JP5620200B2 - Point cloud position data processing device, point cloud position data processing method, point cloud position data processing system, and point cloud position data processing program - Google Patents

Description

  The present invention relates to a three-dimensional point cloud position data processing technique, and more particularly to a technique for efficiently integrating three-dimensional point cloud position data obtained from different viewpoints.

  A technique for generating a three-dimensional shape from three-dimensional point cloud position data of a measurement object is known. In the three-dimensional point cloud position data, a two-dimensional image and a three-dimensional coordinate are linked. That is, in the three-dimensional point cloud position data, the data of the two-dimensional image of the measurement object, a plurality of measurement points (point cloud) associated with the two-dimensional image, and the three-dimensional space of the plurality of measurement points in the three-dimensional space. Are associated with each other (three-dimensional coordinates). According to the three-dimensional point cloud position data, it is possible to obtain a three-dimensional model that reproduces the outer shape of the measurement object using a set of points. In addition, since the three-dimensional coordinates of each point are known, the relative positional relationship of each point in the three-dimensional space can be grasped, and the image of the three-dimensional model displayed on the screen can be rotated or switched to an image viewed from a different viewpoint. Processing is possible.

  For example, in the invention described in Patent Document 1, a scanning laser device scans a three-dimensional object to generate a point cloud. The point cloud is divided into groups of edge points and non-edge points based on depth and normal changes with respect to the scan points. A three-dimensional shape is generated by fitting each group to a geometric original drawing and expanding and intersecting the fitted geometric original drawing.

  In the invention described in Patent Document 2, a segment (triangular polygon) is formed from three-dimensional point cloud position data, and edges and surfaces are extracted based on continuity, normal direction, or distance between adjacent polygons. Further, the planarity or curvedness of the three-dimensional point cloud position data of each segment is replaced with a planar equation or a curved surface equation using a least square method, and grouping is performed to generate a three-dimensional shape.

  In the invention described in Patent Document 3, a two-dimensional rectangular area is set for three-dimensional point cloud position data, and a combined normal vector of measurement points corresponding to the rectangular area is obtained. Then, all measurement points in the rectangular area are rotationally moved so that the combined normal vector coincides with the Z-axis direction. Further, the standard deviation σ of the Z value is obtained for each measurement point in the rectangular area, and when the standard deviation σ exceeds a predetermined value, the measurement point corresponding to the center point of the rectangular area is handled as noise.

Special Table 2000-509150 Japanese Patent Laid-Open No. 2004-272459 JP 2005-024370 A

  When three-dimensional point cloud position data is obtained using a laser scanner, the three-dimensional point cloud position data behind a certain object cannot be obtained because it becomes a shadow as viewed from the laser scanner. The phenomenon in which this shadow occurs is called occlusion. The 3D point cloud position data of the part that could not be acquired as a shadow can be obtained by changing the viewpoint to a position where the laser beam can be applied to the part that was previously a shadow, and performing another scanning. it can.

  However, in order to eliminate occlusion by this method, a process for performing alignment for handling tens of thousands to hundreds of millions of 3D point cloud position data obtained from two viewpoints in a common coordinate system. Is required. This process is complicated and requires a long processing time. In such a background, an object of the present invention is to provide a technique for improving the efficiency of processing for handling three-dimensional point cloud position data obtained from different viewpoints.

The invention according to claim 1 is a point cloud position data acquisition unit that acquires three-dimensional point cloud position data of a measurement object at a first viewpoint, and the measurement object at a second viewpoint different from the first viewpoint. an image data acquisition unit for acquiring image data of the object, the corresponding points in a common region on the image of the measurement object as viewed from the measurement object and the second viewpoint as seen from the first viewpoint By specifying the corresponding relationship between the corresponding point specifying unit to be specified, the three-dimensional point group position data of the corresponding point at the first viewpoint and the screen coordinate value of the corresponding point at the second viewpoint , A point cloud position data processing apparatus comprising: a coordinate calculation unit that performs a calculation for obtaining a three-dimensional position of the second viewpoint associated with the coordinate system at the first viewpoint.

  In the above invention, the common area on the image is the same part of the measurement object (a part that appears to overlap) when viewed from the first viewpoint and when viewed from the second viewpoint. . The images to be compared here include a combination of images obtained by photographing, a three-dimensional model image viewed from the first viewpoint, and a captured image viewed from the second viewpoint. Note that an image using the three-dimensional point cloud position data obtained from the first viewpoint as pixel information can be used instead of the image photographed from the first viewpoint. Further, an image obtained by combining an image photographed from the first viewpoint and a three-dimensional model based on the three-dimensional point cloud position data obtained from the first viewpoint can be used as an image viewed from the first viewpoint.

  Here, the three-dimensional point group position data includes three-dimensional coordinate data at each measurement point of the measurement object. An orthogonal coordinate system or a polar coordinate system is adopted as a coordinate system for displaying three-dimensional coordinates. The image data is image data obtained by photographing with a CCD or the like, or information on the intensity of reflected light from each measurement point and the color and density of each measurement point obtained when acquiring three-dimensional point cloud position data. This is image data in which pixels are configured based on the above.

  In the present invention, corresponding points are designated in the common area, and the three-dimensional point cloud position data of the measuring object obtained from the first viewpoint at the corresponding points and the measuring object obtained from the second viewpoint are used. Based on the screen coordinate values (coordinate data of image elements constituting the image on the screen), the three-dimensional coordinates of the second viewpoint are calculated. Here, the three-dimensional coordinate of the second viewpoint is calculated as a three-dimensional position associated with a coordinate system that handles the three-dimensional point cloud position data obtained from the first viewpoint. The 3D position associated with the coordinate system that handles the 3D point cloud position data obtained from the first viewpoint is specified on the coordinate system that handles the 3D point cloud position data obtained from the first viewpoint. It is the value of 3D coordinates that can be. This association may be direct or via some conversion formula.

  The acquisition of the three-dimensional point cloud position data of the entire measurement object takes longer as the measurement object becomes larger, and the time required for the processing becomes longer. According to the first aspect of the present invention, the image data obtained by shooting from the second viewpoint and the first viewpoint before obtaining the three-dimensional point cloud position data from the second viewpoint. Based on the three-dimensional point group position data obtained from the above, the position of the second viewpoint that can be specified in association with the coordinate system that handles the three-dimensional point group position data is calculated. According to this configuration, it is possible to specify the position of the second viewpoint on the coordinate system used for the first viewpoint. For this reason, the alignment of the 3D point cloud position data of the measurement object obtained from the second viewpoint later with the 3D point cloud position data already obtained from the first viewpoint is the two alignments. This is easier than in the case of alignment by comparing the three-dimensional point cloud position data.

  That is, when the 3D point cloud position data of the measurement object acquired at the first viewpoint and the 3D point cloud position data of the measurement object acquired at the second viewpoint are handled in an integrated manner, It is necessary to compare the 3D point cloud position data, clarify the correspondence, and clarify which one of the 3D point cloud position data corresponds to the other 3D point cloud position data. There is. When the configuration of the present invention is not adopted, three-dimensional point cloud position data from both viewpoints is acquired, and thereafter, both the three-dimensional point cloud position data are compared with each other to calculate the correspondence between them. However, this method requires time and effort to acquire a huge number of three-dimensional point cloud position data from the second viewpoint, and further, enormous calculation by comparing the subsequent three-dimensional point cloud position data. Is required.

  According to the present invention, the screen in the image data obtained by shooting from the second viewpoint (this is a momentary work compared to the acquisition of the 3D point cloud position data) by designating the corresponding points. Based on the coordinates and the three-dimensional point cloud position data obtained at the first viewpoint, the coordinates of the second viewpoint are specified on the coordinate system at the first viewpoint. On the other hand, the 3D point cloud position data obtained from the second viewpoint is acquired as being associated with the coordinates of the second viewpoint (for example, using the second viewpoint as the origin). Therefore, when acquiring the 3D point cloud position data from the second viewpoint of the measurement object, to handle the 3D point cloud position data in which the position of the second viewpoint is obtained in advance from the first viewpoint. If the three-dimensional point group position data are compared with each other, the processing for obtaining the correspondence between the three-dimensional point group position data is simplified as compared with the case where the two three-dimensional point group position data are compared with each other.

  The calculation of the three-dimensional position of the second viewpoint associated with the coordinate system in the first viewpoint first calculates the correspondence between the first viewpoint and the second viewpoint, and based on this correspondence, This is performed by associating the position of the viewpoint with the coordinate system of the first viewpoint (for example, specifying the position of the second viewpoint on the coordinate system of the first viewpoint). Examples of the method for calculating the correspondence include single photo orientation, mutual orientation, and absolute orientation. These methods can also be used in combination.

  Note that the number of corresponding points required for the calculation varies depending on the method. For example, 3 or more points are required for absolute orientation, 4 or more for single photo orientation, and 6 or more for relative orientation. In addition, a stereo matching method can be used to automatically detect and identify corresponding points. In this case, a certain number of corresponding points can be acquired automatically.

  The invention according to claim 2 is the invention according to claim 1, based on the three-dimensional point cloud position data of the measurement object obtained from the first viewpoint, or obtained from the first viewpoint. Further, based on the 3D point cloud position data of the measurement object and the 3D point cloud position data of the measurement object obtained from the second viewpoint, the measurement object viewed from the second viewpoint A three-dimensional model forming unit for forming a three-dimensional model is provided.

  According to the second aspect of the present invention, a three-dimensional model of the measurement object viewed from the second viewpoint can be obtained. Here, the three-dimensional model means a three-dimensional diagram (three-dimensional representation of the contour of the object) created based on the contour information. Here, the three-dimensional model viewed from the second viewpoint includes the three-dimensional model based on the three-dimensional point cloud position data of the measurement object obtained from the first viewpoint, and the measurement obtained from the first viewpoint. There are two types of three-dimensional models based on the three-dimensional point cloud position data of the object and the three-dimensional point cloud position data of the measurement object obtained from the second viewpoint. In the former three-dimensional model, since the three-dimensional point cloud position data from the second viewpoint is missing, the part of the three-dimensional model that is occluded in the first viewpoint is lost. The latter three-dimensional model is occluded from the first viewpoint, but a portion of the three-dimensional model that is not occluded from the second viewpoint is obtained.

  According to a third aspect of the present invention, in the first aspect of the invention, the three-dimensional model forming unit is a three-dimensional unit based on three-dimensional point cloud position data of the measurement object obtained from the first viewpoint. A first viewpoint three-dimensional model that is a model is formed, and the first viewpoint three-dimensional model or the first viewpoint image that is an image of the measurement object obtained from the first viewpoint and the second viewpoint. The second viewpoint image that is the obtained image is displayed, and control is performed to display the corresponding point in at least one of the first viewpoint three-dimensional model, the first viewpoint image, and the second viewpoint image. A display control unit is provided.

  According to the invention described in claim 3, the image of the measurement object or the three-dimensional model viewed from the first viewpoint on the image display device such as a liquid crystal display, and the measurement object viewed from the second viewpoint. An image of an object is displayed, and a corresponding point in at least one of them is displayed. According to this configuration, it is possible to visually grasp the appearance when the user views the measurement target object from the position of the corresponding point or from both viewpoints. For this reason, it is possible to more quickly and with good operability when manually specifying corresponding points or when automatic setting of corresponding points is not successful.

The invention according to claim 4 is the invention according to any one of claims 1 to 3 , wherein the corresponding point designating unit is a three-dimensional point group of the measurement object obtained at the first viewpoint. A corresponding point detection unit for detecting the corresponding point based on position data is provided. According to the fourth aspect of the present invention, the feature point can be detected by calculation.

Invention of Claim 5 is described in any one of Claims 1-4 , The designation | designated of the corresponding point in the said corresponding point designation | designated part is the image obtained from the said 1st viewpoint, and 2nd It is executed by performing stereo matching with an image obtained from a viewpoint.

The invention according to claim 6 is characterized in that, in the invention according to claim 4 or 5 , the corresponding point detection unit detects a portion which is a characteristic point of the measurement object as the corresponding point. As described above, the process of obtaining the correspondence relationship using the corresponding points can be performed more efficiently when the corresponding points function effectively as feature points. For example, the portion that becomes the outline of the measurement object is a portion that characterizes the appearance of the object, and functions as a feature point. Therefore, it is possible to increase the efficiency of processing by calculating a portion that becomes a feature point and making the corresponding point correspond. The feature points are visually easily visible portions such as edge portions, convex portions, and concave portions of the measurement object. A feature point is mathematically detected as a portion where a level that cannot be regarded as a plane is locally large.

The invention according to claim 7 is the invention according to claim 6 , wherein when the corresponding point is specified, the point cloud position data acquisition unit is a three-dimensional point of a peripheral portion including the specified corresponding point. Group position data is acquired at the second viewpoint, the feature points are calculated based on the acquired three-dimensional point group position data, and the designated corresponding points are identified based on the calculation. It is characterized by being.

According to the seventh aspect of the present invention, the acquisition of the three-dimensional point cloud position data of the peripheral portion including the designated portion is performed at the second viewpoint, and the partially acquired three-dimensional point cloud position data. Based on the above, corresponding points are calculated as feature points in the region. For this reason, even if the designated corresponding point deviates from the feature point, the finally obtained corresponding point can be used as the feature point, and the efficiency and accuracy in calculating the correspondence using the corresponding point can be improved. Can do. According to the invention, the 3D point cloud position data newly acquired at the second viewpoint may be a part of the measurement object. For this reason, it is possible to suppress an increase in work time for acquiring new 3D point cloud position data and processing time of the acquired 3D point cloud position data.

The invention according to an eighth aspect is the invention according to any one of the first to seventh aspects, wherein the coordinate calculation unit is associated with the coordinate system at the first viewpoint based on a correspondence relation at at least three points. The calculation for obtaining the three-dimensional position of the second viewpoint is performed.

The invention according to claim 9 is the point cloud position for measuring the three-dimensional point cloud position data acquired by the point cloud position data acquisition unit in the invention according to any one of claims 1 to 7. A data measuring optical system and a photographing optical system for photographing an image serving as a basis of the image data, wherein the point cloud position data measuring optical system and the photographing optical system are rotatable about the same axis; The coordinate calculation unit performs a calculation for obtaining a three-dimensional position of the second viewpoint associated with the coordinate system of the first viewpoint based on a correspondence relationship between at least two points.

The invention according to claim 10 is the invention according to any one of claims 1 to 9 , further comprising a GPS unit that identifies the position of the first viewpoint and the position of the second viewpoint, The coordinate calculation unit performs a calculation based on the position of the first viewpoint and the position of the second viewpoint specified by the GPS unit.

The invention according to claim 11 is a point cloud position data acquisition step for acquiring three-dimensional point cloud position data of the measurement object at the first viewpoint, and the measurement object at a second viewpoint different from the first viewpoint. an image data acquisition step of acquiring image data of the object, the corresponding points in a common region on the image of the measurement object as viewed from the measurement object and the second viewpoint as seen from the first viewpoint By specifying the correspondence between the corresponding point designating step, the three-dimensional point group position data of the corresponding point at the first viewpoint and the screen coordinate value of the corresponding point at the second viewpoint , A point cloud position data processing method comprising: a coordinate calculation step for calculating a three-dimensional position of the second viewpoint associated with the coordinate system at the first viewpoint.

According to a twelfth aspect of the present invention, there is provided point cloud position data acquisition means for acquiring three-dimensional point cloud position data of a measurement object at a first viewpoint, and the measurement object at a second viewpoint different from the first viewpoint. an image data acquisition means for acquiring image data of the object, the corresponding points in a common region on the image of the measurement object as viewed from the measurement object and the second viewpoint as seen from the first viewpoint By specifying a corresponding point specifying means, and a correspondence relationship between the three-dimensional point group position data of the corresponding point in the first viewpoint and the screen coordinate value of the corresponding point in the second viewpoint , A point cloud position data processing system comprising: coordinate calculation means for calculating a three-dimensional position of the second viewpoint associated with the coordinate system at the first viewpoint.

The invention of claim 13 is a program to be executed is read by the computer, the point group location data acquisition unit for acquiring a three-dimensional point cloud position data of the measuring object in the first aspect of the computer, the the measuring viewed the image data acquisition unit for acquiring image data of the measurement object, and the measurement object as viewed from the first viewpoint from the second viewpoint in a second viewpoint different from the first viewpoint A corresponding point designating unit for designating corresponding points in a common area on the image with the object; the three-dimensional point cloud position data of the corresponding points at the first viewpoint; and the corresponding points at the second viewpoint. by identifying the correspondence between the screen coordinates, that function as the coordinate calculation unit which performs calculation for obtaining the three-dimensional position of the associated said second viewpoint coordinate system in the first aspect A point group location data processing program according to symptoms.

  According to the first aspect of the present invention, the processing for handling point cloud position data obtained from different viewpoints is made more efficient than when comparing three-dimensional point cloud position data or image data with a dark cloud. it can.

  According to the invention described in claim 2, it is possible to obtain a three-dimensional model capable of recognizing occlusion at one viewpoint. Further, a three-dimensional model with reduced occlusion can be obtained based on the three-dimensional point cloud position data obtained from two viewpoints.

  According to the third aspect of the present invention, it is possible to specify the corresponding points in a state that is easy to visually grasp.

According to the fourth aspect of the present invention, since the corresponding point is detected using the three-dimensional point cloud position data obtained at the first viewpoint, the characteristic suitable as the corresponding point of the measurement object is effectively calculated. Can be obtained.

According to invention of Claim 5 , a corresponding point can be calculated | required by a calculation.

According to the sixth aspect of the invention, since the feature points are the corresponding points, the efficiency of the calculation for obtaining the correspondence can be further increased.

According to the seventh aspect of the present invention, it is possible to set the feature part more reliably and the point where the correspondence becomes clearer as the corresponding point for associating the two viewpoints.

According to the invention described in claim 8, it is possible to specify the correspondence using absolute orientation, which can perform orientation by specifying three corresponding points.

According to the ninth aspect of the invention, the number of corresponding points can be reduced.

According to the tenth aspect of the present invention, integrated coordinates linked to GPS information can be obtained.

According to the invention described in claims 11 to 13 , the same effect as that of claim 1 can be obtained.

It is a block diagram of a point cloud position data processing device in an embodiment. It is a conceptual diagram which shows a mode that the measurement in embodiment is performed. It is a block diagram of the three-dimensional model formation part in an embodiment. It is a screen display figure which shows an example of the content displayed on the image display apparatus in embodiment. It is a screen display figure which shows an example of the content displayed on the image display apparatus in embodiment. It is a flowchart which shows an example of the procedure of the process in embodiment. It is an example of the screen display which displayed the three-dimensional model. It is a conceptual diagram explaining the principle of mutual orientation. It is a principle figure explaining the principle of stereo matching. It is a conceptual diagram of the point cloud position data processing apparatus provided with the laser scanner. It is a conceptual diagram of the point cloud position data processing apparatus provided with the laser scanner. It is a block diagram of a control part of an embodiment. It is a block diagram of the calculating part of embodiment. It is a conceptual diagram which shows the point cloud position data whose distance between points is not constant. It is a conceptual diagram which shows the formed grid.

1. First Embodiment Hereinafter, an example of a point cloud position data processing apparatus using the invention will be described with reference to the drawings. FIG. 1 shows a point cloud position data processing apparatus 100. The point cloud position data processing apparatus 100 is configured as software on a personal computer. A program that configures the point cloud position data processing apparatus 100 on a personal computer is installed in the personal computer. This program may be recorded on a server or an appropriate recording medium and provided from there.

  The personal computer used is an input unit such as a keyboard or a touch panel display, an image display device such as a liquid crystal display, a GUI (graphical user interface) function unit that is a user interface integrating the input unit and the display unit, a CPU, and the like. Dedicated arithmetic device, semiconductor memory, hard disk storage unit, disk storage device drive unit capable of exchanging information with storage media such as optical disc, and interface capable of exchanging information with portable storage media such as USB memory And a communication interface unit for performing wireless communication and wired communication as necessary. The personal computer may be a notebook type, a portable type, a desktop type, or the like, but the form is not limited. In addition to using a general-purpose personal computer, the position of the point cloud can be determined by dedicated hardware configured using an Application Specific Integrated Circuit (ASIC) or PLD (Programmable Logic Device) such as an FPGA (Field Programmable Gate Array). The data processing apparatus 100 can also be configured.

  Connected to the point cloud position data processing device 100 are a point cloud position data acquisition device 101, an image acquisition device 102, a GPS (Global Positioning System) device 103, an image display device 104, and an operation input device 105. The point cloud position data acquisition device 101 is a laser scanner described later. The point cloud position data acquisition apparatus 101 (laser scanner) acquires 3D point cloud position data by irradiating a measurement target with laser light and detecting the reflected light. The image acquisition device 102 is an imaging device that uses a CCD or a CMOS image sensor. The image acquisition device 102 captures an appearance image of a measurement target that is an acquisition target of 3D point cloud position data, and acquires the image data. The point cloud position data acquisition device 101 and the image acquisition device 102 are integrated so as to face in the same direction, and are rotatable around the same axis.

  The GPS device 103 is a position specifying device used in a navigation system or the like, receives a GPS signal, and acquires position data based on the GPS signal. The image display device 104 is a display (for example, a liquid crystal display) of a personal computer constituting the point cloud position data processing device 100. The operation input device 105 is an operation input device (a pointing device such as a keyboard or a mouse input device) of a personal computer that constitutes the point cloud position data processing device 100. The operation input device 105 can operate the point cloud position data processing device 100 using a known GUI (Graphical User Interface) in conjunction with the image display device 104.

  The point cloud position data processing apparatus 100 includes a point cloud position data acquisition unit 111, an image data acquisition unit 112, a common area specification unit 113, a corresponding point specification unit 114, a coordinate calculation unit 117, a three-dimensional position calculation unit 118, and a display control unit. 120.

  The point cloud position data acquisition unit 111 receives the three-dimensional point cloud position data output from the point cloud position data acquisition device 101 and imports it into the point cloud position data acquisition device 100. The image data acquisition unit 112 receives image data output from the image acquisition device 102 and imports it into the point cloud position data acquisition device 100.

  The common area designating unit 113 designates, as a common area, an overlapping part of measurement objects viewed from two viewpoints, that is, a part of a measurement object that appears common when viewed from two viewpoints. The common area is designated by the user. In addition, it is also possible to calculate a common region by calculation by comparing image data obtained from different viewpoints or three-dimensional point cloud position data.

  The corresponding point specifying unit 114 includes a corresponding point receiving unit 115 and a corresponding point detecting unit 116. The corresponding point receiving unit 115 is a corresponding point that is performed by a user's manual operation on the three-dimensional model (or captured image) from the first viewpoint displayed on the image display device 104 and the captured image from the second viewpoint. The specified operation is accepted. This operation is performed using a GUI function provided in the personal computer. The corresponding point detection unit 116 detects a portion that is easily recognized as a feature point when the measurement object such as an edge portion of the measurement object is visually recognized as a corresponding point by calculation. This process is performed using a calculation described later performed by the three-dimensional model forming unit 119.

  The corresponding point detection unit 116 can also automatically detect corresponding points by stereo matching. In this case, the image data of the measurement object obtained from the first viewpoint is compared with the image data of the measurement object obtained from the second viewpoint, and a feature portion that matches the two is calculated as a corresponding point. . Details of the stereo matching will be described later.

  The coordinate calculation unit 117 associates the three-dimensional point cloud position data of the measurement object acquired at the first viewpoint with the image data of the measurement object obtained by shooting at the second viewpoint different from the first viewpoint. A coordinate system (coordinate system used when viewing the measurement object from the first viewpoint) that calculates the relationship, the calculation related to this calculation, and the three-dimensional point cloud position data obtained from the first viewpoint A process of calculating the position coordinates of the second viewpoint above is performed.

  The coordinate calculation unit 117 includes a three-dimensional position calculation unit 118. The three-dimensional position calculation unit 118 calculates the value of the three-dimensional coordinates of the second viewpoint in the coordinate system that handles the three-dimensional point group position data obtained from the first viewpoint, based on the correspondence relationship described above. By using the result of this calculation, the three-dimensional point cloud position data obtained at the first viewpoint can be handled in the coordinate system in the direction in which the measurement object is viewed from the second viewpoint. Further, 3D point cloud position data obtained from the second viewpoint later and 3D point cloud position data obtained from the first viewpoint can be handled in an integrated manner. Examples of the method for obtaining the correspondence relationship include “single photograph orientation”, “mutual orientation”, and “absolute orientation”, as will be described later.

  The three-dimensional model formation unit 119 forms a three-dimensional model based on the three-dimensional point group position data acquired by the point group position data acquisition unit 111. Details of the function of the three-dimensional model forming unit 119 will be described later. The three-dimensional model here is an image obtained by visualizing the three-dimensional structure of the measurement object in which the outline of the measurement object is expressed as a diagram. The outline is an outline that forms the outline of the measurement object, which is necessary for visually grasping the appearance of the measurement object. Specifically, a bent portion or a portion where the curvature is rapidly reduced becomes a contour line. The contour line is not limited to the outer contour part, but the edge part that characterizes the protruding part of the convex part, or the edge that characterizes the concave part (for example, the part of the groove structure). The part of is also the target. A so-called diagram can be obtained from the contour line, and an image can be displayed so that the appearance of the object can be easily grasped. Note that the three-dimensional model includes not only the above-described line information but also information on points that are characteristic portions when the appearance of the measurement object is visually grasped.

  The display control unit 120 performs control for displaying a captured image or a three-dimensional model of the measurement object on the image display device 104. In particular, the display control unit 120 performs control to perform image display according to FIGS. 4, 5, and 7 described later on the image display device 104.

(Coordinate calculation part)
Hereinafter, the coordinate calculation unit 117 of FIG. 1 will be described. First, a specific example of a state in which 3D point cloud position data, which is a premise for explanation, is measured is described. FIG. 2 conceptually shows an example of a situation in which 3D point cloud position data is measured. FIG. 2 shows a notebook personal computer 130 that functions as the point cloud position data processing apparatus 100 of FIG. A personal computer 130 is connected to a three-dimensional laser scanner 131 which is an example of a point cloud position data acquisition device. FIG. 2 shows image data obtained by obtaining and photographing three-dimensional point cloud position data of measurement objects 134 to 136 by placing a three-dimensional laser scanner 131 at a first installation position 132 serving as a first viewpoint for measurement. Then, the 3D laser scanner 131 is moved to the second installation position 133, which is the second viewpoint, and the 3D point cloud position data and image data are again acquired there. An example is shown. Note that the three-dimensional laser scanner 131 has the functions of the image acquisition device 102 and the GPS device 103 in addition to the functions of the point cloud position data acquisition device 101 of FIG. Further, when the optical axis of the laser scanner 131 is moved, both are integrated so that the optical axis of the image acquisition device is in the same direction.

  In FIG. 2, in the situation where the three-dimensional laser scanner 131 is disposed at the first installation position 132, the measurement object 134 is positioned on the near side as viewed from the three-dimensional laser scanner 131 side, and behind (rear side) The situation where the measurement object 135 is located is shown in FIG. Further, a state in which the measurement object 136 is located at a position hidden behind the measurement object 134 when viewed from the first installation position 132 is shown.

  Hereinafter, the function of the coordinate calculation unit 117 will be described by taking the situation of FIG. 2 as an example. In this case, the coordinate calculation unit 117 includes the three-dimensional point cloud position data of the measurement objects 134 to 136 acquired when the three-dimensional laser scanner 131 is installed at the first installation position 132 (first viewpoint); Correspondence (correlation) with image data obtained by setting the three-dimensional laser scanner 131 at the second installation position 133 (second viewpoint) and performing shooting is obtained.

  Examples of the method for obtaining the above correspondence include “single photo orientation”, “mutual orientation”, and “absolute orientation”. One of these methods may be used, or a plurality of methods may be used in combination. The processing for obtaining the correspondence relationship handles the three-dimensional point cloud position data of the measurement object obtained when the measurement objects 134 to 136 are viewed from the first installation position (first viewpoint) 132 of FIG. Measurement obtained when the measurement objects 134 to 136 are viewed from the coordinate system (first coordinate system) (X, Y, Z) and the second installation position (second viewpoint) 133 used for This can be regarded as a process of clarifying the correspondence with the coordinate system (second coordinate system) (x, y, z) used for handling the three-dimensional point cloud position data of the object.

  In this process, first, a correspondence between both images is obtained by using a three-dimensional model based on the three-dimensional point cloud position data obtained at the first viewpoint and the image data obtained by the image data obtaining unit 112 at the second viewpoint. The part to be performed (corresponding point) is specified. Next, the correspondence between the two coordinate systems is specified using the orientation using the corresponding point as a step.

  By the way, when performing the above-mentioned orientation, the position of each viewpoint becomes the origin of each coordinate system. Therefore, by designating the corresponding points between the three-dimensional model obtained from the first viewpoint and the captured image obtained from the second viewpoint, and obtaining the correspondence between the corresponding points of both images by the above-described orientation, It is possible to know the three-dimensional coordinates of the second viewpoint in the coordinate system of the first viewpoint (the coordinate system for handling the three-dimensional point cloud position data obtained from the first viewpoint).

  Normally, the image data obtained from the second viewpoint is not assembled with the three-dimensional point cloud position data unless the point cloud position data obtaining unit 111 obtains the three-dimensional point cloud position data from the second viewpoint. However, as in the above method, using the orientation by designating corresponding points, the three-dimensional coordinates of the second viewpoint associated with the three-dimensional point cloud position data from the first viewpoint (the second viewpoint) When obtaining (position), the 3D point cloud position data obtained from the second viewpoint is not necessarily acquired.

  Similar processing can also be performed based on image data obtained at the first viewpoint and image data obtained at the second viewpoint. In this case, first, using the image data acquired by the image data acquisition unit 112 at the first viewpoint and the image data acquired by the image data acquisition unit 112 at the second viewpoint, corresponding portions of both images (Corresponding point) is specified. Next, the correspondence between the two coordinate systems is specified using the orientation using the corresponding point as a step.

  Here, the image data obtained from the already acquired first viewpoint is assembled with the three-dimensional point cloud position data obtained by the point cloud position data obtaining unit 111 and its positional relationship. Therefore, by designating the corresponding points between the image data from the first viewpoint and the image data from the second viewpoint, the corresponding points at the second viewpoint and the three-dimensional point group obtained from the first viewpoint The relationship with position data is easily understood. Then, by performing the orientation, it is possible to calculate the three-dimensional position coordinates of the second viewpoint in the coordinate system describing the three-dimensional point group position data obtained from the first viewpoint.

(Single photo orientation)
Single photo orientation refers to the camera position (X0, Y0, Z0) and camera tilt (ω, φ, This is a technique for obtaining κ) and obtaining the relationship between the photographic coordinates x, y and the ground coordinates X, Y, Z. The collinear condition is a condition that the projection center, the photographic image, and the ground object are in a straight line. The camera position (X0, Y0, Z0) and the camera tilt (ω, φ, κ) are called external orientation elements.

  Here, using the principle of single photograph orientation, the first coordinate system when the objects 134 to 136 are viewed from the first installation position 132 (first viewpoint) and the second installation position 133 (first). A method for obtaining the relationship with the second coordinate system when the objects 134 to 136 are viewed from the second viewpoint) will be described. In this case, the image acquisition device 102 provided in the 3D laser scanner 131 from the second installation position 133 in a state where the 3D point cloud position data of the measurement objects 134 to 136 are acquired at the first installation position. An image is acquired by the (CCD camera), and then the following calculation is performed.

  First, it is assumed that the first coordinate system is X, Y, Z, the second coordinate system is x, y, z, and the image acquisition device 102 has taken an image at the second installation position 133. Here, four common points are designated as corresponding points from the three-dimensional point cloud position data obtained at the first installation position 132 and the captured image taken from the second installation position 133. The selection of the corresponding points is performed according to the following procedure. First, the common area is designated by the function of the common area designation unit 113. Further, four corresponding points are designated from the designated common area by the function of the corresponding point designation unit 114. These four points are common coordinate points that serve as a basis for obtaining a correspondence relationship between the two coordinate systems, and a portion to be a feature point is selected from the measurement object. The four points can be selected by a manual operation, a method for automatically extracting a part that can be easily grasped as a feature point such as an edge or a corner of an object, or a method for automatically selecting the four points. Further, a method in which the user manually selects is used. Note that the designation of corresponding points when single photo orientation is used may be five or more, but the correspondence between the two coordinate systems can be obtained by designating at least four points.

  Then, the four screen coordinate values and the corresponding three-dimensional coordinates of the reference point are substituted into the quadratic projective transformation equation shown in Equation 1, and the observation equations are established to obtain the parameters b1 to b8. Here, the screen coordinate values of the four points are the positions of the coordinates on the screen of the corresponding points of the four points designated from the captured image taken from the second installation position 133. The corresponding three-dimensional coordinates of the reference point are the values of the three-dimensional coordinates of the four designated corresponding points obtained from the three-dimensional point group position data obtained at the first installation position 132.

  Using the parameters b1 to b8 of Equation 1, external orientation elements (X0, Y0, Z0) are obtained from Equation 2 below.

  Next, the coordinate system (xp, yp, zp) of the tilted image acquisition device 102 corresponding to (X, Y, Z) is obtained from the following equation 3 based on the principle of single photo orientation. In Equation 3, the inclination (ω, φ, κ) of the image capturing device obtained in Equation 2 is substituted, and a rotation matrix is calculated to obtain parameters a11 to a33.

The obtained parameters a11 to a33 and the camera positions (X0, Y0, Z0) and (X, Y, Z) obtained by Expression 2 are expressed as follows: the projection center, the photographic image, and the object are in a straight line. Substituting into the collinear conditional expression 4 to obtain coordinates (x, y). Here, c is a screen distance (focal length), a _{11 to} a ₃₃ are inclinations of the image acquisition device 102 expressed as elements of a 3 × 3 rotation matrix, and Δx and Δy are internal values of the image acquisition device 102. This is an orientation correction term.

  Thus, the coordinate system (first coordinate system) (X, Y, Z) when the measurement objects 134 to 136 are viewed from the first installation position (first viewpoint) 132 in FIG. Correspondence with the coordinate system (second coordinate system) (x, y, z) when the measurement objects 134 to 136 are viewed from the installation position (second viewpoint) 133 is calculated. By calculating this correspondence, the position of the second viewpoint in the first coordinate system (XYZ) can be specified. In the above calculation method, the relationship between z and Z is not obtained. However, when the first installation position 132 and the second installation position 133 are different in the horizontal plane, z = Z. The above method does not cause any problems.

(Mutual orientation)
In the relative orientation, a first coordinate system that describes a measurement object viewed from the first viewpoint and a second object that describes the measurement object viewed from the second viewpoint, using six or more corresponding points in the image. This is a technique for obtaining the relationship with the coordinate system. FIG. 8 is a conceptual diagram illustrating the principle of relative orientation. Here, it is obtained by a coordinate system (first coordinate system) of an image obtained by photographing from the first viewpoint (photographing by the left camera) and by photographing from the second viewpoint (photographing by the right camera). The procedure for relative orientation will be described with reference to an example in which the relationship with the coordinate system (second coordinate system) of an image is obtained. In relative orientation, each parameter is obtained by the following coplanar conditional expression.

As shown in FIG. 8, the origin of the model coordinate system is taken as the left projection center O ₁ , and the line connecting the right projection center O ₂ is taken as the X axis. For the scale, the base line length is taken as the unit length. The parameters to be obtained at this time are the rotation angle κ ₁ of the left camera, the rotation angle φ _{1 of} the Y axis, the rotation angle κ _{2 of the} right camera, the rotation angle φ ₂ of the Y axis, and the rotation of the X axis. the five of the rotation angle of the corner ω _2. In this case, since the rotation angle ω ₁ of the X axis of the left camera is 0, there is no need to consider it. Under such conditions, the coplanar conditional expression of Equation 5 becomes Equation 6, and each parameter can be obtained by solving this equation.

  Here, the following relational expression for coordinate transformation is established between the model coordinate system XYZ and the camera coordinate system xyz.

Using these equations, unknown parameters are obtained by the following procedure.
(1) The initial approximate value is normally 0.
(2) The coplanar conditional expression of Expression 6 is Taylor-expanded around the approximate value, and the value of the differential coefficient when linearized is obtained by Expression 7 and Expression 8 to form an observation equation.
(3) A least square method is applied to obtain a correction amount for the approximate value.
(4) The approximate value is corrected.
(5) Using the corrected approximate value, the operations (2) to (5) are repeated until convergence.

By obtaining the unknown parameter by the above-described method, the correspondence between the first coordinate system (X ₁ , Y ₁ , Z ₁ ) and the second coordinate system (X ₂ , Y ₂ , Z ₂ ) can be obtained. Then, by calculating this correspondence, it is possible to specify the position of the second viewpoint in the first coordinate system. In addition, after creating a model space (local space) by mutual orientation, the position of the second viewpoint is described on the ground coordinate system by converting to the ground coordinate system (absolute coordinate system) using absolute orientation. Can do.

(Absolute orientation)
Absolute orientation is a method of converting a model coordinate system into a ground coordinate system (absolute coordinate system). When absolute orientation is used, the first coordinate system is associated with the ground coordinate system, while the second coordinate system is associated with the ground coordinate system, and the first coordinate system and the second coordinate system are connected via the ground coordinate system. Is obtained. First, the model coordinate system (XM, YM, ZM) is converted to the ground coordinate system (X, Y, Z). Here, if the scale is s, the rotation about the three axes is ω, φ, κ, and the parallel movement amount is (X0, Y0, Z0), the relational expression of Equation 9 is obtained.

  Next, assuming that ω and φ are small, unknown variables (s, ω, φ, κ, X0, Y0, Z0) are obtained. First, the plane coordinates are adjusted by Helmat transform. When limited to plane coordinates, the following equation 10 holds. In the following formula 10, cosκ = (a / s), sinκ = (− b / s).

  In Equation 10, coefficients a, b, X0, and Y0 are determined by the least square method. Next, the scale is unified. In this case, the following equation 11 holds.

  Next, the height is adjusted. In this case, the following equation 12 holds.

  In Equation 12, ω, φ, and Z0 are obtained by the least square method. Then, using the obtained unknown variable, the model coordinates are corrected by the following equation (13).

  The above processing is repeated until the unknown variable converges, and the correspondence relationship between the model coordinate system (XM, YM, ZM) and the ground coordinate system (X, Y, Z) is obtained. Then, by selecting the first coordinate system from the first viewpoint and the second coordinate system from the second viewpoint as the model coordinate system, the first coordinate system and the second coordinate system via the ground coordinate system are selected. The correspondence with the coordinate system becomes clear. Or the image from two viewpoints and the three-dimensional point cloud position data from two viewpoints can be handled by the ground coordinate system which is a common coordinate. When absolute orientation is used, theoretically, orientation is performed by specifying three corresponding points. For example, as in the case where a coordinate system from two different viewpoints at the same height position is used as a problem. Since the position on one axis is the same even if the coordinate system is changed, the number of corresponding points required for orientation may be two.

(Stereo matching)
The stereo matching method is a method in which coordinate data of images in two coordinate systems are compared with each other, and a correspondence relationship between the two images is obtained by a correlation between the two. In stereo matching, the correspondence between the feature points of the image viewed from each of the two viewpoints is obtained, and the corresponding points can be automatically extracted. FIG. 9 is a principle diagram for explaining the principle of stereo matching. In this method, as shown in the drawing, a template image of N ₁ × N ₁ pixel is moved on a search range (M ₁ −N ₁ +1) ² in an input image of M ₁ × M ₁ pixel larger than that, The upper left position of the template image in which the cross-correlation function C (a, b) expressed by the following equation 14 is maximized (that is, the degree of correlation is maximized) is obtained.

  By using stereo matching, it is possible to know the correspondence between the coordinate systems of two images to be compared. In this method, the relative positional relationship between the two images is determined so as to maximize the correlation between the two images. The correlation between the two images is determined by the feature points of both images (the correlation is difficult to determine even by comparing portions without features). Therefore, by using the threo matching, it is possible to extract corresponding points between the two images. That is, when stereo matching is used, corresponding points can be detected (automatic detection) based on image data obtained from two different viewpoints without manually specifying the corresponding points. However, there are cases in which the calculation cannot be performed well due to the relationship between the positions of the two viewpoints and the object.

(3D model formation department)
Hereinafter, functions of the three-dimensional model forming unit 119 in FIG. 1 will be described. FIG. 3 shows a block diagram of the three-dimensional model forming unit 119. The three-dimensional model forming unit 119 includes a non-surface area removing unit 201, a surface labeling unit 202, and a contour line calculating unit 203. Hereinafter, each of these functional units will be described. The non-surface region removal unit 201 includes a local region acquisition unit 201a that acquires a local region, a normal vector calculation unit 201b that calculates a normal vector of the local region, a local curvature calculation unit 201c that calculates a local curvature of the local region, A local plane calculation unit 201d that calculates a local plane to be fitted to the region is provided.

  The local area acquisition unit 201a acquires, as a local area, a square area (grid-like area) having about 3 to 7 pixels on a side centered on the point of interest based on the three-dimensional point cloud position data. The normal vector calculation unit 201b calculates a normal vector of each point in the local region acquired by the local region acquisition unit 201a. In the process of calculating the normal vector, attention is paid to the three-dimensional point cloud position data in the local region, and the normal vector of each point is calculated. This process is performed for all three-dimensional point cloud position data. That is, the three-dimensional point cloud position data is divided into countless local regions, and the normal vector of each point is calculated in each local region.

  The local curvature calculation unit 201c calculates variation (local curvature) of normal vectors in the above-described local region. Here, the average (mNVx, mNVy, mNVz) of the intensity values (NVx, NVy, NVz) of the three-axis components of each normal vector is obtained in the local region of interest, and the standard deviation (StdNVx, StdNVy, StdNVz) ) Next, the square root of the square sum of the standard deviation is calculated as a local curvature (crv) (see the following formula 15).

  The local plane calculation unit 201d obtains a local plane that is fitted (approximated) to the local region. In this process, an equation of the local plane is obtained from the three-dimensional coordinates of each point in the local region of interest. The local plane is a plane that is fitted to the local region of interest. Here, the equation of the surface of the local plane to be fitted to the local region is calculated using the least square method. Specifically, a plurality of different plane equations are obtained, compared with each other, and a surface equation of the local plane to be fitted to the local region is calculated. If the local region of interest is a plane, the local plane and the local region match. The above processing is repeated so that all the three-dimensional point cloud position data are targeted while sequentially shifting the local area, and the normal vector, local plane, and local curvature in each local area are obtained.

  Next, based on the normal vector, local plane, and local curvature in each local area obtained above, processing for removing points in the non-surface area is performed. That is, in order to extract a surface (a plane and a curved surface), a portion (non-surface region) that can be determined not to be a surface in advance is removed. The non-surface region is a region that is neither a plane nor a curved surface, but may include a curved surface with a high curvature depending on the following threshold values (1) to (3).

  The non-surface area removal process can be performed using at least one of the following three methods. Here, the determination by the following methods (1) to (3) is performed for all the local regions described above, and the local region determined as the non-surface region by one or more methods is configured as the non-surface region. Extract as a local region. Then, the 3D point cloud position data relating to the points constituting the extracted non-surface area is removed.

(1) A portion having a high local curvature: The above-described local curvature is compared with a preset threshold value, and a local region having a local curvature exceeding the threshold value is determined as a non-surface region. Since the local curvature represents the variation of the normal vector at the point of interest and its peripheral points, the value is small for a surface (a flat surface and a curved surface with a small curvature), and the value is large for a surface other than a surface (non-surface). Therefore, if the local curvature is larger than a predetermined threshold, the local region is determined as a non-surface region.

(2) Local plane fitting accuracy: the distance between each point of the local area and the corresponding local plane is calculated, and if the average of these distances is greater than a preset threshold, the local area is determined to be a non-surface area To do. That is, when the local area is in a state of being deviated from the plane, the distance between the local plane corresponding to each point of the local area increases as the degree becomes more severe. Using this fact, the degree of non-surface of the local region is determined.

(3) Coplanarity check: Here, the directions of corresponding local planes are compared in adjacent local regions. If the difference in orientation of the local planes exceeds the threshold value, it is determined that the local area to be compared belongs to the non-plane area. Specifically, if the inner product of the normal vector of the two local planes fitted to each of the two target local regions and the vector connecting the center points is 0, both local planes are on the same plane. Is determined to exist. Moreover, it is determined that the extent that the two local planes are not on the same plane is more remarkable as the inner product becomes larger.

  In the determination by the above methods (1) to (3), a local region determined as a non-surface region by one or more methods is extracted as a local region constituting the non-surface region. Then, the 3D point cloud position data relating to the points constituting the extracted local region is removed from the 3D point cloud position data to be calculated. The non-surface area is removed as described above. Thus, the non-surface area 3D point group position data is removed from the non-surface area removal unit 201 from the 3D point group position data. Since the removed 3D point cloud position data may be used in later processing, it is stored in an appropriate storage area so that it can be identified from the 3D point cloud position data that has not been removed. To make it available for later use.

  Next, the function of the surface labeling unit 202 will be described. The surface labeling unit 202 performs surface labeling on the three-dimensional point group position data from which the non-surface region three-dimensional point group position data has been removed by the non-surface region removing unit 201 based on the continuity of normal vectors. . Specifically, if the angle difference between the normal vector of a specific point of interest and an adjacent point is less than a predetermined threshold, the same label is attached to those points. By repeating this operation, the same label is attached to a continuous flat surface and a continuous gentle curved surface, and these can be identified as one surface. Further, after the surface labeling, it is determined whether the label (surface) is a plane or a curved surface with a small curvature by using the angle difference between the normal vectors and the standard deviation of the three-axis components of the normal vectors. Identification data for identifying the fact is associated with each label.

  Subsequently, the label (surface) having a small area is removed as noise. This noise removal may be performed simultaneously with the surface labeling process. In this case, while performing surface labeling, the number of points of the same label (the number of points constituting the label) is counted, and a process of canceling the label having the number of points equal to or less than a predetermined value is performed. Next, the same label as the nearest surface (closest surface) is given to the point having no label at this time. As a result, the already labeled surface is expanded.

  That is, the equation of the surface with the label is obtained, and the distance between the surface and the point without the label is obtained. If there are a plurality of labels (surfaces) around a point where there is no label, the label with the shortest distance is selected. If there is still a point with no label, the threshold values for non-surface area removal, noise removal, and label expansion are changed, and related processing is performed again. For example, in non-surface area removal, the number of points to be extracted as non-surface is reduced by increasing the threshold value of local curvature. Alternatively, in label expansion, by increasing the threshold of the distance between a point without a label and the nearest surface, more labels are given to the point without a label.

  Next, even if the labels are different surfaces, the labels are integrated when they are the same surface. In this case, even if the surfaces are not continuous, the same label is attached to the surfaces having the same position or orientation. Specifically, by comparing the position and orientation of the normal vector of each surface, the same non-continuous surface is extracted and unified to the label of any surface. The above is the function of the surface labeling unit 202.

  According to the function of the surface labeling unit 202, the amount of data to be handled can be compressed, so that the processing of the three-dimensional point cloud position data can be accelerated. In addition, the required amount of memory can be saved. In addition, it is possible to remove the three-dimensional point cloud position data of a passerby or a vehicle that has passed through during measurement as noise.

  The contour calculation unit 203 calculates (estimates) the contour based on the three-dimensional point cloud position data of the adjacent surfaces. Hereinafter, a specific calculation method will be described. The contour line calculation unit 203 obtains an intersection line between adjacent surfaces sandwiching a non-surface region therebetween, and performs a process of setting it as a contour line. At this time, a method of approximating the non-planar region with a plurality of local planes by fitting a local plane to a non-planar region between adjacent surfaces and connecting a plurality of local planes may be employed. This can be regarded as an approximation of a non-planar region by a polyhedron composed of a plurality of local planes. In this case, the local planes are connected from the adjacent surfaces, and the line of intersection between the adjacent local planes is calculated as the contour line. By calculating the contour line, the contour image of the measurement object becomes clear.

  Next, the two-dimensional edge calculation unit 204 will be described. Hereinafter, an example of processing performed by the two-dimensional edge calculation unit 204 will be described. First, based on the intensity distribution of the reflected light from the object, a region of a two-dimensional image corresponding to a segmented (segmented) surface using a known edge extraction operator such as Laplacian, Pleuwit, Sobel, Canny Extract edges from within. In other words, since the two-dimensional edge is recognized by the difference in shading in the surface, the difference in shading is extracted from the information on the intensity of the reflected light, and by setting a threshold value in the extraction condition, the border between the shading is used as an edge. Extract. Next, the height (z value) of the three-dimensional coordinates of the points constituting the extracted edge, and the height (z value) of the three-dimensional coordinates of the points constituting the neighboring contour line (three-dimensional edge) If the difference is within a predetermined threshold, the edge is extracted as a two-dimensional edge. That is, it is determined whether or not a point constituting the edge extracted on the two-dimensional image is on the segmented surface, and if it is determined that the point is on the surface, it is set as the two-dimensional edge.

  After the calculation of the two-dimensional edge, the contour line calculated by the contour calculation unit 203 and the two-dimensional edge calculated by the two-dimensional edge calculation unit 204 are integrated. Thereby, edge extraction based on the three-dimensional point cloud position data is performed. By extracting the edge, lines constituting the appearance of the measurement object when the measurement object is visually recognized are extracted. As a result, a three-dimensional model (a diagram image) of the measurement object is obtained.

  As a specific example, for example, a case where a building is selected as a measurement object and a three-dimensional model is obtained based on the three-dimensional point cloud position data of the building will be described. In this case, a three-dimensional model in which the appearance of the building, the pattern of the outer wall, the outline of the window, etc. are represented by a diagram is obtained. Note that the contour of a portion with relatively little unevenness, such as a window, may be processed as a contour line or may be processed as a two-dimensional edge depending on the determination of the threshold value.

  In the coordinate calculation unit 117, when the position of the second viewpoint on the coordinates when the measurement object is viewed from the first viewpoint is calculated, the three-dimensional point cloud position data acquired at the first viewpoint is added. The based three-dimensional model can be rotated in the direction seen from the second viewpoint. Further, in this state, if the 3D point cloud position data of the measurement object is newly acquired from the second viewpoint, the 3D point cloud position data obtained from these two viewpoints can be handled in an integrated manner. A three-dimensional model based on three-dimensional point cloud position data obtained from two viewpoints is obtained. In this case, the above-described processing is performed based on the three-dimensional point group position data obtained from one of the viewpoints in the portion where the three-dimensional point group position data overlaps at the same coordinate position. Then, the above-described processing is performed using the three-dimensional point cloud position data obtained from the other viewpoint for a portion that is occluded from one viewpoint and is not occluded from the other viewpoint. Therefore, for example, it is possible to form a three-dimensional model in which the three-dimensional point group position data of the portion that is occluded from the first viewpoint is complemented by the three-dimensional point group position data obtained from the second viewpoint.

(Operation example 1)
Hereinafter, an example of the operation of the point cloud position data processing apparatus 100 shown in FIG. The FIG. 4 (A), the in the first installation position 132 (first viewpoint), and acquires the image data by three-dimensional point group location data and imaging of the measurement object, then in a second installation An example of a screen display on the liquid crystal display of the personal computer 130 in a state where image data obtained by setting a laser scanner 131 at a position 133 (second viewpoint) and performing shooting there is shown.

  FIG. 4 shows a GUI screen in which two screens are displayed side by side on the liquid crystal display of the personal computer 130. In FIG. 4A, a three-dimensional model (line diagram) of the measurement object viewed from the first installation position 132 based on the three-dimensional point cloud position data acquired at the first installation position 132 is displayed on the left side. On the right side, a state of a screen on which an image (Live image) taken from the second installation position 133 is displayed is shown.

  As shown in FIG. 4A, a plurality of display buttons functioning as GUI switches are displayed on the GUI screen. Here, click the display button labeled “Common area specification”, and determine that it is a common area in the left and right images from the surfaces labeled on the screen on which the 3D model on the left is displayed. If the part to be specified is manually specified using a pointing device such as a mouse, the common area specifying unit 113 recognizes that fact. Then, instruction data for performing display based on the designated content is sent from the common area designating unit 113 to the display control unit 120, and an image display reflecting the content operated by the user is performed on the image display device 104. Is called. This is shown in FIG. In FIG. 4B, when the user compares the left three-dimensional model diagram with the live image on the right side and designates a plurality of planes that are determined to be common areas in the left three-dimensional model diagram, The state in which the portion is highlighted with diagonal lines is shown.

  After the common area shown in FIG. 4B is specified, when the “corresponding point calculation” display is clicked, the common area highlighted in FIG. 4B is suitable for the corresponding point ( That is, a corresponding point candidate that is a point (suitable for handling as a corresponding part of two images) is calculated and displayed in a state where it can be easily viewed. This state is shown in FIG. FIG. 4C shows an example in which eight corresponding point candidates indicated by ◯ are displayed.

  The corresponding point candidate is calculated by using the calculation in the three-dimensional model forming unit 119. Hereinafter, this point will be described. The three-dimensional model forming unit 119 calculates a portion where the non-uniform state is remarkable with an adjacent portion such as a portion where the curvature is greatly changed locally such as an edge portion as a non-surface. Since the corresponding point is a point that becomes a foothold when performing the orientation, it is preferable that the corresponding point is a point that can be easily distinguished from the vicinity (referred to as a feature point in this sense). This requirement is a feature of the non-surface area itself. Accordingly, a threshold value is set for one or more of the local curvature, the fitting accuracy of the local plane, and the coplanarity handled by the 3D model forming unit 119, and it is appropriate to be a candidate for a corresponding point based on this threshold value. A critical point is calculated. This process is performed in the corresponding point detection unit 116.

  In a state where the corresponding point candidates shown in FIG. 4C are displayed, the user sets the position in the corresponding image on the right Live screen (the captured image from the second installation position 133) with a mouse or the like. Specify using a pointing device. This operation content is received by the corresponding point receiving unit 114, and the designated point is displayed in the live image on the right side. This state is shown in FIG. In this example, an example in which four points are designated as corresponding points is shown.

  Next, when the “corresponding point determination” display is clicked, as shown in FIG. 5B, the corresponding point on the three-dimensional model display displayed on the left side of the screen obtained from the first installation position 132 is displayed. And the position of the corresponding point on the Live image displayed on the right side of the screen obtained from the second installation position 133 are determined. After that, when the display portion of “Coordinate integration processing” is clicked, an operation based on the selected corresponding points (for example, absolute orientation that can be determined by specifying three corresponding points or standardization by specifying four corresponding points) The first coordinate system for handling the three-dimensional point cloud position data obtained from the first installation position 132, and the three-dimensional point obtained from the second installation position 133. A process of calculating the correspondence with the second coordinate system for handling the group position data is performed in the coordinate calculation unit 117. Based on this correspondence, the three-dimensional position calculation unit 118 calculates the position of the second viewpoint in the coordinate system used when the measurement object is viewed from the first installation position 132, and the coordinate value is calculated. Is required.

  4 and 5 show an example in which a three-dimensional model of a measurement object based on three-dimensional point cloud position data obtained from the first installation position 132 (first viewpoint) is displayed (left side screen). Here, a photographed image taken from the first installation position 132 (first viewpoint) or an image based on the three-dimensional point cloud position data (a pixel is composed of reflected light intensity information possessed by the three-dimensional point cloud position data). Image) may be displayed. At this stage, since the correspondence relationship between the 3D point cloud position data from the first installation position 132 and the captured image is clear, the common area designation and correspondence described with reference to FIGS. It is possible to specify a point and a process related to the point on a captured image from the first viewpoint.

  In addition, a mode in which the user designates the corresponding point candidate without detecting it by calculation is also possible. In this case, the user visually determines on the display the portion to be a feature point and designates it. Further, in the first operation example, when a corresponding point in the image viewed from the second installation position 133 is designated, three-dimensional point cloud position data of the portion and its periphery are acquired and used for orientation. Also good. In this case, since partial 3D point cloud position data is acquired, an increase in the burden associated with the measurement work and its processing can be suppressed as compared with the case where the entire 3D point cloud position data is acquired.

(Operation example 2)
Hereinafter, although there is a part overlapping with the above-mentioned “operation 1”, the relationship between the coordinate systems viewed from the two viewpoints is calculated, and the position of the second viewpoint on the coordinate system viewed from the first viewpoint is calculated based on the calculated relationship. An example of processing to be obtained will be described. FIG. 6 shows an example of this processing flow. When the process is started (step S601), the laser scanner 131 is first installed at the first installation position 132 (first viewpoint) (step S602), and the measurement object is photographed there to acquire image data. The three-dimensional point cloud position data is acquired (step S603). Next, the laser scanner 131 is moved to the second installation position 133 (second viewpoint) (step S604), where the same measurement object as above is photographed, and the photographed image is displayed on the display of the personal computer 130. (Step S605). Next, a three-dimensional model of the measurement object is formed based on the three-dimensional point cloud position data acquired at the first installation position 132, and further displayed on the display of the personal computer 130 (step S606). An example of this state is shown in FIG.

  Next, it is determined whether or not the common area has been designated by the user (step S607). If the common area is designated, it is displayed on the display of the personal computer 130. However, when the corresponding points are designated manually, it is not always necessary to designate the common area, as long as these areas are displayed on the display unit. An example of this state is shown in FIG. Also, corresponding points (feature points) are detected by calculation based on the three-dimensional point group position data obtained at the first installation position 132 (step S608). The detection of the corresponding points (feature points) is performed by the corresponding point detection unit 116, and one or more of the local curvature, the local plane fitting accuracy, and the coplanarity handled by the three-dimensional model forming unit 119 are used. This is performed by providing a threshold value and calculating feature points based on this threshold value. Then, the detected corresponding points are displayed on the display of the personal computer 130 (step S608). An example of this state is shown in FIG.

  Next, it is determined whether or not the corresponding point is designated by the user in the image taken from the second installation position (step S609). If the corresponding point is designated, it is displayed on the display of the personal computer 130. indicate. An example of this state is shown in FIG. Then, with the corresponding point designated by the user in the image taken from the second installation position 133 as the center point, the three-dimensional point cloud position of the square area portion around 5 × 5 to 25 × 25 Data is acquired at the second installation position 133 (step S610). Next, feature points are calculated based on the three-dimensional point cloud position data acquired at the second installation position 133, and are used as corresponding points viewed from the second installation position 133 (step S611). . Here, feature points are calculated in the acquired square area by the same method as in step S608. In this way, the corresponding point viewed from the second installation position 133 is acquired.

  Next, the corresponding points when viewed from the first installation position 132 obtained in step S608 and the corresponding points when viewed from the second installation position 133 obtained in step S611, are obtained as three-dimensional point group position data. Based on this, a process of calculating the correspondence between the two coordinate systems using absolute orientation is performed (step S612).

  If single photo orientation is used here, the acquisition of the 3D point cloud position data at the second installation position 133 (step 610) is not necessary, and the 3D point cloud position data acquired at the first installation position 132 is not necessary. The orientation based on the image data of the image taken from the second installation position 133 is performed. In the case of relative orientation, orientation based on image data obtained at the first installation position 132 and image data obtained at the second installation position 133 is performed. Also, the correspondence can be calculated using stereo matching for automatic position detection of these corresponding points.

  In the calculation of the correspondence relationship in step S612, if it is determined that a problem has occurred such as a retry being repeated in the operation, a delay in the operation, or an error occurring, it is determined that there is a problem in calculating the correspondence relationship. (Step S613), the fact is notified to the user by image display or the like. In this case, the process returns to the previous stage of step S604, the second installation position 133 is selected again, and the processes after step S604 are executed again. When the calculation of the correspondence is completed, based on the result, the value of the three-dimensional coordinate of the second installation position 133 in the coordinate system of the three-dimensional point cloud position data obtained at the first installation position is calculated (step In step S614, the process ends (step S615).

  According to the above processing, the corresponding point can be reliably set as the feature point, and the correspondence of the corresponding point from both viewpoints becomes more accurate. In other words, the designation of the corresponding point when viewed from the first installation position 132 is obtained by calculating a portion to be a feature point based on the three-dimensional point cloud position data obtained from the first installation position 132. Has been done (the feature point is definitely specified at this point).

  On the other hand, identification of corresponding points when viewed from the second installation position 133 is performed in the following steps. That is, (1) First, a corresponding point is temporarily specified by the user's manual operation based on the display of the corresponding point when viewed from the first installation position 132 displayed on the screen. (2) The point temporarily specified by the user Re-acquisition of 3D point cloud position data in the vicinity of (3), and calculation of feature points in the vicinity by calculation based on the reacquired 3D point cloud position data. Therefore, even if there is an error in the designation of the position of the corresponding point when viewed from the second installation position 133 by manual operation, the feature point in the vicinity is calculated again, and the corresponding point is specified based on it. Therefore, also at the second installation position 133, the corresponding points (feature points) designated at the first installation position 132 can be acquired with high accuracy. However, the processes (2) and (3) are not necessary if the corresponding points are specified reliably. Or even if it is provisional designation, it is not necessary when correcting to an accurate position by post-processing. If stereo matching is performed, corresponding point detection and designation can be performed automatically.

  For the reasons described above, it is possible to set the corresponding points between the two coordinate systems with higher accuracy than when the corresponding points when viewed from the second installation position 133 are simply designated by manual operation. . And the precision of the setting of the corresponding point between both coordinate systems becomes high, and the efficiency of the arithmetic processing in calculation of the correspondence between both coordinate systems can be improved. In the method described here, it is necessary to obtain the three-dimensional point cloud position data from the second installation position 133 before calculating the correspondence between the two coordinate systems. Since the acquisition of the three-dimensional point cloud position data from 133 is partial, an increase in processing time can be minimized.

(Operation example 3)
Hereinafter, an example of the operation related to the formation and display of the three-dimensional model will be described. First, at the first installation position 132, photographing of the measurement objects 134 to 136 and three-dimensional point cloud position data of the measurement objects 134 to 136 (to be precise, since occlusion occurs, the measurement object 136 3D point cloud position data cannot be acquired), and a 3D model of the measurement object in the direction viewed from the first installation position 132 is formed (see FIG. 7A).

  Next, the laser scanner 131 is moved to the second installation position 133 and images of the measurement objects 134 to 136 are taken. The correspondence between the 3D point cloud position data acquired at the first installation position 132 (first 3D point cloud position data) and the image data acquired at the second installation position 133 is “single photo orientation”. Calculate using.

  Next, calculation of the coordinate position of the second installation position 133 in the coordinate system (coordinate system of the first three-dimensional point cloud position data already obtained) when the measurement object is viewed from the first installation position 132 is calculated. I do. Based on the calculation result, an operation for displaying the three-dimensional model viewed from the viewpoint of the second installation position 133 is performed. That is, the coordinate conversion is performed on the three-dimensional model viewed from the first installation position 132 into the three-dimensional model viewed from the second installation position 133. This processing is performed based on the coordinates of the second installation position 133 in the coordinate system viewed from the first installation position 132 and the orientation of the laser scanner 131 installed at the second installation position 133 in this coordinate system. It can be performed by a typical calculation. By this process, the position of the viewpoint changes and the three-dimensional model rotates. Thus, a state is obtained in which the three-dimensional model based on the three-dimensional point cloud position data acquired at the first installation position 132 is viewed from the viewpoint of the second installation position 133 (see FIG. 7B).

  In the state of FIG. 7B, the data of the portion 134a that is occlusion is missing from the first installation position 132, and that portion is emphasized as a shadow. At this time, the measurement object 136 that is not occluded from the first installation position 132 is not displayed.

  Next, three-dimensional point group position data (second three-dimensional point group position data) of the measurement objects 134 to 136 is acquired at the second installation position 133. Thereafter, based on the coordinate position of the second installation position 133 in the coordinate system when the measurement object is viewed from the first installation position 132 described above, the first three-dimensional point cloud position data and the second three-dimensional The point cloud position data is handled in an integrated manner, and a 3D model based on the first 3D point cloud position data and the second 3D point cloud position data is formed. An example when this three-dimensional model is displayed on the screen is shown in FIG. In this case, since the three-dimensional model is formed using the three-dimensional point cloud position data obtained at the second installation position 133 where the measurement object 136 of FIG. 2 can be seen, the measurement object 136 does not become an occlusion part. In addition, when the 3D model is rotated to the viewpoint of FIG. 2A, the 3D model portion based on the 3D point cloud position data obtained at the first installation position 132 can be seen. A portion that becomes a blind spot from the installation position 133 is not occluded and is displayed.

(Operation example 4)
In order to obtain the coordinates of the three-dimensional model and the three-dimensional point group position data by associating the coordinate data handled inside the point group position data processing apparatus 100 of FIG. Can be associated with map data. Thereby, for example, the function of displaying the position of the viewpoint on the map data displayed on the personal computer becomes possible.

(Other)
A configuration in which a plurality of functions provided in the point cloud position data processing apparatus 100 of FIG. 1 is distributed is also possible. For example, the functions of the 3D model forming unit 119 and the coordinate calculation unit 117 are performed by another computer or server connected by a communication line, and a point cloud position data processing system having the same functions as the point cloud data processing apparatus 100 is configured. You can also It is also possible to implement the present invention as a system in which a part of the function of the point cloud position data processing apparatus 100 is provided on the laser scanner side and combined with this laser scanner.

(Advantages of the first embodiment)
According to the embodiment described above, after acquiring the three-dimensional point cloud position data of the measurement object at the first viewpoint, the laser scanning device is moved to the second viewpoint, and the three-dimensional point cloud position data at the second viewpoint is obtained. Before acquisition, the position of a new viewpoint (second viewpoint) is specified on the coordinate system used when the measurement object is viewed from the first viewpoint based on image data obtained by shooting. . By using this identified result, the three-dimensional model based on the three-dimensional point cloud coordinate data obtained at the first viewpoint can be changed to the orientation viewed from the second viewpoint. That is, it is possible to perform coordinate transformation so that the three-dimensional point cloud coordinate data obtained at the first viewpoint can be handled in the coordinate system when the measurement object is viewed from the second viewpoint. The three-dimensional model based on the three-dimensional point cloud coordinate data obtained at the viewpoint can be rotated in the direction viewed from the second viewpoint.

  Further, by calculating the position of the second viewpoint in the coordinate system used for the first viewpoint, the three-dimensional point cloud position data obtained at the second viewpoint thereafter, and the first viewpoint acquired previously The correspondence with the 3D point cloud position data from can be known. That is, the second viewpoint is the origin of the three-dimensional point cloud position data obtained from the second viewpoint. Therefore, by obtaining the position of the origin on the coordinates used at the first viewpoint, the three-dimensional point group position data obtained at the first viewpoint and the three-dimensional point group position data obtained at the second viewpoint. No new alignment is required (or a rough correspondence is given, so the burden required for alignment is reduced). For this reason, by comparing the two 3D point cloud position data, the processing required to handle the two point cloud position data in an integrated manner is greatly improved compared to the case of performing the alignment ( Shortened).

  In addition, since the process for obtaining the position of the second viewpoint in the coordinate system used for the first viewpoint does not presuppose acquisition of three-dimensional point cloud position data of the entire measurement object at the second viewpoint, the second viewpoint After acquiring the 3D point cloud position data of the entire measurement object, the problem that a huge amount of processing is wasted is found, such as when the second viewpoint is found to be inappropriate. Can be avoided. In other words, when the measurement object is viewed from the first viewpoint, the coordinate position of the second viewpoint in the coordinate system cannot be well specified (for example, when retry of calculation is repeated), or occlusion is not sufficiently eliminated. For example, the second viewpoint can be easily changed when the second viewpoint is not valid. For this reason, it is possible to avoid the inconvenience of wasteful measurement work and processing that consumes enormous effort and time.

  In the designation of corresponding points shown in FIG. 5, since the operation is a manual operation using a pointing device using a mouse or the like, there may be a case where an error is included in the designated position of the corresponding points. However, even if there is a problem in accuracy, it is possible to give an approximate initial value when the two three-dimensional point cloud position data is handled in an integrated manner. Compared with the case where the processing to integrate them based on both 3D point cloud position data is performed after acquiring the 3D point cloud position data of the entire measurement object, the processing is dramatically improved. (Shortening).

2. Second Embodiment Hereinafter, a point cloud position data processing apparatus including a three-dimensional laser scanner will be described. In this example, the point cloud position data processing apparatus irradiates the measurement object with distance measuring light (laser light) while scanning it, and based on the time of flight of the laser light, the point cloud position data processing apparatus applies a large number on the measurement object. Measure the distance to the measurement point. In addition, the point cloud position data processing device detects the irradiation direction (horizontal angle and elevation angle) of the laser beam, and calculates the three-dimensional coordinates of the measurement point based on the distance and the irradiation direction. Also, the point cloud position data processing device acquires a two-dimensional image (RGB intensity at each measurement point) obtained by photographing the measurement object, and forms three-dimensional point cloud position data by connecting the two-dimensional image and the three-dimensional coordinates. To do. Furthermore, the point cloud position data processing apparatus shown here has a function of performing the processing of the point cloud position data processing apparatus 100 described with reference to FIG.

(Constitution)
10 and 11 are cross-sectional views showing the configuration of the point cloud position data processing apparatus 1 having a three-dimensional laser scanner function. The point cloud position data processing apparatus 1 includes a leveling unit 22, a rotation mechanism unit 23, a main body unit 27, and a rotation irradiation unit 28. The main body 27 includes a distance measuring unit 24, a photographing unit 25, a control unit 26, and the like. For convenience of explanation, FIG. 11 shows a state in which only the rotary irradiation unit 28 is viewed from the side with respect to the cross-sectional direction shown in FIG.

  The leveling unit 22 has a base plate 29, and the rotation mechanism unit 23 has a lower casing 30. The lower casing 30 is supported on the base plate 29 at three points by a pin 31 and two adjustment screws 32. The lower casing 30 tilts with the tip of the pin 31 as a fulcrum. A tension spring 33 is provided between the base plate 29 and the lower casing 30 to prevent the base plate 29 and the lower casing 30 from separating from each other.

  Two leveling motors 34 are provided inside the lower casing 30. The two leveling motors 34 are driven by the control unit 26 independently of each other. When the leveling motor 34 is driven, the adjustment screw 32 is rotated via the leveling drive gear 35 and the leveling driven gear 36, and the amount of downward protrusion of the adjustment screw 32 is adjusted. An inclination sensor 37 (see FIG. 12) is provided inside the lower casing 30. The two leveling motors 34 are driven by the detection signal of the tilt sensor 37, whereby leveling is executed.

  The rotation mechanism unit 23 includes a horizontal angle drive motor 38 inside the lower casing 30. A horizontal rotation drive gear 39 is fitted to the output shaft of the horizontal angle drive motor 38. The horizontal rotation drive gear 39 is meshed with the horizontal rotation gear 40. The horizontal rotation gear 40 is provided on the rotation shaft portion 41. The rotating shaft portion 41 is provided at the center portion of the rotating base 42. The rotating base 42 is provided on the upper portion of the lower casing 30 via a bearing member 43.

  Further, the rotary shaft portion 41 is provided with, for example, an encoder as the horizontal angle detector 44. The horizontal angle detector 44 detects a relative rotation angle (horizontal angle) of the rotation shaft portion 41 with respect to the lower casing 30. The horizontal angle is input to the control unit 26, and the control unit 26 controls the horizontal angle drive motor 38 based on the detection result.

  The main body 27 has a main body casing 45. The main body casing 45 is fixed to the rotating base 42. A lens barrel 46 is provided inside the main body casing 45. The lens barrel 46 has a rotation center concentric with the rotation center of the main body casing 45. The center of rotation of the lens barrel 46 is aligned with the optical axis 47. Inside the lens barrel 46, a beam splitter 48 as a light beam separating means is provided. The beam splitter 48 has a function of transmitting visible light and reflecting infrared light. The optical axis 47 is separated into an optical axis 49 and an optical axis 50 by a beam splitter 48.

  The distance measuring unit 24 is provided on the outer periphery of the lens barrel 46. The distance measuring unit 24 includes a pulse laser light source 51 as a light emitting unit. Between the pulse laser light source 51 and the beam splitter 48, a perforated mirror 52 and a beam waist changing optical system 53 for changing the beam waist diameter of the laser light are arranged. The distance measuring light source unit includes a pulse laser light source 51, a beam waist changing optical system 53, and a perforated mirror 52. The perforated mirror 52 has a role of guiding the pulsed laser light from the hole 52 a to the beam splitter 48, and reflecting the reflected laser light returned from the measurement object toward the distance measuring light receiving unit 54.

  The pulse laser light source 51 emits infrared pulse laser light at a predetermined timing under the control of the control unit 26. The infrared pulse laser beam is reflected by the beam splitter 48 toward the high / low angle rotating mirror 55. The elevation mirror 55 for high and low angles reflects the infrared pulse laser beam toward the measurement object. The elevation mirror 55 is rotated in the elevation direction to convert the optical axis 47 extending in the vertical direction into a projection optical axis 56 in the elevation direction. A condensing lens 57 is disposed between the beam splitter 48 and the elevation mirror 55 and inside the lens barrel 46.

  The reflected laser light from the object to be measured is guided to the distance measuring light receiving unit 54 through the elevation angle turning mirror 55, the condenser lens 57, the beam splitter 48, and the perforated mirror 52. Further, the reference light is also guided to the distance measuring light receiving unit 54 through the internal reference light path. Point cloud position data processing based on the difference between the time until the reflected laser light is received by the distance measuring light receiving unit 54 and the time until the laser light is received by the distance measuring light receiving unit 54 through the internal reference light path The distance from the apparatus 1 to the measurement target (measurement target point) is measured. The ranging light receiving unit 54 is configured by a photoelectric change element such as a CMOS optical sensor, and also has a function of detecting the RGB intensity of the detected light.

  The imaging unit 25 includes an image light receiving unit 58 and functions as a camera corresponding to the image acquisition device 102 of FIG. The image light receiving unit 58 is provided at the bottom of the lens barrel 46. The image light receiving unit 58 is configured by a pixel in which a large number of pixels are arranged in a plane, for example, a CCD (Charge Coupled Device). The position of each pixel of the image light receiving unit 58 is specified by the optical axis 50. For example, an XY coordinate is assumed with the optical axis 50 as the origin, and a pixel is defined as a point of the XY coordinate.

  The rotary irradiation unit 28 is accommodated in the light projection casing 59. A part of the peripheral wall of the light projection casing 59 serves as a light projection window. As shown in FIG. 11, a pair of mirror holder plates 61 are provided facing the flange portion 60 of the lens barrel 46. A rotation shaft 62 is stretched over the mirror holder plate 61. The high / low angle turning mirror 55 is fixed to the turning shaft 62. An elevation gear 63 is fitted to one end of the rotation shaft 62. An elevation angle detector 64 is provided on the other end side of the rotation shaft 62. The elevation angle detector 64 detects the rotation angle of the elevation angle rotation mirror 55 and outputs the detection result to the control unit 26.

  A high and low angle drive motor 65 is attached to one side of the mirror holder plate 61. A drive gear 66 is fitted on the output shaft of the high / low angle drive motor 65. The drive gear 66 is meshed with an elevation gear 63 attached to the rotary shaft 62. The elevation motor 65 is appropriately driven by the control of the control unit 26 based on the detection result of the elevation detector 64.

  On the upper part of the light projection casing 59, there is provided an illumination star turret 67. The sight sight gate 67 is used for roughly collimating the measurement object. The collimation direction using the sight sight gate 67 is a direction orthogonal to the direction in which the projection light axis 56 extends and the direction in which the rotation shaft 62 extends. Further, as shown in FIG. 11, a GPS antenna 81 is disposed on the upper part of the light projection casing 59. GPS information is acquired by the GPS antenna, and the GPS information can be used for calculations performed internally.

  FIG. 12 is a block diagram of the control unit. Detection signals from the horizontal angle detector 44, the elevation angle detector 64, the tilt sensor 37, and the GPS antenna 81 are input to the control unit 26. The control unit 26 receives an operation instruction signal from the operation unit 6. The control unit 26 drives and controls the horizontal angle drive motor 38, the elevation angle drive motor 65, and the leveling motor 34, and controls the display unit 7 that displays the work status, measurement results, and the like. An external storage device 68 such as a memory card or HDD can be attached to and detached from the control unit 26.

  The control unit 26 includes a calculation unit 4, a storage unit 5, a horizontal drive unit 69, a height drive unit 70, a leveling drive unit 71, a distance data processing unit 72, an image data processing unit 73, and the like. The storage unit 5 includes a sequence program, a calculation program, a measurement data processing program for executing measurement data processing, an image processing program for performing image processing, and a three-dimensional point, which are necessary for distance measurement and detection of elevation angle and horizontal angle. A program for extracting a surface from group position data and further calculating a contour line, an image display program for displaying the calculated contour line on the display unit 7, and a process related to reacquisition of three-dimensional point group position data are controlled. In addition to storing various programs such as programs, an integrated management program or the like for integrating and managing these various programs is stored. The storage unit 5 stores various data such as measurement data and image data. The horizontal drive unit 69 drives and controls the horizontal angle drive motor 38, the elevation drive unit 70 controls the drive of the elevation angle drive motor 65, and the leveling drive unit 71 controls the leveling motor 34. The distance data processing unit 72 processes the distance data obtained by the distance measuring unit 24, and the image data processing unit 73 processes the image data obtained by the photographing unit 25.

  The control unit 26 includes a GPS receiving unit 82. The GPS receiver 82 processes a signal from a GPS satellite received by the GPS antenna, and calculates coordinate data on the earth. This is the same as a normal GPS receiver. The position information obtained from the GPS is input to the point cloud position data processing unit 100 '.

  FIG. 13 is a block diagram of the calculation unit 4. The calculation unit 4 includes a three-dimensional coordinate calculation unit 74, a link formation unit 75, a grid formation unit 9, and a point group position data processing unit 100 '. The three-dimensional coordinate calculation unit 74 receives the distance data of the measurement target point from the distance data processing unit 72, and the direction data (horizontal angle and elevation angle) of the measurement target point from the horizontal angle detector 44 and the elevation angle detector 64. Is entered. Based on the input distance data and direction data, the three-dimensional coordinate calculation unit 74 uses the three-dimensional coordinates (orthogonal coordinates) of each measurement point with the position of the point cloud position data processing device 1 as the origin (0, 0, 0). ) Is calculated.

  The link forming unit 75 receives the image data from the image data processing unit 73 and the coordinate data of the three-dimensional coordinates of each measurement point calculated by the three-dimensional coordinate calculation unit 74. The link forming unit 75 forms 3D point group position data 2 in which image data (RGB intensity at each measurement point) and 3D coordinates are linked. That is, when focusing on a point on the measurement object, the link forming unit 75 creates a link in which the position of the point of interest in the two-dimensional image is associated with the three-dimensional coordinates of the point of interest. The associated data is calculated for all the measurement points, and becomes the three-dimensional point group position data 2.

  The link forming unit 75 outputs the above three-dimensional point cloud position data 2 to the grid forming unit 9. When the distance between adjacent points in the three-dimensional point cloud position data 2 is not constant, the grid forming unit 9 forms an equally spaced grid (mesh) and registers the point closest to the grid intersection. Or the grid formation part 9 correct | amends all the points to the intersection position of a grid using a linear interpolation method or a bicubic method. If the distance between the points in the 3D point cloud position data 2 is constant, the processing of the grid forming unit 9 can be omitted.

Hereinafter, the grid formation procedure will be described. FIG. 14 is a conceptual diagram showing three-dimensional point group position data where the distance between points is not constant, and FIG. 15 is a conceptual diagram showing a formed grid. As shown in FIG. 14, the average horizontal intervals H ₁ to _N of each column are obtained, the difference ΔH _{i, j} of the average horizontal interval between the columns is calculated, and the average is set as the horizontal interval ΔH of the grid (Equation 16 ). The vertical interval is calculated by calculating the distance ΔV _{N, H} between the vertical adjacent points in each column, and the average of ΔV _{N, H in} the entire image of the image sizes W, H is defined as the vertical interval ΔV (Equation 17 ). Then, as shown in FIG. 15, a grid having the calculated horizontal interval ΔH and vertical interval ΔV is formed.

  Next, the point closest to the intersection of the formed grids is registered. At this time, a predetermined threshold is provided for the distance from the intersection to each point to limit registration. For example, the threshold value is ½ of the horizontal interval ΔH and the vertical interval ΔV. It should be noted that all points may be corrected by applying a weight according to the distance from the intersection, such as a linear interpolation method or a bicubic method. However, when interpolation is performed, the point is not originally measured.

  The three-dimensional point cloud position data obtained as described above is output to the point cloud position data processing unit 100 '. The point cloud position data processing unit 100 ′ performs the operation described in the first embodiment. In addition, the display of the image presented to the user in the operation (for example, the screen display of FIGS. 4, 5, and 7) is displayed on the display unit 7 which is a liquid crystal display. This point is the same as the case described in relation to the first embodiment. The point cloud position data processing unit 100 ′ is hardware having the same function as that of the point cloud position data processing apparatus 100 of FIG. 1, and is configured by a dedicated integrated circuit using FPGA.

  Coordinate data on the earth obtained from the GPS receiving unit 82 is input to the point cloud position data processing unit 100 ′. According to this configuration, coordinates handled by the point cloud position data processing unit 100 ′ are linked to position data (for example, electronic map information) obtained from GPS. Thereby, for example, the installation position of the point cloud position data processing apparatus 1 provided with the laser scanner can be displayed on the electronic map on the screen.

(Other)
In the configuration of the control unit 26, assuming that the three-dimensional point cloud position data is output from the grid forming unit 9, the apparatus shown in FIGS. 10 and 11 uses the personal computer shown in the first embodiment. The three-dimensional laser scanner can be used in combination with the group position data processing device. A configuration in which the processing performed by the point cloud position data processing unit 100 ′ is performed in a distributed manner is also possible. For example, a configuration in which a part of the function of the point cloud position data processing unit 100 ′ is performed by a server connected by a communication line is also possible. In this case, it is grasped as an example of the point cloud position data processing system of the present invention.

  As a method for acquiring an image, a method by photographing using a CCD camera or the like is common, but an image of a measurement object can also be reproduced based on point cloud data. When three-dimensional point cloud position data is obtained by a laser scanning device, data relating to the light intensity of reflected light from each point is obtained. Therefore, by treating the 3D point cloud position data as pixel data constituting the image of the object, the image of the measurement object can be reproduced based on the 3D point cloud position data. That is, an image of an object to be measured can be obtained using a laser scanning device instead of a photographing means such as a CCD or a CMOS image sensor. In this case, the image data acquisition unit 112 in FIG. 1 acquires image data based on the above-described principle based on the three-dimensional point cloud position data output from the point cloud position data acquisition device 101.

  The present invention can be used in a technique for measuring three-dimensional information.

  DESCRIPTION OF SYMBOLS 1 ... Point cloud position data processing apparatus provided with the three-dimensional laser scanner, 2 ... Three-dimensional point cloud position data, 22 ... Leveling part, 23 ... Rotation mechanism part, 24 ... Distance measuring part, 25 ... Imaging part, 26 ... Control part 27 ... Main body part 28 ... Rotating irradiation part 29 ... Base plate 30 ... Lower casing 31 ... Pin 32 ... Adjustment screw 33 ... Tension spring 34 ... Leveling motor 35 ... Leveling drive gear 36 ... Leveling driven gear, 37 ... Tilt sensor, 38 ... Horizontal rotation motor, 39 ... Horizontal rotation drive gear, 40 ... Horizontal rotation gear, 41 ... Rotating shaft part, 42 ... Rotating base, 43 ... Bearing member , 44 ... Horizontal angle detector, 45 ... Main body casing, 46 ... Lens barrel, 47 ... Optical axis, 48 ... Beam splitter, 49, 50 ... Optical axis, 51 ... Pulse laser light source, 52 ... Perforated mirror, 53 ... Beam waist change optical system, 54 ... Ranging , 55... Rotating mirror for high and low angles, 56... Projecting optical axis, 57... Condensing lens, 58. Driving shaft, 63 ... High and low angle gear, 64 ... High and low angle detector, 65 ... High and low angle drive motor, 66 ... Drive gear, 67 ... High / low drive unit, 71 ... Leveling drive unit, 72 ... Distance data processing unit, 73 ... Image data processing unit, 81 ... GPS antenna, 82 ... GPS reception unit, 100 '... Point cloud position data processing unit, 130 ... Point cloud Personal computer functioning as a position data processing device, 131... 3D laser scanner, 132... 1st installation position for installing 3D laser scanner, 133... 2nd installation position for installing 3D laser scanner, 34,135,136 ... the object to be measured, 134a ... occlusion part,

Claims (13)

A point cloud position data acquisition unit for acquiring 3D point cloud position data of the measurement object at the first viewpoint ;
An image data acquisition unit for acquiring image data of the measurement object at a second viewpoint different from the first viewpoint ;
The corresponding point designating section for designating corresponding points in a common region on the image of the measurement object as viewed from the second viewpoint and the measurement object as viewed from the first viewpoint,
By specifying the correspondence between the three-dimensional point group position data of the corresponding point at the first viewpoint and the screen coordinate value of the corresponding point at the second viewpoint, the coordinate system at the first viewpoint A point cloud position data processing apparatus comprising: a coordinate calculation unit that performs a calculation for obtaining a three-dimensional position of the associated second viewpoint.   Based on the three-dimensional point cloud position data of the measurement object obtained from the first viewpoint, or the three-dimensional point cloud position data of the measurement object and the second viewpoint obtained from the first viewpoint. And a three-dimensional model forming unit that forms a three-dimensional model of the measurement object viewed from the second viewpoint based on the three-dimensional point cloud position data of the measurement object obtained from The point cloud position data processing device according to claim 1. The three-dimensional model forming unit forms a first viewpoint three-dimensional model that is a three-dimensional model based on the three-dimensional point cloud position data of the measurement object obtained from the first viewpoint,
The first viewpoint three-dimensional model, or a first viewpoint image that is an image of the measurement object obtained from the first viewpoint and a second viewpoint image that is an image obtained from the second viewpoint are displayed. And a display control unit that controls to display the corresponding points in at least one of the first viewpoint three-dimensional model, the first viewpoint image, and the second viewpoint image. The point cloud position data processing device described in 1. The said corresponding point designation | designated part is provided with the corresponding point detection part which detects the said corresponding point based on the three-dimensional point cloud position data of the said measurement target obtained in the said 1st viewpoint. The point cloud position data processing apparatus as described in any one of -3 . The designation of the corresponding points in the corresponding point designating unit is executed by performing stereo matching between the image obtained from the first viewpoint and the image obtained from the second viewpoint. The point cloud position data processing device according to any one of claims 1 to 4 . The corresponding point detecting unit, point group location data processing device according to the feature point portion of the measuring object to claim 4 or 5, characterized in that detected as the corresponding point. The point cloud position data acquisition unit acquires, when the corresponding point is specified, the 3D point cloud position data of the peripheral portion including the specified corresponding point at the second viewpoint,
7. The point according to claim 6 , wherein the feature point is calculated based on the acquired three-dimensional point cloud position data, and the designated corresponding point is specified based on the calculation. Group position data processing device. The coordinate calculation unit performs a calculation for obtaining a three-dimensional position of the second viewpoint associated with the coordinate system of the first viewpoint based on a correspondence relationship at least at three points. The point cloud position data processing device according to any one of claims 7 to 9. A point cloud position data measurement optical system for measuring the three-dimensional point cloud position data acquired by the point cloud position data acquisition unit;
A photographing optical system for photographing an image serving as a basis of the image data,
The point cloud position data measurement optical system and the photographing optical system are rotatable about the same axis,
The coordinate calculation unit performs a calculation for obtaining a three-dimensional position of the second viewpoint associated with the coordinate system of the first viewpoint based on a correspondence relationship between at least two points. The point cloud position data processing device according to any one of claims 7 to 9. A GPS unit that identifies the position of the first viewpoint and the position of the second viewpoint;
The coordinate calculation unit, any one of claim 1 to 9, characterized in that a calculation based on the positions of said second viewpoint of the first viewpoint specified by said GPS unit The point cloud position data processing device described. A point cloud position data acquisition step of acquiring 3D point cloud position data of the measurement object at the first viewpoint;
An image data acquisition step of acquiring image data of the measurement object at a second viewpoint different from the first viewpoint;
A designation corresponding point step of designating the corresponding points in a common region on the image of the measurement object as viewed from the second viewpoint and the measurement object as viewed from the first viewpoint,
By specifying the correspondence between the three-dimensional point group position data of the corresponding point at the first viewpoint and the screen coordinate value of the corresponding point at the second viewpoint, the coordinate system at the first viewpoint A point cloud position data processing method comprising: a coordinate calculation step for calculating a three-dimensional position of the associated second viewpoint. Point cloud position data acquisition means for acquiring 3D point cloud position data of the measurement object at the first viewpoint ;
Image data acquisition means for acquiring image data of the measurement object at a second viewpoint different from the first viewpoint ;
A corresponding point specifying means for specifying corresponding points in a common region on the image of the measurement object as viewed from the second viewpoint and the measurement object as viewed from the first viewpoint,
By specifying the correspondence between the three-dimensional point group position data of the corresponding point at the first viewpoint and the screen coordinate value of the corresponding point at the second viewpoint, the coordinate system at the first viewpoint A point cloud position data processing system comprising: coordinate calculation means for calculating a three-dimensional position of the associated second viewpoint. A program that is read and executed by a computer,
Computer
A point cloud position data acquisition unit for acquiring 3D point cloud position data of the measurement object at the first viewpoint ;
An image data acquisition unit for acquiring image data of the measurement object at a second viewpoint different from the first viewpoint ;
The corresponding point designating section for designating corresponding points in a common region on the image of the measurement object as viewed from the second viewpoint and the measurement object as viewed from the first viewpoint,
By specifying the correspondence between the three-dimensional point group position data of the corresponding point at the first viewpoint and the screen coordinate value of the corresponding point at the second viewpoint, the coordinate system at the first viewpoint A point cloud position data processing program that functions as a coordinate calculation unit that performs a calculation for obtaining a three-dimensional position of the associated second viewpoint.

Images