CN116433740A - Stereo matching method based on laser stripe lines - Google Patents
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Abstract
The invention discloses a three-dimensional matching method based on laser stripe lines, which comprises the following steps of: acquiring left laser stripe images and right laser stripe images acquired by a left camera and a right camera; step 2: preprocessing a left laser stripe image and a right laser stripe image respectively; step 3: solving a laser stripe center line by utilizing an improved Steger algorithm; step 4: sequentially selecting center line points of the left laser stripe image and matching the center line points with the line points in the right laser stripe image; step 5: and calculating a parallax threshold of the matching point. According to the method and the device, three-dimensional matching of the sub-pixel coordinate points can be achieved, the precision value is higher, and recovery of the three-dimensional point cloud is facilitated.
Description
Technical Field
The invention relates to the field of computer vision, in particular to a three-dimensional matching method based on laser stripe lines.
Background
The binocular stereo matching algorithm is one of the most important processes in three-dimensional reconstruction, and after the corresponding relation between the three-dimensional space and the image is determined, parallax is needed to be calculated, so that the corresponding relation between the points of the three-dimensional space on the left image and the right image is needed to be known, and the aim of stereo matching is achieved. Through the stereo matching technology, the corresponding relation of the points in the left image and the right image can be clarified, so that parallax is obtained, and the three-dimensional information of the points is recovered.
The stereo matching technology is an important technology in binocular stereo vision, a large number of practical algorithms are proposed at present, a plurality of basic constraint conditions are included, and the constraint conditions are applied to the matching algorithm, so that the matching difficulty can be effectively reduced, and the stereo matching speed is improved. However, compared with a single constraint condition, error matching is easy to cause, and on the basis, local, semi-global and global matching algorithms are provided, so that the matching accuracy is higher, the matching efficiency is lower and the matching speed is slower compared with the prior single constraint. And the algorithm only processes the image pixel points, and the precision can not reach the sub-pixel level. Therefore, it is a problem to be solved to propose a stereo matching algorithm with high accuracy and high efficiency.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a three-dimensional matching method based on laser stripe lines, which is characterized in that on the basis of extracting sub-pixel coordinate values of central line points of the laser stripe lines by utilizing a Steger algorithm, single constraint conditions and local cost matching are combined, and the matched points reach sub-pixel levels.
In order to achieve the above object, the technical scheme adopted for solving the technical problems is as follows:
a three-dimensional matching method based on laser stripe lines comprises the following steps:
step 1: acquiring left laser stripe images and right laser stripe images acquired by a left camera and a right camera;
step 2: preprocessing a left laser stripe image and a right laser stripe image respectively;
step 3: solving a laser stripe center line by utilizing an improved Steger algorithm;
step 4: sequentially selecting center line points of the left laser stripe image and matching the center line points with the line points in the right laser stripe image;
step 5: and calculating a parallax threshold of the matching point.
Further, the step 2 includes: the left and right laser stripe images are subjected to global thresholding treatment, and laser stripe areas are extracted, and the calculation method is as follows:
where f (x, y) is a pixel value at (x, y) of the image coordinates, g (x, y) represents the pixel value after thresholding, and T is a set threshold.
Further, the step 3 includes: extracting laser stripe center lines of the left and right laser stripe images after pretreatment by using a Steger algorithm, wherein the specific steps are as follows:
step 31: obtaining r of each pixel point of image x 、r y 、r xx 、r xy And r yy The formula is as follows:
wherein r is x Representing the first partial derivative of the image in the x-direction, r y Representing the first partial derivative of the image in the y-direction, r xx Representing the second partial derivative of the image in the x-direction, r xy Second order mixed partial derivative representing image firstly deriving along x direction and then deriving for y direction, r yy Representing the second partial derivative in the y-direction, G (x, y) being a two-dimensional gaussian function, G (x, y) being a one-dimensional gaussian function;
step 32: calculating eigenvalues and eigenvectors by using a Hessian matrix, wherein the eigenvector corresponding to the largest eigenvalue of the Hessian matrix corresponds to the normal of the light barLine direction, using n x And n y Expressed, the Hessian matrix is expressed as:
step 33: in points (x) 0 ,y 0 ) As standard points, performing second-order Taylor expansion on the gray level distribution function of the stripe section to obtain sub-pixel coordinates (P) x ,P y )=(x 0 +tn x ,y 0 +tn y ) Wherein the calculation formula of t is as follows:
wherein n is x And n y The eigenvectors corresponding to the largest eigenvalues of the Hessian matrix correspond to the normal direction of the light bar, respectively.
Further, the Steger algorithm is large in the calculation amount of the Hessian matrix, wherein each point is performed 5 times (r x 、r y 、r xx 、r xy And r yy ) The two-dimensional Gaussian convolution results in low calculation efficiency and reduces the real-time performance of the system, so that the two-dimensional Gaussian kernel can be equivalently decomposed into one Gaussian line convolution and one Gaussian column convolution by utilizing the separability and the symmetry of the Gaussian convolution, and the calculation amount is reduced from 5n 2 The multiply-add operation is reduced to 10n multiply-add operations.
Further, the step 4 includes: sequentially selecting center line points of the left laser stripe image, and matching the center line points with line points in the right laser stripe image, wherein the specific method for matching the left laser point and the right laser point is as follows:
step 41: finding out a pixel coordinate value of the sub-pixel coordinate, and extracting a 1*n area by taking the pixel as a central coordinate, wherein n is the width of the central line of the laser stripe, if n is an even number, n is n+1, and if the odd number is unchanged;
step 42: taking the same ordinate as an example, the same region on the right laser stripe image is found, and the matching method is as follows: the 1*n area is also extracted from the right laser stripe image, the divisor of the pixel value of the corresponding pixel point is obtained, the average value of all divisor results is calculated, and the calculation formula is as follows:
wherein, sl j Pixel value representing pixel point of left image selection area, sr j Representing the pixel value of the pixel point of the selected area of the right image, wherein j is (1-n), and the center coordinate is the pixel coordinate where the selected sub-pixel coordinate is located, S j A pixel value divisor representing a corresponding pixel point, S ave An average value of the divisors of the corresponding pixels;
if the difference between each divisor and the average value is within 0.05, the corresponding pixel value point is found, and the specific judgment formula is as follows:
S j -S ave <0.05
step 43: if the pixel value point has the laser stripe line point, the direct matching is successful, if the pixel value point does not have the laser stripe line point, the nearest laser stripe point is respectively taken from the upper part and the lower part nearest to the pixel value, and then the corresponding laser stripe line point coordinates are calculated by the following method, wherein the specific method is as follows:
x L =(x 0 -x 1 )×(y-y 1 )/(y 0 -y 1 )+x 1
wherein x is L Representing the subpixel coordinates, x, of the laser spot being sought 0 、x 1 Respectively representing the abscissa value, y of the upper and lower laser stripe points nearest to the pixel value 0 、y 1 And respectively represent the ordinate values of the upper and lower laser stripe points nearest to the pixel value, and y represents the ordinate value of the sub-pixel coordinate line point.
Compared with the prior art, the invention has the following advantages and positive effects due to the adoption of the technical scheme:
1. the invention adopts global thresholding treatment to obtain a preliminary denoising image, and then extracts the sub-pixel coordinate value of the central line of the laser stripe after the calculation of a Steger algorithm;
2. the invention adopts an improved Steger algorithm, and compared with the traditional Steger algorithm, the speed is improved;
3. the invention combines epipolar constraint and cost matching, so that the matching precision is higher and the calculated amount is relatively small;
4. the stereo matching algorithm integrates image denoising, improved Steger algorithm, epipolar constraint and cost matching, and greatly improves the accuracy and efficiency of matching results.
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In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the invention and that other drawings may be obtained from these drawings by those skilled in the art without inventive effort. In the accompanying drawings:
fig. 1 is a schematic flow chart of a stereo matching method based on laser stripe lines.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the embodiment discloses a stereo matching method based on laser stripe lines, which comprises the following steps:
step 1: acquiring left laser stripe images and right laser stripe images acquired by a left camera and a right camera;
step 2: preprocessing a left laser stripe image and a right laser stripe image respectively;
further, the step 2 includes: the left and right laser stripe images are subjected to global thresholding treatment, and laser stripe areas are extracted, and the calculation method is as follows:
where f (x, y) is a pixel value at (x, y) of the image coordinates, g (x, y) represents the pixel value after thresholding, and T is a set threshold.
Step 3: solving a laser stripe center line by utilizing an improved Steger algorithm;
further, the step 3 includes: extracting laser stripe center lines of the left and right laser stripe images after pretreatment by using a Steger algorithm, wherein the specific steps are as follows:
step 31: obtaining r of each pixel point of image x 、r y 、r xx 、r xy And r yy The formula is as follows:
wherein r is x Representing the first partial derivative of the image in the x-direction, r y Representing the first partial derivative of the image in the y-direction, r xx Representing the second partial derivative of the image in the x-direction, r xy Second order mixed partial derivative representing image firstly deriving along x direction and then deriving for y direction, r yy Representing the second partial derivative in the y-direction, G (x, y) being a two-dimensional gaussian function, G (x, y) being a one-dimensional gaussian function;
in order to reduce the amount of computation, the image pixel is transformed into 10 one-dimensional convolutions by using the separability of Gaussian convolution.
Step 32: calculating eigenvalues and eigenvectors by using a Hessian matrix, wherein the eigenvector corresponding to the largest eigenvalue of the Hessian matrix corresponds to the normal direction of the light bar, using n x And n y Expressed, the Hessian matrix is expressed as:
step 33: in points (x) 0 ,y 0 ) As standard points, performing second-order Taylor expansion on the gray level distribution function of the stripe section to obtain sub-pixel coordinates (P) x ,P y )=(x 0 +tn x ,y 0 +tn y ) Wherein the calculation formula of t is as follows:
wherein n is x And n y The eigenvectors corresponding to the largest eigenvalues of the Hessian matrix correspond to the normal direction of the light bar, respectively.
Further, the Steger algorithm is large in the calculation amount of the Hessian matrix, wherein each point is performed 5 times (r x 、r y 、r xx 、r xy And r yy ) The two-dimensional Gaussian convolution results in low calculation efficiency and reduces the real-time performance of the system, so that the two-dimensional Gaussian kernel can be equivalently decomposed into one Gaussian line convolution and one Gaussian column convolution by utilizing the separability and the symmetry of the Gaussian convolution, and the calculation amount is reduced from 5n 2 The multiply-add operation is reduced to 10n multiply-add operations.
Step 4: sequentially selecting center line points of the left laser stripe image and matching the center line points with the line points in the right laser stripe image;
further, the step 4 includes: sequentially selecting center line points of the left laser stripe image, and matching the center line points with line points in the right laser stripe image, wherein the specific method for matching the left laser point and the right laser point is as follows:
step 41: finding out a pixel coordinate value of the sub-pixel coordinate, and extracting a 1*n area by taking the pixel as a central coordinate, wherein n is the width of the central line of the laser stripe, if n is an even number, n is n+1, and if the odd number is unchanged;
step 42: by using the polar constraint method, taking the same ordinate as an example, the same region on the right laser stripe image is found, and the matching method is as follows: the 1*n area is also extracted from the right laser stripe image, the divisor of the pixel value of the corresponding pixel point is obtained, the average value of all divisor results is calculated, and the calculation formula is as follows:
wherein, sl j Pixel value representing pixel point of left image selection area, sr j Representing the pixel value of the pixel point of the selected area of the right image, wherein j is (1-n), the central coordinate is the pixel coordinate where the selected sub-pixel coordinate is located, S j A pixel value divisor representing a corresponding pixel point, S ave An average value of the divisors of the corresponding pixels;
if the difference between each divisor and the average value is within 0.05, the corresponding pixel value point is found, and the specific judgment formula is as follows:
S j -S ave <0.05
step 43: if the pixel value point has the laser stripe line point, the direct matching is successful, if the pixel value point does not have the laser stripe line point, the nearest laser stripe point is respectively taken from the upper part and the lower part nearest to the pixel value, and then the corresponding laser stripe line point coordinates are calculated by the following method, wherein the specific method is as follows:
x L =(x 0 -x 1 )×(y-y 1 )/(y 0 -y 1 )+x 1
wherein x is L Representing the subpixel coordinates, x, of the laser spot being sought 0 、x 1 Respectively representing the abscissa value, y of the upper and lower laser stripe points nearest to the pixel value 0 、y 1 And respectively represent the ordinate values of the upper and lower laser stripe points nearest to the pixel value, and y represents the ordinate value of the sub-pixel coordinate line point.
Step 5: and calculating a parallax threshold of the matching point.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
1. The stereo matching method based on the laser stripe line is characterized by comprising the following steps of:
step 1: acquiring left laser stripe images and right laser stripe images acquired by a left camera and a right camera;
step 2: preprocessing a left laser stripe image and a right laser stripe image respectively;
step 3: solving a laser stripe center line by utilizing an improved Steger algorithm;
step 4: sequentially selecting center line points of the left laser stripe image and matching the center line points with the line points in the right laser stripe image;
step 5: and calculating a parallax threshold of the matching point.
2. The stereo matching method based on laser stripe line according to claim 1, wherein the step 2 comprises: the left and right laser stripe images are subjected to global thresholding treatment, and laser stripe areas are extracted, and the calculation method is as follows:
where f (x, y) is a pixel value at (x, y) of the image coordinates, g (x, y) represents the pixel value after thresholding, and T is a set threshold.
3. The stereo matching method based on laser stripe line according to claim 1, wherein the step 3 comprises: extracting laser stripe center lines of the left and right laser stripe images after pretreatment by using a Steger algorithm, wherein the specific steps are as follows:
step 31: obtaining r of each pixel point of image x 、r y 、r xx 、r xy And r yy The formula is as follows:
wherein r is x Representing the first partial derivative of the image in the x-direction, r y Representing the first partial derivative of the image in the y-direction, r xx Representing the second partial derivative of the image in the x-direction, r xy Second order mixed partial derivative representing image firstly deriving along x direction and then deriving for y direction, r yy Representing the second partial derivative in the y-direction, G (x, y) being a two-dimensional gaussian function, G (x, y) being a one-dimensional gaussian function;
step 32: calculating eigenvalues and eigenvectors by using a Hessian matrix, wherein the eigenvector corresponding to the largest eigenvalue of the Hessian matrix corresponds to the normal direction of the light bar, using n x And n y Expressed, the Hessian matrix is expressed as:
step 33: in points (x) 0 ,y 0 ) As standard points, performing second-order Taylor expansion on the gray level distribution function of the stripe section to obtain sub-pixel coordinates (P) x ,P y )=(x 0 +tn x ,y 0 +tn y ) Wherein the calculation formula of t is as follows:
wherein n is x And n y The eigenvectors corresponding to the largest eigenvalues of the Hessian matrix correspond to the normal direction of the light bar, respectively.
4. A method according to claim 3The stereo matching method based on laser streak line is characterized in that the Steger algorithm has large operand of Hessian matrix, wherein each point needs to be processed 5 times (r x 、r y 、r xx 、r xy And r yy ) The two-dimensional Gaussian convolution results in low calculation efficiency and reduces the real-time performance of the system, so that the two-dimensional Gaussian kernel can be equivalently decomposed into one Gaussian line convolution and one Gaussian column convolution by utilizing the separability and the symmetry of the Gaussian convolution, and the calculation amount is reduced from 5n 2 The multiply-add operation is reduced to 10n multiply-add operations.
5. The stereo matching method based on laser stripe line according to claim 1, wherein the step 4 comprises: sequentially selecting center line points of the left laser stripe image, and matching the center line points with line points in the right laser stripe image, wherein the specific method for matching the left laser point and the right laser point is as follows:
step 41: finding out a pixel coordinate value of the sub-pixel coordinate, and extracting a 1*n area by taking the pixel as a central coordinate, wherein n is the width of the central line of the laser stripe, if n is an even number, n is n+1, and if the odd number is unchanged;
step 42: taking the same ordinate as an example, the same region on the right laser stripe image is found, and the matching method is as follows: the 1*n area is also extracted from the right laser stripe image, the divisor of the pixel value of the corresponding pixel point is obtained, the average value of all divisor results is calculated, and the calculation formula is as follows:
wherein, sl j Pixel value representing pixel point of left image selection area, sr j Representing the pixel value of the pixel point of the selected area of the right image, wherein j is (1-n), the central coordinate is the pixel coordinate where the selected sub-pixel coordinate is located, S j A pixel value divisor representing a corresponding pixel point, S ave An average value of the divisors of the corresponding pixels;
if the difference between each divisor and the average value is within 0.05, the corresponding pixel value point is found, and the specific judgment formula is as follows:
S j -S ave <0.05
step 43: if the pixel value point has the laser stripe line point, the direct matching is successful, if the pixel value point does not have the laser stripe line point, the nearest laser stripe point is respectively taken from the upper part and the lower part nearest to the pixel value, and then the corresponding laser stripe line point coordinates are calculated by the following method, wherein the specific method is as follows:
x L =(x 0 -x 1 )×(y-y 1 )/(y 0 -y 1 )+x 1
wherein x is L Representing the subpixel coordinates, x, of the laser spot being sought 0 、x 1 Respectively representing the abscissa value, y of the upper and lower laser stripe points nearest to the pixel value 0 、y 1 And respectively represent the ordinate values of the upper and lower laser stripe points nearest to the pixel value, and y represents the ordinate value of the sub-pixel coordinate line point.
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