JP2010210576A - Shape recognition device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To constitute a shape recognition device which can extract strains, on the basis of a shape used as a base of a measuring object. <P>SOLUTION: Based on three-dimensional data, a base-shape data generating means 16 generates base-shape data which express the original surface shape of a measuring object T with a plurality of curvatures, and a detailed shape data generating means 17 generates detailed shape data which express detailed surface shape of the measuring object T with a plurality of curvatures. The device includes a shape-evaluating means 19, which determines the strain, by comparing the curvatures in the identical region of curvature values of these data with each other and evaluates the external shape. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a shape recognition device for recognizing an outer surface shape of an object from three-dimensional data representing the outer surface shape of the object.

  As a shape recognizing apparatus configured as described above, Patent Document 1 discloses a third order of the entire surface shape of a press-processed product by performing fringe analysis on a photographed image of the surface of the press-processed product such as a vehicle body or a door panel. A three-dimensional measurement control unit for generating original measurement data is provided, and a shape recognition device and a distortion evaluation device are provided.

  The shape recognition apparatus includes an approximate curve applying unit, a curvature deriving unit, a uniform range determining unit, and an approximate curve deriving unit. The approximate curve applying means is a part of a circle corresponding to the curve of a plurality of first data groups along the length direction of the cross section among the two-dimensional data generated from the three-dimensional measurement data in the length direction of the cross section. By associating these sections, a plurality of circles are associated with the curve, and the curvature deriving means obtains the curvature of each circle to generate the first approximate curve. Next, the uniform range determining means determines a uniform range from the first approximate curve, and the approximate curve deriving means derives a second approximate curve having a certain curvature. This curvature is the reciprocal of the radius of the circle.

  Next, the strain evaluation device extracts the strain data extraction means using the data whose deviation from the second approximate curve is not less than the set allowable range as the strain data. In other words, in Patent Document 1, the shape of each part of the curve is regarded as a set of circular arcs of circles having a predetermined radius, and the regions where the radii of the adjacent ones of the circles approximate to each other are approximated (the curvature is approximate). A second approximate curve is generated, and it is determined that there is distortion in a region where the distance rate deviates from the second approximate curve.

JP 2007-127610 A (paragraph numbers [0013] to [0028], FIGS. 1 to 5)

  Considering the surface strain detection processing of press-processed products such as vehicle bodies and door panels, for example, comparison with 3D data such as CAD data (for example, CAD data of molds) indicating the ideal surface shape of the processed product It is possible to do. However, in a press-processed product, the shape is usually deformed in a direction in which the shape slightly swells immediately after processing due to the spring back, and detection of surface distortion by comparing with CAD data is relative to CAD data. There are practically difficult aspects including the positioning accuracy.

  Therefore, as described in Patent Document 1, each part of the surface of the measurement object is obtained from the two-dimensional cross-sectional data obtained by cutting the three-dimensional data created based on the image of the surface of the measurement object in a predetermined direction. It is effective to determine the curvature and determine that there is distortion in a region where the curvature deviates by comparing the uniform curvature with the curvature of each part.

  In other words, even if the car body or door panel is distorted so as to be curved as a whole compared to the designed curve, there is almost no visual sense of distortion, and there are some depressions and protrusions. In some cases, even if the amount of depressions or protrusions is small, it often feels visually distorted. With respect to such a problem, in the apparatus described in Patent Document 1, the uniform curvature corresponds to the shape serving as the base of the measurement object, and the curvature of each part is determined based on the shape serving as the base. Since this is a comparison, a good aspect of enabling local distortion detection has been revealed.

  However, in Patent Document 1, in order to detect a relatively small distortion, points (sample points) are set at short intervals in the length direction of the two-dimensional cross-sectional data, and a circle is approximated for each sample point. Since multiple curvatures are obtained, even when a uniform area is set from multiple curvatures, it is inevitable that small irregularities on the surface of the measurement object will affect the value indicating the uniform area. The shape of the base of the object may be inaccurate, and there is room for improvement.

  An object of the present invention is to rationally configure a shape recognition device capable of extracting distortion based on a shape serving as a base of a measurement object.

A feature of the present invention is a shape recognition device for recognizing an outer surface shape of an object from three-dimensional data representing the outer surface shape of the object,
Detailed shape data generating means for acquiring, as detailed shape data, a plurality of curvatures reflected in detail by the shape of the setting region of the three-dimensional data;
A base shape data generating means for acquiring, as base shape data, a plurality of curvatures that reflect the shape of the setting region of the three-dimensional data more roughly than the details;
Of the detailed shape data and the base shape data, there is a shape evaluation means for extracting a strain value based on the curvature in the same region in the three-dimensional data.

According to this configuration, the detailed shape data generating means acquires a plurality of curvatures that are reflected in detail by the shape of the setting area of the three-dimensional data as detailed shape data, and the base shape data generating means is the setting area of the three-dimensional data setting area. A plurality of curvatures that roughly reflect the shape are acquired as base shape data, and the shape evaluation means calculates distortion values based on the three-dimensional data in the same region among the curvatures of the detailed shape data and the base shape data. get.
As a result, a shape recognizing apparatus that can extract a distortion value based on the shape serving as a base of the measurement object is configured.

In the present invention, the detailed shape data generation means generates surface shape data having a two-dimensional data structure in which the shape of the setting region of the three-dimensional data is reflected at the first setting interval in the setting direction. The detailed shape data is generated by calculating the curvature in the setting direction,
The base shape data generating means generates surface shape data having a two-dimensional data structure in which the shape of the setting region of the three-dimensional data is reflected every second setting interval wider than the first setting interval in the setting direction. The base shape data may be generated by calculating the curvature in the setting direction in the shape data.

  According to this configuration, the curvature calculated from the surface shape data that reflects the shape of the three-dimensional data at each first setting interval, and the surface shape that reflects the shape of the three-dimensional data at every second setting interval that is wider than the first setting interval. A distortion value can be obtained from a comparison with the curvature calculated from the data.

  The present invention comprises curvature acquisition means for calculating curvature from the surface shape data generated by the detailed shape data generation means and the surface shape data generated by the base shape data generation means, and the curvature acquisition means Of the three or more sample points indicating the height value in the surface shape data, a quadratic curve passing over or near the point group with the center near the vertex is applied as an approximate curve, and the applied approximation The curvature may be calculated based on the curvature at the center of the sample point of the curve.

  According to this, the surface shape data generated by the detailed shape data generation means and the surface shape data generated by the base shape data generation means can calculate the curvature in the curvature acquisition means, and can share the processing of the curvature acquisition means. . In addition, in the case of approximating a curve indicated by a plurality of sampling points with a circle (circle) as in the conventional technology, when approximating a shape in which a curved line that protrudes downward changes to a curved line that protrudes upward, Since the radius of the circle continues to increase, the numerical value increases infinitely at the inflection point, and then the radius decreases, so a large numerical value is used for calculating the curvature. On the other hand, when a similar shape is approximated by a quadratic curve, the coefficient of the quadratic function corresponding to the quadratic curve decreases from a positive value (may be negative) and reaches “0” at the inflection point. Thereafter, the negative value (which may be positive) increases. In this way, in the case of approximating a curve indicated by a plurality of sampling points by a quadratic curve, it is not necessary to handle a large numerical value for obtaining the curvature, and the processing is simplified.

  In the present invention, the shape evaluation unit compares the curvatures of the same region in the setting direction among the plurality of curvatures acquired from the detailed shape data and the plurality of curvatures acquired from the base shape data, or is generated from the curvature. The distortion value may be extracted by comparing the height values of the curved curves.

  According to this, the curvature of the detailed shape data is compared with the curvature of the base shape data, or the height values of the curves generated from the curvature are compared, so the distortion value is extracted numerically by calculating the numerical value. It becomes possible.

It is a perspective view which shows the whole three-dimensional measuring system. It is a block circuit diagram of a three-dimensional measurement system. It is a block circuit diagram of a shape recognition device It is a flowchart of an evaluation process routine. It is a perspective view which shows a measuring object and a coordinate system. It is a side view of a measuring object. It is a perspective view which shows the conversion form in a data conversion process part. It is a figure which shows the size of two types of area | regions when reflecting the height value of the point group of the cut-out three-dimensional data. It is a figure which shows the surface shape of the three-dimensional data acquired by cutting out. It is a figure which shows typically the process at the time of calculating | requiring the curvature of a shape curve with an approximate curve. It is a figure which shows the process at the time of acquiring a distortion value from the difference in curvature.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
〔System configuration〕
As shown in FIG. 1 and FIG. 2, an image is taken with an articulated robot hand H having a photographing unit V having a digital camera at the tip, a robot hand controller A that controls the robot hand H, and a photographing unit V. A three-dimensional measurement system including an image acquisition unit B that acquires image data of a measurement target T, and a shape recognition device C that acquires image data from the image acquisition unit B, extracts the distortion of the outer shape of the object, and outputs the distortion. Is configured.

  The photographing unit V includes a pattern projection device 1 that projects a lattice pattern onto the surface of the measurement target T via the projection lens 1L, and a digital camera that photographs the surface of the measurement target T via the photographing lens 2L. 2 are provided. The robot hand H includes a plurality of arm units 3 and an electric motor 4 that drives each arm unit 3 independently. The robot hand controller A executes a preset program so that the imaging unit V The operation of photographing the measurement target T from different directions is performed, and the entire surface of the measurement target T is photographed. The image acquisition unit B determines the shape of the measurement target T from the lattice pattern of the image data photographed by the photographing unit V and generates a three-dimensional image.

  The robot hand controller A includes arm control means 6 for controlling the arm unit 3 in accordance with the control program 5. In the image acquisition unit B, the image data acquisition means 7 acquires image data from the camera 2, and the three-dimensional image generation means 8 generates a three-dimensional image from this image data. As a technique for generating a three-dimensional image from image data, a known one is used as disclosed in Japanese Patent Application Laid-Open No. 2002-257528, Japanese Patent Application Laid-Open No. 2004-317495, and the like. A three-dimensional image that is a point group in which the surface shape of the object T is represented in a three-dimensional coordinate system is generated.

  The shape recognition device C is constituted by a general-purpose computer to which a display d, a keyboard k, a mouse m, and the like are connected. The shape recognition device C extracts the amount of distortion on the surface of the measurement target T from the three-dimensional image acquired from the image acquisition unit B and performs evaluation. The evaluation result is output to the display d.

(Control configuration)
As shown in FIGS. 2 and 3, the shape recognition apparatus C includes a data acquisition unit 11, a data conversion unit 12, a noise removal unit 13, an exclusion area setting unit 15, a base shape data generation unit 16, and details. A shape data generation unit 17, a curvature acquisition unit 18, a shape evaluation unit 19, an evaluation image generation unit 20, and a measurement target shape acquisition unit 21 are provided.

These data acquisition means 11, data conversion means 12, noise removal means 13, exclusion region setting means 15, base shape data generation means 16, detailed shape data generation means 17, curvature acquisition means 18, shape evaluation means 19, evaluation image generation The means 20 and the measurement target shape acquisition means 21 are composed of software developed on a storage of a general-purpose computer that functions as the shape recognition device C. These are combinations of software and hardware such as logic. It may be configured only by hardware such as logic.
[Control form]

  The outline of the control by this control configuration is shown in the flowchart of FIG. 4, and the function of the control configuration will be described together with the control flow shown in the flowchart.

  The data acquisition unit 11 acquires the 3D image generated by the 3D image generation unit 8 of the image acquisition unit B via the communication cable (Step # 01). In the shape recognition apparatus C, not only the 3D image generated from the image captured by the imaging unit V is processed, but 3D data having a data structure such as an STL file is processed. It is also possible. That is, the base shape and detailed shape data can be calculated directly from the point group corresponding to the outer surface to be measured by the base shape data generating means and the detailed shape data.

  5 and 6 show a door panel as a measurement object T (an example of an object), the front-rear direction in a posture in which the door panel is provided on the vehicle body is the X-axis direction, the vertical direction is the Y-axis direction, The width direction is shown as Z. A seat surface Ta on which a door handle (not shown) is mounted and an opening Tb are formed in the panel portion of the door panel.

  The data conversion means 12 sets a point group (a collection of points indicating the Z coordinate value / height value) indicating the surface shape of the measurement target T from the acquired three-dimensional image on a lattice point on the XY plane with a set pitch. Three-dimensional data is generated by converting to a point cloud (step # 02). That is, in the three-dimensional coordinate system of the three-dimensional image acquired by the data acquisition unit 11, the front-rear direction and the vertical direction of the door panel do not necessarily coincide with the XY axis direction, and the height value indicating the outer shape (Z direction) The pitch of the point cloud indicating the value at (1) cannot be said to be an appropriate value for processing by the shape recognition apparatus C.

  For this reason, as shown in FIG. 7, the data conversion means 12 sets the front / rear direction of the door panel as the measurement target T to the X-axis direction, sets the vertical direction to the Y-axis direction, and sets the width direction (door Is set in the Z-axis direction, and a three-dimensional data value is generated from the three-dimensional image data value. Note that the point group in the three-dimensional data generated by the data conversion unit 12 is a virtual point existing on the surface of the measurement target T, and indicates a point whose position is specified by three-dimensional coordinates.

  The three-dimensional data generated in this way has a data structure in which the height value is expressed as a Z coordinate with reference to an XY plane with a set pitch of less than several millimeters. When performing this conversion, it is necessary to obtain a height value in an area where the point cloud (shown as a three-dimensional image value in the figure) obtained by the data obtaining unit 11 does not exist. As in the bilinear method and the nearest neighbor method, height values reflecting the height values of adjacent point groups are newly created by interpolation processing. By performing these processes, three-dimensional data having a simple data structure whose height value is indicated in the Z direction with reference to the XY plane is generated, and as a result, the data amount is reduced. Further, the data conversion means 12 performs processing for removing this data including data for generating a polygon, such as an STL file.

  The noise removing unit 13 removes noise included in the three-dimensional data (Step # 03). In this removal, a process of taking a moving average of a plurality of coordinates (height values) indicating the surface shape for each predetermined region is performed. By this process, when the height value at the target coordinate is a value far from the surrounding height value, it is converted to a value reflecting the surrounding height value, and as a result, noise is removed. Note that, as shown in Patent Document 1, the noise removal processing form by the noise removing unit 13 is one of a large number of point groups indicating a surface shape as a specific point, and the coordinate value (height value) of the specific point. ) And the coordinate value (height value) of a point adjacent thereto, and a smoothing process may be performed based on the difference in height value. .

  The excluded area setting means 15 has a curvature acquisition module and an area setting module, and performs an excluded area setting process (step # 04). The curvature acquisition module acquires the three-dimensional data from which noise has been removed and gives it to the curvature acquisition means 18, whereby the measurement object calculated from the point group of the two-dimensional data generated from the three-dimensional data by the curvature acquisition means 18 A curvature indicating the shape of T is acquired. The region setting module extracts a region where the curvature exceeds the set value (the radius of the curve is small), and sets this region or a portion surrounded by this region as an excluded region whose position is expressed in the XY coordinate system. . (The processing mode of the curvature acquisition means 18 will be described later).

  In this process, the process of calculating the curvature of the outer shape of the measurement target T by the curvature acquisition means 18 is essential, and it is desirable to calculate the curvature for each narrow region as much as possible.

  In this embodiment, the exclusion region is set based on the curvature, but if there are not only the curvature but a part protruding beyond the set amount from the average surface or a region recessed beyond the set amount, these The processing mode of the area setting module may be set so that a part including or a part surrounded by these parts is an excluded area.

  In the shape recognition apparatus C according to the present invention, as will be described later, the base shape data generation unit 16 and the detailed shape data generation unit 17 cut out three-dimensional data from the three-dimensional data from which noise has been removed, and a point cloud of the three-dimensional data. The surface shape data having a two-dimensional data structure reflecting the surface shape of the region cut out from the image is generated, and the surface shape data is given to the curvature acquisition means 18. The curvature acquisition means 18 calculates a plurality of curvatures from a given point group of the surface shape data, and the base shape data and the detailed shape data 17 are fed back by the base shape data generation means 16 and the detailed shape data generation means 17, respectively. Generated.

  However, when there is a part whose shape changes with a large curvature in a relatively narrow region such as the seating surface Ta of the measurement target T, the position of the point cloud and the actual curved surface of the measurement target T Depending on the relationship, it is often impossible to obtain an accurate curvature. When such an area is included in the area to be evaluated, the accuracy of the evaluation is reduced. Therefore, in order to exclude an area that causes a decrease in accuracy when the outer shape of the measurement target T is evaluated, The setting means 15 sets an exclusion area.

  The base shape data generation unit 16 and the detailed shape data generation unit 17 include a three-dimensional data extraction module, an exclusion processing module, a two-dimensional data conversion module, and a curvature acquisition module.

  The detailed shape data generation means 17 cuts out three-dimensional data having a set width from the three-dimensional data from which noise has been removed, and single surface shape data having a two-dimensional data structure in which the surface shape of the cut-out three-dimensional data is reflected. And detailed shape data represented by a plurality of curvatures acquired from the surface shape data is generated (step # 05).

  Specifically, the three-dimensional data cutout module cuts out three-dimensional data in a region having a set width in the Y-axis direction and, as shown in FIG. 8, the first set interval D1 (about 30 mm. A process of reflecting the point group included in the range of (an example of one setting range) in the Z coordinate (height value) is performed. In this process, the first setting interval D1 is set as the size of the region reflecting the Z coordinates of a plurality of point groups. The region is moved in the X-axis direction at the pitch of the point group, and the region is moved. Z coordinates (height value) are generated at a pitch equal to the point group pitch in the X-axis direction by processing such as averaging (# 05 step <a>). Next, when there is a point P (height value) included in the exclusion region in the three-dimensional data, the exclusion processing module excludes the point P (#b step <b>).

  Thereafter, the two-dimensional data conversion module performs coordinate transformation to convert the three-dimensional data into two-dimensional data to obtain surface shape data (#c step <c>). Then, the curvature acquisition module gives the surface shape data to the curvature acquisition means 18, thereby acquiring a plurality of curvatures corresponding to the shape of the surface shape data from the curvature acquisition means 18, and obtaining detailed shape data consisting of a plurality of curvatures. (<D> in step # 05).

  Similarly to the detailed shape data generation unit 17, the base shape data generation unit 16 cuts out three-dimensional data having a set width from the three-dimensional data from which noise has been removed, and two-dimensional data in which the surface shape of the cut-out three-dimensional data is reflected. Single surface shape data to be a structure is generated, and base shape data represented by a plurality of curvatures acquired from the surface shape data is generated (step # 06).

  As a specific process, the three-dimensional data cutout module cuts out three-dimensional data in a region having a set width in the Y-axis direction and, as shown in FIG. 8, the second set interval D2 (first set interval in the X-axis direction). A process of reflecting a point group included in the range of about 2 to 10 times D1 (an example of the second setting range) on the Z coordinate (height value) is performed. In this process, the second set interval D2 is set as the size of the region reflecting the Z coordinates of a plurality of point groups, and the region is moved while moving the region of this size in the X-axis direction at the point group pitch. A Z coordinate (height value) is generated at a pitch equal to the point group pitch in the X-axis direction by processing such as averaging (#a in step # 06). Next, when there is a point P (height value) included in the exclusion region in the three-dimensional data, the exclusion processing module excludes the point P (<b> in step # 06).

  Thereafter, the two-dimensional data conversion module performs coordinate transformation to convert the three-dimensional data into two-dimensional data to obtain surface shape data (<c> in step # 06). Then, the curvature acquisition module gives the surface shape data to the curvature acquisition means 18, thereby acquiring a plurality of curvatures from the curvature acquisition means 18 and generating base shape data composed of a plurality of curvatures (in step # 06). <D>).

  As described above, in the process of removing the point P included in the exclusion region in the steps # 05 and # 06, the process of setting the height value to “0” is not performed, but the height value (point P / point group) is not performed. ) Does not exist.

  As shown in FIGS. 5 and 6, when the surface shape of the measurement target T is curved, the height value indicated by the point group of the cut out three-dimensional data also shows an arc shape. Therefore, when Y1, Y2,... Yn are given as coordinate values in the Y-axis direction for the cut out three-dimensional data, the height values corresponding to Y1, Y2,. The height is different from each other in the direction.

  For this reason, as coordinate conversion in the processing of step # 05 and step 06 (c), the normal between each point P in the Y-axis direction and the reference axis parallel to the Z-axis The surface shape data of the two-dimensional data structure is generated by coordinate transformation in which the angle θ is rotated at the corresponding curvature center and projected onto the same plane. The center of curvature is calculated for each point P from the shape indicated by the distribution of the point group (height value) in the Y-axis direction indicating the surface shape in the three-dimensional data.

  The curvature acquisition means 18 has a quadratic curve application module and a curvature calculation module. In the processes of the steps # 05 and # 06 (d), surface shape data is given, and a plurality of curvatures corresponding to the shape curve represented by the point group of the surface shape data are generated. The curvature acquisition means 18 applies a quadratic curve corresponding to the shape curve represented by the point group by the quadratic curve application module, the curvature calculation module calculates the curvature from the quadratic curve, and the calculation result is obtained. This is fed back to the detailed shape data generation means 17 and the base shape data generation means 16.

  The quadratic curve application module of the curvature acquisition means 18 uses a point group corresponding to the shape curve of the surface shape data as the sample point P, as shown in FIGS. 10 (a) and 10 (b), and the plurality of sample points P. Among them, three or more sample points P are set. Then, a quadratic curve passing through the vicinity of the point group with the center near the vertex is applied as the approximate curve F, and the curvature is calculated based on the curvature at the center of the sample point P of the applied approximate curve. Since this approximate curve F is a quadratic curve, a shape passing through a position that matches or approximates a plurality of sample points P is set by increasing or decreasing the coefficient value. The quadratic curve can be understood as a function that can be quadratic differentiated.

  When an approximate curve F composed of a quadratic curve is set, if the shape curve represented by the sample point P is convex downward, the coefficient of the approximate curve F is positive “+”, and is convex upward. The coefficient becomes negative “−”. Further, the closer the shape curve represented by the sample point is to a straight line, the closer the coefficient of the approximate curve F is to “0”. Therefore, even at the inflection point where the shape curve represented by the sample point P changes from convex upward to convex downward, it is only necessary to switch the coefficient value from positive to negative. A curve F can be set. In this curvature acquisition means 18, a quadratic curve is used as the approximate curve F. However, for example, a higher-order function curve higher than a cubic function, a sine curve, a hyperbola, or the like may be used.

  In order to obtain the curvature by the curvature calculation module, it is also possible to obtain directly from the degree of curvature at the sample point center portion (near the apex) of the approximate curve. As one specific example, it is conceivable to process the degree of curvature of the approximate curve in numerical form and store it as a table, and obtain the curvature value from the table based on the degree of curvature. Further, as a specific processing form for obtaining the curvature, the curvature calculation module sets the center line Q, and in the direction of the center line Q, the distance from the center sample point P and the direction orthogonal to the center line The center point S is assumed to be a position where the distance to the approximate curve F coincides with the distance to the central sample point P, and the radius of the center point S that is inscribed in the vertex is the curvature. The radius r may be calculated, and the reciprocal of the radius r may be used as the curvature. Thus, in the process of obtaining the curvature, the setting of the center line Q and the setting of the center point S are not limited to those shown in FIG. 10. For example, the center line Q is set between the two sample points P. It is also possible to set an arbitrary one suitable for processing.

  From such processing, the base shape data is composed of a plurality of curvatures, and the detailed shape data is also composed of a plurality of curvatures.

  In this embodiment, the curvature acquisition unit 18 calculates the curvature according to instructions from the exclusion region setting unit 15, the base shape data generation unit 16, and the detailed shape data generation unit 17, but instead, the curvature calculation unit 18 calculates the curvature. The exclusion area setting unit 15, the base shape data generation unit 16, and the detailed shape data generation unit 17 may include a module that performs the same process as the curvature acquisition unit 18, and the module may perform the process of calculating the curvature. .

  When generating the detailed shape data and the base shape data, in order to evaluate the shape of the entire surface of the measurement target T, the three-dimensional data cutout module shifts the cutout area in the Y-axis direction while # 05 and # 06. Step processing is repeated (step # 07). In particular, when shifting the object to be cut out in the Y-axis direction, the processing mode is adopted so that the three-dimensional data cut out first and the three-dimensional data cut out after that do not overlap in the Y-axis direction. The processing mode may be set so as to partially overlap in the Y-axis direction.

  The shape evaluation means 19 obtains a distortion value by comparing the curvatures in the same region in the X-axis direction among the plurality of curvatures of the base shape data and the detailed shape data (step # 08). This distortion value is a simple process of calculating a difference in curvature value, but as shown in FIG. 11, the difference from the detailed shape data curvature is extracted as a distortion value based on the curvature of the base shape data.

[Different processing forms of shape evaluation means]
In particular, in the present invention, when acquiring the strain value, two types of external curves (not shown) assumed from a plurality of curvatures of the base shape data and the detailed shape data are generated by calculation, and the base shape data is obtained. The processing form of the shape evaluation means 19 may be set so as to obtain the distortion value from the difference (offset amount) between the corresponding outer shape curve and the outer shape curve corresponding to the detailed shape data. By adopting this processing mode, the unevenness amount of the detailed shape data based on the base shape data is acquired with a value close to the actual size.

  The evaluation image generation unit 20 acquires shape image data indicating an outline indicating the outer edge of the measurement target T from the measurement target shape acquisition unit 21, and sets an area including the same distortion value with respect to an internal region of the shape image data. Then, for each area, the hue / density corresponding to the distortion value is applied to the area, and this image is displayed on the display d (step # 09).

  As a result of the processing of the evaluation image generating means 20, an image of the shape of the measurement target T is displayed on the display d, and distortion in the protruding direction and depressions are generated with respect to the internal region of the image of this shape. By displaying the distortion of the direction in different hues, and by changing the density and hue according to the amount of distortion, it is possible to visually grasp the area where the distortion exists, the direction of the distortion, and the degree of distortion To do.

[Outline of Embodiment]
As described above, according to the present invention, three-dimensional data is generated from photographing data having a three-dimensional data structure acquired by the photographing unit V, and after removing noise, the shape of a predetermined region of the three-dimensional data is roughly reflected. The base shape data including the curvature group is generated, and the detailed shape data including the curvature group reflecting the shape of the predetermined region of the three-dimensional data in detail is generated. After this, since the distortion value is obtained by comparing the curvatures of the same region in the generated base shape data and detailed shape data, for example, a narrow region of a part of the outer surface shape that becomes a generally gentle convex shape Even when there is a concave distortion, the concave distortion can be accurately detected.

  In addition, when there is distortion in the measurement target T, an image having the same shape as the measurement target T is displayed on the display d, a distortion distribution area is displayed inside the image, and distortion is performed for each area. Since the paint of the hue / density corresponding to is performed, the position where the distortion exists visually can be grasped, and at the same time, the direction of the distortion (convex or concave) and the degree of the distortion can be visually grasped.

[Another embodiment]
In the present invention, the processing form is set so that the detailed shape data and the base shape data are generated directly from the acquired three-dimensional data, whether it is three-dimensional data acquired by photographing or three-dimensional data having an STL file structure. May be set. As a specific example, the detailed shape data generation means 17 acquires the coordinates of a point group having a set width in a predetermined direction among the coordinates of the point group of the three-dimensional data indicating the outer surface shape of the measurement target T. A point group for each first set interval D1 is acquired from the group, an approximate plane is defined from the peripheral point group of this point group by the method of least squares, and the origin 0 of this approximate plane is set.

  Next, a map (two-dimensional data) in which the coordinates of the peripheral point group are reflected on a plane including the Z ′ axis (for example, the Z′-X ′ plane) with the normal direction of the approximate plane as the Z ′ axis. Is generated. By applying an approximate curve to this map and calculating the curvature of the approximate curve, the curvature of the detailed shape data with the origin 0 as the inspection point is obtained. Similarly, the base shape data generating means 16 obtains the curvature of the base shape data with the origin 0 as the inspection point from the point group for each second set interval D2. By this principle, it is possible to obtain the curvature of the detailed shape data and the base shape data based on a plurality of origins 0, and by obtaining and displaying the distortion value from the curvature of the detailed shape data and the base shape data by the same processing as described above. The distortion can be grasped.

  In this alternative embodiment, the process of excluding the excluded area is not described, but the excluded area is set in the three-dimensional data acquired by the base shape data generating unit 16 or the detailed shape data generating unit 17 and excluded from the processing target. If the acquired three-dimensional data may contain noise, the processing form is set so that noise is removed after the base shape data generating unit 16 or the detailed shape data generating unit 17 acquires it. May be.

  INDUSTRIAL APPLICABILITY The present invention can be used for evaluation of three-dimensional data for manufacturing a mold and three-dimensional data for machining with a machine tool.

16 Base shape data generation means 17 Detailed shape data generation means 18 Curvature acquisition means 19 Shape evaluation means F Approximation curve P Sample point

Claims (4)

A shape recognition device for recognizing an outer shape of an object from three-dimensional data representing the outer shape of the object,
Detailed shape data generating means for acquiring, as detailed shape data, a plurality of curvatures reflected in detail by the shape of the setting region of the three-dimensional data;
A base shape data generating means for acquiring, as base shape data, a plurality of curvatures that reflect the shape of the setting region of the three-dimensional data more roughly than the details;
A shape recognition apparatus comprising shape evaluation means for extracting a distortion value based on a curvature in the same region in the three-dimensional data among the detailed shape data and the base shape data. The detailed shape data generating means generates surface shape data having a two-dimensional data structure in which the shape of the setting region of the three-dimensional data is reflected in the setting direction at every first setting interval. The detailed shape data is generated by calculating the curvature of
The base shape data generating means generates surface shape data having a two-dimensional data structure in which the shape of the setting region of the three-dimensional data is reflected every second setting interval wider than the first setting interval in the setting direction. The shape recognition apparatus according to claim 1, wherein the base shape data is generated by calculating a curvature in the setting direction in the shape data.   Curvature acquisition means for calculating curvature from the surface shape data generated by the detailed shape data generation means and the surface shape data generated by the base shape data generation means, the curvature acquisition means comprising the surface shape Of the three or more sample points indicating the height value in the data, a quadratic curve that passes on or near the point cloud with the center near the vertex is applied as an approximate curve, and the sample points of the applied approximate curve The shape recognition apparatus according to claim 2, wherein the curvature is calculated based on a curvature at a central portion. Among the plurality of curvatures acquired from the detailed shape data and the plurality of curvatures acquired from the base shape data, the shape evaluation unit compares the curvatures of the same region in the setting direction, or curves generated from the curvatures The shape recognition apparatus according to any one of claims 1 to 3, wherein the distortion value is extracted by comparing height values.

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