CN107680159B - Space non-cooperative target three-dimensional reconstruction method based on projection matrix - Google Patents

Space non-cooperative target three-dimensional reconstruction method based on projection matrix Download PDF

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CN107680159B
CN107680159B CN201710957020.1A CN201710957020A CN107680159B CN 107680159 B CN107680159 B CN 107680159B CN 201710957020 A CN201710957020 A CN 201710957020A CN 107680159 B CN107680159 B CN 107680159B
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宁昕
张丕辉
李帅
张家巍
袁建平
李羿霏
任璐
张敬敏
高尔心
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Northwestern Polytechnical University
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Abstract

The invention provides a space non-cooperative target three-dimensional reconstruction method based on a projection matrix, which comprises the steps of firstly extracting characteristic points of images shot by a left camera and a right camera, and then matching the corresponding characteristic points according to an image matching principle; then, according to the epipolar geometric constraint principle, a rotation matrix R and a translation matrix T are solved; and finally, calculating the coordinates of the three-dimensional space points through the projection matrix relationship between the three-dimensional space points and the image plane. Under the condition that the image feature points are correctly matched, the internal and external parameters of the camera can be solved only through epipolar geometric constraint, so that the complicated steps of calibrating the camera are omitted, the calculated amount is reduced, the reconstruction time is shortened, and the requirement of spacecraft operation on real-time performance can be met.

Description

Space non-cooperative target three-dimensional reconstruction method based on projection matrix
Technical Field
The invention relates to a three-dimensional reconstruction method for a space non-cooperative target, and belongs to the field of three-dimensional reconstruction.
Background
With the development of aerospace technology, the exploration and development of outer space resources by human beings are more and more intensive. The spacecraft is abandoned after the spacecraft is out of order, fails or completes a task, and the spacecraft can float freely in space, namely space garbage is formed. Therefore, a space non-cooperative target capturing technology aiming at on-orbit maintenance, failed satellite cleaning, space debris cleaning, space attack and defense and the like of the traditional spacecraft becomes a new development direction in the field of space robots. And the accurate position information of the target is the premise for realizing the operations of detection, approximation, rendezvous and docking, maintenance and the like on the target. The spatial non-cooperative target three-dimensional reconstruction technology is an effective technology for acquiring target information, thereby becoming a research hotspot.
At present, scholars at home and abroad have developed related research work around spatial non-cooperative target three-dimensional reconstruction. Tommasi proposes a photographic reconstruction method, and the core idea is to recover the geometric structure of a scene and the motion information of a camera from two images by using a factorization method. Faugeras proposes to adopt different geometric constraint information to convert projective reconstruction into Euclidean space metric reconstruction, but the method is only suitable for objects under various geometric condition constraints and requires the existence of projective reconstruction. Pollefeees gives a more general approach to metric reconstruction using an automatic scaling method within a parameter range under varying focal length of the camera. Longuet-Higgins proposes that a three-dimensional Structure (SFM) is restored in the Motion of a camera, and the main idea is to calculate the internal parameters of the camera and the information of the direction, the position and the like of the camera by using key technologies such as feature point detection, computer geometric constraint relation and the like and then reconstruct a three-dimensional Structure model of a scene. Yasutaka Furukawa and Jean Ponce propose a Patch-Based three-dimensional Multi-View Stereo vision algorithm (PMVS), and the idea is mainly divided into three parts: initializing feature point matching, generating and expanding patches and screening the patches. Most of the methods solve the three-dimensional space point coordinates based on a triangulation method, and the calculation of the solving process consumes long time and cannot meet the real-time requirement of space operation.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a three-dimensional reconstruction method of a non-cooperative target based on a projection matrix space, which can solve and obtain a three-dimensional space point coordinate through a projection matrix and internal and external parameters of a camera under the condition of giving a pixel point coordinate position of an image plane, and further carry out three-dimensional reconstruction on the non-cooperative target in the space.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
(1) projecting three-dimensional space points to a camera pixel plane by adopting a pinhole model, and setting O-x-y-z as a cameraIn a coordinate system, the z axis points to the front of the camera, x points to the right, y points to the bottom, and O is the optical center of the camera; a point P in the three-dimensional space is projected by an optical center O and then falls on a camera image plane O '-x' -y ', and an imaging point is P'; let P coordinate be [ X, Y, Z ]]TAnd the coordinate of P 'is [ X', Y ', Z']TThe distance from the image plane of the camera to the optical center is f, then
Figure BDA0001434139890000021
Figure BDA0001434139890000022
(2) A pixel plane o-u-v is fixed on the image plane, the origin o of a pixel coordinate system is positioned at the upper left corner of the image plane, the u axis is parallel to the x axis towards the right, and the v axis is parallel to the y axis towards the lower side; the pixel coordinate system differs from the imaging plane by a zoom and a translation of the origin, assuming that the pixel coordinates are scaled by a times on the u-axis and by β times on v, while the origin is translated by cx,cy]TThen exist
Figure BDA0001434139890000023
The pixel p' coordinate is [ u, v ] at this time]TThe space point P coordinate is [ X, Y, Z ]]TK is a camera reference matrix;
(3) according to the spatial point and image pixel conversion relation obtained in the step (2), a pair of registered feature points p is obtained from the two images1'，p'2Then there is Zp'1＝KP、Zp'2K (RP + T), R, T being a rotation matrix and a translation matrix; get x1＝K-1p′1，x2＝K-1p'2Then there is x2＝Rx1+ T; at this time, antipodal geometric constraint is obtained
Figure BDA0001434139890000024
Determining R, T the pixel position of the registration point;
(4) suppose the ith image projection matrix is Ni and the camera intrinsic parameters are
Figure BDA0001434139890000025
The rotation matrix is RiTranslation vector is Ti(ii) a The (i + 1) th image projection matrix is Ni+1The camera intrinsic parameters are
Figure BDA0001434139890000026
The rotation matrix is Ri+1Translation vector is Ti+1(ii) a Then there is Ni＝Ki(Ri/Ti)，Ni+1＝Ki+1(Ri+1/Ti+1) (ii) a Obtaining Zp 'according to the coordinate system conversion relation in the step (3)'i=Ni·P,Zp'i+1=Ni+1P; wherein the content of the first and second substances,
Figure BDA0001434139890000028
Figure BDA0001434139890000031
solving by substitution
Figure BDA0001434139890000032
Wherein
Figure BDA0001434139890000033
Figure BDA0001434139890000034
I.e. [ X ]w,Yw,Zw]＝BA-1。
The invention has the beneficial effects that: under the condition that the image feature points are correctly matched, the internal and external parameters of the camera can be solved only through epipolar geometric constraint, the complicated steps of camera calibration are omitted, and finally the three-dimensional state of an unknown object is recovered through the projection matrix relation, so that the calculated amount is reduced, the reconstruction time is shortened, the requirement of spacecraft operation on real-time performance can be met, and the method is favorable for detecting, approaching, rendezvous and docking, maintaining and other operations of satellites, space robots and the like.
Drawings
FIG. 1 is a schematic diagram of a pinhole camera model;
FIG. 2 is a schematic view of similar triangles;
FIG. 3 is a schematic diagram of an image coordinate system and a pixel coordinate system;
FIG. 4 is a schematic antipodal geometric constraint;
FIG. 5 is a schematic view of a binocular stereo camera;
fig. 6 binocular stereoscopic input images;
FIG. 7 is a three-dimensional reconstruction point cloud result under different viewing angles;
fig. 8 adds the three-dimensional reconstruction results of the texture.
Detailed Description
The present invention will be further described with reference to the following drawings and examples, which include, but are not limited to, the following examples.
The idea of the invention is as follows: firstly, extracting characteristic points of images shot by a left camera and a right camera, and matching the corresponding characteristic points according to an image matching principle; then, according to the epipolar geometric constraint principle, a rotation matrix R and a translation matrix T are solved; and finally, calculating the coordinates of the three-dimensional space points through the projection matrix relationship between the three-dimensional space points and the image plane.
The specific solving method is as follows:
(1) three-dimensional spatial points are first projected into the camera pixel plane, and for this projection relationship, we use a pinhole model to model the mapping relationship. And setting O-x-y-z as a camera coordinate system, pointing the z axis to the front of the camera, setting x to the right and y to the down, and setting O as the optical center of the camera. A point P in the three-dimensional space, after being projected by the optical center O, falls on the camera image plane O '-x' -y ', and the imaging point is P'. Let P coordinate be [ X, Y, Z ]]TP 'is [ X', Y ', Z']TAnd let f (focal length) be the distance from the camera image plane to the optical center. According to the similar triangle relation
Figure BDA0001434139890000041
Therefore, it is not only easy to use
Figure BDA0001434139890000042
(2) Based on the pinhole model given in step (1), described isThe spatial relationship between point P and its image. However, in the camera, pixels are finally obtained one by one, and it is also necessary to sample and quantize an image on an imaging plane. To describe the process by which the sensor converts the perceived light into image pixels, a pixel plane o-u-v is fixed to the image plane. The pixel coordinate system is usually defined as follows: the origin o is located in the upper left corner of the image plane, the u-axis is parallel to the x-axis to the right, and the v-axis is parallel to the y-axis downward. The difference between the pixel coordinate system and the imaging plane is a zoom and a translation of the origin, where it is assumed that the pixel coordinates are scaled by a times on the u-axis, by β times on v, while the origin is translated by cx,cy]T. Exist of
Figure BDA0001434139890000043
The pixel p' coordinate is [ u, v ] at this time]TThe space point P coordinate is [ X, Y, Z ]]TAnd K is an internal reference matrix.
(3) And (3) obtaining a pair of registered feature points from the two images according to the spatial point and image pixel conversion relation obtained in the step (2). Suppose that the pixels are each p1'，p'2Then there is Zp'1＝KP,Zp'2K (RP + T), R, T are rotation and translation matrices. Get x1＝K-1p′1，x2＝K-1p'2Then there is x2＝Rx1+ T. Then both sides simultaneously take left multiplication by T^To obtain T^x2＝T^Rx1(ii) a Then simultaneously left-multiplying by x2 TTo obtain
Figure BDA0001434139890000044
The above formula is the epipolar geometric constraint. The constraint simply gives the spatial position relationship of the two registration points, and we only need to solve for R, T according to the pixel positions of the registration points.
(4) According to step (3), we have found R, T. Suppose that the ith sub-image projection matrix is NiThe camera intrinsic parameter is KiThe rotation matrix is RiTranslation vector is Ti(ii) a The (i + 1) th image projection matrix is Ni+1The camera intrinsic parameter is Ki+1The rotation matrix is Ri+1Translation vector is Ti+1. Then there is Ni＝Ki(Ri/Ti)，Ni+1＝Ki+1(Ri+1/Ti+1). And (4) converting the relation according to the coordinate system in the step (3) to obtain Zp'i=Ni·P,Zp'i+1=Ni+1P. Solving by substitution
Figure BDA0001434139890000052
Wherein
Figure BDA0001434139890000053
Figure BDA0001434139890000054
I.e. [ X ]w,Yw,Zw]＝BA-1。
An embodiment of the invention comprises the following steps:
in the first step, three-dimensional space points are first projected onto a camera plane, and for the projection relationship, a pinhole model is used to model the mapping relationship. Fig. 1 is a schematic diagram of a pinhole camera model. The coordinate system of the camera is O-X-Y-Z, and the coordinate P of the three-dimensional space point is (X, Y, Z)T(ii) a The image coordinate system is O ' -X ' -Y ', the imaging point is P ' (X ', Y ', Z ')T。
Second, as can be seen from fig. 1, the three-dimensional space point coordinate P and the imaging point P' have a similar triangular relationship as in fig. 2, where:
Figure BDA0001434139890000055
finishing to obtain:
Figure BDA0001434139890000056
in a third step, a pixel plane o-u-v is fixed on the imaging plane, as shown in fig. 3. Let the pixel coordinates be scaled by a factor of alpha on the u-axis and by vBeta times while the origin is shifted by [ c ]x,cy]T. Then, the coordinates of P' are associated with the pixel coordinates [ u, v ]]TIn a relationship of
Figure BDA0001434139890000061
Bringing formula (3) into formula (2) and combining α f into fxCombining β f into fyObtaining:
Figure BDA0001434139890000062
where f is in meters and alpha, beta is in pixels/meter, so fx，fyThe unit of (2) is a pixel. Write the equation in matrix form:
Figure BDA0001434139890000063
wherein K is a parameter matrix in the camera. At this time, the left camera coordinate system is taken as the world coordinate system, and the right coordinate system and the left camera coordinate system have a rotation and translation relationship.
Fourthly, as shown in FIG. 4, we find a pair of matched feature points p from two images1',p2' the right image and the left image have a relationship of a rotation matrix R and a translation matrix T. The centers of the two cameras are respectively O1,O2，O1,O2And the plane determined by the three points P is a polar plane. O is1O2Referred to as baseline, O1O2Connecting line and image plane I1,I2Respectively at the intersection points of e1,e2Referred to as poles. Polar plane and two image planes I1,I2Cross line l between1,l2Is the polar line. From the pinhole model, there were:
Zp'1=KP,Zp'2=K(RP+T) (6)
if homogeneous coordinates are used, the above equation can also be written as an equation that holds true with a non-zero constant:
p′1＝KP,p'2＝K(RP+T) (7)
taking:
x1＝K-1p′1,x2＝K-1p'2 (8)
x1,x2is the coordinate on the normalized plane of the two pixels. Bringing into formula (7) to obtain:
x2＝Rx1+T (9)
both sides simultaneously left-hand T^Equivalently, both sides do outer products with T at the same time:
T^x2＝T^Rx1 (10)
then simultaneously left-multiplying by x2 T:
Figure BDA0001434139890000065
Observe equation left side, T^x2Is one of the radicals and T and x2Are all perpendicular vectors. Re-mixing it with x2When inner product is made, 0 is obtained.
Figure BDA0001434139890000071
Re-substituting p1',p2', there are:
p'2 TK-TT^RK-1p1'＝0 (13)
both equations are referred to as epipolar geometric constraints. It has the meaning of O1,O2And P is coplanar, and the antipodal geometric constraint simultaneously comprises translation and rotation. We can solve for R and T by matching the pixel locations of the points.
Fifthly, setting the projection matrixes as N according to the relation between the camera coordinate system and the pixel coordinate system1、N2. And (3) viewing the left camera coordinate system as a world coordinate system, wherein the projection relation of the left image and the right image is as follows:
Figure BDA0001434139890000072
where I is a 3 × 3 identity matrix, R is a rotation matrix, and T is a translation matrix. K1，K2The left and right camera intrinsic parameters, respectively.
The world coordinate system is projected to the pixel coordinate system with the following relationship:
Figure BDA0001434139890000073
the algorithm is applied to the ith sub-picture and the (i + 1) th sub-picture. Suppose that the ith sub-image projection matrix is NiThe camera intrinsic parameter is KiThe rotation matrix is RiTranslation vector is Ti(ii) a The (i + 1) th image projection matrix is Ni+1The camera intrinsic parameter is Ki+1The rotation matrix is Ri+1Translation vector is Ti+1. The formula (14) is substituted by:
Figure BDA0001434139890000074
from the formula (16), N can be obtainedi、Ni+1. Can be substituted by formula (15):
Figure BDA0001434139890000075
will be provided with
Figure BDA0001434139890000076
Substituting formula (17) to obtain:
Figure BDA0001434139890000081
finishing to obtain:
Figure BDA0001434139890000082
removing Z to obtain:
Figure BDA0001434139890000083
wherein
Figure BDA0001434139890000084
Figure BDA0001434139890000085
Therefore, the three-dimensional spatial point P coordinates are:
[Xw,Yw,Zw]＝BA-1
finally, a simple simulation experiment is given:
the satellite model is placed on a precise controllable rotary table, and the rotating speed of the controllable rotary table is controlled to be 3 degrees/s. Then, a binocular camera is used for imaging the satellite model, the first frames (shown in figure 6) of the left camera and the right camera are selected as the input of the three-dimensional reconstruction system, and the reconstruction result is used as the output.
After the work of image feature point extraction, feature point matching, depth information acquisition and the like, the three-dimensional space point coordinates of the satellite image are calculated according to the algorithm provided by the invention, the number of the point clouds displayed in Matlab is 458048, and the time required for reconstruction is 15 s. Two three-dimensional point clouds from different viewing angles are also taken, as shown in fig. 7.
And finally, adding texture information to the three-dimensional point cloud to obtain a final reconstruction result as shown in fig. 8.

Claims (1)

1. A space non-cooperative target three-dimensional reconstruction method based on a projection matrix is characterized by comprising the following steps:
(1) projecting the three-dimensional space point to a camera pixel plane by adopting a pinhole model, setting O-x-y-z as a camera coordinate system, pointing a z axis to the front of a camera, pointing x to the right, pointing y to the lower, and setting O as shootingA camera optical center; a point P in the three-dimensional space is projected by an optical center O and then falls on a camera image plane O '-x' -y ', and an imaging point is P'; let P coordinate be [ X, Y, Z ]]TAnd the coordinate of P 'is [ X', Y ', Z']TThe distance from the image plane of the camera to the optical center is f, then
Figure FDA0002723253400000011
Figure FDA0002723253400000012
(2) A pixel plane o-u-v is fixed on the image plane, the origin o of a pixel coordinate system is positioned at the upper left corner of the image plane, the u axis is parallel to the x axis towards the right, and the v axis is parallel to the y axis towards the lower side; the pixel coordinate system differs from the imaging plane by a zoom and a translation of the origin, assuming that the pixel coordinates are scaled by a times on the u-axis and by β times on v, while the origin is translated by cx,cy]TThen exist
Figure FDA0002723253400000013
The pixel p' coordinate is [ u, v ] at this time]TThe space point P coordinate is [ X, Y, Z ]]TK is a camera reference matrix;
(3) according to the spatial point and image pixel conversion relation obtained in the step (2), a pair of registered feature points p is obtained from the two images1′，p′2Then there is Zp'1＝KP、Zp′2K (RP + T), R, T being a rotation matrix and a translation matrix; get x1＝K- 1p′1，x2＝K-1p′2Then there is x2＝Rx1+ T; both sides simultaneously left multiply T ^ which is equivalent to that both sides simultaneously make outer products with T, and at the moment, antipodal geometric constraint is obtained
Figure FDA0002723253400000014
Determining R, T the pixel position of the registration point;
(4) suppose that the ith sub-image projection matrix is NiThe camera intrinsic parameters are
Figure FDA0002723253400000015
The rotation matrix is RiTranslation vector is Ti(ii) a The (i + 1) th image projection matrix is Ni+1The camera intrinsic parameters are
Figure FDA0002723253400000016
The rotation matrix is Ri+1Translation vector is Ti+1(ii) a Then there is Ni＝Ki(Ri/Ti)，Ni+1＝Ki+1(Ri+1/Ti+1) (ii) a Obtaining Zp 'according to the coordinate system conversion relation in the step (3)'i＝Ni·P,Zp′i+1＝Ni+1P; wherein the content of the first and second substances,
Figure FDA0002723253400000021
Figure FDA0002723253400000022
solving by substitution
Figure FDA0002723253400000023
Wherein
Figure FDA0002723253400000024
Figure FDA0002723253400000025
I.e. [ X ]w,Yw,Zw]＝BA-1。
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CN111582293B (en) * 2019-02-19 2023-03-24 曜科智能科技（上海）有限公司 Plane geometry consistency detection method, computer device and storage medium
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101033972A (en) * 2007-02-06 2007-09-12 华中科技大学 Method for obtaining three-dimensional information of space non-cooperative object
CN103472668A (en) * 2013-09-24 2013-12-25 东北大学 Stereo imaging device and method

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103914874B (en) * 2014-04-08 2017-02-01 中山大学 Compact SFM three-dimensional reconstruction method without feature extraction
US20150302575A1 (en) * 2014-04-17 2015-10-22 Siemens Aktiengesellschaft Sun location prediction in image space with astronomical almanac-based calibration using ground based camera
CN106204717B (en) * 2015-05-28 2019-07-16 长沙维纳斯克信息技术有限公司 A kind of stereo-picture quick three-dimensional reconstructing method and device
CN106204656A (en) * 2016-07-21 2016-12-07 中国科学院遥感与数字地球研究所 Target based on video and three-dimensional spatial information location and tracking system and method
CN106600645B (en) * 2016-11-24 2019-04-09 大连理工大学 A kind of video camera space multistory calibration rapid extracting method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101033972A (en) * 2007-02-06 2007-09-12 华中科技大学 Method for obtaining three-dimensional information of space non-cooperative object
CN103472668A (en) * 2013-09-24 2013-12-25 东北大学 Stereo imaging device and method

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