CN103617328A - Airplane three-dimensional attitude computation method - Google Patents

Abstract

The invention provides an airplane three-dimensional attitude computation method. Firstly, preprocessing is carried out on an image to be processed by the adoption of Gaussian smooth filtering to eliminate influences of noise on follow-up algorithms; secondly, the smoothened image is segmented by the adoption of a fuzzy C-means clustering algorithm (FCM) to obtain a binary image, then Hough conversion is carried out on the binary image obtained through segmentation, a part, with obvious linear features, on a target is detected, feature points of the part, with non-obvious linear features, on the target are extracted by the adoption of a framework, and then linear fitting is carried out on the feature points on the framework to obtain a target axis; finally, the obtained axis, the actual size of the target, camera parameters and other information are combined, and a three-dimensional attitude parameter of the target under a camera coordinate system is calculated by means of projective geometry knowledge. On the basis of geometrical features capable of reflecting the structure of an object and by means of Hough conversion and framework extraction of the whole target image, the obtained axis is more accurate, and the obtained three-dimensional attitude parameter is more precise.

Description

A kind of aircraft 3 d pose calculation method

Technical field

The present invention relates to a kind of aircraft 3 d pose calculation method, particularly a kind of Hough of utilization conversion, skeletal extraction and perspective geometry are resolved the method for the 3 d pose of aircraft, are mainly used in vision guided navigation, spacecraft launching site and target following.

Background technology

3 d pose (angle of pitch, crab angle, roll angle) is the reflection aircraft important parameter of state of flight aloft, it is accurately measured has important using value to experimental analysis, target following and identification, accident evaluation, training assessment etc., has been subjected to relevant scholar both at home and abroad and has more and more paid close attention to.And the location of aerial sports target, identification and flight path are measured, optoelectronic device is the highest instrument of precision so far, it can be in real time, dynamic tracking target record the continuous sequence image that target moves, and be not subject to the ground impacts such as " black barrier ", ground clutter interference.In photoelectric measurement, widespread use optoelectronic device carries out track up to aircraft at present, to obtain the real-time continuous sequence image of airplane motion, then by image, processes to obtain its 3 d pose.

Current, conventional Mono-view determines that the method for the 3 d pose of aircraft comprises based on modelling, feature correspondent method and optical flow method.Method based on model can process to single station flash ranging data that to obtain three-dimension altitude angle and the precision of aircraft high, but the template matches process of image processing exists " np problem "; The method corresponding based on feature need to define some unique points on Aircraft Targets, line and shape, but the decay of characteristic information brings very large difficulty can to the corresponding relation of setting up between feature and pixel; Method based on light stream is according to the difference of multiple image, to estimate the instantaneous velocity of airplane motion, thus the attitude of estimation/tracking aircraft, but the method is very responsive to noise, thereby easily introduce larger error.Therefore,, the in the situation that of existing how much prioris, finding a kind of attitude algorithm method simple, that computational accuracy is high is current problem in the urgent need to address.

Summary of the invention

The technology of the present invention is dealt with problems: for the deficiencies in the prior art, provide a kind of method of the Hough of utilization conversion, skeletal extraction and perspective geometry to resolve the 3 d pose of aircraft, the in the situation that of existing priori, utilize the geometric properties of reference planes and the known parameters of picture plane projection, solid geometry relation in conjunction with object plane and image planes projection solves pose parameter again, solution procedure is only simple triangle geometric relationship, thereby has greatly reduced calculated amount, has improved work efficiency.

For realizing such object, technical scheme of the present invention: a kind of aircraft 3 d pose calculation method, comprises the steps:

Step 1, image pre-service: adopt Gaussian smoothing filtering to process pending image, remove the impact of noise, obtain filtered smoothed image;

Step 2, use the Image Segmentation Using after level and smooth that Fuzzy C-Means Cluster Algorithm FCM (Fuzzy C-Means Cluster) obtains step 1, obtain bianry image;

Step 3, the bianry image that utilizes Hough transfer pair step 2 to obtain are processed, and detect the straight line of the obvious part of linear feature on aircraft as the axis of this part;

Step 4, utilize the image that the method for skeletal extraction obtains step 2 to process, extract after the skeleton point on aircraft, choose some feature skeleton points and carry out fitting a straight line, the straight line obtaining is as the axis of this part on aircraft;

Step 5, obtain the axial equation that step 3 and four obtains, calculate angle and the azimuth of these two axis, the range information of combining camera parameter and camera and aircraft calculates aircraft 3 d pose parameter.

Wherein, in described step 2, use the Image Segmentation Using after level and smooth that Fuzzy C-Means Cluster Algorithm FCM (Fuzzy C-Means Cluster) obtains step 1, the method that obtains bianry image is:

Step (21), initialization: given cluster classification is counted C=2 in C(the present invention), set iteration stopping threshold epsilon, initialization fuzzy partition matrix, iterations l=0, Fuzzy Weighting Exponent m (m=2 in the present invention);

Step (22), by U ⁽⁰⁾substitution formula (7), calculates cluster centre matrix V ^(l):

$v_i=\frac{1}{\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{\left(u_{\mathrm{ik}}\right)}^m}\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{\left(u_{\mathrm{ik}}\right)}^m{x}_k,i=1,...,c---\left(7\right)$

Wherein n is number of pixels to be clustered, and m is FUZZY WEIGHTED index, and c is cluster classification number, u _ikfuzzy classification matrix U while being the l time iteration ^(l)in the capable k column element of i, x _kfor k pixel value in image to be clustered, v _icluster centre matrix V while being the l time iteration ^(l)in i cluster centre value;

Step (23), according to formula (8), utilize V ^(l)upgrade U ^(l), obtain new fuzzy classification matrix U ^(l+1):

$u_{\mathrm{ik}}=\frac{1}{\underset{j=1}{\overset{c}{\mathrm{\Σ}}}{\left(\frac{d_{\mathrm{ik}}}{d_{\mathrm{jk}}}\right)}^{\frac{2}{m-1}}},i=1,...,c---\left(8\right)$

D wherein _ikfor the Euclidean distance of k element and i cluster centre in image to be clustered, similarly, d _jkeuclidean distance for k element and j cluster centre in image to be clustered;

Step (24) if || U ^(l)-U ^l+1|| < ε, iteration stopping.Otherwise put l=l+1, return to step (22);

Step (25), calculate in image to be clustered the Euclidean distance of the cluster centre value that each pixel distance above-mentioned steps (21)-(24) obtains, the pixel value nearest apart from cluster centre is set to 1, otherwise is set to 0, the bianry image after being cut apart thus.

Wherein, in described step 3, the bianry image that utilizes Hough transfer pair step 2 to obtain is processed, detect linear feature on aircraft obviously the straight line of part as the method for the axis of this part, be:

Step (31), the target image that step 2 is obtained are asked size, according to the possible span of parameter in parameter space, quantize, and according to a quantized result structure totalizer array A (ρ, θ), are initialized as 0;

Step (32), the set point in each XY space is got all over all probable values by θ, with formula (9), calculated ρ, according to the value of ρ and θ A:A (ρ, θ)=A (ρ, θ)+1 of adding up;

ρ＝xcosθ+ysinθ (9)

Wherein ρ and θ are respectively two parameter-amplitudes and the angle in parameter space, and (x, y) is the point coordinate in image space.

Corresponding ρ and the θ of maximal value in step (33), the cumulative rear A of basis, makes the straight line (being axes of aircraft) in XY by formula (9), and the maximal value in A has represented the number of set point on this straight line.

Wherein, in described step 4, utilize the image that the method for skeletal extraction obtains step 2 to process, extract after the skeleton point on aircraft, choose some feature skeleton points and carry out fitting a straight line, the straight line obtaining is as the method for the axis of this part on aircraft, and the present invention adopts successively the iterative refinement algorithm of cancellation frontier point to extract skeleton, and algorithm is as follows:

If known target point is labeled as 1, background dot is labeled as 0.Definition frontier point is that itself is labeled as 1, and in its 8-connected region, has at least a point to be labeled as 0 point.Algorithm is considered the 8-neighborhood centered by frontier point, and note central point is p ₁, 8 points of its neighborhood are designated as respectively p around central point clockwise ₂, p ₃..., p ₉, p wherein ₂at p ₁top.

Algorithm comprises frontier point is carried out to two step operations:

(41) mark meets the frontier point of following condition simultaneously:

（411）2≤N(p ₁)≤6;

（412）S(p ₁)＝1;

（413）p ₂·p ₄·p ₆＝0;

（414）p ₄·p ₆·p ₈＝0;

N (p wherein ₁) be p ₁non-zero adjoint point number, S (p ₁) be with p ₂, p ₃..., p ₉, p ₂the number of the value of these points from 0 → 1 during for order.When to after all boundary point check, by all marks point remove.

(42) mark meets the frontier point of following condition simultaneously:

（421）1≤N(p ₁)≤6;

（422）S(p ₁)＝1;

（423）p ₂·p ₄·p ₈＝0;

（424）p ₂·p ₆·p ₈＝0;

Above two steps operations form an iteration, algorithm iterate until not point meet again flag condition, at this moment remaining point forms skeleton point.Extracted after the skeleton point of target, using the tie point in skeleton point as unique point, adopted the straight line of acquisition repeatedly of fitting a straight line, usingd this axis as fuselage on aircraft.

Wherein, in described step 5, the axial equation that utilizes step 3 and four to obtain, calculates angle and the azimuth of these two axis, and the method that the range information of combining camera parameter and camera and aircraft calculates aircraft 3 d pose parameter is:

The calculating of step (51), crab angle: the direction vector of establishing projecting plane middle machine body main shaft is i' _bx, be also i' _bxcoordinate at projection plane is (x ₁, y ₁), therefore:

Ψ＝arctan(y ₁/x ₁)(10)

The scope that arctan function obtains is (pi/2, pi/2), in practice according to the positive and negative value of adjusting crab angle of the ratio of direction vector transverse and longitudinal coordinate and transverse and longitudinal coordinate;

The calculating of step (52), the angle of pitch: the focal length of establishing camera is f, photocentre range-to-go is D, and fuselage physical length is L, and in image planes, fuselage length is L', has:

$\mathrm{\&theta;}=\mathrm{ar}\cos \left(\frac{D\&CenterDot;L^{\&prime;}}{f\&CenterDot;L}\right)---\left(11\right)$

Angular range obtained above is (0, pi/2), and the span of actual crab angle should be (pi/2, pi/2).When solving practical problems, can be progressive or away from optical measuring device, determine the symbol of θ by aircraft, just when progressive, getting, away from time get negative;

The calculating of step (53), roll angle: as the formula (12):

In formula, f is camera focus, and D is photocentre range-to-go, and L and W are respectively the physical length of fuselage and wing, and L' and W' are respectively fuselage and the length of wing in image planes, and α is the actual angle of fuselage and wing, and α ' is the angle of image planes middle machine body and wing.The scope that above formula is tried to achieve is (0, pi/2), and the scope of actual roll angle is [π, π].When solving practical problems, should determine according to geometry priori and flight path.

The present invention's beneficial effect is compared with prior art:

(1) the present invention adopts fuzzy mean cluster FCM(Fuzzy C-Means Cluster to the image after level and smooth) method is by target and background segment out, compare with traditional method based on gray level threshold segmentation, owing to considering the property complicated and changeable of natural image, FCM considers that from the angle of fuzzy clustering image cuts apart, and the image of acquisition more meets truth.

(2) the present invention carries out Hough conversion to the whole target image after cutting apart, and target is not carried out to rim detection, can effectively retain the correlativity between object pixel like this, strengthens the noise immunity of algorithm.

(3) the present invention adopts the method for skeletal extraction to obtain the architectural feature point on Aircraft Targets to image after cutting apart, then unique point is carried out to fitting a straight line, has solved the problem that Hough change detection does not go out the axis of the unconspicuous object of linear feature.

(4) the present invention is in conjunction with the method for Hough conversion and skeletal extraction, can accurately extract fuselage on aircraft and the axis of wing, accurately resolving and laying the first stone for follow-up attitude.

(5) the invention provides a kind of method of utilizing the knowledge such as known priori and geometric projection to resolve the 3 d pose of aircraft, compare with the method based on model and Feature Points Matching, the inventive method is only utilized simple geometric relationship, has greatly reduced calculated amount, has improved work efficiency.

Accompanying drawing explanation

Fig. 1 is the inventive method realization flow figure;

Fig. 2 is that the present invention adopts Hough change detection wing straight line result;

Fig. 3 is that the present invention adopts Hough conversion and skeletal extraction combine detection fuselage and wing axis result;

Fig. 4 is body axis system and the attitude angle definition that the present invention adopts; Wherein (a) is body axis system definition, is (b) attitude angle definition;

Fig. 5 is that the present invention solves the geometric graph of roll angle under camera system coordinate system.

Embodiment

Below in conjunction with accompanying drawing, embodiments of the invention are elaborated.The present embodiment is implemented take technical solution of the present invention under prerequisite, provided detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.

The present invention is based on a kind of Hough of utilization conversion, skeletal extraction and geometric projection and resolve the method for aircraft 3 d pose, input picture is single station flash ranging model plane image.

As shown in Figure 1, the invention provides a kind of method that aircraft 3 d pose is resolved in the Hough of utilization conversion, skeletal extraction and geometric projection, comprise following steps:

Step 1, image pre-service.Due to the defect of illumination or imaging system, the pending image obtaining can be subject to the impact of noise, thereby affects follow-up processing.Therefore,, before carrying out follow-up Processing Algorithm, pending image is carried out to pre-service.This method adopts Gaussian smoothing filtering to remove the impact of noise, obtains filtered smoothed image.

Step 2, use the Image Segmentation Using after level and smooth that Fuzzy C-Means Cluster Algorithm FCM (Fuzzy C-Means Cluster) obtains step 1, obtain bianry image.In essence, to cut apart be a process of pixel being classified based on certain attribute to image.The property complicated and changeable of natural image has determined that it is uncertain that many pixels belong in the problem of which cluster at it, thereby considers that from the angle of fuzzy clustering image ratio of division is more reasonable.Fuzzy C-Means Cluster Algorithm (FCM, Fuzzy C-Means Cluster) be from hard C mean algorithm (HCM, Hard C-Means Cluster) develop, its essence is a kind of nonlinear iteration optimization method of based target function, the Weighted Similarity in objective function employing image between each pixel and each cluster centre is estimated.The task of FCM algorithm is exactly by iteration, selects a rational fuzzy membership matrix cluster centre, makes objective function reach minimum, thereby obtains optimal segmentation result.

Fuzzy C-Means Cluster Algorithm is divided by the iteration optimization of objective function is realized to set, and it can express the degree that each pixel of image belongs to a different category.If n is pixel count to be clustered, c is classification number (c=2 in the present invention), and m is FUZZY WEIGHTED index (getting m=2 in the present invention), and it controls degree of membership all kinds of shared degree.The value of objective function is that in image, each pixel, to the weighted sum of squares of C cluster centre, can be expressed as:

$J_m(U,V)=\underset{i=1}{\overset{c}{\mathrm{\&Sigma;}}}\underset{k=1}{\overset{n}{\mathrm{\&Sigma;}}}{u}_{\mathrm{ik}}^m{\left(d_{\mathrm{ik}}\right)}^2---\left(13\right)$

Wherein, u _ikbe the degree of membership of k pixel to i class, d _ikbe k pixel to the distance of i class, U is fuzzy classification matrix, V is cluster centre set.

Clustering criteria will be sought best group exactly to (U, V), makes J _m(U, V) minimum.J _mminimization can be realized by iterative algorithm below:

(2.1) initialization: given cluster classification is counted C=2 in C(the present invention), set iteration stopping threshold epsilon, initialization fuzzy partition matrix U ⁽⁰⁾, iterations l=0, Fuzzy Weighting Exponent m (m=2 in the present invention);

(2.2) by U ^(l)substitution formula (14), calculates cluster centre matrix V ^(l):

$v_i=\frac{1}{\underset{k=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(u_{\mathrm{ik}}\right)}^m}\underset{k=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(u_{\mathrm{ik}}\right)}^m{x}_k,i=1,...,c---\left(14\right)$

Wherein n is number of pixels to be clustered, and m is FUZZY WEIGHTED index, and c is cluster classification number, u _ikfuzzy classification matrix U while being the l time iteration ^(l)in the capable k column element of i, x _kfor k pixel value in image to be clustered, v _icluster centre matrix V while being the l time iteration ^(l)in i cluster centre value;

(2.3) according to formula (15), utilize V ^(l)upgrade U ^(l), obtain new fuzzy classification matrix U ^(l+1):

$u_{\mathrm{ik}}=\frac{1}{\underset{j=1}{\overset{c}{\mathrm{\&Sigma;}}}{\left(\frac{d_{\mathrm{ik}}}{d_{\mathrm{jk}}}\right)}^{\frac{2}{m-1}}},i=1,...,c---\left(15\right)$

D wherein _ikfor the Euclidean distance of k element and i cluster centre in image to be clustered, similarly, d _jkeuclidean distance for k element and j cluster centre in image to be clustered;

(2.4) if || U ^(l)-U ^l+1|| < ε, iteration stopping.Otherwise put l=l+1, return to step (2.2);

(2.5) calculate the Euclidean distance of the cluster centre value that in image to be clustered, each pixel distance above-mentioned steps (2.1)-(2.4) obtains, the pixel value nearest apart from cluster centre is set to 1, otherwise is set to 0, the bianry image after being cut apart thus.

Experiment is found, adopts fuzzy C-means clustering method to cut apart image and can obtain than the better effect of method that adopts Threshold segmentation, especially obvious to natural image.This is because natural image is complicated and changeable, and level is complicated, and each pixel belongs to the definite boundary of which kind of neither one.The degree that fuzzy C-means clustering belongs to each pixel which kind of shows with the form of probability, and unlike hard C mean cluster (HCM) method, directly think that each pixel determines which kind of belongs to, therefore fuzzy C-means clustering method is cut apart to the property complicated and changeable that can better embody natural image for image.

Step 3, the bianry image that utilizes Hough transfer pair step 2 to obtain are processed, and detect the straight line of the obvious part of linear feature on aircraft as the axis of this part.The principle of Hough conversion is that the straight-line detection problem in image space is converted to the maximizing problem in parameter Accumulator space, and the parameter corresponding to unit of accumulated value maximum is the parameter of required straight line.Because the line feature of aircaft configuration is obvious, therefore consider to adopt Hough to convert to detect the axis on aircraft.Adopt the method for Hough change detection straight line as follows:

(3.1) target image step 2 being obtained is asked size, according to the possible span of parameter in parameter space, quantizes, and according to a quantized result structure totalizer array A (ρ, θ), is initialized as 0;

(3.2) set point in each XY space is got all over all probable values by θ, by formula (9), calculated ρ, according to cumulative A:A (ρ, θ)=A (ρ, θ)+1 of the value of ρ and θ;

ρ＝xcosθ+ysinθ （16）

Wherein ρ and θ are respectively two parameters in parameter space, and (x, y) is the point coordinate in image space.

(3.3) according to corresponding ρ and θ-amplitude and the angle of maximal value in A after cumulative, by formula (16), make the straight line (being axes of aircraft) in XY, the maximal value in A has represented the number of set point on this straight line.

Utilize the binary object image that method obtains above-mentioned steps two described in step 3 to carry out Hough conversion, testing result as shown in Figure 2.In figure, black line is detected wing axis.As can be seen from the figure,, for the obvious aircraft wing of linear feature position, use Hough conversion can well detect its axis, and to the unconspicuous fuselage of line feature position, Hough conversion can not detect.For resolving of follow-up attitude angle, wing and fuselage axis all need be detected.On the other hand, skeleton has the topological sum shape information identical with the original, can effectively describe object, is a kind of geometric properties of function admirable.Similarly, axis is also the geometry feature of reflection object.Therefore, consider Hough conversion to combine with skeletal extraction, utilize the obvious straight line of Hough change detection linear feature, and utilize skeleton point to carry out the unconspicuous part of fitting a straight line feature, and then obtain whole axis of object.

Step 4, utilize the image that the method for skeletal extraction obtains step 2 to process, extract after the skeleton point on aircraft, choose some feature skeleton points and carry out fitting a straight line, the straight line obtaining is as the axis of this part on aircraft.Skeleton has the topological sum shape information identical with the original, can effectively describe object, is a kind of geometric properties of function admirable.The method that realizes skeletal extraction has multiple thinking, and Medial-Axis Transformation (medial axis transform, MAT) is a kind of more effective technology.Yet the method need to be calculated all frontier points to the distance of All Ranges internal point, and calculated amount is very large.Therefore, the present invention adopts successively the iterative refinement algorithm of cancellation frontier point to extract skeleton.

If known target point is labeled as 1, background dot is labeled as 0.Definition frontier point is that itself is labeled as 1, and in its 8-connected region, has at least a point to be labeled as 0 point.Algorithm is considered the 8-neighborhood centered by frontier point, and note central point is p ₁, 8 points of its neighborhood are designated as respectively p around central point clockwise ₂, p ₃..., p ₉, p wherein ₂at p ₁top.

Algorithm comprises frontier point is carried out to two step operations:

(4.1) mark meets the frontier point of following condition simultaneously:

（4.1.1）2≤N(p ₁)≤6;

（4.1.2）S(p ₁)＝1;

（4.1.3）p ₂·p ₄·p ₆＝0;

（4.1.4）p ₄·p ₆·p ₈＝0;

N (p wherein ₁) be p ₁non-zero adjoint point number, S (p ₁) be with p ₂, p ₃..., p ₉, p ₂the number of the value of these points from 0 → 1 during for order.When to after all boundary point check, by all marks point remove.

(4.2) mark meets the frontier point of following condition simultaneously:

（4.2.1）1≤N(p ₁)≤6

（4.2.2）S(p ₁)＝1;

（4.2.3）p ₂·p ₄·p ₈＝0;

（4.2.4）p ₂·p ₆·p ₈＝0;

Above two steps operations form an iteration, algorithm iterate until not point meet again flag condition, at this moment remaining point forms skeleton point.Extracted after the skeleton point of target, using the tie point in skeleton point as unique point, adopted the straight line of acquisition repeatedly of fitting a straight line, usingd this axis as fuselage on aircraft.Model plane image is tested, and the axis obtaining after the method that adopts Hough conversion and skeletal extraction to combine as shown in Figure 3.The axis of black line for adopting the method for skeletal extraction to obtain on figure middle machine body.Can see, Hough conversion be combined with skeletal extraction and can accurately extract fuselage and wing axis on aircraft, for follow-up attitude algorithm lays the first stone.

Step 5, the axial equation that utilizes step 3 and four to obtain, calculate angle and the azimuth of these two axis, and the range information of combining camera parameter and camera and aircraft calculates aircraft 3 d pose parameter.Because body axis system and the camera system coordinate system of aircraft is the basis that defines aircraft three-dimension altitude angle under camera system coordinate system, first define several concepts here.The body axis system O of aircraft _bx _by _bz _btake aircraft barycenter as initial point, as in Figure 4 (a), X _bpoint to head dead ahead, Y _baxle points to its left side, Z _baxle, perpendicular to fuselage and wing place plane (supposing that fuselage and wing are positioned at same plane), defines vectorial i _bx, i _by, i _bzbe parallel to respectively three axles, mould value is respectively fuselage length, wing is long and aircraft altitude.By the O of body axis system _bx _by _bbe defined as the reference planes of aircraft.

(5.1) crab angle is calculated: (a) in Fig. 4 is known, and the direction vector of projecting plane middle machine body main shaft is i' _bx, be also i' _bxcoordinate at projection plane is (x ₁, y ₁), therefore:

Ψ＝arctan(y ₁/x ₁) (17)

The scope that arctan function obtains is (pi/2, pi/2), in practice according to the positive and negative value of adjusting crab angle of the ratio of direction vector transverse and longitudinal coordinate and transverse and longitudinal coordinate;

(5.2) angle of pitch calculates: (b) in Fig. 4 is known, and the camera system coordinate system angle of pitch of getting off the plane is the angle of fuselage and OXY plane.In the situation that aircraft and optical measuring device distance is certain, the angle of pitch that camera system coordinate system is got off the plane is determining that fuselage is in the straight length of projection plane shape.According to above-mentioned known, at known fuselage length and aircraft, to optical measuring device distance in the situation that, in conjunction with optical measuring device image-forming principle, according to similarity, can obtain the projected length of fuselage on the projecting plane parallel with the plane of delineation:

$\frac{f}{D}=\frac{L^{\&prime;}}{L_p}\&DoubleRightArrow;L_p=\frac{D}{f}\&CenterDot;L^{\&prime;}---\left(18\right)$

$\mathrm{\&theta;}=\mathrm{ar}\cos \left(\frac{L_p}{L}\right)=\mathrm{ar}\cos \left(\frac{D\&CenterDot;L^{\&prime;}}{f\&CenterDot;L}\right)---\left(19\right)$

Wherein, L' is the length of fuselage on the plane of delineation, L _pfor fuselage length on projecting plane, L is actual fuselage length, D be photocentre apart from the distance of aircraft, f is camera focus.Angular range obtained above is (0, pi/2), and the scope of the actual angle of pitch is (pi/2, pi/2).When solving practical problems, can be progressive or away from optical measuring device, determine the symbol of θ by aircraft, just when progressive, getting, away from time get negative.

(5.3) roll angle is calculated: (b) in Fig. 4 is known, and the roll angle that camera system coordinate system is got off the plane is aircraft wing vector i _bywith the angle of plane HKNP, HKNP was airframe and the vertical plane vertical with the earth.In existing known image plane, the length of fuselage and wing is respectively L' and W', and its angle is α ', and the actual angle of fuselage and wing is α.Utilize the perspective geometry relation between face face can calculate roll angle.As shown in Figure 5, by aircraft wing and fuselage, respectively to XOY plane projection, projection vector is respectively OD and Oi' _bx.So, face i _bxoi _bywith face DOi' _bxbetween angle (being also the angle on aircraft reference planes and projecting plane) be desired roll angle.By triangle sine, can be obtained:

$S_{{\mathrm{\&Delta;DOi}}_{\mathrm{bx}}^{\&prime;}}=\frac{1}{2}\&CenterDot;\left|\stackrel{\&RightArrow;}{\mathrm{OD}}\right|\&CenterDot;\left|\stackrel{\&RightArrow;}{{\mathrm{Oi}}_{\mathrm{bx}}^{\&prime;}}\right|\&CenterDot;\sin {\mathrm{\&alpha;}}^{\&prime;}---\left(20\right)$

$S_{{\mathrm{\&Delta;i}}_{\mathrm{bx}}{\mathrm{Oi}}_{\mathrm{by}}}=\frac{1}{2}\&CenterDot;\left|\stackrel{\&RightArrow;}{{\mathrm{Oi}}_{\mathrm{bx}}}\right|\&CenterDot;|\stackrel{\&RightArrow;}{{\mathrm{Oi}}_{\mathrm{by}}|}\&CenterDot;\sin \mathrm{\&alpha;}---\left(21\right)$

In formula

with

be respectively wing and the projection vector of fuselage on the projecting plane parallel with the plane of delineation.According to similarity principle, can obtain the length of wing and the projection vector of fuselage on the projecting plane parallel with the plane of delineation:

$\frac{f}{D}=\frac{L^{\&prime;}}{L_p}\&DoubleRightArrow;L_p=\frac{D}{f}\&CenterDot;L^{\&prime;}---\left(22\right)$

$\frac{f}{D}=\frac{W^{\&prime;}}{W_p}\&DoubleRightArrow;W_p=\frac{D}{f}\&CenterDot;W^{\&prime;}---\left(23\right)$

L in formula _pand W _pthe length that represents respectively fuselage and the projection vector of wing on the projecting plane parallel with the plane of delineation.Therefore,

According to perspective geometry principle, can obtain face i _bxoi _bywith face DOi' _bxbetween angle

for:

Formula (22) and (23) difference substitution formulas (24) are obtained:

The scope that above formula is tried to achieve is (0, pi/2), and the scope of actual roll angle is [π, π].When solving practical problems, should determine according to geometry priori and flight path.

In order to verify the accuracy of the inventive method, experiment adopts model plane image, as shown in Figure 2.Camera focus f=2m used, pixel dimension is 0.011mm, camera photocentre is to the distance B=550m of aircraft, the long L=1.1m of the actual fuselage of aircraft, span W=1.4m, fuselage and wing angle α=90 °.After known these prioris, adopt the aircraft 3 d pose parameter of the inventive method calculating chart 2 to be: crab angle Ψ=27.253 °, pitching angle theta=48.073 °, roll angle

Non-elaborated part of the present invention belongs to those skilled in the art's known technology.

Those of ordinary skill in the art will be appreciated that, above embodiment is only for the present invention is described, and be not used as limitation of the invention, as long as within the scope of connotation of the present invention, the above embodiment is changed, and modification all will drop in the scope of the claims in the present invention book.

Claims (5)

1. an aircraft 3 d pose calculation method, its feature comprises the steps:

Step 1, image pre-service: adopt Gaussian smoothing filtering to process pending image, remove the impact of noise, obtain filtered smoothed image;

Step 2, use the Image Segmentation Using after level and smooth that Fuzzy C-Means Cluster Algorithm FCM (Fuzzy C-Means Cluster) obtains step 1, obtain bianry image;

Step 3, the bianry image that utilizes Hough transfer pair step 2 to obtain are processed, and detect the straight line of the obvious part of linear feature on aircraft as the axis of this part;

Step 4, utilize the image that the method for skeletal extraction obtains step 2 to process, extract after the skeleton point on aircraft, choose some feature skeleton points and carry out fitting a straight line, the straight line obtaining is as the axis of this part on aircraft;

Step 5, obtain the axial equation that step 3 and four obtains, calculate angle and the azimuth of these two axis, the range information of combining camera parameter and camera and aircraft calculates aircraft 3 d pose parameter.

2. aircraft 3 d pose calculation method according to claim 1, is characterized in that: the method for utilizing the Image Segmentation Using after level and smooth that Fuzzy C-Means Cluster Algorithm FCM (Fuzzy C-Means Cluster) obtains step 1 described in step 2 is achieved as follows:

Step (21), initialization: given cluster classification is counted C, set iteration stopping threshold epsilon, initialization fuzzy partition matrix U ⁽⁰⁾, iterations l=0, Fuzzy Weighting Exponent m;

Step (22), by U ^(l)substitution formula (1), calculates cluster centre matrix V ^(l):

Wherein n is number of pixels to be clustered, and m is FUZZY WEIGHTED index, and c is cluster classification number, u _ikfuzzy classification matrix U while being the l time iteration ^(l)in the capable k column element of i, x _kfor k pixel value in image to be clustered, v _icluster centre matrix V while being the l time iteration ^(l)in i cluster centre value;

Step (23), according to formula (2), utilize V ^(l)upgrade U ^(l), obtain new fuzzy classification matrix U ^(l+1):

D wherein _ikfor the Euclidean distance of k element and i cluster centre in image to be clustered, similarly, d _jkeuclidean distance for k element and j cluster centre in image to be clustered;

Step (24) if || U ^(l)-U ^l+1|| < ε, iteration stopping.Otherwise, put l=l+1, return to step (22);

Step (25), calculate in image to be clustered the Euclidean distance of the cluster centre value that each pixel distance above-mentioned steps (21)-(24) obtains, the pixel value nearest apart from cluster centre is set to 1, otherwise is set to 0, the bianry image after being cut apart thus.

3. aircraft 3 d pose calculation method according to claim 1, it is characterized in that: the bianry image that utilizes Hough transfer pair step 2 to obtain described in step 3 is processed, detect linear feature on aircraft obviously the straight line of part as the method for the axis of this part, be achieved as follows:

Step (31), the target image that step 2 is obtained are asked size, according to the possible span of parameter in parameter space, quantize, and according to a quantized result structure totalizer array A (ρ, θ), are initialized as 0;

Step (32), the set point in each XY space is got all over all probable values by θ, with formula (3), calculated ρ, according to the value of ρ and θ A:A (ρ, θ)=A (ρ, θ)+1 of adding up;

ρ＝xcosθ+ysinθ (3)

Wherein ρ and θ are respectively two parameter-amplitudes and the angle in parameter space, and (x, y) is the point coordinate in image space.

Corresponding ρ and the θ of maximal value in step (33), the cumulative rear A of basis, makes the straight line (being axes of aircraft) in XY by formula (3), and the maximal value in A has represented the number of set point on this straight line.

4. aircraft 3 d pose calculation method according to claim 1, it is characterized in that: the image that the method for utilizing skeletal extraction described in step 4 obtains step 2 is processed, extract after the skeleton point on aircraft, choose some feature skeleton points and carry out fitting a straight line, the straight line obtaining is as the method for the axis of this part on aircraft, and the present invention adopts successively the iterative refinement algorithm of cancellation frontier point to extract skeleton and is achieved as follows:

If known target point is labeled as 1, background dot is labeled as 0, and definition frontier point is that itself is labeled as 1, and in its 8-connected region, has at least a point to be labeled as 0 point, considers the 8-neighborhood centered by frontier point, and note central point is p ₁, 8 points of its neighborhood are designated as respectively p around central point clockwise ₂, p ₃..., p ₉, p wherein ₂at p ₁top;

Comprise frontier point carried out to two step operations:

(41) mark meets the frontier point of following condition simultaneously:

（411）2≤N(p ₁)≤6;

（412）S(p ₁)＝1;

（413）p ₂·p ₄·p ₆＝0;

（414）p ₄·p ₆·p ₈＝0;

N (p wherein ₁) be p ₁non-zero adjoint point number, S (p ₁) be with p ₂, p ₃..., p ₉, p ₂the number of the value of these points from 0 → 1 during for order, after to the check of all boundary point, by all marks point remove;

(42) mark meets the frontier point of following condition simultaneously:

（421）1≤N(p ₁)≤6；

（422）S(p ₁)＝1;

（423）p ₂·p ₄·p ₈＝0;

（424）p ₂·p ₆·p ₈＝0;

Above two steps operations form an iteration, iterate until not point meet again flag condition, at this moment remaining point forms skeleton point.Extracted after the skeleton point of target, using the tie point in skeleton point as unique point, adopted the straight line of acquisition repeatedly of fitting a straight line, usingd this axis as fuselage on aircraft.

5. aircraft 3 d pose calculation method according to claim 1, it is characterized in that: the axial equation that utilizes step 3 and four to obtain described in step 5, calculate angle and the azimuth of these two axis, the range information of combining camera parameter and camera and aircraft calculates the method for aircraft 3 d pose parameter, is achieved as follows:

The calculating of step (51), crab angle: the direction vector of establishing projecting plane middle machine body main shaft is i' _bx, be also i' _bxcoordinate at projection plane is (x ₁, y ₁), therefore:

Ψ＝arctan(y ₁/x ₁)(4)

The scope that arctan function obtains is (pi/2, pi/2), and in fact the scope of crab angle is [π, π], in practice should be according to the positive and negative value of adjusting crab angle of the ratio of direction vector transverse and longitudinal coordinate and transverse and longitudinal coordinate;

The calculating of step (52), the angle of pitch: the focal length of establishing camera is f, photocentre range-to-go is D, and fuselage physical length is L, and in image planes, fuselage length is L', has:

Angular range obtained above is (0, pi/2), and the span of the actual angle of pitch should be (pi/2, pi/2), when solving practical problems, can be progressive or away from optical measuring device, determine the symbol of θ by aircraft, just when progressive, get, away from time get negative;

The calculating of step (53), roll angle: as the formula (6):

In formula, f is camera focus, D is photocentre range-to-go, and L and W are respectively the physical length of fuselage and wing, and L' and W' are respectively fuselage and the length of wing in image planes, α is the actual angle of fuselage and wing, α ' is the angle of image planes middle machine body and wing, and the scope that above formula is tried to achieve is (0, pi/2), and the scope of actual roll angle is [π, π], when solving practical problems, should determine according to geometry priori and flight path.

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