CN107300377B - A kind of rotor wing unmanned aerial vehicle objective localization method under track of being diversion - Google Patents
A kind of rotor wing unmanned aerial vehicle objective localization method under track of being diversion Download PDFInfo
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- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The present invention discloses the rotor wing unmanned aerial vehicle objective localization method under a kind of track of being diversion, and using the single camera photographic subjects image being mounted on unmanned plane, and image is passed back to earth station；The marker with obvious characteristic is selected, and carries out visual identity；Then rotor wing unmanned aerial vehicle is diversion centered on the marker, carries out multipoint images measurement, calculates height and course deviation of the unmanned plane relative to landform where target based on the method for binocular vision model and the mutual iteration of linear regression model (LRM)；Next, any static or moving target in camera coverage may be selected in operator, realize that the three-dimensional of target is accurately positioned.The present invention is carried out in same primary aerial mission, and flight leading portion calculates course deviation and relative altitude, and flight back segment carries out three-dimensional accurate positioning；The present invention traditional triangulation location method under track that solves the problems, such as to be diversion can not calculate relative altitude, to realize the three-dimensional localization to target.
Description
Technical field
The invention belongs to vision measurement fields, and in particular to the rotor wing unmanned aerial vehicle objective under a kind of track of being diversion is fixed
Position method.
Background technique
The features such as rotor wing unmanned aerial vehicle is at low cost, VTOL and hovering, in investigation, agricultural insurance, environmental protection and calamity
The fields such as rescue are applied widely afterwards.
And the rotor wing unmanned aerial vehicle target positioning of view-based access control model has been current one of research hotspot problem, using vision side
Method carries out three-dimensional localization to target, determines the relative altitude of unmanned plane and target by triangulation location method first, then could
Carry out the positioning of target.Course deviation brought by the low precision AHRS attitude heading reference system being equipped in view of rotor wing unmanned aerial vehicle
Larger, when carrying out vision measurement using the image of unmanned plane shooting, certain offset occurs for the light projected in image.If
Using traditional triangulation location method, for the two groups of light projected from left and right view due to being deviated, what is solved is opposite
Height will generate biggish error, therefore can not accurately calculate the relative altitude between unmanned plane and object, thus cannot to target into
The effective three-dimensional localization of row.
Summary of the invention
In view of this, the present invention provides the rotor wing unmanned aerial vehicle objective localization method under a kind of track of being diversion, it can
Course deviation is calculated, reduces the calculating error to relative altitude, to improve rotor wing unmanned aerial vehicle to the three-dimensional localization of target
Ability.
The utility model has the advantages that
(1) method provided by the present invention is directed to the rotor wing unmanned aerial vehicle for being equipped with low precision AHRS attitude heading reference system system,
It can be precisely calculated course deviation existing for AHRS attitude heading reference system, and then calculate rotor wing unmanned aerial vehicle and mesh under track of being diversion
Height where mark between landform, to realize that rotor wing unmanned aerial vehicle positions the 3D vision of target.
Detailed description of the invention
Fig. 1 is rotor wing unmanned aerial vehicle target 3 D positioning system structure chart of the invention；
Fig. 2 is the flow chart of method provided by the present invention；
Fig. 3 is revolved view binocular vision model schematic used in the present invention；
Fig. 4 is monocular-camera ranging model schematic used in the present invention；
Fig. 5 is the iterative process flow chart in method provided by the present invention；
Fig. 6 is in method provided by the present inventionData matched curve；
Fig. 7 is in method provided by the present inventionData matched curve；
Fig. 8 is the locating effect figure of method provided by the present invention.
Specific embodiment
The present invention will now be described in detail with reference to the accompanying drawings and examples.
Following experiment porch is built to verify effectiveness of the invention, using a frame T650 quadrotor drone, one
Platform notebook can carry out real time communication as earth station between unmanned plane and earth station, system structure is as shown in Figure 1.
For unmanned plane, GPS positioning system, AHRS attitude heading reference system, altimeter, wireless image transmission are had on machine
Module and wireless data transceiver module make the APM flight control system of 3D Robotics company work and are protecting from steady mode
Demonstrate,prove the stabilized flight of unmanned plane.Video camera is installed in the Handpiece Location of unmanned plane, depression angle β is 45 °, and passes through wireless image
Transmission module returns image to earth station, and obtained respectively by GPS positioning system, AHRS attitude heading reference system and altimeter
Position, posture and the elevation information of unmanned plane then pass through wireless data transceiver module and are transferred to earth station.
Earth station runs unmanned plane vision positioning scheduling algorithm based on computer, is connected using USB interface without line number
According to transceiver module, being in communication with each other for unmanned plane and earth station is realized.
Based on the experiment porch, as shown in Fig. 2, the rotor wing unmanned aerial vehicle objective localization method under a kind of track of being diversion,
The following steps are included:
Step 1: shooting image using the video camera being mounted on unmanned plane, and image is passed back to after system starting
Earth station；
Step 2: selecting the stationary body with clear profile as marker from the image of passback, and to marker
Carry out visual identity；
Carrying out visual identity for marker in step 2, detailed process is as follows:
Marker is identified with SIFT algorithm, obtains m characteristic point P1,P2...Pm-1,Pm, and by these features
Point carries out storage as template, and m is integer；
Step 3: rotor wing unmanned aerial vehicle is diversion centered on the marker, and using the result of visual identity to marker into
Row multipoint images measurement, based on the method for binocular vision model and the mutual iteration of linear regression model (LRM) calculate unmanned plane relative to
The height and course deviation of landform where target；
The flow chart of step 3 is as shown in figure 5, detailed process is as follows:
Step 3.1, rotor wing unmanned aerial vehicle under track of being diversion using visual identity respectively to N number of image in chronological order into
Row measurement carries out feature extraction (1≤i≤N) to current i-th of image using SIFT algorithm, then utilizes the feature in template
Point is matched with the characteristic point of present image, obtains w group match point P1,P2...Pw-1,Pw(w≤m) finally takes these matchings
The geometric center P of pointf(f≤w) represents the location of pixels of marker in the picture, is denoted asAnd it is recorded in i-th
Measured value when image measurement, comprising: unmanned plane shooting point OiIn the position of inertial reference system { I }And posture
(ψi,θi,φi), ψi,θi,φiRespectively azimuth, pitch angle and roll angle.
Any two image in step 3.2, the N number of image of selection, shares n group, whereinIt is surveyed first
The image of amount as left view L, rear measurement image as right view R, constitute the binocular vision model of revolved view, such as
Shown in Fig. 3.
Calculate relative altitude h of the unmanned plane relative to markerj, 1≤j≤n
Wherein, marker is respectively in the location of pixels of left and right viewRl, TlPoint
It Wei not the corresponding unmanned plane shooting point O of left viewlRelative to the spin matrix and translation matrix of inertial coordinate system,
Wherein, ψl,θl,φlRespectively left view unmanned plane shooting point OlCourse angle, pitch angle and roll angle, ψr,θr,
φrRespectively right view unmanned plane shooting point OlCourse angle, pitch angle and roll angle, δ ψ are course deviation, there is ψl=ψi-δψ
(k), k is the number of iterations, θl=θi, φl=φi(1≤i < N), if initial value δ ψ (0)=0；
Rr, TrThe respectively corresponding unmanned plane shooting point O of right viewrRelative to inertial coordinate system spin matrix and
Translation matrix,
Wherein, ψr=ψm- δ ψ (k), θr=θm, φr=φm(i < m≤N)
The coordinate of left view and the corresponding unmanned plane shooting point of right view under inertial coordinate system is respectivelyWithR, T are the corresponding camera coordinate system of right view relative to left view
Scheme spin matrix, the translation matrix of corresponding camera coordinate system, R=RrRl T, T=Tl-RTTr=Rl(Or-Ol)；M=[Pl -
RTPr Pl×RTPr]-1T。
Step 3.3, for the n group relative altitude h being calculatedj, gross error is rejected with 3 σ criterion, then asks n group flat
Mean value
Step 3.4 obtains relative altitudeAfterwards, using the N point measured value in step 3.2 and based on linear regression mould
Type calculates course deviation δ ψ (k)；
Generally, [x y z]T, [xp yp zp]TUnmanned plane and object are respectively indicated in the seat of inertial coordinate system { I }
Mark, (xf′,yf') indicating that the location of pixels of object in the picture, f are the focal length of video camera, the ranging model of video camera is
Attitude matrixFor
Wherein, relative altitude of the h' between unmanned plane and object, (ψ ', θ ', φ ') indicate unmanned plane in some measurement
Course angle, pitch angle and the roll angle of point, wherein pitching angle theta ', the measurement accuracy of roll angle φ ' it is high, error is ignored not
Meter, and there are biggish course deviations for the measurement of course angle ψ '.
In the present embodiment, in order to calculate the course deviation of course angle, the mark shot using unmanned plane in different location
The N point measured value of object, and solved by linear regression method, specific calculating process is as follows: [xG yG zG]TIndicate mark
Object enables [x in the coordinate of inertial coordinate system { I }p yp zp]T=[xG yG zG]T,For the phase of unmanned plane and marker
To the average value of height, enableIt substitutes into formula (4), can obtain
Setting parameter θ=[θa,θb]T, θa=[xG,yG]T, θb=δ ψ (k),Position and appearance
The measurement equation of state is respectively formula (6) and formula (7):
z1(i)=y1(i)+v1,v1~N (0, R1) (6)
Wherein v1, v2To measure noise, R1, R2For real symmetry positive definite matrix.Then formula (5) is deformed into
Wherein,For attitude misalignment, with Taylor expansion, formula (8) becomes
By formula (8) and formula (9), obtain
If matrixWherein a1,3~a2,5Representing matrix AiIn it is right
The element answered；MatrixWherein b1,1~b2,3It indicates in matrix BiIn it is corresponding
Element.In the present embodiment, N point vision measurement is carried out to same marker, thereforeIt is corresponding
Matrix is A1,…,AN, B1,…,BN, following linear regression model (LRM) is obtained by these measured values,
Wherein, I2For 2 × 2 unit matrix, noise is
V~N (0, R)
Covariance matrix is
The estimated value of parameter θ is
Course deviation δ ψ (k) can be solved by formula (12).
Step 3.5 sets e as constant, if | δ ψ (k)-δ ψ (k-1) | < e obtains the estimated value of final relative altitude With the estimated value of course deviation And execute step 4；Otherwise, step 3.2 is gone to,
Current δ ψ (k) is substituted into the calculation formula of left and right view course angle, is found outTo be iterated calculating.
Step 4: selecting any in camera coverage under conditions of relative altitude and course deviation are effectively estimated
The true course of unmanned plane, and then basis are calculated using the course deviation obtained for target and the measured value for obtaining the target
True course and Height Estimation valueIt realizes and the three-dimensional of target is accurately positioned.
Specifically, it is assumed that selected target and marker is in same plane, that is, the relative altitude estimatedIt is considered that
The relative altitude of unmanned plane and target, [xt yt zt]TIndicate that target in the coordinate of inertial coordinate system { I }, hasEnable [xp yp zp]T=[xt yt zt]T,By the true course generation of the measured value of target and unmanned plane
Enter formula (4), the coordinate of target is calculated, to realize the three-dimensional localization to target.
The validity of the iterative process is specifically described below, by taking track of being diversion is circumference as an example, radius R=73m, radian
Rad=1.5 π, δ ψ=0,1 ..., 59,60deg obtains corresponding 61 groups by formula (1)Then the side of maintenance data fitting
Method, withFor dependent variable, δ ψ is independent variable, as shown in fig. 6, obtainingMathematical relationship expression formula:
In the same manner, it enablesIt is solved by formula (10) and obtains 36 groups of δ ψ, then maintenance data
The method of fitting, using δ ψ as dependent variable,For independent variable, as shown in fig. 7, obtainingMathematical relationship expression formula:
Ifeδψ=δ ψ-δ ψt, wherein ht, δ ψ t is the true value of relative altitude and course deviation, by formula (9)
,
It is obtained by formula (10),
Wherein, k1, k2For relevant parameter.
The relative altitude that binocular vision model calculates substitutes into equation of linear regression, can effectively calculate course deviation.So
Afterwards, by the estimated value back substitution of course deviation to binocular vision model, relative altitude can be precisely calculated.Generally, AHRS system
Course deviation be no more than 30deg, so having | k2| > k1> 0, and due to k2< 0, according to formula (15), (16) it is found that by
After finite iteration, the estimated value of relative altitudeWith the estimated value of course deviationTrue value will be converged to.
Under conditions of UAV flight's camera, carried out flight test, unmanned plane under track of being diversion to marker into
Row image measurement, wherein the radius R=73m for track of being diversion, radian rad=1.5 π, true height of the unmanned plane relative to marker
Spend ht=45m, flying speed V=3.44m/s, fGPS=4Hz, the true value δ ψ of course deviationt=30deg, if e=
0.02deg, the effect of method provided by the present invention such as table 1, table 2, as shown in Fig. 8.Wherein listed error e in tableh, eδψ, exy,
ezAll referring to root-mean-square error.
1 iterative process of table
2 target positioning result of table
| Index | Three-dimensional localization of the invention |
| Relative altitude evaluated error eh/m | 0.93 |
| Course estimation error eδψ/deg | 1.89 |
| Position error exy/m | 10.89 |
| Position error ez/m | 0.43 |
In conclusion the above is merely preferred embodiments of the present invention, being not intended to limit protection model of the invention
It encloses.All within the spirits and principles of the present invention, any modification, equivalent replacement, improvement and so on should be included in this hair
Within bright protection scope.
Claims (4)
1. the rotor wing unmanned aerial vehicle objective localization method under a kind of track of being diversion, it is characterised in that:
Step 1: extracting stationary body from the image that rotor wing unmanned aerial vehicle is shot as marker；
Step 2: rotor wing unmanned aerial vehicle is diversion centered on the marker, N is carried out to the marker during being diversion
The shooting of a angle, and obtain the measured value of every width shooting image；N is positive integer；
Step 3: N number of shooting image is pairwise grouping, every group of shooting image utilizes the binocular vision model meter of revolved view
The relative altitude of the relatively described marker of rotor wing unmanned aerial vehicle is calculated, then N group is averaged, as the phase in current iteration round k
To height error
Wherein, when calculating relative altitude, course deviation δ ψ (k) needed for the binocular vision model of revolved view uses upper one
A iteration round k-1 calculates the course deviation δ ψ (k-1) obtained, and binocular vision model is calculated required course angle ψ (k) more
It is newly ψ (k)=ψi- δ ψ (k), wherein ψiFor course angle of the unmanned plane when shooting i-th of image；Initial value δ ψ (0) takes 0；
Step 4: utilizing relative altitude errorWith the measured value of N width shooting image, current iteration round k is calculated
Course deviation δ ψ (k)；
Step 5: judging whether δ ψ (k) and the deviation of δ ψ (k-1) are less than given threshold, if it is, by last time iteration knot
Fruit is as relative altitude estimated valueWith the estimated value of course deviationAnd execute step 6；Otherwise, iteration round k is enabled to add 1,
It is transferred to step 3；
Step 6: to the arbitrary target in rotor wing unmanned aerial vehicle camera coverage, using the estimated value of course deviation calculate rotor without
Man-machine true course, and then according to true course and Height Estimation valueRealize the three-dimensional localization to target；
Wherein, in the step 4, the concrete mode of course deviation δ ψ (k) is calculated are as follows:
[xGyGzG]TIndicate that marker in the coordinate of inertial coordinate system { I }, can obtain
The ranging model of video camera are as follows:
Wherein, f is the focal length of video camera,For the attitude matrix of the unmanned plane when shooting i-th of image,
1≤i≤N；
Wherein,(ψi,θi,φi) it is unmanned plane shooting point O when shooting i-th of imageiAt inertial reference system { I }
Position and posture, ψi,θi,φiRespectively azimuth, pitch angle and roll angle,It is marker in i-th image
Location of pixels.
Setting parameter θ=[θa,θb]T, θa=[xG,yG]T, θb=δ ψ (k),Measurement equation group is
Wherein v1, v2To measure noise, R1, R2For real symmetry positive definite matrix, then formula (3) is deformed into
WhereinFor attitude misalignment, with Taylor expansion, formula (4) becomes
By formula (4) and formula (5), obtain
If matrixWherein a1,3~a2,5Representing matrix AiIn corresponding member
Element；
MatrixWherein b1,1~b2,3It indicates in matrix BiIn corresponding element；It obtains
Following linear regression model (LRM),
Wherein, I2For 2 × 2 unit matrix, noise is V~N (0, R)
Covariance matrix is
The estimated value of parameter θ is
Course deviation δ ψ (k) can be solved by formula (8).
2. rotor wing unmanned aerial vehicle objective localization method as described in claim 1, which is characterized in that the rotor wing unmanned aerial vehicle phase
To the relative altitude h of the markerj, 1≤j≤n calculation method are as follows:
Wherein, T=Tl-RTTr=Rl(Or-Ol), M=[Pl -RTPr Pl×RTPr]-1T, R=RrRl T；Pl、PrRespectively marker
In the location of pixels of left and right view；R, T are that the corresponding camera coordinate system of right view is sat relative to the corresponding video camera of left view
Mark spin matrix, the translation matrix of system, Rl, TlThe respectively corresponding unmanned plane shooting point O of left viewlIt is sat relative to inertial reference
Mark the spin matrix and translation matrix of system, Rr, TrThe respectively corresponding unmanned plane shooting point O of right viewrIt is sat relative to inertial reference
Mark the spin matrix and translation matrix of system.
3. rotor wing unmanned aerial vehicle objective localization method as claimed in claim 2, which is characterized in that marker pixel position
The measurement method set are as follows:
Mark object image obtained in step 1 is identified, several characteristic points are obtained；During being diversion each image into
Row identification obtains several characteristic points, and the characteristic point of each image is matched with the characteristic point of mark object image, will be matched
Location of pixels of the geometric center of point as marker in the picture.
4. rotor wing unmanned aerial vehicle objective localization method as described in claim 1, which is characterized in that the step 3 is carrying out
Before N group is averaged, the gross error of relative altitude is first rejected using 3 σ criterion.
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| CN112567201B (en) * | 2018-08-21 | 2024-04-16 | 深圳市大疆创新科技有限公司 | Distance measuring method and device |
| CN110632941B (en) * | 2019-09-25 | 2020-12-15 | 北京理工大学 | Trajectory generation method for target tracking of unmanned aerial vehicle in complex environment |
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| CN115272892B (en) * | 2022-07-29 | 2023-07-11 | 同济大学 | Unmanned aerial vehicle positioning deviation monitoring management and control system based on data analysis |
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