe points in the tree are clustered according to cosine similarity of seed points, the process is repeated until no point meeting the condition is generated, and then the cluster is formed againSelecting seed points from the rest point cloud, and repeating the above process until/>Ending without point cloud;
s32, calculating the average Hausdorff distance of each clustering point cloud relative to other point clouds And the normal/>, of each cluster point cloudAnd the number/>, of each cluster point cloudAnd similarity to template point cloud/>，；
S33, carrying out weight voting on each cluster in a weight voting based mode, and finally obtaining the score of each cluster as followsThe calculation formula is/>Wherein/>，/>，/>，/>For/>The normal direction of the straight line is fitted,Is the modular length of the two vectors, will/>The two clusters with the largest value are extracted to be used as the inner point cloud, and the point cloud is treated as the outer point cloud to obtain the effective point cloud/>。
Further, the step S4 is implemented through a point cloudPerforming small-scale point cloud fairing to solve the problem of rough point cloud in a local area and obtain final filtered point cloud/>The specific process comprises the following steps:
S41, determining a proper small-scale smooth radius, wherein the radius is used for defining a smooth operation range, and the smooth radius is selected according to the characteristics of point clouds and application requirements;
s42, for the effective point cloud Each point/>Determining its neighborhood/> from the smooth radiusNeighborhood, method of generating a neighborhood, and computer program productIs an adjacent point contained within a smooth radius range;
S43, for neighborhood Calculating a normal vector thereof by using a least square method;
S44, for each point By means of neighborhood/>The normal line information in the neighborhood is subjected to smoothing operation, a weighted average method is used, the new position of the point is determined by the weighted average of the points in the neighborhood, the weight is calculated according to the consistency of the point and the normal line directions of other points in the neighborhood, the processes of S41-S44 are repeated until the whole point cloud is subjected to smoothing treatment, and the final filtered point cloud/>, is obtained。
By means of the technical scheme, the invention provides a multi-scale large-scale component assembly gap point cloud filtering method, which has at least the following beneficial effects:
Firstly, the original point cloud is segmented into the ROI areas based on the point cloud density sensing method, and the segmentation process can identify areas with higher density in the point cloud, so that effective point cloud data are reserved. And then, filtering invalid points and large-scale outliers from the segmented ROI region point cloud to improve the accuracy of subsequent processing. Then, clustering operation is carried out on the filtered point cloud by adopting a region growing method so as to form a point cloud cluster with continuity, then, the clustered point cloud data is segmented and filtered by adopting a weight voting-based method, and whether the point cloud belongs to the point cloud irrelevant to the gap is judged by analyzing the weight of each point, and then the segmentation and filtering operation is carried out so as to improve the accuracy of the point cloud relevant to the gap. And finally, aiming at the situation of rough point cloud of the local area, carrying out small-scale point cloud fairing processing on the point cloud. The method can smooth the point cloud data, so that the point cloud data is more continuous and finer, and the quality of the point cloud data is improved; the invention realizes rapid and comprehensive filtering treatment of the point cloud by utilizing a multi-scale filtering method.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a multi-scale large part assembly gap point cloud filtering method provided by the invention;
FIG. 2 is a schematic diagram of an overall bounding box of a point cloud constructed in accordance with the present invention;
FIG. 3 is a schematic diagram of the effect of the large-scale filtering and extraneous point filtering of the three-dimensional point cloud of the assembly gap of the large-scale component of the present invention;
fig. 4 is a schematic diagram of the final filtering effect of the filtering of the three-dimensional point cloud of the large part assembly gap of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. Therefore, the realization process of how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in a method of implementing an embodiment described above may be implemented by a program to instruct related hardware, and thus, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Referring to fig. 1-4, a specific implementation manner of the present embodiment is shown, first, an ROI region is segmented from an original point cloud by a method based on point cloud density sensing. The segmentation process can identify areas with higher density in the point cloud, so that effective point cloud data can be reserved. And then, filtering invalid points and large-scale outliers from the segmented ROI region point cloud to improve the accuracy of subsequent processing. And then, clustering the filtered point cloud by adopting a region growing method, so as to form a point cloud cluster with continuity. And secondly, dividing and filtering the clustered point cloud data based on a weight voting method. And judging whether the point belongs to the point cloud irrelevant to the gap or not by analyzing the weight of each point, and further performing segmentation and filtering operations so as to improve the accuracy of the point cloud relevant to the gap. And finally, aiming at the situation of rough point cloud of the local area, carrying out small-scale point cloud fairing processing on the point cloud. The processing can smooth the point cloud data, so that the point cloud data is more continuous and finer, and the quality of the point cloud data is improved. The invention realizes rapid and comprehensive filtering treatment of the point cloud by utilizing a multi-scale filtering method.
Referring to fig. 1, the present embodiment provides a multi-scale large-scale component assembly gap point cloud filtering method, which includes the following steps:
s1, segmenting an ROI (region of interest) region of an original point cloud P of a large component assembly gap, and extracting by a point cloud density sensor to obtain a point cloud density Minimum ROI region Point cloud/>；
As a preferred embodiment of step S1, the specific procedure comprises the steps of:
S11, initializing a minimum coordinate set of three dimensions of the point cloud X, Y, Z The middle element is infinite and maximum coordinate set/>The medium elements are infinitely small, all point clouds are traversed through a point cloud iterator, and new/>' is finally obtained through continuous iteration updatingAnd/>And constructing an integral bounding box B of the point cloud according to the two sets of parameters, wherein the effect is shown in figure 2;
s12, constructing a point cloud density sensor, wherein 、/>、/>Is the length, width and height of the point cloud density sensor,，/>Is 2 times of the gap width based on prior；
S13, in the point cloud integral bounding box B, performing iterative search through a point cloud density sensor to perform density calculation, and performing point cloud density calculation，/>For the number of points in the current density sensor range, the point cloud density is determined by searchingMinimum ROI region Point cloud/>。
In this embodiment, the ROI area is segmented for the original point cloud by a method based on the point cloud density sensing. The segmentation process can identify areas with higher density in the point cloud, so that effective point cloud data can be reserved.
S2, for the ROI region point cloudFiltering the invalid points and the large-scale outlier points to obtain filtered point clouds；
As a preferred embodiment of step S2, the specific procedure comprises the steps of:
S21, performing constraint on the object by a multi-condition constraint method Filtering the invalid point cloud to make constraint conditionsCo/>Judging whether the point cloud is invalid or not according to the individual conditions, and judging the conditions，/>For a point in the current point cloud, when/>When the point cloud is an invalid point cloud, the point cloud after filtering the invalid point cloud is/>；
S22, point cloudEach point/>Defining a local neighborhood/>For each point/>Curvature/>And based on local neighborhood/>Curvature characteristics/>Based on the dot/>Curvature/>And its local curvature characteristics/>Filtering outliers by a standard deviation statistical method to obtain filtered point cloud/>；
Wherein,，/>、/>Is a dot/>And/>；
；
For local neighborhood/>Number of points in/>For/>Neighborhood points/>Is provided for the curvature of the lens.
In this embodiment, the invalid points and large-scale outliers of the segmented ROI region point cloud are filtered to improve accuracy of subsequent processing.
S3, extracting by using a region growing clustering methodClustering all point clouds, and dividing and filtering the gap irrelevant point clouds based on a weight voting method to obtain effective point clouds/>；
As a preferred embodiment of step S3, the specific procedure comprises the steps of:
s31, clustering point clouds in a mode based on region growth, and selecting The inner single point is used as a seed point to be used as a clustering starting point to construct k-near neighborhood/>Based on cosine similarity/>The manner of measurement determines the similarity between points,/>The value of the product is within the range of [ -1,1 ]/>Representing the current seed point,/>Representing a current neighborhoodThe points in the tree are clustered according to cosine similarity of seed points, the process is repeated until no point meeting the condition is generated, and then the cluster is formed againSelecting seed points from the rest point cloud, and repeating the above process until/>Ending without point cloud;
s32, calculating the average Hausdorff distance of each clustering point cloud relative to other point clouds And the normal/>, of each cluster point cloudAnd the number/>, of each cluster point cloudAnd similarity to template point cloud/>，；
Wherein:，/>，/> and/> For/>Clustering of middle divisions;
S33, carrying out weight voting on each cluster in a weight voting based mode, and finally obtaining the score of each cluster as follows The calculation formula is/>Wherein/>，/>，/>，/>For/>The normal direction of the straight line is fitted,Is the modular length of the two vectors, will/>The two clusters with the largest value are extracted to be used as the inner point cloud, and the point cloud is treated as the outer point cloud to obtain the effective point cloud/>The effect is shown in fig. 3.
In this embodiment, a region growing method is used to perform clustering operation on the filtered point cloud, so as to form a point cloud cluster with continuity. And secondly, dividing and filtering the clustered point cloud data based on a weight voting method. And judging whether the point belongs to the point cloud irrelevant to the gap or not by analyzing the weight of each point, and further segmenting and filtering the operation so as to improve the accuracy of the point cloud relevant to the gap.
S4, through effective point cloudPerforming small-scale point cloud fairing to solve the problem of rough point cloud in a local area and obtain final filtered point cloud/>。
As a preferred embodiment of step S4, the specific process comprises the steps of:
S41, determining a proper small-scale smooth radius, wherein the radius is used for defining a smooth operation range, and the smooth radius is selected according to the characteristics of point clouds and application requirements;
s42, for the effective point cloud Each point/>Determining its neighborhood/> from the smooth radiusNeighborhood, method of generating a neighborhood, and computer program productIs an adjacent point contained within a smooth radius range;
S43, for neighborhood Calculating a normal vector thereof by using a least square method;
S44, for each point By means of neighborhood/>The normal line information in the neighborhood is subjected to smoothing operation, a weighted average method is used, the new position of the point is determined by the weighted average of the points in the neighborhood, the weight is calculated according to the consistency of the point and the normal line directions of other points in the neighborhood, the processes of S41-S44 are repeated until the whole point cloud is subjected to smoothing treatment, and the final filtered point cloud/>, is obtainedThe effect is shown in fig. 4.
Correction of sample point position using weighted average of neighboring sample points is expressed asWherein/>Is the original point cloud,/>Is a filtered point cloud,/>Is a filtering factor, n is/>A normal of the location;
；
Calculation of For/>And/>Spatial distance of/>For/>At/>Distance in the normal direction,/>For/>Neighborhood points of/>Representing a vector product,/>、As Gaussian kernel function, represents the nearby point pair/>The weight is affected. /(I)For/>The distance to each of the adjacent points,For/>To each nearby point to/>The projection distance on the normal has an impact weight on this point.
In this embodiment, aiming at the situation that the local area point cloud is rough, small-scale point cloud fairing processing is performed on the point cloud. The processing can smooth the point cloud data, so that the point cloud data is more continuous and finer, and the quality of the point cloud data is improved.
In conclusion, the method utilizes a multi-scale filtering method to rapidly filter the point cloud of the assembly gap of the large-scale component, and achieves rapid and comprehensive filtering treatment of the point cloud.
The above is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, and all technical solutions belonging to the concept of the present invention are within the scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
The foregoing embodiments have been presented in a detail description of the invention, and are presented herein with a particular application to the understanding of the principles and embodiments of the invention, the foregoing embodiments being merely intended to facilitate an understanding of the method of the invention and its core concepts; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims (4)

1. The multi-scale large-scale component assembly gap point cloud filtering method is characterized by comprising the following steps of:
S1, segmenting an ROI (region of interest) region of an original point cloud P of a large component assembly gap, initializing an infinite element in a minimum coordinate Set min＝(xmin,ymin,zmin) and an infinite element in a maximum coordinate Set max＝(xmax,ymax,zmax) of three dimensions of the point cloud X, Y, Z, extracting a point cloud P' of an ROI region with the minimum point cloud density rho by a point cloud density sensor, wherein the length, width and height of the point cloud density sensor are l, w and h, l=y max-ymin, w is 2 times of the gap width based on prior, and h=z max-zmin;
S2, performing invalid point filtering on the ROI region point cloud P 'to obtain a point cloud P', defining a local neighborhood N 'i for each point P' i in the point cloud P 'by a large-scale outlier point filtering method, for the curvature F i of each point P' i and the curvature characteristic F i based on the local neighborhood N 'i, filtering outliers based on the curvature F i of the point P' i and the standard deviation statistics method of the local curvature characteristic F i, and obtaining a filtered point cloud P a;
S3, selecting a single point in P a as a seed point to serve as a clustering starting point by a region growing clustering method, constructing a k-near neighborhood N (P i), determining the similarity between the points based on a cosine similarity S θ measurement mode, The value of the method is within the range of [ -1,1], P i represents the current seed point, P j represents the points in the current neighborhood N (P i), clustering is carried out according to the cosine similarity of the seed points, the process is continuously repeated until no points meeting the conditions are generated, then the process is repeated by selecting seed points from the rest point clouds of P a again until no point clouds in P a are finished to extract the clusters of all the point clouds of P a, and the gap irrelevant point clouds are segmented and filtered based on a weight voting method to obtain an effective point cloud P g;
S4, carrying out small-scale point cloud fairing on the effective point cloud P g to solve the problem of rough point cloud of a local area, and obtaining a final filtered point cloud P f; the specific process comprises the following steps:
S41, determining a proper small-scale smooth radius, wherein the radius is used for defining a smooth operation range, and the smooth radius is selected according to the characteristics of point clouds and application requirements;
S42, for each point in the effective point cloud P g Determining its neighborhood/> from the smooth radiusNeighborhood/>Is an adjacent point contained within a smooth radius range;
S43, for neighborhood Calculating a normal vector thereof by using a least square method;
S44, for each point By means of neighborhood/>And (3) carrying out smoothing operation on the normal line information in the neighborhood, and using a weighted average method, wherein the new position of the point is determined by the weighted average of the points in the neighborhood, the weight is calculated according to the consistency of the point and the normal line directions of other points in the neighborhood, and repeating the processes S41-S44 until the whole point cloud is smoothed, so as to obtain the final filtered point cloud P f.
2. The multi-scale large component assembly gap point cloud filtering method according to claim 1, wherein the specific process of S1 comprises the following steps:
S11, traversing all point clouds through a point cloud iterator, finally obtaining new Set min and Set max through continuous iterative updating, and constructing an integral bounding box B of the point clouds according to parameters of the two sets;
s12, in the point cloud integral bounding box B, performing iterative search through a point cloud density sensor to perform density calculation, and performing point cloud density calculation Points are the number of points in the current density sensor range, and the ROI area point cloud P' with the minimum point cloud density rho is determined through searching.
3. The multi-scale large part assembly gap point cloud filtering method of claim 1, wherein S2 further comprises: filtering the invalid point cloud in the P' by a multi-condition constraint method, enabling constraint condition C 1,C2,…Cn to judge whether the point cloud is the invalid point cloud or not under n conditions, judging that condition J=C 1(p'i)*C2(P'i)…Cn(p'i),p'i is a point in the current point cloud, and when J=0, the point cloud is the invalid point cloud, and the point cloud after filtering the invalid point cloud is P ".
4. The multi-scale large component assembly gap point cloud filtering method according to claim 1, wherein the method based on weight voting in S3 segments and filters gap irrelevant point clouds to obtain an effective point cloud P g, and the specific process comprises the following steps:
S31, calculating an average Hausdorff distance H i of each clustering point cloud relative to other point clouds, a normal N ci of each clustering point cloud, the number num i of each clustering point cloud and the similarity S ci,Sci＝0or Sci =1 with the template point cloud;
S32, carrying out weight voting on each cluster in a weight voting mode, finally obtaining a score of each cluster as value i, and processing the point cloud as external point cloud by using a calculation formula as value i＝α*Hi+β*|Nci-Npaline|+γ*numi+Sci, wherein alpha=0.1, beta=0.5, gamma=0.02, N paline is the normal direction of a P a fitting straight line, N ci-Npaline is the modular length of two vectors, extracting two clusters with the maximum value of value i as internal point cloud, and obtaining effective point cloud P g.
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