Method for measuring joint occurrence of bedrock mass

Abstract

The embodiment of the invention relates to the field of photogrammetry, in particular to a method for measuring the joint attitude of a bedrock mass, which comprises the following steps: S10: acquiring image information of the bedrock mass; s20, processing the image information to obtain a point cloud model of the bedrock mass; and S30, the point cloud model is measured to obtain the joint occurrence of the bedrock mass, and in the step S10, when the image information of the bedrock mass is obtained, the distance between a shooting device and the bedrock mass is kept unchanged. According to the method, when the bedrock image information is obtained, the distance between the shooting device and the bedrock is kept unchanged by changing the mode of obtaining the image information, so that the image information obtained by the shooting device fits the terrain trend of the bedrock, and image distortion caused by the terrain trend is reduced; in the step of constructing the point cloud model according to the image information, a more accurate point cloud model can be obtained, so that the accuracy of the joint attitude obtained by measuring the point cloud model is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of photogrammetry, and in particular to a method for measuring the occurrence of joints in a bedrock body. Background Art

[0002] The statements herein merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] Outcrops are the parts of strata and rock masses that are exposed on the surface. The joints of outcrops are geological interfaces with a length ranging from a few centimeters to tens of meters and a certain thickness. They are the structural traces of the rock mass after tectonic movement. Investigating and obtaining the occurrence of outcrop joints can help understand the structural evolution of the regional rock mass, thereby guiding rock mass engineering practice. Summary of the invention

[0004] A brief overview of the present application is provided below in order to provide a basic understanding of certain aspects of the present application. It should be understood that this overview is not an exhaustive overview of the present application. It is not intended to identify the key or important parts of the present application, nor is it intended to limit the scope of the present application. Its purpose is merely to present certain concepts in a simplified form as a prelude to a more detailed description discussed later.

[0005] An embodiment of the present application provides a method for measuring the joint occurrence of a bedrock body, which includes the following steps: S10: acquiring image information of the bedrock body; S20: processing the image information to obtain a point cloud model of the bedrock body; S30: measuring the point cloud model to obtain the joint occurrence of the bedrock body, wherein in step S10, when acquiring the image information of the bedrock body, the distance between the shooting device and the bedrock body is set to remain unchanged.

[0006] The method for measuring the joint occurrence of a bedrock body provided in the present application sets a constant distance between a photographing device and the bedrock body when acquiring image information of the bedrock body, so that the image information acquired by the photographing device fits the topography of the bedrock body, thereby reducing image distortion caused by the topography, so that a more accurate point cloud model can be obtained when constructing a point cloud model based on the image information, thereby improving the accuracy of the joint occurrence obtained by measuring the point cloud model. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to further illustrate the above and other advantages and features of the present application, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. The accompanying drawings together with the following detailed description are included in this specification and form a part of this specification. Elements with the same function and structure are represented by the same reference numerals. It should be understood that these drawings only describe typical examples of the present application and should not be regarded as limiting the scope of the present application.

[0008] Figure 1 is a schematic diagram of a method provided in an embodiment of the present application using a camera to perform surround photography of a bedrock body; Figure 2 is a schematic diagram of the influence of the key point and connection point restrictions on the number of points of an image provided by an embodiment of the present application; Figure 3a It is a schematic diagram of a point cloud model obtained by the traditional frontal shooting method; Figure 3b The embodiments of the present application provide Figure 3a The same area uses Figure 1 Schematic diagram of a point cloud model obtained by the shooting method; Figure 4 It is a schematic diagram of a three-dimensional real-time cloud model of a bedrock body provided in an embodiment of the present application; Figure 5a The embodiments of the present application provide a comparison Figure 3b The point cloud model obtained Figure 4 A schematic diagram of the three-dimensional real-world point cloud model and statistical deviation values ​​of the points within the relative distance error range in the X-axis direction; Figure 5b The embodiments of the present application provide a comparison Figure 3b The point cloud model obtained Figure 4 A schematic diagram of the three-dimensional real-world point cloud model and statistical deviation values ​​of the points within the relative distance error range in the Y-axis direction; Figure 5c The embodiments of the present application provide a comparison Figure 3b The point cloud model obtained Figure 4 A schematic diagram of the three-dimensional real-world point cloud model and statistical deviation values ​​of points in the Z-axis direction within the relative distance error range; Figure 5d The embodiments of the present application provide a comparison Figure 3b The point cloud model obtained Figure 4 A schematic diagram of a 3D real-world point cloud model and statistical deviation values ​​of points in space within the relative distance error range; Figure 6a The embodiments of this application provide Figure 3b Schematic diagram of a closed joint on the point cloud model in ; Figure 6b The traditional automatic picking method is used Figure 6a Schematic diagram of the closed joint picking trace in; Figure 6c is based on Figure 6b Schematic diagram of automatically picked trace fitting plane to extract occurrence; Figure 7a The embodiments of the present application provide Figure 6aSchematic diagram of manually selecting picking points for closed joints in; Figure 7b The embodiments of this application provide the basis Figure 7a Schematic diagram of the trace generated by manually selected picking points; Figure 7c The embodiments of this application provide the basis Figure 7b Schematic diagram of the generated trace fitting plane to extract the occurrence; Figure 8a The embodiments of this application provide Figure 3b Schematic diagram of another closed joint on the point cloud model in ; Figure 8b The embodiments of this application provide Figure 8a Schematic diagram of the true trace of the closed joint in; Figure 8c The embodiments of the present application provide Figure 8a A schematic diagram of manually selecting pick points and generating traces for closed joints in the figure; Figure 9a The embodiments of the present application provide Figure 3b Schematic diagram of manually selecting a picking point on an exposed joint surface on the point cloud model in the figure; Figure 9b The embodiments of the present application provide Figure 9a Schematic diagram of manually selected picked points in fitting into a plane; Fig.9c The embodiments of the present application provide Figure 3b Schematic diagram of manually selecting a pick point for a closed joint with a trace interruption on the point cloud model in ; Figure 9d The embodiments of the present application provide Fig.9c Schematic diagram of manually selected picking points fitting into a plane; Fig.10 The embodiments of the present application provide Figure 3b Schematic diagram of the location distribution of all joints extracted by manually selecting picking points in the point cloud model.

[0009] Description of reference numerals: 100. Shooting device; 200. Bedrock; 300. Shooting range; 40. Manually picked traces; 401. Real traces of joints; 51. Curve showing the influence of key points and connection point restrictions on the number of points when the connection point limit is 30,000; 52. Curve showing the influence of key points and connection point restrictions on the number of points when the connection point limit is 20,000; 53. Curve showing the influence of key points and connection point restrictions on the number of points when the connection point limit is 10,000. DETAILED DESCRIPTION

[0010] Exemplary embodiments of the present application will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual implementation are described in the specification. However, it should be understood that many implementation-specific decisions must be made in the process of developing any such actual implementation in order to achieve the developer's specific goals, such as meeting those constraints related to the system and business, and these constraints may vary from implementation to implementation. In addition, it should be understood that although the development work may be very complex and time-consuming, it is only a routine task for those skilled in the art who benefit from the content of this application.

[0011] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, only the device structure and / or processing steps closely related to the scheme according to the present application are shown in the accompanying drawings, while other details that are not very relevant to the present application are omitted.

[0012] The disclosure below provides a plurality of different embodiments or examples to implement the present application. In order to simplify the disclosure of the present application, the parts and methods of specific examples are described below. Of course, they are merely examples, and the purpose is not to limit the present application. In the description of the embodiments of the present application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically limited.

[0013] Generally speaking, the investigation and acquisition of joint attitude is mainly achieved through field geological surveys by surveyors. Specifically, each joint is measured on the spot using tools such as laser rangefinders and geological compasses, and the data is recorded manually. The implementation of the above method has high labor costs and a small coverage range, resulting in low measurement efficiency. Therefore, a new technical solution for joint attitude measurement is needed to improve measurement efficiency.

[0014] An embodiment of the present application provides a method for measuring the joint occurrence of a bedrock body, which includes the following steps: S10: acquiring image information of the bedrock body; S20: processing the image information to obtain a point cloud model of the bedrock body; S30: measuring the point cloud model to obtain the joint occurrence of the bedrock body, wherein in step S10, when acquiring the image, it is set so that the point cloud model constructed in step S20 is more accurate.

[0015] The method for measuring the joint occurrence of a bedrock body provided in the present application sets the distance between the shooting device 100 and the bedrock body 200 to remain unchanged when acquiring image information of the bedrock body, so that the image information acquired by the shooting device 100 fits the topography of the bedrock body 200, reduces the image distortion caused by the topography, and enables a more accurate point cloud model to be obtained when constructing a point cloud model based on the image information, thereby improving the accuracy of the joint occurrence obtained by measuring the point cloud model.

[0016] In some embodiments, before shooting, it is necessary to collect remote sensing images and conduct field surveys on the predetermined shooting area to obtain terrain information of the predetermined shooting area to determine the actual shooting area. The shooting device is set to be a drone, and the shooting object is the bedrock body 200 exposed on the surface.

[0017] In some embodiments, step S10 also includes: S11: setting a positioning system at a position higher than the bedrock body 200, and the positioning system is communicated with the shooting device 100 to correct the position of the shooting device 100; S12: setting the shooting device 100 to fly to a position at a predetermined distance from the bedrock body 200; S13: setting the shooting device 100 to fly around the bedrock body 200, and setting the shooting angle of the shooting device 100 to remain perpendicular to the slope of the bedrock body 200.

[0018] In some embodiments, in step S11, a positioning system is installed in the selected shooting area to perform real-time dynamic measurement of the position of the shooting device 100, provide a reference benchmark for the moving shooting device 100, and correct the coordinate position of the shooting device 100 to improve the coordinate accuracy of the shooting device 100. Installing the positioning system at a position higher than the bedrock body 200 of the shooting object can prevent the positioning system from being blocked by terrain higher than its body, avoid the signal being shielded by the terrain and causing the connection between the shooting device 100 and the positioning system to be disconnected, and ensure smooth signal flow.

[0019] Figure 1 A schematic diagram showing a method provided in an embodiment of the present application using a camera to perform surround photography of a bedrock body. In some embodiments, for example, Figure 1 As shown, in steps S12 and S13, the shooting device 100 is a drone, and the drone is set to fly to a predetermined height above the bedrock body 200 (for example, a relative height of 40 meters), and the drone is set to fly around the bedrock body 200, and the angular bisector of the vertex angle of the shooting range 300 of the drone is set to be perpendicular to the slope of the bedrock body 200 at the corresponding position, that is, the lens of the drone is set to be parallel to the slope of the bedrock body 200, that is, the drone flies along the terrain undulations of the bedrock body 200. In the case of using the traditional shooting method, if the terrain undulations are large, the relative distance between the drone lens and the surface of the bedrock body will change, thereby aggravating the effect of the acquired image distortion, resulting in a large deformation of the subsequently generated model. The use of the above-mentioned surrounding shooting method with a constant relative distance can reduce the impact of image distortion and make the subsequently constructed model more accurate.

[0020] In some embodiments, the heading overlap rate and lateral overlap rate of the UAV are set to specific values ​​(for example, the heading overlap rate is set to 60%, and the lateral overlap rate is set to 80%). Technicians in the relevant technical field can also set the parameters of the heading overlap rate and the lateral overlap rate based on experience to ensure that all images captured can fully contain the surface information of the bedrock body 200.

[0021] In some embodiments, in step S20, the following steps are also included: S21: correcting the image information to eliminate distortion in the image information.

[0022] A predetermined number of bedrock body images captured by the shooting device in step S13 are preprocessed. For example, the captured images can be imported into image processing software, and lens distortion can be corrected in the image processing software to further reduce image distortion.

[0023] In some embodiments, step S20 also includes the following steps: S22: selecting key points and connection points from the image information obtained in step S21, where the key points are the parts of the same pixels of two images in the image information, and the connection points are used to connect adjacent key points; S23: setting the number of connection points to 50% of the number of key points; S24: generating a point cloud model using key points and connection points.

[0024] The bedrock images captured in step S21 are imported into the image recognition software. There is a certain overlap rate between the bedrock images. The easily distinguishable points in each image are found as feature points for easy positioning. The feature points in multiple images are matched in pairs. The overlapping points with the same pixels are used as key points. The adjacent key points are connected as connection points to obtain the projections of the points in the three-dimensional scene at different shooting angles, and they are deflected and stacked to generate a three-dimensional point cloud model.

[0025] Figure 2 A schematic diagram showing the influence of the key point and connection point restrictions on the number of points provided by the embodiments of the present application. In some embodiments, for example, Figure 2As shown in the figure, under the condition of the same other parameters, when the connection point limit is 10000, the influence curve 53 of the key point and connection point limit on the number of points reaches a maximum value of 150000 when the key point limit is 20000, and then the key point limit increases, but the number of points shows a trend of gradually decreasing; when the connection point limit is 20000, the influence curve 52 of the key point and connection point limit on the number of points reaches a maximum value of 300000 when the key point limit is 40000, and then the key point limit increases, but the number of points shows a trend of gradually decreasing; when the connection point limit is 30000, the influence curve 51 of the key point and connection point limit on the number of points reaches a maximum value when the key point limit is 60000. In summary, when the connection point limit is 1 / 2 of the key point limit, the number of points is the largest, and more points are conducive to building a dense point cloud, making the point cloud model more accurate.

[0026] Figure 3a The schematic diagram of the point cloud model obtained by the traditional frontal shooting method is shown. Figure 3b The embodiment of the present application provides Figure 3a The same area uses Figure 1 A schematic diagram of a point cloud model obtained by a shooting method, in some embodiments, such as Figure 3a and Figure 3b As shown in the figure, compared with the traditional frontal shooting method, the point cloud model generated by the image obtained by shooting around the bedrock body has a smaller curvature of the structural fracture curve. Figure 4 A schematic diagram of a three-dimensional real-time cloud model of a bedrock body provided by an embodiment of the present application is shown as follows: Figure 3a , Figure 3b and Figure 4 As shown, it can be seen from the same angle that the point cloud model generated by the image obtained by shooting around the bedrock body is closer to the real scene shown by the three-dimensional real-life point cloud model of the bedrock body.

[0027] Figure 5a The embodiments of the present application provide a comparative example. Figure 3b The point cloud model obtained Figure 4 A schematic diagram showing the deviation values ​​of the points in the X-axis direction within the relative distance error range using the 3D real-world point cloud model. Figure 5b The embodiments of the present application provide a comparative example. Figure 3b The point cloud model obtained Figure 4 A schematic diagram of the three-dimensional real-world point cloud model and statistical deviation values ​​of the points within the relative distance error range in the Y-axis direction; Figure 5c The embodiments of the present application provide a comparative example. Figure 3b The point cloud model obtained Figure 4A schematic diagram of the three-dimensional real-world point cloud model and statistical deviation values ​​of points in the Z-axis direction within the relative distance error range; Figure 5d The embodiments of the present application provide a comparative example. Figure 3b The point cloud model obtained Figure 4 A schematic diagram of a three-dimensional real-world point cloud model in the space and counting the deviation values ​​of points within the relative distance error range. In some embodiments, as Figure 5a-5d As shown in the figure, the point cloud model constructed according to the above steps is compared with Figure 4 For example, the two point cloud models can be imported into the point cloud computing processing software to compare the three-dimensional real-world point cloud models. Figure 4 The 3D real-world point cloud in is used as the reference point cloud, and the point cloud model constructed according to the above steps is used as the comparison point cloud. The point closest to a specific point in the reference point cloud is selected, and the distance between the two points is calculated as the point distance deviation value. Similarly, all point distance deviation values ​​between the two point clouds are calculated, and the number of points in the X, Y, and Z coordinate directions and in the point distance deviation value range in space is counted, such as Figure 5a and Figure 5b As shown in the figure, the deviation of the relative distance of the point cloud in the X-axis and Y-axis directions is controlled within the range of ±0.5m. Figure 5c As shown, the deviation value of the relative distance of the point cloud in the Z-axis direction is controlled within the range of ±1m, and in space, the relative distance of the point cloud space is controlled within 0-1m. In summary, the error of the point cloud model constructed by the above method is controlled within a smaller range compared with the three-dimensional real-life point cloud model. In other words, the accuracy of the point cloud model constructed by the above method is improved, and it is closer to the real scene of the bedrock body.

[0028] In some embodiments, in step S30, it also includes: S31: classifying the types of joints; S32: determining the measurement method for the point cloud model according to the types classified in step S31.

[0029] Figure 6a The embodiments of the present application provide Figure 3b Schematic diagram of a closed joint on the point cloud model in . Figure 6b The conventional automatic picking method is shown Figure 6a Schematic diagram of the closed joint picking trace in, Figure 6c Show according to Figure 6b The schematic diagram of the automatically picked trace fitting plane extraction is as follows: Figure 6a-6cAs shown in the figure, the traditional steps of extracting joint occurrence are as follows: import the constructed point cloud model into the point cloud processing software for joint identification and parameter extraction; use the software's automatic trace delineation function to pick up the trace, and further fit the plane according to the trace of Shencheng to calculate the joint occurrence. The traditional method does not distinguish the types of joints, so when the joint traces are not obvious or not continuous, the traces will be distorted and deviate from the real joint traces. Therefore, it is necessary to classify the joint types and treat them differently when extracting the joint occurrence.

[0030] In some embodiments, step S31 also includes the following steps: S311: obtaining the morphology of the joints in the point cloud model; S312: classifying the types of joints according to the morphology of the joints: if the morphology of the joints has only a single trace, the type of joints is a closed joint; if the morphology of the joints is that the surface containing the joints is exposed outward, the type of joints is an exposed joint.

[0031] like Figure 6a As shown in the figure, the joints whose joint traces appear as linear joints are called closed joints; Figure 9a The embodiment of the present application provides Figure 3b Schematic diagram of manually selecting a picking point on an exposed joint surface on the point cloud model in Figure 9a As shown in the figure, the joints whose traces appear as surface joints exposed on the surface are called exposed joints.

[0032] In some embodiments, the type of joint is a closed joint, the head and tail endpoints of the joint trace are picked up, and a fitting line is generated based on the two endpoints; a fitting plane is generated based on the fitting line; and the joint occurrence is determined based on the fitting plane.

[0033] Figure 7a The embodiment of the present application provides Figure 6a Schematic diagram of manually selecting picking points for closed joints in. Figure 7b The embodiment of the present application provides Figure 7a Schematic diagram of the trace generated by manually selected picking points, Figure 7c The embodiment of the present application provides Figure 7b The generated trace fitting plane is a schematic diagram of the occurrence extraction, in some embodiments, such as Figure 7a-7c As shown in the figure, after determining that the joint type is a closed joint, a predetermined number of points are evenly selected on the joint trace, and the number of selected points should be greater than 3 to ensure that the generated trace fits the trace of the real trace; after the trace is generated, the plane is fitted to calculate the occurrence.

[0034] In some embodiments, the type of joint is an exposed joint, and no less than three points are selected on the exposed surface; a fitting plane is generated based on the three points; and the joint occurrence is determined based on the fitting plane.

[0035] Figure 9b The embodiment of the present application provides Figure 9a A schematic diagram of fitting manually selected picked points in a plane, in some embodiments, such as Figure 9b As shown, after determining that the joint type is an exposed joint, a predetermined number of points are selected on the exposed surface of the joint. The number of selected points should be greater than 3 to ensure that the generated joint surface is close to the real joint surface.

[0036] Fig.9c The embodiment of the present application provides Figure 3b Schematic diagram of manually selecting picking points for a closed joint with a broken trace on the point cloud model in . Figure 9d The embodiment of the present application provides Fig.9c Schematic diagram of manually selected picking points fitting into a plane, in some embodiments, such as Fig.9c As shown, the joint traces in the circle do not show clear traces. In the case of interrupted joint traces, it is necessary to skip the disconnected part, select only the joint traces visible in other areas, select the picking points, and directly fit the plane to reduce the impact of fuzzy joints on the occurrence extraction.

[0037] Figure 8a The embodiments of the present application provide Figure 3b A schematic diagram of another closed joint on the point cloud model in Figure 1. Figure 8b The embodiments of the present application provide Figure 8a Schematic diagram of the true trace of the closed joint in Figure 8c The embodiment of the present application provides Figure 8a A schematic diagram of a closed joint in which a pick point is manually selected and a trace is generated. In some embodiments, Figure 8a As shown, Figure 8a The joint traces in the Figure 8a Select more than 3 pick-up points on the joint traces and connect the pick-up points to obtain Figure 8c The manually picked trace 40 in Figure 8b-Figure 8c As shown, Figure 8c The manually picked trace 40 generated by the method of manually selecting the picking point is Figure 8b The deviation from the true trace 401 of the joints in FIG. 4 is small.

[0038] Fig.10 The embodiment of the present application provides Figure 3b The point cloud model in the figure is a schematic diagram of the position distribution of all joints extracted by manually selecting picking points, as shown in Fig.10As shown, this embodiment randomly selected 30 structural fractures in the bedrock body, and measured their occurrences by traditional photography methods and by the method provided by this application. Table 1 shows the comparison of the occurrence results before and after the improvement of the measurement method, where the occurrence results are expressed by two indicators: dip angle and inclination. In order to verify the comparative effect of the measurement method provided by the present application, 30 structural fractures were measured by compass, and the measured data by compass were the actual occurrence data. Fifteen joints among the 30 structural fractures were randomly selected and measured twice by compass, and the errors were calculated as the allowable errors. The calculation results were: the allowable error of dip was 10.0°, and the allowable error of dip was 7.1°. The errors of the data obtained by the traditional method and the method improved by the present application were compared, as shown in Table 1. According to the judgment results of the allowable errors mentioned above, 12 occurrence data measured by the conventional method exceeded the allowable errors, which were the data corresponding to numbers 2, 6, 11, 13, 16, 17, 19, 20, 21, 22, 24, and 28 respectively; 4 occurrence data measured by the method improved by the present application exceeded the allowable errors, which were the data corresponding to numbers 5, 23, 25, and 26 respectively.

[0039] Table 1 Comparison of measured occurrence results before and after the improvement of the measurement method In summary, the measurement accuracy of the conventional method is 60%, while the measurement accuracy of the improved method provided in the present application is 86.7%. In comparison, the measurement accuracy of the improved method provided in the present application is significantly improved.

[0040] The above are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. The protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for measuring the joint occurrence of a bedrock body, characterized in that: It includes the following steps: S10: Acquire image information of the bedrock body; S20: Processing the image information to obtain a point cloud model of the bedrock body; S30: measuring the point cloud model to obtain the joint occurrence of the bedrock body, Wherein, in step S10, when acquiring the image information of the bedrock body, the distance between the photographing device and the bedrock body is set to remain unchanged.

2. The method according to claim 1, characterized in that In step S10, it also includes: S11: setting a positioning system at a position higher than the bedrock body, wherein the positioning system is in communication connection with the photographing device to correct the position of the photographing device; S12: Setting the photographing device to fly to a position at a predetermined distance from the bedrock body; S13: Setting the photographing device to fly around the bedrock body, and setting the photographing angle of the photographing device to remain perpendicular to the slope of the bedrock body.

3. The method according to claim 1, characterized in that In step S20, the following steps are also included: S21: Correct the image information to eliminate distortion in the image information.

4. The method according to claim 3, characterized in that: The step S20 further includes the following steps: S22: Select key points and connection points from the image information obtained in step S21, wherein the key points are the parts of the same pixels of two images in the image information, and the connection points are used to connect adjacent key points; S23: setting the number of the connection points to be 50% of the number of the key points; S24: Generate the point cloud model using the key points and the connection points.

5. The method according to claim 1, characterized in that In step S30, it also includes: S31: classifying the types of the joints; S32: Determine a measurement method for the point cloud model according to the type classified in step S31.

6. The method according to claim 5, characterized in that In step S31, the following steps are also included: S311: Acquire the shape of joints in the point cloud model; S312: Classify the types of the joints according to the morphology of the joints.

7. The method according to claim 6, characterized in that The morphology of the joint has only a single trace, and the type of the joint is a closed joint; The morphology of the joints is that the surface including the joints is exposed outwards, and the type of the joints is an exposed joint.

8. The method according to claim 7, characterized in that The type of joint is a closed joint. Pick the two end points of the joint trace, generating a fitted line according to the two endpoints; Generate a fitting plane according to the fitting line; The joint occurrence is determined according to the fitting plane.

9. The method according to claim 8, characterized in that The type of joint is an exposed joint. Select no less than three points on the exposed surface; Generate a fitting plane according to the three points; The joint occurrence is determined according to the fitting plane.

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