CN108388854A - A kind of localization method based on improvement FAST-SURF algorithms - Google Patents
A kind of localization method based on improvement FAST-SURF algorithms Download PDFInfo
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Abstract
The present invention is claimed a kind of based on the localization method for improving FAST SURF algorithms, and time-consuming for positioning when carrying out crawl object manipulation for industrial robot, and the low problem of precision, it uses a kind of localization method of improved FAST SURF algorithms：According to the target object pictorial information that binocular vision system acquires, preliminary treatment is carried out using improved FAST algorithms, obtains characteristic point；The matching of characteristic point pair and RANSAC are screened using SURF algorithm；By the characteristic point matched to carrying out triangulation principle processing, it is accurately positioned object dimensional coordinate.This method has to be reduced and is promoted in accuracy in time, is positioned more suitable for the target object under this paper industrial robot backgrounds.
Description
Technical field
The invention belongs to field of locating technology, especially a kind of target positioning side based on improved FAST-SURF algorithms
Method.
Background technology
Industrial robot is widely used in the industrial tasks such as logistics classification, object stacking.And pick up is with drop target object
One of vital task of industrial robot.The crawl of most of industrial robot remains in the layer of repeated work after advance described point
It is secondary.Such working method is high to the stability requirement of environment, cannot cope with the variation of burst.It is continuous with social production
Development, it is also higher and higher to the practicability of robot system, intelligent requirements.By vision system, industrial robot can be to mesh
Mark object be identified and position, to improve system robustness and real-time come the case where coping with burst.In recent years, vision
The application of identification positioning industrially is also more and more extensive.
Binocular visual positioning technology is to simulate the principle of mankind's binocular perceived distance, realizes the three-dimensional space position to scenery
Measurement, be based on principle of triangulation, same scenery be imaged from different location with two cameras, to restoring from parallax
Range information is an important content of robot vision research, and object location techniques are disadvantageous in that image at present
Processing speed is not fast enough, and positioning is inaccurate.
Invention content
Present invention seek to address that the above problem of the prior art.Propose it is a kind of improve whole matching precision based on changing
Into the localization method of FAST-SURF algorithms.Technical scheme is as follows：
A kind of localization method based on improvement FAST-SURF algorithms comprising following steps：
S11：The image information of positioning object is acquired using binocular vision system；
S12：The positioning object image information that step S11 is obtained carries out preliminary treatment using improved FAST algorithms, obtains
Characteristic point；Improved FAST algorithm improvements are mainly reflected in：The number for detecting contrast points is reduced to 12 by 16, accelerates detection
Speed.
S13：According to the characteristic point that step S12 is obtained, the matching of characteristic point pair is carried out to target area using SURF algorithm
And screening；
S14：According to the matching pair that step S13 is obtained, multigroup spy is obtained to screening to matching using RANSAC algorithms
Levy matching double points；
S15：The multigroup characteristic matching point pair obtained according to step S14, point is calculated using triangulation algorithm
Three-dimensional coordinate is accurately positioned object dimensional coordinate.
Further, the step S12 carries out preliminary treatment using improved FAST algorithms, obtains characteristic point and specifically includes
Step：
S21：FAST algorithms：It is the center of circle that a pixel P is chosen from picture, and radius is the discretization of 3 pixels
Bresenhan justifies, and there are 16 pixels on round boundary；
S22：The improvement of FAST algorithms
16 pixels compared with surrounding are reduced to 12 pixels, detection principle is that continuous 9 pixels all compare Ip+
T is big or compares Ip- t is small, then the pixel is characterized a candidate point；
S23：The gray value difference of the gray value and candidate point of a circle pixel around point to be detected is enough big, can recognize
It is a characteristic point for this candidate point；
S24：Non-maxima suppression is carried out to characteristic point, obtains characteristic point output.
Further, the step S23 is specifically included：Any point central coordinate of circle (x, y) being located in two dimensional image, it is special
Sign point description algorithmic formula is as follows：
As the gray value I of center pixelPIt punctuates grey scale pixel value I at x less than surroundingxWhen+t, then it is assumed that the gray-scale pixels category
In darker, then SP→x=d；And so on similar s and brighter gray-scale pixels point b, in this way at one with candidate feature point p
Gray-scale pixels point d, s and b of 3 types are just had found for the border circular areas edge in the center of circle, count the number of d or b, if it is greater than
N, then it is assumed that the point is candidate feature point；Then the gray value for justifying upper pixel by radii fixus is compared acquisition candidate feature
Point, according to 12 features, judges whether this candidate feature point is characterized a little using ID3 graders；By above step obtain d,
S, the number of b is denoted as P respectivelyd、Ps、Pb, the S that is calculatedP→xMust corresponding (2) certain situation, enable IPWhen being characterized
Kp=true, otherwise false, to judge whether some pixel is characterized a little.
Further, the step S13 using SURF algorithm to target area carry out characteristic point pair matching, then to
It carries out distance discrimination respectively with result and angle differentiates screening, obtain the high characteristic point of accuracy to specifically including：
Point of interest is extracted with the Gaussian derivative of integral image property detector, it will be square centered on the point of interest of extraction
Shape region, this region are divided into 4 × 4 grid grids, have the sampled point of 5 × 5 aturegularaintervals in every grid, for each net
Lattice, Haar small echos respond dx and dy and are calculated by 5 × 5 sampled points at these intervals, then by small echo respond dx be added with dy with
Formation 4-D vectors V, V=(∑ dx, ∑ dy, ∑ | dx |, ∑ | dy |), it connects this and is described for all 4 × 4 grid protocol 64-D
Symbol vector.
Further, the first matching double points obtained to SURF algorithm using this method are into row distance and angle character
Consistency check with parameter judges that the point of interest is correct characteristic point, otherwise makees if meeting two match parameters simultaneously
For the matching double points of mistake, removing does not make reference.
Further, the step S14 is according to obtained matching pair, using RANSAC algorithms to matching to carrying out screening tool
Body includes step：
S51：It extracts four sample datas out at random from data set, calculates transformation matrix, be denoted as M；
S52：The projection error of all data and model M in data set is calculated, if error is less than threshold value, interior point set I is added；
S53：If point set I element numbers are more than point set I_best in u in current, then I_best=I is updated；It updates simultaneously
Iterations k；
S54:If iterations are more than k, then exit；Otherwise iterations add 1, and repeat the above steps.
Further, multigroup characteristic matching point pair that the step S14 is obtained, is calculated a little using triangulation algorithm
The three-dimensional coordinate of position, is accurately positioned object dimensional coordinate and specifically includes：
S51：Left camera O1 is respectively being set as origin system, it is known that point P (X, Y, Z)
Left image feature point coordinates (u0,v0), right image feature point coordinates (u1,v1), b is two cameras
Distance, f are camera focus
According to the obtained characteristic point pair of above-mentioned algorithm, the three-dimensional coordinate of each characteristic point is calculated with above formula；
S52：The three-dimensional coordinate of each characteristic point is handled, calculating summarizes coordinate, and the three-dimensional of object need to be positioned by, which obtaining, sits
Mark.
It advantages of the present invention and has the beneficial effect that：
Innovative point of the present invention is 16 detection contrast points of FAST algorithms being reduced to 12, reduces FAST and detect spy
Levy the time of point.Error hiding pair is rejected using RANSAC algorithms simultaneously, improves accuracy；
Time-consuming for positioning when carrying out crawl object manipulation for industrial robot, and the low problem of precision proposes a kind of new point
Laminar target object positioning strategy.Image characteristic point is extracted using improved FAST algorithms.It is used for the characteristic point of extraction
SURF algorithm carries out Feature Points Matching to target area, compared with the erroneous matching for again reducing Independent Point for SURF algorithm.It carries
High whole matching precision, accelerates whole matching speed.
Description of the drawings
Fig. 1 is that the present invention provides localization method system flow chart of the preferred embodiment based on improved FAST-SURF algorithms；
Fig. 2 is to improve FAST algorithm principle figures；
Fig. 3 is triangulation schematic diagram.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, detailed
Carefully describe.Described embodiment is only a part of the embodiment of the present invention.
The present invention solve above-mentioned technical problem technical solution be：
In industrial environment, time-consuming for positioning when carrying out crawl object manipulation for industrial robot, and the low problem of precision carries
Go out a kind of layer-stepping positioning strategy of improved FAST-SURF algorithms, quickly and accurately positions object.First according to binocular vision
The target object pictorial information of system acquisition carries out preliminary treatment using improved FAST algorithms, extracts the characteristic point of image；So
Carry out the matching and screening of characteristic point pair to target area using SURF algorithm afterwards；Finally by the characteristic point matched to carrying out
The processing of triangulation principle, obtains object dimensional coordinate.
As shown in Figure 1, be the localization method system block diagram proposed by the present invention based on improved FAST-SURF algorithms, it should
The target object pictorial information that method is acquired according to binocular vision system carries out preliminary treatment using improved FAST algorithms, carries
Take image characteristic point；Then the matching and screening of characteristic point pair are carried out to target area using SURF algorithm；It finally will matching
Good characteristic point is accurately positioned object dimensional coordinate to carrying out triangulation principle processing.
As shown in Fig. 2, for FAST algorithm principle figures in the present invention.Compare in surrounding 16 pixels are reduced to 12 pictures
Vegetarian refreshments.Detection principle is that continuous 9 pixels all compare Ip+ t is big or compares Ip- t is small, then the pixel is characterized a candidate point.It waits for
The gray value of a circle pixel and the gray value difference of candidate point around the point of detection reach big, it is believed that this candidate point is
One characteristic point, any point central coordinate of circle (x, y) in two dimensional image.Feature point description algorithmic formula is as follows：
As the gray value I of center pixelPIt punctuates grey scale pixel value I at x less than surroundingxWhen+t, then it is assumed that the gray-scale pixels category
In darker, then SP→x=d；And so on similar s and brighter gray-scale pixels point b.In this way at one with candidate feature point p
Gray-scale pixels point d, s and b of 3 types are just had found for the border circular areas edge in the center of circle.The number for counting d or b, if it is greater than
N (when it is 9 to divide test point number, i.e. IxNumber 3/4), then it is assumed that the point be candidate feature point.Then pass through fixation
The gray value of the upper pixel of radius circle, which is compared, obtains candidate feature point.Judge this according to 12 features using ID3 graders
Whether candidate feature point is characterized a little.Above step acquisition d, s, b are denoted as Pd、Ps、Pb, the S that is calculatedP→xIt must be right
Answer certain situation of formula (2).Enable IPKp=true when being characterized, otherwise false, to judge whether some pixel is characterized
Point.
Non-maxima suppression is carried out to characteristic point, obtains characteristic point output.
As shown in figure 3, for the triangle location algorithm in the present invention.Above-mentioned improvement SURF algorithm first to characteristic point into
Row matching, then the calculating by characteristic point to progress triangle algorithm, obtains the three-dimensional coordinate of each characteristic point.It is directed to each spy again
The three-dimensional coordinate of sign point is handled, and calculating summarizes coordinate, obtains the three-dimensional information of target object.
The above embodiment is interpreted as being merely to illustrate the present invention rather than limit the scope of the invention.
After the content for having read the record of the present invention, technical staff can make various changes or modifications the present invention, these equivalent changes
Change and modification equally falls into the scope of the claims in the present invention.
Claims (7)
1. a kind of based on the localization method for improving FAST-SURF algorithms, which is characterized in that include the following steps：
S11：The image information of positioning object is acquired using binocular vision system；
S12：The positioning object image information that step S11 is obtained carries out preliminary treatment using improved FAST algorithms, obtains feature
Point；Improved FAST algorithm improvements are mainly reflected in：The number for detecting contrast points is reduced to 12 by 16, accelerates the speed of detection
Degree；
S13：According to the characteristic point that step S12 is obtained, using SURF algorithm to target area carry out characteristic point pair matching and
Screening；
S14：According to the matching pair that step S13 is obtained, multigroup feature is obtained to screening to matching using RANSAC algorithms
With point pair；
S15：The multigroup characteristic matching point pair obtained according to step S14, the three-dimensional of point is calculated using triangulation algorithm
Coordinate is accurately positioned object dimensional coordinate.
2. according to claim 1 based on the localization method for improving FAST-SURF algorithms, which is characterized in that the step
S12 carries out preliminary treatment using improved FAST algorithms, obtains characteristic point and specifically includes step：
S21：FAST algorithms：It is the center of circle that a pixel P is chosen from picture, and radius is the Bresenhan of the discretization of 3 pixels
Circle, there are 16 pixels on round boundary；
S22：The improvement of FAST algorithms
16 pixels compared with surrounding are reduced to 12 pixels, detection principle is that continuous 9 pixels all compare Ip+ t it is big or
Person's ratio Ip- t is small, then the pixel is characterized a candidate point；
S23：The gray value difference of the gray value and candidate point of a circle pixel around point to be detected is enough big, it is believed that this
A candidate point is a characteristic point；
S24：Non-maxima suppression is carried out to characteristic point, obtains characteristic point output.
3. according to claim 2 based on the localization method for improving FAST-SURF algorithms, which is characterized in that the step
S23 is specifically included：Any point central coordinate of circle (x, y) being located in two dimensional image, feature point description algorithmic formula are as follows：
As the gray value I of center pixelPIt punctuates grey scale pixel value I at x less than surroundingxWhen+t, then it is assumed that the gray-scale pixels belong to more
It is dark, then SP→x=d；And so on similar s and brighter gray-scale pixels point b, in this way one with candidate feature point p be circle
The border circular areas edge of the heart just has found gray-scale pixels point d, s and b of 3 types, counts the number of d or b, if it is greater than n, then
Think that the point is candidate feature point；Then the gray value for justifying upper pixel by radii fixus is compared acquisition candidate feature point,
Using ID3 graders, according to 12 features, judge whether this candidate feature point is characterized a little；Above step is obtained into d, s, b
Number be denoted as P respectivelyd、Ps、Pb, the S that is calculatedP→xMust corresponding (2) certain situation, enable IPKp=when being characterized
True, otherwise false, to judge whether some pixel is characterized a little.
4. according to claim 2 based on the localization method for improving FAST-SURF algorithms, which is characterized in that the step
S13 using SURF algorithm to target area carry out characteristic point pair matching, then matching result is carried out respectively distance discrimination and
Angle differentiates screening, obtains the high characteristic point of accuracy to specifically including：
Point of interest is extracted with the Gaussian derivative of integral image property detector, by square region centered on the point of interest of extraction
Domain, this region are divided into 4 × 4 grid grids, per the sampled point for having 5 × 5 aturegularaintervals in grid, for each grid,
Haar small echos respond dx and dy and are calculated by 5 × 5 sampled points at these intervals, are then summed to form small echo response dx and dy
4-D vectors V, V=(∑ dx, ∑ dy, ∑ | dx |, ∑ | dy |), connect this for all 4 × 4 grid protocol 64-D descriptors to
Amount.
5. according to claim 4 based on the localization method for improving FAST-SURF algorithms, which is characterized in that the use
The first matching double points that this method obtains SURF algorithm into row distance and angle character match parameter consistency check, if simultaneously
Meet two match parameters, then judge that the point of interest is correct characteristic point, otherwise the matching double points as mistake, removing is not done
With reference to.
6. according to claim 4 based on the localization method for improving FAST-SURF algorithms, which is characterized in that the step
S14 specifically includes step to matching according to obtained matching pair, using RANSAC algorithms to carrying out screening：
S51：It extracts four sample datas out at random from data set, calculates transformation matrix, be denoted as M；
S52：The projection error of all data and model M in data set is calculated, if error is less than threshold value, interior point set I is added；
S53：If point set I element numbers are more than point set I_best in u in current, then I_best=I is updated；Update iteration simultaneously
Number k；
S54:If iterations are more than k, then exit；Otherwise iterations add 1, and repeat the above steps.
7. according to claim 4 based on the localization method for improving FAST-SURF algorithms, which is characterized in that the step
Multigroup characteristic matching point pair that S14 is obtained, the three-dimensional coordinate of point is calculated using triangulation algorithm, is accurately positioned object
Three-dimensional coordinate specifically includes：
S51：Left camera O1 is respectively being set as origin system, it is known that point P (X, Y, Z)
Feature point coordinates (u in left image0,v0), feature point coordinates (u in right image1,v1), b is two cameras
Distance, f are camera focus；
According to the obtained characteristic point pair of above-mentioned algorithm, the three-dimensional coordinate of each characteristic point is calculated with above formula；
S52：The three-dimensional coordinate of each characteristic point is handled, calculating summarizes coordinate, obtains the three-dimensional coordinate that need to position object.
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