CN112802120A - Camera external parameter calibration method based on non-uniform segmentation accumulation table and orthogonal blanking points - Google Patents

Camera external parameter calibration method based on non-uniform segmentation accumulation table and orthogonal blanking points Download PDF

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CN112802120A
CN112802120A CN202110040970.4A CN202110040970A CN112802120A CN 112802120 A CN112802120 A CN 112802120A CN 202110040970 A CN202110040970 A CN 202110040970A CN 112802120 A CN112802120 A CN 112802120A
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刘思宁
郭恩特
吴林煌
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Fuzhou Ivisionic Technology Co ltd
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Abstract

The invention relates to a camera external reference calibration method based on a non-uniform segmentation accumulation table and orthogonal blanking points. Establishing a spherical coordinate system, and projecting the intersection points of all the detection line segments onto a unit sphere under the coordinate system; dividing azimuth angles according to angles, dividing zenith angles by combining angles and accumulation cell areas, establishing a non-uniform division accumulation table, and updating by using projection of line segment intersection points; randomly sampling a plurality of groups of line segments, wherein each group comprises two line segments; respectively establishing rotation matrix hypotheses in a spherical coordinate system or orthogonality of line segments in the spherical coordinate system according to the condition that two line segments correspond to the same blanking point or different blanking points; and respectively calculating the accumulated value of each rotation matrix in the non-uniform segmentation accumulation table, and outputting the rotation matrix with the maximum corresponding accumulated value as a final result. The method can accurately detect the blanking point, acquire the rotation matrix of the camera relative to the world coordinate system, and greatly improve the accuracy of external reference calibration of the camera.

Description

Camera external parameter calibration method based on non-uniform segmentation accumulation table and orthogonal blanking points
Technical Field
The invention relates to camera external reference calibration in the field of calibration, in particular to a camera external reference calibration method based on a non-uniform segmentation accumulation table and orthogonal blanking points.
Background
Computer vision techniques are widely applied to practical scenes, such as detection tracking, three-dimensional reconstruction, velocity estimation, and the like. The camera external reference calibration is a key link in computer vision application, determines the transformation relation of rotation and translation between a camera coordinate system and a world coordinate system, and the calibration result directly influences the accuracy of subsequent detection, tracking and other steps. Therefore, it is very important to design an accurate camera external reference calibration method in practical application.
In an actual scene structure, there are many mutually parallel straight lines which pass through the perspective projection and intersect at a point on the image, which is called a blanking point. The detection of the blanking point plays an important role in the external reference calibration of the camera, and one common method is to calculate the position of the blanking point by using the line segment information in the image to obtain the rotation transformation of the camera relative to the scene, but the rotation matrix in the method is not constrained by orthogonality, and the rotation matrix cannot ensure orthogonality under the known internal reference of the camera. The other method is to generate a plurality of hypotheses of the rotation matrix in the space, project the hypotheses to the image to obtain a plurality of groups of orthogonal blanking points, and optimize according to the line segment information to obtain a final result.
Disclosure of Invention
The invention aims to provide a camera external reference calibration method based on a non-uniform segmentation accumulation table and orthogonal blanking points, which overcomes the problems of the existing external reference calibration, has simple principle and high efficiency, ensures the orthogonality of a rotation matrix, and can be widely applied to scenes needing the camera external reference calibration.
In order to achieve the purpose, the technical scheme of the invention is as follows: a camera external parameter calibration method based on a non-uniform segmentation accumulation table and orthogonal blanking points comprises the following steps:
step S1, establishing a spherical coordinate system by taking the optical center of the camera as an origin, projecting the intersection point of the line segments on the image onto a unit sphere under the coordinate system to obtain coordinates (theta, phi), wherein theta represents the azimuth angle of the point and the positive half axis of the x axis, and phi represents the zenith angle of the point and the positive half axis of the z axis;
step S2, carrying out non-uniform segmentation on the unit sphere, and constructing a non-uniform segmentation accumulation table T (i, j) on the spherical surface, wherein the non-uniform segmentation accumulation table T is used for accumulating the projection of the line segment intersection points in the image on the spherical surface, and i corresponds to an azimuth angle theta and j corresponds to a zenith angle phi;
step S3, arbitrarily sampling S groups of line segments on the image, each group including two line segments, and constructing a plurality of rotation matrix hypotheses R ═ R in a spherical coordinate system according to the parallelism and orthogonality of the corresponding blanking point line segments in the image1 r2 r3]；
Step S4, searching an accumulated value corresponding to each rotation matrix hypothesis in the non-uniform segmentation accumulated table;
and step S5, outputting the rotation matrix with the maximum corresponding accumulated value in the hypothesis as the final result.
In an embodiment of the present invention, the step S1 specifically includes the following steps:
step S11, detecting N line segments in the image, and calculating the intersection points (u, v) of all the line segments;
step S12, converting the point (u, v) on the image plane into the camera coordinate system by using the camera internal reference to obtain PC＝(X，Y，Z)；
Step S13, converting the P coordinate system into a spherical coordinate system by using a conversion formula from a camera coordinate system to the spherical coordinate systemCProjecting the coordinate (theta, phi) of the unit sphere in a spherical coordinate system to obtain the coordinate (theta, phi) of the unit sphere in the spherical coordinate system
Figure BDA0002896058080000021
The value ranges are respectively theta ∈ [0, 2 pi) and
Figure BDA0002896058080000022
in an embodiment of the present invention, the step S2 specifically includes the following steps:
step S21, alignmentDividing by an angle theta into N with 1 degree as precisionθ360 equal parts, where the azimuth angle θ can be expressed as
Figure BDA0002896058080000023
Step S22, zenith angle
Figure BDA0002896058080000024
Dividing into N with 1 degree as precisionφEqual to 60 parts, where the zenith angle phi may be expressed as
Figure BDA0002896058080000025
Step S23, zenith angle
Figure BDA0002896058080000026
Dividing into N 'while ensuring the same area of each accumulation unit cell'φ200 equal parts, where the zenith angle phi may be expressed as
Figure BDA0002896058080000027
Step S24, building a non-uniform segmentation accumulation table T (i, j) with the size of
Figure BDA0002896058080000028
Step S25, using the non-uniform segmentation accumulation table to match the spherical coordinates obtained in step S1
Figure BDA0002896058080000029
Accumulating and updating the accumulation table to obtain
Figure BDA00028960580800000210
Wherein lmAnd lnRepresenting two line segments corresponding to the intersection point in the image, and alpha represents the included angle between the two line segments.
In an embodiment of the present invention, the step S3 specifically includes the following steps:
s31, sampling the N line segments detected in the S1 to obtain S groups of line segments, wherein each group comprises two line segments;
step S32, assuming that two line segments correspond to the same blanking point, i.e. are parallel in space, calculating the intersection point of the two line segments to obtain a first blanking point v1Blank point v1Corresponding rotation matrix in space
Figure BDA00028960580800000211
With r1The normal vector plane intersects with the unit sphere to form a circle, the azimuth angle theta is divided on the circle by taking 1 degree as the precision, and the zenith angle corresponding to each azimuth angle is obtained
Figure BDA00028960580800000212
R is calculated according to the coordinates (theta, phi)2(ii) a Finally, r is calculated3Is r1And r2Cross multiplication of (1);
step S33, assuming that the two line segments correspond to different blanking points, i.e. are orthogonal in space, which corresponds to r of the rotation matrix in space1And r2(ii) a Constructing a plane passing through the optical center of the camera for each line segment, wherein the normal vectors of the plane are n1And n2Each plane intersects a unit sphere to form a circle; in a manner that
Figure BDA0002896058080000031
On a circle of normal vector with an accuracy of 1 DEG to an azimuth angle theta1Dividing to obtain zenith angle corresponding to each azimuth angle
Figure BDA0002896058080000032
According to the coordinates (theta)1，φ1) Calculated to obtain r1(ii) a If r1×n2Not equal to 0, then r2Is r1And n2Cross product of, if r1×n2When r is equal to 0, then2＝n1(ii) a Finally, r is calculated3Is r1And r2Cross multiplication of (1);
step S34, repeating step S32 and step S33S times, considering spinSymmetry of the rotation matrix, co-construction
Figure BDA0002896058080000033
Figure BDA0002896058080000034
A rotation matrix hypothesis.
In an embodiment of the present invention, the step S4 specifically includes the following steps:
step S41, calculating r for each rotation matrix hypothesis1、r2And r3Corresponding T (i) on the non-uniform partition accumulation table1，j1)、T(i2，j2) And T (i)3，j3)；
In step S42, the accumulated value of each rotation matrix hypothesis is T (i)1，j1)+T(i2，j2)+T(i3，j3)。
Compared with the prior art, the invention has the following beneficial effects: the method can accurately detect the blanking point, acquire the rotation matrix of the camera relative to the world coordinate system and greatly improve the accuracy of external parameter calibration of the camera; the method has the advantages of simple principle and high efficiency, ensures the orthogonality of the rotation matrix, and can be widely applied to scenes needing external parameter calibration of the camera.
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Fig. 1 is a block diagram of the structure of the embodiment of the present invention.
Fig. 2 is a schematic diagram of the point on the image in step S1 projected to the spherical coordinate system in the embodiment of the present invention.
FIG. 3 is a schematic diagram of the step S22 of dividing the zenith angle φ according to angle in this embodiment of the present invention.
FIG. 4 is a diagram illustrating the step S23 of dividing the zenith angle phi according to the area of the accumulated cells according to an embodiment of the present invention.
FIG. 5 is a schematic diagram of the step S32 of constructing the rotation matrix hypothesis according to the parallelism according to the embodiment of the present invention.
FIG. 6 is a schematic diagram of the step S32 for constructing the rotation matrix hypothesis according to the orthogonality according to the embodiment of the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the embodiment provides a camera external reference calibration method based on a non-uniform segmentation accumulation table and orthogonal blanking points, including the following steps:
step S1, establishing a spherical coordinate system by taking the optical center of the camera as an origin, projecting the intersection point of the line segments on the image onto a unit sphere under the coordinate system to obtain coordinates (theta, phi), wherein theta represents the azimuth angle of the point and the positive half axis of the x axis, and phi represents the zenith angle of the point and the positive half axis of the z axis;
step S2, carrying out non-uniform segmentation on the unit sphere, and constructing a non-uniform segmentation accumulation table T (i, j) on the spherical surface, wherein the non-uniform segmentation accumulation table T is used for accumulating the projection of the line segment intersection points in the image on the spherical surface, and i corresponds to an azimuth angle theta and j corresponds to a zenith angle phi;
step S3, arbitrarily sampling S groups of line segments on the image, each group including two line segments, and constructing a plurality of rotation matrix hypotheses R ═ R in a spherical coordinate system according to the parallelism and orthogonality of the corresponding blanking point line segments in the image1 r2 r3]；
Step S4, searching an accumulated value corresponding to each rotation matrix hypothesis in the non-uniform segmentation accumulated table;
and step S5, outputting the rotation matrix with the maximum corresponding accumulated value in the hypothesis as the final result.
As shown in fig. 2, in this embodiment, the step S1 specifically includes the following steps:
step S11, detecting N line segments in the image, and calculating the intersection points (u, v) of all the line segments;
step S12, converting the point (u, v) on the image plane into the camera coordinate system by using the camera internal reference to obtain PC＝(X，Y，Z)；
Step S13, converting the P coordinate system into a spherical coordinate system by using a conversion formula from a camera coordinate system to the spherical coordinate systemCProjecting the coordinate (theta, phi) of the unit sphere in a spherical coordinate system to obtain the coordinate (theta, phi) of the unit sphere in the spherical coordinate system
Figure BDA0002896058080000041
The value ranges are respectively theta ∈ [0, 2 pi) and
Figure BDA0002896058080000042
in this embodiment, the step S2 specifically includes the following steps:
step S21, dividing the azimuth angle theta into N with 1 degree as precisionθ360 equal parts, where the azimuth angle θ can be expressed as
Figure BDA0002896058080000043
Step S22, zenith angle
Figure BDA0002896058080000044
Dividing into N with 1 degree as precisionφEqual to 60 parts, where the zenith angle phi may be expressed as
Figure BDA0002896058080000045
As shown in fig. 3.
Step S23, zenith angle
Figure BDA0002896058080000046
To carry outDividing into N 'by ensuring the same area of each accumulation unit cell'φ200 equal parts, where the zenith angle phi may be expressed as
Figure BDA0002896058080000047
As shown in fig. 4.
Step S24, building a non-uniform segmentation accumulation table T (i, j) with the size of
Figure BDA0002896058080000048
Step S25, accumulating the spherical coordinates (theta, phi) obtained in the step S1 by utilizing a non-uniform segmentation accumulation table, and updating the accumulation table to obtain
Figure BDA0002896058080000049
Wherein lmAnd lnRepresenting two line segments corresponding to the intersection point in the image, and alpha represents the included angle between the two line segments.
In this embodiment, the step S3 specifically includes the following steps:
s31, sampling the N line segments detected in the S1 to obtain S groups of line segments, wherein each group comprises two line segments;
step S32, assuming that two line segments correspond to the same blanking point, i.e. are parallel in space, calculating the intersection point of the two line segments to obtain a first blanking point v1Blank point v1Corresponding rotation matrix in space
Figure BDA0002896058080000051
With r1The normal vector plane intersects with the unit sphere to form a circle, the azimuth angle theta is divided on the circle by taking 1 degree as the precision, and the zenith angle corresponding to each azimuth angle is obtained
Figure BDA0002896058080000052
R is calculated according to the coordinates (theta, phi)2(ii) a Finally, r is calculated3Is r1And r2Cross product of (d), as shown in fig. 5.
Step S33, assume that two line segments correspond to each otherDifferent blanking points, i.e. orthogonal in space, corresponding in space to r of the rotation matrix1And r2(ii) a Constructing a plane passing through the optical center of the camera for each line segment, wherein the normal vectors of the plane are n1And n2Each plane intersects a unit sphere to form a circle; in a manner that
Figure BDA0002896058080000053
On a circle of normal vector with an accuracy of 1 DEG to an azimuth angle theta1Dividing to obtain zenith angle corresponding to each azimuth angle
Figure BDA0002896058080000054
According to the coordinates (theta)1，φ1) Calculated to obtain r1(ii) a If r1×n2Not equal to 0, then r2Is r1And n2Cross product of, if r1×n2When r is equal to 0, then2＝n1(ii) a Finally, r is calculated3Is r1And r2Cross product of (d), as shown in fig. 6.
Step S34, repeating the steps S32 and S33S times, considering symmetry of rotation matrix, and constructing
Figure BDA0002896058080000055
Figure BDA0002896058080000056
A rotation matrix hypothesis.
In an embodiment of the present invention, the step S4 specifically includes the following steps:
step S41, calculating r for each rotation matrix hypothesis1、r2And r3Corresponding T (i) on the non-uniform partition accumulation table1，j1)、T(i2，j2) And T (i)3，j3)；
In step S42, the accumulated value of each rotation matrix hypothesis is T (i)1，j1)+T(i2，j2)+T(i3，j3)。
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.

Claims (5)

1. A camera external reference calibration method based on a non-uniform segmentation accumulation table and orthogonal blanking points is characterized by comprising the following steps:
step S1, establishing a spherical coordinate system by taking the optical center of the camera as an origin, projecting the intersection point of the line segments on the image onto a unit sphere under the coordinate system to obtain coordinates (theta, phi), wherein theta represents the azimuth angle of the point and the positive half axis of the x axis, and phi represents the zenith angle of the point and the positive half axis of the z axis;
step S2, carrying out non-uniform segmentation on the unit sphere, and constructing a non-uniform segmentation accumulation table T (i, j) on the spherical surface, wherein the non-uniform segmentation accumulation table T is used for accumulating the projection of the line segment intersection points in the image on the spherical surface, and i corresponds to an azimuth angle theta and j corresponds to a zenith angle phi;
step S3, arbitrarily sampling S groups of line segments on the image, each group including two line segments, and constructing a plurality of rotation matrix hypotheses R ═ R in a spherical coordinate system according to the parallelism and orthogonality of the corresponding blanking point line segments in the image1 r2 r3]；
Step S4, searching an accumulated value corresponding to each rotation matrix hypothesis in the non-uniform segmentation accumulated table;
and step S5, outputting the rotation matrix with the maximum corresponding accumulated value in the hypothesis as the final result.
2. The method for calibrating camera external parameters based on the non-uniform segmentation accumulation table and the orthogonal blanking points as claimed in claim 1, wherein the step S1 specifically comprises the following steps:
step S11, detecting N line segments in the image, and calculating the intersection points (u, v) of all the line segments;
step S12, converting the point (u, v) on the image plane into the camera coordinate system by using the camera internal reference to obtain PC＝(X，Y，Z)；
Step S13, converting the P coordinate system into a spherical coordinate system by using a conversion formula from a camera coordinate system to the spherical coordinate systemCProjecting the coordinate (theta, phi) of the unit sphere in a spherical coordinate system to obtain the coordinate (theta, phi) of the unit sphere in the spherical coordinate system
Figure FDA0002896058070000011
The value ranges are respectively theta ∈ [0, 2 pi) and
Figure FDA0002896058070000012
3. the method for calibrating camera external parameters based on the non-uniform segmentation accumulation table and the orthogonal blanking points as claimed in claim 1, wherein the step S2 specifically comprises the following steps:
step S21, dividing the azimuth angle theta into N with 1 degree as precisionθ360 equal parts, where the azimuth angle θ can be expressed as
Figure FDA0002896058070000013
Step S22, zenith angle
Figure FDA0002896058070000014
Dividing into N with 1 degree as precisionφEqual to 60 parts, where the zenith angle phi may be expressed as
Figure FDA0002896058070000015
Step S23, zenith angle
Figure FDA0002896058070000016
Dividing into N 'while ensuring the same area of each accumulation unit cell'φ200 equal parts, wherein,the zenith angle phi can be expressed as
Figure FDA0002896058070000017
Step S24, building a non-uniform segmentation accumulation table T (i, j) with the size of
Figure FDA0002896058070000018
Step S25, accumulating the spherical coordinates (theta, phi) obtained in the step S1 by utilizing a non-uniform segmentation accumulation table, and updating the accumulation table to obtain
Figure FDA0002896058070000021
Wherein lmAnd lnRepresenting two line segments corresponding to the intersection point in the image, and alpha represents the included angle between the two line segments.
4. The method for calibrating camera external parameters based on the non-uniform segmentation accumulation table and the orthogonal blanking points as claimed in claim 1, wherein the step S3 specifically comprises the following steps:
s31, sampling the N line segments detected in the S1 to obtain S groups of line segments, wherein each group comprises two line segments;
step S32, assuming that two line segments correspond to the same blanking point, i.e. are parallel in space, calculating the intersection point of the two line segments to obtain a first blanking point v1Blank point v1Corresponding rotation matrix in space
Figure FDA0002896058070000022
With r1The normal vector plane intersects with the unit sphere to form a circle, the azimuth angle theta is divided on the circle by taking 1 degree as the precision, and the zenith angle corresponding to each azimuth angle is obtained
Figure FDA0002896058070000023
R is calculated according to the coordinates (theta, phi)2(ii) a Finally, r is calculated3Is r1And r2Is cross multiplication of；
Step S33, assuming that the two line segments correspond to different blanking points, i.e. are orthogonal in space, which corresponds to r of the rotation matrix in space1And r2(ii) a Constructing a plane passing through the optical center of the camera for each line segment, wherein the normal vectors of the plane are n1And n2Each plane intersects a unit sphere to form a circle; in a manner that
Figure FDA0002896058070000024
On a circle of normal vector with an accuracy of 1 DEG to an azimuth angle theta1Dividing to obtain zenith angle corresponding to each azimuth angle
Figure FDA0002896058070000025
According to the coordinates (theta)1，φ1) Calculated to obtain r1(ii) a If r1×n2Not equal to 0, then r2Is r1And n2Cross product of, if r1×n2When r is equal to 0, then2＝n1(ii) a Finally, r is calculated3Is r1And r2Cross multiplication of (1);
step S34, repeating the steps S32 and S33S times, considering symmetry of rotation matrix, and constructing
Figure FDA0002896058070000026
Figure FDA0002896058070000027
A rotation matrix hypothesis.
5. The method for calibrating camera external parameters based on the non-uniform segmentation accumulation table and the orthogonal blanking points as claimed in claim 1, wherein the step S4 specifically comprises the following steps:
step S41, calculating r for each rotation matrix hypothesis1、r2And r3Corresponding T (i) on the non-uniform partition accumulation table1，j1)、T(i2，j2) And T (i)3，j3)；
In step S42, the accumulated value of each rotation matrix hypothesis is T (i)1，j1)+T(i2，j2)+T(i3，j3)。
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