CN108171713A - The robot of view-based access control model quickly identifies and three-dimensional visual positioning method - Google Patents
The robot of view-based access control model quickly identifies and three-dimensional visual positioning method Download PDFInfo
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- CN108171713A CN108171713A CN201611117481.XA CN201611117481A CN108171713A CN 108171713 A CN108171713 A CN 108171713A CN 201611117481 A CN201611117481 A CN 201611117481A CN 108171713 A CN108171713 A CN 108171713A
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/10—Image acquisition modality
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Abstract
The invention discloses a kind of robot of view-based access control model, quickly identification and three-dimensional visual positioning method, this method include：S1 carries out Image Acquisition to fruit；S2 carries out image segmentation and regional characteristics analysis to fruit image；S3 schemes left and right to carry out Stereo matching；S4 obtains the three-dimensional coordinate information of fruit.The present invention program extracts objective fruit using 2R G B Model for chromatic aberration Threshold segmentation is normalized, connected component labeling and region shape feature description are quickly carried out using Run- Length Coding, the matching algorithm Stereo matching being combined using centroid feature point with neighborhood gray scale cross correlation chooses best picking object by optimal picking target strategy.
Description
Technical field
The invention belongs to Robot visual location fields, and being related to a kind of robot of view-based access control model, quickly identification is regarded with three-dimensional
Feel localization method.
Background technology
As south jiangxi specialty industries, cultivated area occupies first of the world navel orange, and picking fruit is an onerous toil.With
Aging of population, rural labor population constantly reduce, and realize that navel orange automation picking is to improve agricultural automation, solve labour
Insufficient inevitable development trend.To space fruit, accurate, real-time position successfully is picked in navel orange automates picking process
It is crucial.
There is binocular vision technology perception information amount to enrich, it is of overall importance it is good, precision is high, non-contact measurement etc. can not be substituted
Advantage, be widely used to robot navigation, commercial measurement etc..For the picking robot worked under natural environment
Target identification and positioning are the heat subjects of Recent study.
Picking robot is a high real-time clock, wherein identification positioning is the key that influence picking robot real-time ring
Section.The real-time and accuracy of vision positioning system are improved, and then it is current to improve picking robot performance, improve operating efficiency
Picking robot application major issue urgently to be resolved hurrily.
Application publication number is that the application for a patent for invention of CN105144992A discloses " a kind of strawberry picks collection device ", should
Device includes pedestal and the picking mechanism on pedestal, which includes the second sliding rail being vertical on pedestal, the
Two sliding rails are equipped with the second motor, and the second motor is connected with the second screw, which is equipped with the second screw；Second screw
Upper horizontal equipped with third sliding rail, third sliding rail is equipped with third motor, and third motor is connected with third screw, on the third screw
Equipped with third nut seat；Third nut seat is equipped with Picker arm, and the end of Picker arm is equipped with cutter, in the case where Picker arm is fixed a cutting tool
Side is equipped with Picking basket.The invention can be by way of controlling multiple sliding rails to carry out right angle movement by the Picking basket of Picker arm leading portion
Operation cuts off fruit to the underface of the fruit, and by cutter, and fruit passes under the effect of gravity along the fruit of Picking basket lower end
Defeated cylinder deceleration slips into collecting box, completes the picking of a strawberry, has stability high, at low cost, picking precision is high, not easy damaged
Fruit appearance, picking efficiency is high, but it can not realize the identification and positioning of ripening fruits without machine vision positioning technology.
" a kind of kiwifruit fruit picking robot and picking method, application number CN201310719150.3 " disclose one to patent
Kind of kiwifruit fruit picking robot and picking method, including mechanical execution system and control system, by fruit and barrier
Image Acquisition and feature extraction, the intelligent mobile of telescopic slide platform, the obstacle of five degree of freedom picking mechanical arm and fruit are determined
The crawl of position and two finger formula end effectors is integrated, and realizes mechanization, the automation of kiwifruit fruit picking, but its
Image identifies and location algorithm is more complicated, can not realize the quick of fruit and in real time identification and positioning.
Invention content
Present invention aims at a kind of robot of view-based access control model quickly identification and three-dimensional visual positioning method is provided, utilize
2R-G-B Model for chromatic aberration Threshold segmentation extraction objective fruit is normalized, connected component labeling and area are quickly carried out using Run- Length Coding
Domain shape feature description, the matching algorithm Stereo matching being combined using centroid feature point with neighborhood gray scale cross correlation are passed through
Optimal picking target strategy chooses best picking object, efficiently solve at present due to artificial picking efficiency is low and caused by
Economic loss and the adverse effect to navel orange plant production development.
In order to solve the above technical problems, the present invention adopts the following technical scheme that：A kind of robot of view-based access control model is quick
Identification and three-dimensional visual positioning method, the method includes：S1 carries out Image Acquisition to fruit；S2 carries out figure to fruit image
As segmentation and regional characteristics analysis；S3 schemes left and right to carry out Stereo matching；S4 obtains the three-dimensional coordinate information of fruit.
Further, in the step S1, Image Acquisition is carried out to fruit using binocular camera.
Further, it in the step S2, is split using normalization 2R-G-B Model for chromatic aberration threshold values, is compiled using the distance of swimming
Code method carries out regional characteristics analysis.
Further, it in the step S3, is calculated using centroid feature point with the matching that neighborhood gray scale cross correlation is combined
Method.
The present invention has following advantageous effect compared with prior art：
The present invention program extracts objective fruit using 2R-G-B Model for chromatic aberration Threshold segmentation is normalized, fast using Run- Length Coding
Speed carries out connected component labeling and region shape feature description, is combined using centroid feature point with neighborhood gray scale cross correlation
Matching algorithm Stereo matching chooses best picking object by optimal picking target strategy.
Description of the drawings
Fig. 1 is that the robot of view-based access control model quickly identifies and three-dimensional visual positioning method flow diagram.
Fig. 2 is the image after fruit image is binarized.
Fig. 3 is the image after fruit Boundary Extraction.
Fig. 4 is fruit binary image and the progress of Boundary Extraction image or operation.
Fig. 5 is fruit image by the image after morphological erosion, expansion.
Fig. 6 is Run- Length Coding schematic diagram.
Fig. 7 is more candidate matches figures in the range of binocular parallax.
Fig. 8 is binocular camera illustraton of model.
Specific embodiment
Below in conjunction with the accompanying drawings and specific embodiment the present invention is carried out in further detail with complete explanation.It is appreciated that
It is that specific embodiment described herein is only used for explaining the present invention rather than limitation of the invention.
With reference to Fig. 1, a kind of robot of view-based access control model of the invention quickly identifies and three-dimensional visual positioning method, the side
Method includes：
S1 carries out Image Acquisition to fruit；
This method carries out Image Acquisition using binocular camera to fruit, for post-treatment operations.
S2 carries out image segmentation and regional characteristics analysis to fruit image；
S21, color characteristic analysis are divided with image；
Apparent difference feature is had according to fruit color and background color, image point is carried out by color component characteristic relation
It cuts.Image sampling is carried out to the fruit that grows naturally under different illumination conditions, RGB color analysis target and background R,
G, B component value relationship.It can be obtained by each color channel values of image line sampled pixel, objective fruit is with background in Model for chromatic aberration 2R-
Grey-scale contrast is larger under G-B, and 2R-G-B Model for chromatic aberration grey level histograms have the characteristics that it is apparent bimodal, therefore can given threshold
Target is detached.In order to have better adaptability to different illumination intensity using normalization aberration (2R-G-B)/(2R+G+B)<T
Threshold segmentation.Global threshold T=0.29 is taken to be split, the image after being divided filters out noise by medium filtering, conversion
Into bianry image, such as Fig. 2.
For part fruit, there are adhesion situations, and fruit region after Threshold segmentation is carried out colour switching and asks for gray-scale map
Picture, then extract using Gauss-Laplace operator the borderline region of fruit, such as Fig. 3.Again by Fig. 3 and binary image, that is, scheme
2, progress or operation can preferably separate adhesion fruit, such as Fig. 4.Finally by the morphological erosion of 33 × 3 templates and swollen
Noise and small holes in image are removed in swollen processing, obtain connection fruit region, such as Fig. 5.
S22, regional characteristics analysis；
Since picking process is to win fruit one by one, optimal picking target strategy to be used to choose preferential picking pair
As.Multiple target connected regions are presented in image after segmentation, and regional analysis is typically that longer link is taken in image procossing.Often
Regional analysis has seed fill algorithm and two step scanning methods etc., is all analyzed by basic unit of pixel, meter
It is poor to calculate cost height, real-time.Run- Length Coding method is using the distance of swimming as basic operation unit, and connected component analysis basic unit is by Pixel-level
Distance of swimming grade is gone to, improves regional analysis efficiency.The present invention carries out the fruit connected region detected using Run- Length Coding method
Analysis only needs a scanning that zone marker and shape feature calculating can be completed, is as follows：
1st step initializes distance of swimming Array for structural body, from left to right progressive scanning picture, the distance of swimming in coding record a line
Number RLEi, row coordinate Ri, start-stop position Xi、YiAnd by pointer QiAdding in abutted father's distance of swimming, (current run lastrow is swum
Journey) pointed by the root distance of swimming (first father's distance of swimming) chained list.The distance of swimming for belonging to the same root distance of swimming forms a section object.
2nd step judges the syntople of current run and father's distance of swimming, and by four neighborhood walking direction overlapping regions, (K represents weight
Fold-over prime number), such as Fig. 6, there are three types of situations：If 1. do not abut (such as RLE with the lastrow distance of swimming0), then create the new root distance of swimming
Indicator linking, and add in root distance of swimming chained list；If 2. abut (such as RLE with lastrow list1With RLE0It is adjacent), then by the distance of swimming pointer
Add in the root distance of swimming pointer chained list of father's distance of swimming；3. if (such as RLE is abutted again with the multiple distances of swimming of lastrow3With RLE1、RLE2Multiple neighbour
Connect), then member's update in the corresponding distance of swimming chained list of father's distance of swimming abutted is merged into wherein number least root distance of swimming chain
In table, and current run pointer is added in into the root distance of swimming chained list after merging.
3rd step, the area S of statistical regions, perimeter L, regional center coordinateWait characteristic parameters.Toward root distance of swimming chain
Distance of swimming RLE is added in tablei+1The following formula of time zone characteristic of field (1)~formula (4)：
Si+1=Si+Yi+1-Xi+1+1 (1)
Li+1=Li+2(Yi+1-Xi+1+1)+2-2K (2)
Usual small area region is noise jamming, distance is remote and the fruit image not in the range of picking, all row scannings
After complete, these small area regions are first removed, then region roundness measurement is carried out by formula (5).
Wherein, SiIt is the area (area pixel sum) in region, LiPerimeter (zone boundary number of pixels) for region.Ci
It is to represent region shape closer to round closer to 1 for the circularity in region.By border circular areas mark of the area in preset range
Remember and add in storehouse.Since close-in target is bigger than distant object imaging, the unobstructed fruit circularity in outside is preferable.With low coverage
Principle from the picking of, outside target priority selects relative importance value function such as formula (6).
Wherein γ is proportionality coefficient.Preferential angle value P is pressed to storehouse inner region after scanning whole regioniIt is ranked up, selects
The highest region of relative importance value for picking target, extract zone boundary, using random ring method fitting ask for target central coordinate of circle and
Radius parameter.
S3 schemes left and right to carry out Stereo matching；
Stereo matching process finds left figure specified point corresponding point in right figure.Traditional matching process has based on region
Matching and feature based matching.It is to carry out differentiating image to journey of coincideing with the similitude of intensity profile in window based on Region Matching
Degree, matching primitives amount is big, and real-time is poor；Feature-based matching is carried out according to features such as clarification of objective point, edge shapes
Matching has matching speed fast, and matching precision is high, but characteristic matching is likely that there are multiple times when finding Corresponding matching point
Reconnaissance or match point are not present, and are susceptible to error hiding, such as Fig. 7.
The matching algorithm that the present invention is combined using centroid feature point with neighborhood gray scale cross correlation, by by slightly to smart real
Existing object matching.It is subject to epipolar-line constraint and disparity range constraint in centroid feature point matching process.Point in left image is on the right side
It is only searched in corresponding polar curve height in image, it is contemplated that search range in vertical direction is limited to correspondence by distortion error
In three pixel coverages up and down of polar curve height.Search is limited to robot manipulating task space and corresponds to effective parallax in the horizontal direction
Range X-DmaxIn~X.Target's center's point in region of search is candidate matches point, has more candidate for region of search
Match, be susceptible to the situation of error hiding, use neighborhood area grayscale correlation as unique constraint.The present invention is fitted using intensity of illumination
The preferable normalized cross-correlation coefficients R (x, y) of answering property, such as formula (7).
Wherein, P (x, y) is target's center's point gray scale in left figure, and Q (x ', y ') corresponds to candidate matches point gray scale, 2M for right figure
+ 1,2N+1 are height, the width of template.
Matching step is as follows：
Step (1) chooses picking target in left figure, 9 × 9 template windows, the centre of form is established with the target dot center P (x, y)
Matching is subject to epipolar-line constraint disparity constraint, determines candidate matches point Q (x ', y ').
Step (2) chooses a candidate matches point, the moving die plate in its 15 × 15 neighborhood windows, search and left figure
Template gray is distributed correlation R (x, y) highest match point.
Step (3), judges whether the highest match point of similitude meets matching requirement, | R (x, y) |>L is considered correct
Match, receive the matching double points；Otherwise, it fails to match, goes to step (2), and next candidate matches point is matched.
Step (4) records last matching double points coordinate and participates in disparity computation, if institute's candidate matches are unsatisfactory for, matches
Target is not present, and goes to step (1) and carries out next object matching.
S4 obtains the three-dimensional coordinate information of fruit；
Camera model uses the linear model of pinhole imaging system, and binocular camera main shaft is arranged in parallel, binocular model such as Fig. 8
It is shown.
Wherein f is the focal length of video camera, and b is the base length of two video cameras.P1(x1,y1) and P2(x2,y2) exist for target point P
Imaging point coordinates in two imaging planes.The three dimensional space coordinate point P (x, y, z) of objective fruit can be obtained by principle of triangulation
Coordinate such as formula (10)：
Target information is obtained by CMOS binocular cameras in real time, extracts the pixel coordinate of objective fruit；Pass through video camera
Demarcate the intrinsic parameters of the camera external parameter obtained.Fruit is calculated according to image-forming principle to sit in the three dimensions of camera coordinate system
Mark.
The foregoing is merely the preferred embodiment of the present invention, are not intended to restrict the invention, for those skilled in the art
For, the present invention can have various modifications and changes.All any modifications made within spirit and principles of the present invention are equal
Replace, improve etc., it should all be included in the protection scope of the present invention.
Claims (4)
1. the robot of view-based access control model quickly identifies and three-dimensional visual positioning method, which is characterized in that the method includes：S1,
Image Acquisition is carried out to fruit；S2 carries out image segmentation and regional characteristics analysis to fruit image；S3 founds left and right figure
Body matches；S4 obtains the three-dimensional coordinate information of fruit.
2. quickly identification and three-dimensional visual positioning method, feature exist for the robot of view-based access control model according to claim 1
In in the step S1, using binocular camera to fruit progress Image Acquisition.
3. quickly identification and three-dimensional visual positioning method, feature exist for the robot of view-based access control model according to claim 1
In, in the step S2, it is split using normalization 2R-G-B Model for chromatic aberration threshold values, it is special using Run- Length Coding method progress region
Sign analysis.
4. quickly identification and three-dimensional visual positioning method, feature exist for the robot of view-based access control model according to claim 1
In, in the step S3, the matching algorithm that is combined using centroid feature point with neighborhood gray scale cross correlation.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109739237B (en) * | 2019-01-09 | 2020-08-18 | 华南理工大学 | AGV visual navigation and positioning method based on novel coding marks |
| CN111753577A (en) * | 2019-03-28 | 2020-10-09 | 天津工业大学 | Apple identification and positioning method in automatic picking robot |
| CN111784655A (en) * | 2020-06-24 | 2020-10-16 | 江苏科技大学 | Underwater robot recovery positioning method |
-
2016
- 2016-12-07 CN CN201611117481.XA patent/CN108171713A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109739237B (en) * | 2019-01-09 | 2020-08-18 | 华南理工大学 | AGV visual navigation and positioning method based on novel coding marks |
| CN111753577A (en) * | 2019-03-28 | 2020-10-09 | 天津工业大学 | Apple identification and positioning method in automatic picking robot |
| CN111753577B (en) * | 2019-03-28 | 2024-01-30 | 天津工业大学 | Apple identification and positioning method in automatic picking robot |
| CN111784655A (en) * | 2020-06-24 | 2020-10-16 | 江苏科技大学 | Underwater robot recovery positioning method |
| CN111784655B (en) * | 2020-06-24 | 2023-11-24 | 江苏科技大学 | Underwater robot recycling and positioning method |
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