A dual-robot large-scale collaborative measurement method and system

Abstract

A method and system for large-scale collaborative measurement of dual robots, the method comprising: 1. Measuring the coordinates of a marker point; 2. Scanning the target object; during scanning, if the field of view is limited, proceed to step 3, otherwise continue scanning until the scanning is completed; 3. Calculate the rotation matrix and translation vector of the marker point coordinate system of the adjacent measuring station to the tracking robot coordinate system, construct the relationship between the rotation matrix and translation vector of the adjacent measuring station, calculate the rotation matrix and translation vector of the next measuring station to the previous measuring station, and construct the relationship between the point scanned by the next measuring station and the previous measuring station; 4. During scanning, if the field of view is limited, use the relationship between the point scanned by the next measuring station and the previous measuring station to merge the scanned point clouds of the two measuring stations until the scanning is completed. The present invention can realize the automatic station switching between any two adjacent measuring stations, and can transform the scanning results of any measuring station to the coordinate system of other measuring stations, so as to realize the merging of the scanning results between measuring stations.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional scanning technology, and in particular to a dual-robot large-range collaborative measurement method and system. Background Art

[0002] There are many 3D scanning systems at present, but they are applicable to different scenarios. Among them, the tracking 3D scanning system is applied to the 3D scanning tasks of various large workpieces due to its significant advantages of no need to stick points, high efficiency, high precision, and wide scanning range. When the tracking 3D scanning system is applied to tasks such as scanning large aircraft models and scanning the front and back of large models, the tracking 3D scanning system is limited by the tracking camera. The current maximum scanning depth and scanning width are only less than 10m, and it is impossible to complete the scanning task at one time. Therefore, it is necessary to manually move the tracking camera to the next measuring station to perform the scanning task, and the point cloud of the two scanning tasks needs to be aligned to realize the merging of the two scanning results. However, it is likely to lose some accuracy in the point cloud alignment, and the manual movement of the tracking camera is not conducive to the application of the 3D scanning system in the automation scene; another method of moving the measuring station is to preset some marker points around the target. When changing the measuring station, the two scanning results can be merged according to these marker points, but the marker points need to be manually preset, which is not conducive to the application in the automation scene. Summary of the invention

[0003] The present invention provides a dual-robot large-scale collaborative measurement method and system to solve the technical problems mentioned in the background technology.

[0004] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0005] The present invention provides a dual-robot large-scale collaborative measurement method, comprising the following steps:

[0006] S1, first use a photogrammetric device to measure the three-dimensional coordinates of multiple marking points on the scanner in the measuring robot;

[0007] S2, use the scanner on the measuring robot to scan the target object according to the set route, and use the tracking robot to track the measuring robot in real time to obtain the spatial position of the measuring robot; during the scanning process, it is determined in real time whether the field of view of the tracking robot is limited, if so, enter S3, otherwise continue scanning until the scanning of the target object is completed;

[0008] S3, based on the three-dimensional coordinates of multiple marker points on the scanner in S1, and using the Gauss-Newton method, calculate the rotation matrix and translation vector of the marker point coordinate system of two adjacent measuring stations to the tracking robot coordinate system, and construct the relationship between the rotation matrix and translation vector of the two adjacent measuring stations, and calculate the rotation matrix and translation vector of the next measuring station to the previous measuring station based on this relationship, and finally construct the relationship between the points scanned by the next measuring station and the previous measuring station based on the rotation matrix and translation vector of the next measuring station to the previous measuring station, so as to realize the merging of the scanned point clouds of the two measuring stations;

[0009] S4. Continue to use the scanner on the measuring robot to scan the target object according to the set route. If the field of view of the tracking robot is limited, the point clouds scanned by the next measuring station are merged using the relationship formula in S3 from the point scanned by the next measuring station to the previous measuring station until the scanning of the target object is completed.

[0010] Furthermore, the coordinate system where the three-dimensional coordinates of the multiple marker points in S1 are located is the marker point coordinate system. The three-dimensional coordinates of the landmark points are:

[0011] ;

[0012] in, Indicates the coordinate system of the landmark point A landmark point, , and Indicates the coordinate system of the landmark point The landmarks are X , Y and Z The coordinate values ​​on the axis.

[0013] Furthermore, the S3 specifically includes the following steps:

[0014] S31, in The tracking robot is used to measure multiple landmarks around the scanner at each station. The three-dimensional coordinates of multiple markers in the robot coordinate system are tracked at the 0th frame of each measuring station as follows:

[0015] ;

[0016] in, Indicates The first frame captured by the station at the 0th frame is located in the tracking robot coordinate system. landmark points; , and Indicates The 0th frame of the station tracks the The landmarks are X , Y and Z Coordinate values ​​on the axis;

[0017] S32, using the coordinates of the marker points measured by the tracking robot in S31 and the corresponding real coordinates as constraints, according to the three-dimensional coordinates of the multiple marker points on the scanner in S1, and using the Gauss-Newton method to calculate the coordinates of the marker points located at the first The rotation matrix from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of The translation vector from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of

[0018] S33, construct the first The three-dimensional coordinates of the marker points in the tracking robot coordinate system and the rotation matrix finally obtained by S32 , translation vector The relationship between

[0019] S34. With the scanner stationary, the measuring robot moves to the station, and use the steps from S31 to S32 to calculate the The rotation matrix from the marker coordinate system of the 0th frame of each station to the tracker coordinate system The optimal value of The translation vector from the marker coordinate system of the 0th frame of the station to the tracker coordinate system The optimal value of

[0020] S35, construct the first The three-dimensional coordinates of the landmark points in the tracking robot coordinate system and the rotation matrix finally obtained by S34 and translation vector The relationship between

[0021] S36, constructing a relational expression of a rotation matrix and a translation vector of two adjacent measuring stations according to the relational expression in S33 and the relational expression in S35;

[0022] S37, calculate the first The point of the station is transferred to the The rotation matrix of the station and translation vector ;

[0023] S38, using the rotation matrix obtained in S37 and translation vector , construct the The target object scanned at the station Go to the The relational expression on the two measuring stations, that is, the relational expression of the points scanned by the next measuring station to the previous measuring station, realizes the merging of the scanned point clouds of the two measuring stations.

[0024] Furthermore, the S32 specifically includes the following steps:

[0025] S341, The coordinate value of the marker point is transferred from the marker point coordinate system to the tracking robot coordinate system and is combined with the coordinate value of the marker point in S31. Subtract the three-dimensional coordinate values ​​of the marker points in the tracking robot coordinate system to get the Conversion error of the marker points ;

[0026] S342, then use Conversion error of the marker points , and construct the optimization function of the conversion errors of multiple landmarks with the help of Gauss-Newton method;

[0027] S343, by iteratively optimizing the optimization function, an update amount is obtained in each iteration , and then use the update amount For The rotation matrix from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot and translation vector After updating and optimizing for multiple times, we get The rotation matrix from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of The translation vector from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of .

[0028] Furthermore, in S341, Conversion error of the marker points , expressed by the formula, as follows:

[0029] .

[0030] Furthermore, the optimization function in S342 is specifically as follows:

[0031] ;

[0032] in, Indicates the number of point pairs between the landmark points measured by the tracking robot and the corresponding real landmark points; represents the transpose of a matrix, Represents the function to be optimized using the Gauss-Newton method.

[0033] Furthermore, the relationship in S33 is specifically:

[0034] ;

[0035] The relationship in S35 is specifically:

[0036] ;

[0037] The relationship between the rotation matrix and the translation vector of two adjacent measuring stations in S36 is specifically:

[0038] .

[0039] Furthermore, the rotation matrix in S37 and translation vector The calculation formula is as follows:

[0040] .

[0041] Further, the first The target object scanned at the station Go to the The relationship at each station is as follows:

[0042] ;

[0043] in, Indicates the converted The first of the target objects scanned at the station points.

[0044] On the other hand, the present invention further provides a dual-robot large-scale collaborative measurement system, which uses the above dual-robot large-scale collaborative measurement method to perform measurement. The dual-robot large-scale collaborative measurement system includes:

[0045] A measuring robot, comprising a first automatic guided vehicle, a mechanical arm mounted on a first lifting mechanism in the first automatic guided vehicle, and a scanner mounted on an execution end of the mechanical arm;

[0046] The tracking robot comprises a second automatic guided vehicle, a pan platform installed on a second lifting mechanism in the second automatic guided vehicle, and a binocular three-dimensional tracker installed on the pan platform.

[0047] Beneficial effects of the present invention:

[0048] 1. The dual-robot large-scale collaborative measurement method provided by the present invention can realize the automatic station exchange between any two adjacent measuring stations, and can transform the scanning results of any measuring station into the coordinate system of other measuring stations to realize the merging of scanning results between measuring stations.

[0049] 2. The present invention also provides a dual-robot large-scale collaborative measurement system, including a measuring robot and a tracking robot. Both the measuring robot and the tracking robot are driven by their own automatic guided vehicles, and automatic three-dimensional scanning is realized without manual movement. The degree of automation is high, which reduces the labor intensity of the scanner. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the large-scale collaborative measurement method of dual robots in the present invention;

[0051] Figure 2 It is a schematic diagram of the structure of the dual-robot large-scale collaborative measurement system in the present invention. DETAILED DESCRIPTION

[0052] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Preferred embodiments of the present invention are provided in the drawings. However, the present invention can be implemented in many other different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0053] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. 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.

[0054] 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 the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0055] Reference Figure 1 The present application provides a dual-robot large-scale collaborative measurement method, comprising the following steps:

[0056] S1, first use a photogrammetric device to measure the three-dimensional coordinates of multiple marking points on the scanner in the measuring robot;

[0057] S2, use the scanner on the measuring robot to scan the target object according to the set route, and use the tracking robot to track the measuring robot in real time to obtain the spatial position of the measuring robot; during the scanning process, it is determined in real time whether the field of view of the tracking robot is limited, if so, enter S3, otherwise continue scanning until the scanning of the target object is completed;

[0058] Specifically, a depth camera is installed on the tracking robot, and a visual recognition algorithm is built into the tracking robot. The depth camera and the visual recognition algorithm can be used to determine in real time whether the field of view of the tracking robot is limited. In addition, the rule for determining whether the field of view is limited is: when the tracking robot cannot track the target (i.e., multiple markers on the scanner), the field of view is determined to be limited;

[0059] S3, based on the three-dimensional coordinates of multiple marker points on the scanner in S1, and using the Gauss-Newton method, calculate the rotation matrix and translation vector of the marker point coordinate system of two adjacent measuring stations to the tracking robot coordinate system, and construct the relationship between the rotation matrix and translation vector of the two adjacent measuring stations, and calculate the rotation matrix and translation vector of the next measuring station to the previous measuring station based on this relationship, and finally construct the relationship between the points scanned by the next measuring station and the previous measuring station based on the rotation matrix and translation vector of the next measuring station to the previous measuring station, so as to realize the merging of the scanned point clouds of the two measuring stations;

[0060] S4. Continue to use the scanner on the measuring robot to scan the target object according to the set route. If the field of view of the tracking robot is limited, the point clouds scanned by the next measuring station are merged using the relationship formula in S3 from the point scanned by the next measuring station to the previous measuring station until the scanning of the target object is completed.

[0061] In some embodiments, the coordinate system where the three-dimensional coordinates of the multiple marker points in S1 are located is the marker point coordinate system. The three-dimensional coordinates of the landmark points are:

[0062] (1)

[0063] in, Indicates the coordinate system of the landmark point A landmark point, , and Indicates the coordinate system of the landmark point The landmarks are X , Y and Z The coordinate values ​​on the axis.

[0064] In some embodiments, S3 specifically includes the following steps:

[0065] S31, in The tracking robot is used to measure multiple landmarks around the scanner at each station. The three-dimensional coordinates of multiple markers in the robot coordinate system are tracked at the 0th frame of each measuring station as follows:

[0066] (2)

[0067] in, Indicates The first frame captured by the station at the 0th frame is located in the tracking robot coordinate system. landmark points; , and Indicates The 0th frame of the station tracks the The landmarks are X , Y and Z Coordinate values ​​on the axis;

[0068] S32, using the coordinates of the marker points measured by the tracking robot in S31 and the corresponding real coordinates as constraints, according to the three-dimensional coordinates of the multiple marker points on the scanner in S1, and using the Gauss-Newton method to calculate the coordinates of the marker points located at the first The rotation matrix from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of The translation vector from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of

[0069] S33, construct the first The three-dimensional coordinates of the marker points in the tracking robot coordinate system and the rotation matrix finally obtained by S32 , translation vector The relationship between

[0070] S34. With the scanner stationary, the measuring robot moves to the station, and use the steps from S31 to S32 to calculate the The rotation matrix from the marker coordinate system of the 0th frame of each station to the tracker coordinate system The optimal value of The translation vector from the marker coordinate system of the 0th frame of the station to the tracker coordinate system The optimal value of

[0071] S35, construct the first The three-dimensional coordinates of the landmark points in the tracking robot coordinate system and the rotation matrix finally obtained by S34 and translation vector The relationship between

[0072] S36, constructing a relational expression of a rotation matrix and a translation vector of two adjacent measuring stations according to the relational expression in S33 and the relational expression in S35;

[0073] S37, calculate the first The point of the station is transferred to the The rotation matrix of the station and translation vector ;

[0074] S38, using the rotation matrix obtained in S37 and translation vector , construct the The target object scanned at the station Go to the The relational expression on the two measuring stations, that is, the relational expression of the points scanned by the next measuring station to the previous measuring station, realizes the merging of the scanned point clouds of the two measuring stations.

[0075] In some embodiments, the S32 specifically includes the following steps:

[0076] S341, The coordinate value of the marker point is transferred from the marker point coordinate system to the tracking robot coordinate system and is combined with the coordinate value of the marker point in S31. Subtract the three-dimensional coordinate values ​​of the marker points in the tracking robot coordinate system to get the Conversion error of the marker points ;

[0077] S342, then use Conversion error of the marker points , and construct the optimization function of the conversion errors of multiple landmarks with the help of Gauss-Newton method;

[0078] S343, by iteratively optimizing the optimization function, an update amount is obtained in each iteration , and then use the update amount For The rotation matrix from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot and translation vector After updating and optimizing for multiple times, we get The rotation matrix from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of The translation vector from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of

[0079] Specifically, is a third-order matrix, is a three-dimensional vector.

[0080] In some embodiments, the step S341 Conversion error of the marker points , expressed by the formula, as follows:

[0081] (3)

[0082] In some embodiments, the optimization function in S342 is specifically as follows:

[0083] (4)

[0084] in, Indicates the number of point pairs between the landmark points measured by the tracking robot and the corresponding real landmark points; represents the transpose of a matrix, Represents the function to be optimized using the Gauss-Newton method.

[0085] The S343 specifically includes the following steps:

[0086] S3431, first calculate the conversion error and , The partial derivative of , as shown in the following formula:

[0087] (5)

[0088] (6)

[0089] (7)

[0090] in, is a third-order unit matrix, is the antisymmetric symbol; is the symbol of partial derivative;

[0091] S3432, then use the Gauss-Newton method to solve the update amount of each iteration of the optimization function , as shown in the following formula:

[0092] (8)

[0093] (9)

[0094] (10)

[0095] in, Represents the first cumulative number, which is a 6-order matrix. Represents the second cumulative number, which is a 6-dimensional vector. is a 6-dimensional vector;

[0096] S3433, update the amount Convert to rotation matrix , translation vector The rotation matrix and translation vector After updating and optimizing for multiple times, we get The rotation matrix from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of The translation vector from the coordinate system of the marker point in the 0th frame of the measuring station to the coordinate system of the tracking robot The optimal value of is expressed by the formula as follows:

[0097] (11)

[0098] (12)

[0099] (13)

[0100] (14)

[0101] (15)

[0102] in, represents the translation vector increment for each iteration, represents the rotation vector increment for each iteration, , , , are three-dimensional vectors, is a scalar, , , is a third-order matrix, Represents the iteration The rotation matrix is Represents the iteration The translation vector and rotation matrix The optimal value of the translation vector The optimal value of can be obtained by iterating formulas (14)-(15).

[0103] In some embodiments, the relationship in S33 is specifically:

[0104] (16)

[0105] The relationship in S35 is specifically:

[0106] (17)

[0107] The relationship between the rotation matrix and the translation vector of two adjacent measuring stations in S36 is specifically:

[0108] (18)

[0109] In some embodiments, the rotation matrix in S37 and translation vector The calculation formula is as follows:

[0110] (19)

[0111] (20).

[0112] In some embodiments, the first The target object scanned at the station Go to the The relationship at each station is as follows:

[0113] (twenty one)

[0114] in, Indicates the converted The first of the target objects scanned at the station points.

[0115] The dual-robot large-scale collaborative measurement method provided by the present invention can realize automatic station switching between any two adjacent measuring stations, and can transform the scanning results of any measuring station into the coordinate system of other measuring stations to realize the merging of scanning results between measuring stations.

[0116] On the other hand, the present invention further provides a dual-robot large-scale collaborative measurement system, which uses the above dual-robot large-scale collaborative measurement method to perform measurement. The dual-robot large-scale collaborative measurement system includes:

[0117] A measuring robot, comprising a first automatic guided vehicle, a mechanical arm mounted on a first lifting mechanism in the first automatic guided vehicle, and a scanner mounted on an execution end of the mechanical arm;

[0118] The tracking robot comprises a second automatic guided vehicle, a pan platform installed on a second lifting mechanism in the second automatic guided vehicle, and a binocular three-dimensional tracker installed on the pan platform.

[0119] The present invention provides a dual-robot large-scale collaborative measurement system, including a measuring robot and a tracking robot. The measuring robot and the tracking robot are both driven by their own automatic guided vehicles, and automatic three-dimensional scanning is realized without manual movement. The degree of automation is high, and the labor intensity of the scanner is reduced.

[0120] When the dual-robot large-scale collaborative measurement system is in use, it is necessary to obtain a map of the scene where the target is located through other hardware (such as lidar, etc.), and then set measuring stations and scanning points for the first automatic guided vehicle and the second automatic guided vehicle according to the map.

[0121] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered within the protection scope of the present invention. In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A dual-robot large-scale collaborative measurement method, characterized in that: The steps include: S1. First, use a photogrammetric device to measure the three-dimensional coordinates of multiple marking points on the scanner in the measuring robot; S2, use the scanner on the measuring robot to scan the target object according to the set route, and use the tracking robot to track the measuring robot in real time to obtain the spatial position of the measuring robot; during the scanning process, it is judged in real time whether the field of view of the tracking robot is limited, if so, enter S3, otherwise continue scanning until the scanning of the target object is completed; the judgment rule of whether the field of view is limited is: when the tracking robot cannot track multiple marking points on the scanner, it is judged that the field of view is limited; S3, based on the three-dimensional coordinates of multiple marker points on the scanner in S1, and using the Gauss-Newton method, calculate the rotation matrix and translation vector of the marker point coordinate system of two adjacent measuring stations to the tracking robot coordinate system, and construct the relationship between the rotation matrix and translation vector of the two adjacent measuring stations, and calculate the rotation matrix and translation vector of the next measuring station to the previous measuring station based on this relationship, and finally construct the relationship between the points scanned by the next measuring station and the previous measuring station based on the rotation matrix and translation vector of the next measuring station to the previous measuring station, so as to realize the merging of the scanned point clouds between the two measuring stations; The S3 specifically includes the following steps: S31, using a tracking robot to measure multiple marker points around the scanner at the mth measuring station, and obtaining the three-dimensional coordinates of the multiple marker points on the tracking robot coordinate system of the mth measuring station at the 0th frame, as follows: ； in, Indicates The first frame captured by the station at the 0th frame is located in the tracking robot coordinate system. landmark points; , and Indicates The 0th frame of the station tracks the The coordinate values ​​of the marker points on the X, Y and Z axes; S32, using the coordinates of the marker points measured by the tracking robot in S31 and the corresponding real coordinates as constraints, according to the three-dimensional coordinates of multiple marker points on the scanner in S1, and using the Gauss-Newton method, calculate the rotation matrix from the coordinate system of the marker points at the mth measuring station in the 0th frame to the coordinate system of the tracking robot The optimal value of and the translation vector from the coordinate system of the mth station's 0th frame marker point to the tracking robot's coordinate system The optimal value of S33, construct the three-dimensional coordinates of the i-th marker point obtained in S31 in the tracking robot coordinate system and the rotation matrix finally obtained in S32 , translation vector The relationship between The relationship in S33 is specifically: ； Represents the i-th landmark point in the landmark point coordinate system; S34: Under the premise that the scanner is stationary, the measuring robot moves to the m+1th measuring station, and uses the steps from S31 to S32 to calculate the rotation matrix from the 0th frame marker coordinate system of the m+1th measuring station to the tracker coordinate system. The optimal value of and the translation vector from the coordinate system of the 0th frame marker point of the m+1th station to the tracker coordinate system The optimal value of S35, construct the three-dimensional coordinates of the i-th marker point obtained in S31 in the tracking robot coordinate system and the rotation matrix finally obtained in S34 and translation vectors The relationship between The relationship in S35 is specifically: ； S36, constructing a relational expression of a rotation matrix and a translation vector of two adjacent measuring stations according to the relational expression in S33 and the relational expression in S35; The relationship between the rotation matrix and the translation vector of two adjacent measuring stations in S36 is specifically: ； S37, calculate the first The point of the measuring station goes to The rotation matrix of the station and translation vectors ; S38, using the rotation matrix obtained in S37 and translation vectors , construct the relational expression of the kth point of the target object scanned at the m+1th station to the mth station, that is, the relational expression of the point scanned by the next station to the previous station, so as to realize the merging of the scanned point clouds of the two stations; The relationship between the kth point in the target object scanned at the m+1th measuring station and the point on the mth measuring station in S38 is as follows: ； in, represents the kth point of the target object scanned at the mth measuring station after transformation; S4. Continue to use the scanner on the measuring robot to scan the target object according to the set route. If the field of view of the tracking robot is limited, the point clouds scanned by the next measuring station are merged using the relationship formula in S3 from the point scanned by the next measuring station to the previous measuring station until the scanning of the target object is completed.

2. The dual-robot large-scale collaborative measurement method according to claim 1 is characterized in that: The coordinate system where the three-dimensional coordinates of the multiple marker points in S1 are located is the marker point coordinate system. The three-dimensional coordinates of the i-th marker point in the marker point coordinate system are specifically: ； in, represents the i-th landmark point in the landmark point coordinate system, , and Represents the coordinate values ​​of the i-th marker point in the marker point coordinate system on the X, Y and Z axes.

3. The dual-robot large-scale collaborative measurement method according to claim 1 is characterized in that: The S32 specifically includes the following steps: S341, convert the coordinate value of the i-th marker point from the marker point coordinate system to the tracking robot coordinate system, and subtract it from the three-dimensional coordinate value of the i-th marker point in the tracking robot coordinate system in S31 to obtain the conversion error of the i-th marker point ; S342, then use the conversion error of the i-th marker point , and construct the optimization function of the conversion errors of multiple landmarks with the help of Gauss-Newton method; S343, by iteratively optimizing the optimization function, an update amount is obtained in each iteration , and then use the update amount The rotation matrix from the coordinate system of the marker point of the mth station in the 0th frame to the coordinate system of the tracking robot and translation vectors After multiple iterations of optimization, the rotation matrix from the coordinate system of the mth station's 0th frame marker point to the tracking robot's coordinate system is obtained. The optimal value of, the translation vector from the coordinate system of the mth station's 0th frame marker point to the tracking robot's coordinate system The optimal value of .

4. The dual-robot large-scale collaborative measurement method according to claim 3 is characterized in that: The conversion error of the i-th marker point in S341 , expressed by the formula, as follows: 。 5. The dual-robot large-scale collaborative measurement method according to claim 4 is characterized in that: The optimization function in S342 is specifically as follows: ； in, represents the number of point pairs between the landmark points measured by the tracking robot and the corresponding real landmark points; T represents the transpose of the matrix, Represents the function to be optimized using the Gauss-Newton method.

6. The dual-robot large-scale collaborative measurement method according to claim 1 is characterized in that: The S37 rotation matrix and translation vectors The calculation formula is as follows: ； 。 7. A dual-robot large-scale collaborative measurement system, characterized in that: The dual-robot large-scale collaborative measurement method according to any one of claims 1 to 6 is used for measurement, and the dual-robot large-scale collaborative measurement system comprises: A measuring robot, comprising a first automatic guided vehicle, a mechanical arm mounted on a first lifting mechanism in the first automatic guided vehicle, and a scanner mounted on an execution end of the mechanical arm; The tracking robot comprises a second automatic guided vehicle, a pan platform installed on a second lifting mechanism in the second automatic guided vehicle, and a binocular three-dimensional tracker installed on the pan platform.

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