CN106504288A - A kind of domestic environment Xiamen localization method based on binocular vision target detection - Google Patents

Abstract

The invention discloses a kind of domestic environment Xiamen localization method based on binocular vision target detection, the method includes：Image is obtained by binocular vision video camera；Intercept image and obtain binary image；Obtain to be a serial of line sections of doorjamb；Find the line segment pair matched in left images；Optimum Matching line segment is obtained to queue using the principle that global optimum mates；Doubtful door line pair is obtained to the distance of corresponding actual line by mating line segment；Doubtful door optimal solution is obtained using characteristic-integration principle, so as to opposite house is positioned.Technical solution of the present invention adopts binocular vision, by the bilateral frame of highly effective algorithm recognitiion gate, effectively realizes the door positioning under domestic environment, with low cost, easy to use and flexible, the extensive advantage of application scenarios.

Description

A kind of domestic environment Xiamen localization method based on binocular vision target detection

Technical field

The present invention relates to robot vision field, is a kind of real-time object localization method, especially a kind of based on binocular The domestic environment Xiamen localization method of sensation target detection.

Background technology

In recent years, roboticses have obtained constantly developing, in family, office, hospital and work as high and new technology Factory etc. is widely adopted.It is used for the industrial robot of Intelligent assembly to for public service, home services from workshop Intellect service robot, robot have increasingly been pressed close to and have been entered in our work and life.Server emerging at present Qi Ren enterprises and service robot application market are also constantly increasing, so amusement miscellaneous, rescue, monitoring robot The every aspect of we life is gradually penetrated into.With to the more and more high of service robot degree of intelligence demand, to people Work technology of identification, human-computer interaction technology, autonomous patrol technology and intelligent control technology etc. require also to heal for the intellectual technology of representative Come higher.For home-services robot, robot is especially monitored, indoor navigation and independently go on patrol the work(being commonly necessary Energy.Therefore for the maximum challenge of such robot be on the basis of limited understanding is carried out to surrounding enviroment, can be autonomous hold The increasing complex task of row.Wherein to robot, map structuring, indoor navigation, autonomous patrol in domestic environment have door Extremely important meaning.Its reason is as follows：

1. it is geostationary in the position of domestic environment；

2. it is the unique passage for connecting a room and another room；

3. width and shape has unified standard.

But some home services humanoid robots are more because of its integrated functionality, and volume is relatively bulky, but under domestic environment Door is relatively small.Realize that opposite house is positioned, and pass through door, become the difficult point of robotics development.It is achieved that to household The positioning of environment Xiamen, for the development tool of roboticses and industry is of great significance.

There are a lot of scientific research institutions and manufacturer all in the orientation problem of research domestic environment Xiamen in the world, imaged by employing Head, sonar sensor, laser radar sensor etc. are realizing sensor information or multi-sensor information fusion, and propose one and be The solution of row, such as：

1. in door both sides adhesive label, and the positioning of opposite house is further realized to the positioning of label by monocular vision；

2. using whether having depth saltation zone in sonar sensor or laser radar sensor sniffing robot direction of advance, And the door positioning to open is realized with reference to known cartographic information；

3., using RGB-D sensors, extracted by the feature of opposite house, and the depth information with reference to each object in the visual field Realize the positioning of opposite house；

4. monocular vision is adopted, by extracting image center line feature, further mates the various combined situations of door line feature, Realize the positioning of opposite house.

But said method has certain defect.First, sonar sensor, laser radar sensor price are very high, RGB-D The price of sensor is not relatively low yet, so the robot with the sensor is difficult the family for being generalized to each economic class In；And this scheme there is also deficiency, it is impossible to realize positioning to the door that close.Next, although 4 cost of said method 1 and method Than relatively low, but method 1 needs to modify home environment, and operates relatively complicated, it is difficult to received by user；Method 4 need robot need from door have with a certain distance from just can achieve opposite house positioning, otherwise door with ground boundary can be located at photographic head Dead range in, be subject in actual applications certain to limit to.

Content of the invention

The present invention proposes a kind of domestic environment Xiamen localization method based on binocular vision target detection, and the method is by adopting The positioning to domestic environment Xiamen is realized with binocular vision target detection, is effectively solved using sensing by efficient algorithm The expensive defect of the sensor plans such as device, laser radar sensor, reduces hardware cost；And, this method need not Increase unnecessary mark on door, more simple and convenient in practical operation；Meanwhile, this method only needs to two side frames of door Simultaneously appear in the positioning that opposite house is just can achieve in binocular field of view, actually used more flexible；It is right that this method can also be realized Under domestic environment, the door of different conditions realizes positioning, such as closing, the door of half-open, standard-sized sheet, with more wide application prospect.

Technical scheme provides a kind of domestic environment Xiamen localization method based on binocular vision target detection, Comprise the following steps：

S101, binocular vision video camera (left video camera and right video camera) is placed in the support apart from ground certain altitude On, horizontal positioned receives the image I from two-way binocular vision video camera_L、I_R；

S102, with the respective image principal point of left video camera and right video cameraCentered on, respectively Intercept image I_LMiddle coordinate isRectangular area and image I_RMiddle coordinate isRectangular area (y_upAnd y_downFor the upside threshold value and downside threshold value of image interception, W is image I_LAnd I_RThe width of image) can intercept after RGB image I_L,C, I_R,C；

Above-mentioned photographic head parameter is to demarcate to obtain by monocular, such as Zhang Zhengyou standardizitions,

S103, to image I_L,C, I_R,CGray processing, obtains gray level image I respectively_L,GAnd I_R,G, then respectively to I_L,GAnd I_R,GEnter Row Canny operator edge detections, obtain binary image I_L,BAnd I_R,B；

S104, using accumulated probability Hough transformation algorithm to binary image I_L,BAnd I_R,BStraight-line segment detection is carried out, and Extracting wherein slope isLine segment (T_dFor angle change threshold value), every line segment is expressed asIn formulaIt is line segment L_a,iWith straight line y^I=y_up+¹The x of intersection point^IValue, and according to both Set pattern is then screened to the line segment obtained in two width images, can further obtain line segment group L in left image_L={ L_L,1, L_L,2,…,L_L,nLAnd right image in line segment group L_R={ L_R,1,L_R,2,…,L_R,nR(quantity of the nL for left image middle conductor, nR Quantity for right image middle conductor)；

S105, with line segment group L of left image_LMiddle conductor L_L,iOn the basis of, calculate L_L,iCorrespond to right image middle conductor group L_R? The matching degree of all line segments in the range of matching somebody with somebody, can obtain meeting the spurious matches line segment of screening threshold value to group { L_L,i,:}={ (L_L,i, L_R,i1),(L_L,i,L_R,i2),…,(L_L,i,L_R,in)}；

S106, judge L_LIn all of line segment whether complete spurious matches line segment to organize acquisition, if it is not, then turn to step S105；

S107, obtain line segment group L_LIn corresponding line segment group L of each line segment_RIn spurious matches line segment to a group set CMP= {{L_L,0,:},{L_L,1,:},…,{L_L,nL,:}}；

S108, the principle that is mated using global optimum select L from CMP_LAnd L_RMiddle Optimum Matching line segment is to queue(wherein ix and jx represent line segment respectivelyAnd line segmentIn line segment group L_LAnd L_RIn sequence number), its corresponding actual vertical line group be L={ L₁,L₂,…,L_n}；

S109, formula of being found range using binocular

Calculate coordinate of corresponding actual vertical line groups L of OMP Q under photographic head coordinate systemWherein, f',For binocular vision photographic head parameter, demarcated by monocular and obtained , T is the spacing of two photographic head light between centers,The two lines section for respectively matchingWithCentre coordinate x^IValue, x^CAnd z^CX of the corresponding actual vertical line of two lines section for matching under photographic head coordinate system^C-z^CCoordinate；

S110, according to formula(i and j represent line L_iWith line L_jSequence number in L) meter Calculate vertical line L in L_iWith vertical line L_jDistance, and think：

Work as d_i,j∈[W_D,min,W_D,max](W_D,minAnd W_D,maxThe respectively minima of domestic environment gate-width and maximum) when, Line L_iWith line L_jMay be two outer rims of door, be designated as doubtful door

Work as d_i,j∈[W_F,min,W_F,max](W_F,minAnd W_F,maxThe respectively wide minima of domestic environment doorframe and maximum) When, line L_iWith line L_jMay be the both sides sideline of doorframe, be designated as doubtful doorframe

Calculate in L that institute is the distance between wired, doubtful door group is obtained (ix and jx represent vertical line L respectively_ix,xAnd L_jx,xSequence number in vertical line group L, D are doubtful door label, and x represents doubtful doorIn D_susIn sequence number), and doubtful doorframe group(ix Vertical line L is represented respectively with jx_ix,xAnd L_jx,xSequence number in vertical line group L, F are doubtful doorframe label, and x represents doubtful doorframeIn F_susIn sequence number)

S111, using characteristic-integration principle from doubtful door group D_susMiddle selection optimal solution, integrates maximum doubtful door for most Excellent solution, that is, realize the identification of opposite house.

Further, in step S104, line segment group L obtained in left image_L={ L_L,1,L_L,2,…,L_L,nLAnd Line segment group L in right image_R={ L_R,1,L_R,2,…,L_R,nR(quantity of the nL for left image middle conductor, nR are right image middle conductor Quantity), further include：

S201, leave out length in pixels less than T_lengthLine segment；

S202, when there is center distance less than T_disDuring the two lines section of length in pixels, leave out the less line segment of length；

S203, to the line segment in two images according to x^IIt is ranked up from small to large；

S204, final line segment group L obtained in left image_L={ L_L,1,L_L,2,…,L_L,nLAnd right image in line segment group L_R ={ L_R,1,L_R,2,…,L_R,nR}.

Further, in step S105, line segment group L with left image_LMiddle conductor L_L,iOn the basis of, calculate L_L,iRight Should be in right image middle conductor group L_RThe matching degree of all line segments in matching range, can obtain the spurious matches line for meeting screening threshold value Section is to group { L_L,i,:}={ (L_L,i,L_R,i1),(L_L,i,L_R,i2),…,(L_L,i,L_R,in), further include：

Line segment group L with left image_L={ L_L,1,L_L,2,…,L_L,nLIn line segment L_L,iAs a example by for item to be matched, if which is sat It is designated asMatching process is as follows：

S301, formula of being found range according to binocularIn conjunction with the scope that actually fathomsDisparity range [d can be obtained_min,d_max], i.e. line segment group L_RIn with L_L,iMatch line segment L_R,jX^ISpan isL_RIn all meet require line segments be designated as line segment L_L,iSpurious matches line segment group；

S302, the matching area for calculating matched line and line to be matched, it is N to be defaulted as width_p, a height of y_up+y_down+ 1 square Shape region, if there are other line segments in the default zone, matching area is changed into the rectangular area between two lines section, if this is silent Recognize region and exceed image boundary, then matching area is changed into the rectangular area between the line segment and image boundary, and ensures left and right figure As two pieces of rectangular zone widths to be matched identical；

S303, further calculating obtain line segment L_L,iThe left and right sides and a line segment L in its spurious matches line segment group_R,_ixLeft Right matching areaAnd (color space is straight to calculate the feature histogram in 4 Block- matching regions respectively Fang Tu, such as RGB, HSV, YUV etc., such as Texture similarity, LBP etc., and other feature histograms)；

S304, by using similarity measurement (such as Euclidean distance, mahalanobis distance, Pasteur distance etc.) respectively calculate line segment L_L,iAnd L_R,ixLeft side matching areaWithLine segment L_L,iAnd L_R,ixRight side matching areaWithVarious features straight The similarity of square figure isWith(n is characterized label), and then obtain line segment L_L,_iAnd L_R,ixThe similarity power of the left and right sides Weight is：

In formulaWithRespectively line segment L_L,iWith line segment L to be matched_R,ixSimilarity of the left and right sides with regard to feature k, a_kIt is characterized the weight of k matching degrees；

S305, note w_i,ixForWithMiddle the greater, if w_i,ix＞ T_m, then it is assumed that line segment L to be matched_R,ixFor L_L,iDoubt Like coupling line segment, spurious matches line segment is constituted to (L_L,i,L_R,ix)；

S306, determine whether other line segments do not complete coupling, if so, then turn to step S302；

S307, the spurious matches line segment pair for further all being met screening threshold value, constitute spurious matches line segment to group {L_L,i,:}={ (L_L,i,L_R,i1),(L_L,i,L_R,i2),…,(L_L,i,L_R,in), n be mate line segment to mate in group to number.

Further, in step S108, the principle of the employing global optimum coupling selects L from CMP_LAnd L_RIn most Excellent coupling line segment is to queue(wherein ix and jx are represented respectively Line segmentAnd line segmentIn line segment group L_LAnd L_RIn sequence number), its corresponding actual vertical line group be L={ L₁,L₂,…,L_n, Further include：

S401, L is extracted from CMP_LAnd L_RIn all of coupling line segment to queue MPQ={ (L_L,i1,L_R,j1),(L_L,i2, L_R,j2),…,(L_L,in,L_R,jn) (wherein ix and jx represent line segment L respectively_L,ixWith line segment L_R,_jxIn line segment group L_LAnd L_RIn sequence Number), each MPQ={ (L_L,i1,L_R,j1),(L_L,i2,L_R,j2),…,(L_L,in,L_R,jn) following rule should be met：

1. to be respectively less than which to the label of each the spurious matches line segment centering two lines section in queue direct for coupling line segment The corresponding label of follow-up spurious matches line segment centering two lines section, i.e., for spurious matches line segment pairWith doubtful Match somebody with somebody line segment pairI need to be met_k＜ i_k+1&&j_k＜ j_k+1；

2. the label of the internal two lines section of first spurious matches line segment during coupling line segment is to queue need to meet current With line segment to the label for not having the internal two lines section of other spurious matches line segments in queue less than its reference numeral, i.e., for doubtful Like coupling line segment pairTo there is no spurious matches line segment pair in queue in current matching line segmentMeet i_k ＜ i₁||j_k＜ j₁；

3. coupling line segment need to be met currently to the label of the internal two lines section of last spurious matches line segment in queue Coupling line segment to the label that do not have the internal two lines section of other spurious matches line segments in queue more than its reference numeral, i.e., for Coupling line segment pairTo there is no coupling line segment pair in queue in current matching line segmentMeet i_k＞ i_n| |j_k＞ j_n；

S402, calculate each coupling the total weight of line segment to queue：

In formula i be coupling sequence number of the line segment to queue, n be current matching line segment to queue mate to number, rho_Li,Rj It is by L_LIn Li-th article of line segment and L_RIn the Rj-th article line segment composition spurious matches line segment pair matching degree；

S403, all coupling total weights of the line segment to queue of calculating, the maximum coupling line segment of weighted value is complete to queue Office's optimal solution, is designated as

Further, in step S111, the characteristic-integration principle is further included：

With doubtful door(ix and jx is line L_ix,iAnd L_jx,iSequence number in L, D are doubtful door label, and i is doubtful Like doorIn doubtful door group D_susIn sequence number) as a example by, initial integration is 0, and characteristic-integration computational methods include：

If there is doorjamb on the left of the doubtful doors of S501, i.e., doubtful doorLeft side bearing L_ixThere is line L in right side_mFull Foot (L_ix,L_m)∈F_sus, then bonus point S_F,L；

If there is doorjamb on the right side of the doubtful doors of S502, i.e., doubtful doorRight side bearing L_jxThere is line L in left side_nFull Foot (L_n,L_jx)∈F_sus, then bonus point S_F,R；

For line L_xIt is imaged as mating in the photographic head of left and right line segment in binocular to (L_L,ix,L_R,jx), for line segment L_L,ixWith L_R,jxThe matching degree of the left and right sidesWithThere are three kinds of situations：

T in formula_mFor matching degree threshold value；

If the doubtful doors of S503Sideline L_ixLeft and right sides matching degreeWithMeet formula (1), then plus Divide S_M,1；Bonus point S if formula (2) is met_M,2；If meeting formula (3), deduction S_M,3；

If the doubtful doors of S504Sideline L_jxLeft and right sides matching degreeWithMeet formula (1), then plus Divide S_M,1；Bonus point S if formula (3) is met_M,2；If meeting formula (2), deduction S_M,3；

If the doubtful doors of S505There is left frame (L_ix,L_m) when, if sideline L_mLeft and right sides matching degreeWithMeet formula (1) or formula (2), then bonus point S_M,2；If meeting formula (3), deduction S_M,3；

If the doubtful doors of S506There is left frame (L_n,L_jx) when, if sideline L_nLeft and right sides matching degreeWithMeet formula (1) or formula (3), then bonus point S_M,2；If meeting formula (2), deduction S_M,3；

If S507 is in doubtful doorThe depth that there is actual line in the middle of two side lines is undergone mutation, that is, there is L_kIn L_ixAnd L_jxBetween, and meet(T_ZIt is distance mutation threshold value), then bonus point S_z,1；

If S508 is in doubtful doorThe depth that there is actual point in the middle of two side lines is undergone mutation, that is, there is matching angle Point P_mIn L_ixAnd L_jxBetween, and meetThen bonus point S_z,1.

Further, in step S508, the matching angle point is further included：

The coupling angle point refers to by ORB Corner Detection Algorithms, SURF Corner Detections, FAST Corner Detection scheduling algorithms to image I_L,CAnd I_R,CThe angle point for detecting, and coupling angle point the GM={ (P obtained using BF matching algorithms or FLANN matching algorithms_L,i1, P_R,j1),(P_L,i2,P_R,j2),…,(P_L,in,P_R,jn), and its coordinate under photographic head coordinate system is obtained by binocular range finding formula

Further, in step S111, the employing characteristic-integration principle is from doubtful door group D_susMiddle selection is optimum Solution, it is optimal solution to integrate maximum doubtful door, further includes：

Calculate doubtful door group D_susIn all doubtful doors integration S_sus={ S₁,S₂,…,S_n, take integration maximum and meet S_max≥T_s(T_sIntegral threshold) doubtful door be optimal solution；If there is the doubtful door of maximum integration identical, doubtful door is selected Central point to photographic head zero distance most short doubtful door be optimal solution.

Technical solution of the present invention effectively realizes the door under domestic environment using binocular vision video camera by efficient algorithm Positioning, and unnecessary mark need not be increased on door, it is only necessary to two side frames of door are simultaneously appeared in binocular field of view Just the positioning of opposite house is can achieve, the door in various states such as closing, half-open, standard-sized sheets effectively can be recognized, with cost The extensive advantage of low, easy to use and flexible, application scenarios.

Other features and advantages of the present invention will be illustrated in the following description, also, partly be become from description Obtain it is clear that or being understood by implementing the present invention.The purpose of the present invention and other advantages can pass through in the explanation that is write In book, claims and accompanying drawing, specifically noted structure is realizing and obtain.

Below by drawings and Examples, technical scheme is described in further detail.

Description of the drawings

Accompanying drawing is used for providing a further understanding of the present invention, and constitutes a part for description, the reality with the present invention Applying example is used for explaining the present invention together, is not construed as limiting the invention.In the accompanying drawings：

Fig. 1 is the flow process of the domestic environment Xiamen localization method in the embodiment of the present invention one based on binocular vision target detection Figure；

Fig. 2 is illustrated by carrying out the pixel coordinate system that image interception is used in the embodiment of the present invention one to left and right video camera Figure；

Fig. 3 is the flow process of the domestic environment Xiamen localization method in the embodiment of the present invention one based on binocular vision target detection Schematic diagram；

Fig. 4 is photographic head coordinate system schematic diagram in step S109 in the embodiment of the present invention one；

Fig. 5 is the width information integration schematic diagram of door in step S501, S502 in the embodiment of the present invention one；

Fig. 6 is to judging to be the method flow of the line segment of doorjamb in two road images of left and right in the embodiment of the present invention one Figure；

Fig. 7 is the method flow diagram for finding left and right coupling line segment in the embodiment of the present invention one；

Fig. 8 is to obtain method flow diagram of the coupling line segment to group according to global registration principle in the embodiment of the present invention one；

Fig. 9 is the method for carrying out bonus point according to characteristic-integration principle to doubtful doorjamb line segment in the embodiment of the present invention one Flow chart.

Specific embodiment

The preferred embodiments of the present invention are illustrated below in conjunction with accompanying drawing, it will be appreciated that preferred reality described herein Apply example to be merely to illustrate and explain the present invention, be not intended to limit the present invention.

As shown in Figures 1 to 5, the flow process is comprised the following steps：

S101, binocular vision video camera (left video camera and right video camera) is placed in the support apart from ground certain altitude On, horizontal positioned receives the image I from two-way binocular vision video camera_L、I_R；

S102, with the respective image principal point of left video camera and right video cameraCentered on, respectively Intercept image I_LMiddle coordinate isRectangular area and image I_RMiddle coordinate isRectangular area (y_upAnd y_downFor the upside threshold value and downside threshold value of image interception, W is image I_LAnd I_RThe width of image) can intercept after RGB image I_L,C, I_R,C；

Above-mentioned photographic head parameter is to demarcate to obtain by monocular, such as Zhang Zhengyou standardizitions.

S103, to image I_L,C, I_R,CGray processing, obtains gray level image I respectively_L,GAnd I_R,G.Again respectively to I_L,GAnd I_R,GEnter Row Canny operator edge detections, obtain binary image I_L,BAnd I_R,B；

S104, using accumulated probability Hough transformation algorithm to binary image I_L,BAnd I_R,BStraight-line segment detection is carried out, and Extracting wherein slope isLine segment (T_dFor angle change threshold value).Every line segment is expressed asIn formulaIt is line segment L_a,iWith straight line y^I=y_upThe x of+1 intersection point^IValue.And according to Established rule is screened to the line segment obtained in two width images.Line segment group L in left image can further be obtained_L={ L_L,1, L_L,2,…,L_L,nLAnd right image in line segment group L_R={ L_R,1,L_R,2,…,L_R,nR(quantity of the nL for left image middle conductor, nR Quantity for right image middle conductor)；

S105, with line segment group L of left image_LMiddle conductor L_L,iOn the basis of, calculate L_L,iCorrespond to right image middle conductor group L_R? The matching degree of all line segments in the range of matching somebody with somebody, can obtain meeting the spurious matches line segment of screening threshold value to group { L_L,i,:}={ (L_L,i, L_R,i1),(L_L,i,L_R,i2),…,(L_L,i,L_R,in)}；

S106, judge L_LIn all of line segment whether complete spurious matches line segment to organize acquisition, if it is not, then turn to step S105；

S107, obtain line segment group L_LIn corresponding line segment group L of each line segment_RIn spurious matches line segment to a group set CMP= {{L_L,0,:},{L_L,1,:},…,{L_L,nL,:}}；

S108, the principle that is mated using global optimum select L from CMP_LAnd L_RMiddle Optimum Matching line segment is to queue(wherein ix and jx represent line segment respectivelyAnd line segmentIn line segment group L_LAnd L_RIn sequence number), its corresponding actual vertical line group be L={ L₁,L₂,…,L_n}；

S109, formula of being found range using binocular

Calculate coordinate of corresponding actual vertical line groups L of OMPQ under photographic head coordinate systemWherein, f',For binocular vision photographic head parameter, demarcated by monocular and obtained , T is the spacing of two photographic head light between centers,The two lines section for respectively matchingWithCentre coordinate x^IValue, x^CAnd z^CX of the corresponding actual vertical line of two lines section for matching under photographic head coordinate system^C-z^CCoordinate；

S110, according to formula(i and j represent line L_iWith line L_jSequence number in L) meter Calculate vertical line L in L_iWith vertical line L_jDistance, and think：

Work as d_i,j∈[W_D,min,W_D,max](W_D,minAnd W_D,maxThe respectively minima of domestic environment gate-width and maximum) when, Line L_iWith line L_jMay be two outer rims of door, be designated as doubtful door

Work as d_i,j∈[W_F,min,W_F,max](W_F,minAnd W_F,maxThe respectively wide minima of domestic environment doorframe and maximum) When, line L_iWith line L_jMay be the both sides sideline of doorframe, be designated as doubtful doorframe

Calculate in L that institute is the distance between wired, doubtful door group is obtained (ix and jx represent vertical line L respectively_ix,xAnd L_jx,xSequence number in vertical line group L, D are doubtful door label, and x represents doubtful doorIn D_susIn sequence number), and doubtful doorframe group (ix and jx represent vertical line L respectively_ix,xAnd L_jx,xSequence number in vertical line group L, F are doubtful doorframe label, and x represents doubtful doorframeIn F_susIn sequence number)

S111, using characteristic-integration principle from doubtful door group D_susMiddle selection optimal solution, integrates maximum doubtful door for most Excellent solution, that is, realize the identification of opposite house.

Fig. 6 is to judging to be the method flow of the line segment of doorjamb in two road images of left and right in the embodiment of the present invention one Figure, i.e., in step S104, obtain line segment group L in left and right image_LAnd L_RMethod and step.As shown in fig. 6, the flow process includes Following steps：

S201, leave out length in pixels less than T_lengthLine segment；

S202, when there is center distance less than T_disDuring the two lines section of length in pixels, leave out the less line segment of length；

S203, to the line segment in two images according to x^IIt is ranked up from small to large；

S204, final line segment group L obtained in left image_L={ L_L,1,L_L,2,…,L_L,nLAnd right image in line segment group L_R ={ L_R,1,L_R,2,…,L_R,nR}.

Fig. 7 is the method flow diagram for finding left and right coupling line segment in the embodiment of the present invention one, i.e., in step S105, with a left side Line segment group L of image_LMiddle conductor L_L,iOn the basis of, calculate L_L,iCorrespond to right image middle conductor group L_RAll line segments in matching range Matching degree, obtain meeting the spurious matches line segment of screening threshold value to group { L_L,i,:}={ (L_L,i,L_R,i1),(L_L,i,L_R,i2),…, (L_L,i,L_R,in) flow process.As shown in fig. 7, the flow process is comprised the following steps：

S301, formula of being found range according to binocularIn conjunction with the scope that actually fathomsDisparity range [d can be obtained_min,d_max], i.e. line segment group L_RIn with L_L,iMatch line segment L_R,jXI spans beL_RIn all meet require line segments be designated as line segment L_L,iSpurious matches line segment group；

S302, the matching area for calculating matched line and line to be matched, it is N to be defaulted as width_p, a height of y_up+y_down+ 1 square Shape region, if there are other line segments in the default zone, matching area is changed into the rectangular area between two lines section, if this is silent Recognize region and exceed image boundary, then matching area is changed into the rectangular area between the line segment and image boundary, and ensures left and right figure As two pieces of rectangular zone widths to be matched identical；

S303, further calculating obtain line segment L_L,iThe left and right sides and a line segment L in its spurious matches line segment group_R,ixLeft Right matching areaAnd (color space is straight to calculate the feature histogram in 4 Block- matching regions respectively Fang Tu, such as RGB, HSV, YUV etc., such as Texture similarity, LBP etc., and other feature histograms)；

S304, by using similarity measurement (such as Euclidean distance, mahalanobis distance, Pasteur distance etc.) respectively calculate line segment L_L,iAnd L_R,ixLeft side matching areaWithLine segment L_L,iAnd L_R,ixRight side matching areaWithVarious features straight The similarity of square figure isWith(n is characterized label), and then obtain line segment L_L,_iAnd L_R,ixThe similarity power of the left and right sides Weight is：

In formulaWithRespectively line segment L_L,iWith line segment L to be matched_R,ixSimilarity of the left and right sides with regard to feature k, a_kIt is characterized the weight of k matching degrees；

S305, note w_i,ixForWithMiddle the greater.If w_i,ix＞ T_m, then it is assumed that line segment L to be matched_R,ixFor L_L,iDoubt Like coupling line segment, spurious matches line segment is constituted to (L_L,i,L_R,ix)；

S306, determine whether other line segments do not complete coupling, if so, then turn to step S302；

S307, the spurious matches line segment pair for further all being met screening threshold value, constitute spurious matches line segment to group {L_L,i,:}={ (L_L,i,L_R,i1),(L_L,i,L_R,i2),…,(L_L,i,L_R,in), n be mate line segment to mate in group to number.

Fig. 8 is to obtain method flow diagram of the coupling line segment to group according to global registration principle in the embodiment of the present invention one, i.e., In step S108, the principle that is mated using global optimum selects L from CMP_LAnd L_RMiddle Optimum Matching line segment is to queue(wherein ix and jx represent line segment respectivelyAnd line segmentIn line segment group L_LAnd L_RIn sequence number, its corresponding actual vertical line group be L={ L₁,L₂,…,L_n) method.Such as Fig. 8 institutes Show, the flow process is comprised the following steps：

S401, L is extracted from CMP_LAnd L_RIn all of coupling line segment to queue MPQ={ (L_L,i1,L_R,j1),(L_L,i2, L_R,j2),…,(L_L,in,L_R,jn) (wherein ix and jx represent line segment L respectively_L,ixAnd line segmentIn line segment group L_LAnd L_RIn sequence Number), each MPQ={ (L_L,i1,L_R,j1),(L_L,i2,L_R,j2),…,(L_L,in,L_R,jn) following rule should be met：

1. to be respectively less than which to the label of each the spurious matches line segment centering two lines section in queue direct for coupling line segment The corresponding label of follow-up spurious matches line segment centering two lines section, i.e., for spurious matches line segment pairWith doubtful Match somebody with somebody line segment pairI need to be met_k＜ i_k+1&&j_k＜ j_k+1；

2. the label of the internal two lines section of first spurious matches line segment during coupling line segment is to queue need to meet current With line segment to the label for not having the internal two lines section of other spurious matches line segments in queue less than its reference numeral, i.e., for doubtful Like coupling line segment pairTo there is no spurious matches line segment pair in queue in current matching line segmentMeet i_k ＜ i₁||j_k＜ j₁；

3. coupling line segment need to be met currently to the label of the internal two lines section of last spurious matches line segment in queue Coupling line segment to the label that do not have the internal two lines section of other spurious matches line segments in queue more than its reference numeral, i.e., for Coupling line segment pairTo there is no coupling line segment pair in queue in current matching line segmentMeet i_k＞ i_n| |j_k＞ j_n；

S402, calculate each coupling the total weight of line segment to queue：

In formula i be coupling sequence number of the line segment to queue, n be current matching line segment to queue mate to number, rho_Li,Rj It is by L_LIn Li-th article of line segment and L_RIn the Rj-th article line segment composition spurious matches line segment pair matching degree；

S403, all coupling total weights of the line segment to queue of calculating, the maximum coupling line segment of weighted value is complete to queue Office's optimal solution, is designated as

Fig. 9 is the method for carrying out bonus point according to characteristic-integration principle to doubtful doorjamb line segment in the embodiment of the present invention one In flow chart, i.e. step S111, characteristic-integration computational methods.As shown in figure 9, the flow process is comprised the following steps：

If there is doorjamb on the left of the doubtful doors of S501, i.e., doubtful doorLeft side bearing L_ixThere is line L in right side_mFull Foot (L_ix,L_m)∈F_sus, then bonus point S_F,L；

If there is doorjamb on the right side of the doubtful doors of S502, i.e., doubtful doorRight side bearing L_jxThere is line L in left side_nFull Foot (L_n,L_jx)∈F_sus, then bonus point S_F,R；

For line L_xIt is imaged as mating in the photographic head of left and right line segment in binocular to (L_L,ix,L_R,jx), for line segment L_L,ixWith L_R,jxThe matching degree of the left and right sidesWithThere are three kinds of situations：

T in formula_mFor matching degree threshold value；

If the doubtful doors of S503Sideline L_ixLeft and right sides matching degreeWithMeet formula (1), then plus Divide S_M,1；Bonus point S if formula (2) is met_M,2；If meeting formula (3), deduction S_M,3；

If the doubtful doors of S504Sideline L_jxLeft and right sides matching degreeWithMeet formula (1), then plus Divide S_M,1；Bonus point S if formula (3) is met_M,2；If meeting formula (2), deduction S_M,3；

If the doubtful doors of S505There is left frame (L_ix,L_m) when, if sideline L_mLeft and right sides matching degree WithMeet formula (1) or formula (2), then bonus point S_M,2；If meeting formula (3), deduction S_M,3；

If the doubtful doors of S506There is left frame (L_n,L_jx) when, if sideline L_nLeft and right sides matching degreeWithMeet formula (1) or formula (3), then bonus point S_M,2；If meeting formula (2), deduction S_M,3；

If S507 is in doubtful doorThe depth that there is actual line in the middle of two side lines is undergone mutation, that is, there is L_kIn L_ixAnd L_jxBetween, and meet(T_ZIt is distance mutation threshold value), then bonus point S_z,1；

If S508 is in doubtful doorThe depth that there is actual point in the middle of two side lines is undergone mutation, that is, there is coupling Angle point P_mIn L_ixAnd L_jxBetween, and meetThen bonus point S_z,1.

Coupling angle point refers to by ORB Corner Detection Algorithms, SURF Corner Detections, FAST Corner Detections scheduling algorithm to image I_L,C And I_R,CThe angle point for detecting, and coupling angle point the GM={ (P obtained using BF matching algorithms or FLANN matching algorithms_L,i1, P_R,j1),(P_L,i2,P_R,j2),…,(P_L,in,P_R,jn), and its coordinate under photographic head coordinate system is obtained by binocular range finding formula

Technical scheme in above-described embodiment is due to using binocular vision video camera, effectively realizing house by efficient algorithm Door positioning under habitat environment, and unnecessary mark need not be increased on door, it is only necessary to two side frames of door occur simultaneously Just the positioning of opposite house is can achieve in binocular field of view, and the door in various states such as closing, half-open, standard-sized sheets effectively can be known Not, with low cost, easy to use and flexible, the extensive advantage of application scenarios.

Those skilled in the art are it should be appreciated that embodiments of the invention can be provided as method, system or computer program Product.Therefore, the present invention can adopt complete hardware embodiment, complete software embodiment or with reference to software and hardware in terms of reality Apply the form of example.And, the present invention can be adopted in one or more computers for wherein including computer usable program code The shape of the upper computer program that implements of usable storage medium (including but not limited to disk memory and optical memory etc.) Formula.

The present invention is the flow process with reference to method according to embodiments of the present invention, equipment (system) and computer program Figure and/or block diagram are describing.It should be understood that can be by computer program instructions flowchart and/or each stream in block diagram Journey and/or the combination of square frame and flow chart and/or the flow process in block diagram and/or square frame.These computer programs can be provided Instruct the processor of general purpose computer, special-purpose computer, Embedded Processor or other programmable data processing devices to produce A raw machine so that produced for reality by the instruction of computer or the computing device of other programmable data processing devices The device of the function of specifying in present one flow process of flow chart or one square frame of multiple flow processs and/or block diagram or multiple square frames.

These computer program instructions may be alternatively stored in and can guide computer or other programmable data processing devices with spy Determine in the computer-readable memory that mode works so that the instruction being stored in the computer-readable memory is produced to be included referring to Make the manufacture of device, the command device realize in one flow process of flow chart or one square frame of multiple flow processs and/or block diagram or The function of specifying in multiple square frames.

These computer program instructions can be also loaded in computer or other programmable data processing devices so that in meter Series of operation steps is executed on calculation machine or other programmable devices to produce computer implemented process, so as in computer or The instruction executed on other programmable devices is provided for realization in one flow process of flow chart or multiple flow processs and/or block diagram one The step of function of specifying in individual square frame or multiple square frames.

Obviously, those skilled in the art can carry out the essence of various changes and modification without deviating from the present invention to the present invention God and scope.So, if these modifications of the present invention and modification belong to the scope of the claims in the present invention and its equivalent technologies Within, then the present invention is also intended to comprising these changes and modification.

Claims (7)

1. a kind of domestic environment Xiamen localization method based on binocular vision target detection, it is characterised in that comprise the following steps：

S101, binocular vision video camera (left video camera and right video camera) is placed on the support of ground certain altitude, water Placing flat, receives the image I from two-way binocular vision video camera_L、I_R；

S102, with the respective image principal point of left video camera and right video cameraCentered on, intercept respectively Image I_LMiddle coordinate isRectangular area and image I_RMiddle coordinate isRectangular area (y_upAnd y_downFor the upside threshold value and downside threshold value of image interception, W is image I_LAnd I_RThe width of image) can intercept after RGB image I_L,C, I_R,C；

Above-mentioned photographic head parameter is to demarcate to obtain by monocular, such as Zhang Zhengyou standardizitions,

S103, to image I_L,C, I_R,CGray processing, obtains gray level image I respectively_L,GAnd I_R,G, then respectively to I_L,GAnd I_R,GCarry out Canny operator edge detections, obtain binary image I_L,BAnd I_R,B；

S104, using accumulated probability Hough transformation algorithm to binary image I_L,BAnd I_R,BStraight-line segment detection is carried out, and is extracted wherein oblique Rate isLine segment (T_dFor angle change threshold value), every line segment is expressed as In formulaIt is line segment L_a,iWith straight line y^I=y_upThe x of+1 intersection point^IValue, and according to established rule to the line segment that obtains in two width images Screened, can further be obtained line segment group L in left image_L={ L_L,1,L_L,2,…,L_L,nLAnd right image in line segment group L_R ={ L_R,1,L_R,2,…,L_R,nR(quantity of the nL for left image middle conductor, quantity of the nR for right image middle conductor)；

S105, with line segment group L of left image_LMiddle conductor L_L,iOn the basis of, calculate L_L,iCorrespond to right image middle conductor group L_RCoupling model The matching degree of interior all line segments is enclosed, can obtain meeting the spurious matches line segment of screening threshold value to group { L_L,i,:}={ (L_L,i, L_R,i1),(L_L,i,L_R,i2),…,(L_L,i,L_R,in)}；

S106, judge L_LIn all of line segment whether complete spurious matches line segment to organize acquisition, if it is not, then turn to step S105；

S107, obtain line segment group L_LIn corresponding line segment group L of each line segment_RIn spurious matches line segment to a group set CMP= {{L_L,0,:},{L_L,1,:},…,{L_L,nL,:}}；

S108, the principle that is mated using global optimum select L from CMP_LAnd L_RMiddle Optimum Matching line segment is to queue(wherein ix and jx represent line segment respectivelyAnd line segment In line segment group L_LAnd L_RIn sequence number), its corresponding actual vertical line group be L={ L₁,L₂,…,L_n}；

S109, formula of being found range using binocular

$\left\{\begin{array}{c}{z}^C=-\frac{f^{\′}\·T}{x_l^I-x_r^I-(C_x^l-C_x^r)}\\ x^C=\frac{z\·(x_l^I-C_x^l)}{f}\end{array}\right.$

Calculate coordinate of corresponding actual vertical line groups L of OMPQ under photographic head coordinate system Wherein, f',For binocular vision photographic head parameter, demarcated by monocular and obtained, T is the spacing of two photographic head light between centers,The two lines section for respectively matchingWithThe x of centre coordinate^IValue, x^CAnd z^CFor the two lines section for matching X of the corresponding actual vertical line under photographic head coordinate system^C-z^CCoordinate；

S110, according to formula(i and j represent line L_iWith line L_jSequence number in L) calculate in L Vertical line L_iWith vertical line L_jDistance, and think：

Work as d_i,j∈[W_D,min,W_D,max](W_D,minAnd W_D,maxThe respectively minima of domestic environment gate-width and maximum) when, line L_i With line L_jMay be two outer rims of door, be designated as doubtful door

Work as d_i,j∈[W_F,min,W_F,max](W_F,minAnd W_F,maxThe respectively wide minima of domestic environment doorframe and maximum) when, line L_iWith line L_jMay be the both sides sideline of doorframe, be designated as doubtful doorframe

Calculate in L that institute is the distance between wired, doubtful door group is obtained (ix and jx represent vertical line L respectively_ix,xAnd L_jx,xSequence number in vertical line group L, D are doubtful door label, and x represents doubtful doorIn D_susIn sequence number), and doubtful doorframe group(ix Vertical line L is represented respectively with jx_ix,xAnd L_{Jx, x}Sequence number in vertical line group L, F are doubtful doorframe label, and x represents doubtful doorframeIn F_susIn sequence number)

S111, using characteristic-integration principle from doubtful door group D_susMiddle selection optimal solution, integrates maximum doubtful door for optimum Solution, that is, realize the identification of opposite house.

2. method according to claim 1, it is characterised in that in step S104, the line segment obtained in left image Group L_L={ L_L,1,L_L,2,…,L_L,nLAnd right image in line segment group L_R={ L_R,1,L_R,2,…,L_R,nR(nL is left image center line The quantity of section, quantity of the nR for right image middle conductor), further include：

S201, leave out length in pixels less than T_lengthLine segment；

S202, when there is center distance less than T_disDuring the two lines section of length in pixels, leave out the less line segment of length；

S203, to the line segment in two images according to x^IIt is ranked up from small to large；

S204, final line segment group L obtained in left image_L={ L_L,1,L_L,2,…,L_L,nLAnd right image in line segment group L_R= {L_R,1,L_R,2,…,L_R,nR}.

3. method according to claim 1, it is characterised in that in step S105, the calculating L_L,iCorrespond to right image Middle conductor group L_RThe matching degree of all line segments in matching range, can obtain meeting the spurious matches line segment of screening threshold value to group {L_L,i,:}={ (L_L,i,L_R,i1),(L_L,i,L_R,i2),…,(L_L,i,L_R,in), further include：

Line segment group L with left image_L={ L_L,1,L_L,2,…,L_L,nLIn line segment L_L,iAs a example by for item to be matched, if its coordinate isMatching process is as follows：

S301, formula of being found range according to binocularIn conjunction with the scope that actually fathoms Disparity range [d can be obtained_min,d_max], i.e. line segment group L_RIn with L_L,iMatch line segment L_R,jX^ISpan isL_RIn all meet require line segments be designated as line segment L_L,iSpurious matches line segment group；

S302, the matching area for calculating matched line and line to be matched, it is N to be defaulted as width_p, a height of y_up+y_down+ 1 rectangle region Domain, if there are other line segments in the default zone, matching area is changed into the rectangular area between two lines section, if the acquiescence area Domain exceeds image boundary, then matching area is changed into the rectangular area between the line segment and image boundary, and ensures that left images are treated Two pieces of rectangular zone widths of coupling are identical；

S303, further calculating obtain line segment L_L,iThe left and right sides and a line segment L in its spurious matches line segment group_R,ixLeft and right coupling RegionAnd calculate respectively 4 Block- matching regions feature histogram (color space histogram, Such as RGB, HSV, YUV etc., such as Texture similarity, LBP etc., and other feature histograms)；

S304, by using similarity measurement (such as Euclidean distance, mahalanobis distance, Pasteur distance etc.) respectively calculate line segment L_L,iWith L_R,ixLeft side matching areaWithLine segment L_L,iAnd L_R,ixRight side matching areaWithVarious features histogrammic Similarity isWith(n is characterized label), and then obtain line segment L_L,iAnd L_R,ixThe similarity weight of the left and right sides is：

$\left\{\begin{array}{c}{w}_{i,ix}^L=\underset{k=0}{\overset{N}{\Σ}}{a}_k\·w_{i,ix,k}^L(\underset{k=0}{\overset{N}{\Σ}}{a}_k=1)\\ w_{i,ix}^R=\underset{k=0}{\overset{N}{\Σ}}{a}_k\·w_{i,ix,k}^R(\underset{k=0}{\overset{N}{\Σ}}{a}_k=1)\end{array}\right.$

In formulaWithRespectively line segment L_L,iWith line segment L to be matched_R,ixSimilarity of the left and right sides with regard to feature k, a_kFor The weight of feature k matching degree；

S305, note w_i,ixForWithMiddle the greater, if w_i,ix＞ T_m, then it is assumed that line segment L to be matched_R,ixFor L_L,iDoubtful With line segment, spurious matches line segment is constituted to (L_L,i,L_R,ix)；

S306, determine whether other line segments do not complete coupling, if so, then turn to step S302；

S307, the spurious matches line segment pair for further all being met screening threshold value, constitute spurious matches line segment to group {L_L,i,:}={ (L_L,i,L_R,i1),(L_L,i,L_R,i2),…,(L_L,i,L_R,in), n be mate line segment to mate in group to number.

4. method according to claim 1, it is characterised in that in step S108, the principle of the employing global optimum coupling from L is selected in CMP_LAnd L_RMiddle Optimum Matching line segment is to queue (wherein ix and jx represent line segment respectivelyAnd line segmentIn line segment group L_LAnd L_RIn sequence number), its corresponding actual vertical line group For L={ L₁,L₂,…,L_n, further include：

S401, L is extracted from CMP_LAnd L_RIn all of coupling line segment to queue MPQ={ (L_L,i1,L_R,j1),(L_L,i2, L_R,j2),…,(L_L,in,L_R,jn) (wherein ix and jx represent line segment L respectively_L,ixAnd line segmentIn line segment group L_LAnd L_RIn sequence Number), each MPQ={ (L_L,i1,L_R,j1),(L_L,i2,L_R,j2),…,(L_L,in,L_R,jn) following rule should be met：

1. to be respectively less than which to the label of each the spurious matches line segment centering two lines section in queue directly follow-up for coupling line segment The corresponding label of spurious matches line segment centering two lines section, i.e., for spurious matches line segment pairWith spurious matches line Section is rightI need to be met_k＜ i_k+1&&j_k＜ j_k+1；

2. the label of the internal two lines section of first spurious matches line segment during coupling line segment is to queue need to meet current matching line Section is less than its reference numeral to the label for not having the internal two lines section of other spurious matches line segments in queue, i.e., for doubtful Match somebody with somebody line segment pairTo there is no spurious matches line segment pair in queue in current matching line segmentMeet i_k＜ i₁ ||j_k＜ j₁；

3. coupling line segment need to meet current matching to the label of the internal two lines section of last spurious matches line segment in queue Line segment is more than its reference numeral to the label for not having the internal two lines section of other spurious matches line segments in queue, i.e., for coupling Line segment pairTo there is no coupling line segment pair in queue in current matching line segmentMeet i_k＞ i_n||j_k＞ j_n；

S402, calculate each coupling the total weight of line segment to queue：

${\mathrm{rho}}_i=\underset{k=1}{\overset{n}{\Σ}}{\mathrm{rho}}_{Li,Rj}$

In formula i be coupling sequence number of the line segment to queue, n be current matching line segment to queue mate to number, rho_Li,RjServe as reasons L_LIn Li-th article of line segment and L_RIn the Rj-th article line segment composition spurious matches line segment pair matching degree；

S403, all coupling total weights of the line segment to queue of calculating, the maximum coupling line segment of weighted value are the overall situation most to queue Excellent solution, is designated as

5. method according to claim 1, it is characterised in that in step S111, the characteristic-integration principle enters Step includes：

With doubtful door(ix and jx is line L_ix,iAnd L_jx,iSequence number in L, D are doubtful door label, and i is doubtful doorIn doubtful door group D_susIn sequence number) as a example by, initial integration is 0, and characteristic-integration computational methods include：

If there is doorjamb on the left of the doubtful doors of S501, i.e., doubtful doorLeft side bearing L_ixThere is line L in right side_mMeet (L_ix,L_m)∈F_sus, then bonus point S_F,L；

If there is doorjamb on the right side of the doubtful doors of S502, i.e., doubtful doorRight side bearing L_jxThere is line L in left side_nMeet (L_n,L_jx)∈F_sus, then bonus point S_F,R；

For line L_xIt is imaged as mating in the photographic head of left and right line segment in binocular to (L_L,ix, L_R,jx), for line segment L_L,ixAnd L_R,jx The matching degree of the left and right sidesWithThere are three kinds of situations：

T in formula_mFor matching degree threshold value；

If the doubtful doors of S503Sideline L_ixLeft and right sides matching degreeWithMeet formula (1), then bonus point S_M,1；Bonus point S if formula (2) is met_M,2；If meeting formula (3), deduction S_M,3；

If the doubtful doors of S504Sideline L_jxLeft and right sides matching degreeWithMeet formula (1), then bonus point S_M,1；Bonus point S if formula (3) is met_M,2；If meeting formula (2), deduction S_M,3；

If the doubtful doors of S505There is left frame (L_ix,L_m) when, if sideline L_mLeft and right sides matching degreeWithMeet formula (1) or formula (2), then bonus point S_M,2；If meeting formula (3), deduction S_M,3；

If the doubtful doors of S506There is left frame (L_n,L_jx) when, if sideline L_nLeft and right sides matching degreeWith Meet formula (1) or formula (3), then bonus point S_M,2；If meeting formula (2), deduction S_M,3；

If S507 is in doubtful doorThe depth that there is actual line in the middle of two side lines is undergone mutation, that is, there is L_kIn L_ixWith L_jxBetween, and meet(T_ZIt is distance mutation threshold value), then bonus point S_z,1；

If S508 is in doubtful doorThe depth that there is actual point in the middle of two side lines is undergone mutation, that is, there is coupling angle point P_m In L_ixAnd L_jxBetween, and meetThen bonus point S_z,1.

6. method according to claim 1 or 5, it is characterised in that in step S508, the matching angle point is further wrapped Include：

The coupling angle point refers to by ORB Corner Detection Algorithms, SURF Corner Detections, FAST Corner Detections scheduling algorithm to image I_L,C And I_R,CThe angle point for detecting, and coupling angle point the GM={ (P obtained using BF matching algorithms or FLANN matching algorithms_L,i1, P_R,j1),(P_L,i2,P_R,j2),…,(P_L,in,P_R,jn), and its coordinate under photographic head coordinate system is obtained by binocular range finding formula

7. method according to claim 1, it is characterised in that in step S111, the employing characteristic-integration principle From doubtful door group D_susMiddle selection optimal solution, it is optimal solution to integrate maximum doubtful door, further includes：

Calculate doubtful door group D_susIn all doubtful doors integration S_sus={ S₁,S₂,…,S_n, take integration maximum and meet S_max≥T_s (T_sIntegral threshold) doubtful door be optimal solution；If there is the doubtful door of maximum integration identical, doubtful door central point is selected It is optimal solution to the photographic head zero most short doubtful door of distance.