JP6803745B2 - Inspection equipment - Google Patents

Description

The present invention relates to an inspection device for in-core inspection work of a nuclear power plant.

The reactors of nuclear power plants are regularly inspected to maintain the soundness of the equipment inside the reactors. In particular, in the inspection of the inner wall of the shroud, it is necessary to arrange an inspection device in the shroud, and a remote-controlled robot is utilized. The inside of the furnace under inspection is filled with water, and the remote-controlled robot equipped with the inspection device is a type that swims underwater. Position estimation of the remote-controlled robot is indispensable for improving the accuracy of inspection or reducing the workload of inspectors. An optical imager, an inertial sensor, or the like is used to detect the position of the underwater robot. A typical conventional method is a stereo measurement method using a plurality of optical imagers. In the stereo measurement method, feature points common to images taken by a plurality of optical imagers are extracted, and the self-position of the robot and the position of the feature points in the surrounding environment are calculated by the triangulation method.

In the stereo measurement method, since the robot self-position and the feature point position in the surrounding environment are calculated from the feature points common to the images, the field of view capable of stereo measurement is limited to the overlapping region of the fields of view of the plurality of imagers. Therefore, for example, there is a proposal of Japanese Patent Application Laid-Open No. 2011-123051. In this patent, when performing stereo measurement with a plurality of imagers, a plane including a corresponding point on the surface of the first imager and a focal position of the imager for a feature point not imaged by the second imager. A method of calculating three-dimensional coordinates using a second imager plane and a group of feature points on the second imager image is described.

Japanese Unexamined Patent Publication No. 2011-123051

In order to improve the accuracy of the robot self-position calculation, it is desirable to be able to calculate the feature point coordinates over a wider area and suppress the number of repetitions of the self-position calculation due to the sequential movement of the robot. In the method described in Patent Document 1 described above, a method of calculating the three-dimensional coordinates of a feature point that is out of the field of view with one of the imagers is described, but the feature of the repeating structure is used. There is no description about the wide-area three-dimensional coordinate calculation method.

Therefore, the present invention has been made in view of such circumstances, and an object of the present invention is to utilize the characteristics of the repeating structure to widen the range in which stereo measurement can be performed by a plurality of imagers, and to calculate the robot self-position. The purpose is to provide a method for improving accuracy.

In order to solve the above problems, the present invention extracts line segments and extension lines of repetitive feature points from the captured image, calculates the intersection coordinates of the plurality of line segments and extension lines, and uses the coordinates of the plurality of intersection points of the inspection device. It is characterized in that the coordinates of its own position and peripheral feature points are calculated.

According to the present invention, the coordinate calculation range of the peripheral feature points is expanded, and the self-position calculation can be calculated with high accuracy by suppressing the number of repetitions of the self-position calculation.

It is a figure which shows schematic the whole structure of the inspection apparatus of Example 1 and the reactor to which it applies. It is a figure which shows schematic structure of the control apparatus of Example 1. FIG. It is a flow chart which shows the detail of each function in the inspection apparatus of Example 1. FIG. It is a figure which shows typically the apparatus for demonstrating the method of calculating a position using a plurality of imagers. It is a figure which shows typically the captured image for demonstrating the method of calculating a position using a plurality of imagers. It is a figure which shows schematic about the apparatus and the reference structure for demonstrating the position calculation before moving an imager. It is a figure which shows schematic the image | captured image for demonstrating the position calculation before moving an imager. It is a figure which shows schematicly about the apparatus and the reference structure for demonstrating the position calculation after moving an imager. It is a figure which shows schematic the image | image for explaining the position calculation after moving the imager. It is a figure which shows the feature point which can be extracted from the captured image by a conventional method. It is a figure which shows the feature point which can be extracted from the captured image using the virtual extension line by the proposed method. It is a figure which shows the feature point which can be extracted from the captured image by the proposed method using the repeat feature shape extension line. It is a figure which shows schematicly about the apparatus and the reference structure for explaining the proposed method about the position calculation before moving an imager. It is a figure which shows typically the captured image for demonstrating the proposed method about the position calculation before moving an imager. It is a figure which shows schematic about the apparatus and the reference structure for demonstrating the proposed method about the position calculation after moving an imager. It is a figure which shows typically the image | photographed image for explaining the proposed method about the position calculation after moving an imager. It is a figure for demonstrating the method of calculating a virtual feature point from a polygonal image. It is a figure for demonstrating the method of calculating a virtual feature point from a circular photograph image. It is a figure for demonstrating the method of calculating a virtual feature point from an elliptical image.

Hereinafter, examples will be described with reference to the drawings.

The outline of the confirmation method using the apparatus will be described with reference to FIGS. 1 to 16.

FIG. 1 is a diagram schematically showing the overall configuration of the inspection apparatus and the reactor to be applied in the first embodiment. The reactor 1 has structures such as a shroud 2, an upper lattice plate 3, a core support plate 4, and a shroud support 5, and pipes such as PLR pipe 6 are connected to the reactor 1. Further, an operation floor 7, which is a work space, is located above the reactor 1, and a refueling carriage 8 is installed directly above the core. When carrying out a visual inspection of the inside of the reactor 1, the underwater inspection device 9 is turned on, and the inspector 14 on the refueling carriage 8 operates the controller 13 while confirming the position of the underwater inspection device 9 on the display device 12. .. The underwater inspection device 9 is controlled by the control device 11 and is connected via the cable 10. The display device 12 and the controller 13 are connected to the control device 11.

FIG. 2 is a diagram schematically showing the configuration of the control device 11 in this embodiment. The image acquired by the imager 15 mounted on the underwater inspection device 9 is taken into the control device 11 by the image acquisition unit 16. The acquired image is sent to the line segment extraction processing unit 17, and the line segment region in the image is extracted by a specific determination. Further, the feature point calculation processing unit 18 extracts the intersection of the line segments as the feature point. The position calculation processing unit 19 calculates the position of the imager 15 by using the plurality of feature points extracted from the image and the position information of the peripheral structures stored in the first storage unit 21. The calculated position information is displayed on the display device 12 or stored in the second storage unit 24. Further, the mapping processing unit 20 additionally updates the position information of the peripheral structure stored in the first storage unit 21 by using the imager position calculated by the position calculation processing unit 19 and the feature points in the image. On the other hand, the inspection data acquired by the sensor 22 is sent to the inspection data acquisition unit 23, and is displayed on the display device 12 or stored in the second storage unit 24 together with the position information calculated by the position calculation processing unit 19. Will be done.

FIG. 3 is a flow chart showing details of each function in the inspection device of this embodiment. When the inspection is started in the inspection process start step S101, the robot starts moving toward the arrangement location of the position reference structure in the robot movement step S102. When the robot is stationary around the position reference structure that is the reference for position calculation in the reference position rest step S103, the image acquisition by the imager 15 mounted on the robot is performed in the image acquisition step S104. The acquired image has feature points extracted in the plurality of feature point selection steps S105. In the robot position calculation step S106, the position of the imager 15 in the world coordinates is calculated from the imager coordinates of the plurality of feature points extracted in the plurality of feature point selection steps S105 and the world coordinates of the feature points. In the peripheral feature point extraction step S107, the world coordinates of the peripheral feature points are calculated from the world coordinates of the imager 15 and the feature points newly extracted from the acquired image. In the robot position determination step S108, it is determined whether or not the robot position is the place to perform the inspection, and if it is the place to be inspected, the process proceeds to the inspection data acquisition step S109, and various inspections are performed by the sensor 22. If the data is acquired and it is not the place to be inspected, the process proceeds to the robot movement step S111, and the movement of the robot is continued. When the acquisition of the inspection data is completed in the inspection data acquisition step S109, the process proceeds to the inspection process determination step S110, and it is determined whether or not all the inspection processes have been completed. When the inspection is completed, the process proceeds to the robot recovery step S113, and the robot is recovered on the refueling carriage 8. If the inspection is not completed, the process proceeds to the robot movement step S111, and the movement of the robot is continued. In the robot movement step S111, the movement of the robot is restarted, and in the arbitrary position rest step S112, the robot is stopped. After the robot is stationary, the process proceeds to the image acquisition step S104, and thereafter, the robot position calculation step, the peripheral feature point position calculation step, and the inspection data acquisition step are repeated until all the inspection steps are completed. When the inspection is completed and the robot is recovered in the robot recovery step S113, the process proceeds to the inspection completion step S114 to complete the inspection.

Hereinafter, the details of each step will be described individually.

First, the details of image acquisition by the imager 15 and calculation of the imager coordinates of the feature points will be described with reference to FIGS. 4 to 5. FIG. 4 is a diagram schematically showing an apparatus for explaining a method of calculating a position using a plurality of imagers, and shows an imaging relationship of feature points in the imager. The spatial feature points 27a are imaged as feature points 27A on the image on the optical element 26 via the lens 25 in the two left and right imagers 15. The coordinate axes of the imager 15 are such that the imager arrangement direction is the x-axis, the direction orthogonal to the imager arrangement is the y-axis, and the image pickup direction by the imager is the z-axis. FIG. 5 is a diagram schematically showing captured images for explaining a method of calculating a position using a plurality of imagers, and shows images captured by two left and right imagers 15. Feature points 27A are imaged on the left imager image 28L and the right imager image 28R, respectively. When the origin of the imager coordinates is the lens center position of the left imager, the coordinates (X, Y, Z) in the imager coordinates of the spatial feature point 27a are expressed by the following equations (1) to (3). Will be done.

Here, B is the distance between the left and right imagers, dx1 is the distance from the vertical center position of the left imager image 28L to the feature point 27A on the image, and dx2 is the distance from the vertical center position of the right imager image 28R on the image. The distance to the feature point 27A, dy is the distance from the horizontal center position of the left imager image 28L and the right imager image 28R to the feature point 27A on the image, and L is the focal length of the lens 25. In the plurality of feature point selection step S105, three or more spatial feature points 27a are selected, and the coordinates in the imager coordinates are calculated for each. It should be noted that the spatial feature point 27a refers to a point that is uniquely defined in space and is captured in the image as the same point by the two left and right imagers, such as the intersection of the grid shapes described later. The feature points are not limited to the intersections of the grid shape, but the points in space such as the points where straight lines such as the corners of the structure intersect, the points drawn on the structure, and the center point of the circle are uniquely determined. Any point is fine.

Next, the details of the processing in the robot position calculation step S106 will be described. The position of the world coordinates of the imager 15 is calculated by using three or more spatial feature points 27a whose world coordinates are known in advance. Let the world coordinates of the three spatial feature points 27a be (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3), respectively. Further, the imager coordinates when the three spatial feature points 27a are photographed by the imager 15 are set to (X1, Y1, Z1), (X2, Y2, Z2), and (X3, Y3, Z3), respectively. The conversion between the world coordinates and the imager coordinates at this time is represented by the following determinants (4) to (6).

Here, R is a rotation matrix and T is a translation matrix. φ is roll rotation (rotation around z axis), θ is pitch rotation (rotation around y axis), ψ is yaw rotation (rotation around x axis), tx is movement in x axis direction, ty is movement in y axis direction, tz is z Represents axial movement. The position of the imager 15 in world coordinates is represented by (tx, ty, tz) using a translation matrix. When the imager position is obtained from n feature points, which are three or more points, the sum of squares of each matrix component is minimized in the matrix A represented by the following equation.

By the above method, the position of the imager 15 in the world coordinates can be obtained. Next, the details of the processing in the peripheral feature point extraction step S107 will be described. After determining the position of the imager 15, a new peripheral feature point is selected on the captured image. This feature point does not need to have world coordinates known in advance. For the feature points selected on the left and right captured images, the imager coordinates (X4, Y4, Z4) can be obtained using the equations (1) to (3). Then, using the imager coordinates and the determinant represented by the equation (7), the world coordinates (x4, y4, z4) of the feature points are obtained by the following equation (8).

By the above method, the position of the peripheral feature points in the world coordinates can be obtained. The feature points for which the world coordinates have been obtained are stored in the first storage unit 21, and an image is acquired in a state after the imager has moved through the robot moving step S111 and the arbitrary position resting step S112, and a plurality of features are obtained. In the point selection step S105, when the spatial feature point 27a is selected, it is used as the spatial feature point 27a whose world coordinates are known in advance.

Next, using FIGS. 6 to 9, when the virtual feature points proposed in the present invention are not used, the peripheral feature points are taken from the image acquisition step S104 through the robot moving step S111 and the arbitrary position resting step S112. Details of the case where the extraction step S107 is repeated will be described. FIG. 6 is a diagram schematically showing an apparatus and a reference structure for explaining the position calculation before the imager moves, and shows the appearance of the apparatus arrangement before the imager 15 moves. Spatial feature points 27a, 27b, and 27c are spatial feature points whose world coordinates are known, and are located in overlapping regions of the imaging regions 29 of the left and right imagers 15. FIG. 7 is a diagram schematically showing a captured image for explaining the position calculation before the imager moves, and shows the captured images of the two left and right imagers 15. Using the feature points 27A, 27B, and 27C on the images captured in the left imager image 28L and the right imager image 28R, the positions on the spatial coordinates of the imager 15 according to equations (1) to (6). Ask for. After that, the feature points 27D and 27E on the image were selected in the left imager image 28L and the right imager image 28R, respectively, and were unknown until then by the formulas (1) to (3) and (7). The world coordinates of the spatial feature points 27d and 27e are obtained. After that, the imager 15 moves in the y-axis direction of FIG.

FIG. 8 is a diagram schematically showing an apparatus and a reference structure for explaining the position calculation after the imager has moved, and shows the appearance of the apparatus arrangement after the imager 15 has moved. The moving distance of the imager 15 so that the overlapping area of the imaging area 29 of the imager 15 includes the spatial feature points 27c whose world coordinates are known and the spatial feature points 27d and 27e whose world coordinates are obtained before the movement. Is adjusted. FIG. 9 is a diagram schematically showing a captured image for explaining the position calculation after the imager has moved, and shows the captured images of the two left and right imagers 15. The equations (1) to (6) are used by using the feature points 27C, 27D, and 27E on the images captured in the left imager image 28L and the right imager image 28R as before the imager 15 is moved. The position on the spatial coordinates after the movement of the imager 15 is obtained by. After that, the feature points 27F, 27G, and 27H on the images are selected in the left imager image 28L and the right imager image 28R, respectively, and the spatial feature points 27f, according to the equations (1) to (3) and (7), Find the world coordinates of 27g and 27h. After that, by repeating this procedure, the position of the imager and the coordinates of the feature points of the surrounding structures are alternately calculated, and the robot position until the end of the inspection is sequentially calculated.

The details of the method of obtaining the virtual feature point proposed in the present invention will be described with reference to FIGS. 10 to 12. FIG. 10 is a diagram showing feature points that can be extracted from a captured image by a conventional method, and shows the feature points that can be extracted when the virtual feature points proposed by the present invention are not used. In the captured image of the reference structure having a regular grid shape, the intersection of the grids in the captured image 28 indicated by the black circle in FIG. 10 can be extracted as a feature point. As a method of extracting intersections, extraction by image processing is possible. For example, in the acquired image, the binarization process extracts a plurality of straight line portions of lattices having different brightness in the image, and the thinning process extracts the central portion of the extracted straight line region and approximates the extracted linear central region. By fitting a straight line and calculating the intersection of approximate straight lines, the lattice intersection can be extracted as a feature point. The method of extracting the lattice intersections is not limited to this method, and the processing may be performed by using another image processing means that can obtain the same effect. Further, the method of extracting the lattice intersections is not limited to automatic extraction by image processing, and for example, intersections may be extracted by visual inspection by a person and manual image position selection. Further, the visibility improving process or the image correction process may be applied as necessary before performing the feature point extraction process. Visibility improving processing includes, for example, correction of luminance distribution by contrast correction processing and correction of defocus by focus correction processing and the like. The image correction process includes correction of image distortion by lens distortion correction processing or the like. Here, the details of each process are omitted.

FIG. 11 is a diagram showing feature points that can be extracted from the captured image using the virtual extension line by the proposed method, and shows the feature points that can be extracted when the virtual extension line of the grid line is used and the virtual feature points. It shows. When the grid shape of the reference structure is composed of straight lines, the intersections of the grids also exist on the extension lines of the straight lines imaged on the image. By extending the linear component on the captured image 28 and assuming the virtual extension line 31 shown by the long broken line in FIG. 11, the virtual feature point 30 which is the intersection thereof can be extracted.

FIG. 12 is a diagram showing feature points that can be extracted from the captured image using the repeat feature shape extension line by the proposed method, and is extracted when the virtual extension line of the lattice straight line and the extension line of the repeat feature shape are used. It shows possible feature points and virtual feature points. When the grid shape of the reference structure is configured at equal intervals, there are grid intersections on the extension of the straight line passing through the plurality of grid intersections for the plurality of grid intersections that can be extracted as feature points. By extending the straight line passing through the grid intersection on the captured image 28 and assuming the repeating feature shape extension line 32 shown by the short broken line in FIG. 12, the intersection between the virtual extension lines 31 and the virtual extension line 31 and the repeating feature The virtual feature point 30 including the intersection of the shape extension lines 32 can be extracted. By the above method, it is possible to extract virtual feature points outside the range of the captured image and use them as feature points used for position calculation.

Next, when the virtual feature points proposed in the present invention are used with reference to FIGS. 13 to 16, the peripheral feature points are taken from the image acquisition step S104 through the robot moving step S111 and the arbitrary position resting step S112. Details of the case where the extraction step S107 is repeated will be described. FIG. 13 is a diagram schematically showing a device and a reference structure for explaining the proposed method for position calculation before the imager moves, and shows the appearance of the device arrangement before the imager 15 moves. .. Spatial feature points 27a, 27b, and 27c are spatial feature points whose world coordinates are known, and are located in overlapping regions of the imaging regions 29 of the left and right imagers 15.

FIG. 14 is a diagram schematically showing an image captured for explaining the proposed method for position calculation before the imager moves, and shows images captured by the two left and right imagers 15. Using the feature points 27A, 27B, and 27C on the images captured in the left imager image 28L and the right imager image 28R, the positions on the spatial coordinates of the imager 15 according to equations (1) to (6). Ask for. After that, in the left imager image 28L and the right imager image 28R, the feature points 27D, 27E, and 27F are selected on the image by using the virtual feature point extraction method, and the formulas (1) to (3), respectively, are selected. The world coordinates of the previously unknown spatial feature points 27d, 27e, and 27f are obtained by the equation (7). After that, the imager 15 moves in the y-axis direction of FIG.

FIG. 15 is a diagram schematically showing a device and a reference structure for explaining the proposed method for position calculation after the imager has moved, and shows the appearance of the device arrangement after the imager 15 has moved. .. The moving distance of the imager 15 is adjusted so that the range in which the coordinates are obtained from the image captured by the imager 15 includes the spatial feature points 27d, 27e, and 27f whose world coordinates are obtained before the movement.

FIG. 16 is a diagram schematically showing an image captured for explaining the proposed method for position calculation after the imager has moved, and shows images captured by the two left and right imagers 15. After moving the imager 15 according to equations (1) to (6) using the feature points 27D, 27E, and 27F on the image whose coordinates are obtained from the captured image in the left imager image 28L and the right imager image 28R. Find the position on the spatial coordinates of. After that, the feature points 27G, 27H, and 27I on the image are selected in the left imager image 28L and the right imager image 28R, respectively, and the spatial feature points 27g according to the equations (1) to (3) and (7). Find the world coordinates of 27h and 27i. After that, by repeating this procedure, the position of the imager and the coordinates of the feature points of the surrounding structures are alternately calculated, and the robot position until the end of the inspection is sequentially calculated.
In the position calculation using the virtual feature points shown in FIGS. 13 to 16, the imager between the steps for calculating the imager position is compared with the case where the virtual feature points shown in FIGS. 6 to 9 are not used. The movement distance becomes large, and the number of times the robot position is calculated until the inspection is completed decreases. On the other hand, in the calculation of the imager position and the feature point coordinates of the peripheral structure, an error occurs due to the pixel size of the imager. This error occurs every time the robot position calculation calculation is performed, and accumulates in proportion to the number of calculations by repeating the movement and calculation of the robot. Therefore, as the number of calculations decreases, the number of times of error accumulation also decreases, and as a result, highly accurate position detection is achieved.

By the above method, the robot self-position calculation in the inspection process can be performed with high accuracy.

In this embodiment, a feature point whose world coordinates are known and a feature point whose world coordinates are calculated in the peripheral feature point extraction step S107 are illustrated and described as being located on the same grid shape. However, the feature points for which the world coordinates are calculated in the peripheral feature point extraction step S107 do not have to be on the same grid shape as the known feature points, and the grid spacing, grid orientation, and grid angle of the grid shape. Do not have to be the same. Further, the repeating structure to which the repeating feature shape extension line 32 is applied is not limited to a lattice shape as long as it has a periodic feature shape, for example, a central point of a circular shape arranged periodically, a tangent line of the circumference, and the like. , A similar one may be used.

Hereinafter, the second embodiment will be described with reference to FIGS. 17 to 19.

The inspection process according to this embodiment is the same as the process shown in FIG. 3 of Example 1. In this embodiment, virtual feature points outside the field of view of the captured image are extracted by a method different from the feature point extraction method shown in Example 1.

FIG. 17 is a diagram for explaining a method of calculating virtual feature points from a polygonal captured image, and specifically describes a method of extracting out-of-field feature points in a regular octagonal feature pattern. In the captured image 28, when the feature points 27A, 27B, and 27C on the image, which are the vertices of the regular octagon, are imaged, the feature points on the image outside the field of view are formed by fitting the geometric shape fitting virtual line 33 having the regular octagon shape. It is possible to calculate the coordinates of 27D, 27E, 27F, 27G, 27H. Although a regular octagon is shown here as an example of a regular polygon, the feature pattern is not limited to this, and can be applied to feature point calculation using other regular polygon feature patterns.

FIG. 18 is a diagram for explaining a method of calculating virtual feature points from a circular captured image, and specifically describes a method of extracting out-of-field feature points on the circumference of a circular feature pattern. When the feature points 27A, 27B, and 27C on the image are located on the circumference, the feature point 27O on the image, which is the center coordinate of the circle, is used as the axis by fitting the geometric shape fitting virtual line 33 which is a circle shape. It is possible to calculate the coordinates of the feature points 27E, 27F, and 27G on the image outside the field of view, which are point-symmetrical positions. Further, when the feature points 27A, 27B and 27C on the image have a repeating structure and are located at equal intervals, the coordinates of the feature points 27D and 27H on the image outside the field of view can be calculated.

FIG. 19 is a diagram for explaining a method of calculating virtual feature points from an elliptical captured image, and specifically describes a method of extracting out-of-field feature points on the elliptical circumference in an elliptical feature pattern. is there. When the feature points 27A, 27B, and 27C on the image are located on the circumference of the ellipse, the feature point 27O on the image, which is the center coordinate of the ellipse, is used as the axis by fitting the geometric shape fitting virtual line 33 which is an ellipse. It is possible to calculate the coordinates of the feature points 27E, 27F, and 27G on the image outside the field of view, which are the point-symmetrical positions. Further, it is possible to calculate the coordinates of the feature points 27D and 27H on the image outside the field of view, where the feature points 27B on the image are symmetrical with respect to the elliptical major axis or the elliptical minor axis.

The present invention is not limited to the above-mentioned examples, and includes various modifications. For example, the above-described embodiment has been described in detail in order to explain the present invention in an easy-to-understand manner, and is not necessarily limited to those having all the described configurations. Further, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Further, it is possible to add / delete / replace a part of the configuration of each embodiment with another configuration.

1: Reactor 2: Shroud 3: Upper lattice plate 4: Core support plate 5: Shroud support 6: PLR piping 7: Operation floor 8: Refueling trolley 9: Underwater inspection device 10: Cable 11: Control device 12: Display device 13: Controller 14: Inspector 15: Imager 16: Image acquisition unit 17: Line segment extraction processing unit 18: Feature point calculation processing unit 19: Position calculation processing unit 20: Mapping processing unit 21: First storage unit 22: Sensor 23: Inspection data acquisition unit 24: Second storage unit 25: Lens 26: Optical elements 27a, 27b, 27c, 27d, 27e, 27f, 27g, 27h, 27i: Spatial feature points 27A, 27B, 27C, 27D, 27E, 27F, 27G, 27H, 27I, 27O: Feature points on the image 28: Captured image 28L: Left imager captured image 28R: Right imager captured image 29: Imaging area 30: Virtual feature point 31: Virtual extension line 32: Repeated feature Shape extension line 33: Geometric shape fitting virtual line S101: Inspection process start step S102: Robot movement step S103: Reference position stationary step S104: Image acquisition step S105: Multiple feature point selection step S106: Robot position calculation step S107: Peripheral feature point Extraction step S108: Robot position determination step S109: Inspection data acquisition step S110: Inspection process determination step S111: Robot movement step S112: Arbitrary position stationary step S113: Robot recovery step S114: Inspection completion step

Claims (6)

An inspection device that travels remotely in a structure
An imaging unit that captures the surrounding environment and
An image processing unit that extracts a plurality of line segments or extension lines of repeated feature shapes from the image acquired by the imaging unit, and an image processing unit.
A feature point calculation unit for obtaining the camera coordinates of a plurality of intersections from a plurality of line segment data or extension line data obtained by the image processing unit, and
A position calculation unit that calculates the world coordinates of the inspection device from the camera coordinates of a plurality of intersections calculated by the feature point calculation unit, and
A mapping processing unit that obtains the world coordinates of peripheral feature points of the inspection device from the camera coordinates of the intersections obtained by the feature point calculation unit and the world coordinates of the inspection device obtained by the position calculation unit.
An inspection device including a display device for displaying the peripheral feature points and a position calculation result of the inspection device. In the inspection device according to claim 1,
An inspection apparatus characterized in that the image processing unit extracts a line segment forming a repeating grid shape or an extension line of a repeating grid intersection from an image acquired by the imaging unit. In the inspection device according to claim 1,
In the image processing unit, the vertices constituting the regular polygon geometry are extracted from the image acquired by the imaging unit, and in the feature point calculation unit, the vertex coordinates obtained by the image processing unit and the regular polygon geometry fitting are used. An inspection device characterized by obtaining the coordinates of regular polygon vertices. In the inspection device according to claim 1,
The image processing unit extracts vertices located on the circumference of the elliptical geometry from the image acquired by the imaging unit, and the feature point calculation unit extracts the vertex coordinates obtained by the image processing unit and the elliptical circumference by fitting the elliptical geometry. An inspection device characterized by obtaining the coordinates of the upper vertex. In the inspection device according to claim 4,
An inspection device characterized in that the elliptic geometry is a circle. It is an inspection method in an inspection device that travels remotely in a structure.
Steps to move a robot equipped with an imaging unit that captures the surrounding environment,
A step of extracting a plurality of line segments or extension lines of a repeating feature shape from the image acquired by the imaging unit, and
The step of obtaining the camera coordinates of a plurality of intersections from the plurality of line segment data or extension line data, and
The step of obtaining the world coordinates of the inspection device from the camera coordinates of the plurality of intersections obtained,
A step of obtaining the world coordinates of peripheral feature points of the inspection device from the photographer coordinates of the obtained intersection and the world coordinates of the obtained inspection device.
An inspection method comprising: a display step for displaying the peripheral feature points and the position calculation result of the inspection device.

Images