Method and device for automatically detecting three-dimensional information of segment steel mold

Abstract

The invention discloses an automatic detection method for three-dimensional information of a segment steel mold. The automatic detection method comprises the following steps: S1, constructing a detection device and a system; s2, establishing a movable axis coordinate system Q; S3, calibrating a coordinate system relation; s4, confirming a coordinate system transformation relation between the structured light 3D camera and the tracker; s5, the optical tracker identifies and captures the position information of the self-luminous tracker; s6, a teaching detection step: enabling the structured light 3D camera to collect 3D point cloud information of the detection area; s7, a system automatic detection step; and S8, collecting the point cloud data of all the detection areas, and converting the point cloud data into an optical tracker coordinate system to obtain global point cloud data of the segment steel mold. And according to the global point cloud data of the segment steel mold, analyzing vertex position information, line segment position information and plane position information of the detection area, and according to an analysis result, outputting a detection report of the segment steel mold. Three-dimensional information of the inner cavity of the large-size steel mold is automatically collected and detected, and field installation and maintenance are facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of segment steel mold detection, and in particular relates to a method and device for automatically detecting three-dimensional information of a segment steel mold. Background Art

[0002] Prefabricated segments are mainly used in tunnel engineering, especially in subway shield tunnel construction. Prefabricated segment construction technology has high work efficiency and a wide range of applications. The manufacture of prefabricated segments mainly adopts high-precision segment steel molds to form by pouring concrete. During the production process, multiple steel molds are used to form a fully automatic assembly line production mode, which has high requirements on the dimensional error, precision, and consistency of the segments.

[0003] The segments are cast in steel molds, and the shape of the inner cavity of the steel mold determines the outer dimensions of the segments. Therefore, in order to ensure the production quality of the segments, the shape and dimensional accuracy of the inner cavity of the steel mold need to be detected and monitored during the production process. However, due to the large size of the steel mold (usually the dimensions are about: 5 meters long, 2 meters wide, 0.6 meters thick, and with an arc-shaped inner mold), conventional laser trackers can meet the needs of fast measurement and high precision, but the tester needs to manually place the measurement points, the detection efficiency is extremely low, and it is easy to produce human errors, which cannot meet the requirements of assembly line operations. Other three-coordinate detection methods have limited detection points and cannot meet the requirements. The large-scale robotic arm plus ground rail solution has high testing costs, extremely high requirements for the use environment, and is difficult to maintain, making it difficult to achieve rapid measurement of the segment steel mold. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that due to the large shape of the steel mold (usually the dimensions are about: 5 meters long, 2 meters wide, 0.6 meters thick, and with a curved inner mold), the conventional laser tracker can meet the needs of fast measurement and high precision, but the tester needs to manually place the measurement points, the detection efficiency is extremely low, and it is easy to produce human errors, and it cannot meet the requirements of assembly line operations. Other types of three-coordinate detection methods have limited detection points and cannot meet the requirements. The large-scale mechanical arm plus ground rail solution has high testing costs, extremely high requirements on the use environment, and is difficult to maintain. It is difficult to achieve rapid measurement of the segment steel mold, so a method for automatic detection of three-dimensional information of the segment steel mold is proposed.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a method for automatically detecting three-dimensional information of a segment steel mold, comprising the following steps:

[0006] S1: Testing device and system construction;

[0007] a: Build a large steel structure frame on the segment production line, the size of which covers the segment steel mold inspection range;

[0008] b: Install the linear motor module on the steel structure frame to build three movable axes for position movement: X-axis, Y-axis, and Z-axis;

[0009] c: Install a hollow rotating motor at the end of the Z axis to construct the rotating axes Rx, Ry and Rz, wherein the structured light 3D camera is fixed on the Rx axis through a connecting plate;

[0010] d: The self-luminous tracker is installed on the structured light 3D camera, and the optical tracker is installed on the steel structure frame through a fixed bracket;

[0011] e: connecting the driving control and information of the X-axis, the Y-axis, the Z-axis, the Rx-axis, the Ry-axis, the Rz-axis, the structured light 3D camera, the self-luminous tracker and the optical tracker to the workstation through cables;

[0012] S2: Establishment of the active axis coordinate system Q: according to the size of the linear motor stroke, the coordinate range of the X-axis, the Y-axis, and the Z-axis and the origin of the coordinate system are set, and the attitude angle setting range of the Rx-axis, the Ry-axis, and the Rz-axis is -180° to +180°, and the establishment of the 6-axis coordinate system Q is completed;

[0013] S3: Coordinate system relationship calibration: collect and analyze the three-dimensional data, and calculate the coordinate system transformation relationship between the structured light 3D camera and the optical tracker;

[0014] S4: The coordinate transformation relationship between the structured light 3D camera and the self-luminous tracker is confirmed, and the rotation matrix R C-Z =R C-Ref ×R Z-Ref and the translation vector T C-Z =T C-Ref ×T Z-Ref ;

[0015] S5: The optical tracker identifies and captures the position information of the self-luminous tracker itself;

[0016] S6: teaching detection step, controlling the X axis, the Y axis, the Z axis, the Rx axis, the Ry axis, and the Rz axis, moving the structured light 3D camera to the detection area, and adjusting the camera posture through the Rx axis, the Ry axis, and the Rz axis, so that the structured light 3D camera can collect the 3D point cloud information of the detection area, ensuring that at least one luminous surface of the self-luminous tracker faces the optical tracker, and ensuring that the optical tracker can capture the position of the self-luminous tracker; after the adjustment is completed, recording the position information PQ1 in the control system; teaching all detection areas according to this method, and planning the walking route of the structured light 3D camera, and recording the position information PQ i of all detection areas;

[0017] S7: System automated inspection step; after the segment steel mold moves to the inspection station, the system starts automated inspection; according to the taught inspection area and the planned inspection path, the system automatically controls the structured light 3D camera to the inspection area, and after arriving at the inspection area, the structured light 3D camera collects 3D point cloud data of the inspection area while the optical tracker collects the position information of the self-luminous tracker; S8: After collecting the point cloud data of all inspection areas and converting them to the optical tracker coordinate system, the global point cloud data of the segment steel mold is obtained, and based on the global point cloud data of the segment steel mold, the vertex position information, line segment position information, and plane position information of the inspection area are analyzed, and based on the results of the analysis and calculation, the inspection report of the segment steel mold is output.

[0018] As a further description of the above technical solution:

[0019] Three-dimensional data acquisition in S3: placing the calibration plate within the tracking field of view of the optical tracker, and using the structured light 3D camera to collect 3D point cloud data of the luminous surface of the calibration plate. During the acquisition process, ensure that at least one surface of the self-luminous tracker faces the optical tracker, and the optical tracker simultaneously captures the position information of the self-luminous tracker and the calibration plate.

[0020] As a further description of the above technical solution:

[0021] Three-dimensional coordinate analysis in S3: When the optical tracker captures the position information of the calibration plate, the distribution of the light-emitting points on the calibration plate can obtain the positions of all the light-emitting points on the calibration plate in the optical tracker coordinate system. After the structured light 3D camera collects the point cloud of the calibration plate, the position information of the light-emitting points on the calibration plate in the structured light 3D camera coordinate system can be analyzed based on the point cloud of the circular light-emitting points on the calibration plate.

[0022] As a further description of the above technical solution:

[0023] The specific steps of calculating the transformation relationship between the structured light 3D camera and the optical tracker coordinate system in S4 specifically include:

[0024] a: According to the point cloud data collected by the structured light 3D camera, obtain the coordinates P1 of the three calibration plate light points in the camera coordinate system C 、P2 C and P3 C , according to the three-dimensional data collected by the optical tracker, the coordinates P1 of the three luminous points of the calibration plate in the optical tracker coordinate system are obtained. Ref , P R 2ef and P3 Ref ;in,

[0025]

[0026]

[0027] b: Based on P1 C 、P2 C and P3 C Calculate the center point C of the three luminous points in the three-dimensional coordinate system of the structured light 3D camera C , based on P1 Ref , and Calculate the center points of the three luminous points in the three-dimensional coordinate system of the optical tracker in,

[0028] c: Construct a decentralized point set of three luminous points in the three-dimensional coordinate system of the structured light 3D camera and a decentralized point set in the three-dimensional coordinate system of the optical tracker in, i takes the value 1, 2, 3, P i C Including P1 C 、P2 C and P3 C , P i Ref Including P1 Ref 、P2 Ref and P3 Ref , include and include and

[0029] d: Calculate the rotation matrix R of the three-dimensional coordinate system of the optical tracker and the three-dimensional coordinate system of the structured light 3D camera according to the SVD algorithm C-Ref ,in

[0030] e: Calculate the translation vector T of the three-dimensional coordinate system of the optical tracker and the three-dimensional coordinate system of the structured light 3D camera C-Ref , where T C-Ref =C C -R C-Ref C2 Ref ;

[0031] f: The rotation matrix R based on the three-dimensional coordinate system of the optical tracker and the three-dimensional coordinate system of the camera C-Ref and the translation vector T C-Ref, calculate the coordinate transformation relationship between the three-dimensional coordinate system of the optical tracker and the three-dimensional coordinate system of the structured light 3D camera.

[0032] As a further description of the above technical solution:

[0033] The self-luminous tracker described in S5 has 7 light-emitting surfaces, each of which has 4 light-emitting points. The shapes formed by the 4 light-emitting points in each surface are different. The optical tracker can capture the positions of the light-emitting points and distinguish the corresponding light-emitting surfaces according to the shape characteristics of each of the 4 light-emitting points. As long as the optical tracker can capture any one or more light-emitting surfaces of the self-luminous tracker, it can capture the position information of the self-luminous tracker itself.

[0034] As a further description of the above technical solution:

[0035] The present invention also provides a device for automatically detecting three-dimensional information of a segment steel mold, comprising:

[0036] A production line track, on which a transport trolley and a steel structure frame are arranged, a fixed bracket is connected to the steel structure frame, the optical tracker is connected to the fixed bracket, and the segment steel mold is positioned on the transport trolley;

[0037] X-axis, Y-axis and Z-axis, which are connected to the steel structure frame;

[0038] The visual monitoring unit comprises an Rx axis, an Ry axis, an Rz axis and a structured light 3D camera, wherein the structured light 3D camera is connected to the Rx axis, and the Rz axis is connected to the Z axis.

[0039] As a further description of the above technical solution:

[0040] The structured light 3D camera is provided with a self-luminous tracker, and the self-luminous tracker is provided with a plurality of infrared light emitting points.

[0041] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0042] 1. In the present invention, a large steel structure frame is built on the segment production line, the size of which covers the steel mold detection range, and the linear motor module is installed on the steel structure frame to build three movable axes for position movement: X / Y / Z axis; a hollow rotary motor is installed at the end of the Z axis to build the rotating axes Rx, Ry, and Rz axes, the structured light 3D camera is fixed on the Rx axis through a connecting plate, the self-luminous tracker is installed on the structured light 3D camera, and the optical tracker is installed on the steel structure frame through a fixed bracket. The drive control of the 6 movable axes, the structured light 3D camera, the self-luminous tracker and the optical tracker The information is connected to the workstation through cables to complete the construction of the detection device. A coordinate system is established according to the orientation of each component, and the calibration plate is used as a reference. Then, a structured light 3D camera is used to collect 3D point cloud data of the luminous surface of the self-luminous calibration plate. During the collection process, at least one side of the self-luminous tracker is guaranteed to face the optical tracker. The optical tracker simultaneously captures the position information of the tracker and the calibration plate, and then analyzes the three-dimensional coordinates to obtain the calculation method of the coordinate system transformation relationship between the structured light 3D camera and the optical tracker. Because the self-luminous tracker and the structured light 3D camera are rigidly connected, and the calibration plate is used The coordinate change relationship between the self-luminous tracker and the 3D camera can be obtained, so the 3D point cloud data collected by the structured light 3D camera can be converted to the self-luminous tracker coordinate system. Because the optical tracker also captures the position information of the self-luminous tracker, the real-time position change relationship between the optical tracker and the self-luminous tracker is known, so the 3D point cloud data can be converted to the coordinate system of the optical tracker again. When the point cloud data of all the detection areas are collected and converted to the optical tracker coordinate system, the global point cloud data of the steel mold can be obtained. This structure can be based on the global point cloud data of the steel mold. By analyzing the vertex position information, line segment position information, and plane position information of the detection area, the three-dimensional information of the steel mold inner cavity can be calculated through point, line, and surface information, such as the distance between the two end faces of the inner cavity, the angle of the inner cavity plane, the distance between the inner cavity vertices, the radius of the inner cavity arc surface, and other information. Based on the results of the analysis and calculation, the inspection report of the steel mold is output. Because multiple steel molds are used in a cycle during the production of pipe segments, the dimensional data of the steel mold after each use can be recorded and compared to detect the health of the steel mold. At the same time, the dimensional data between different steel molds can also be compared to ensure the consistency of the pipe segments produced by each steel mold.

[0043] 2. In the present invention, the automatic collection of three-dimensional information of the inner cavity of a large-sized steel mold is realized, and the structure is simple and reliable, which is convenient for on-site installation and maintenance. The cooperation of the optical tracker and the structured light 3D camera can realize high-precision detection of the inner cavity of a large-sized steel mold; the accuracy of the system is determined by the optical tracker, the tracker and the 3D camera, and does not depend on the 6 active axes of the system. Therefore, the technical requirements of the active axis can be greatly reduced, which greatly reduces the difficulty of installation and debugging of the system, reduces the requirements for the use environment, and reduces the maintenance requirements for the system. Therefore, the system also greatly reduces costs and expenses. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 The present invention is a step diagram of a method for automatically detecting three-dimensional information of a segment steel mold.

[0046] Figure 2 A schematic diagram of the structure of a three-dimensional information automatic detection device for segment steel molds Figure 1 .

[0047] Figure 3 A schematic diagram of the structure of a three-dimensional information automatic detection device for segment steel molds Figure 2 .

[0048] Figure 4 for Figure 3 Enlarged view of point A in the middle.

[0049] Legend:

[0050] 1- production line track; 2- transport trolley; 3- steel structure frame; 41- X-axis; 42- Y-axis; 43- Z-axis; 5- structured light 3D camera; 61- Rx-axis; 62- Ry-axis; 63- Rz-axis; 7- self-luminous tracker; 8- workstation; 9- segment steel mold; 10- optical tracker. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0055] In the description of the embodiments of the present invention, it should be noted that the terms "upper", "inner", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the inventive product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0056] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] See also Figure 1 The present invention provides a technical solution: a three-dimensional information automatic detection method for segment steel mold

[0058] The method comprises the following steps:

[0059] S1: Testing device and system construction;

[0060] a: Build a large steel structure frame 3 on the segment production line, the size of which covers the segment steel mold 9 for inspection

[0061] scope;

[0062] b: Install the linear motor module on the steel structure frame 3 to build three positions for movement

[0063] Active axes: X axis 41, Y axis 42, Z axis 43;

[0064] c: Install the hollow rotating motor at the end of the Z axis 43 to build the rotating axes RX axis 61, RY axis 62, and RZ axis 63, wherein the structured light 3D camera 5 is fixed on the RX axis 61 through a connecting plate;

[0065] d: The self-luminous tracker 7 is installed on the structured light 3D camera 5, and the optical tracker 10 is

[0066] The fixing bracket is installed on the steel structure frame 3;

[0067] e: connecting the driving control and information of the X-axis 41, the Y-axis 42, the Z-axis 43, the RX-axis 61, the RY-axis 62, the RZ-axis 63, the structured light 3D camera 5, the self-luminous tracker 7 and the optical tracker 10 to the workstation 8 through cables;

[0068] S2: Establishment of the active axis coordinate system Q: according to the size of the linear motor stroke, the coordinate ranges of the X axis 41, the Y axis 42, and the Z axis 43 and the origin of the coordinate system are set, and the attitude angles of the RX axis 61, the RY axis 62, and the RZ axis 63 are set in the range of -180° to +180°, and the establishment of the 6-axis coordinate system Q is completed;

[0069] S3: Coordinate system relationship calibration: collect and analyze the three-dimensional data, and calculate the coordinate system transformation relationship between the structured light 3D camera 5 and the optical tracker 10;

[0070] S4: The transformation relationship between the structured light 3D camera 5 and the tracker coordinate system is confirmed, and the rotation matrix R C-Z =R C-Ref ×R Z -Ref and the translation vector T C-Z =T C-Ref ×T Z-Ref ;

[0071] S5: The optical tracker 10 identifies and captures the position information of the self-luminous tracker 7 itself;

[0072] S6: Teaching detection step, controlling the X axis 41, the Y axis 42, the Z axis 43, the RX axis 61, the RY axis 62, and the RZ axis 63, moving the structured light 3D camera 5 to the detection area, and adjusting the camera posture through the RX axis 61, the RY axis 62, and the RZ axis 63, so that the structured light 3D camera 5 can collect the 3D point cloud information of the detection area, ensuring that at least one luminous surface of the self-luminous tracker 7 faces the optical tracker 10, and ensuring that the optical tracker 10 can capture the position of the self-luminous tracker 7; after the adjustment is completed, recording the position information PQ1 in the control system; teaching all the detection areas according to this method, and planning the walking route of the structured light 3D camera 5, and recording all the detection area position information PQi;

[0073] S7: System automated inspection step; after the segment steel mold 9 is moved to the inspection station, the system starts automated inspection; according to the taught inspection area and the planned inspection path, the system automatically controls the structured light 3D camera 5 to the inspection area. After arriving at the inspection area, the structured light 3D camera 5 collects the 3D point cloud data of the inspection area while the optical tracker 10 collects the position information of the self-luminous tracker 7; and because the tracker and the 3D camera are rigidly connected, and the coordinate change relationship between the tracker and the structured light 3D camera can be obtained from the calibration plate, the 3D point cloud data collected by the structured light 3D camera can be converted to the self-luminous tracker coordinate system; and because the optical tracker also captures the position information of the self-luminous tracker, the real-time position change relationship between the optical tracker and the tracker is known, so the 3D point cloud data can be converted to the coordinate system of the optical tracker again; when the point cloud data of all inspection areas are collected and converted to the optical tracker coordinate system, the global point cloud data of the steel mold can be obtained.

[0074] S8: After collecting the point cloud data of all the detection areas and converting them to the coordinate system of the optical tracker 10, the global point cloud data of the segment steel mold 9 is obtained. According to the global point cloud data of the segment steel mold 9, the vertex position information, line segment position information, and plane position information of the detection area are analyzed. According to the results of the analysis and calculation, the detection report of the segment steel mold 9 is output. And because the tracker and the 3D camera are rigidly connected, and the coordinate change relationship between the tracker and the 3D camera can be obtained by calibration, the 3D point cloud data collected by the 3D camera can be converted to the tracker coordinate system. And because the optical tracker also captures the position information of the tracker, the real-time position change relationship between the optical tracker and the tracker is known, so the 3D point cloud data can be converted to the coordinate system of the optical tracker again. When the point cloud data of all the detection areas are collected and converted to the optical tracker coordinate system, the global point cloud data of the steel mold can be obtained.

[0075] Three-dimensional data acquisition in S3: Place the calibration plate within the tracking field of view of the optical tracker 10, and use the structured light 3D camera 5 to collect 3D point cloud data of the luminous surface of the calibration plate. During the acquisition process, ensure that at least one surface of the self-luminous tracker 7 faces the optical tracker 10, and the optical tracker 10 simultaneously captures the position information of the self-luminous tracker 7 and the calibration plate.

[0076] Three-dimensional coordinate analysis in S3: When the optical tracker 10 captures the position information of the calibration plate, the distribution of the light-emitting points on the calibration plate can obtain the positions of all the light-emitting points on the calibration plate in the coordinate system of the optical tracker 10. After the structured light 3D camera 5 collects the point cloud of the calibration plate, the position information of the light-emitting points on the calibration plate in the coordinate system of the structured light 3D camera 5 can be analyzed based on the point cloud of the circular light-emitting points on the calibration plate.

[0077] The specific steps of calculating the coordinate transformation relationship between the structured light 3D camera 5 and the optical tracker 10 in S4 include:

[0078] a: According to the point cloud data collected by the structured light 3D camera 5, the coordinates P1 of the three calibration plate light-emitting points in the camera coordinate system are obtained. C 、P2 C and P3 C , according to the three-dimensional data collected by the optical tracker 10, the coordinates P1 of the three luminous points of the calibration plate in the coordinate system of the optical tracker 10 are obtained. Ref , and in,

[0079]

[0080] b: Based on P1 C 、P2 C and P3 C Calculate the center point C of the three luminous points in the three-dimensional coordinate system of the structured light 3D camera 5 C , based on P1 Ref , and Calculate the center points of the three luminous points in the three-dimensional coordinate system of the optical tracker 10 in,

[0081] c: Construct a decentralized point set of three luminous points in the three-dimensional coordinate system of the structured light 3D camera 5 and a decentralized point set in the three-dimensional coordinate system of the optical tracker 10 in, i takes the values ​​1, 2, 3, Pi C Including P1 C 、P2 C and P3 C , P i Ref Including P1 Ref 、P2 Ref and P3 Ref , include and include and

[0082] d: Calculate the rotation matrix R of the three-dimensional coordinate system of the optical tracker 10 and the three-dimensional coordinate system of the structured light 3D camera 5 according to the SVD algorithm C-Ref ,in

[0083] e: Calculate the three-dimensional coordinate system of the optical tracker 10 and the three-dimensional coordinate system of the structured light 3D camera 5

[0084] The translation vector T of the system C-Ref , where T C-Ref =C C -R C-Ref C2 Ref ;

[0085] f: rotation matrix R based on the three-dimensional coordinate system of the optical tracker 10 and the three-dimensional coordinate system of the camera C-Ref and the translation vector T C-Ref , calculate the coordinate transformation relationship between the three-dimensional coordinate system of the optical tracker 10 and the three-dimensional coordinate system of the structured light 3D camera 5.

[0086] The self-luminous tracker 7 described in S5 has 7 light-emitting surfaces, each of which has 4 light-emitting points. The shapes formed by the 4 light-emitting points in each surface are different. The optical tracker 10 can capture the positions of the light-emitting points and distinguish the corresponding light-emitting surfaces according to the shape characteristics of each of the 4 light-emitting points. As long as the optical tracker 10 can capture any one or more light-emitting surfaces of the self-luminous tracker 7, the position information of the self-luminous tracker 7 itself can be captured.

[0087] See also Figure 2-4 The present invention also provides a device for automatically detecting three-dimensional information of a segment steel mold, comprising:

[0088] A production line track 1, on which a transport trolley 2 and a steel structure frame 3 are arranged, a fixed bracket is connected to the steel structure frame 3, the optical tracker 10 is connected to the fixed bracket, and a segment steel mold 9 is positioned on the transport trolley 2;

[0089] The X-axis 41, the Y-axis 42, and the Z-axis 43 are connected to the steel structure frame 3 to facilitate movement in different directions.

[0090] The visual monitoring unit includes an RX axis 61, an RY axis 62, an RZ axis 63 and a structured light 3D camera 5, wherein the structured light 3D camera is connected to the RX axis 61, and the RZ axis 63 is connected to the Z axis 43. This facilitates the structured light 3D camera to adjust its angle.

[0091] The structured light 3D camera 5 is provided with a self-luminous tracker 7, and the self-luminous tracker 7 is provided with a plurality of infrared light emission points. The self-luminous tracker has 7 light-emitting surfaces, each of which has 4 light-emitting points (infrared light emission points), and the shapes formed by the 4 light-emitting points in each surface are different. The optical tracker can capture the position of the light-emitting points and distinguish the corresponding light-emitting surfaces according to the shape characteristics of each of the 4 light-emitting points. Therefore, as long as the optical tracker can capture any one or more light-emitting surfaces of the tracker, the position information of the tracker itself can be captured.

[0092] Working principle: By building a large steel structure frame on the segment production line, the size of which covers the steel mold detection range, the linear motor module is installed on the steel structure frame to build three movable axes for position movement: X / Y / Z axis; the hollow rotary motor is installed at the end of the Z axis to build the rotating axes Rx, Ry, and Rz axes. The structured light 3D camera is fixed to the Rx axis through a connecting plate, the self-luminous tracker is installed on the structured light 3D camera, and the optical tracker is installed on the steel structure frame through a fixed bracket. The drive control and The information is connected to the workstation through cables to complete the construction of the detection device. A coordinate system is established according to the orientation of each component, and the calibration plate is used as a reference. Then, a structured light 3D camera is used to collect 3D point cloud data of the luminous surface of the self-luminous calibration plate. During the collection process, it is ensured that at least one side of the self-luminous tracker is facing the optical tracker. The optical tracker simultaneously captures the position information of the tracker and the calibration plate, and then analyzes the three-dimensional coordinates to obtain the calculation method of the coordinate system transformation relationship between the structured light 3D camera and the optical tracker. Because the self-luminous tracker and the structured light 3D camera are rigidly connected, and the calibration plate can The coordinate change relationship between the self-luminous tracker and the 3D camera is obtained, so the 3D point cloud data collected by the structured light 3D camera can be converted to the self-luminous tracker coordinate system. Because the optical tracker also captures the position information of the self-luminous tracker, the real-time position change relationship between the optical tracker and the self-luminous tracker is known, so the 3D point cloud data can be converted to the coordinate system of the optical tracker again. When the point cloud data of all the detection areas is collected and converted to the optical tracker coordinate system, the global point cloud data of the steel mold can be obtained. This structure can be based on the global point cloud data of the steel mold. By analyzing the vertex position information, line segment position information, and plane position information of the detection area, the three-dimensional information of the steel mold inner cavity can be calculated through point, line, and surface information, such as the distance between the two end faces of the inner cavity, the angle of the inner cavity plane, the distance between the inner cavity vertices, the radius of the inner cavity arc surface, and other information. Based on the results of the analysis and calculation, the inspection report of the steel mold is output. Because multiple steel molds are used in a cycle during the production of pipe segments, the dimensional data of the steel mold after each use can be recorded and compared to detect the health of the steel mold. At the same time, the dimensional data between different steel molds can also be compared to ensure the consistency of the pipe segments produced by each steel mold.

[0093] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for automatically detecting three-dimensional information of a segment steel mold, characterized in that: The following steps are involved: S1: Testing device and system construction; a: Build a large steel structure frame on the segment production line, the size of which covers the segment steel mold inspection range; b: Install the linear motor module on the steel structure frame to build three movable axes for position movement: X-axis, Y-axis, and Z-axis; c: Install a hollow rotating motor at the end of the Z axis to construct the rotating axes Rx, Ry and Rz, wherein the structured light 3D camera is fixed on the Rx axis through a connecting plate; d: The self-luminous tracker is installed on the structured light 3D camera, and the optical tracker is installed on the steel structure frame through a fixed bracket; e: connecting the driving control and information of the X-axis, the Y-axis, the Z-axis, the Rx-axis, the Ry-axis, the Rz-axis, the structured light 3D camera, the self-luminous tracker and the optical tracker to the workstation through cables; S2: Establishment of the active axis coordinate system Q: according to the size of the linear motor stroke, the coordinate range of the X-axis, the Y-axis, and the Z-axis and the origin of the coordinate system are set, and the attitude angle setting range of the Rx-axis, the Ry-axis, and the Rz-axis is -180° to +180°, and the establishment of the 6-axis coordinate system Q is completed; S3: Coordinate system relationship calibration: collect and analyze the three-dimensional data, and calculate the coordinate system transformation relationship between the structured light 3D camera and the optical tracker; S4: The coordinate transformation relationship between the structured light 3D camera and the self-luminous tracker is confirmed, and the rotation matrix R C-z =R C-Ref ×R Z -Ref and the translation vector T C-z =T C-Ref ×T Z-Ref ; S5: The optical tracker identifies and captures the position information of the self-luminous tracker itself; S6: Teaching detection step, controlling the X axis, the Y axis, the Z axis, the Rx axis, the Ry axis, and the Rz axis, moving the structured light 3D camera to the detection area, and adjusting the camera posture through the Rx axis, the Ry axis, and the Rz axis, so that the structured light 3D camera can collect 3D point cloud information of the detection area, ensuring that at least one light-emitting surface of the self-luminous tracker faces the optical tracker, and ensuring that the optical tracker can capture the position of the self-luminous tracker; After the adjustment is completed, the position information PQ1 is recorded in the control system; all detection areas are taught according to this method, and the route of the structured light 3D camera is planned, and the position information PQ i of all detection areas is recorded; S7: System automatic detection step; after the segment steel mold is moved to the detection station, the system starts automatic detection; according to the taught detection area and the planned detection path, the system automatically controls the structured light 3D camera to the detection area. After arriving at the detection area, the structured light 3D camera collects 3D point cloud data of the detection area, while the optical tracker collects the position information of the self-luminous tracker; S8: After collecting point cloud data of all inspection areas and converting them to the optical tracker coordinate system, the global point cloud data of the segment steel mold is obtained. According to the global point cloud data of the segment steel mold, the vertex position information, line segment position information, and plane position information of the inspection area are analyzed. According to the results of the analysis and calculation, the inspection report of the segment steel mold is output.

2. The method for automatic detection of three-dimensional information of a segment steel mold according to claim 1, characterized in that: The three-dimensional data acquisition in S3 is as follows: placing the calibration plate within the tracking field of view of the optical tracker, and using the structured light 3D camera to acquire 3D point cloud data of the luminous surface of the calibration plate. During the acquisition process, ensure that at least one surface of the self-luminous tracker faces the optical tracker, and the optical tracker simultaneously captures the position information of the self-luminous tracker and the calibration plate.

3. The method for automatic detection of three-dimensional information of a segment steel mold according to claim 2, characterized in that: Three-dimensional coordinate analysis in S3: when the optical tracker captures the position information of the calibration plate, the distribution of the light-emitting points on the calibration plate can obtain the positions of all the light-emitting points on the calibration plate in the optical tracker coordinate system. After the structured light 3D camera collects the point cloud of the calibration plate, the position information of the light-emitting points on the calibration plate in the structured light 3D camera coordinate system can be analyzed based on the point cloud of the circular light-emitting points on the calibration plate.

4. The method for automatic detection of three-dimensional information of a segment steel mold according to claim 1, characterized in that: The specific steps of calculating the transformation relationship between the structured light 3D camera and the optical tracker coordinate system in S4 specifically include: a: According to the point cloud data collected by the structured light 3D camera, obtain the coordinates P1 of the three calibration plate light points in the camera coordinate system C , and P3 C , according to the three-dimensional data collected by the optical tracker, the coordinates P1 of the three luminous points of the calibration plate in the optical tracker coordinate system are obtained. Ref , and in, b: Based on P1 C , and P3 C Calculate the center point C of the three luminous points in the three-dimensional coordinate system of the structured light 3D camera C , based on P1 Ref , and Calculate the center points of the three luminous points in the three-dimensional coordinate system of the optical tracker in, c: Construct a decentralized point set of three luminous points in the three-dimensional coordinate system of the structured light 3D camera and a decentralized point set in the three-dimensional coordinate system of the optical tracker in, i takes the values ​​1, 2, 3, P i C Including P1 C , and P3 C , P i Ref Including P1 Ref , and include and include and d: Calculate the rotation matrix R of the three-dimensional coordinate system of the optical tracker and the three-dimensional coordinate system of the structured light 3D camera according to the SVD algorithm C-Ref ,in e: Calculate the translation vector T of the three-dimensional coordinate system of the optical tracker and the three-dimensional coordinate system of the structured light 3D camera C-Ref , where T C-Ref =C C -R C-Ref C2 Ref ; f: The rotation matrix R based on the three-dimensional coordinate system of the optical tracker and the three-dimensional coordinate system of the camera C-Ref and the translation vector T C -Ref , calculate the coordinate transformation relationship between the three-dimensional coordinate system of the optical tracker and the three-dimensional coordinate system of the structured light 3D camera.

5. The method for automatic detection of three-dimensional information of a segment steel mold according to claim 1, characterized in that: The self-luminous tracker described in S5 has 7 light-emitting surfaces, each of which has 4 light-emitting points. The shapes formed by the 4 light-emitting points in each surface are different. The optical tracker can capture the positions of the light-emitting points and distinguish the corresponding light-emitting surfaces according to the shape characteristics of each of the 4 light-emitting points. As long as the optical tracker can capture any one or more light-emitting surfaces of the self-luminous tracker, it can capture the position information of the self-luminous tracker itself.

6. An automatic detection device for three-dimensional information of segment steel mold, characterized in that: include: A production line track, on which a transport trolley and a steel structure frame are arranged, a fixed bracket is connected to the steel structure frame, the optical tracker is connected to the fixed bracket, and the segment steel mold is positioned on the transport trolley; X-axis, Y-axis and Z-axis, which are connected to the steel structure frame; The visual monitoring unit comprises an Rx axis, an Ry axis, an Rz axis and a structured light 3D camera, wherein the structured light 3D camera is connected to the Rx axis, and the Rz axis is connected to the Z axis.

7. The method for automatic detection of three-dimensional information of a segment steel mold according to claim 6, characterized in that: The structured light 3D camera is provided with a self-luminous tracker, and the self-luminous tracker is provided with a plurality of infrared light emitting points.

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