A long-distance and large-field-of-view binocular camera calibration method and three-dimensional measurement method

Abstract

The present invention discloses a binocular camera calibration method and a three-dimensional measurement method with a long distance and a large field of view. The binocular camera calibration method comprises: configuring a binocular camera in a long-distance measurement scene, and calibrating the internal parameters of two cameras of the binocular camera respectively; selecting at least one calibration object in the long-distance measurement scene, all calibration objects are provided with a total of a plurality of feature points, and collecting a plurality of calibration object images respectively through the two cameras of the binocular camera; obtaining a plurality of groups of feature point pairs in the plurality of calibration object images respectively collected by the two cameras, combining the internal parameters of the two cameras, and calculating the three-dimensional coordinates of the plurality of groups of feature point pairs in the corresponding camera coordinate system; combining the three-dimensional coordinates of the plurality of groups of feature point pairs in the corresponding camera coordinate system, using a relative orientation algorithm, and calculating the rotation matrix and translation vector between the two cameras; adding a scale factor to the translation vector between the two cameras, and obtaining the translation matrix between the two cameras. The present invention is simple, easy to implement, flexible, convenient, and low in cost.

Description

Technical Field

[0001] The present invention relates to the field of vision-based three-dimensional measurement technology, and in particular to a long-distance and large-field-of-view binocular camera calibration method and a three-dimensional measurement method. Background Art

[0002] Binocular vision is often used in vision-based 3D measurement, among which camera calibration is the premise and basis of stereo vision measurement. Binocular calibration plays a vital role in photogrammetry and computer vision by solving the camera's intrinsic and extrinsic parameters to obtain the camera's geometric model and the relative position relationship between the left and right cameras. In the environment of long distance and large field of view, binocular vision measurement is often difficult, and the accuracy of camera calibration is difficult to guarantee.

[0003] Existing calibration methods include traditional calibration method, self-calibration method, and active vision calibration method. Among them, the traditional calibration method requires the use of precision-made calibration plates. Calibration is simple and accurate in close-range and small fields of view, but it is difficult to implement calibration using calibration plates due to the large field of view when calibrating at long distances. The self-calibration method can calibrate the internal and external parameters of the camera only through the matching relationship between multiple frames of images, without the need for external calibration objects. This method is flexible and convenient, but not very accurate, and is suitable for online calibration. The active vision calibration method requires adjusting the camera for rotation and translation, and the operation process is complicated and costly.

[0004] Therefore, the existing calibration methods have the following disadvantages: (1) Traditional methods and equipment are mostly suitable for close-range scene measurement, but not for long-distance and large field of view environments; (2) Large calibration plates are expensive to produce, difficult to operate, and time-consuming and labor-intensive to use; (3) They require the use of some expensive measuring instruments or equipment, such as total stations, theodolites, drones, etc.

[0005] The disclosure of the above background technology content is only used to assist in understanding the concept and technical solution of the present invention. It does not necessarily belong to the prior art of this patent application. In the absence of clear evidence that the above content has been disclosed on the filing date of this patent application, the above background technology should not be used to evaluate the novelty and creativity of the present application. Summary of the invention

[0006] In view of the fact that traditional methods are not applicable to long-distance three-dimensional measurement scenarios, and have the disadvantages of high cost and complex technical processes, the present invention provides a long-distance and large-field-of-view binocular camera calibration method and a three-dimensional measurement method.

[0007] In order to solve the above problems, the present invention adopts the following technical solutions:

[0008] The present invention discloses a long-distance and large-field binocular camera calibration method, comprising the following steps:

[0009] S1: configuring a binocular camera in a long-distance measurement scene, and calibrating the intrinsic parameters of two cameras of the binocular camera respectively;

[0010] S2: selecting at least one calibration object in the long-distance measurement scene, all of the calibration objects having a total of a plurality of feature points, and collecting a plurality of calibration object images respectively through two cameras of the binocular camera, wherein the distance between the binocular camera and the at least one calibration object is at least 100 m;

[0011] S3: Acquire multiple groups of feature point pairs from a plurality of images of the calibration object captured by two cameras respectively;

[0012] S4: combining the intrinsic parameters of the two cameras, calculating the three-dimensional coordinates of the plurality of sets of feature point pairs in the corresponding camera coordinate system;

[0013] S5: combining the three-dimensional coordinates of the plurality of sets of feature point pairs in the corresponding camera coordinate system, and using a relative orientation algorithm to calculate a rotation matrix and a translation vector between the two cameras;

[0014] S6: Add a scale factor to the translation vector between the two cameras to obtain the translation matrix between the two cameras.

[0015] Preferably, the step S1 of calibrating the intrinsic parameters of the two cameras of the binocular camera includes: the two cameras collect at least 8 groups of calibration plate images in different orientations within a range of 10 meters from the calibration plate, calibrate the cameras using a bundle adjustment algorithm, and calculate the intrinsic parameters of the two cameras.

[0016] Preferably, the distance between every two of the plurality of feature points in step S2 is greater than 5 m, and the distribution area of ​​the plurality of feature points in the collected calibration object image occupies at least 1 / 3 of the area of ​​the calibration object image.

[0017] Preferably, step S4 comprises: combining the intrinsic parameters of the two cameras, and using the following formula to calculate the three-dimensional coordinates of the plurality of feature point pairs in the corresponding camera coordinate system:

[0018]

[0019] In the formula, (X C ,Y C ,Z C ) represents the three-dimensional coordinates of the feature point in the corresponding camera coordinate system, dx is the pixel size, (u,v) is the pixel coordinates of the image point corresponding to the feature point in the image taken by the camera, (u0,v0) is the pixel coordinates of the camera principal point, (c x ,c y ) is the deviation of the camera principal point, (Δx, Δy) is the distortion parameter, and f is the focal length of the camera intrinsic parameter.

[0020] Preferably, step S5 comprises: combining the three-dimensional coordinates of the plurality of feature point pairs in the corresponding camera coordinate system, using an iterative algorithm to solve the following two equations to obtain a rotation matrix R and a translation vector t=(B x ,B y ,B z ) T :

[0021]

[0022]

[0023] In the formula, (X Cl ,Y Cl ,Z Cl ) T represents the coordinates of the image point of the feature point on the image captured by the first camera in the binocular camera in the camera coordinate system of the first camera, (X Cr ,Y Cr ,Z Cr ) T represents the coordinates of the image point of the feature point on the image captured by the second camera in the binocular camera in the camera coordinate system of the second camera, (X′ Cr ,Y′ Cr ,Z′ Cr ) T The coordinates of an image point of a feature point on an image captured by a second camera in the binocular camera in a transformed camera coordinate system of the second camera, where the transformed camera coordinate system of the second camera is obtained by performing a rotation transformation according to a rotation matrix R on the camera coordinate system of the second camera.

[0024] Preferably, step S6 comprises: using a rangefinder to measure the distance L between any two of the feature points, and using the following formula to calculate the proportional factor s:

[0025]

[0026] In the formula, (X a ,Y a ,Z a ) and (X b ,Y b ,Z b ) are respectively the three-dimensional coordinates of the two feature points measured by the rangefinder obtained by solving the intrinsic parameters of the two cameras and the rotation matrix and translation vector between the two cameras;

[0027] The translation matrix is ​​calculated based on the translation vector and the scale factor.

[0028] The present invention also discloses a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the steps of the above-mentioned binocular camera calibration method.

[0029] The present invention also discloses a long-distance and large-field three-dimensional measurement method, comprising calibrating the intrinsic parameters and extrinsic parameters of the binocular camera according to the above binocular camera calibration method, wherein the extrinsic parameters include a rotation matrix and a translation matrix between the two cameras, and:

[0030] S7: Perform feature detection on the object to be measured, and perform three-dimensional measurement on the object to be measured in combination with the intrinsic parameters and extrinsic parameters of the binocular camera.

[0031] Preferably, performing three-dimensional measurement of the object to be measured using the intrinsic parameters and extrinsic parameters of the binocular camera in step S7 specifically includes: solving the following equation using the least squares method to calculate the three-dimensional coordinates (X, Y, Z) of the object point:

[0032]

[0033] In the formula, (u l ,v l ) represents the pixel coordinates of the image point of the object point on the image taken by the first camera in the binocular camera, (u r ,v r ) represents the pixel coordinates of the image point of the object point on the image taken by the second camera in the binocular camera, A matrix consisting of the intrinsic parameters and extrinsic parameters of the first camera in the binocular camera is represented, A matrix consisting of intrinsic parameters and extrinsic parameters of the second camera in the binocular camera is represented.

[0034] The present invention also discloses a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the steps of the above-mentioned three-dimensional measurement method.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: the long-distance and large-field binocular camera calibration method proposed by the present invention is simple, easy to implement, flexible and convenient, with high calibration accuracy, and no expensive measuring instruments or equipment are used, so the cost is low. The long-distance and large-field three-dimensional measurement method proposed by the present invention solves the problem of difficulty and high cost of three-dimensional measurement under long-distance and large-field conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1It is a flow chart of a binocular camera calibration method with a long distance and a large field of view according to a preferred embodiment of the present invention;

[0037] Figure 2 It is a schematic diagram of the camera center projection;

[0038] Figure 3 It is the coplanar relationship diagram of the two cameras of the binocular camera;

[0039] Figure 4 It is a flow chart of a long-distance and large-field-of-view three-dimensional measurement method according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0041] It should be noted that when a component is referred to as being "fixed to" or "disposed on" another component, it can be directly on the other component or indirectly on the other component. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element. In addition, connection can be used for both fixing and circuit / signal communication.

[0042] It should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0043] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0044] like Figure 1 As shown, the preferred embodiment of the present invention discloses a long-distance and large-field binocular camera calibration method, comprising the following steps:

[0045] S1: Configure a binocular camera in the long-distance measurement scene and calibrate the intrinsic parameters of the two cameras of the binocular camera respectively;

[0046] In a specific embodiment, two CMOS cameras, two tripods, two computers, and one laser rangefinder are configured in a long-distance measurement scene, wherein the two CMOS cameras are fixed on the two tripods, and the two computers are respectively connected to the two CMOS cameras, wherein the base distance between the two cameras is greater than 20m.

[0047] The intrinsic parameters of the two cameras of the binocular camera are calibrated separately, specifically including: the two cameras collect 15 groups of calibration plate images in different orientations within a range of 10 meters from the calibration plate, calibrate the cameras using the bundle adjustment algorithm, and calculate the intrinsic parameters of the two cameras.

[0048] Specifically, when an object point P on the calibration plate is projected from the optical center S of the camera to the image point p, the following coordinate system transformation relationship exists. Figure 2 As shown, O W -X W Y W Z W is the world coordinate system, O C -X C Y C Z C is the camera coordinate system, o-xy is the image plane coordinate system, and o′-uv is the image coordinate system.

[0049] 1) The transformation relationship from the world coordinate system to the camera coordinate system is:

[0050] The transformation of the object point in the world coordinate system to the camera coordinate system can be obtained by the rotation matrix and translation matrix It is expressed as:

[0051]

[0052] In the formula, (X C ,Y C ,Z C ) is the coordinate of the object point P in the camera coordinate system, (X W ,Y W ,Z W ) is the coordinate of the object point P in the world coordinate system, (X S ,Y S ,Z S ) is the coordinate of the camera optical center S in the world coordinate system.

[0053] 2) The transformation relationship from the camera coordinate system to the image plane coordinate system is:

[0054]

[0055] In the formula, (X C ,Y C ,Z C) is the coordinate of the object point P in the camera coordinate system, (x, y) is the image coordinate of the image point p, and f is the focal length.

[0056] 3) The transformation relationship from the image plane coordinate system to the image pixel coordinate system is:

[0057]

[0058] Where (x, y) is the image coordinate of the image point p, (u0, v0) is the pixel coordinate of the principal point (the pixel coordinate of the center of the image), (dx, dy) is the camera pixel size, and (u, v) is the pixel coordinate of the image point p.

[0059] After considering the actual camera principal point deviation and lens distortion, the collinear equation of the actual camera imaging is obtained as follows:

[0060]

[0061] Where (x, y) is the image coordinate of the image point p, (c x ,c y ) is the principal point deviation, f is the focal length, and (Δx, Δy) is the lens distortion deviation.

[0062] An iterative algorithm is used to solve the nonlinear equation (Equation (4)) and calculate the camera intrinsic parameters.

[0063] S2: at least one calibration object is selected in the long-distance measurement scene, and a plurality of feature points are set on all the calibration objects. A plurality of calibration object images are respectively collected by two cameras of the binocular camera, wherein the distance between the binocular camera and at least one calibration object is at least 100m;

[0064] In a specific embodiment, some stationary objects with feature point information or texture information are selected as calibration objects, such as large buildings; if the calibration object feature points or texture information in the environment are very few, some large-format calibration objects with only one feature point are added, and then two cameras are used to collect multiple calibration object images. Among them, the distance between the binocular camera and the long-distance calibration object is about 150m (for example, 100m to 200m), the distance between the calibration object feature points is greater than 5m (more preferably, the distance between the calibration object feature points is greater than 10m), and the distribution area of ​​the calibration object feature points is greater than 1 / 3 of the image area.

[0065] S3: Acquire multiple sets of feature point pairs from multiple calibration object images captured by two cameras respectively;

[0066] In one embodiment, at least 15 feature point pairs in the left and right images are obtained by manual picking and feature point detection in computer software; specifically, an image detection algorithm is used to identify target feature points, and manual inspection is performed in the software interface to obtain feature point pairs with smaller deviations.

[0067] S4: combining the intrinsic parameters of the two cameras, calculating the three-dimensional coordinates of multiple sets of feature point pairs in the corresponding camera coordinate system;

[0068] In one embodiment, the image coordinates of the feature points are dedistorted according to the intrinsic parameters of the camera, and the three-dimensional coordinates of the image points corresponding to the multiple groups of feature points in the camera coordinate system are calculated using the principle of projective geometry.

[0069] Specifically, if Figure 2 As shown, the image point of the object point P in the camera is p (that is, the image point p is the image point corresponding to the object point P in the image taken by the camera). Assume that the pixel coordinates of the image point p are (u, v), the pixel coordinates of the camera principal point are (u0, v0), the pixel size is dx, the focal length of the camera internal parameters is f, the distortion parameters are (Δx, Δy), and the principal point deviation is (c x ,c y ). Then, the image point p is in O C -X C Y C Z C The three-dimensional coordinates (X C ,Y C ,Z C )for:

[0070]

[0071] S5: Combine the three-dimensional coordinates of multiple sets of feature point pairs in the corresponding camera coordinate system and use the relative orientation algorithm to calculate the rotation matrix and translation vector between the two cameras;

[0072] Specifically, if Figure 3 As shown in the figure, the image points of a point P in space on the left and right images taken by the left and right cameras are p l 、p r , the two camera coordinate systems are S Cl -X Cl Y Cl Z Cl and S Cr -X Cr Y Cr Z Cr , image point p l In S Cl -X Cl Y Cl Z Cl The coordinates in (X Cl ,Y Cl ,Z Cl ) T , image point p r In S Cr -X Cr YCr Z Cr The coordinates in (X Cr ,Y Cr ,Z Cr ) T , let the projection center S cr In S Cl -X Cl Y Cl Z Cl The coordinates in the coordinate system are (B x ,B y ,B z ) T , S Cr -X Cr Y Cr Z Cr To S Cl -X Cl Y Cl Z Cl The rotation matrix of image point p is R. r In S Cr -X′ Cr Y′ Cr Z′ Cr The coordinates in the coordinate system are (X′ Cr ,Y′ Cr ,Z′ Cr ) T , S Cr -X′ Cr Y′ Cr Z′ Cr By S Cr -X Cr Y Cr Z Cr According to the rotation matrix R, the coplanar relationship in the epipolar geometry is obtained:

[0073]

[0074] in,

[0075]

[0076] The coplanar equation for relative orientation is:

[0077]

[0078] The iterative algorithm is used to solve equations (6) and (7) to calculate the relative orientation elements and obtain the rotation matrix R and translation vector t = (B x ,B y ,B z ) T .

[0079] S6: adding a scale factor to the translation vector between the two cameras to obtain a translation matrix between the two cameras;

[0080] In one embodiment, a set of feature point distances are measured by a laser rangefinder, a scaling factor is added, and a translation matrix is ​​calculated.

[0081] Specifically, the distance L between any two feature points a and b is measured using a laser rangefinder, and the three-dimensional coordinates of the feature points a and b are solved using the camera's intrinsic parameters, rotation matrix, and translation vector, respectively: a ,Y a ,Z a ) and (X b ,Y b ,Z b ); Calculate the translation matrix and add the scale factor s to get the final camera extrinsic parameters. s is calculated by equation (8), and the translation matrix T is calculated by equation (9).

[0082]

[0083]

[0084] Through the above steps, the calibration of the binocular camera with a long distance and a large field of view is completed. It is simple, easy, flexible and convenient. The camera calibration method of bundle adjustment is adopted, and the calibration accuracy is high. No expensive measuring instruments or equipment are used, so the cost is low.

[0085] Another embodiment of the present invention discloses a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the steps of the above-mentioned binocular camera calibration method.

[0086] like Figure 4 As shown, the preferred embodiment of the present invention also discloses a long-distance and large-field-of-view three-dimensional measurement method, comprising: first obtaining the intrinsic parameters and extrinsic parameters of the binocular camera (including the rotation matrix and the translation matrix between the two cameras) through the above steps S1 to S6, and:

[0087] S7: Perform feature detection on the object to be measured, and perform three-dimensional measurement of the object to be measured by combining the intrinsic parameters and extrinsic parameters of the binocular camera.

[0088] In a specific embodiment, four objects to be tested are arranged at a distance of 150 m, and a circular marking point with a diameter of 800 mm×800 mm is arranged on the surface of the object to be tested; the image coordinates of the marking point of the object to be tested are calculated using a feature point detection algorithm.

[0089] Based on the principle of triangulation, the least square method is used to solve equation (10) and calculate the three-dimensional coordinates (X, Y, Z) of the marked points of the object to be measured.

[0090]

[0091]

[0092] In the formula, (u l ,v l ) is the pixel coordinate of the left image point (i.e., the image point of the object point on the image taken by the left camera), (u r ,v r ) is the pixel coordinate of the right image point (i.e., the image point of the object point on the image taken by the right camera), m is the matrix composed of the camera intrinsic parameters and extrinsic parameters, the superscript l of m represents the left camera, and the superscript r of m represents the right camera.

[0093] Finally, the point distance of the object to be measured in the world coordinate system can be calculated according to formula (11), and the results can be compared by measuring with a laser rangefinder.

[0094]

[0095] Where (X, Y, Z) is the three-dimensional coordinate of a marking point of the object to be measured, and (X′, Y′, Z′) is the three-dimensional coordinate of a marking point of the other object to be measured.

[0096] Through the above steps, a three-dimensional measurement method with a long distance and a large field of view is realized, which solves the problem of difficulty and high cost of three-dimensional measurement under the conditions of a long distance and a large field of view.

[0097] Another embodiment of the present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the steps of the above-mentioned three-dimensional measurement method.

[0098] The background section of the present invention may contain background information about the problem or environment of the present invention, rather than describing the prior art by others. Therefore, the content contained in the background section is not an admission by the applicant that the prior art is available.

[0099] The above content is a further detailed description of the present invention in combination with specific / preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, without departing from the concept of the present invention, it can also make several substitutions or modifications to these described embodiments, and these substitutions or modifications should be regarded as belonging to the protection scope of the present invention. In the description of this specification, the description of reference terms "an embodiment", "some embodiments", "preferred embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in combination with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, in the absence of mutual contradiction, the technical personnel in this field can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples. Although the embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions and modifications can be made herein without departing from the scope defined by the attached claims.

Claims

1. A long-distance and large-field binocular camera calibration method, characterized in that: The following steps are involved: S1: configuring a binocular camera in a long-distance measurement scene, and calibrating the intrinsic parameters of two cameras of the binocular camera respectively; S2: selecting at least one calibration object in the long-distance measurement scene, all of the calibration objects having a total of a plurality of feature points, and collecting a plurality of calibration object images respectively through two cameras of the binocular camera, wherein the distance between the binocular camera and the at least one calibration object is at least 100 m; S3: Acquire multiple groups of feature point pairs from a plurality of images of the calibration object captured by two cameras respectively; S4: combining the intrinsic parameters of the two cameras, calculating the three-dimensional coordinates of the plurality of sets of feature point pairs in the corresponding camera coordinate system; S5: combining the three-dimensional coordinates of the plurality of sets of feature point pairs in the corresponding camera coordinate system, and using a relative orientation algorithm to calculate a rotation matrix and a translation vector between the two cameras; S6: Add a scale factor to the translation vector between the two cameras to obtain the translation matrix between the two cameras.

2. The binocular camera calibration method according to claim 1, characterized in that: In step S1, the intrinsic parameters of the two cameras of the binocular camera are calibrated separately, including: the two cameras collect at least 8 groups of calibration plate images in different orientations within a range of 10 meters from the calibration plate, calibrate the cameras using a bundle adjustment algorithm, and calculate the intrinsic parameters of the two cameras.

3. The binocular camera calibration method according to claim 1, characterized in that: In step S2, the distance between every two of the plurality of feature points is greater than 5 m, and the distribution area of ​​the plurality of feature points in the collected calibration object image occupies at least 1 / 3 of the area of ​​the calibration object image.

4. The binocular camera calibration method according to claim 1, characterized in that: Step S4 includes: combining the intrinsic parameters of the two cameras, and using the following formula to calculate the three-dimensional coordinates of the plurality of feature point pairs in the corresponding camera coordinate system: In the formula, (X C ,Y C ,Z C ) represents the three-dimensional coordinates of the feature point in the corresponding camera coordinate system, dx is the pixel size, (u,v) is the pixel coordinates of the image point corresponding to the feature point in the image taken by the camera, (u0,v0) is the pixel coordinates of the camera principal point, (c x ,c y ) is the deviation of the camera principal point, (Δx, Δy) is the distortion parameter, and f is the focal length of the camera intrinsic parameter.

5. The binocular camera calibration method according to claim 1, characterized in that: Step S5 includes: combining the three-dimensional coordinates of the plurality of feature point pairs in the corresponding camera coordinate system, using an iterative algorithm to solve the following two equations to obtain a rotation matrix R and a translation vector t=(B x ,B y ,B z ) T : In the formula, (X Cl ,Y Cl ,Z Cl ) T represents the coordinates of the image point of the feature point on the image captured by the first camera in the binocular camera in the camera coordinate system of the first camera, (X Cr ,Y Cr ,Z Cr ) T represents the coordinates of the image point of the feature point on the image captured by the second camera in the binocular camera in the camera coordinate system of the second camera, (X′ Cr ,Y′ Cr ,Z′ Cr ) T The coordinates of an image point of a feature point on an image captured by a second camera in the binocular camera in a transformed camera coordinate system of the second camera, where the transformed camera coordinate system of the second camera is obtained by performing a rotation transformation according to a rotation matrix R on the camera coordinate system of the second camera.

6. The binocular camera calibration method according to claim 1, characterized in that: Step S6 includes: using a rangefinder to measure the distance L between any two of the feature points, and using the following formula to calculate the proportional factor s: In the formula, (X a ,Y a ,Z a ) and (X b ,Y b ,Z b ) are respectively the three-dimensional coordinates of the two feature points measured by the rangefinder obtained by solving the intrinsic parameters of the two cameras and the rotation matrix and translation vector between the two cameras; The translation matrix is ​​calculated based on the translation vector and the scale factor.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the steps of the binocular camera calibration method according to any one of claims 1 to 6.

8. A three-dimensional measurement method with a long distance and a large field of view, characterized in that: The method comprises calibrating the binocular camera according to any one of claims 1 to 6 to obtain the intrinsic parameters and extrinsic parameters of the binocular camera, wherein the extrinsic parameters include a rotation matrix and a translation matrix between the two cameras, and: S7: Perform feature detection on the object to be measured, and perform three-dimensional measurement on the object to be measured in combination with the intrinsic parameters and extrinsic parameters of the binocular camera.

9. The three-dimensional measurement method according to claim 8, characterized in that: In step S7, the three-dimensional measurement of the object to be measured using the intrinsic parameters and extrinsic parameters of the binocular camera specifically includes: using the least squares method to solve the following equation to calculate the three-dimensional coordinates (X, Y, Z) of the object point: In the formula, (u l ,v l ) represents the pixel coordinates of the image point of the object point on the image taken by the first camera in the binocular camera, (u r ,v r ) represents the pixel coordinates of the image point of the object point on the image taken by the second camera in the binocular camera, A matrix consisting of the intrinsic parameters and extrinsic parameters of the first camera in the binocular camera is represented, A matrix consisting of intrinsic parameters and extrinsic parameters of the second camera in the binocular camera is represented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the steps of the three-dimensional measurement method according to claim 8 or 9.

Images