CN111275764A - Depth camera visual mileage measurement method based on line segment shadow - Google Patents

Depth camera visual mileage measurement method based on line segment shadow Download PDF

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CN111275764A
CN111275764A CN202010089290.7A CN202010089290A CN111275764A CN 111275764 A CN111275764 A CN 111275764A CN 202010089290 A CN202010089290 A CN 202010089290A CN 111275764 A CN111275764 A CN 111275764A
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苑晶
周光召
孙沁璇
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Abstract

The invention belongs to the field of autonomous positioning and navigation of mobile robots, and provides a RGB-D (red, green and blue) -visual odometer method based on linear shadow, which constructs a minimum reprojection error equation through the geometric relationship between a plane constraint blocking line and a blocked point line, and solves a pose through nonlinear optimizationLCarrying out pose estimation; finally, the accuracy of pose estimation is improved through shielded line matching constraint. The invention is mainly applied to the occasions of autonomous positioning and navigation of the mobile robot.

Description

Depth camera visual mileage measurement method based on line segment shadow
Technical Field
The invention belongs to the field of autonomous positioning and navigation of mobile robots, and particularly relates to a visual odometry method of a depth camera RGB-D.
Background
A visual odometer is a method of incrementally estimating the trajectory of a person or object's motion as perceived by a visual sensor fixed by a sequence of input images. Compared with the traditional inertial navigation and wheel type odometer, the visual odometer solves the problems of measurement errors of inertial navigation drift and tire slip, the visual sensor has the advantages of low power consumption, low price, rich acquired information and the like, and the visual odometer is widely concerned and applied in the fields of mobile robot positioning and navigation.
At present, the methods of the visual odometer mainly comprise a characteristic point method and a direct method. The feature point method mainly comprises the steps of extracting feature points of an image, matching the feature points, constructing a minimized reprojection error, and estimating the pose between frames by using nonlinear optimization. The characteristic point method is a traditional visual odometry method, has more successful application examples, and has some problems. The characteristic point extraction and matching steps of the characteristic point method have the problems of time consumption and mismatching, the obtained characteristic points are sparse, the map multiplexing cannot be realized, and when the image has the conditions of motion blur, low illumination, repeated texture or lack of texture and the like, the accuracy of the characteristic point method is greatly influenced. In view of the problem of the feature point method, researchers have proposed a direct method and a semi-direct method for directly aligning the pixel brightness values of two frames of pictures, the direct method estimating the pose between two frames by minimizing photometric errors. Compared with a characteristic point method, the direct method does not need to extract and match characteristic points any more, but based on the assumption that the brightness values of corresponding pixel points of two frames of pictures are unchanged, the camera model directly uses the values of the pixel points to construct photometric errors, and the photometric errors are minimized to estimate pose parameters. The direct method in the general visual odometer is semi-dense, namely only the pixel points with certain gradient information are used for calculating the luminosity error, so that the relative accuracy of pose estimation can be kept, and the real-time performance of the direct method is achieved. The direct method can obtain a robust and accurate pose estimation result when the camera does not move violently, and increases the robustness of the algorithm to motion blur, repeated textures and lack of textures due to more fully utilizing the whole image information. The main disadvantage of the direct method is that two frames of images to be aligned need to conform to the assumption that the brightness is not changed, and the brightness difference degree of the images determines the accuracy of the estimation result of the direct method. The direct method still works under small brightness difference, and the direct method can obtain wrong pose estimation under large brightness difference.
The visual sensor used for the visual odometer is generally a monocular camera, a binocular camera or an RGB-D camera, and the visual odometer can be implemented based on a feature point method or a direct method regardless of which of the three cameras is used. The visual odometry method based on the pure monocular camera is complex, three-dimensional map points need to be reconstructed while the pose is estimated, and the pose and the three-dimensional points obtained by estimation have no absolute scale, so that scale drift easily occurs, and better initialization is needed. The binocular camera can obtain the depth value of the pixel point in the image scene through binocular stereo matching, the image processing workload is large, and the binocular camera is not suitable for dark or environment with unobvious texture features. The RGB-D camera can simultaneously acquire a color image and a depth image of a scene, the depth image is acquired through a hardware structure of infrared structured light or a flight time method, the depth image is mostly used indoors due to the fact that the depth image is easily influenced by sunlight, and the range of depth measurement is limited. The vision odometer based on the existing depth can estimate and obtain a motion track containing absolute scales, scale drift is generally avoided, and a pose estimation result is more accurate.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a RGB-D visual odometer method based on linear shadow, which constructs a minimum reprojection error equation through the geometric relationship between a plane constraint blocking line and a blocked point line and solves the pose through nonlinear optimizationLCarrying out pose estimation; and finally, improving the pose estimation precision through shielded line matching constraint.
The method comprises the following specific steps:
s1, acquiring color and depth image information of an environment through a depth camera RGB-D sensor, and extracting a plane and a straight line structure in an image by using the color and depth information, wherein the straight line comprises a shielding part and a shielded point;
s2, defining a coordinate expression of Pl ü cker of a three-dimensional space straight line L, as follows:
L＝[uT，vT]T
wherein the vector u ∈ R3Perpendicular analytical plane piLIs composed of an origin O and a straight line L, and a vector v is formed by R3Represents the direction vector of the straight line L, and L satisfies the Pl ü cker constraint:
uTv＝0
the Pl ü cker matrix for L is defined as:
Figure BDA0002383176350000021
plane pi formed by origin and shielding lineLIs defined as:
Figure BDA0002383176350000022
wherein
Figure BDA0002383176350000023
Homogeneous coordinate of origin, L*For a dual Pl ü cker matrix of L, the calculation method is as follows:
Figure BDA0002383176350000024
s3, defining a constraint plane pi ═ nT，d]TStraight line expression L formed by upper shielded pointsπThe Pl ü cker matrix of (A) is as follows:
Lπ＝[-duT，(u×n)T]T(4)
wherein n isTRepresents the normal vector of the plane, d represents the distance from the plane to the origin, LπThe dual Pl ü cker matrix of (A) is:
Figure BDA0002383176350000025
s4, using a plane pi and a blocking line L to estimate the pose, wherein the upper right corner marks c and r respectively represent a current frame and a reference frame, and the rigid body transformation T of the planecrThe expression (π) is:
Figure BDA0002383176350000026
rigid body transformation T of shield linecr(L) is:
Figure BDA0002383176350000027
to solve the pose, a reprojection error function is defined:
Figure BDA0002383176350000028
wherein
Figure BDA0002383176350000031
Figure BDA0002383176350000032
The rotational variable R can be determined by minimizing the formula (9)crMinimizing the expression (10) to obtain the displacement variable tcr；
S5, further optimizing the pose through shielded line matching, wherein the coordinates of the reference frame relative to the original point of the current frame are
Figure BDA0002383176350000033
Plane under reference frame coordinate system
Figure BDA0002383176350000034
By a shielding line LcAnd the origin of the reference frame
Figure BDA0002383176350000035
The calculation method is as follows：
Figure BDA0002383176350000036
The shielded line is defined by a constraint plane pi and a plane
Figure BDA0002383176350000037
The intersection results in a dual Pl ü cker matrix as follows:
Figure BDA0002383176350000038
wherein
Figure BDA0002383176350000039
Figure BDA00023831763500000310
The observations of the occlusion line, occluded line and constraint plane of the reference frame are represented as: l isr，
Figure BDA00023831763500000311
πrBy matching the occluded lines of the current frame with the reference frame
Figure BDA00023831763500000312
Refining R found abovecr，tcrValue if Rcr，tcrCorrectly converge and satisfy pic＝Tcr(πr)，Lc＝Tcr(Lr) Then, then
Figure BDA00023831763500000313
And
Figure BDA00023831763500000314
is matched, a new objective function is defined:
Figure BDA00023831763500000315
the final rotation and translation matrix is found by minimizing equation (15).
The invention has the characteristics and beneficial effects that:
1) the invention provides a latest RGB-D visual mileage calculation method, which constructs a geometric relation solving pose through a plane constraint plane and a blocking line in an image, and is different from the traditional milemeter method based on plane and line segment characteristic matching, and the method is more efficient.
2) The projection relation of the shielded lines is fused, the pose is further optimized, the accuracy of the algorithm is improved, wrong line segment feature matching is avoided through the planar constraint relation, and the robustness of the algorithm is improved.
Description of the drawings:
FIG. 1 is an illustration of an RGB-D visual odometer based on line segment shading.
A: spatial rectilinear shadow model, sub-graph B: a shading line motion estimation model subgraph C: the occluded line matches the model.
Detailed Description
The technical scheme adopted by the invention is as follows: an RGB-D visual odometry method based on straight line shadow comprises the following steps:
s1, acquiring color and depth image information of an environment through an RGB-D sensor, and extracting a plane and a straight line structure in an image by using the color and depth information, wherein the straight line comprises a shielding position and a shielded point.
S2, defining a coordinate expression of Pl ü cker of a three-dimensional space straight line L, as follows:
L＝[uT，vT]T
wherein the vector u ∈ R3Perpendicular analytical plane piL(consisting of origin O and straight line L), vector v ∈ R3Represents the direction vector of the straight line L, and L satisfies the Pl ü cker constraint:
uTv＝0
the Pl ü cker matrix for L is defined as:
Figure BDA0002383176350000041
plane pi formed by origin and shielding lineLIs defined as:
Figure BDA0002383176350000042
wherein
Figure BDA0002383176350000043
Homogeneous coordinate of origin, L*For a dual Pl ü cker matrix of L, the calculation method is as follows:
Figure BDA0002383176350000044
s3, defining a constraint plane pi ═ nT，d]TStraight line expression L formed by upper shielded pointsπThe Pl ü cker matrix of (A) is as follows:
Lπ＝[-duT，(u×n)T]T(4)
wherein n isTRepresents the normal vector of the plane, d represents the distance from the plane to the origin, LπThe dual Pl ü cker matrix of (A) is:
Figure BDA0002383176350000045
and S4, carrying out pose estimation by using the plane pi and the blocking line L. The top right corner marks c and r represent the rigid body transformation T of the current frame, the reference frame and the plane respectivelycrThe expression (π) is:
Figure BDA0002383176350000046
rigid body transformation T of shield linecr(L) is:
Figure BDA0002383176350000047
to solve the pose, a reprojection error function is defined:
Figure BDA0002383176350000048
wherein
Figure BDA0002383176350000049
Figure BDA00023831763500000410
The rotational variable R can be determined by minimizing the formula (9)crMinimizing the expression (10) to obtain the displacement variable tcr。
And S5, further optimizing the pose through shielded line matching. The origin coordinates of the reference frame relative to the current frame are
Figure BDA00023831763500000411
Plane under reference frame coordinate system
Figure BDA00023831763500000412
By a shielding line LcAnd the origin of the reference frame
Figure BDA00023831763500000413
The calculation method is as follows:
Figure BDA00023831763500000414
the shielded line is defined by a constraint plane pi and a plane
Figure BDA00023831763500000415
The intersection results in a dual Pl ü cker matrix as follows:
Figure BDA00023831763500000416
wherein
Figure BDA00023831763500000417
Figure BDA00023831763500000418
The observations of the occlusion line, occluded line and constraint plane of the reference frame are represented as: l isr，
Figure BDA00023831763500000419
πrBy matching the occluded lines of the current frame with the reference frame
Figure BDA0002383176350000051
Refining R found abovecr，tcrThe value is obtained. If R iscr，tcrCorrectly converge and satisfy pic＝Tcr(πr)，Lc＝Tcr(Lr) Then, then
Figure BDA0002383176350000052
And
Figure BDA0002383176350000053
is matched, a new objective function is defined:
Figure BDA0002383176350000054
the final rotation and translation matrix is found by minimizing equation (15).
The present invention will be described in further detail with reference to the accompanying drawings and specific examples.
First, using the existing algorithm to extract the plane and line segment structure in the image and perform parameter fitting, defining the representation mode of the square line segment and plane under the Pl ü cker coordinate system, where the occlusion line L in the sub-graph a in fig. 1 is represented as follows:
L＝[uT，vT]T
wherein the vector u ∈ R3Perpendicular plane piL(consisting of origin O and straight line L) and vector v ∈ R3Represents the direction vector of the straight line L, and L satisfies the Pl ü cker constraint:
uTv＝0
the Pl ü cker matrix for L is defined as:
Figure BDA0002383176350000055
plane piLIs defined as:
Figure BDA0002383176350000056
wherein
Figure BDA0002383176350000057
Homogeneous coordinate of origin, L*For a dual Pl ü cker matrix of L, the calculation method is as follows:
Figure BDA0002383176350000058
then defining a constraint plane pi ═ nT，d]TStraight line expression L formed by upper shielded pointsπThe Pl ü cker matrix of (A) is as follows:
Lπ＝[-duT，(u×n)T]T
wherein n isTRepresents the normal vector of the plane, d represents the distance from the plane to the origin, LπThe dual Pl ü cker matrix of (A) is:
Figure BDA0002383176350000059
second, using the plane pi, the shielding line L and the plane piLAnd (3) performing pose estimation, for example, in a sub-graph B, defining the representation mode of each variable under the current coordinate system:
Figure BDA00023831763500000510
and
Figure BDA00023831763500000511
respectively representing the coordinates of the origin, L, of the reference frame and the current framecDenotes a shading line, picA plane of constraint is represented by a plane of constraint,
Figure BDA00023831763500000512
and
Figure BDA00023831763500000513
representing the plane formed by the origin and the shading line, picFor constraining the planes, the top right hand corner labels c and r represent the current and reference frames, respectively, the rigid transformation T of the plane πcrThe expression (π) is:
Figure BDA00023831763500000514
rigid body transformation T of the shield line Lcr(L) is:
Figure BDA00023831763500000515
defining an objective function, representing the reprojection error of the constraint plane and the occlusion line between two frames, and calculating the method as follows:
Figure BDA00023831763500000516
wherein
Figure BDA0002383176350000061
Figure BDA0002383176350000062
By minimizing E1(Rcr) Can obtain the rotation variable RcrMinimization of E2(Rcr，tcr) Calculating a displacement variable tcr。
And finally, improving the pose estimation precision through shielded line matching constraint. The origin coordinates of the reference frame in the subgraph C relative to the current frame are
Figure BDA0002383176350000063
Analytic plane under reference frame coordinate system
Figure BDA0002383176350000064
By a shielding line LcAnd the origin of the reference frame
Figure BDA0002383176350000065
The calculation method is as follows:
Figure BDA0002383176350000066
shielded line
Figure BDA0002383176350000067
By constraining planes pi and plane
Figure BDA0002383176350000068
The intersection results in a dual Pl ü cker matrix as follows:
Figure BDA0002383176350000069
wherein
Figure BDA00023831763500000610
Figure BDA00023831763500000611
The observations of the occlusion line, occluded line and constraint plane of the reference frame are represented as: l isr，
Figure BDA00023831763500000612
πrBy matching the current frame with the referenceOccluded line of frame
Figure BDA00023831763500000613
Ridging Rcr，tcrThe value of (c). If R iscr，tcrCorrectly converge and satisfy pic＝Tcr(πr)，Lc＝Tcr(Lr) Then, then
Figure BDA00023831763500000614
And
Figure BDA00023831763500000615
if the matching is correct, the orange part in the sub-image C is represented by a pose TcrThe transformed line segment or plane obtains a new reprojection error function as follows:
Figure BDA00023831763500000616
by minimizing F (R)cr，tcr) And solving a final rotation and translation matrix.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (2)

1. A depth camera visual mileage measurement method based on line segment shadow is characterized in that a plane and a line segment structure in an image are extracted and subjected to parameter fitting, a representation mode of a square line segment and a plane are defined under a Planckian (Pl ü cker) coordinate system, and a plane pi, a shielding line L and a plane pi are usedLCarrying out pose estimation; and finally, improving the pose estimation precision through shielded line matching constraint.
2. The method for measuring the visual mileage of the depth camera based on the line segment shadow as claimed in claim 1, which is characterized by comprising the following steps:
s1, acquiring color and depth image information of an environment through a depth camera RGB-D sensor, and extracting a plane and a straight line structure in an image by using the color and depth information, wherein the straight line comprises a shielding part and a shielded point;
s2, defining a coordinate expression of Pl ü cker of a three-dimensional space straight line L, as follows:
L＝[uT，vT]T
wherein the vector u ∈ R3Perpendicular analytical plane piLIs composed of an origin O and a straight line L, and a vector v is formed by R3Represents the direction vector of the straight line L, and L satisfies the Pl ü cker constraint:
uTv＝0
the Pl ü cker matrix for L is defined as:
Figure FDA0002383176340000011
plane pi formed by origin and shielding lineLIs defined as:
Figure FDA0002383176340000012
wherein
Figure FDA0002383176340000013
Homogeneous coordinate of origin, L*For a dual Pl ü cker matrix of L, the calculation method is as follows:
Figure FDA0002383176340000014
s3, defining a constraint plane pi ═ nT，d]TStraight line expression L formed by upper shielded pointsπThe Pl ü cker matrix of (A) is as follows:
Lπ＝[-duT，(u×n)T]T(4)
wherein n isTRepresents the normal vector of the plane, d represents the distance from the plane to the origin, LπThe dual Pl ü cker matrix of (A) is:
Figure FDA0002383176340000015
s4, using a plane pi and a blocking line L to estimate the pose, wherein the upper right corner marks c and r respectively represent a current frame and a reference frame, and the rigid body transformation T of the planecrThe expression (π) is:
Figure FDA0002383176340000016
rigid body transformation T of shield linecr(L) is:
Figure FDA0002383176340000017
to solve the pose, a reprojection error function is defined:
Figure FDA0002383176340000018
wherein
Figure FDA0002383176340000019
Figure FDA00023831763400000110
The rotational variable R can be determined by minimizing the formula (9)crMinimizing the expression (10) to obtain the displacement variable tcr；
S5, further optimizing the pose through shielded line matching, wherein the coordinates of the reference frame relative to the original point of the current frame are
Figure FDA0002383176340000021
Plane under reference frame coordinate system
Figure FDA0002383176340000022
By a shielding line LcAnd the source of the reference frameDot
Figure FDA0002383176340000023
The calculation method is as follows:
Figure FDA0002383176340000024
the shielded line is defined by a constraint plane pi and a plane
Figure FDA0002383176340000025
The intersection results in a dual Pl ü cker matrix as follows:
Figure FDA0002383176340000026
wherein
Figure FDA0002383176340000027
Figure FDA0002383176340000028
The observations of the occlusion line, occluded line and constraint plane of the reference frame are represented as: l isr，
Figure FDA0002383176340000029
πrBy matching the occluded lines of the current frame with the reference frame
Figure FDA00023831763400000210
Refining R found abovecr，tcrValue if Rcr，tcrCorrectly converge and satisfy pic＝Tcr(πr)，Lc＝Tcr(Lr) Then, then
Figure FDA00023831763400000211
And
Figure FDA00023831763400000212
is matched, a new objective function is defined:
Figure FDA00023831763400000213
the final rotation and translation matrix is found by minimizing equation (15).
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CN117197229A (en) * 2023-09-22 2023-12-08 北京科技大学顺德创新学院 Multi-stage estimation monocular vision odometer method based on brightness alignment
WO2024018605A1 (en) * 2022-07-21 2024-01-25 株式会社ソニー・インタラクティブエンタテインメント Image information processing device, image information processing method, and program

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