CN106813662A - A kind of air navigation aid based on light stream - Google Patents
A kind of air navigation aid based on light stream Download PDFInfo
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- CN106813662A CN106813662A CN201710070841.3A CN201710070841A CN106813662A CN 106813662 A CN106813662 A CN 106813662A CN 201710070841 A CN201710070841 A CN 201710070841A CN 106813662 A CN106813662 A CN 106813662A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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Abstract
The present invention relates to navigation field, more particularly to a kind of air navigation aid based on light stream.The air navigation aid based on light stream of present invention design, the point that optical flow algorithm tracks error is rejected by way of distance between calculating not characteristic point in the same time first, the accuracy of effective lifting optical flow algorithm, then speed of the characteristic point under image coordinate system is calculated by LK pyramid optical flow algorithms, inertial navigation information is converted to the optical flow velocity information under image coordinate system, optical flow velocity information is corrected using the characteristic point pixel speed of inertial navigation system release, the ability of optical flow algorithm resistance image local interference can effectively be strengthened, the information of the two is merged finally by Kalman filter, to obtain more accurate navigation information.The method can be as alternative of the Small and micro-satellite without independent navigation under GPS environment, with theoretical and practical value.
Description
Technical field
The present invention relates to navigation field, more particularly to a kind of air navigation aid based on light stream.
Background technology
Usual multi-rotor unmanned aerial vehicle provides the navigation information of aircraft using GPS/MEMS inertia combined navigation systems.But
It is that GPS inherently belongs to radionavigation, losing lock can occurs because of dropout in particular surroundings such as jungle, valley and interiors
Situation.Because the precision of MEMS inertia devices is relatively low, error is very fast with time diverging, therefore effective navigation cannot be provided separately
Information.So needing to introduce a kind of error diverging of new navigation mode to suppress inertia device.
Scientific investigations showed that, the honeybee in nature is entered by using the change of its special eyes perception ambient
Row navigation and avoidance, and then inspired people using optical flow method and navigated.With the development of computer vision, light stream navigation
There is low cost, small volume because of it, lightweight Small and micro-satellite navigation field is applied to.
Because classical optical flow algorithm is built upon on three hypothesis, therefore multi-rotor aerocraft practical flight environment
In be likely to occur intensity of illumination change, image is blocked and the factor such as Large Scale Motion all can be to the reliability of light stream navigation information
Impact.
Using light stream/inertia combined navigation system, it is possible to use the velocity information of light stream navigation algorithm come suppress inertial navigation letter
The diverging of breath, while the precision of Optic flow information solution can also be lifted using the information of inertial navigation output, so as to lift navigation system
The reliability of system, survival ability of the enhancing multi-rotor unmanned aerial vehicle under without GPS environment.
The content of the invention
In view of the above problems, the present invention provides a kind of air navigation aid based on light stream.
A kind of air navigation aid based on light stream, wherein, including：
Coordinate system under the front right of aircraft is set up, forward direction points to the head of aircraft, and light flow sensor gathers some images,
And the coordinate set of characteristic point in every described image is detected, the characteristic point for selecting wantonly two described images is contrasted, and is screened
Go out coordinate set of the same characteristic features point on different images, calculate geometry of the same characteristic features point between coordinate points on different images away from
From calculating flying speed of the characteristic point on image with this；
The navigation for defining the aircraft is coordinate, and records the horizontal flight speed of the aircraft；
Flying speed and the horizontal flight speed by characteristic point on image carry out fusion treatment, to obtain described flying
The navigation information of row device.
Above-mentioned method, wherein, methods described also includes：
The key point displacement as the ideal value of light stream is solved using inertial navigation information, is solved with reference to the characteristic point
Light flow valuve calculates the angle error of each feature-point optical flow, the characteristic point small so as to filter out angle error.
Above-mentioned method, wherein, methods described also includes：
Some error state amounts are chosen, state equation is set up, is amount with velocity error of the characteristic point under pixel coordinate system
Measurement, sets up measurement equation, and the flying speed of the aircraft is estimated using Kalman filtering recurrence equation.
Above-mentioned method, wherein, flying speed of the characteristic point on image is calculated using LK pyramid optical flow algorithms.
Above-mentioned method, wherein, methods described also includes：
The feature spot speed released using inertial navigation system is calculated characteristic point correcting LK pyramid optical flow algorithms and existed
Flying speed on image.
Above-mentioned method, wherein, the navigation information includes the deflection of the aircraft, and gyro error and position are sat
Mark.
Above-mentioned method, wherein, the mathematical platform error angle of the error state amount including strapdown inertial navigation system of selection,
The error of the velocity error of the aircraft, the site error of the flight, gyro error and accelerometer.
Above-mentioned method, wherein, characteristic point is in another figure in estimating an image using LK pyramid optical flow algorithms
Coordinate set as in.
In sum, a kind of air navigation aid based on light stream of present invention design, changes by by the velocity information of inertial navigation
To under image coordinate system, depth is carried out to the information of light stream and inertia as measurement using the pixel speed of image characteristic point and is melted
Close, so that the error of inertial navigation is modified using the information of light stream, by calculating the angle error of feature-point optical flow, using inertial navigation
Information lift the reliability of light stream, final integrated navigation system exports more structurally sound navigation information to be made for multi-rotor unmanned aerial vehicle
With.
Brief description of the drawings
With reference to appended accompanying drawing, more fully to describe embodiments of the invention.However, appended accompanying drawing be merely to illustrate and
Illustrate, and be not meant to limit the scope of the invention.
Fig. 1 is the signal of relation between the optical flow field that multi-rotor aerocraft sports ground of the invention and Airborne Camera are measured
Figure；
Fig. 2 is optical flow algorithm structural representation of the present invention；
Fig. 3 is the light stream/inertia combined navigation algorithm structure schematic diagram of distinguished point based pixel speed combination.
Specific embodiment
The present invention is further illustrated with specific embodiment below in conjunction with the accompanying drawings, but not as limit of the invention
It is fixed.
Embodiment
According to Fig. 1, multi-rotor unmanned aerial vehicle body system as " under front right " coordinate system (i.e. the direction of head) is set, by phase
Machine is connected as a rigid body with body.The x of camera coordinates system, y, z coincide with three directions of body system respectively.When p is t
Characteristic point P coordinates in the picture under world coordinate system are carved, p ' is t+k moment characteristic points P coordinates in the picture.Thus, may be used
It is with the light stream for deriving image characteristic point
Wherein [u v]TIt is the T moment to the light stream of T+k moment characteristic point P both directions under image coordinate system, it is asked
Lead the pixel speed that can obtain characteristic point in both direction：
The imaging model of combining camera after pixel speed is obtained, you can launch plan is as optical flow field and carrier movement field
Between relation：
F is the focal length of camera in formula, and Z is the flying height of carrier, ωx,ωyBe carrier in x, the angle speed of y both directions
Degree, [Tx Ty]TCarrier speed in the horizontal direction；
It is the flow chart of ORB feature-point optical flow algorithms according to Fig. 2, its main step is：
1st, camera is in t image It, and carried out greyscale transformation and obtain gray-scale map Gt；
2nd, using ORB feature point detection algorithms, G is obtainedtLocal feature point coordinates pt；
3rd, collection t+k time charts are as It+k, gray-scale map G is obtained after carrying out gradation conversiont+k；
4th, by Gt, Gt+k, ptAs input, G is estimated using pyramid LK optical flow algorithmst+kFeature point coordinates p on imaget+k；
5th, by Gt+k,Gt,pt+kAs input, using step in 4, G is calculatedtCharacteristics of image point coordinates
6th, calculateWith ptThe distance between d and it is averaged, the point that will be greater than average is considered that tracking error is larger
Point, is rejected；
7th, using pt+kWith ptSolve accurate light stream；
Fig. 3 is the light stream/inertia combined navigation algorithm structure schematic diagram, its main step of distinguished point based pixel speed
For：
1st, navigation system is defined as east northeast ground coordinate system, the carrier that the measurement of Airborne Inertial navigation system is obtained between the k moment
The speed of horizontal direction is [v 'bx,v′by]T：
2nd, the feature spot speed calculated under the image coordinate system derived by inertial navigation system is：
Wherein, f is the focal length of camera, and Z is camera height from the ground, the i.e. flying height of carrier, [ω 'x,ω′y]TFor
The measured value of x, y both direction gyroscope.Therefore, the characteristic point position under the image coordinate system derived by inertial navigation system
Move and be：
3rd, the angle error of each feature-point optical flow is calculated
It is thereinThe ideal value of ith feature point light stream is represented,Represent that ith feature point uses LK gold
Light flow valuve estimated by word tower optical flow algorithm, k is the frame number being separated by between two images.During due to interval between the frame of camera two
Between it is shorter, the error of inertial navigation information is smaller, using inertial navigation information solve key point displacement as light stream ideal value, by angle error
Larger point is rejected, and can be effectively reduced image and the light stream estimation error that local interference is caused is occurred.
4th, the mathematical platform error angle of strapdown inertial navigation system is chosenThree direction velocity error [δ Vn,δ
Ve,δVd]T, the site error [δ L, δ λ, δ H] in longitude and latitude three directions highTAnd the error of gyro and accelerometer constitutes 15 and ties up shape
Amount：
The noise matrix W of system is set to：
Wherein,It is the white noise of the measurement of gyroscope,It is acceleration
The white noise of measurement；
5th, the velocity error with characteristic point under pixel coordinate system is as measurement
Known by formula (2), the speed contained under body system in the image characteristic point velocity information calculated by inertial navigation information
Error, because velocity error contained in quantity of state is the lower error of navigation system, transformational relation therebetween can be approximately：
6th, (9) are combined, (10) two formulas can build measurement equation as follows：
Wherein [nx,ny]TTo measure noise, the setting of measurement matrix is as follows：
By above-mentioned the integrated navigation system outgoing position and speed that calculate unmanned vehicle, control is reached with this
The effect of the state of flight of unmanned vehicle.
In sum, the air navigation aid based on light stream of the application design, using calculating characteristic point conductWith pIBetween
The point of optical flow algorithm tracking error is rejected apart from the mode of d, the accuracy of optical flow algorithm can be effectively lifted；Using inertia
The characteristic point pixel speed that navigation system is released corrects optical flow velocity information, can effectively strengthen optical flow algorithm resistance image
The ability of local interference；Using the light stream/inertia combined navigation algorithm of distinguished point based pixel speed compared to simple by used
Property navigation scheme accuracy and reliability have lifting, can be as multi-rotor unmanned aerial vehicle without the standby of GPS environment independent navigation
Select scheme.
By explanation and accompanying drawing, the exemplary embodiments of the ad hoc structure of specific embodiment are given, based on essence of the invention
God, can also make other conversions.Although foregoing invention proposes existing preferred embodiment, however, these contents are not intended as
Limitation.
For a person skilled in the art, after reading described above, various changes and modifications undoubtedly will be evident that.
Therefore, appending claims should regard the whole variations and modifications for covering true intention of the invention and scope as.In power
Any and all scope and content of equal value, are all considered as still belonging to the intent and scope of the invention in the range of sharp claim.
Claims (8)
1. a kind of air navigation aid based on light stream, it is characterised in that including：
Coordinate system under the front right of aircraft head is set up, light flow sensor gathers some images, and detects every described image
The coordinate set of middle characteristic point, the characteristic point for selecting wantonly two described images is contrasted, and filters out same characteristic features point in different figures
As upper coordinate set, geometric distance of the same characteristic features point between coordinate points on different images is calculated, characteristic point is calculated with this
Flying speed on image；
The navigation for defining the aircraft is coordinate, and records the horizontal flight speed of the aircraft；
Flying speed and the horizontal flight speed by characteristic point on image carry out fusion treatment, to obtain the aircraft
Navigation information.
2. method according to claim 1, it is characterised in that methods described also includes：
The key point displacement as the ideal value of light stream is solved using inertial navigation information, with reference to the light flow valuve meter that the characteristic point is solved
The angle error of each feature-point optical flow is calculated, the characteristic point small so as to filter out angle error.
3. method according to claim 2, it is characterised in that methods described also includes：
Some error state amounts are chosen, state equation is set up, with velocity error of the characteristic point under pixel coordinate system as measurement,
Measurement equation is set up, the flying speed of the aircraft is estimated using Kalman filtering recurrence equation.
4. method according to claim 1, it is characterised in that characteristic point is calculated in figure using LK pyramid optical flow algorithms
As upper flying speed.
5. method according to claim 4, it is characterised in that methods described also includes：
LK pyramid optical flow algorithms are corrected using the feature spot speed of inertial navigation system release calculate characteristic point in image
On flying speed.
6. method according to claim 3, it is characterised in that the navigation information includes the deflection of the aircraft,
Gyro error and position coordinates.
7. method according to claim 3, it is characterised in that the error state amount of selection includes strapdown inertial navigation system
Mathematical platform error angle, the velocity error of the aircraft, the site error of the flight, gyro error and accelerometer
Error.
8. method according to claim 1, it is characterised in that in estimating an image using LK pyramid optical flow algorithms
Coordinate set of the characteristic point in another image.
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| CN107798691A (en) * | 2017-08-30 | 2018-03-13 | 西北工业大学 | A kind of unmanned plane independent landing terrestrial reference real-time detecting and tracking method of view-based access control model |
| CN108007474A (en) * | 2017-08-31 | 2018-05-08 | 哈尔滨工业大学 | A kind of unmanned vehicle independent positioning and pose alignment technique based on land marking |
| CN108298101A (en) * | 2017-12-25 | 2018-07-20 | 上海歌尔泰克机器人有限公司 | The control method and device of holder rotation, unmanned plane |
| CN109062238A (en) * | 2018-09-19 | 2018-12-21 | 张洋 | Control the device of unmanned plane hovering |
| CN109407103A (en) * | 2018-09-07 | 2019-03-01 | 昆明理工大学 | A kind of unmanned plane greasy weather obstacle recognition system and its recognition methods |
| CN109459025A (en) * | 2018-11-08 | 2019-03-12 | 中北大学 | A kind of class brain air navigation aid based on light stream UWB combination |
| CN109975844A (en) * | 2019-03-25 | 2019-07-05 | 浙江大学 | A kind of anti-bleach-out process of GPS signal based on optical flow method |
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| CN115790574A (en) * | 2023-02-14 | 2023-03-14 | 飞联智航(北京)科技有限公司 | Unmanned aerial vehicle optical flow positioning method and device and unmanned aerial vehicle |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798691A (en) * | 2017-08-30 | 2018-03-13 | 西北工业大学 | A kind of unmanned plane independent landing terrestrial reference real-time detecting and tracking method of view-based access control model |
| CN107798691B (en) * | 2017-08-30 | 2019-02-19 | 西北工业大学 | A kind of unmanned plane independent landing terrestrial reference real-time detection tracking of view-based access control model |
| CN108007474A (en) * | 2017-08-31 | 2018-05-08 | 哈尔滨工业大学 | A kind of unmanned vehicle independent positioning and pose alignment technique based on land marking |
| CN108298101A (en) * | 2017-12-25 | 2018-07-20 | 上海歌尔泰克机器人有限公司 | The control method and device of holder rotation, unmanned plane |
| CN109407103A (en) * | 2018-09-07 | 2019-03-01 | 昆明理工大学 | A kind of unmanned plane greasy weather obstacle recognition system and its recognition methods |
| CN109062238A (en) * | 2018-09-19 | 2018-12-21 | 张洋 | Control the device of unmanned plane hovering |
| CN109459025A (en) * | 2018-11-08 | 2019-03-12 | 中北大学 | A kind of class brain air navigation aid based on light stream UWB combination |
| CN109975844A (en) * | 2019-03-25 | 2019-07-05 | 浙江大学 | A kind of anti-bleach-out process of GPS signal based on optical flow method |
| CN109975844B (en) * | 2019-03-25 | 2020-11-24 | 浙江大学 | GPS signal anti-drift method based on optical flow method |
| CN111024067A (en) * | 2019-12-17 | 2020-04-17 | 国汽(北京)智能网联汽车研究院有限公司 | Information processing method, device and equipment and computer storage medium |
| CN111024067B (en) * | 2019-12-17 | 2021-09-28 | 国汽(北京)智能网联汽车研究院有限公司 | Information processing method, device and equipment and computer storage medium |
| CN112985388A (en) * | 2021-02-08 | 2021-06-18 | 福州大学 | Combined navigation method and system based on large-displacement optical flow method |
| CN115790574A (en) * | 2023-02-14 | 2023-03-14 | 飞联智航(北京)科技有限公司 | Unmanned aerial vehicle optical flow positioning method and device and unmanned aerial vehicle |
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Application publication date: 20170609 |