A wide-range robot hand-eye calibration method integrating internal and external vision

Abstract

This invention discloses a large-scale robot hand-eye calibration method integrating internal and external vision, relating to the field of hand-eye calibration, including: S1, calibrating the internal vision system; S2, calibrating the photogrammetry system; S3, using the internal vision system to acquire images of a common target and performing 3D reconstruction to obtain the 3D data {P} of the common target measured by the internal vision system. i}; Using a photogrammetric system, images of the internal target and the common target were acquired separately and 3D reconstructions were performed on each to obtain the 3D data of the internal target measured by the photogrammetric system {Q}. i} and public target 3D data {M i};S4, according to {P i}、{Q i} and {M i Calculate the transformation matrix Z between the internal target and the internal vision system; S5, calculate the pose transformation matrix Y between the end effector flange and the internal target; S6, based on Z and Y, calculate the relative transformation matrix X between the end effector flange and the internal vision system to complete the calibration. This invention avoids the use of robot end effector pose relationships and improves the accuracy of hand-eye calibration.

Description

Technical Field

[0001] This invention relates to the field of hand-eye calibration, and more particularly to a large-scale robot hand-eye calibration method that integrates internal and external vision. Background Technology

[0002] Robot hand-eye calibration technology is one of the key technologies for realizing automated measurement and processing by robots, and it is also a key research area in machine vision. However, traditional hand-eye calibration methods introduce cumulative kinematic errors of the robotic arm, resulting in low calibration accuracy.

[0003] There are two main ways to assemble a vision measurement system with a robot: one is to directly fix the vision measurement system to the flange of the robot's end effector, forming an "eye on the hand" assembly. In this assembly, the vision measurement system moves with the robot, and its phase pose relative to the robot's end effector remains constant. The other is an "eye outside the hand" assembly, where the vision measurement system is fixed to the outside of the robot. In this case, the pose of the external vision measurement system relative to the robot's base coordinate system remains constant. However, both methods introduce the robot's end effector pose when solving the hand-eye matrix, leading to accumulated errors in the robotic arm and resulting in low accuracy in the final hand-eye calibration. Furthermore, the hybrid calibration method using external and internal binocular vision is limited by the field of view of the external binocular vision, restricting the robot's movement over a wide area. Using a close-range photography system as the external vision not only provides high-precision 3D coordinates of marker points over a large area but also avoids measurement problems caused by occlusion and measurement range limitations due to the freedom of the shooting angle. Summary of the Invention

[0004] To address the above issues, this invention proposes a large-scale robot hand-eye calibration method that integrates internal and external vision. By integrating internal and external vision, the method avoids the use of robot end-effector pose relationships and solves problems such as cumulative errors in the robotic arm.

[0005] The specific steps of a large-scale robot hand-eye calibration method that integrates internal and external vision are as follows:

[0006] S1, Fix the internal vision system and calibrate it;

[0007] S2, calibrate the photogrammetry system;

[0008] S3, the mobile robot arm moves to several different poses, and simultaneously uses its internal vision system to acquire images of a common target in each pose. Based on the common target images, 3D reconstruction is performed to obtain the 3D data of the common target measured by the internal vision system {P}. iA fixed robotic arm is used to acquire images of the internal target and the common target at different locations using a photogrammetry system, and then performs 3D reconstruction on each image to obtain the 3D data of the internal target measured by the photogrammetry system {Q}. i} and the three-dimensional data of the common target measured by the photogrammetry system {M i The common target is set in a fixed position.

[0009] S4, according to {P i}、{Q i} and {M i Calculate the transformation matrix Z between the internal target and the internal vision system;

[0010] S5, establish the pose of the robot arm flange in the photogrammetry system coordinate system, and at the same time obtain the pose of the internal target in the photogrammetry system coordinate system, and calculate the pose transformation matrix Y between the end flange and the internal target.

[0011] S6. Solve the relative transformation matrix X between the end flange and the internal vision system based on the transformation matrix Z between the internal target and the internal vision system and the pose transformation matrix Y between the end flange and the internal target, and complete the robot hand-eye calibration.

[0012] Preferably, in step S1, an external calibration board is used to calibrate the internal vision system, specifically as follows:

[0013] The internal vision system is fixed, the external calibration board is moved to several different poses, and the calibration board images at each pose are acquired using the internal vision system. The calibration of the internal vision system is achieved using the Zhang Zhengyou calibration method.

[0014] Preferably, S2 is as follows:

[0015] A fixed photogrammetric system is used to keep the pose of the photogrammetric system's coordinate system relative to the robot's base coordinate system. The photogrammetric system takes pictures of the calibration rod and several coding points placed in the measurement space to obtain the three-dimensional coordinates of the photogrammetric system's base coordinate system, the calibration rod, and the coding points under the photogrammetric system, thus completing the calibration.

[0016] Preferably, S3 is as follows:

[0017] S31, using the internal vision system to acquire images of common marker points of the common target, and using the Otsu thresholding method to binarize the images of common marker points, separating the reflective marker point region from the background region; inverting the binarized image; calling the OpenCV library to identify and extract sub-pixel marks from the inverted binarized image, obtaining the sub-pixel center coordinates; and performing 3D reconstruction using the sub-pixel center coordinates to obtain the 3D data of the common marker points, i.e., the 3D data of the common target measured by the internal vision system {P}. i};

[0018] S32, using a photogrammetric system, images of the internal target and the common target are acquired separately, and 3D reconstruction is performed according to the 3D reconstruction steps in S31 to obtain the 3D data of the internal target {Q} measured by the photogrammetric system. i} and the three-dimensional data of the common target measured by the photogrammetry system {M i}

[0019] Preferably, S4 is as follows:

[0020] S41, based on {P i Construct a first local coordinate system, and calculate the transformation matrix between the origin coordinate system of the internal vision system and the common target coordinate system under the internal vision system based on the first local coordinate system. C F T; and using the transformation matrix {P i} Redirecting to the origin of the vision system, we obtain the 3D data {P' of the common target in the internal vision origin coordinate system. i};

[0021] S42, {P i} and {M i Perform a rigid body transformation to obtain the transformation matrix between the common target coordinate system under the photogrammetry system and the common target coordinate system under the internal vision system. Using the transformation matrix {P i} Transform to the photogrammetric system coordinate system to obtain the common target 3D data {P” of the internal visual measurement in the photogrammetric system coordinate system coordinate system. i};

[0022] S43, based on {Q i Construct a second local coordinate system, and calculate the transformation matrix between the origin coordinate system and the internal target coordinate system of the photogrammetric system based on the second local coordinate system.

[0023] S44, using the transformation matrix {P” i} Transform to the internal target coordinate system to obtain the common target 3D data {P”' of the internal visual measurement in the internal target coordinate system. i}, based on {P”' i Construct a third local coordinate system based on {P' i}、{P” i The transformation matrix between the internal target coordinate system and the internal visual origin coordinate system is calculated using the third local coordinate system. That is, Z; the construction methods of the second and third local coordinate systems are the same as those of the first local coordinate system.

[0024] Preferably, the one based on {P i Construct the first local coordinate system as follows:

[0025] Arbitrarily select {P i In the coordinate system, there are three points P1, P2, and P3 that are not on the same straight line; P1 is taken as the origin; a local coordinate system is constructed using points P1 and P2, and its unit direction vector is represented as... Construct a temporary y-axis for a local coordinate system using points P1 and P3, with its unit direction vector represented as: The unit direction vector of the z-axis can be calculated using the right-hand rule. Based on the unit direction vector X G and Z G The unit direction vector Y along the y-axis of the local coordinate system is calculated using the right-hand rule. G , represented as in, | represents the product of vectors; | represents the magnitude of a vector.

[0026] Preferably, step S5 is as follows:

[0027] S51, using the rotation and translation of the TCP coordinate system, the transformation relationship from the photogrammetric system to the target center is determined: the end flange of the robot arm is controlled to rotate multiple times around the z-axis of the TCP coordinate system, and the photogrammetric system is used to collect the reflective marker point P. z Then, the robot arm is controlled to move multiple times along the positive x and y axes of the TCP coordinate system to collect three-dimensional data P of the reflective marker points. x and P y and for P z Perform circle fitting, respectively for P x and P y A straight line fit is performed to determine the transformation matrix between the origin coordinate system of the photogrammetric system and the flange coordinate system fitted to the internal target.

[0028] S52, as the robotic arm moves along the positive y-axis of the TCP coordinate system, it simultaneously acquires the 3D data of the internal target and the last set of marker point 3D data. Based on the 3D data of the internal target, a fourth local coordinate system is constructed, and the transformation matrix between the origin coordinate system of the photogrammetric system and the internal target coordinate system is calculated.

[0029] S53, based on the thickness of the reflective markers, calculate the transformation matrix between the end flange coordinate system and the flange coordinate system fitted to the internal target.

[0030] S54, according to formula Calculate the transformation matrix between the end flange coordinate system and the internal target coordinate system. That is, Y; where ( ) -1 Let represent the inverse matrix.

[0031] Preferably, the relative transformation matrix X between the end flange and the internal vision system is solved using the formula X = YZ; where Z represents the transformation matrix between the internal target and the internal vision system; and Y represents the pose transformation matrix between the end flange and the internal target.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] This invention does not use the robot end-effector pose relationship during the entire calibration process, thus avoiding the impact of accumulated errors of the robotic arm on the accuracy of hand-eye calibration. It also reduces the impact of errors in internal target processing and installation on the calibration accuracy, thereby achieving high-precision hand-eye calibration. Attached Figure Description

[0034] The present invention will now be described in further detail with reference to the accompanying drawings;

[0035] Figure 1 This is a flowchart of a large-scale robot hand-eye calibration method that integrates internal and external vision, according to an embodiment of the present invention.

[0036] Figure 2 This is a schematic diagram illustrating the basic principle of the hybrid calibration of the internal target and the internal vision system in the large-scale robot hand-eye calibration method that integrates internal and external vision according to an embodiment of the present invention.

[0037] Figure 3 This is a schematic diagram of the experimental platform for the large-scale robot hand-eye calibration method that integrates internal and external vision, according to an embodiment of the present invention.

[0038] Figure 4 This is a schematic diagram illustrating the basic principle of solving the flange and internal target conversion relationship in the large-scale robot hand-eye calibration method that integrates internal and external vision, according to an embodiment of the present invention.

[0039] Figure 5 This is a schematic diagram illustrating the calibration solution principle of the large-scale robot hand-eye calibration method that integrates internal and external vision according to an embodiment of the present invention. Detailed Implementation

[0040] The present invention will be further described below through specific embodiments.

[0041] In this embodiment, the internal vision system and the internal target are respectively mounted on the robot's robotic arm, while the photogrammetry system (or imaging system, close-range measurement system) is mounted outside the robot, and the common target is also mounted outside the robot. In this embodiment, the internal vision system can be a binocular vision measurement system.

[0042] The calibration process in this embodiment involves multiple coordinate systems, as shown below:

[0043] {O} Origin coordinate system of photogrammetry system

[0044] {E} Internal target coordinate system

[0045] {F} Common target coordinate system under internal vision system

[0046] {C} Internal vision system origin coordinate system

[0047] {G} Common target coordinate system under photogrammetry system

[0048] {B} Coordinate system of common target origin under internal target coordinate system

[0049] Flange coordinate system fitted to the internal target of {H}

[0050] {I} End flange coordinate system

[0051] The transformation matrix between coordinate systems is represented as follows:

[0052] The transformation matrix between the origin coordinate system of the internal vision system and the common target coordinate system under the internal vision system;

[0053] The transformation matrix between the common target coordinate system under the photogrammetric system and the common target coordinate system under the internal vision system;

[0054] Transformation matrix between the origin coordinate system and the internal target coordinate system of the photogrammetric system;

[0055] Transformation matrix between the internal target coordinate system and the internal visual origin coordinate system;

[0056] Transformation matrix between the end flange coordinate system and the internal target coordinate system;

[0057] The transformation matrix between the end flange coordinate system and the flange coordinate system fitted to the internal target;

[0058] The transformation matrix between the origin coordinate system of the photogrammetric system and the flange coordinate system fitted to the internal target.

[0059] like Figure 1 As shown in the figure, the large-scale robot hand-eye calibration method that integrates internal and external vision in this embodiment specifically includes the following steps.

[0060] S1, fix the internal vision system and calibrate it.

[0061] Specifically, the internal vision system is fixed, the external calibration board is moved to N different poses, and the internal vision system is used to acquire images of the calibration board at each pose. The Zhang Zhengyou calibration method is used to calibrate the internal vision system.

[0062] S2, calibrate the photogrammetry system.

[0063] A fixed photogrammetric system is used to keep the pose of the photogrammetric system's coordinate system relative to the robot's base coordinate system. The photogrammetric system takes pictures of the calibration rod and several coding points placed in the measurement space to obtain the three-dimensional coordinates of the photogrammetric system's base coordinate system, the calibration rod, and the coding points under the photogrammetric system.

[0064] Specifically, such as Figure 2 As shown, the internal vision system and the external photogrammetry system are respectively installed at the first position on the end flange of the robot arm and at the first position outside the robot; the external common target and the internal target are respectively installed at the second position outside the robot and at the second position on the end flange of the robot arm. Figure 3 This is the hand-eye calibration experimental platform for this embodiment. The robot is moved to a suitable posture, in which the internal vision system at the robot's end can always "see" the external common target in space, and the external photogrammetry system outside the robot can always "see" the internal target and the external common target on the end of the robotic arm.

[0065] S3, the mobile robot arm moves to several different poses, and simultaneously uses its internal vision system to acquire images of a common target in each pose. Based on the common target images, 3D reconstruction is performed to obtain the 3D data of the common target measured by the internal vision system {P}. i A fixed robotic arm is used to acquire images of the internal target and the common target at different locations using a photogrammetry system, and then performs 3D reconstruction on each image to obtain the 3D data of the internal target measured by the photogrammetry system {Q}. i} and the three-dimensional data of the common target measured by the photogrammetry system {Mi The common target is set in a fixed position. The specific steps are as follows:

[0066] S31, according to the method described above, the image of the external common target is acquired using internal vision. The image of the common marker points on the common target is binarized using the Otsu thresholding method to separate the reflective marker point area from the background area. The binarized image is inverted to obtain the background that OpenCV can recognize. The OpenCV library is called to identify and extract sub-pixel marker points, obtaining the coordinates of the sub-pixel center. Then, the images are reconstructed in three dimensions to obtain the three-dimensional data of the common marker points.

[0067] S32 uses an external photogrammetry system to acquire three-dimensional data of the internal target and the external common target.

[0068] S4, based on the common target 3D data {P} measured by the internal vision system i}, Photogrammetric system measures the internal target's three-dimensional data {Q} i} and the three-dimensional data of the common target measured by the photogrammetry system {M i A hand-eye calibration model is constructed, and the transformation matrix Z between the internal target and the internal vision system is calculated. The specific steps are as follows:

[0069] S41, based on the common target 3D data {P} obtained from the internal vision system i Establish a local coordinate system, and use the measured three-dimensional data {P} to represent the local coordinate system. i Return to the origin of the vision measurement system. Arbitrarily select 3D data {P} i In the coordinate system, there are three points P1, P2, and P3 that are not on the same straight line. Point P1 is taken as the origin, and a local coordinate system is constructed using points P1 and P2, with the x-axis denoted as . Construct a temporary y-axis for a coordinate system using points P1 and P3, with its unit direction vector... Finally, using the right-hand rule, the unit direction vector of the z-axis can be calculated. Given the unit direction vector X G and Z G Similarly, the unit direction vector along the final y-axis can be calculated using the right-hand rule. Therefore, the transformation matrix between the origin coordinate system of the internal vision system and the common target coordinate system under the internal vision system can be calculated. And using the transformation matrix Point cloud data {P i} Redirecting to the origin of the vision system, we obtain the 3D data {P' of the common target in the internal vision origin coordinate system. i}

[0070] S42, according to the above, the three-dimensional data of the common target {P} obtained by the internal vision system is used. i} and the three-dimensional data of the common target obtained by the external photogrammetry system {M i A rigid body transformation is performed to register the internal visual measurement point cloud data to the photogrammetric system, resulting in the transformation matrix between the common target coordinate system in the photogrammetric system and the common target coordinate system in the internal visual system. Using the transformation matrix The common target in the internal visual coordinate system is transformed to the photogrammetric system coordinate system to obtain the 3D data {P” of the common target in the internal visual measurement under the photogrammetric system. i}

[0071] S43, Based on the three-dimensional data of the internal target obtained from the external photogrammetry system {Q i A local coordinate system is constructed using the method in S41. By constructing the local coordinate system, the transformation matrix between the origin coordinate system and the internal target coordinate system of the photogrammetric system is calculated.

[0072] S44, according to the transformation matrix The common target 3D data {P” of the internal visual measurement under the photogrammetry system i} Transform to the internal target coordinate system to obtain the common target 3D data {P”' of the internal visual measurement in the internal target coordinate system. i}, and based on {P”' i Construct a local coordinate system, which is the common target origin coordinate system {B} under the internal target coordinate system. Finally, use the 3D data {P' i} and {P”' i} and its constructed local coordinate system {B}, calculate the transformation matrix between the internal target coordinate system and the internal visual origin coordinate system. That is, the transformation matrix Z between the internal target and the internal vision system needs to be solved.

[0073] S5 establishes the pose of the robot arm's flange in the photogrammetric system coordinate system, and simultaneously obtains the pose of the internal target in the photogrammetric system coordinate system. Calculates the pose transformation matrix Y between the end flange and the internal target. (See also...) Figure 4 As shown; the specific steps are as follows:

[0074] S51, the transformation relationship from the photogrammetric system to the flange coordinate system fitted to the internal target is determined by the rotation and translation of the TCP coordinate system. The robot's end effector flange is controlled to rotate multiple times around the z-axis of the TCP coordinate system, and the photogrammetric system is used to collect the reflective marker point P. zThen, the robot is controlled to move multiple times along the positive x and y axes of the TCP coordinate system to collect 3D data P of the marker points. x and P y and for P z Perform circle fitting, respectively for P x and P y Linear fitting is performed to determine the transformation matrix between the origin coordinate system of the photogrammetric system and the flange coordinate system fitted to the internal target.

[0075] S52, based on the above, while the robot moves along the positive y-axis of the TCP coordinate system, simultaneously acquiring the 3D data of the last set of marker points and the 3D data of the internal target, and according to the form of constructing the local coordinate system, the transformation matrix between the origin coordinate system of the photogrammetric system and the coordinate system of the internal target is calculated.

[0076] S53, based on the thickness of the affixed reflective markers, calculate the transformation matrix between the end flange coordinate system and the flange coordinate system fitted to the internal target.

[0077] S54, according to formula Find the transformation matrix between the final end flange coordinate system and the internal target coordinate system. That is, Y.

[0078] S6. Solve the relative transformation matrix X between the end flange and the internal vision system based on the transformation matrix Z between the internal target and the internal vision system and the pose transformation matrix Y between the end flange and the internal target, and complete the robot hand-eye calibration.

[0079] For details, see Figure 5 As shown, based on the transformation matrix Z between the internal target and the vision system and the pose transformation matrix Y between the end flange and the internal target, the final relative transformation matrix (or hand-eye matrix) X between the end flange and the internal vision system is solved using the formula X = YZ.

[0080] The above are merely specific embodiments of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept shall be considered as infringing upon the protection scope of the present invention.

Claims

1. A wide-range robot hand-eye calibration method fusing inside and outside vision, characterized in that, It comprises the following steps: S1, fix the internal vision system and calibrate; S2, calibrate the photogrammetry system; S3, moving the robot arm to several different sets of poses, while collecting images of the common target at each pose using the internal vision system, and performing 3D reconstruction based on the images to obtain 3D data of the common target measured by the internal vision system ; fixing the robot arm, and using the photogrammetry system to collect images of the internal target and the common target at different positions respectively and perform 3D reconstruction respectively to obtain 3D data of the internal target measured by the photogrammetry system and 3D data of the common target measured by the photogrammetry system ; the common target is arranged at a fixed position S4, calculating a transformation matrix Z between the internal target and the internal vision system according to , and ; S5, establish the coordinate system pose of the flange plate of the robot mechanical arm under the coordinate system of the photogrammetry system, and simultaneously obtain the pose of the internal target under the coordinate system of the photogrammetry system, and calculate the pose conversion matrix Y between the end flange plate and the internal target; S6, solve the relative conversion matrix X between the end flange plate and the internal vision system according to the conversion matrix Z between the internal target and the internal vision system and the pose conversion matrix Y between the end flange plate and the internal target, and complete the robot hand-eye calibration; The S3 is specifically as follows: S31, acquiring a common mark point image of a common target by using an internal vision system, binarizing the common mark point image by using a Otsu threshold method, separating a reflective mark point region and a background region; inverting the binarized image; calling an Opencv library to recognize and sub-pixel extract a circular mark point of the inverted binarized image, obtaining a sub-pixel circle center coordinate; performing three-dimensional reconstruction on the sub-pixel circle center coordinate, obtaining three-dimensional data of the common mark point, i.e. three-dimensional data of the common target measured by the internal vision system ; S32, images of the internal target and the common target are respectively collected by the photogrammetry system and three-dimensional reconstruction is performed according to the three-dimensional reconstruction steps of S31 to obtain internal target three-dimensional data measured by the photogrammetry system and common target three-dimensional data measured by the photogrammetry system .

2. The wide-range robot hand-eye calibration method of fusing internal and external vision according to claim 1, wherein, In the S1, the internal vision system is calibrated by using an external calibration board, and specifically as follows: Fix the internal vision system, move the external calibration board to several groups of different poses, and collect the calibration board images of each pose by using the internal vision system, and realize the calibration of the internal vision system by using Zhang Zhengyou calibration method.

3. The wide-range robot hand-eye calibration method of fusing internal and external vision according to claim 1, wherein, The S2 is specifically as follows: Fix the photogrammetry system, so that the coordinate system of the photogrammetry system is invariant relative to the robot base coordinate system, the photogrammetry system photographs the calibration rod and a plurality of code points placed in the measurement space, obtains the three-dimensional coordinates of the photogrammetry system base coordinate system, the calibration rod and the code points under the photogrammetry system, and completes the calibration.

4. The method of claim 1, wherein, The S4 is specifically as follows: S41, based on constructing a first local coordinate system, calculating a conversion matrix between the internal vision system origin coordinate system and the common target coordinate system under the internal vision system based on the first local coordinate system ; and using the conversion matrix going to the vision system origin to obtain three-dimensional data of the common target under the internal vision origin coordinate system ;​ S42, the with rigid body transformation, get the conversion matrix between the common target coordinate system under the photogrammetry system and the common target coordinate system under the internal vision system ; convert the to the photogrammetry system coordinate system, get the common target three-dimensional data of internal vision measurement under the photogrammetry system coordinate system ;​ S43, based on constructing a second local coordinate system, and calculating a conversion matrix between the photogrammetry system origin coordinate system and the internal target coordinate system based on the second local coordinate system ; S44, using the conversion matrix will be converted to the internal target coordinate system to obtain the common target three-dimensional data of the internal vision measurement in the internal target coordinate system , based on a third local coordinate system is constructed, based on , and the third local coordinate system, the conversion matrix between the internal target coordinate system and the internal vision origin coordinate system is calculated , that is, Z; the construction method of the second local coordinate system and the third local coordinate system is the same as the construction method of the first local coordinate system.

5. The wide-range robot hand-eye calibration method of fusing internal and external vision according to claim 4, wherein, The method comprises the following steps: A first local coordinate system is constructed, specifically as follows: arbitrarily selected three points not on the same straight line , and ; With point As the origin; with point And The x-axis of the local coordinate system is constructed, and the unit direction vector is represented as ; with point And The temporary y-axis of the local coordinate system is constructed, and the unit direction vector is represented as The unit direction vector of the z-axis is calculated by the right-hand rule ; Based on the unit direction vector And , the unit direction vector of the y-axis direction of the local coordinate system is calculated according to the right-hand rule , expressed as ; wherein Indicates the vector product; Indicates the modulus of the vector.

6. The method of claim 1, wherein The S5 is specifically as follows: S51, determine the conversion relationship from the photogrammetry system to the center of the target circle by rotation and translation of the TCP coordinate system: control the robot arm end flange to rotate multiple times around the z-axis of the TCP coordinate system, and collect the retro-reflective marker points by using the photogrammetry system Then control the robot arm to move multiple times along the positive direction of the x-axis and y-axis of the TCP coordinate system, and collect the three-dimensional data of the retro-reflective marker points and Perform circle fitting on , and perform linear fitting on and respectively, so as to determine the conversion matrix from the photogrammetry system origin coordinate system to the flange coordinate system fitted by the internal target ; S52, while the robot mechanical arm moves along the positive direction of the y-axis of the TCP coordinate system, collect the three-dimensional data of the internal target while collecting the three-dimensional data of the last set of mark points, construct a fourth local coordinate system based on the three-dimensional data of the internal target, and calculate the conversion matrix between the origin coordinate system of the photogrammetry system and the internal target coordinate system ; S53, according to the thickness of the retro-reflective marker points, calculate the conversion matrix between the coordinate system of the end flange plate and the coordinate system of the flange plate fitted with the inner target ; S54, the conversion matrix between the end flange coordinate system and the internal target coordinate system is calculated according to the formula The conversion matrix between the end flange coordinate system and the internal target coordinate system is calculated , that is, Y; wherein represents an inverse matrix.

7. The method of claim 1, wherein Solving the relative transformation matrix X between the end flange and the internal vision system by using the formula wherein Z represents the transformation matrix between the internal target and the internal vision system; and Y represents the pose transformation matrix between the end flange and the internal target.

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