A method and device for extracting casting grinding points based on three-dimensional point cloud information

Abstract

The present invention relates to a casting grinding point extraction method based on three-dimensional point cloud information, comprising the following steps: performing edge extraction on a casting flash point cloud to obtain flash contour data; performing point cloud fusion on the flash contour data and the casting body point cloud to obtain a three-dimensional point cloud model of the two; determining the inner edge point of the flash, which is also the local grinding point of the casting, taking the average value of the height of the local grinding point as the value of the projection height, projecting the casting body point cloud in the height direction, and converting the casting body point cloud into a first plane point cloud; performing edge extraction on the plane point cloud to obtain the inner cavity edge point and outer contour point of the casting body, retaining the outer contour point; downsampling the outer contour point and performing data smoothing processing to obtain the overall grinding point of the casting. Compared with the robot grinding method using teaching, the present invention can reduce manual intervention, thereby shortening the casting grinding point extraction time, reducing unnecessary design processes, and better reflecting the intelligence of robot-automated casting grinding.

Description

Technical Field

[0001] The present invention relates to the technical field of casting processing, and in particular to a method and device for extracting casting grinding points based on three-dimensional point cloud information. Background Art

[0002] Flash is a thin solidified substance that extends slightly outward at the corners of the blank. It is caused by the casting liquid that seeps through the mold due to the fact that the mold docking position is not particularly tight. When processing the blank, the flash needs to be polished.

[0003] Traditional methods of flash grinding are mostly done manually, which has the disadvantages of low grinding efficiency, uneven grinding quality, large grinding dust, and affecting the physical and mental health of workers.

[0004] If intelligent flash grinding is to be performed, grinding points must be extracted so that the robot can perform the corresponding work. How to provide an effective method for extracting casting grinding points is an urgent problem to be solved in the current market. Summary of the Invention

[0005] The purpose of the present invention is to solve at least one of the deficiencies of the prior art and to provide a method and device for extracting grinding points of castings based on three-dimensional point cloud information.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] Specifically, a casting grinding point extraction method based on three-dimensional point cloud information is proposed, which includes the following:

[0008] Obtain the point cloud information of the target casting, and perform point cloud segmentation on the target casting to obtain the corresponding casting body point cloud and casting flash point cloud;

[0009] Performing edge extraction on the casting flash point cloud to obtain flash contour data;

[0010] Performing point cloud fusion on the flash contour data and the casting body point cloud to obtain a three-dimensional point cloud model of the two;

[0011] Taking the flash contour data in the three-dimensional point cloud model as the target area and the casting body point cloud as the search area, extracting the inner edge points of the flash through nearest neighbor search, and using the inner edge points as local grinding points of the flash;

[0012] Taking the average value of the heights of the local polishing points as the value of the projection height, projecting the casting body point cloud in the height direction, and converting the casting body point cloud into a first plane point cloud;

[0013] Performing edge extraction on the plane point cloud to obtain inner cavity edge points and outer contour points of the casting body;

[0014] Removing the inner cavity edge points by a clustering algorithm and retaining the outer contour points;

[0015] Down-sampling and data smoothing processing are performed on the outer contour points obtained by clustering to obtain the overall polishing points of the casting.

[0016] Further, specifically, obtaining the point cloud information of the target casting, performing point cloud segmentation on the target casting to obtain its corresponding casting body point cloud and casting flash point cloud, including the following:

[0017] The target casting is scanned by a laser scanner to obtain point cloud information of the target casting. The obtained point cloud information is subjected to a statistical filtering algorithm to remove irrelevant noise points and a point cloud segmentation algorithm to obtain the corresponding casting body point cloud and casting flash point cloud. Statistical filtering is used to remove sparse outlier noise points. The average distance, variance and average value between each point and all its adjacent points are calculated, and the distance and variance of all points are used to define the standard range. Points outside the standard range are outliers, and finally these outliers are deleted from the original point cloud.

[0018] Further, specifically, edge extraction is performed on the casting flash point cloud to obtain flash contour data, including the following:

[0019] Determine the search radius and number of search points of the target point, use the projection of the surrounding points on the tangent plane of the target point, connect the projection points of the surrounding points with the target point to form multiple adjacent angles, and make the difference between the adjacent angles clockwise to obtain Ω=(θ1,θ2,θ3...θ n ), find the largest angle θ in Ω max , if θ max If the value is greater than the first threshold, the selected target point is considered to be an edge point, otherwise it is a non-edge point, and all edge points are output to obtain the burr contour data.

[0020] Furthermore, specifically, the inner edge point of the burr is obtained by:

[0021] The point cloud data of the target area and the search area are converted into a kd-tree model. The search radius and the minimum number of search points are set for the search points in the search area. When searching at a search point, if the number of searched points is higher than the minimum number of search points, the search point is determined to be an inner edge point of the fin. If the number of searched points is lower than the minimum number of search points, the search point is determined to be an outer edge point of the fin.

[0022] Furthermore, the method further comprises:

[0023] When all the inner edge points are extracted, they will also be highly smoothed and used as local polishing points for the burrs.

[0024] Further, specifically, removing the inner cavity edge points by a clustering algorithm and retaining the outer contour points includes:

[0025] The inner cavity edge points and outer contour points of the obtained casting body were processed by the DBSCAN clustering method. The parameters in the DBSCAN clustering method, the radius epsilon of the neighborhood around a point, and the minimum number of points contained in the neighborhood, minPts, were set to 10 to remove the inner cavity edge points and retain the outer contour points.

[0026] Further, specifically, the value of the first threshold is π.

[0027] Furthermore, specifically, the radius range is set to 5 mm and the minimum number of search points is set to 30.

[0028] The present invention also proposes a casting grinding point extraction device based on three-dimensional point cloud information, comprising:

[0029] The target casting point cloud acquisition module is used to obtain the point cloud information of the target casting, and perform point cloud segmentation on the target casting to obtain the corresponding casting body point cloud and casting flash point cloud;

[0030] A first edge extraction module is used to extract edges from the casting flash point cloud to obtain flash contour data;

[0031] A point cloud fusion module is used to fuse the flash contour data with the casting body point cloud to obtain a three-dimensional point cloud model of the two;

[0032] A local grinding point acquisition module is used to use the flash contour data in the three-dimensional point cloud model as the target area and the casting body point cloud as the search area to extract the inner edge points of the flash through nearest neighbor search, and use the inner edge points as the local grinding points of the flash;

[0033] a point cloud conversion module, configured to project the casting body point cloud in a height direction using the average value of the heights of the local polishing points as a projection height value, and convert the casting body point cloud into a first plane point cloud;

[0034] A second edge extraction module is used to perform edge extraction on the plane point cloud to obtain inner cavity edge points and outer contour points of the casting body;

[0035] An outer contour point extraction module, configured to remove the inner cavity edge points by a clustering algorithm and retain the outer contour points;

[0036] The overall polishing point acquisition module is used to perform down-sampling and data smoothing on the outer contour points obtained by clustering to obtain the overall polishing points of the casting.

[0037] The present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of a casting grinding point extraction method based on three-dimensional point cloud information as described in any of the above items.

[0038] The beneficial effects of the present invention are:

[0039] The present invention proposes a casting grinding point extraction method based on three-dimensional point cloud information. The extracted target casting is analyzed by an algorithm based on point cloud analysis, and the grinding points of the target casting are finally extracted. Compared with the robot grinding method using teaching, manual intervention can be reduced, thereby shortening the extraction time of casting grinding points, reducing unnecessary design processes, having greater generalization capabilities, and better reflecting the intelligence of robot-automated casting grinding. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and other features of the present disclosure will become more apparent through a detailed description of the embodiments shown in conjunction with the accompanying drawings. The same reference numerals in the drawings of the present disclosure represent the same or similar elements. Obviously, the drawings described below are only some embodiments of the present disclosure. It is possible for a person skilled in the art to derive other drawings based on these drawings without inventive effort. In the drawings:

[0041] Figure 1 Shown is a flow chart of a casting grinding point extraction method based on three-dimensional point cloud information according to the present invention;

[0042] Figure 2 The figure shows a flow chart of extracting local flash grinding points from casting grinding points based on three-dimensional point cloud information according to the present invention;

[0043] Figure 3 The figure shows a flow chart of extracting overall casting grinding points based on three-dimensional point cloud information according to the present invention;

[0044] Figure 4 FIG2 is a schematic diagram of a complete point cloud of a casting according to the first embodiment of the present invention;

[0045] Figure 5 Schematic diagram of the point cloud of the casting body after segmentation according to the first embodiment of the present invention;

[0046] Figure 6 Schematic diagram of the casting flash point cloud after segmentation according to the first embodiment of the present invention;

[0047] Figure 7 Shown is a schematic diagram of the burr profile of the first embodiment of the present invention;

[0048] Figure 8 FIG2 is a schematic diagram of a point cloud model of a flash outline and a casting body after being spliced ​​together according to the first embodiment of the present invention;

[0049] Figure 9 Schematic diagram of local grinding points of burrs according to the first embodiment of the present invention;

[0050] Figure 10 Schematic diagram of the plane point cloud of the casting body according to the first embodiment of the present invention;

[0051] Figure 11 Schematic diagram of edge extraction of plane point cloud of casting body according to the first embodiment of the present invention;

[0052] Figure 12 FIG2 is a schematic diagram of the outermost contour of the casting body according to the first embodiment of the present invention;

[0053] Figure 13 Shown is a schematic diagram of the overall grinding points of a casting according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict. The same reference numerals used throughout the drawings indicate the same or similar parts.

[0055] Reference Figure 1 、 Figure 2 as well as Figure 3 In embodiment 1, the present invention proposes a method for extracting casting grinding points based on three-dimensional point cloud information, comprising the following steps:

[0056] Obtain the point cloud information of the target casting, and perform point cloud segmentation on the target casting to obtain the corresponding casting body point cloud and casting flash point cloud;

[0057] Performing edge extraction on the casting flash point cloud to obtain flash contour data;

[0058] Performing point cloud fusion on the flash contour data and the casting body point cloud to obtain a three-dimensional point cloud model of the two;

[0059] Taking the flash contour data in the three-dimensional point cloud model as the target area and the casting body point cloud as the search area, extracting the inner edge points of the flash through nearest neighbor search, and using the inner edge points as local grinding points of the flash;

[0060] Taking the average value of the heights of the local polishing points as the value of the projection height, projecting the casting body point cloud in the height direction, and converting the casting body point cloud into a first plane point cloud;

[0061] Figure 10 This is the planar point cloud model of the casting body after projection in the first embodiment. This step mainly utilizes the projection of the casting body point cloud in the direction of the mean height of the inner edge of the burr, that is, the projection plane is the plane where the inner edge of the burr is located, thereby converting the three-dimensional point cloud into a planar point cloud.

[0062] Performing edge extraction on the plane point cloud to obtain inner cavity edge points and outer contour points of the casting body;

[0063] After obtaining the plane point cloud of the casting body, it is necessary to perform edge extraction operation on it. The purpose is to filter out most of the internal points. The retained points include the inner cavity edge points and the outer contour points, which are mainly prepared for the next step of extracting the overall edge contour. Figure 11 This is the result of edge extraction of the plane point cloud of the casting body in the first embodiment;

[0064] Removing the inner cavity edge points by a clustering algorithm and retaining the outer contour points;

[0065] After edge extraction of the above casting body plane point cloud, the extracted result includes not only the overall outline of the outer layer of the casting, but also the inner cavity outline of the casting. Therefore, it is necessary to remove the inner cavity outline points of the casting and retain the overall outline points. Figure 12 The result of the outermost contour points of the casting extracted after clustering in the first embodiment

[0066] Down-sampling and data smoothing processing are performed on the outer contour points obtained by clustering to obtain the overall polishing points of the casting.

[0067] Figure 13 Shown are the overall polishing points obtained after downsampling in the first embodiment.

[0068] This embodiment 1 analyzes the extracted target casting through an algorithm based on point cloud analysis, and finally extracts the grinding points of the target casting. Compared with the robot grinding method using teaching, it can reduce manual intervention, thereby shortening the extraction time of the casting grinding points, reducing unnecessary design processes, and has greater generalization capabilities, which can better reflect the intelligence of robot automated casting grinding.

[0069] As a preferred embodiment of the present invention, specifically, obtaining point cloud information of a target casting, performing point cloud segmentation on the target casting to obtain its corresponding casting body point cloud and casting flash point cloud, including the following:

[0070] The target casting is scanned by a laser scanner to obtain point cloud information of the target casting. The obtained point cloud information is subjected to a statistical filtering algorithm to remove irrelevant noise points and a point cloud segmentation algorithm to obtain the corresponding casting body point cloud and casting flash point cloud. Statistical filtering is used to remove sparse outlier noise points. The average distance, variance and average value between each point and all its adjacent points are calculated, and the distance and variance of all points are used to define the standard range. Points outside the standard range are outliers, and finally these outliers are deleted from the original point cloud.

[0071] as follows Figure 4 、 Figure 5 and Figure 6 Shown are the casting point cloud obtained after filtering in the first embodiment, the complete casting body point cloud obtained after segmentation, and the casting flash point cloud.

[0072] like Figure 7 This is the flash contour after processing in the first embodiment. As a preferred embodiment of the present invention, specifically, edge extraction is performed on the casting flash point cloud to obtain flash contour data, including the following:

[0073] Determine the search radius and number of search points of the target point, use the projection of the surrounding points on the tangent plane of the target point, connect the projection points of the surrounding points with the target point to form multiple adjacent angles, and make the difference between the adjacent angles clockwise to obtain Ω=(θ1,θ2,θ3...θ n ), find the largest angle θ in Ω max , if θ max If the value is greater than the first threshold, the selected target point is considered to be an edge point, otherwise it is a non-edge point, and all edge points are output to obtain the burr contour data.

[0074] Figure 8 This is the point cloud model after splicing in the first embodiment. As a preferred embodiment of the present invention, specifically, the inner edge points of the burr are obtained by the following method:

[0075] The point cloud data of the target area and the search area are converted into a kd-tree model. The search radius and the minimum number of search points are set for the search points in the search area. When searching at a search point, if the number of searched points is higher than the minimum number of search points, the search point is determined to be an inner edge point of the fin. If the number of searched points is lower than the minimum number of search points, the search point is determined to be an outer edge point of the fin.

[0076] Figure 9 For the local grinding point of the processed flash, as a preferred embodiment of the present invention, the method further includes:

[0077] When all the inner edge points are extracted, they will also be highly smoothed and used as local polishing points for the burrs.

[0078] As a preferred embodiment of the present invention, specifically, removing the inner cavity edge points by a clustering algorithm and retaining the outer contour points includes:

[0079] The inner cavity edge points and outer contour points of the obtained casting body were processed by the DBSCAN clustering method. The parameters in the DBSCAN clustering method, the radius epsilon of the neighborhood around a point, and the minimum number of points contained in the neighborhood, minPts, were set to 10 to remove the inner cavity edge points and retain the outer contour points.

[0080] As a preferred embodiment of the present invention, specifically, the value of the first threshold is π.

[0081] As a preferred embodiment of the present invention, specifically, the radius range is set to 5 mm and the minimum number of search points is set to 30.

[0082] The present invention also proposes a casting grinding point extraction device based on three-dimensional point cloud information, comprising:

[0083] The target casting point cloud acquisition module is used to obtain the point cloud information of the target casting, and perform point cloud segmentation on the target casting to obtain the corresponding casting body point cloud and casting flash point cloud;

[0084] A first edge extraction module is used to extract edges from the casting flash point cloud to obtain flash contour data;

[0085] A point cloud fusion module is used to fuse the flash contour data with the casting body point cloud to obtain a three-dimensional point cloud model of the two;

[0086] A local grinding point acquisition module is used to use the flash contour data in the three-dimensional point cloud model as the target area and the casting body point cloud as the search area to extract the inner edge points of the flash through nearest neighbor search, and use the inner edge points as the local grinding points of the flash;

[0087] a point cloud conversion module, configured to project the casting body point cloud in a height direction using the average value of the heights of the local polishing points as a projection height value, and convert the casting body point cloud into a first plane point cloud;

[0088] A second edge extraction module is used to perform edge extraction on the plane point cloud to obtain inner cavity edge points and outer contour points of the casting body;

[0089] An outer contour point extraction module, configured to remove the inner cavity edge points by a clustering algorithm and retain the outer contour points;

[0090] The overall polishing point acquisition module is used to perform down-sampling and data smoothing on the outer contour points obtained by clustering to obtain the overall polishing points of the casting.

[0091] The present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of a casting grinding point extraction method based on three-dimensional point cloud information as described in any of the above items.

[0092] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

[0093] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0094] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or system that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0095] Although the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but rather should be construed as providing a broad possible interpretation of these claims in view of the prior art by reference to the appended claims, thereby effectively encompassing the intended scope of the invention. In addition, the invention has been described above in terms of embodiments foreseen by the inventors for the purpose of providing a useful description, and those insubstantial modifications of the invention that are not currently foreseen may still represent equivalent modifications of the invention.

[0096] The above description is merely a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. As long as the technical effects of the present invention are achieved by the same means, they shall fall within the scope of protection of the present invention. Within the scope of protection of the present invention, various modifications and variations of the technical solutions and / or implementation methods may be made.

Claims

1. A casting grinding point extraction method based on three-dimensional point cloud information, characterized in that: These include: Obtain the point cloud information of the target casting, and perform point cloud segmentation on the target casting to obtain the corresponding casting body point cloud and casting flash point cloud; Performing edge extraction on the casting flash point cloud to obtain flash contour data; The flash contour data is integrated with the casting body point cloud to obtain a three-dimensional point cloud model of the two; Taking the flash contour data in the three-dimensional point cloud model as the target area and the casting body point cloud as the search area, extracting the inner edge points of the flash through nearest neighbor search, and using the inner edge points as local grinding points of the flash; Taking the average value of the heights of the local polishing points as the value of the projection height, projecting the casting body point cloud in the height direction, and converting the casting body point cloud into a first plane point cloud; Performing edge extraction on the plane point cloud to obtain inner cavity edge points and outer contour points of the casting body; Removing the inner cavity edge points by a clustering algorithm and retaining the outer contour points; Downsampling the outer contour points obtained by clustering and performing data smoothing processing to obtain the overall polishing points of the casting; Specifically, the inner edge point of the burr is obtained by the following method: The point cloud data of the target area and the search area are converted into a kd-tree model. The search radius and the minimum number of search points are set for the search points in the search area. When searching at a search point, if the number of searched points is higher than the minimum number of search points, the search point is determined to be an inner edge point of the fin. If the number of searched points is lower than the minimum number of search points, the search point is determined to be an outer edge point of the fin.

2. The method for extracting casting grinding points based on three-dimensional point cloud information according to claim 1, characterized in that: Specifically, the point cloud information of the target casting is obtained, and the point cloud of the target casting is segmented to obtain the corresponding casting body point cloud and casting flash point cloud, including the following: The target casting is scanned by a laser scanner to obtain point cloud information of the target casting. The obtained point cloud information is subjected to a statistical filtering algorithm to remove irrelevant noise points and a point cloud segmentation algorithm to obtain the corresponding casting body point cloud and casting flash point cloud. Statistical filtering is used to remove sparse outlier noise points. The average distance, variance and average value between each point and all its adjacent points are calculated, and the distance and variance of all points are used to define the standard range. Points outside the standard range are outliers, and finally these outliers are deleted from the original point cloud.

3. The method for extracting casting grinding points based on three-dimensional point cloud information according to claim 1, characterized in that: Specifically, edge extraction is performed on the casting flash point cloud to obtain flash contour data, including the following: Determine the search radius and number of search points of the target point, use the projection of the surrounding points on the tangent plane of the target point, connect the projection points of the surrounding points with the target point to form multiple adjacent angles, and make the difference between the adjacent angles clockwise to obtain , looking for The largest angle ,like If the value is greater than the first threshold, the selected target point is considered to be an edge point, otherwise it is a non-edge point, and all edge points are output to obtain the burr contour data.

4. The method for extracting casting grinding points based on three-dimensional point cloud information according to claim 1, characterized in that: The method further comprises, When all the inner edge points are extracted, they will also be highly smoothed and used as local polishing points for the burrs.

5. The method for extracting casting grinding points based on three-dimensional point cloud information according to claim 1, characterized in that: Specifically, the inner cavity edge points are removed by a clustering algorithm, and the outer contour points are retained, including: The inner cavity edge points and outer contour points of the obtained casting body were processed by the DBSCAN clustering method. The parameters in the DBSCAN clustering method, the radius epsilon of the neighborhood around a point, and the minimum number of points contained in the neighborhood, minPts, were set to 10 to remove the inner cavity edge points and retain the outer contour points.

6. The method for extracting casting grinding points based on three-dimensional point cloud information according to claim 3, characterized in that: Specifically, the value of the first threshold is π.

7. The method for extracting casting grinding points based on three-dimensional point cloud information according to claim 1, characterized in that: Specifically, the radius range is set to 5 mm and the minimum number of search points is set to 30.

8. A casting grinding point extraction device based on three-dimensional point cloud information, characterized in that: The steps of the method according to any one of claims 1 to 7 are applied, wherein the device comprises: The target casting point cloud acquisition module is used to obtain the point cloud information of the target casting, and perform point cloud segmentation on the target casting to obtain the corresponding casting body point cloud and casting flash point cloud; A first edge extraction module is used to extract edges from the casting flash point cloud to obtain flash contour data; A point cloud fusion module is used to fuse the flash contour data with the casting body point cloud to obtain a three-dimensional point cloud model of the two; A local grinding point acquisition module is used to use the flash contour data in the three-dimensional point cloud model as the target area and the casting body point cloud as the search area to extract the inner edge points of the flash through nearest neighbor search, and use the inner edge points as the local grinding points of the flash; a point cloud conversion module, configured to project the casting body point cloud in a height direction using the average value of the heights of the local polishing points as a projection height value, and convert the casting body point cloud into a first plane point cloud; A second edge extraction module is used to perform edge extraction on the plane point cloud to obtain inner cavity edge points and outer contour points of the casting body; An outer contour point extraction module, configured to remove the inner cavity edge points by a clustering algorithm and retain the outer contour points; An overall polishing point acquisition module is used to perform downsampling and data smoothing on the outer contour points obtained by clustering to obtain overall polishing points of the casting; Specifically, the inner edge point of the burr is obtained by the following method: The point cloud data of the target area and the search area are converted into a kd-tree model. The search radius and the minimum number of search points are set for the search points in the search area. When searching at a search point, if the number of searched points is higher than the minimum number of search points, the search point is determined to be an inner edge point of the fin. If the number of searched points is lower than the minimum number of search points, the search point is determined to be an outer edge point of the fin.

9. A computer-readable storage medium storing a computer program, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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