CN107330911B - Pure rotation motion judgment method based on intersection constraint - Google Patents

Pure rotation motion judgment method based on intersection constraint Download PDF

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CN107330911B
CN107330911B CN201710318964.4A CN201710318964A CN107330911B CN 107330911 B CN107330911 B CN 107330911B CN 201710318964 A CN201710318964 A CN 201710318964A CN 107330911 B CN107330911 B CN 107330911B
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matrix
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CN107330911A (en
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武元新
蔡奇
郁文贤
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Shanghai Jiaotong University
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/20Analysis of motion
    • G06T7/246Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/30Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/50Depth or shape recovery
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10016Video; Image sequence

Abstract

The invention provides a pure rotational motion judgment method based on intersection constraint, which comprises the following steps: the method comprises the following steps: extracting correct feature point pairs (x) from the dual viewi,x′i) I is 1,2 … m; step two: defining a matrix; step three: performing singular value decomposition on the matrix; step four: defining a vector; step five: if p/m falls within a certain interval with 0.5 as the center, the pure rotation motion is determined; if p/m is close to 0 or close to 1, non-pure rotational motion is determined. The invention is suitable for various feature point structures; compared with a homography matrix method, the pure rotation judgment of the method is more precise and accurate.

Description

Pure rotation motion judgment method based on intersection constraint
Technical Field
The invention relates to a pure rotational motion judgment method, in particular to a pure rotational motion judgment method based on intersection constraint.
Background
The calculation of pose and three-dimensional reconstruction by using double views is a key problem in computer vision and is also a basic technology for forming a complex vision system, such as instant positioning and map building (SLAM). In a specific scene, the more obvious the motion between the two views is, the better the pose and the three-dimensional reconstruction effect are, and the more beneficial the normal work of the SLAM system is. Three-dimensional reconstruction is almost impossible when there is only pure or nearly pure rotational (small displacement/depth of field ratio) motion between the dual views. The pure rotation motion is accurately judged, and the method has significance for improving the working robustness of a visual system.
At present, the computer vision field usually adopts the interior point proportion of a Homography (homographic) model to realize the judgment of pure rotation motion, but the threshold value selection has larger arbitrariness and fuzziness, and the practical effect of the engineering is not good.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a pure rotational motion judgment method based on Intersection Constraint, which utilizes the Intersection Constraint (Intersection Constraint) of dual-view imaging geometry, and the theoretical value of the Intersection Constraint is zero under pure rotational motion. The constraint is made equal in probability to the number of pairs greater than zero and less than zero, taking into account the effect of random noise.
According to an aspect of the present invention, there is provided a pure rotational motion determination method based on intersection constraints, comprising:
the method comprises the following steps: extracting correct feature point pairs (x) from the dual viewi,xi'), i is 1,2, m, wherein the number m of the characteristic point pairs is more than or equal to 4, xiAnd xi' normalized homogeneous coordinates of the feature point pairs, respectively;
step two: defining a matrix, taking a singular vector of the matrix corresponding to the minimum singular value, and constructing a 3 x 3 matrix from the singular vector according to the sequence of columns;
step three: performing singular value decomposition on the 3 x 3 matrix;
step four: defining a vector;
step five: the number of elements larger than zero in the counting vector is p, and if p/m falls in a certain interval with 0.5 as the center, the counting vector is judged to be pure rotational motion; if p/m is close to 0 or close to 1, non-pure rotational motion is determined.
Compared with the prior art, the invention has the following beneficial effects: the invention is suitable for various characteristic point structures, such as plane characteristic point structures; compared with a homography matrix method, the pure rotation judgment of the method is more precise and accurate.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by those skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
The pure rotation motion judgment method based on the intersection constraint comprises the following steps:
the method comprises the following steps: extracting correct feature point pairs (x) from the dual viewi,xi'), i is 1,2, m, wherein the number m of the characteristic point pairs is more than or equal to 4, xiAnd xi' normalized coordinates of the dual views, respectively;
the feature points are extracted from the dual-view image, various mainstream feature extraction algorithms such as SIFT, SURF or ORB can be adopted, and the error matching point pairs are removed by using a robust algorithm such as random sample consensus (RANSAC).
Step two: defining a matrix, as in formula (1):
Figure GDA0003362375550000021
wherein
Figure GDA0003362375550000023
Expressed as the kronecker product. Taking a singular vector Q of the matrix A corresponding to the minimum singular value, and constructing a 3 x 3 matrix Q from the singular vector Q according to the sequence of columns; the superscript T represents the transpose of a matrix or vector.
Step three: singular Value Decomposition (SVD) is performed on the 3 x 3 matrix Q, i.e. Q ═ U Σ VT=(u1,u2,u3)Σ(v1,v2,v3)TWherein Σ is a diagonal matrix composed of singular values; u and V are orthogonal matrices obtained by singular value decomposition, UiAnd vi(i ═ 1,2,3) column vectors for U and V, respectively;
step four: vector M is defined, as in formula (2):
Figure GDA0003362375550000022
wherein, | | · |, represents the length of the vector, det (·) represents the determinant of the matrix;
step five: the number of elements larger than zero in the counting vector M is p, and if p/M falls in a certain interval with 0.5 as the center, the counting vector M is judged to be pure rotational motion; if p/m is close to 0 or close to 1, non-pure rotational motion is determined.
The length setting of the interval depends on the specific decision accuracy requirement. Taking intervals according to past experience[0.4,0.6]Pure or near-pure rotation can be effectively determined.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.

Claims (1)

1. A pure rotation motion judgment method based on intersection constraint is characterized by comprising the following steps:
the method comprises the following steps: extracting correct feature point pairs (xi, x' i) from the double-view map, wherein i is 1,2, m, the number m of the feature point pairs is more than or equal to 4, and x is larger than or equal to 4iAnd xi' normalized coordinates of the dual views, respectively;
step two: defining a matrix, taking a singular vector of the matrix corresponding to the minimum singular value, and constructing a 3 x 3 matrix Q from the singular vector according to the sequence of columns;
step three: performing singular value decomposition on the 3 x 3 matrix Q;
Q=UΣVT=(u1,u2,u3)Σ(v1,v2,v3)Twherein Σ is a diagonal matrix composed of singular values; u and V are orthogonal matrices obtained by singular value decomposition, UiAnd vi(i ═ 1,2,3) column vectors for U and V, respectively;
step four: defining a vector;
step five: the number of elements larger than zero in the counting vector is p, and if p/m falls in a certain interval with 0.5 as the center, the counting vector is judged to be pure rotational motion; if p/m is close to 0 or close to 1, determining that the rotation is not pure rotation;
the second step is as follows: defining a matrix, as in formula (1):
Figure FDA0003362375540000011
wherein
Figure FDA0003362375540000012
Expressed as the kronecker product; taking a singular vector Q of the matrix A corresponding to the minimum singular value, and constructing a 3 x 3 matrix Q from the singular vector Q according to the sequence of columns; superscript T represents the transpose of a matrix or vector;
the fourth step is that: vector M is defined, as in formula (2):
Figure FDA0003362375540000013
where | l | · | |, represents the length of the vector, det (·) represents the determinant of the matrix.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007066445A1 (en) * 2005-12-05 2007-06-14 Kyoto University Singular value decomposition device and singular value decomposition method
CN104322052A (en) * 2012-05-09 2015-01-28 恩卡姆技术有限公司 A system for mixing or compositing in real-time, computer generated 3D objects and a video feed from a film camera
CN105551047A (en) * 2015-12-21 2016-05-04 小米科技有限责任公司 Picture content detecting method and device

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007066445A1 (en) * 2005-12-05 2007-06-14 Kyoto University Singular value decomposition device and singular value decomposition method
CN104322052A (en) * 2012-05-09 2015-01-28 恩卡姆技术有限公司 A system for mixing or compositing in real-time, computer generated 3D objects and a video feed from a film camera
CN105551047A (en) * 2015-12-21 2016-05-04 小米科技有限责任公司 Picture content detecting method and device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
3D Reconstruction Based on Homography Mapping;Zhongfei Zhang et al.;《ARPA image understanding workshop》;19960115;第1007-1012页 *

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