CN109829950B - Method and device for detecting calibration parameters of binocular camera and automatic driving system - Google Patents
Method and device for detecting calibration parameters of binocular camera and automatic driving system Download PDFInfo
- Publication number
- CN109829950B CN109829950B CN201910037745.8A CN201910037745A CN109829950B CN 109829950 B CN109829950 B CN 109829950B CN 201910037745 A CN201910037745 A CN 201910037745A CN 109829950 B CN109829950 B CN 109829950B
- Authority
- CN
- China
- Prior art keywords
- parameters
- original
- calibration
- sub
- coordinates
- 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.)
- Active
Links
Images
Landscapes
- Image Analysis (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention provides a method and a device for detecting calibration parameters of a binocular camera and an automatic driving system, which are applied to a binocular camera system. The binocular camera calibration parameter detection method comprises the following steps: acquiring a sub-pixel coordinate of a corner point of a preset calibration plate; calibrating the sub-pixel coordinates for the first time to obtain original internal parameters and original external parameters of the sub-pixel coordinates; after random noise is added to the sub-pixel coordinates, the original internal parameters and the original external parameters, the calibration is carried out for the second time, and new internal parameters and new external parameters are obtained; respectively comparing the new internal parameters and the new external parameters with the original internal parameters and the original external parameters to obtain comparison results; and judging whether the comparison result meets a preset threshold condition, and determining the correctness of the original internal parameters and the original external parameters when the comparison result meets the preset threshold condition. The calibration parameters of the binocular camera system are detected, and the correctness and stability of the calibration parameters are verified.
Description
Technical Field
The invention relates to the field of binocular cameras, in particular to a method and a device for detecting calibration parameters of a binocular camera and an automatic driving system.
Background
In actual use, the binocular camera imaging system must be calibrated firstly, but the calibration purpose of the binocular camera is different from that of a monocular camera, mainly aiming at ensuring that the left camera and the right camera reach the polar line alignment state and further providing convenient data support for subsequent other processing steps.
As shown in FIG. 1, in the binocular imaging system, two optical centers are connected with each other (O)lOr) Intersection with the imaging plane (e)l，er) Image (P) of an object in space P on left and right imaging planes, called polesl，pr) Connection to pole (p)lel，prer) Referred to as left and right epipolar lines, respectively. The purpose of binocular camera calibration is as follows: (1) correcting distortion; (2) the polar lines are aligned.
However, due to the limitation of the processing and assembling processes, the binocular camera system cannot completely reach the theoretical design state after being calibrated, so how to evaluate the calibration result of the binocular camera does not have a detailed mode at present.
In view of this, the present invention is proposed.
Disclosure of Invention
The invention provides a method and a device for detecting calibration parameters of a binocular camera and an automatic driving system, which are used for solving the problem of detecting the correctness of the calibration parameters of the binocular camera in the prior art.
In order to achieve the above object, according to an aspect of the present invention, a method for detecting calibration parameters of a binocular camera is provided, and the following technical solution is adopted:
the binocular camera calibration parameter detection method comprises the following steps: acquiring a sub-pixel coordinate of a corner point of a preset calibration plate; calibrating the sub-pixel coordinates for the first time to obtain original internal parameters and original external parameters of the sub-pixel coordinates; after random noise is added to the sub-pixel coordinates, the original internal parameters and the original external parameters, calibrating for the second time to obtain new internal parameters and new external parameters; comparing the new internal parameters and the new external parameters with the original internal parameters and the original external parameters respectively to obtain comparison results; and judging whether the comparison result meets a preset threshold condition, and determining the correctness of the original internal parameter and the original external parameter when the comparison result meets the preset threshold condition.
According to another aspect of the present invention, a binocular camera calibration parameter detection apparatus is provided, and the following technical scheme is adopted:
the binocular camera calibration parameter detection device comprises: the acquisition module is used for acquiring the sub-pixel coordinates of the corner points of the preset calibration plate; the first calibration module is used for carrying out first calibration on the sub-pixel coordinates to obtain original internal parameters and original external parameters of the sub-pixel coordinates; the second calibration module is used for performing second calibration after random noise is added to the sub-pixel coordinates, the original internal parameters and the original external parameters to obtain new internal parameters and new external parameters; the comparison module is used for comparing the new internal parameters and the new external parameters with the original internal parameters and the original external parameters respectively to obtain comparison results; and the determining module is used for judging whether the comparison result meets a preset threshold condition or not, and determining the correctness of the original internal parameter and the original external parameter when the comparison result meets the preset threshold condition.
According to another aspect of the invention, an automatic driving system is provided, and the following technical scheme is adopted:
the automatic driving system comprises the detection device.
According to the invention, the calibration board corner points of the target chessboard are extracted, random noise is added after the coordinates of the corner points are calibrated, and then the calibration parameters are subjected to constraint detection, so that the calibration correctness of the internal and external parameters in the calibration process of the binocular camera is verified.
Drawings
In order to more clearly illustrate the embodiments of the present application or technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a schematic diagram of the basic principle of binocular calibration according to the background art of the present invention;
fig. 2 is a flowchart illustrating a method for detecting calibration parameters of a binocular camera according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a corner sub-pixel accurate extraction method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a binocular camera calibration parameter detection apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 2 is a flowchart illustrating a method for detecting calibration parameters of a binocular camera according to an embodiment of the present invention.
Referring to fig. 2, a method for detecting calibration parameters of a binocular camera includes:
s101: acquiring a sub-pixel coordinate of a corner point of a preset calibration plate;
s103: calibrating the sub-pixel coordinates for the first time to obtain original internal parameters and original external parameters of the sub-pixel coordinates;
s105: after random noise is added to the sub-pixel coordinates, the original internal parameters and the original external parameters, calibrating for the second time to obtain new internal parameters and new external parameters;
s107: comparing the new internal parameters and the new external parameters with the original internal parameters and the original external parameters respectively to obtain comparison results;
s109: and judging whether the comparison result meets a preset threshold condition, and determining the correctness of the original internal parameter and the original external parameter when the comparison result meets the preset threshold condition.
In step S101, corner sub-pixel coordinates of a preset calibration plate are obtained. The specific method comprises the following steps:
the preset calibration board is selected as the chessboard grid calibration board in this embodiment. And carrying out three times of shooting on the chessboard pattern calibration plate under different spatial poses, and carrying out primary positioning on the corner point position of the shot image by using a mode based on gray gradient. Namely, the gray level image is respectively subjected to first-order difference from left to right and from top to bottom, and the local extremum of the first-order difference image is extracted as shown in formula (1). And it is required that the differential pixel values for these extreme positions should be greater than 150 or less than-150.
Where corner (x, y) represents the extractionIs equal in value to a particular neighborhood in the x-direction or y-direction
Inner maximum value
Or minimum value
And (3) performing sub-pixel coordinate extraction on the corner point coordinates (x, y) acquired by the formula (1). As a preferred implementation manner, fig. 3 shows a schematic diagram of a corner sub-pixel accurate extraction method according to an embodiment of the present invention.
Referring to fig. 3, it is assumed that a start corner q is near the actual sub-pixel corner. p points are in the neighborhood around the q point, and if the p point is inside a homogeneous region, as in the case of neighhorwood shown in (a) the graph, the gradient of the p point is 0,
if the p point is on the edge, as in the case of Gradient on edge shown in (b), the Gradient direction of the p point is perpendicular to the edge direction,
vertically upwards. If the direction of the vector q-p is consistent with the edge direction, the dot product operation result of the gradient vector of the q-p vector and the p point is 0. The gradients of many sets of points and the related vectors q-p can be collected near the initial corner (the initial corner may not be on the edge), where q is the more accurate corner position that we require, and then the dot product of the vectors of each set is set to 0, and based on this idea, the equations with the dot product of 0 are combined to form a system equation, and the solution of the system equation is the more accurate corner sub-pixel position. The new q point is taken as the center of the region, and the method can be continuously used for iteration to obtain the sub-pixel coordinates of the corner point with high precision.
In step S103, the sub-pixel coordinates obtained in step S101 are calibrated for the first time, so as to obtain the original internal parameters and the original external parameters of the sub-pixel coordinates. In step S105, after random noise is added to the sub-pixel coordinates, the original internal parameters, and the original external parameters, a second calibration is performed to obtain new internal parameters and new external parameters.
Specifically, random uniform noise between [ -0.1,0.1] is added to the sub-pixel coordinates.
Further, uniform random noise between Δ ∈ [ -0.1,0.1] is added to the abscissa and ordinate of each corner coordinate (x, y) according to the following formula (2).
x is x + delta, y is y + delta formula (2)
Furthermore, random uniform noise is added to the original external parameters by the following method:
random uniform noise between [ -1,1] is added to the original extrinsic parameters according to equation (3).
Where om1 represents the rotation angle around the x-axis, om2 represents the rotation angle around the y-axis, and om3 represents the rotation angle around the z-axis; t1 denotes translation in the x-axis direction, t2 denotes translation in the y-axis direction, and t3 denotes translation in the z-axis direction; a uniform random noise between delta e-0.1, 0.1.
In steps S107 to S109, comparing the new internal parameter and the new external parameter with the original internal parameter and the original external parameter, respectively, to obtain a comparison result; and then judging whether the comparison result meets a preset threshold condition, and determining the correctness of the original internal parameter and the original external parameter when the comparison result meets the preset threshold condition.
By re-calibrating the sub-pixel coordinates and the original internal parameters after the uniform random noise is added, the obtained calibrated internal parameters are approximately equal to the calibrated internal parameters before the random noise is added. If the condition is met, the calibration of the original internal parameters and the original external parameters is stable and correct. Otherwise, the original internal parameter and the original external parameter are not calibrated correctly.
As a preferred implementation, comparing the new internal parameter and the new external parameter with the original internal parameter and the original external parameter, respectively, to obtain a comparison result; then, judging whether the comparison result meets a preset threshold condition, and if the comparison result does not meet the preset threshold condition, the detection method further comprises the following steps:
removing the external parameters and the internal parameters from the corner point coordinates of the binocular image with the new internal parameters and the new external parameters to obtain original coordinates;
and carrying out binocular calibration again on the original coordinates.
Specifically, the removing the extrinsic parameters and the intrinsic parameters from the corner coordinates of the binocular image with the new intrinsic parameters and the new extrinsic parameters to obtain the original coordinates includes: the method for removing the external parameters comprises the following steps:
the left corner point coordinate LeftPoint0 is unchanged, and the right corner point coordinate RightPoint0 removes the influence of the rotation matrix R and the translation matrix T of the extrinsic parameters, as shown in formula (4):
the method for removing the internal parameters comprises the following steps:
respectively mapping the left image and the right image to angular point coordinates under a normalized coordinate system;
wherein, C refers to the coordinate of the principal point in the internal reference calibration, and f refers to the equivalent focal length in the internal reference calibration.
For LeftPoint2 and RightPoint2, a sequence of two-dimensional point coordinates, where each corner point coordinate may be represented as (x)i,yi) Wherein x isiDenotes the abscissa, y, of the ith point in leftPoint2 (or rightPoint2)iIs expressed by LeftPoint2 (or RightPoint 2). Lens distortion is then added according to the following formula. Radial distortion:
tangential distortion:
wherein k1 and k2 are radial distortion parameters, p1 and p2 are tangential distortion parameters, and the distortion parameters are calculated by a Zhang Yong calibration method together with calibration internal parameters.
From the above equation (6) and equation (7), the distortion-added corner point coordinates LeftPoint3 and RightPoint3 can be obtained. Then, the coordinates of the upper corner points of the original image can be obtained by using the formula (8).
And (3) performing binocular calibration based on a Zhang Zhengyou method on the coordinates of the corner points of the original image obtained by the formula (8), wherein the used internal parameters are the original calibrated internal parameters, and the generated external parameters have little difference with the original calibrated external parameters. Otherwise, the external parameters of the original calibration are considered to be incorrect.
Through the verification steps, the correctness of the calibration of the internal parameter and the external parameter in the calibration process of the binocular camera can be verified, and the constraint of the two verifications is met, which shows that the calibration of the internal parameter and the external parameter of the binocular camera is correct and stable.
Fig. 4 is a schematic structural diagram of a binocular camera calibration parameter detection apparatus according to an embodiment of the present invention.
Referring to fig. 4, the apparatus for detecting calibration parameters of a binocular camera includes: the acquisition module 40 is used for acquiring the sub-pixel coordinates of the corner points of the preset calibration plate; the first calibration module 42 is configured to perform first calibration on the sub-pixel coordinate to obtain an original internal parameter and an original external parameter of the sub-pixel coordinate; a second calibration module 44, configured to perform a second calibration after adding random noise to the sub-pixel coordinates, the original internal parameters, and the original external parameters, so as to obtain new internal parameters and new external parameters; a comparison module 46, configured to compare the new internal parameter and the new external parameter with the original internal parameter and the original external parameter, respectively, to obtain a comparison result; a determining module 48, configured to determine whether the comparison result meets a preset threshold condition, and determine correctness of the original internal parameter and the original external parameter when the comparison result meets the preset threshold condition.
Optionally, the obtaining module 40 includes: a shooting module (not shown in the figure) for shooting the preset calibration plate for preset times in different spatial poses to obtain a shot image; a positioning module (not shown) for performing preliminary positioning of the corner positions of the captured image by using a gray gradient-based manner, i.e. performing first-order difference from left to right and from top to bottom on the gray image, and extracting local extrema of the first-order difference map as formula (1), and requiring that the difference pixel values of the extremum positions should be greater than 150 or less than-150:
where corenrx, y represents the extracted corner coordinates (x, y) with a value equal to the x-direction or y-direction of a particular neighborhood
Inner maximum value
Or minimum value
Optionally, the second calibration module 44 is further configured to:
adding random uniform noise between [ -0.1,0.1] to the sub-pixel coordinates;
adding uniform random noise between delta E [ -0.1,0.1] to the horizontal and vertical coordinates of each corner point coordinate (x, y) of the original internal parameters according to the following formula (2);
x + Δ, y + Δ formula (2)
Adding random uniform noise between [ -1,1] to the original external parameters according to a formula (3);
where om1 represents the rotation angle around the x-axis, om2 represents the rotation angle around the y-axis, and om3 represents the rotation angle around the z-axis; t1 denotes translation in the x-axis direction, t2 denotes translation in the y-axis direction, and t3 denotes translation in the z-axis direction; a uniform random noise between delta e-0.1, 0.1.
Optionally, the detection device further comprises: a removing module (not shown) for removing the external parameters and the internal parameters from the corner point coordinates of the corrected binocular image to obtain original coordinates; and the recalibration module (not shown) is used for carrying out binocular calibration again on the original coordinates.
The automatic driving system provided by the invention comprises the detection device.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A method for detecting calibration parameters of a binocular camera is characterized by comprising the following steps:
acquiring a sub-pixel coordinate of a corner point of a preset calibration plate;
calibrating the sub-pixel coordinates for the first time to obtain original internal parameters and original external parameters of the sub-pixel coordinates;
after random noise is added to the sub-pixel coordinates, the original internal parameters and the original external parameters, calibrating for the second time to obtain new internal parameters and new external parameters;
comparing the new internal parameters and the new external parameters with the original internal parameters and the original external parameters respectively to obtain comparison results;
judging whether the comparison result meets a preset threshold condition, and determining the correctness of the original internal parameter and the original external parameter when the comparison result meets the preset threshold condition;
the acquiring of the sub-pixel coordinates of the corner point of the preset calibration plate comprises the following steps:
shooting the preset calibration plate for preset times under different spatial poses to obtain shot images;
using a gray gradient-based mode to perform preliminary positioning on the diagonal positions of the shot image, namely performing left-to-right and top-to-bottom first-order differences on the gray image respectively, and extracting local extrema of the first-order difference image through a formula (1), wherein the difference pixel values of the extrema positions are required to be more than 150 or less than-150:
where corner (x, y) represents the extracted corner coordinates (x, y) with a value equal to the x-direction or y-direction of a particular neighborhood
Inner maximum value
Or minimum value
2. The method according to claim 1, wherein the obtaining of the new internal parameters and the new external parameters comprises:
adding random uniform noise between [ -0.1,0.1] to the sub-pixel coordinates;
adding uniform random noise between delta E [ 0.1,0.1] to the horizontal and vertical coordinates of each corner point coordinate (x, y) of the original internal parameters according to the following formula (2);
x + Δ, y + Δ formula (2)
Adding random uniform noise between [ -1,1] to the original external parameters according to a formula (3);
where om1 represents the rotation angle around the x-axis, om2 represents the rotation angle around the y-axis, and om3 represents the rotation angle around the z-axis; t1 denotes translation in the x-axis direction, t2 denotes translation in the y-axis direction, and t3 denotes translation in the z-axis direction; a uniform random noise between delta e-0.1, 0.1.
3. The detection method according to claim 1, wherein when the comparison result does not satisfy a preset threshold condition, the detection method further comprises:
removing the external parameters and the internal parameters from the corner point coordinates of the binocular image with the new internal parameters and the new external parameters to obtain original coordinates;
and carrying out binocular calibration again on the original coordinates.
4. The utility model provides a detection apparatus of binocular camera calibration parameter which characterized in that includes:
the acquisition module is used for acquiring the sub-pixel coordinates of the corner points of the preset calibration plate;
the first calibration module is used for carrying out first calibration on the sub-pixel coordinates to obtain original internal parameters and original external parameters of the sub-pixel coordinates;
the second calibration module is used for performing second calibration after random noise is added to the sub-pixel coordinates, the original internal parameters and the original external parameters to obtain new internal parameters and new external parameters;
the comparison module is used for comparing the new internal parameters and the new external parameters with the original internal parameters and the original external parameters respectively to obtain comparison results;
the determining module is used for judging whether the comparison result meets a preset threshold condition or not, and determining the correctness of the original internal parameter and the original external parameter when the comparison result meets the preset threshold condition;
the acquisition module includes:
the shooting module is used for shooting the preset calibration plate for preset times under different spatial poses to obtain a shot image;
a positioning module, configured to perform preliminary positioning on the positions of the diagonal points of the captured image in a gray-scale-gradient-based manner, that is, performing left-to-right and top-to-bottom first-order differences on the gray-scale image, and extracting local extrema of the first-order difference map according to formula (1), where the difference pixel values of the extrema positions are required to be greater than 150 or less than-150:
where corner (x, y) represents the extracted corner coordinates (x, y) with a value equal to the x-direction or y-direction of a particular neighborhood
Inner maximum value
Or minimum value
5. The detection apparatus as claimed in claim 4, wherein the second calibration module is further configured to: adding random uniform noise between [ -0.1,0.1] to the sub-pixel coordinates;
adding uniform random noise between delta E [ -0.1,0.1] to the horizontal and vertical coordinates of each corner point coordinate (x, y) of the original internal parameters according to the following formula (2);
x + Δ, y + Δ formula (2)
Adding random uniform noise between [ -1,1] to the original external parameters according to a formula (3);
where om1 represents the rotation angle around the x-axis, om2 represents the rotation angle around the y-axis, and om3 represents the rotation angle around the z-axis; t1 denotes translation in the x-axis direction, t2 denotes translation in the y-axis direction, and t3 denotes translation in the z-axis direction; Δ ∈ [ 0.1,0.1] of uniform random noise.
6. The detection device of claim 4, further comprising:
the removing module is used for removing the external parameters and the internal parameters from the corner point coordinates of the binocular image with the new internal parameters and the new external parameters to obtain original coordinates;
and the recalibration module is used for recalibrating the binocular calibration of the original coordinates.
7. An autopilot system, characterized in that it comprises a detection device according to any one of claims 4 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910037745.8A CN109829950B (en) | 2019-01-16 | 2019-01-16 | Method and device for detecting calibration parameters of binocular camera and automatic driving system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910037745.8A CN109829950B (en) | 2019-01-16 | 2019-01-16 | Method and device for detecting calibration parameters of binocular camera and automatic driving system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109829950A CN109829950A (en) | 2019-05-31 |
| CN109829950B true CN109829950B (en) | 2021-05-18 |
Family
ID=66860333
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910037745.8A Active CN109829950B (en) | 2019-01-16 | 2019-01-16 | Method and device for detecting calibration parameters of binocular camera and automatic driving system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109829950B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105588563A (en) * | 2016-01-15 | 2016-05-18 | 武汉光庭科技有限公司 | Joint calibration method of binocular camera and inertial navigation unit in automatic driving |
| CN108629810A (en) * | 2017-03-23 | 2018-10-09 | 展讯通信（上海）有限公司 | Scaling method, device and the terminal of binocular camera |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012019370A1 (en) * | 2010-08-13 | 2012-02-16 | 武汉大学 | Camera calibration method, image processing device and motor vehicle |
| CN105631853B (en) * | 2015-11-06 | 2018-01-30 | 湖北工业大学 | Vehicle-mounted binocular camera demarcation and Verification method |
| CN105608693B (en) * | 2015-12-18 | 2018-08-28 | 上海欧菲智能车联科技有限公司 | The calibration system and method that vehicle-mounted panoramic is looked around |
-
2019
- 2019-01-16 CN CN201910037745.8A patent/CN109829950B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105588563A (en) * | 2016-01-15 | 2016-05-18 | 武汉光庭科技有限公司 | Joint calibration method of binocular camera and inertial navigation unit in automatic driving |
| CN108629810A (en) * | 2017-03-23 | 2018-10-09 | 展讯通信（上海）有限公司 | Scaling method, device and the terminal of binocular camera |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109829950A (en) | 2019-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107633536B (en) | Camera calibration method and system based on two-dimensional plane template | |
| CN108230397B (en) | Multi-view camera calibration and correction method and apparatus, device, program and medium | |
| CN112070845B (en) | Calibration method and device of binocular camera and terminal equipment | |
| CN111179356A (en) | Binocular camera calibration method, device and system based on Aruco code and calibration board | |
| JP2003254748A (en) | Stereo image characteristic inspection system | |
| CN113920205B (en) | Calibration method of non-coaxial camera | |
| CN113610929B (en) | Combined calibration method of camera and multi-line laser | |
| CN114549652A (en) | Camera calibration method, device, equipment and computer readable medium | |
| JP5228614B2 (en) | Parameter calculation apparatus, parameter calculation system and program | |
| Ding et al. | A robust detection method of control points for calibration and measurement with defocused images | |
| CN108520541B (en) | Calibration method of wide-angle camera | |
| CN114463437A (en) | Camera calibration method, device, equipment and computer readable medium | |
| CN109829950B (en) | Method and device for detecting calibration parameters of binocular camera and automatic driving system | |
| CN112907677B (en) | Camera calibration method and device for single-frame image and storage medium | |
| CN112489141A (en) | Production line calibration method and device for single board single-image relay lens of vehicle-mounted camera | |
| TWI424259B (en) | Camera calibration method | |
| EP3564747B1 (en) | Imaging device and imaging method | |
| CN115719387A (en) | 3D camera calibration method, point cloud image acquisition method and camera calibration system | |
| Hamzah et al. | A practical method for camera calibration in stereo vision mobile robot navigation | |
| CN114897851A (en) | Coordinate compensation method, device, equipment and medium based on central projection | |
| KR101703715B1 (en) | The apparatus for measuring camera principal point and the method thereof | |
| CN114648544A (en) | Sub-pixel ellipse extraction method | |
| CN115239816A (en) | Camera calibration method, system, electronic device and storage medium | |
| CN110232715B (en) | Method, device and system for self calibration of multi-depth camera | |
| CN115937321B (en) | Gesture detection method and device of electronic equipment |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |