CN109269511A - The Curve Matching vision navigation method that circumstances not known lower planet lands - Google Patents
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Abstract
The Curve Matching vision navigation method that circumstances not known lower planet disclosed by the invention lands, belongs to field of deep space exploration.Implementation method of the present invention is as follows: combining inertia measurement information to establish lander kinematics model first, then the measurement model based on interframe Curve Matching is established using acquisition sequence image during lander decline, real-time estimation is carried out finally by absolute movement state of the Kalman filtering algorithm to lander, and then realize the Curve Matching vision guided navigation that circumstances not known lower planet lands, improve navigation system precision, guarantee the stability of navigation system, guarantees that lander precise and safety is landed.The present invention do not need priori cartographic information can the absolute movement state to lander estimate, improve navigation system stability.The present invention is applicable not only in planetary landing task, is also applied for small feature loss landing task.
Description
Technical field
The present invention relates to the Curve Matching vision navigation methods that circumstances not known lower planet lands, and belong to deep space exploration technology neck
Domain.
Background technique
Landing detection and sampling return are the main direction of development of the following deep space exploration.Following small feature loss and mars exploration
Task requires that detector has the ability landed in the higher region pinpoint of scientific value.And target celestial body is apart from the earth
Farther out, communication time-delay is serious, and this requires detectors to have the ability of independent navigation.Meanwhile the priori letter of target celestial body environment
Ceasing the uncertainty such as deficiency, environmental perturbation, more stringent requirements are proposed to autonomous navigation system.
The air navigation aid based on Inertial Measurement Unit IMU boat position recursion, but this method are mainly used in landing mission at present
Initial deviation can not be modified, and there are random drifts and error for Inertial Measurement Unit, with the accumulated error meeting of time
It gradually spreads, it is difficult to meet the requirement of high-precision navigation.For deficiency existing for above-mentioned air navigation aid, it is based on celestial surface feature
The autonomous vision navigation method of image information is increasingly becoming the emphasis of scholars' research.Based on celestial surface characteristic image information
Autonomous vision navigation method be broadly divided into two classes: the first kind is air navigation aid known to celestial surface road sign feature locations；The
Two classes are the unknown air navigation aids of celestial surface road sign feature locations.But in the absence of the priori cartographic information, i.e. celestial surface
When road sign feature locations can not obtain, first kind method will be no longer applicable in.It leads based on celestial surface road sign feature locations are unknown
Boat method is divided into the air navigation aid based on Feature Points Matching and leading based on Curve Matching according to the different of road sign characteristics of image again
Boat method.But characteristic point visual line measurement information is vulnerable to influence of noise.In consideration of it, it is necessary to move for lander under circumstances not known
This problem of state estimation designs a kind of fast and effeciently lander vision navigation method, guarantees that lander precise and safety is landed.
Summary of the invention
In order to solve the problems, such as interspace landing independent navigation under circumstances not known, the object of the present invention is to provide a kind of unknown rings
The Curve Matching vision navigation method that border lower planet lands, in conjunction with inertia measurement use of information Kalman filtering algorithm to lander
Absolute movement state carry out real-time estimation, realize circumstances not known under interspace landing independent navigation, guarantee lander precise and safety
It lands.
The purpose of the present invention is what is be achieved through the following technical solutions.
The Curve Matching vision navigation method that circumstances not known lower planet disclosed by the invention lands combines inertia measurement first
Information establishes lander kinematics model, is then established using acquisition sequence image during lander decline and is based on interframe curve
Matched measurement model carries out real-time estimation finally by absolute movement state of the Kalman filtering algorithm to lander, in turn
It realizes the Curve Matching vision guided navigation that circumstances not known lower planet lands, improves navigation system precision, guarantee the stabilization of navigation system
Property.
The Curve Matching vision navigation method that circumstances not known lower planet disclosed by the invention lands, includes the following steps:
Step 1: establishing lander kinematics model.
In order to describe the skyborne position of lander and posture and its phase between the visual signature of target celestial body surface
To geometrical relationship, and the equation of motion of the lander in coherent reference system is defined, is firstly introduced into following relative coordinate system: landing point
Coordinate system { L }, navigate camera body coordinate system { C } and lander body coordinate system { B }.The Position and orientation parameters of lander are all
It is described in landing point coordinate system.Lander body coordinate system with navigation camera coordinates system is overlapped, i.e., optical navigation camera and
The installation matrix of land device is unit battle array.Do not consider that planetary rotation influences, establishes lander landing kinematics using IMU metrical information
Equation are as follows:
Wherein Inertia information acceleration aimuAnd angular velocity omegaimuMeasurement model is
Wherein,LR andLV respectively indicates position and speed of the lander under landing point coordinate system；For attitude quaternion,It is transition matrix of the landing point coordinate system to lander body coordinate system, is abbreviated as C (q)；LG is gravitational acceleration, ng
For gravitational acceleration disturbance；baAnd bωRespectively indicate accelerometer and gyroscope zero bias；naAnd nωIt is accelerometer and gyro respectively
Instrument measures noise；nwaAnd nwωRespectively accelerometer and gyroscope deviation noise；LA expression acts on lander in addition to gravitation
Resultant force generate acceleration；Bω indicates that lander is fast with respect to the angle of rotation of landing point coordinate system under lander body coordinate system
Degree；For any angular velocity omega=[ωx ωy ωz]T, Ω () is defined as
Step 2: the measurement model based on interframe Curve Matching is established, for realizing the update of lander motion state.
Touchdown area almost plane, crater are expressed as at landing point coordinate system { L }
WhereinFor any point on aerolite pit edge under landing point coordinate system.
Any point using national forest park in Xiaokeng, in landing planeLX=[Lx Ly Lz]TPicture in the i-th width decline image
Point u=[u v]TFor
Wherein σ is non-zero constant,F is camera focus, Ri=C (qi)。
Since touchdown area is approximately plane, thenLZ=0, formula (4) are written as
Wherein
WhereinIndicate lander location components under landing point coordinate system.
Crater is expressed as in the i-th width decline image
Then by formula (3), formula (5) and formula (8), crater is obtained as curve EiFor
Therefore decline the measurement of j-th of crater in image in the i-th widthIt is expressed as
WhereinFor crater boundary curve parameter,It makes an uproar for measurement
Sound, vech () indicate the vectorization form of symmetrical matrix, and vec () indicates the vectorization form of Arbitrary Matrix, and matrix Η is
Transition matrix between vech () and vec (),
Since crater absolute location information Q is unknown, formula (10) cannot be directly used to state estimation.
Crater Q is observed in continuous two width decline image, and lander is in t1And t2The crater picture of moment observation is bent
Line is respectively
It is obtained by formula (13) and formula (14)
Crater is in t2Moment measurement modelFor
WhereinTo measure noise,
Step 3: the lander kinematics model and step 2 established in conjunction with step 1 establish based on unknown curvilinear characteristic
Measurement model carries out real-time estimation using absolute movement state of the Kalman filtering algorithm to lander, realizes under circumstances not known
Interspace landing independent navigation guarantees that lander precise and safety is landed.
For the nonlinear problem based on unknown curvilinear characteristic measurement model that settlement steps to deal 2 is established, karr described in step 3
The graceful preferred Unscented kalman filtering algorithm of filtering algorithm.
When step 3 selects Unscented kalman filtering algorithm, step 3 concrete methods of realizing is as follows:
Step 3.1: based in step 1 using inertia measurement information establish lander kinematics model obtain lander state
Equation is
WhereinThe differential form of expression state,LrcWithBe illustrated respectively in previous imaging moment lander position and
Attitude quaternion, w indicate state-noise, Qk=E [wwT] it is state-noise covariance matrix.
Step 3.2: based on the measurement model based on unknown curvilinear characteristic established in step 2, obtaining measurement model isFor
zk=h (vech (Ek))+vk (19)
Wherein k=1,2,3 ...,vkTo survey
Measure noise, Rk=E { vk(vk)TIt is measurement noise covariance matrix.
Step 3.3: carrying out real-time estimation using absolute movement state of the Unscented kalman filtering algorithm to lander, realize
Interspace landing independent navigation under circumstances not known guarantees that lander precise and safety is landed.
Step 3.3.1: lander motion state is initialized
Wherein x0WithRespectively indicate lander original state and its mean value, P0Indicate lander original state covariance square
Battle array.
Step 3.3.2: lander motion state sigma sampled point is calculated
Step 3.3.3: lander motion state time propagation equation is established using formula (18).
Step 3.3.4: lander motion state measurement updaue equation is established using formula (19).
N indicates state dimension in above-mentioned formula,
Wherein 10-4≤ α≤1, κ=3-n, β=2
Step 3.3.5: the real-time absolute movement state of lander is obtained using formula (22) and (23)WithRealize
Interspace landing independent navigation under circumstances not known guarantees that lander precise and safety is landed.
The utility model has the advantages that
1, the Curve Matching vision navigation method that circumstances not known lower planet disclosed by the invention lands provides and a kind of utilizes frame
The matched lander vision navigation method of half interval contour, in conjunction with inertia measurement use of information Unscented kalman filtering algorithm to lander
Absolute movement state carry out real-time estimation, can guarantee the real-time of navigation system.
2, the Curve Matching vision navigation method that circumstances not known lower planet disclosed by the invention lands, utilizes interframe curve
With as measurement model, therefore, there is no need to priori cartographic information can the absolute movement state to lander estimate, improve
Navigation system stability.
3, since planet and small feature loss surface have curvilinear characteristic, what circumstances not known lower planet disclosed by the invention landed
Curve Matching vision navigation method is applicable not only in planetary landing task, is also applied for small feature loss landing task.
Detailed description of the invention
Fig. 1 is the Curve Matching vision navigation method flow chart that circumstances not known lower planet lands；
Fig. 2 is that lander position estimation error and its 3 σ filter performance criterias are poor；
Fig. 3 is that lander speed estimation error and its 3 σ filter performance criterias are poor；
Fig. 4 is that lander attitude estimation error and its 3 σ filter performance criterias are poor.
Specific embodiment
Objects and advantages in order to better illustrate the present invention, with reference to the accompanying drawing with example to the contents of the present invention do into
One step explanation.
As shown in Figure 1, the Curve Matching vision navigation method that circumstances not known lower planet disclosed in this example lands, specific to walk
It is rapid as follows:
Step 1: establishing lander kinematics model.
In order to describe the skyborne position of lander and posture and its phase between the visual signature of target celestial body surface
To geometrical relationship, and the equation of motion of the lander in coherent reference system is defined, is firstly introduced into following relative coordinate system: landing point
Coordinate system { L }, navigate camera body coordinate system { C } and lander body coordinate system { B }.The Position and orientation parameters of lander are all
It is described in landing point coordinate system.Lander body coordinate system with navigation camera coordinates system is overlapped, i.e., optical navigation camera and
The installation matrix of land device is unit battle array.Do not consider that planetary rotation influences, establishes lander landing kinematics using IMU metrical information
Equation are as follows:
Wherein Inertia information acceleration aimuAnd angular velocity omegaimuMeasurement model is
Wherein,LR andLV respectively indicates position and speed of the lander under landing point coordinate system；For attitude quaternion,It is transition matrix of the landing point coordinate system to lander body coordinate system, is abbreviated as C (q)；LG is gravitational acceleration, ng
For gravitational acceleration disturbance；baAnd bωRespectively indicate accelerometer and gyroscope zero bias；naAnd nωIt is accelerometer and gyro respectively
Instrument measures noise；nwaAnd nwωRespectively accelerometer and gyroscope deviation noise；LA expression acts on lander in addition to gravitation
Resultant force generate acceleration；Bω indicates that lander is fast with respect to the angle of rotation of landing point coordinate system under lander body coordinate system
Degree；For any angular velocity omega=[ωx ωy ωz]T, Ω () is defined as
Step 2: the measurement model based on interframe Curve Matching is established, for realizing the update of lander motion state.
Touchdown area almost plane, crater are expressed as at landing point coordinate system { L }
Wherein1]TFor any point on aerolite pit edge under landing point coordinate system.
Any point using national forest park in Xiaokeng, in landing planeLX=[Lx Ly Lz]TPicture in the i-th width decline image
Point u=[u v]TFor
Wherein σ is non-zero constant,F is camera focus, Ri=C (qi)。
Since touchdown area is approximately plane, thenLZ=0, formula (28) are written as
Wherein
WhereinIndicate lander location components under landing point coordinate system.
Crater is expressed as in the i-th width decline image
Then by formula (27), formula (29) and formula (32), crater is obtained as curve EiFor
Therefore decline the measurement of a crater in image in the i-th widthIt is expressed as
WhereinFor crater boundary curve parameter,It makes an uproar for measurement
Sound, vech () indicate the vectorization form of symmetrical matrix, and vec () indicates the vectorization form of Arbitrary Matrix, and matrix Η is
Transition matrix between vech () and vec (),
Since crater absolute location information Q is unknown, formula (34) cannot be directly used to state estimation.
Crater Q is observed in continuous two width decline image, and lander is in t1And t2The crater picture of moment observation is bent
Line is respectively
It is obtained by formula (37) and formula (38)
Crater is in t2Moment measurement modelFor
WhereinTo measure noise,
Step 3: the lander kinematics model and step 2 established in conjunction with step 1 establish based on unknown curvilinear characteristic
Measurement model carries out real-time estimation using absolute movement state of the Kalman filtering algorithm to lander, realizes under circumstances not known
Interspace landing independent navigation guarantees that lander precise and safety is landed.
For the nonlinear problem for the measurement model based on unknown curvilinear characteristic that settlement steps to deal 2 is established, card described in step 3
The preferred Unscented kalman filtering algorithm of Kalman Filtering algorithm.
When step 3 selects Unscented kalman filtering algorithm, step 3 concrete methods of realizing is as follows:
Step 3.1: based in step 1 using inertia measurement information establish lander kinematics model obtain lander state
Equation is
WhereinThe differential form of expression state,LrcWithIt is illustrated respectively in previous imaging moment lander
Position and attitude quaternion, w indicate state-noise, Qk=E [wwT] it is state-noise covariance matrix.
Step 3.2: based on the measurement model based on unknown curvilinear characteristic established in step 2, obtaining measurement model isFor
zk=h (vech (Ek))+vk (43)
Wherein k=1,2,3 ...,vkTo survey
Measure noise, Rk=E { vk(vk)TIt is measurement noise covariance matrix.
Step 3.3: carrying out real-time estimation using absolute movement state of the Unscented kalman filtering algorithm to lander, realize
Interspace landing independent navigation under circumstances not known guarantees that lander precise and safety is landed.
Step 3.3.1: lander motion state is initialized
Wherein x0WithRespectively indicate lander original state and its mean value, P0Indicate lander original state covariance square
Battle array.
Step 3.3.2: lander motion state sigma sampled point is calculated
Step 3.3.3: lander motion state time propagation equation is established using formula (42).
Step 3.3.4: lander motion state measurement updaue equation is established using formula (43).
N expression state dimension in above-mentioned formula, n=23,
Wherein 10-4≤ α≤1, κ=3-n, β=2
Step 3.3.5: the real-time absolute movement state of lander is obtained using formula (46) and (47)WithIt is i.e. real
Existing circumstances not known lower planet landing independent navigation guarantees that lander precise and safety is landed.
Mathematical simulation simulating, verifying has been carried out using a curve using Mars landing detection as background under Matlab environment.
If emulation terminates when lander reaches above landing point at 100m, landing times 120s.45 ° of viewing field of camera angle of navigation, focal length
14.6mm measures 1 pixel of noise.IMU uses LN-200, sample frequency 50HZ.Lander original state is as shown in table 1, position
All directions initial error is 500m, and speed all directions initial error is 1m/s, and posture all directions initial error is 1 °.Process noise
Covariance Q is
Q=diag ([2.4 × 10-13I 2.4×10-13I 2.5×10-7I 1.2×10-7I 1.2×10-8I])
1 simulation parameter of table
Above-described specific descriptions have carried out further specifically the purpose of invention, technical scheme and beneficial effects
It is bright, it should be understood that the above is only a specific embodiment of the present invention, the protection model being not intended to limit the present invention
It encloses, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in the present invention
Protection scope within.
Claims (6)
1. the Curve Matching vision navigation method that circumstances not known lower planet lands, characterized by the following steps:
Step 1: establishing lander kinematics model；
Step 2: the measurement model based on interframe Curve Matching is established, for realizing the update of lander motion state；
Step 3: the measurement based on unknown curvilinear characteristic that the lander kinematics model and step 2 established in conjunction with step 1 are established
Model carries out real-time estimation using absolute movement state of the Kalman filtering algorithm to lander, realizes interspace under circumstances not known
Landing independent navigation guarantees that lander precise and safety is landed.
2. the Curve Matching vision navigation method that circumstances not known lower planet as described in claim 1 lands, it is characterised in that: step
Rapid 1 concrete methods of realizing is,
In order to describe the skyborne position of lander and posture and it is relatively several between the visual signature of target celestial body surface
What relationship, and the equation of motion of the lander in coherent reference system is defined, it is firstly introduced into following relative coordinate system: landing point coordinate
It is { L } navigate camera body coordinate system { C } and lander body coordinate system { B }；The Position and orientation parameters of lander all
It is described in the point coordinate system of land；Lander body coordinate system is overlapped with navigation camera coordinates system, i.e. optical navigation camera and lander
Installation matrix be unit battle array；Do not consider that planetary rotation influences, establishes lander landing kinematical equation using IMU metrical information
Are as follows:
Wherein Inertia information acceleration aimuAnd angular velocity omegaimuMeasurement model is
Wherein,LR andLV respectively indicates position and speed of the lander under landing point coordinate system；For attitude quaternion,It is transition matrix of the landing point coordinate system to lander body coordinate system, is abbreviated as C (q)；LG is gravitational acceleration, ng
For gravitational acceleration disturbance；baAnd bωRespectively indicate accelerometer and gyroscope zero bias；naAnd nωIt is accelerometer and gyro respectively
Instrument measures noise；nwaAnd nwωRespectively accelerometer and gyroscope deviation noise；LA expression acts on lander in addition to gravitation
Resultant force generate acceleration；Bω indicates that lander is fast with respect to the angle of rotation of landing point coordinate system under lander body coordinate system
Degree；For any angular velocity omega=[ωx ωy ωz]T, Ω () is defined as
3. the Curve Matching vision navigation method that circumstances not known lower planet as claimed in claim 2 lands, it is characterised in that: step
Rapid 2 concrete methods of realizing is,
Touchdown area almost plane, crater are expressed as at landing point coordinate system { L }
WhereinFor any point on aerolite pit edge under landing point coordinate system；
Any point using national forest park in Xiaokeng, in landing planeLX=[Lx Ly Lz]TPicture point u in the i-th width decline image
=[u v]TFor
Wherein σ is non-zero constant,F is camera focus, Ri=C (qi)；
Since touchdown area is approximately plane, thenLZ=0, formula (4) are written as
Wherein
WhereinLri x,Lri y,Lri zIndicate lander location components under landing point coordinate system；
Crater is expressed as in the i-th width decline image
Then by formula (3), formula (5) and formula (8), crater is obtained as curve EiFor
Therefore decline the measurement of j-th of crater in image in the i-th widthIt is expressed as
WhereinFor crater boundary curve parameter,To measure noise,
Vech () indicates the vectorization form of symmetrical matrix, and vec () indicates the vectorization form of Arbitrary Matrix, and matrix Η is vech
Transition matrix between () and vec (),
Since crater absolute location information Q is unknown, formula (10) cannot be directly used to state estimation；
Crater Q is observed in continuous two width decline image, and lander is in t1And t2The crater of moment observation is as curve point
It is not
It is obtained by formula (13) and formula (14)
Crater is in t2Moment measurement modelFor
WhereinTo measure noise,
4. the Curve Matching vision navigation method that circumstances not known lower planet as claimed in claim 3 lands, it is characterised in that: be
The nonlinear problem based on unknown curvilinear characteristic measurement model that settlement steps to deal 2 is established, Kalman filtering algorithm described in step 3
Select Unscented kalman filtering algorithm.
5. the Curve Matching vision navigation method that circumstances not known lower planet as claimed in claim 4 lands, it is characterised in that: when
When step 3 selects Unscented kalman filtering algorithm, step 3 concrete methods of realizing is as follows,
Step 3.1: based in step 1 using inertia measurement information establish lander kinematics model obtain lander state equation
For
WhereinThe differential form of expression state,LrcWithIt is illustrated respectively in position and the posture four of previous imaging moment lander
First number, w indicate state-noise, Qk=E [wwT] it is state-noise covariance matrix；
Step 3.2: based on the measurement model based on unknown curvilinear characteristic established in step 2, obtaining measurement model isFor
zk=h (vech (Ek))+vk (19)
Wherein k=1,2,3,vkTo measure noise,
Rk=E { vk(vk)TIt is measurement noise covariance matrix；
Step 3.3: carrying out real-time estimation using absolute movement state of the Unscented kalman filtering algorithm to lander, realize unknown
Interspace landing independent navigation under environment guarantees that lander precise and safety is landed.
6. the Curve Matching vision navigation method that circumstances not known lower planet as claimed in claim 5 lands, it is characterised in that: step
Rapid 3.3 concrete methods of realizing is,
Step 3.3.1: lander motion state is initialized
Wherein x0WithRespectively indicate lander original state and its mean value, P0Indicate lander original state covariance matrix；
Step 3.3.2: lander motion state sigma sampled point is calculated
Step 3.3.3: lander motion state time propagation equation is established using formula (18)；
Step 3.3.4: lander motion state measurement updaue equation is established using formula (19)；
N indicates state dimension in above-mentioned formula,
Wherein 10-4≤ α≤1, κ=3-n, β=2
Step 3.3.5: the real-time absolute movement state of lander is obtained using formula (22) and (23)WithIt realizes unknown
Interspace landing independent navigation under environment guarantees that lander precise and safety is landed.
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| CN110702122B (en) * | 2019-10-22 | 2021-03-30 | 北京理工大学 | Comprehensive optimization method for autonomous optical navigation characteristics of extraterrestrial celestial body landing |
| CN111652896A (en) * | 2020-05-29 | 2020-09-11 | 北京理工大学 | Inertial navigation auxiliary meteorite crater coarse-to-fine detection method |
| CN111652896B (en) * | 2020-05-29 | 2023-06-23 | 北京理工大学 | Method for detecting coarse-fine meteorite crater by inertial navigation assistance |
| CN112066999A (en) * | 2020-09-16 | 2020-12-11 | 北京控制工程研究所 | Method for determining gravity direction in real time in planet landing process |
| CN113022898A (en) * | 2021-02-18 | 2021-06-25 | 北京理工大学 | State estimation method for flexible attachment system in weak gravity environment |
| CN113022898B (en) * | 2021-02-18 | 2022-05-17 | 北京理工大学 | State estimation method for flexible attachment system in weak gravity environment |
| CN114485678A (en) * | 2021-12-31 | 2022-05-13 | 上海航天控制技术研究所 | Heaven and earth integrated lunar surface landing navigation method |
| CN114485678B (en) * | 2021-12-31 | 2023-09-12 | 上海航天控制技术研究所 | Navigation method for land, ground and lunar landing |
| CN114577205A (en) * | 2022-02-10 | 2022-06-03 | 北京空间飞行器总体设计部 | Planet soft landing autonomous navigation landmark optimization method based on sequence images |
| CN114577205B (en) * | 2022-02-10 | 2023-06-06 | 北京空间飞行器总体设计部 | Satellite soft landing autonomous navigation landmark optimization method based on sequence images |
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