CN107392954A - A kind of gross error point elimination method based on sequence image - Google Patents
A kind of gross error point elimination method based on sequence image Download PDFInfo
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
- G06T7/521—Depth or shape recovery from laser ranging, e.g. using interferometry; from the projection of structured light
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
- G06T7/55—Depth or shape recovery from multiple images
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
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- G—PHYSICS
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- G06T2207/30—Subject of image; Context of image processing
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- G06T2207/30164—Workpiece; Machine component
Abstract
A kind of gross error point elimination method based on sequence image of the present invention belongs to reverse-engineering field, is related to a kind of gross error point elimination method based on sequence image.This method, using the auxiliary laser striped that is formed on measured object of left and right video camera shooting generating laser, obtains the cloud data for representing measured object surface information first by way of laser combination binocular vision.The point of selection is fitted to by a curve according to least square method, using adjacent two curves as point cloud zone boundary, completes the division in point cloud sector domain；Obtain respectively again in the domain of every piece of point cloud sector o'clock to two boundary curves beeline, gross error point is judged according to ratio size.This method is simple to operate, first building topology structure is not needed, the information such as the density of point cloud is calculated to delete unnecessary point cloud, improves the efficiency that a cloud gross error point removes, the limitation of single width laser striation picture point cloud processing is overcome, ensure that the accuracy of the local message of cloud data.
Description
Technical field
The invention belongs to reverse-engineering field, is related to a kind of gross error point elimination method based on sequence image.
Background technology
With the continuous development of aviation digital technology, industry competition will be more and more fierce, to aircraft product quality
Requirement also more and more higher, so development substitutes conventionally manufactured gimmick to compel in eyebrow the reverse modeling technology of airplane parts
Eyelash.Committed step of the processing as reverse-engineering of cloud data, its processing accuracy is by the reconstruction precision of direct decision model.
During Point Cloud Processing, the gross error point rejecting for putting cloud is the first step of points cloud processing.During points cloud processing
The method that the gross error point of point cloud is rejected, domestic and international many scholars have carried out corresponding research, such as .kd-tree methods, space cell
Lattice method, Octree method etc., but at present in these methods, it is, it is necessary to which the extremely long time builds kd- the shortcomings that kd-tree methods
Tree, so that asking neighborhood also to take a significant amount of time each point；Selection requirement of the space cell lattice method to grid is compared
Height, complex operation；Octree is linear structure, it is necessary to substantial amounts of memory storage pointer.In laser measurement system data acquisition
Cheng Zhong, because measured object can produce impulsive noise point with errors caused by reason such as measuring environments in itself, i.e., so-called gross error,
Its irregular distribution and deviation True Data is larger, but negligible amounts, influences reconstruction precision.To avoid above mentioned problem, just
It is necessary to carry out point cloud data gross error point rejecting processing.
Gross error point rejecting for cloud data is handled, Gao Jianmin of Xi'an Communications University et al.,《Modern scientist
Engineering》7th phase,《Data preprocessing technical research based on reverse-engineering》It is proposed that one kind is counted using isolated point in one text
The method that rejection method rejects gross error.This method obtains mean μ and variances sigma by calculating in wall scroll point cloud scan line2Afterwards,
Establish normal distribution N (μ, the σ of consecutive points distance2).Then, using adjacent 2 points of air line distance as statistics pair in scan line
As utilizing normal distribution N (μ, the σ of consecutive points distance2) 3 σ rules judge point going or staying, can preferably reject wall scroll scanning
Impulse noise data on line.But this method is merely able to be handled for single width laser optical strip image acquisition cloud data, can not
Gross error point rejecting is carried out to the point cloud face of a plurality of cloud line composition, there is significant limitation.
Liu Guanzhou of Beijing Mine and Metallurgy General Inst et al., in the patent No.：201210496277.9 patent《It is a kind of three-dimensional sharp
The denoising of light cloud data and compressing method and system》In propose the processing method and system of a kind of cloud data, this method
Current point is with the distance of its neighborhood each point and the average of distance and for representing data in point cloud after main calculating topological structure
The standard deviation of dispersion degree, deletion judgement is carried out to current point.By using the points cloud processing method, noise spot filtering is improved
Accuracy rate and cloud data accuracy, and significantly reduce the redundancy of cloud data.But this method needs first to establish
Topological structure, and delete unnecessary point cloud by calculating the information such as the point curvature of cloud, density, has computationally intensive, and efficiency is low,
The problems such as raw scanning data local message can not be ensured.
The content of the invention
The present invention is in order to solve under existing big visual field, the limitation during large aerospace flat-type part Point Cloud Processing
Property, invent a kind of gross error point elimination method based on sequence image.The purpose is to the gross error for cloud data
Point, which is rejected, needs first building topology structure in removal process, and the information such as curvature, density by calculating point cloud is unnecessary to delete
Point cloud, computationally intensive, efficiency is low, can not ensure raw scanning data local message, can not be to the point of a plurality of cloud line composition
The problems such as cloud face is handled, scanned for by the cloud data to acquisition according to the direction of scan line, be fitted cloud data,
Division points cloud sector domain, by the ratio between judgement o'clock to the beeline of two boundary curves, realize the quick, high-precision of cloud data
Gross error point remove.Overcoming needs building topology structure during existing points cloud processing, can not ensure original scan number
According to local message, the problems such as can not handling the point cloud face of a plurality of cloud line composition, it is with a wide range of applications.
The technical solution adopted by the present invention is a kind of gross error point elimination method based on sequence image, it is characterized in that,
This method shoots generating laser c tested first by way of laser combination binocular vision, using left and right video camera a, b
The auxiliary laser striped f formed on thing e, obtain the cloud data for representing measured object e surface informations；Secondly by every point cloud line two
Boundary point corresponding to end connects, and obtains straight line g, by the point between two boundary points near straight line g, one is taken every n point
After point, the match point J that treats of selection is fitted to by a curve according to least square method, using adjacent two curves as point cloud sector
Domain border, complete the division in point cloud sector domain；Finally obtain respectively in the domain of every piece of point cloud sector o'clock to the most short of two boundary curves
Distance h1、h2, afterwards, according to h1With h2Ratio size judge gross error point；Method comprises the following steps that：
The first step, obtain cloud data
Measuring apparatus is installed, auxiliary laser transmitter c is opened and irradiates measured object e, after collection is started, opens turntable
D drives generating laser c to rotate, and makes laser scanning measured object e.Then, the position of integral translation left and right cameras a, b, carry out
Repeatedly shooting, ensure the integrality of measured object e shapes face information.Auxiliary laser striation f images are collected by information acquisition system
Afterwards, it is necessary to be extracted to laser striation f center line, the present invention is the side using the extraction of optical strip image center grey scale centre of gravity
Method, formula (1) are：
Wherein：, (ui,vi) it is the i-th row striation grey scale centre of gravity coordinate, IijFor the i-th row jth row gray value；.Pass through the method
Auxiliary laser striation f characteristic point two-dimensional signal is obtained, in conjunction with calibration result and reconstruction formula, obtains boundary point and striation
D coordinates value of the central point under world coordinate system, reconstruction formula are as follows：
Where it is assumed that xi'=(Xi',Yi'), Xi', Yi' it is respectively in the sharp point or striation that left video camera a is gathered
Heart point xi' horizontal stroke, ordinate under image coordinates system；xi′'=(Xi′′,Yi′'), Xi′', Yi′' it is respectively right video camera b collections
Image spot central point xi‘' horizontal stroke, ordinate under image coordinates system；f1、f2Respectively left and right video camera a, b demarcate to obtain
Focal length；It is spin matrixs of the right video camera b relative to left video camera a, [tx ty tz] it is right video camera b
Relative to left video camera a translation matrix, obtained by calibration experiment；Then (xi,yi,zi) it is the three-dimensional for rebuilding corresponding points out
Coordinate, thus obtain the three dimensional point cloud on whole measured object e surfaces.
Second step, put the division in cloud sector domain
For the point cloud of acquisition, two points of head and the tail of every point cloud line are numbered, i.e. the boundary point to point cloud chart picture
Numbering 1 ..., 2n, boundary point corresponding to every point cloud line both ends is connected, obtain straight line g, by between two boundary points in straight line
Point near g, after n point takes a point, using formula (3) (4), selection is treated by match point according to least square method
J is fitted to a curve；
yi=a0+a1xi+...+akxi k (3)
Using adjacent two curves as the left and right border l in monolithic point cloud sector domain1、l2, complete the division in point cloud sector domain；
3rd step, the removal of gross error point
After point cloud region division, the point in a cloud sector domain is scanned for successively, obtained respectively in the domain of every piece of point cloud sector
O'clock to two boundary curves beeline h1、h2, afterwards, according to h1With h2Ratio ρ sizes judge gross error point I；
Wherein, h1To put the beeline to left margin, h2To put the beeline to right margin；When in a cloud sector domain
Point is close to left margin l1When, ρ tends to 0, when the point in a cloud sector domain is close to right margin l2When, ρ tends to ∞, therefore, sets threshold value
α1、α2, work as α1≤ρ≤α2When, this point is judged for gross error point I, and this point is rejected；As ρ≤α1Or ρ >=α2When, judge this point
For normal point, this point is retained；This completes the gross error of point cloud data point I rejectings.
The invention has the advantages that scan-type cloud data is obtained by the way of laser combination binocular vision, to obtaining
The cloud data taken scans for according to the direction of scan line, is fitted cloud data, division points cloud sector domain, passes through judgement o'clock to two
The ratio between beeline of bar boundary curve, realize that quick, the high-precision gross error point of cloud data removes.Overcome existing
Building topology structure is needed during points cloud processing, raw scanning data local message can not be ensured；Overcome single width laser light
The limitation of bar image points cloud processing, the problems such as can not handling the point cloud face of a plurality of cloud line composition.Improve a cloud
The efficiency that gross error point removes, and the accuracy of the local message of cloud data is ensure that, it is with a wide range of applications.
Brief description of the drawings
Fig. 1 is the acquisition schematic diagram of cloud data, wherein, the left video cameras of a-, the right video cameras of b-, c- generating lasers, d-
Turntable, e- measured objects, f- laser striations.
Fig. 2 is a cloud region division signal, wherein, 1,3 ..., 2n-1- coboundaries point numbering, 2,4 ..., 2n- lower boundaries
Point numbering, boundary point line corresponding to g- both ends, I- gross errors point, what J- chose treats match point, l1- monolithic point cloud sector domain is left
Border, l2- monolithic point cloud sector domain right margin, h1- arrive left margin beeline, h2- arrive right margin beeline；
Fig. 3 is that gross error point removes flow chart
Embodiment
Describe the embodiment of the present invention in detail below in conjunction with technical method and accompanying drawing.
Method shoots generating laser c first by way of laser combination binocular vision, using left and right video camera a, b
The auxiliary laser striped f formed on measured object e, obtain the cloud data for representing measured object e surface informations；Secondly by every point
Boundary point corresponding to cloud line both ends connects, and obtains straight line g, by the point between two boundary points near straight line g, every n point
Take a point；Selection is treated that match point J is fitted to a curve according to least square method afterwards, using adjacent two curves as
Point cloud zone boundary, complete the division in point cloud sector domain；Finally obtain respectively in the domain of every piece of point cloud sector o'clock to two boundary curves
Beeline h1、h2, afterwards, according to h1With h2Ratio size judge gross error point；Method comprises the following steps that：
The first step, obtain cloud data
The model industrial cameras of VC-12MC-M/C 65 of Vieworks companies of South Korea production are chosen in this measurement, and this camera is
Progressive scan formula Surface scan industrial camera, that select herein is the Lasiris that generating laser is the production of Coherent companies
PowerLine generating lasers, measured object e are aviation flat-type part.After experimental facilities is installed, generating laser c is opened simultaneously
Measured object e is irradiated, after collection is started, turntable d is opened and drives generating laser c to rotate, make laser scanning measured object e.So
Afterwards, the position of left and right cameras a, b is converted, is repeatedly shot, ensures the integrality of measured object e shapes face information.Pass through information
After acquisition system collects auxiliary laser striation f images, laser striation f center line is extracted using formula (1), obtained
Laser striation f characteristic point two-dimensional signal is taken, in conjunction with calibration result reconstruction formula (2), left and right camera a, b can be shot
Striation information is matched, and two-dimensional signal reduction thus is turned into three-dimensional point information.Afterwards according to calibration result, finally obtain whole
The three dimensional point cloud on individual measured object e surfaces.
Second step, put the division in cloud sector domain
For the point cloud of acquisition, two points of head and the tail of every point cloud line are numbered, i.e. the boundary point to point cloud chart picture
Numbering 1 ..., 2n, boundary point corresponding to every point cloud line both ends is connected, obtain straight line g, by between two boundary points in straight line
Point near g, after 5 points take a point, selection is treated by match point according to least square method using formula (3), (4)
J is fitted to a curve；Using adjacent two curves as the left and right border l in monolithic point cloud sector domain1、l2, complete drawing for point cloud sector domain
Point.
3rd step, the removal of gross error point
After point cloud region division, the point in a cloud sector domain is scanned for successively, obtained respectively in the domain of every piece of point cloud sector
O'clock to two boundary curves beeline h1、h2, afterwards, according to h1With h2Ratio size judge gross error point.According to
For formula (5) when the point in a cloud sector domain is close to left margin, ρ tends to 0, and when the point in a cloud sector domain is close to right margin, ρ tends to
∞, therefore, threshold alpha is set1=0.1, α2=10, as 0.1≤ρ≤10, judge that this point for gross error point I, this is put and picked
Remove；As ρ≤0.1 or ρ >=10, this point is judged for normal point, by this point reservation, this completes the thick mistake of point cloud data
Not good enough I rejecting.
The present invention, using the measuring method of laser combination binocular vision, improves existing on the basis of least square method
The limitation of reverse process of reconstruction point cloud data gross error point minimizing technology, it is quick, high-precision to realize gross error point
Removal.
Claims (1)
1. a kind of gross error point elimination method based on sequence image, it is characterized in that, this method is combined double by laser first
The mode visually felt, the auxiliary laser formed using left and right video camera (a, b) shooting generating laser (c) on measured object (e)
Striped (f), obtain the cloud data for representing measured object (e) surface information；Secondly by boundary point corresponding to every point cloud line both ends
Connection, straight line g is obtained, by the point between two boundary points near straight line g, a point is taken every n point；Further according to a most young waiter in a wineshop or an inn
Multiplication treats that match point J is fitted to a curve by selection, using adjacent two curves as point cloud zone boundary, completes point cloud sector
The division in domain；Finally obtain respectively in the domain of every piece of point cloud sector o'clock to two boundary curves beeline h1、h2, afterwards, according to
h1With h2Ratio size judge gross error point；Method comprises the following steps that：
The first step, obtain cloud data
Measuring apparatus is installed, auxiliary laser transmitter (c) is opened and irradiates measured object (e), after collection is started, opens turntable
(d) drive generating laser (c) to rotate, make laser scanning measured object (e)；Then, the left and right video camera of integral translation (a, b)
Position, repeatedly shot, ensure the integrality of measured object (e) shape face information；Auxiliary is collected by information acquisition system to swash
, it is necessary to be extracted to the center line of laser striation (f) after light striation (f) image, optical strip image center grey scale centre of gravity is utilized
The method of extraction, formula (1) are：
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Wherein：, (ui,vi) it is the i-th row striation grey scale centre of gravity coordinate, IijFor the i-th row jth row gray value；；Can be with by the method
The characteristic point two-dimensional signal of auxiliary laser striation (f) is obtained, in conjunction with calibration result and reconstruction formula, obtains boundary point and light
D coordinates value of the bar central point under world coordinate system, reconstruction formula (2) are as follows：
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Where it is assumed that xi'=(Xi',Yi'), Xi', Yi' it is respectively sharp point or optical losses that left video camera (a) gathers
Point xi' horizontal stroke, the ordinate under image coordinates system；xi'=(Xi′,Yi'), Xi', Yi' it is respectively the figure that right video camera (b) gathers
As spot center point xi‘' horizontal stroke, the ordinate under image coordinates system；f1、f2Respectively left and right video camera (a, b) demarcation obtains
Focal length；It is spin matrix of the right video camera (b) relative to left video camera (a), [tx ty tz] it is right shooting
Machine (b) is obtained relative to the translation matrix of left video camera (a) by calibration experiment；Then (xi,yi,zi) it is to rebuild corresponding points out
Three-dimensional coordinate, thus obtain the three dimensional point cloud on whole measured object (e) surface；
Second step, put the division in cloud sector domain
For the point cloud of acquisition, two points of head and the tail of every point cloud line are numbered, i.e., the boundary point of point cloud chart picture numbered
1 ..., 2n, boundary point corresponding to every point cloud line both ends is connected, obtain straight line g, will be attached in straight line g between two boundary points
Near point, after n point takes a point, using formula (3), (4), selection is treated by match point J according to least square method
It is fitted to a curve；
yi=a0+a1xi+…+akxi k (3)
Using adjacent two curves as the left and right border l in monolithic point cloud sector domain1、l2, complete the division in point cloud sector domain；
3rd step, the removal of gross error point
After point cloud region division, the point in a cloud sector domain is scanned for successively, obtains the point in the domain of every piece of point cloud sector respectively
To the beeline h of two boundary curves1、h2, afterwards, according to h1With h2Ratio ρ sizes judge gross error point I；
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Wherein, h1To put the beeline to left margin, h2To put the beeline to right margin；When the point in a cloud sector domain leans on
Nearly left margin l1When, ρ tends to 0, when the point in a cloud sector domain is close to right margin l2When, ρ tends to ∞, therefore, sets threshold alpha1、α2,
Work as α1≤ρ≤α2When, this point is judged for gross error point I, and this point is rejected；As ρ≤α1Or ρ >=α2When, judge that this point is normal
Point, retain this point；This completes the gross error of point cloud data point I rejecting.
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