CN112767494A - Precise measurement positioning method based on calibration algorithm - Google Patents
Precise measurement positioning method based on calibration algorithm Download PDFInfo
- Publication number
- CN112767494A CN112767494A CN202110070836.9A CN202110070836A CN112767494A CN 112767494 A CN112767494 A CN 112767494A CN 202110070836 A CN202110070836 A CN 202110070836A CN 112767494 A CN112767494 A CN 112767494A
- Authority
- CN
- China
- Prior art keywords
- image
- camera
- points
- coordinates
- steps
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000005259 measurement Methods 0.000 title claims abstract description 26
- 238000012545 processing Methods 0.000 claims abstract description 47
- 239000011159 matrix material Substances 0.000 claims abstract description 18
- 238000013519 translation Methods 0.000 claims abstract description 4
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 9
- 238000003384 imaging method Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 230000006872 improvement Effects 0.000 abstract description 7
- 238000004364 calculation method Methods 0.000 abstract 1
- 238000003754 machining Methods 0.000 description 10
- 230000000694 effects Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000000691 measurement method Methods 0.000 description 4
- 230000009466 transformation Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011426 transformation method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20164—Salient point detection; Corner detection
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses an accurate measurement positioning method based on a calibration algorithm, which comprises the steps of carrying out multi-image acquisition on any position and any posture of a planar chessboard calibration plate by using a camera for determining a focal length, converting an image into a gray-scale image and carrying out binarization processing, acquiring chessboard calibration plate angular points on the image, obtaining accurate sub-pixel coordinates of the chessboard calibration plate angular points of the image, calculating internal parameters of camera equipment for shooting a picture, obtaining external parameters of rotation, translation matrix and the like of the camera equipment for shooting through a multi-point positioning method, and calculating to obtain the accurate position of a world coordinate point corresponding to a desired positioning point on the picture. The invention can acquire the relative position of a real space corresponding to any pixel point on an image shot by the camera in any pose state, and the accurate target position positioning is a very important link in the processing and daily measurement calculation of a precise instrument, and the improvement of the positioning and measurement precision and speed is a target and direction which are always tried by the industry.
Description
Technical Field
The invention relates to the field of laser precision machining, in particular to a precise measurement positioning method based on a calibration algorithm.
Background
In the field of conventional image processing, it is an important ring to restore the exact position of the object itself by an image. For example, in the field of industrial measurement and positioning, accurate measurement needs to be performed manually to obtain actual parameters of an object, such as length, width, and the like, the implementation process of the online measurement method is complicated, and for some large or small devices which are difficult to measure or require high precision, it is often difficult to obtain accurate parameters of the object by a direct measurement method. At present, a plurality of off-line measurement methods are commonly used, such as a method for measuring a precise object by microscope confocal scanning and the like, although the method has high precision, coaxial measurement is mostly required when the object is precisely machined, the machining platform and the microscope are difficult to coaxially and simultaneously measure and machine, the coaxial measurement method has high requirements on hardware equipment and external conditions, the machining platform and the object to be machined need to be accurately calibrated and matched, and the calibration and the matching of the machining platform and the object to be machined need to be carried out again after the machining platform or the object to be machined is moved every time, so the coaxial method is not suitable for simultaneously and accurately measuring during machining.
Although paraxial measurement and processing are also used, image transformation or projection transformation and other methods are generally used at present, the image transformation method has low measurement precision and high requirements on external factors, the distortion of a camera needs to be strictly controlled, the positions of the camera, a processing platform and an object are required to be relatively accurate to shoot an ideal picture for subsequent measurement operation, and the process is complicated and the cost is high. In the field of accurate positioning, the relative position of a measured object can be quickly and reliably acquired through a laser radar at present, but relevant equipment and corresponding external conditions are required. Although the image can reflect the relative position and relative parameters of the actual object, the actual parameters of the actual object cannot be accurately obtained through the image due to the influence of the shooting conditions and external factors, and spatial position information is difficult to obtain. Therefore, it is important to solve such problems.
Disclosure of Invention
Aiming at the problems, the invention provides an accurate measurement positioning method based on a calibration algorithm, which obtains a one-to-one corresponding relation between a camera and a world three-dimensional coordinate system of an object to be measured through camera imaging to obtain an image and a conversion matrix under a world coordinate, obtains a relative position relation of the object to be processed relative to a laser processing plane through the conversion matrix by utilizing the conversion relation between the world coordinate and a pixel coordinate, and thus skips the complicated step of re-matching the processing plane and the processed object after moving the object every time.
In order to realize the technical scheme, the invention provides an accurate measurement positioning method based on a calibration algorithm, which comprises the following steps:
the method comprises the following steps: acquiring sub-pixel coordinates of corner points of a shot picture, performing multi-shot of changing any position and any posture on a chessboard calibration plate with known size parameters by using a camera with fixed focal length to acquire an image, converting the image into a gray-scale image, binarizing the processed gray-scale image, judging whether the corner points exist by using a corner point detection algorithm, and if the corner points exist, acquiring accurate sub-pixel coordinates of the corner points;
step two: acquiring internal parameters of camera equipment, defining corresponding world coordinates corresponding to angular points of an image, acquiring one-to-one correspondence between the world coordinates of the angular points and the image coordinates, selecting the angular points of a plurality of groups of images and points on a corresponding world coordinate system, and acquiring the internal parameters and distortion coefficients for shooting the group of image camera equipment through a calibration algorithm;
step three: acquiring external parameters of camera equipment for shooting the group of pictures, keeping internal parameters such as focal length of the shooting camera unchanged, selecting three non-collinear points around the object to be processed on a laser processing plane, shooting the object to be processed by the camera at any position and in any posture to obtain an image, and obtaining the external parameters of the shot camera equipment through a multi-point positioning algorithm;
step four: fixing the position of the camera with the obtained internal and external parameters, keeping the imaging of the object to be processed in the camera view finding range, moving the object to be processed at will, acquiring the moved image again by the camera, obtaining pixel points on the shot image pixel by pixel, obtaining the visual relation between the laser processing plane and the image through the conversion matrix calculated by the internal and external parameters, and performing precision processing operation on any position on the image on the laser processing plane.
In a further improvement, in the step one, the gray scale map of the image is to set the gray scale value of the image to be 0 to 255, 255 represents all white, and 0 represents all black; the white and black are divided into a plurality of equal steps according to the logarithmic relation, and the gray level is divided into 256 steps.
In the first step, the corner detection algorithm includes the following steps of continuously taking out each picture of a group of pictures which are taken and subjected to graying processing, and executing the following operations for each picture:
the method comprises the following steps: preprocessing an image, binarizing the image, and converting a gray level image into a binary image;
step two: searching adjacent grids of each grid, recording the number of the adjacent grids, and classifying all the adjacent grids according to the principle that all the grids in the class are adjacent;
step three: judging whether the squares in each class are the sought chessboard squares or not according to the known number of the angular points, confirming whether the positions and the number of the squares are correct or not, if so, extracting the connection points of any two squares of the sought chessboard squares, namely the sought angular points, and obtaining the accurate sub-pixel coordinates of all the angular points; if not, the chessboard angular point detection fails.
In a further improvement, in the second step, the calibration algorithm includes the following steps, for each image from which a sub-pixel coordinate corner is successfully acquired, the following operations are performed:
the method comprises the following steps: defining world coordinate points corresponding to the coordinates of the angular points of the chessboard pattern calibration board on the image, wherein the world coordinate points need to be the same as the theoretical condition, namely the coordinate difference between two angular points is the real distance between the chessboard pattern calibration board grids used for shooting;
step two: and (4) inputting the world coordinates and the pixel coordinates of the defined corresponding corner points into a function in a matrix form to calculate the internal parameters and the distortion coefficients of the camera.
In a further improvement, the relationship between the pixel coordinates and the world coordinates is as follows:
wherein u and v represent coordinates in a pixel coordinate system, s represents a scale factor, fx, fy, u0, v0 and gamma represent 5 camera internal parameters, R and t represent camera external parameters, Xw, Yw and Zw (assuming that a calibration chessboard is positioned on a plane of which Zw is 0 in the world coordinate system) represent coordinates in the world coordinate system, that is, a plurality of sets of world coordinates and corresponding pixel coordinate points are input to obtain required camera internal parameters.
In a further improvement, the relation of the distortion coefficients is:
u1＝u+(u-u0)[k1(x2+y2)+k2(x2+y2)2]
v1＝v+(v-v0)[k1(x2+y2)+k2(x2+y2)2]
wherein, (u, v) represents pixel coordinates of ideal distortion-free, (u1, v1) represents pixel coordinates in the case of distortion of an actual radial image, and (u0, v0) represents principal points, and is obtained directly in the obtained internal reference matrix regardless of the presence or absence of distortion.
In a further improvement, the camera device external parameter acquisition comprises the following steps, for the camera model with the obtained internal parameters, the following operations are carried out: keeping the internal parameters such as the focal length of a camera and the like unchanged, placing an object to be processed on a laser processing operation platform, selecting three non-collinear points around the object to be processed on a laser processing plane, shooting the object to be processed by using a laser, shooting an image containing the object to be processed and the three non-collinear points shot by using the laser by using the camera at any position and posture of a paraxial of a laser processing platform, and obtaining the external parameters of the shooting camera at the moment through a multi-point positioning algorithm.
The further improvement lies in that: the multi-point positioning algorithm comprises the following steps of executing the following operations on the obtained camera shooting images:
the method comprises the following steps: inputting laser processing plane coordinates of three points around an object to be processed selected on a laser processing plane;
step two: inputting corresponding world coordinates of three points on a laser processing plane which is shot by the laser;
step three: and by means of cosine law, a translation and rotation matrix of the camera system image coordinate system shot at the moment relative to the world coordinate system can be obtained by utilizing a point cloud registration method.
The invention has the beneficial effects that: the invention provides a laser precision machining method based on paraxial offline measurement, which obtains a one-to-one corresponding relation between a camera and a world three-dimensional coordinate system of an object to be machined through camera imaging to obtain an image and a conversion matrix under a world coordinate, obtains the relative position relation of the object to be machined relative to a laser machining plane through the conversion matrix by utilizing the conversion relation between the world coordinate and a pixel coordinate, and thus skips the complicated step of re-matching the machining plane and the machining object after moving the object every time. Meanwhile, the method can realize the paraxial arrangement of the camera and the object to be processed at any pose, effectively solves the problems of complicated process and low precision of coaxial processing, has higher processing precision and simple process, and provides a novel method with low cost, high efficiency and accuracy for the field of laser precision processing.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic diagram illustrating an exemplary operation effect of the present invention.
Fig. 3 is a schematic diagram illustrating an exemplary operation effect of the present invention.
Fig. 4 is a schematic diagram illustrating an exemplary effect of the present invention.
Detailed Description
In order to further understand the present invention, the following detailed description will be made with reference to the following examples, which are only used for explaining the present invention and are not to be construed as limiting the scope of the present invention.
As shown in fig. 1, the present embodiment provides a calibration algorithm based accurate measurement positioning method, which includes the following steps:
the method comprises the following steps: acquiring sub-pixel coordinates of corner points of a shot picture, performing multi-shot of changing any position and any posture on a chessboard calibration plate with known size parameters by using a camera with fixed focal length to acquire an image, converting the image into a gray-scale image, binarizing the processed gray-scale image, judging whether the corner points exist by using a corner point detection algorithm, and if the corner points exist, acquiring accurate sub-pixel coordinates of the corner points;
step two: acquiring internal parameters of camera equipment, defining corresponding world coordinates corresponding to angular points of an image, acquiring one-to-one correspondence between the world coordinates of the angular points and the image coordinates, selecting the angular points of a plurality of groups of images and points on a corresponding world coordinate system, and acquiring the internal parameters and distortion coefficients for shooting the group of image camera equipment through a calibration algorithm;
step three: acquiring external parameters of camera equipment for shooting the group of pictures, keeping internal parameters such as focal length of the shooting camera unchanged, selecting three non-collinear points around the object to be processed on a laser processing plane, shooting the object to be processed by the camera at any position and in any posture to obtain an image, and obtaining the external parameters of the shot camera equipment through a multi-point positioning algorithm;
step four: fixing the position of the camera with the obtained internal and external parameters, keeping the imaging of the object to be processed in the camera view finding range, moving the object to be processed at will, acquiring the moved image again by the camera, obtaining pixel points on the shot image pixel by pixel, obtaining the visual relation between the laser processing plane and the image through the conversion matrix calculated by the internal and external parameters, and performing precision processing operation on any position on the image on the laser processing plane.
In the embodiment, in the first step, the grayscale map of the image is to set the grayscale value of the image to 0 to 255, 255 represents full white, and 0 represents full black; the white and black are divided into a plurality of equal steps according to the logarithmic relation, and the gray level is divided into 256 steps.
In this embodiment, in the first step, the corner detection algorithm includes the following steps of continuously taking out each picture of a group of pictures which are taken and subjected to graying processing, and executing the following operations for each picture:
the method comprises the following steps: preprocessing an image, binarizing the image, and converting a gray level image into a binary image;
step two: searching adjacent grids of each grid, recording the number of the adjacent grids, and classifying all the adjacent grids according to the principle that all the grids in the class are adjacent;
step three: judging whether the squares in each class are the sought chessboard squares or not according to the known number of the angular points, confirming whether the positions and the number of the squares are correct or not, if so, extracting the connection points of any two squares of the sought chessboard squares, namely the sought angular points, and obtaining the accurate sub-pixel coordinates of all the angular points; if not, the chessboard angular point detection fails.
In this embodiment, in the second step, the calibration algorithm includes the following steps, for each image from which a sub-pixel coordinate corner is successfully acquired, the following operations are performed:
the method comprises the following steps: defining world coordinate points corresponding to the coordinates of the angular points of the chessboard pattern calibration board on the image, wherein the world coordinate points need to be the same as the theoretical condition, namely the coordinate difference between two angular points is the real distance between the chessboard pattern calibration board grids used for shooting;
step two: and (4) inputting the world coordinates and the pixel coordinates of the defined corresponding corner points into a function in a matrix form to calculate the internal parameters and the distortion coefficients of the camera.
In this embodiment, the relationship between the pixel coordinates and the world coordinates is as follows:
wherein u and v represent coordinates in a pixel coordinate system, s represents a scale factor, fx, fy, u0, v0 and gamma represent 5 camera internal parameters, R and t represent camera external parameters, Xw, Yw and Zw (assuming that a calibration chessboard is positioned on a plane of which Zw is 0 in the world coordinate system) represent coordinates in the world coordinate system, that is, a plurality of sets of world coordinates and corresponding pixel coordinate points are input to obtain required camera internal parameters.
In this embodiment, the relationship of the distortion coefficients is:
u1＝u+(u-u0)[k1(x2+y2)+k2(x2+y2)2]
v1＝v+(v-v0)[k1(x2+y2)+k2(x2+y2)2]
wherein, (u, v) represents pixel coordinates of ideal distortion-free, (u1, v1) represents pixel coordinates in the case of distortion of an actual radial image, and (u0, v0) represents principal points, and is obtained directly in the obtained internal reference matrix regardless of the presence or absence of distortion.
In this embodiment, the camera device extrinsic parameter acquisition includes the following steps, for the camera model with the obtained intrinsic parameters, to perform the following operations: keeping the internal parameters such as the focal length of a camera and the like unchanged, placing an object to be processed on a laser processing operation platform, selecting three non-collinear points around the object to be processed on a laser processing plane, shooting the object to be processed by using a laser, shooting an image containing the object to be processed and the three non-collinear points shot by using the laser by using the camera at any position and posture of a paraxial of a laser processing platform, and obtaining the external parameters of the shooting camera at the moment through a multi-point positioning algorithm.
In this embodiment, the multi-point positioning algorithm includes the following steps, for the obtained camera shooting image, to perform the following operations:
the method comprises the following steps: inputting laser processing plane coordinates of three points around an object to be processed selected on a laser processing plane;
step two: inputting corresponding world coordinates of three points on a laser processing plane which is shot by the laser;
step three: and by means of cosine law, a translation and rotation matrix of the camera system image coordinate system shot at the moment relative to the world coordinate system can be obtained by utilizing a point cloud registration method.
Fig. 2-3 are schematic diagrams illustrating exemplary operation effects of the present invention, and as shown in fig. 2, a checkerboard is used as an example of the present invention for related explanation, parameters of a checkerboard calibration board are 3mm × 3mm, a camera used for shooting has been used for calibration steps of obtaining external parameters and camera distortion, the checkerboard can be shot by the camera after any pose is changed to obtain a required image, any 3 non-collinear points are selected in the actual shooting on the left side, and corresponding 3 points are selected on the simulated image on the right side. After the steps are completed, as shown in fig. 3, a desired positioning measurement point is selected on the left actual shot image, and after the steps of the invention are completed, the actual positioning measurement point is displayed on the right simulated image. This example is merely an example, and to facilitate direct observation, a checkerboard calibration board is taken as an example for description, and in actual operation, any object may be selected to perform the operation flow described in this example.
Fig. 4 is a schematic diagram illustrating an exemplary effect of the present invention, and as shown in fig. 4, a checkerboard is taken as an example of the present invention for relevant explanation, where an object to be processed is a checkerboard of 2mm × 2mm, and is placed on a laser processing platform, at this time, a camera can select any position capable of shooting the object to be processed, and at this time, the camera has performed a calibration step of acquiring an external parameter and a camera distortion coefficient, and selects any 3 non-collinear points on the object to be processed on the left side, and by using the method of the present invention, a desired processing point and an obtained transformation matrix are input, so as to obtain a processing point at an actual spatial position, and perform precision processing, thereby obtaining a processing effect shown in the right drawing. In the implementation process of the embodiment, the position of the camera is kept unchanged, the position of the object to be processed can be rotated or moved randomly, and the obtained results are consistent. The feasibility and the accuracy of the invention are proved by the example.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (8)
1. An accurate measurement positioning method based on a calibration algorithm is characterized by comprising the following steps:
the method comprises the following steps: acquiring sub-pixel coordinates of corner points of a shot picture, performing multi-shot of changing any position and any posture on a chessboard calibration plate with known size parameters by using a camera with fixed focal length to acquire an image, converting the image into a gray-scale image, binarizing the processed gray-scale image, judging whether the corner points exist by using a corner point detection algorithm, and if the corner points exist, acquiring accurate sub-pixel coordinates of the corner points;
step two: acquiring internal parameters of camera equipment, defining corresponding world coordinates corresponding to angular points of an image, acquiring one-to-one correspondence between the world coordinates of the angular points and the image coordinates, selecting the angular points of a plurality of groups of images and points on a corresponding world coordinate system, and acquiring the internal parameters and distortion coefficients for shooting the group of image camera equipment through a calibration algorithm;
step three: acquiring external parameters of camera equipment for shooting the group of pictures, keeping internal parameters such as focal length of the shooting camera unchanged, selecting three non-collinear points around the object to be processed on a laser processing plane, shooting the object to be processed by the camera at any position and in any posture to obtain an image, and obtaining the external parameters of the shot camera equipment through a multi-point positioning algorithm;
step four: fixing the position of the camera with the obtained internal and external parameters, keeping the imaging of the object to be processed in the camera view finding range, moving the object to be processed at will, acquiring the moved image again by the camera, obtaining pixel points on the shot image pixel by pixel, obtaining the visual relation between the laser processing plane and the image through the conversion matrix calculated by the internal and external parameters, and performing precision processing operation on any position on the image on the laser processing plane.
2. The method for accurate measurement and positioning based on calibration algorithm as claimed in claim 1, wherein in the step one, the gray scale map of the image is to set the gray scale value of the image to 0 to 255, 255 for white and 0 for black; the white and black are divided into a plurality of equal steps according to the logarithmic relation, and the gray level is divided into 256 steps.
3. The calibration algorithm based precise measurement positioning method according to claim 1, wherein in the step one, the corner point detection algorithm comprises the following steps:
the method comprises the following steps: preprocessing an image, binarizing the image, and converting a gray level image into a binary image;
step two: searching adjacent grids of each grid, recording the number of the adjacent grids, and classifying all the adjacent grids according to the principle that all the grids in the class are adjacent;
step three: judging whether the squares in each class are the sought chessboard squares or not according to the known number of the angular points, confirming whether the positions and the number of the squares are correct or not, if so, extracting the connection points of any two squares of the sought chessboard squares, namely the sought angular points, and obtaining the accurate sub-pixel coordinates of all the angular points; if not, the chessboard angular point detection fails.
4. The calibration algorithm based precise measurement positioning method according to claim 1, wherein in the second step, the calibration algorithm comprises the following steps:
the method comprises the following steps: defining world coordinate points corresponding to the coordinates of the angular points of the chessboard pattern calibration board on the image, wherein the world coordinate points need to be the same as the theoretical condition, namely the coordinate difference between two angular points is the real distance between the chessboard pattern calibration board grids used for shooting;
step two: and (4) inputting the world coordinates and the pixel coordinates of the defined corresponding corner points into a function in a matrix form to calculate the internal parameters and the distortion coefficients of the camera.
5. A calibration algorithm based method for accurate measurement positioning according to claim 1 or 4, wherein the pixel coordinates and world coordinates are related as follows:
wherein u and v represent coordinates in a pixel coordinate system, s represents a scale factor, fx, fy, u0, v0 and gamma represent 5 camera internal parameters, R and t represent camera external parameters, and Xw, Yw and Zw represent coordinates in a world coordinate system, namely, a plurality of sets of world coordinates and corresponding pixel coordinate points are input to obtain the required camera internal parameters.
6. The calibration algorithm based precise measurement positioning method according to claim 1 or 4, wherein the relation of the distortion coefficients is as follows:
u1＝u+(u-u0)[k1(x2+y2)+k2(x2+y2)2]
v1＝v+(v-v0)[k1(x2+y2)+k2(x2+y2)2]
wherein, (u, v) represents pixel coordinates of ideal distortion-free, (u1, v1) represents pixel coordinates in the case of distortion of an actual radial image, and (u0, v0) represents principal points, and is obtained directly in the obtained internal reference matrix regardless of the presence or absence of distortion.
7. The calibration algorithm based precise measurement positioning method according to claim 1, wherein the camera equipment external parameter acquisition comprises the following steps: keeping the internal parameters such as the focal length of a camera and the like unchanged, placing an object to be processed on a laser processing operation platform, selecting three non-collinear points around the object to be processed on a laser processing plane, shooting the object to be processed by using a laser, shooting an image containing the object to be processed and the three non-collinear points shot by using the laser by using the camera at any position and posture of a paraxial of a laser processing platform, and obtaining the external parameters of the shooting camera at the moment through a multi-point positioning algorithm.
8. The calibration algorithm based precise measurement positioning method according to claim 1 or 7, characterized in that: the multipoint positioning algorithm comprises the following steps:
the method comprises the following steps: inputting laser processing plane coordinates of three points around an object to be processed selected on a laser processing plane;
step two: inputting corresponding world coordinates of three points on a laser processing plane which is shot by the laser;
step three: and by means of cosine law, a translation and rotation matrix of the camera system image coordinate system shot at the moment relative to the world coordinate system can be obtained by utilizing a point cloud registration method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110070836.9A CN112767494A (en) | 2021-01-19 | 2021-01-19 | Precise measurement positioning method based on calibration algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110070836.9A CN112767494A (en) | 2021-01-19 | 2021-01-19 | Precise measurement positioning method based on calibration algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112767494A true CN112767494A (en) | 2021-05-07 |
Family
ID=75703264
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110070836.9A Pending CN112767494A (en) | 2021-01-19 | 2021-01-19 | Precise measurement positioning method based on calibration algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112767494A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113888640A (en) * | 2021-09-07 | 2022-01-04 | 浙江大学 | Improved calibration method suitable for unmanned aerial vehicle pan-tilt camera |
| CN115174879A (en) * | 2022-07-18 | 2022-10-11 | 峰米(重庆)创新科技有限公司 | Projection picture correction method, projection picture correction device, computer equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106595700A (en) * | 2015-10-15 | 2017-04-26 | 南京理工大学 | Target channel space reference calibration method based on three-point coordinate measurement |
| CN109949373A (en) * | 2019-03-22 | 2019-06-28 | 深圳市博维远景科技有限公司 | A kind of improved checkerboard angle point detection process |
| CN110449749A (en) * | 2019-07-19 | 2019-11-15 | 东莞理工学院 | A kind of laser cutting scanning system |
| CN110751680A (en) * | 2019-10-17 | 2020-02-04 | 中国科学院云南天文台 | Image processing method with fast alignment algorithm |
| CN111429532A (en) * | 2020-04-30 | 2020-07-17 | 南京大学 | Method for improving camera calibration accuracy by utilizing multi-plane calibration plate |
| CN111922510A (en) * | 2020-09-24 | 2020-11-13 | 武汉华工激光工程有限责任公司 | Laser visual processing method and system |
| CN112161586A (en) * | 2020-11-20 | 2021-01-01 | 苏州睿牛机器人技术有限公司 | Line structured light vision sensor calibration method based on coding checkerboard |
-
2021
- 2021-01-19 CN CN202110070836.9A patent/CN112767494A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106595700A (en) * | 2015-10-15 | 2017-04-26 | 南京理工大学 | Target channel space reference calibration method based on three-point coordinate measurement |
| CN109949373A (en) * | 2019-03-22 | 2019-06-28 | 深圳市博维远景科技有限公司 | A kind of improved checkerboard angle point detection process |
| CN110449749A (en) * | 2019-07-19 | 2019-11-15 | 东莞理工学院 | A kind of laser cutting scanning system |
| CN110751680A (en) * | 2019-10-17 | 2020-02-04 | 中国科学院云南天文台 | Image processing method with fast alignment algorithm |
| CN111429532A (en) * | 2020-04-30 | 2020-07-17 | 南京大学 | Method for improving camera calibration accuracy by utilizing multi-plane calibration plate |
| CN111922510A (en) * | 2020-09-24 | 2020-11-13 | 武汉华工激光工程有限责任公司 | Laser visual processing method and system |
| CN112161586A (en) * | 2020-11-20 | 2021-01-01 | 苏州睿牛机器人技术有限公司 | Line structured light vision sensor calibration method based on coding checkerboard |
Non-Patent Citations (1)
| Title |
|---|
| 周辉辉等: "经络可视化系统中的双目立体视觉定位", 《中国医疗设备》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113888640A (en) * | 2021-09-07 | 2022-01-04 | 浙江大学 | Improved calibration method suitable for unmanned aerial vehicle pan-tilt camera |
| CN113888640B (en) * | 2021-09-07 | 2024-02-02 | 浙江大学 | Improved calibration method suitable for unmanned aerial vehicle pan-tilt camera |
| CN115174879A (en) * | 2022-07-18 | 2022-10-11 | 峰米(重庆)创新科技有限公司 | Projection picture correction method, projection picture correction device, computer equipment and storage medium |
| CN115174879B (en) * | 2022-07-18 | 2024-03-15 | 峰米(重庆)创新科技有限公司 | Projection screen correction method, apparatus, computer device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110555889B (en) | CALTag and point cloud information-based depth camera hand-eye calibration method | |
| CN110021046B (en) | External parameter calibration method and system for camera and laser radar combined sensor | |
| CN109215108B (en) | Panoramic three-dimensional reconstruction system and method based on laser scanning | |
| CN110689579A (en) | Rapid monocular vision pose measurement method and measurement system based on cooperative target | |
| CN111369630A (en) | Method for calibrating multi-line laser radar and camera | |
| CN107270810B (en) | The projector calibrating method and device of multi-faceted projection | |
| CN109360230A (en) | A kind of method for registering images and system based on 2D camera Yu 3D camera | |
| CN111707187B (en) | Measuring method and system for large part | |
| CN112614188B (en) | Dot-matrix calibration board based on cross ratio invariance and identification method thereof | |
| CN113119129A (en) | Monocular distance measurement positioning method based on standard ball | |
| CN112767494A (en) | Precise measurement positioning method based on calibration algorithm | |
| CN111637851A (en) | Aruco code-based visual measurement method and device for plane rotation angle | |
| CN115082538A (en) | System and method for three-dimensional reconstruction of surface of multi-view vision balance ring part based on line structure light projection | |
| CN111047586A (en) | Pixel equivalent measuring method based on machine vision | |
| CN109506629B (en) | Method for calibrating rotation center of underwater nuclear fuel assembly detection device | |
| CN114062265A (en) | Method for evaluating stability of supporting structure of visual system | |
| Zexiao et al. | A novel approach for the field calibration of line structured-light sensors | |
| CN112935562A (en) | Laser precision machining method based on paraxial offline measurement | |
| CN109272551B (en) | Visual positioning method based on circular mark point layout | |
| Danyu et al. | A simple calibration method for line-structured light vision sensor based on planar target of different positions | |
| CN113983951B (en) | Three-dimensional target measuring method, device, imager and storage medium | |
| Ju et al. | Multi-camera calibration method based on minimizing the difference of reprojection error vectors | |
| WO2023060717A1 (en) | High-precision positioning method and system for object surface | |
| CN115810055A (en) | Annular structure light calibration method based on planar checkerboard | |
| CN110706292B (en) | Two-dimensional workbench error self-calibration method based on machine vision |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210507 |