Measurement system, measurement method, and storage medium

Abstract

A measurement system has an extraction section and an alignment section. The extraction section extracts a point group of a first marker from a measurement point group including a point group of a measurement object and a point group of the first marker arranged at a known position with respect to the measurement object. The alignment section aligns the point group of the measurement object with a known point group by alignment of the point group of the first marker with a point group of a second marker, the position of which is known with respect to the known point group of the measurement object.

Description

Technical Field

[0001] The implementation methods involve measurement systems, measurement methods, and storage media. Background Technology

[0002] Previously, the alignment of two point groups was achieved by deriving information indicating which point in the point group on one side corresponds to which point in the point group on the other side, and by deriving geometric transformation information between the two point groups.

[0003] If two point groups have characteristic structures, high-precision alignment can be achieved by comparing the characteristic quantities between the two point groups. Conversely, when the two point groups have fewer characteristic structures, the alignment accuracy is more likely to decrease. For example, if the two point groups have different positions and orientations, and the shapes of the point groups are symmetrical, establishing a correspondence between the closest points may lead to incorrect geometric transformation information.

[0004] The implementation provides a measurement system, measurement method, and storage medium that can perform alignment with high precision even for point groups with non-characteristic structures. Summary of the Invention

[0005] One type of measurement system includes an extraction unit and an alignment unit. The extraction unit extracts a group of points marked with first marks from a group of measurement points containing a group of points of the object to be measured and a group of points marked with first marks positioned at known locations relative to the object to be measured. The alignment unit aligns the group of points of the object to the known group of points by aligning the group of points marked with first marks with a group of points marked with second marks that are associated with known positions relative to the known group of points of the object to be measured.

[0006] One measurement method comprises: extracting a first-marked point group from a measurement point group containing a point group of the measurement object and a point group of first-marked points disposed at a known position relative to the measurement object; and aligning the point group of the measurement object with the known point group by matching the point group of the first-marked points with a point group of second-marked points that are associated with a known position relative to a known point group of the measurement object.

[0007] A computer-readable storage medium stores a measurement program for causing the computer to perform the following steps, the steps comprising: extracting a first marked point group from a measurement point group containing a point group of a measurement object and a point group of first marked points disposed at a known position relative to the measurement object; and aligning the point group of the measurement object with the known point group by means of alignment of the first marked point group with a second marked point group that is associated with a known position relative to a known point group of the measurement object. Attached Figure Description

[0008] Figure 1 This is a block diagram illustrating the configuration of an example of a measurement system according to one embodiment.

[0009] Figure 2 It is a diagram representing measurement marks.

[0010] Figure 3 It is a graph that shows the relationship between known point group data and known labeled point group data.

[0011] Figure 4 This is a diagram illustrating an example of the hardware configuration of a measurement system.

[0012] Figure 5 It is a flowchart representing the actions of the measurement system.

[0013] Figure 6 It is a graph representing clustering.

[0014] Figure 7 It is a multi-layered graph representing a group of points with known labels. Detailed Implementation

[0015] The embodiments will now be described with reference to the accompanying drawings. Figure 1 This is a block diagram illustrating the configuration of an example of a measurement system according to one embodiment. Figure 1 The measurement system 1 shown can be used for measurements in a component assembly system. The object measured by the measurement system 1 is, for example, a component p placed on an assembly substrate B. In this embodiment, the measurement system 1 compares a group of points on component p measured by the camera 2 with a pre-prepared group of known points on component p and displays the comparison results to the user. The user is, for example, a worker determining whether the assembly of component p has been performed correctly.

[0016] Substrate B is a flat plate having, for example, a holding portion for holding component p in a predetermined position. A measurement mark M1 is disposed on substrate B. The measurement mark M1 is a known-sized mark disposed at a predetermined position on substrate B with a predetermined orientation. Information about the dimensions of the measurement mark M1 may include, for example, the length of each side of the measurement mark M1 and the length of its diagonal. Here, in the embodiment, component p is placed on substrate B in a predetermined, known positional relationship with the measurement mark M1. Figure 1 In this embodiment, the horizontal distance between component p and measurement mark M1 on the plane of substrate B is x1, and the vertical distance is y1. Alternatively, substrate B can be a worktable or similar facility for assembling component p. Furthermore, substrate B can also be a substrate for mounting electronic circuits.

[0017] The measurement marker M1 is, for example, an AR (Augmented Reality) marker, which can be identified from the image acquired by camera 2. The measurement marker M1 is, for example, a marker on a plane with a quadrilateral shape and a black and white pattern. Figure 2 This is a diagram representing measurement mark M1. For example... Figure 2 As shown, the measurement mark M1 preferably has an asymmetrical pattern in both the left-right and up-down directions. The asymmetrical pattern of the measurement mark M1 allows for identification of its orientation within the image. Alternatively, two or more measurement marks M1 may be disposed on the substrate B. Furthermore, the shape of the measurement mark M1 may not be quadrilateral.

[0018] like Figure 1 As shown, the measurement system 1 includes a first extraction unit 11, a plane detection unit 12, a clustering unit 13, a second extraction unit 14, an alignment unit 15, a shape database (DB) 16, and a display control unit 17. The measurement system 1 is configured to communicate with a camera 2. Communication between the measurement system 1 and the camera 2 can be wireless or wired. Furthermore, the measurement system 1 is configured to communicate with a display device 3. Communication between the measurement system 1 and the display device 3 can be wireless or wired. Here, in Figure 1 In this process, the first extraction unit 11, the plane detection unit 12, the clustering unit 13, and the second extraction unit 14 constitute the extraction unit for extracting the point group of the measurement mark M1.

[0019] Camera 2, for example, is held by a user and is configured to measure together measurement point group data, including a group of points containing the component p being measured and the measurement mark M1, with an image of the component p being measured and the measurement mark M1. Camera 2 can be either a depth camera or a 3D scanner. For example, an RGB-D camera can be used as camera 2. An RGB-D camera is a camera configured to measure RGB-D images. An RGB-D image includes a depth image and a color image. The depth image is an image with the depth of each point of the measured object as pixel values. The color image is an image with the RGB values ​​of each point of the measured object as pixel values.

[0020] Display device 3 is a display device such as a liquid crystal display (LCD) or an organic EL display. Display device 3 displays various images based on data transmitted from measurement system 1.

[0021] The first extraction unit 11 extracts point group data from the measurement point group data measured by the camera 2 that has a color similar to the measurement mark M1. For example, if the measurement mark M1 is a mark with a black and white pattern, the first extraction unit 11 compares the RGB values ​​of each pixel in the color image measured by the camera 2 with the upper limit value corresponding to black, thereby identifying pixels with RGB values ​​below the upper limit value as black pixels. Furthermore, the first extraction unit 11 extracts point group data corresponding to black pixels from the measurement point group data.

[0022] The plane detection unit 12 detects the plane unfolded from the point group data extracted by the first extraction unit 11, and extracts the point group data on the plane from the point group data extracted by the first extraction unit 11. Plane detection can be performed, for example, using RANSAC (Random Sample Consensus) Plate Fitting. In RANSAC Plate Fitting, outliers are removed using a RANSAC algorithm based on a fundamental matrix calculated from randomly sampled points in the point group data. In RANSAC Plate Fitting, the points in the point group data are grouped into two segments—inline set and outline set—using RANSAC, thereby detecting the plane unfolded from points belonging to the inline set. Plane detection can be performed using any method other than RANSAC Plate Fitting, such as using the Hough transform method. Through plane detection, the point group data extracted by the first extraction unit 11 is filtered into point group data on the plane.

[0023] The clustering unit 13 clusters the point cluster data on the plane detected by the plane detection unit 12. Clustering may be performed using, for example, DBSCAN (Density-based spatial clustering of applications with noise). DBSCAN is a method for clustering point cluster data by repeatedly making the following judgment while changing the evaluation points: if the number of points near the evaluation point in the point cluster data exceeds a certain amount, it is determined that the evaluation point and the nearby points belong to the same cluster; if the number does not exceed a certain amount, it is determined that the evaluation point and the nearby points do not belong to the same cluster. As in the embodiment, if the point cluster data of component p is separated from the point cluster data of measurement marker M1, then the probability that each point cluster data of measurement marker M1 belongs to the same cluster is high. Clustering may also be performed using any method other than DBSCAN, such as k-means clustering.

[0024] The second extraction unit 14 extracts the point group data of measurement marker M1 from the clusters obtained by the clustering unit 13. If the size of measurement marker M1 is known, the point group data of measurement marker M1 can be specifically determined based on, for example, the length of the diagonal of the bounding box of the point group. The bounding box of the point group is the region formed by the boundary lines of each cluster. That is, the second extraction unit 14 extracts the point group data of the cluster whose length of the diagonal of the bounding box of the point group is closest to the length of the diagonal of measurement marker M1 as the point group data of measurement marker M1. Not limited to the diagonal, the point group data of measurement marker M1 can also be extracted based on the length of the side of the bounding box, etc.

[0025] The alignment unit 15 aligns the point group data of the measurement object with the known point group data stored in the shape DB 16 by aligning the point group data of the measurement mark M1 extracted by the second extraction unit 14 with the point group data of the known mark M2 stored in the shape DB 16. Alignment can be performed using methods such as ICP (Iterative Closest Point) and BCPD (Bayesian Coherent Point Drift).

[0026] Shape DB 16 stores known point group data of the measured object. Known point group data can be 3D CAD (Computer-Aided Design) based design data of part p of the measured object, etc. Known point group data is not limited to design drawing data; it can be any point group data or data that can be converted into point group data.

[0027] Furthermore, shape DB 16 stores the dot group data of known mark M2 along with the known dot group data. The dot group data of known mark M2 is the dot group data of a mark with the same black and white pattern as the measurement mark M1, and the dot group data of known mark M2 is associated with the known dot group data in a predetermined position and a predetermined orientation. When two or more measurement marks M1 are arranged on substrate B, dot group data of two or more known marks M2 can be prepared.

[0028] Figure 3This is a diagram showing the relationship between the known point group data and the known marker M2's point group data. In the embodiment, the known point group data d of component p and the known marker M2's point group data are configured on the same virtual plane with a predetermined orientation. Furthermore, the known point group data d and the known marker M2's point group data are associated with data representing their positional relationship on the virtual plane. The data representing the positional relationship includes data on the horizontal distance x2 and the vertical distance y2 on the virtual plane configuring the known point group data d of component p and the known marker M2's point group data. Here, the horizontal distance x2 is a multiple of the horizontal distance x1 by k1 (k1 is a positive real number), and the vertical distance y2 is a multiple of the vertical distance y1 by k2 (k2 is a positive real number). k1 and k2 can be equal or unequal. That is, the positional relationship between the measured object component p and the measured marker M1, and the positional relationship between the known point group data d and the known marker M2, can also be different.

[0029] Furthermore, the number of points in the known point group data does not need to be the same as the number of points in the point group data of the object being measured. On the other hand, the number of points in the point group data of the known marker M2 is preferably the same as the number of points in the point group data of the measuring marker M1. That is, the density of the known point group data and the point group data of the object being measured can be different, but the density of the point group data of the known marker M2 and the point group data of the measuring marker M1 is preferably the same. This is because, as explained in detail later, in the embodiment, the alignment of the measuring point group data and the known point group data is performed by aligning the measuring marker M1 with the known marker M2. In order to achieve high-precision alignment between the measuring marker M1 and the known marker M2, it is preferable that the number of points on the two markers is the same.

[0030] Known point group data and known labeled point group data can also constitute independent point group data. Even in this case, the horizontal distance x2 and vertical distance y2 representing the positional relationship between the known point group data and the known labeled point group data are defined. Of course, known point group data and known labeled point group data can also constitute a single point group data.

[0031] Alternatively, the shape DB 16 can be located outside the measuring system 1. In this case, the alignment section 15 of the measuring system 1 obtains information from the shape DB 16 as needed.

[0032] The display control unit 17 displays information related to the alignment results performed by the alignment unit 15 on the display device 3. This information, related to the shape comparison results, includes, for example, an image based on a point group measured by the camera 2, and an image superimposed on an image based on a known point group stored in the shape DB 16. Image superposition can be achieved by moving one image to the other image based on geometric transformation information obtained from the alignment in the alignment unit 15.

[0033] Figure 4This diagram illustrates an example of the hardware configuration of measurement system 1. Measurement system 1 can be various terminal devices such as personal computers (PCs) and tablet computers. Figure 2 As shown, the measurement system 1 has a processor 101, a ROM 102, a RAM 103, a memory 104, an input interface 105, and a communication device 106 as hardware.

[0034] Processor 101 is a processor that controls the overall operation of measurement system 1. For example, processor 101 operates as the first extraction unit 11, the plane detection unit 12, the clustering unit 13, the second extraction unit 14, the alignment unit 15, and the display control unit 17 by executing a program stored in memory 104. Processor 101 is, for example, a CPU (Central Processing Unit). Processor 101 can also be an MPU (Micro-Processing Unit), GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), etc. Processor 101 can be a single CPU or multiple CPUs.

[0035] ROM (Read Only Memory) 102 is a non-volatile memory. ROM 102 stores the startup program of measurement system 1, etc. RAM (Random Access Memory) 103 is a volatile memory. RAM 103 can be used, for example, as working memory during processing in processor 101.

[0036] The memory 104 is, for example, a hard disk drive, a solid-state drive, or other type of memory. The memory 104 stores various programs, such as measurement programs, that are executed by the processor 101. Furthermore, the memory 104 can store a shape DB 16. The shape DB 16 does not necessarily need to be stored in the memory 104.

[0037] Input interface 105 includes input devices such as a touch panel, keyboard, and mouse. When an input device of input interface 105 is operated, a signal corresponding to the operation is input to processor 101. Processor 101 performs various processes based on the signal.

[0038] The communication device 106 is a communication device used for communication between the measurement system 1 and external devices such as the camera 2 and the display device 3. The communication device 106 can be used for wired communication or wireless communication.

[0039] Next, the operation of measurement system 1 will be explained. Figure 5 This is a flowchart illustrating the actions of measurement system 1. Figure 5 The processing is performed by processor 101.

[0040] In step S1, the processor 101 acquires measurement point group data from the camera 2, which includes point group data of the component p of the object being measured and the measurement mark M1. Here, when the camera 2 measures the measurement point group data, the measurement is performed in such a way that both the component p of the object being measured and the measurement mark M1 are contained within the field of view of the camera 2.

[0041] In step S2, the processor 101 extracts, for example, black measurement point group data from the measurement point group data acquired from the camera 2. If component p does not contain black areas and the measurement mark M1 is a black and white pattern mark, the point group data of the measurement mark M1 is extracted through this process. However, if component p contains black areas or low-brightness areas that are considered black areas, the point group data of the black areas or low-brightness areas of component p can also be extracted. The subsequent processing takes into account the case where component p contains black or low-brightness areas.

[0042] In step S3, the processor 101 detects the plane unfolded from the extracted point group and extracts the point group data on the plane. The plane detection is performed, for example, to account for tilting of the point group data caused by factors such as the shooting orientation of the camera 2. Subsequent processing is performed on the extracted point group data on the plane.

[0043] In step S4, the processor 101 clusters the detected point cluster data on each plane. The clustering result is that the black measurement point cluster data extracted in step S2 is divided into... Figure 6 Multiple clusters C1, C2, ..., Cn as shown (in) Figure 6 (n=13). Figure 6 For example, cluster C10 is a cluster of point group data for measurement marker M1. Additionally, Figure 6 The results of clustering corresponding to point cluster data on a plane are shown. In fact, clustering is performed on the point cluster data on each plane detected in step S3.

[0044] In step S5, the processor 101 extracts the point group data of the measurement mark M1 based on the dimensions of the bounding boxes of each point group data. For example, the processor 101 extracts the point group data whose bounding box shape is the same as the shape of the measurement mark M1 and whose diagonal length is closest to the diagonal length of the measurement mark M1 as the point group data of the measurement mark M1. Furthermore, it is also conceivable that a component with a bounding box shape the same as that of the mark M1 is disposed on the substrate B. Considering this case, it is necessary that the diagonal length of the measurement mark M1 is different from the diagonal length of any component conceivably disposed on the substrate B. By making the diagonal length of the measurement mark M1 different from the diagonal length of each component, the point group data of the measurement mark M1 can be extracted correctly.

[0045] In step S6, the processor 101 virtually multi-layers the point group data of the known marker M2 stored in shape DB 16. For example... Figure 7 As shown, multi-layering is performed as follows: along the normal direction of the face of the original known marker M2 point group data stored in shape DB 16, multiple copies of the known marker M2 point group data M21, M22 are generated at positions where the data has been moved a certain distance. Here, the number of copies is not limited to two. That is, more than three point group data can also be generated.

[0046] In step S7, the processor 101 aligns the point group data of component p with the known point group data by matching the point group data of measurement mark M1 extracted in step S5 with the point group data of the known mark M2 after multi-layering in step S6. Due to factors such as the tilt of camera 2 during shooting, the measurement point group data may rotate around the normal direction. Furthermore, due to the tilt of camera 2 during shooting, the measurement point group data may also be tilted. In these cases, even if the point group data of measurement mark M1 is simply aligned with the point group data of known mark M2, the amount of information in the three-dimensional direction may be insufficient, making accurate alignment impossible. Figure 7As shown, by aligning the point group data of the multi-layered known marker M2 with the point group data of the measurement marker M1, the lack of three-dimensional information during alignment can be compensated for. Therefore, the point group data of the measurement marker M1 and the point group data of the known marker M2 are correctly aligned. Here, the positional relationship between the measurement marker M1 and the component p of the measurement object, as well as the positional relationship between the point group data of the known marker M2 and the known point group data, are predetermined. Therefore, by aligning the point group data of the measurement marker M1 and the point group data of the known marker M2, the point group data of the component p is also correctly aligned with the known point group data. Furthermore, when the positional relationship between the component p of the measurement object and the measurement marker M1, and the positional relationship between the known point group data d and the known marker M2, are different, the alignment of the point group data of the component p with the known point group data is performed according to the difference in their positional relationships.

[0047] In step S8, the processor 101 overlays a three-dimensional image of the measured object based on measurement point group data measured by the camera 2 with a three-dimensional image of the measured object based on known point group data, and displays the image on the display device 3. Afterward, the processor 101 terminates the process. Figure 5 The processing is as follows. Additionally, when overlaying and displaying, the locations of differences between the measured point group data and the known point group data can be emphasized. Emphasis can be achieved by changing the color of the differences, changing the density of the differences, or any other method.

[0048] As explained above, according to the embodiment, a measurement marker M1 is set at a known location relative to the object being measured, and a group of known points, marked with known marker M2, is set at a known location relative to a known group of points relative to the object being measured. Furthermore, the point group data of the object being measured is aligned with the point group data of the known marker M2 by aligning the point group data of the measurement marker M1 extracted from the point group data. That is, information about the characteristic quantities of the object being measured is not used for the alignment of the point group data of the object being measured with the known point group data. Therefore, high-precision alignment can be achieved even if the object being measured does not have a characteristic structure.

[0049] Furthermore, according to the embodiment, in order to extract the point group data of measurement marker M1 from the measurement point group data, extraction of measurement marker M1 and point group data of similar colors, plane detection, clustering, and extraction of point group data based on the diagonal size of the bounding box are performed. Thus, only the point group data of measurement marker M1 can be correctly extracted. Therefore, in this embodiment, even if the performance of camera 2 cannot obtain an image of measurement marker M1 with sufficient resolution, the point group data of measurement marker M1 can be extracted with high accuracy.

[0050] Furthermore, during alignment, the point group data with known marker M2 is multi-layered. This enables high-precision alignment that also includes three-dimensional orientation.

[0051] [Variation Example]

[0052] A variation will be described. In this embodiment, measurement system 1 is used for measurement in a component assembly system. In contrast, the measurement system of this embodiment can be applied to any measurement system.

[0053] Furthermore, in some embodiments, the camera 2 can also be integrated with the measurement system 1. In this case, the position and attitude control of the camera 2 can also be implemented by the measurement system 1.

[0054] Furthermore, in this embodiment, the measurement mark M1 is set as a black and white pattern mark. However, the measurement mark M1 is not necessarily a black and white pattern mark. For example, the measurement mark M1 could also be a pattern mark with a predetermined color. In this case, the first extraction unit 11 compares the RGB values ​​of each pixel in the color image measured by the camera 2 with the upper and lower limits corresponding to the color of the mark M1, thereby identifying pixels whose RGB values ​​are in the range above the lower limit and below the upper limit. Moreover, the first extraction unit 11 extracts the point group data corresponding to the specific pixel from the measurement point group data.

[0055] Furthermore, the measurement mark M1 can also be a mark identified by brightness. For example, the measurement mark M1 can also be a mark depicting a black and white pattern using retroreflective paint. In this case, a LiDAR (Light Detecting and Ranging) camera can be used as camera 2. The first extraction unit 11 extracts the measurement point group data of the measurement mark M1 from the measurement point group data using information about the infrared brightness of the measurement object measured by camera 2. Specifically, the first extraction unit 11 extracts point group data with brightness values ​​higher than a predetermined value. This is because high-brightness infrared light returns from the mark depicted by retroreflective paint due to retroreflection. In addition, the measurement mark M1 depicted by retroreflective paint can also be measured using a camera other than a LiDAR camera, such as an RGB-D camera.

[0056] Furthermore, in this implementation, we envision a situation where the object being measured has a three-dimensional structure. In contrast, if the object being measured is a plane and three-dimensional information is not required for alignment, then plane detection and multi-layer processing of the point group data of the known marker M2 can be omitted.

[0057] While several embodiments of the invention have been described, these embodiments are provided as examples and are not intended to limit the scope of the invention. These new embodiments can be implemented in various other ways, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included within the scope and spirit of the invention, as well as within the scope of the invention as described in the claims and its equivalents.

Claims

1. A measurement system, comprising: a measurement point group of a measurement object; and a first marker having a known position with respect to a known point group of the measurement object, the first marker being a planar marker, a position aligning section that aligns the point group of the measurement object with the known point group by aligning a point group of the first marker with a point group of a second marker whose position with respect to the known point group is known, the second marker being a planar marker, The extraction unit extracts a point group of a first marker, which is a planar marker, from among a measurement point group including a point group of a measurement object and a point group of the first marker arranged at a known position with respect to the measurement object. the extracting section having: a first extracting section that extracts a first point group having a color or brightness similar to that of the first marker from the measurement point group; a clustering section that clusters the first point group; and a second extracting section that extracts a second point group having a size corresponding to that of the first marker from the clustered first point group as the point group of the first marker, on the basis of information on the size of the first marker, the position aligning section that aligns the second point group with the point group of the second marker that has been multilayered by copying the point group of the second marker in a direction normal to the plane of the second marker.

2. The measurement system according to claim 1, further comprising a plane detecting section that detects at least one plane corresponding to the first point group, the clustering section clustering the first point group on each of the planes.

3. The measurement system according to claim 1, wherein the information on the size is a length of a diagonal line of the first marker.

4. The measurement system according to claim 1, wherein the first marker is a marker drawn by retroreflective paint.

5. A measurement method, comprising: a measurement point group of a measurement object; and a first marker having a known position with respect to a known point group of the measurement object, the first marker being a planar marker, a position aligning step of aligning the point group of the measurement object with the known point group by aligning a point group of the first marker with a point group of a second marker whose position with respect to the known point group is known, the second marker being a planar marker, the extracting step including: extracting a first point group having a color or brightness similar to that of the first marker from the measurement point group; clustering the first point group; and extracting a second point group having a size corresponding to that of the first marker from the clustered first point group as the point group of the first marker, on the basis of information on the size of the first marker, the aligning step including: multilayering the point group of the second marker by copying the point group of the second marker in a direction normal to the plane of the second marker; and aligning the second point group with the point group of the second marker that has been multilayered.

6. A computer-readable storage medium storing a measurement program for causing a computer to execute the steps including: a measurement point group of a measurement object; and a first marker having a known position with respect to a known point group of the measurement object, the first marker being a planar marker, a position aligning step of aligning the point group of the measurement object with the known point group by aligning a point group of the first marker with a point group of a second marker whose position with respect to the known point group is known, the second marker being a planar marker, the extracting step including: extracting a first point group having a color or brightness similar to that of the first marker from the measurement point group; clustering the first point group; and extracting a second point group having a size corresponding to that of the first marker from the clustered first point group as the point group of the first marker, on the basis of information on the size of the first marker, ​ ​ ​ ​ ​ ​ ​ extracting a point group of a first marker from among a measurement point group including a point group of a measurement object and a point group of the first marker configured at a known position with respect to the measurement object, the first marker being a planar marker; ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ extracting a point group of a first marker from among a measurement point group including a point group of a measurement object and a point group of the first marker configured at a known position with respect to the measurement object, the first marker being a planar marker; ​ ​ ​ ​ ​ extracting a second point group having a size corresponding to the size of the first marker from the first point group after clustering, as a point group of the first marker, based on information of the size of the first marker, the step of aligning the point group of the measurement object with the known point group includes: the point group of the second marker is multilayered by copying the point group of the second marker in a direction of a normal line of a plane of the second marker; and the second point group is aligned with the point group of the second marker after multilayering. the step of aligning the point group of the measurement object with the known point group includes: the point group of the second marker is multilayered by copying the point group of the second marker in a direction of a normal line of a plane of the second marker; and the second point group is aligned with the point group of the second marker after multilayering.

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