CN110308459B - Model-independent non-cooperative satellite relative pose measurement method - Google Patents

Model-independent non-cooperative satellite relative pose measurement method Download PDF

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CN110308459B
CN110308459B CN201910582131.8A CN201910582131A CN110308459B CN 110308459 B CN110308459 B CN 110308459B CN 201910582131 A CN201910582131 A CN 201910582131A CN 110308459 B CN110308459 B CN 110308459B
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straight line
depth map
line segment
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CN110308459A (en
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赵高鹏
范佳杰
薄煜明
王超尘
韦雷
黄皓冉
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Nanjing University of Science and Technology
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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
    • G01C21/24—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for cosmonautical navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4808—Evaluating distance, position or velocity data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a model-independent non-cooperative Wei Xingwei pose measurement method, which aims at a completely non-cooperative satellite with an unknown model, utilizes a point cloud and a depth map obtained by a laser imaging radar to construct a target coordinate system of the non-cooperative satellite, and utilizes the target coordinate system and the laser imaging radar coordinate system to calculate to obtain a relative pose value. The method has the advantages that the method is suitable for non-cooperative satellites with unknown models, and the construction of the target coordinate system is completed by only using a linear frame of a typical single circular part and body on the satellite. The method can stably construct the target coordinate system, finish the measurement of the relative pose and has high precision.

Description

Model-independent non-cooperative satellite relative pose measurement method
Technical Field
The invention belongs to the technical field of space autonomous navigation, and particularly relates to a model-independent non-cooperative Wei Xingwei pose measurement method.
Background
With the development of aerospace technology and the increasing frequency of space activities, more and more failed spacecrafts (such as failed satellites and space debris) can cause space orbit crowding, influence the normal operation of the on-orbit spacecrafts, and have important significance for guaranteeing the normal space activities by on-orbit maintenance or cleaning and removal. How to realize accurate measurement of close-range relative position and relative attitude parameters (abbreviated as relative attitude) is one of the core problems to be solved in realizing autonomous proximity and relative navigation, and for a completely non-cooperative satellite with unknown model, due to lack of priori information and auxiliary manual identification, two measurement modes exist when the relative attitude is measured in the autonomous proximity process: one way is to build a target spacecraft model, but the method needs to track the spacecraft to fly around the target spacecraft on a safe distance, and builds the target model through an on-orbit three-dimensional reconstruction method, which has higher requirements on the fly-around control, the pointing control, the on-orbit three-dimensional reconstruction and the like; and the other is to directly calculate the relative pose based on the measurement data of the sensor, directly extract the characteristics of the measurement data of the sensor without depending on a target model, and construct a target coordinate system in real time so as to obtain the relative pose. The invention patent (application number CN201210066492, publication number CN 102759358A) provides a method for constructing a target coordinate system of a failure satellite, wherein the construction method needs to calculate the inertial pointing direction of a rotating shaft and use a reference star to construct the target coordinate system, and the method does not fully utilize the structural characteristics of a non-cooperative satellite and is complex.
The non-scanning laser imaging radar can acquire three-dimensional measurement data of a target in real time, comprises three-dimensional point cloud, two-dimensional depth map and intensity map data, has the advantages of strong background stray light inhibition capability, long detection distance, no motion blur, high frame rate and the like, and can meet real-time measurement.
Disclosure of Invention
The invention aims to provide a model-independent relative pose measurement method of a non-cooperative satellite, which utilizes measurement data of a non-scanning laser imaging radar to realize the relative pose measurement of a completely non-cooperative satellite.
The technical solution for realizing the purpose of the invention is as follows: a method for measuring the relative pose of a non-cooperative satellite independent of a model comprises the following steps:
step (1), acquiring a target point cloud C of a kth frame by using a laser imaging radar k Depth map D corresponding to the same k ；
Step (2), detecting straight line segments of a kth frame depth map;
step (3), when k=1, selecting the longest straight line segment l of the body frame in the 1 st frame depth map k . When k > 1, find the straight line segment l in the kth frame depth map k-1 Corresponding straight line segment l k Finding a corresponding point cloud point set p according to the corresponding relation between the point cloud and the depth map k Fitting out straight line segment vector L of three-dimensional space by utilizing random sampling consistency algorithm 1 ；
Step (4), detecting an elliptical arc of a kth frame depth map;
step (5), when k=1, selecting an elliptical arc r formed by non-cooperative satellite circular features in the 1 st frame depth map 1 . When k > 1, find the elliptical arc r in the kth frame depth map k-1 Corresponding elliptical arc r k Finding a corresponding point cloud point set q according to the corresponding relation between the point cloud and the depth map k By utilizing a random sampling consistency algorithm, a point set q is obtained k The normal vector of the space plane P is L 2 ；
Step (6), according to the elliptic arc r k Solving an origin Q of a target coordinate system according to the circle center and the plane P of the target coordinate system;
step (7), according to L 1 、L 2 Q, establishing a target coordinate system;
and (8) calculating the translation amount and the rotation amount.
Compared with the prior art, the invention has the remarkable advantages that: (1) The method is suitable for non-cooperative satellites with unknown models, and the construction of the target coordinate system is completed by only using one linear frame of a typical single circular part and body on the satellite. (2) The method can stably construct the target coordinate system, finish the measurement of the relative pose, and has the characteristic of high precision.
Drawings
FIG. 1 is a flow chart of a method of model independent, non-cooperative satellite relative pose measurement of the present invention.
FIG. 2 is a ray schematic diagram corresponding to the origin of the target coordinate system.
FIG. 3 is a schematic illustration of three-dimensional object coordinate system origin calculation.
Fig. 4 is a diagram of an effect of constructing a simulated point cloud target coordinate system in an embodiment of the present invention.
FIG. 5 is a graph of rotation amount for simulating pose estimation of a point cloud sequence in an embodiment of the invention. (a) a roll angle estimation profile, (b) a pitch angle measurement profile, (c) a yaw angle measurement profile.
FIG. 6 is a graph of translation amount of simulated point cloud sequence pose estimation in an embodiment of the invention. (a) a graph of translational mass estimation on the X axis, (b) a graph of translational mass estimation on the Y axis, and (c) a graph of translational mass estimation on the Z axis.
Detailed Description
The invention relates to a relative pose measuring method for a completely non-cooperative satellite with unknown model. The direction of the optical axis of the laser imaging radar coordinate system is the X axis, and the direction of the target coordinate system is consistent with the direction of the laser imaging radar coordinate system.
The method is characterized in that aiming at a completely non-cooperative satellite with unknown model, a point cloud and a depth map obtained by a laser imaging radar are utilized to construct a target coordinate system of the non-cooperative satellite, and the target coordinate system and the laser imaging radar coordinate system are utilized to calculate to obtain a relative pose value.
The invention is further described below with reference to the drawings.
As shown in FIG. 1, the invention relates to a non-cooperative satellite relative pose measurement method independent of a model, which comprises the following steps:
step 1: acquisition of target point cloud C of kth frame by laser imaging radar k Depth map D corresponding to the same k
Step 2: for the kth frame depth map D k The straight line segment detection is carried out, and the step 2 specifically comprises the following steps:
step 2-1: for the kth frame depth map D k Bilateral filtering is adopted, the size of a kernel window is 9 multiplied by 9, the sigma value of a color space filter is 15, and the sigma value of a coordinate space filter is 9. Preserving image edge details and filtering noise of low frequency components.
Step 2-2: for the filtered k frame depth map D k Proceeding withThe method for detecting the straight line segments adopts LSD, and a group of straight line segments are obtained through an LSD algorithm.
Step 3: searching corresponding straight line segment l in kth frame depth map k Finding a corresponding point cloud point set p according to the corresponding relation between the point cloud and the depth map k Fitting out straight line segment vector L of three-dimensional space by utilizing random sampling consistency algorithm 1 The step 3 specifically comprises the following steps:
step 3-1: if k=1, find the longest body frame straight line segment l in the 1 st frame depth map k . If k > 1, find the straight line segment l in the kth frame depth map k-1 Corresponding straight line segment l k 。
Step 3-2: straight line section l k The matching of the straight line segments is determined according to the similarity function of the straight line segment description characteristics, and the similarity function of the straight line segment characteristics and the matching function of the straight line segments are respectively defined as follows:
straight line segment length similarity measurement function
Figure BDA0002113431530000031
Linear segment gradient magnitude similarity metric function
Figure BDA0002113431530000032
Straight line segment direction similarity measurement function
sm 3 (l k-1 ,l k )＝cos(φ(l k )-φ(l k-1 ))
Straight line segment midpoint position similarity measurement function
Figure BDA0002113431530000041
Where k represents the kth frame, l k Represents a straight line segment in the kth frame, len (l k ) Represents the length of the straight line segment of the kth frame, G (l) k ) Represents the gradient magnitude of the k-th frame line, phi (l) k ) The direction angle of the straight line of the kth frame is indicated,
Figure BDA0002113431530000042
and represents the midpoint coordinates of the straight line segment of the kth frame.
Finally defining a matching function of the straight line segment
Figure BDA0002113431530000043
Wherein w is i Representing the weight.
Step 3-3, finding l according to the corresponding relation between the point cloud and the depth map k Corresponding point cloud point set p k 。
Step 3-4, fitting a straight line segment vector L of the three-dimensional space by utilizing a random sampling consistency algorithm k 。
And 4, detecting an elliptical arc of the kth frame depth map, and solving the detection of the elliptical arc by adopting an ELSD algorithm. The object for detecting the elliptical arc selects a circular target such as a docking ring, a parabolic antenna and the like of the satellite.
Step 5, when k=1, selecting an elliptical arc r formed by non-cooperative satellite circular features in the 1 st frame depth map 1 . When k > 1, find the elliptical arc r in the kth frame depth map k-1 Corresponding elliptical arc r k Finding a corresponding point cloud point set q according to the corresponding relation between the point cloud and the depth map k By utilizing a random sampling consistency algorithm, a point set q is obtained k The normal vector of the space plane P is L 2 ；
Step 6, according to the elliptical arc r k The origin Q of the target coordinate system is solved by the circle center and the plane P, and the specific steps are as follows:
any point in the depth map may correspond to a ray in space, as shown in fig. 2, ob is a projection of the ray OR to the plane XOY, α is an angle between the ray OR and Ob, and β is an angle between Ob and Oa. The unique alpha and beta values can be obtained at any point in the depth map, and the parametric equation of the rays is shown below.
y＝x×tan(β)
Figure BDA0002113431530000044
As shown in fig. 3, the plane P is the plane where the docking ring is located, R is the center of the antenna guard ring, P 'is the depth map, and R' is the center of the guard ring obtained on the depth map. Given the pixel coordinates of a point R 'on the depth map, a ray from the origin O of coordinates of the laser imaging radar can be obtained from space, and the point R' corresponds to the spatial point R and is located on the ray. The intersection point of the plane of the butt joint ring and the ray is calculated to be the circle center of the three-dimensional space circle of the antenna protection ring, and the circle center R is taken as the origin Q of the target coordinate system.
Step 7, according to L 1 、L 2 Q, establishing a target coordinate system;
the target coordinate system adopts a right-hand coordinate system, L 1 Y-axis, L as target coordinate system 2 The X axis and the Z axis of the target coordinate system are L 1 And L 2 The point Q is the origin of the target coordinate system.
Step 8, calculating translation and rotation;
the target coordinate system adopts a right-hand coordinate system, and the positive and negative directions of rotation around the coordinate axis are defined as follows: the right thumb points in the positive direction about the axis, and then the four-finger bending direction is positive and the direction opposite to the four-finger direction is negative.
The translation amount is equal to the coordinate of the origin Q;
the resolving step is based on an active rotation of the point cloud. The coordinate transformation matrix actively rotated by the point cloud is as follows:
Figure BDA0002113431530000051
Figure BDA0002113431530000052
Figure BDA0002113431530000053
the 3 x 3 rotation matrix R is noted as follows:
Figure BDA0002113431530000054
the point cloud first rotates about the X-axis
Figure BDA0002113431530000055
And the angle is rotated by an angle theta around the Y axis, and finally rotated by an angle phi around the Z axis. Let the target coordinate system be the stationary coordinate system, the rotation matrix of the camera relative to the target coordinate system be:
Figure BDA0002113431530000056
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0002113431530000061
-90°≤θ≤90°，-180°≤φ≤180°
if R is 31 Not equal to 1, there are
Figure BDA0002113431530000062
Figure BDA0002113431530000063
Otherwise
If R is 31 ＝-1
Figure BDA0002113431530000064
Otherwise
Figure BDA0002113431530000065
The final rotation angle is
Figure BDA0002113431530000066
And->
Figure BDA0002113431530000067
A group with smaller middle mould length.
Examples
In order to explain the effectiveness of the algorithm, the method is fully shown to have more accurate pose acquisition performance, and the completion experiment is as follows:
(1) Experiment initial condition and parameter setting
The simulation experiment adopts a virtual laser radar and a target model point cloud, and the measurement data of the sensor is generated through software simulation according to the parameters and imaging principles of the laser imaging radar. Parameters of the laser imaging radar are set as follows: resolution 500×500, field angle 20×20°. The initial position was 8m. The pose change in the relative motion process is set as follows: the translation amount is linear motion from 8m to 3m at a constant speed of 30mm/s along the X-axis, the rotation amount is rotation from 0 DEG to 1.5 DEG/s around the X-axis, and nutation variation within 7 DEG exists.
(2) Analysis of experimental results
Fig. 4 is a diagram of the effect of constructing a simulated point cloud target coordinate system, wherein a red-marked straight line is an established coordinate system X-axis, a green-marked straight line is a coordinate system Y-axis, and a blue-marked straight line is a coordinate system Z-axis. Fig. 5 is a rotation amount of the sequence pose estimation, and fig. 6 is a translation amount of the sequence pose estimation. The movement speed in the X-axis direction was calculated from the data of the time period from 20s to 110s, and the result of fitting was shown in fig. 5 (a). As can be seen from the equation of motion y= -1.5289x-1.7515 obtained in fig. 5 (a), the roll angle velocity of the target spacecraft is-1.5289 °/s, which is very small different from the true rotational speed of 1.5 °/s; from the equation of motion y= -0.030079x+8.1978 obtained in fig. 6 (a), the translational motion velocity in the X-axis direction is-0.030079 m/s, which is close to the motion velocity of the tracking spacecraft by 30mm/s, and the negative signs in the above represent the motion directions.

Claims (3)

1. A non-cooperative Wei Xingwei pose measurement method independent of a model is characterized by comprising the following steps of: aiming at a completely non-cooperative satellite with an unknown model, constructing a target coordinate system of the non-cooperative satellite by utilizing a point cloud and a depth map obtained by a laser imaging radar, and calculating by utilizing the target coordinate system and the laser imaging radar coordinate system to obtain a relative pose value; the construction of the target coordinate system is completed by using a linear frame of a typical single circular part and body on a satellite; comprising the following steps:
step (1), acquiring a target point cloud C of a kth frame by using a laser imaging radar k Depth map D corresponding to the same k ；
Step (2), detecting straight line segments of a kth frame depth map;
step (3), when k=1, selecting the longest straight line segment l of the body frame in the 1 st frame depth map k The method comprises the steps of carrying out a first treatment on the surface of the When k > 1, find the straight line segment l in the kth frame depth map k-1 Corresponding straight line segment l k Finding a corresponding point cloud point set p according to the corresponding relation between the point cloud and the depth map k Fitting out straight line segment vector L of three-dimensional space by utilizing random sampling consistency algorithm 1 ；
Step (4), detecting an elliptical arc of a kth frame depth map;
step (5), when k=1, selecting an elliptical arc r formed by non-cooperative satellite circular features in the 1 st frame depth map 1 The method comprises the steps of carrying out a first treatment on the surface of the When k > 1, find the elliptical arc r in the kth frame depth map k-1 Corresponding elliptical arc r k Finding a corresponding point cloud point set q according to the corresponding relation between the point cloud and the depth map k By utilizing a random sampling consistency algorithm, a point set q is obtained k The normal vector of the space plane P is L 2 ；
Step (6), according to the elliptic arc r k Solving an origin Q of a target coordinate system according to the circle center and the plane P of the target coordinate system; the specific implementation method comprises the following steps:
any point in the depth map may correspond to a ray in space, ob is a projection of the ray OR to the plane XOY, α is an included angle between the ray OR and Ob, and β is an included angle between Ob and Oa; the unique alpha and beta values can be obtained at any point in the depth map, and the parameter equation of the rays is shown as follows
y＝x×tan(β)
Figure FDA0004074664970000011
The plane P is the plane where the butt joint ring is positioned, R is the circle center of the antenna protection ring, P 'is the depth map, and R' is the circle center of the protection ring obtained on the depth map; under the condition that the pixel coordinates of a point R 'on the depth map are known, a ray starting from a laser imaging radar coordinate origin O is obtained from the space, and the point R' corresponds to the space point R and is positioned on the ray; calculating the intersection point of the plane of the docking ring and the ray to be the circle center of the three-dimensional space circle of the antenna protection ring, and taking the circle center R as the origin Q of a target coordinate system;
step (7), according to L 1 、L 2 Q, establishing a target coordinate system;
and (8) calculating the translation amount and the rotation amount.
2. The model independent non-cooperative Wei Xingwei pose measurement method of claim 1, wherein the method of implementing step (3) is:
step 3-1: if k=1, find the longest body frame straight line segment l in the 1 st frame depth map k The method comprises the steps of carrying out a first treatment on the surface of the If k > 1, find the straight line segment l in the kth frame depth map k-1 Corresponding straight line segment l k ；
Step 3-2: straight line section l k Matching, namely determining the matching of the straight line segments according to the similarity function of the straight line segment description characteristics, and respectively defining the similarity function of the straight line segment characteristics and the matching function of the straight line segments as follows:
straight line segment length similarity measurement function
Figure FDA0004074664970000021
Linear segment gradient magnitude similarity metric function
Figure FDA0004074664970000022
Straight line segment direction similarity measurement function
sm 3 (l k-1 ,l k )＝cos(φ(l k )-φ(l k-1 ))
Straight line segment midpoint position similarity measurement function
Figure FDA0004074664970000023
Where k represents the kth frame, l k Represents a straight line segment in the kth frame, len (l k ) Represents the length of the straight line segment of the kth frame, G (l) k ) Represents the gradient magnitude of the k-th frame line, phi (l) k ) The direction angle of the straight line of the kth frame is indicated,
Figure FDA0004074664970000024
a midpoint coordinate of a straight line segment of a kth frame is represented;
finally defining a matching function of the straight line segment
Figure FDA0004074664970000025
Wherein w is i Representing the weight;
step 3-3, finding l according to the corresponding relation between the point cloud and the depth map k Corresponding point cloud point set p k ；
Step 3-4, fitting a straight line segment vector L of the three-dimensional space by utilizing a random sampling consistency algorithm k 。
3. The model independent non-cooperative Wei Xingwei pose measurement method of claim 1, wherein: in the step (4), the object for detecting the elliptical arc selects a circular target on the satellite, and the circular target comprises a docking ring and a parabolic antenna.
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