JP2012230594A - Three-dimensional point group position data processor, three-dimensional point group position data processing method, three-dimensional point group position data processing system, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technology for learning interim progress of three-dimensional point group position data.SOLUTION: A three-dimensional models whose resolutions are gradually changed are created (S505) on the basis of three-dimensional point group position data (S502) measured by a three-dimensional laser scanner. In this case, creation of a three-dimensional model with low resolution and its display (S506) are performed, after that, creation of a three-dimensional model with high resolution and its display are further performed by succeeding labelled data (S509). Here, a user can grasp a step in the middle of completion of the final three-dimensional model by displaying the three-dimensional model with low resolution.

Description

  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

  The number of points of the three-dimensional point cloud position data is tens of thousands to hundreds of millions. Therefore, a certain amount of time is required to process the acquired 3D point cloud position data and create a 3D model. Of course, this processing time depends on the performance of the computing device used, the shape of the measurement object, the required resolution, etc., but when a general personal computer is used, it may require several hours or more. is there. During this calculation, it is unclear what kind of result will be obtained, but there is a user's desire to know only a rough outline even in the middle. In addition, when the 3D model is obtained, the viewpoint setting for acquiring 3D point cloud position data is not appropriate, and it may be necessary to change the viewpoint and acquire 3D point cloud position data again. Even in this case, if the 3D model can be seen with some outline even though the resolution is low, it may be found that the viewpoint is not appropriate before reaching the final result. In such a case, if the validity of this calculation is known at the stage before the calculation is performed to the end, useless processing can be reduced and the work efficiency can be improved. In such a background, an object of the present invention is to provide a technique capable of knowing the progress of processing of three-dimensional point cloud position data.

  The invention according to claim 1 is a three-dimensional point group position data acquisition unit that acquires three-dimensional point group position data of a measurement object, and the three-dimensional point group position acquired by the three-dimensional point group position data acquisition unit. A density changing unit for three-dimensional point cloud position data for changing the density of the data, and a non-plane area removing unit for removing points of the non-plane area from the three-dimensional point cloud position data whose density has been changed by the density changing unit; A surface labeling unit that assigns the same label to points on the same surface with respect to points other than the points removed by the non-surface region removal unit, and a three-dimensional model based on the surface divided by the surface labeling unit A three-dimensional point cloud position data processing device comprising a three-dimensional model creation unit for creating.

  According to the first aspect of the present invention, the density changing unit changes the density of the three-dimensional point cloud position data, and creates a three-dimensional model based on the three-dimensional point cloud position data with the changed density. By reducing the density of the 3D point cloud position data, which is the basic data for the calculation, the resolution is reduced, but the processing load required for creating the 3D model is reduced. That is, a rough three-dimensional model is obtained, but the time required for processing is shortened. Therefore, when creating a 3D model without changing the density of the 3D point cloud position data by reducing the density of the acquired 3D point cloud position data and creating a 3D model based on it In comparison, a three-dimensional model can be obtained in a shorter processing time. By using this function, it is possible to obtain a low-accuracy 3D model in a shorter time before the final 3D model is obtained. By presenting this low-accuracy 3D model to the user, The user can know the progress of the processing of the 3D point cloud position data. In addition, if this function is used, low resolution is allowed by selecting a hierarchy with a reduced resolution, but it is possible to meet a request for obtaining a three-dimensional model in a short processing time.

  According to a second aspect of the present invention, in the first aspect of the present invention, the density change in the density changing unit is performed in two or more layers.

  The invention according to claim 3 is the invention according to claim 2, wherein the density changing unit determines the number of layers based on a time required to create the three-dimensional model. When a three-dimensional model is created without reducing the resolution, the density of the basic three-dimensional point cloud position data is large (that is, the amount of data is large), so the time required for processing becomes long. On the other hand, when the resolution is lowered, the density of the basic three-dimensional point cloud position data is reduced (that is, the data amount is reduced), so that the time required for processing can be shortened. When the number of layers is increased, the processing can be started from the creation of a lower-resolution three-dimensional model, so that the processing time for one layer is relatively short. Therefore, by setting the number of hierarchies based on the time required for processing of one layer, settings are performed so that processing according to the user's request is performed.

  The invention according to claim 4 is the invention according to any one of claims 1 to 3, wherein the three-dimensional model creation unit creates a relatively low resolution after creating a relatively low resolution three-dimensional model. It is characterized by creating a high three-dimensional model.

  According to the invention described in claim 4, the high-resolution three-dimensional model is created after the low-resolution three-dimensional model is created. That is, a three-dimensional model whose resolution increases step by step is created. In creating a three-dimensional model with a stepwise increase in resolution, the amount of three-dimensional point cloud position data used for one-step (one-layer) processing is smaller than when the step-wise processing is not performed. Therefore, the time required for processing per stage (per layer) can be shortened. Therefore, a three-dimensional model can be obtained at an earlier stage although the resolution is low.

  According to a fifth aspect of the present invention, in the fourth aspect of the present invention, the three-dimensional model creation unit uses the same label information given when the three-dimensional model having a relatively low resolution is created. Based on the above, a three-dimensional model having a relatively high resolution is created.

  According to the fifth aspect of the present invention, the label data created in the relatively low resolution layer is taken over by the processing in the relatively high resolution layer below it. In other words, coarse labeling is performed first, and then the labeling data is taken over to perform more detailed labeling. By doing so, the processing efficiency is increased. Assuming that the labeling is performed from the beginning every time the layer changes, the processing load increases as the layer becomes a higher resolution layer, and the processing in the prior art does not change at the last layer.

  The invention according to claim 6 is the invention according to any one of claims 1 to 5, further comprising a three-dimensional model image creation unit that creates an image of the three-dimensional model, and the three-dimensional model image creation. The unit is characterized in that after the image of the three-dimensional model having a relatively low resolution is created, the image of the three-dimensional model having a relatively high resolution is created. According to the invention described in claim 6, since the image of the three-dimensional model whose resolution is gradually increased is displayed, the user can observe the state of the three-dimensional model whose resolution is gradually increased.

  According to a seventh aspect of the invention, in the sixth aspect of the invention, after the image of the relatively low-resolution three-dimensional model is created, the creation of the relatively high-resolution three-dimensional model is stopped. A reception unit that receives an instruction is provided.

  According to the seventh aspect of the present invention, the processing can be stopped before the final high-resolution three-dimensional model is created. If the final high-resolution 3D model is created without going through multiple stages, even if you notice an error such as setting the viewpoint when the 3D model is obtained, Since it has been completed, the processing up to that point is wasted. On the other hand, in the method of obtaining a 3D model by dividing the resolution step by step, an error is noticed at the low resolution stage where the amount of calculation is small, and the processing is stopped at that point, and this is wastefully performed. Can be reduced.

  The invention according to claim 8 is the invention according to any one of claims 1 to 7, wherein the three-dimensional edge extracting unit extracts a three-dimensional edge at a boundary portion between the surfaces divided by the surface labeling unit. And a two-dimensional edge extraction unit that extracts a two-dimensional edge from the plane divided by the surface labeling unit, and an edge integration unit that integrates the three-dimensional edge and the two-dimensional edge. According to the invention described in claim 8, the creation of the three-dimensional model is efficiently performed.

  The invention according to claim 9 is the invention according to claim 8, further comprising a boundary region surface forming unit that forms a boundary region surface that connects adjacent surfaces, wherein the three-dimensional edge extraction unit includes the boundary A feature is that a three-dimensional edge is extracted based on a region plane. By creating a boundary region surface and extracting a three-dimensional edge based on the boundary region surface, it is possible to create a three-dimensional model that accurately reproduces the appearance features of the measurement object.

  A tenth aspect of the present invention is the invention according to any one of the first to ninth aspects, wherein the non-surface region removing unit is a plane of a local surface formed by a plurality of three-dimensional point group position data. The non-surface region is removed based on at least one of the fitting accuracy, curvature, and coplanarity with other local surfaces. According to the invention described in claim 10, the surface can be extracted with high accuracy.

  The invention according to claim 11 is the invention according to any one of claims 1 to 10, wherein the label of the nearest surface is given to the point where the label is not given by the surface labeling part, The apparatus further includes a label expansion unit that expands the label. The degree of completeness of the three-dimensional model can be increased by extending the label in the label extension unit.

  The invention according to claim 12 is a three-dimensional point cloud position data acquisition step for acquiring three-dimensional point cloud position data of a measurement object, and the three-dimensional point cloud position acquired in the three-dimensional point cloud position data acquisition step. A density changing step of 3D point cloud position data for changing the density of the data, and a non-plane area removing step for removing points of the non-plane area from the 3D point cloud position data whose density has been changed in the density changing step; A surface labeling step for assigning the same label to points on the same surface with respect to points other than the points removed in the non-surface region removal step; and a three-dimensional model based on the surfaces divided in the surface labeling step. A three-dimensional point cloud position data processing method comprising: a three-dimensional model creation step for creating.

  The invention according to claim 13 is a three-dimensional point group position data acquisition unit that acquires three-dimensional point group position data of a measurement object, and the three-dimensional point group position acquired by the three-dimensional point group position data acquisition unit. 3D point cloud position data density changing means for changing data density, and non-plane area removing means for removing non-plane area points from the 3D point cloud position data whose density has been changed by the density changing means; The surface labeling means for assigning the same label to the points on the same surface with respect to the points other than the points removed by the non-surface area removing means, and the three-dimensional model based on the surface divided by the surface labeling means A three-dimensional point cloud position data processing system comprising a three-dimensional model creating means for creating.

  The invention according to claim 14 is a program that is read and executed by a computer, the computer acquiring a three-dimensional point cloud position data acquisition unit for acquiring three-dimensional point cloud position data of the measurement object, and the three-dimensional point From the density changing unit of the 3D point cloud position data for changing the density of the 3D point cloud position data acquired by the group position data acquiring unit, and the 3D point cloud position data whose density has been changed by the density changing unit A non-surface area removing unit that removes points in the non-surface area; a surface labeling unit that gives the same label to points on the same surface with respect to points other than the points removed by the non-surface area removing unit; and It is a program characterized by functioning as a three-dimensional model creation unit that creates a three-dimensional model based on the surfaces classified by the surface labeling unit.

  ADVANTAGE OF THE INVENTION According to this invention, the technique which can know the progress in the middle of the process of 3D point cloud position data is provided.

It is a block diagram of an embodiment. It is a conceptual diagram explaining an example of the distribution state of three-dimensional point cloud position data. It is a conceptual diagram explaining the density of three-dimensional point cloud position data. It is a block diagram of an embodiment. It is a flowchart which shows an example of the procedure of the process in embodiment. It is a conceptual diagram explaining an example of hierarchization. It is an image display figure which shows an example of the image of the hierarchized three-dimensional model. It is a conceptual diagram explaining the relationship of each parameter. It is an internal structure figure which shows an example of the internal structure of a laser scanner. It is an internal structure figure which shows an example of the internal structure of a laser scanner. It is a block diagram concerning a laser scanner. It is a block diagram concerning a laser scanner. It is a conceptual diagram which shows an example of three-dimensional point cloud position data. It is a conceptual diagram which shows an example of three-dimensional point cloud position data.

1. First embodiment (configuration)
Hereinafter, an example of a three-dimensional point cloud position data processing apparatus using the invention will be described with reference to the drawings. In the following description, the three-dimensional point cloud 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.

  FIG. 1 shows a three-dimensional point cloud position data processing device 100. The three-dimensional point cloud position data processing apparatus 100 has a function of creating a three-dimensional model of the measurement object based on the three-dimensional point cloud position data of the measurement object. The three-dimensional point cloud position data processing apparatus 100 is configured as software on a personal computer. A program that configures the three-dimensional 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 operation 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 which is a user interface integrating the operation input function and the image display function, Between a CPU and other dedicated arithmetic devices, semiconductor memory, hard disk storage unit, disk storage device drive unit capable of exchanging information with a storage medium such as an optical disk, and its interface unit, portable storage medium such as a USB memory As necessary, an interface unit that can exchange information and a communication interface unit that performs wireless communication and wired communication are provided. 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, a dedicated computer can be prepared. In addition, the 3D point cloud position data processing apparatus 100 may be configured by dedicated hardware configured using a PLD (Programmable Logic Device) such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). Is possible.

  Connected to the three-dimensional point cloud position data processing apparatus 100 is a laser scanner that measures the three-dimensional point cloud position data of the measurement object. The three-dimensional point cloud position data measured by the laser scanner is input to the three-dimensional point cloud position data processing apparatus 100. It is also possible to create a 3D model by acquiring 3D point cloud position data of the same measurement object from a plurality of viewpoints and integrating them. In this case, processing is performed on a target obtained by integrating three-dimensional point cloud position data obtained from a plurality of viewpoints.

  The laser scanner irradiates the measurement target with laser light and receives the reflected light to obtain information on the distance, azimuth, and elevation (or depression) from the laser scanner installation position (viewpoint) to the measurement point. Based on these pieces of information, information related to the three-dimensional position coordinates of the measurement point is calculated. Further, the laser scanner acquires information on the intensity, color, hue, saturation, etc. of the reflected light from the measurement point. Based on these pieces of information, the laser scanner calculates point cloud position data including three-dimensional coordinate values, and outputs it to the three-dimensional point cloud position data processing apparatus 100. An example of the detailed configuration of the laser scanner will be described later. As a method for acquiring the three-dimensional point cloud position data, the method for acquiring the three-dimensional point cloud position data from the stereoscopic photograph image using the image processing software is used in addition to the method using the laser scanner. An extraction method can also be adopted.

  The 3D point cloud position data processing apparatus 100 shown in FIG. 1 includes a 3D point cloud position data acquisition unit 101, a resolution setting unit 102, a 3D model creation unit 103, and a 3D model image creation unit 104. The three-dimensional point cloud position data processing device 100 is connected to an operation input unit 106 and an image display device (for example, a liquid crystal display) 105 included in a personal computer that constitutes the three-dimensional point cloud position data processing device 100.

  The three-dimensional point group position data acquisition unit 101 acquires three-dimensional point group position data from the laser scanner described above. Note that a mode in which the 3D point cloud position data acquisition unit 101 calculates the 3D point cloud position data based on the output of the laser scanner is also possible. The resolution setting unit 102 is an example of a density changing unit for 3D point cloud position data, and sets the resolution of a 3D model created based on the 3D point cloud position data. The operation for setting the resolution is performed by operating the operation input unit 106 by the user. The resolution is set by changing the density of the 3D point cloud position data to be processed.

  The resolution setting will be described below. FIG. 2 conceptually shows the distribution state of the three-dimensional point cloud position data indicated by ◯ distributed two-dimensionally. FIG. 2 shows a state in which three-dimensional point cloud data is distributed on a two-dimensional grid. FIG. 3 shows a case where the resolution layer is set to three layers in the three-dimensional point cloud position data of FIG. In FIG. 3, when creating the 3D model, the 3D point cloud position data of the portion ● is used for creating the 3D model, and the 3D point cloud position data of the portion ○ is not used.

  FIG. 3A shows the case where the resolution is the lowest, FIG. 3B shows the case where the resolution is medium, and FIG. 3C shows the case where the resolution is the highest. Has been. That is, FIG. 3A shows a case where the density of 3D point group position data to be sampled is the lowest, and FIG. 3B shows 3D point group position data to be sampled. The case where the density of the three-dimensional point cloud position data to be sampled is the highest is shown in FIG. 3C. In FIG. 3, the point ratio is the ratio of the number of 3D point cloud position data to the 3D point cloud position data in the state of FIG. As shown in FIG. 3, the smaller the point ratio, the lower the density of the three-dimensional point cloud position data used, and the correspondingly lower the resolution. This is because if the density of the basic three-dimensional point cloud position data is low, the pixels become coarse and the accuracy of reproducing fine portions is reduced. The score ratio of the nth layer (n is a natural number) is expressed by the following formula 1.

  Compared to the case of FIG. 3A, the density of the three-dimensional point cloud position data used is higher in the case of FIG. 3B, so the resolution (accuracy) of the obtained three-dimensional model is higher. In the case of FIG. 3C, since the density of the three-dimensional point cloud position data used is further increased, the resolution (accuracy) of the obtained three-dimensional model is further increased. The resolution setting unit 102 in FIG. 1 sets the resolution exemplified in FIG.

  FIG. 3 describes a method of thinning out the 3D point cloud position data and reducing the density of the 3D point cloud position data to be used each time the hierarchy is raised (every time the resolution is lowered). As a method for obtaining the same effect as this method, an average value of a plurality of three-dimensional point cloud position data is adopted as a representative value, and the three-dimensional point cloud position data obtained by reducing the resolution with respect to the 0th layer by the representative value is obtained. A configuration method can also be adopted. For example, in the second layer, the average value of the 3D point cloud position data of 5 × 5 pixels is adopted as the representative value of the 3D point cloud position data in the 5 × 5 pixel region, and then the first layer In the hierarchy, the average value of the 3D point cloud position data of 3 × 3 pixels is adopted as a representative value of the 3D point cloud position data in the 3 × 3 pixel region, and the 3D point cloud position data A method of setting the density is also possible. FIG. 3 shows a case where the resolution setting is set to three layers (three levels). However, the number of layers can be two or more, and can be four layers, five layers, or more. There may be.

  Returning to FIG. 1, the 3D point cloud position data processing apparatus 100 includes a 3D model creation unit 103. The three-dimensional model creation unit 103 calculates a non-surface region, removes the non-surface region, labeling processing, calculates a characteristic part such as a contour line, creates a three-dimensional model composed of the contour line, and operations related thereto I do. The three-dimensional model 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. An outline is a three-dimensional edge of a measurement object, and is an outline that forms the outline of the measurement object, which is necessary to visually grasp 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. It is also possible to represent a three-dimensional model as a polyhedron composed of labeled surfaces.

  The details of the three-dimensional model creation unit 103 in FIG. 1 will be described below. FIG. 4 shows a block diagram of the three-dimensional model creation unit 103. The three-dimensional model creation unit 103 includes a non-surface region calculation unit 201, a non-surface region removal unit 202, a surface labeling unit 203, a label expansion unit 204, a boundary region surface formation unit 205, a contour line calculation unit 206, and a two-dimensional edge calculation unit. 207 and an edge integration unit 208. Hereinafter, each of these functional units will be described.

  The non-surface region calculation unit 201 includes a local region acquisition unit 201a that acquires a local region that is a local surface, a normal vector calculation unit 201b that calculates a normal vector of the local region, and a local curvature calculation that calculates a local curvature of the local region. The unit 201c includes a local plane calculation unit 201d that calculates a local plane to be fitted to the local region.

  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. At this time, the target 3D point cloud position data is the 3D point cloud position data of the hierarchy selected by the resolution setting unit 102. For example, when the hierarchy (resolution) in FIG. 3B is selected, not all the 3D point cloud position data are processed, and the 3D point cloud position indicated by ● in FIG. Data is subject to processing. FIG. 3 shows an example in which the two-dimensional simplification is performed, but 3 × 3 pixel local regions 401, 402, and 403 at each resolution are shown.

  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. Based on this result, standard deviations (StdNVx, StdNVy, StdNVz) are further obtained. Next, the square root of the square sum of the standard deviation is calculated as a local curvature (crv) (Equation 2).

  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.

  The non-surface area removing unit 202 performs a process of removing points in the non-surface area based on the normal vector, local plane, and local curvature in each local area obtained above. 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 unit 202 removes the 3D point group position data of the non-surface area calculated from the 3D point group position data acquired by the 3D point group position data acquisition unit 101. 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) Fitting accuracy to the local plane: When the distance between each point of the local area and the corresponding local plane is calculated and the average of these distances is greater than a preset threshold, the local area is determined as a non-surface area. judge. 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. Further, as the inner product increases, it is determined that the degree that the two local planes are not on the same plane is more remarkable, and the degree of non-surface is determined to be higher.

  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. 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 surface labeling unit 203 will be described. The surface labeling unit 203 is an example of a surface extraction unit. The surface labeling unit 203 performs surface labeling on the 3D point cloud position data from which the 3D point cloud position data of the non-surface area is removed 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. The label expansion unit 204 performs processing related to the expansion of this surface.

  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. This process is also performed in the label extension unit 204.

  According to the function of the surface labeling unit 203, 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 boundary region surface forming unit 205 forms a boundary region surface that connects adjacent surfaces. In this example, a local plane is fitted to a non-surface region between adjacent surfaces, and a plurality of these local planes are connected to form a boundary region surface that approximates the non-surface region by a plurality of local planes. This can be regarded as a process of approximating a non-surface region with a polyhedron composed of a plurality of local planes and forming a boundary region surface that is a boundary portion between the surfaces. It is also possible to set a smooth curved surface to be fitted to the boundary region surface having this polyhedral structure and replace the boundary region surface with this curved surface.

  The contour calculation unit 206 calculates (estimates) a contour line that becomes a three-dimensional edge of the measurement object based on the three-dimensional point cloud position data of the adjacent surfaces. In this case, in the formation of the boundary area surface, a process of connecting the local plane from the adjacent surface is performed. At this time, 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. As a method for calculating the contour line using the boundary region surface, the contour line is set so that the contour line passes through the center position of the boundary region surface, or the contour line passes through the portion where the curvature of the boundary region surface is minimized. A method of setting the contour line as described above is also possible. In addition, the process which makes the intersection line of an adjacent surface and a surface an outline without forming a boundary area surface is also possible.

  Next, the two-dimensional edge calculation unit 207 will be described. Based on the intensity distribution of the reflected light from the object, the two-dimensional edge calculation unit 207 corresponds to a segmented (segmented) surface using a known edge extraction operator such as Laplacian, Plewwit, Sobel, Canny, etc. An edge is extracted from the area of the two-dimensional image to be processed. 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 206 and the two-dimensional edge calculated by the two-dimensional edge calculation unit 207 are integrated. This processing is performed in the edge integration unit 208. Thereby, extraction of the edge characterizing the object is performed based on the three-dimensional point cloud position data. By extracting the edge, a characteristic portion constituting the appearance of the measurement object when the measurement object is visually recognized is extracted, and three-dimensional model data is obtained.

  Returning to FIG. 1, the 3D point cloud position data processing apparatus 100 includes a 3D model image creation unit 104. The 3D model image creation unit 104 creates image data for displaying an image of the 3D model created by the 3D model creation unit 103. This image data is sent to the image display device 105 and displayed there. Thereby, the image display of the three-dimensional model of the measurement object is performed on the image display device 105.

(Example of processing)
FIG. 5 is a flowchart for explaining an example of processing performed by the three-dimensional point cloud position data processing device 100 of FIG. The program for executing the flowchart shown in FIG. 5 is stored in a storage unit of a personal computer constituting the three-dimensional point cloud position data processing device 100, read into an appropriate storage area, and executed by the personal computer. Is done.

  When the process is started (step S501), first, three-dimensional point cloud position data of the measurement object is acquired (step S502). This process is performed in the three-dimensional point cloud position data acquisition unit 101. When the three-dimensional point cloud position data of the measurement object is acquired, information on the number of set layers (the set number of resolution steps) performed by the user is acquired from the operation input unit 106 (step S503). Make settings (resolution settings). This process is performed in the resolution setting unit 102.

  Next, labeling is performed on the highest layer (hierarchy with the lowest resolution) of the layers acquired in step S503. This process is performed in the 3D model creation unit 103 according to the contents described with reference to FIG. Then, after labeling, a three-dimensional model is created based on the result of the labeling process (step S505). This processing is also performed in the 3D model creation unit 103.

  When the three-dimensional model is created, it is imaged by the three-dimensional model image creation unit 104, and the image data is sent to the image display device 105 where it is displayed (step S506). After this image display, it is determined whether or not an operation for instructing the end of the process in FIG. 5 is performed on the operation input unit 106 by the user (step S507), and an operation for instructing the end of the process in FIG. If not, the process ends (step S511), and if such an operation has not been performed, the process proceeds to step S508.

  In step S508, it is determined whether or not the labeling layer at this stage is at the lowest level (whether or not the resolution is maximum). If the labeling layer is at the lowest layer, the process ends (step S511). If it is not the lowest layer, the process proceeds to step (509).

  In step 509, the label is handed over to the hierarchy one rank lower than the hierarchy determined in step S508, and the hierarchy one rank lower than the hierarchy determined in step S508 (one rank resolution is higher). Re-labeling is performed (step S510). Details of step S509 and step S510 will be described below.

  On the left side of FIG. 6, a modeled state of three levels of labeling is described. On the right side of FIG. 6, an image of the three levels of the measurement object corresponding to the left labeling hierarchy is shown. An image is shown. In the case of FIG. 6, three layers of the second layer, the first layer, and the zeroth layer are shown. Here, the second layer has the lowest resolution and the zeroth has the highest resolution. In the case of FIG. 6, in the second layer hierarchy of the lowest resolution, the image in the range shown is labeled with a resolution that is divided into four, and in the first layer hierarchy of the middle resolution, Labeling is performed at a resolution that divides the image into 16 (four times the resolution in the case of the second layer), and in the 0th layer of the highest resolution, a resolution (second layer) that divides the image in the range shown in the figure. In this example, labeling is performed at a resolution 16 times that of the case of (4) and 4 times that of the case of the first layer.

  On the right side of FIG. 6, the wall 601 is first labeled as one surface in the second layer, and the resolution of the wall 601 is increased in the first layer where the resolution is increased by one layer, corresponding to the left resolution. In the plane, the window 602 is labeled as a plane, and in the layer of the 0th layer where the resolution is further increased by one layer, a tile that is a surface finer than the window 602 on the surface of the wall 601 (surface of the tile constituting the wall 601) A state where 603 to 605 are labeled as finer surfaces is shown.

  That is, in the example of FIG. 6, in the second layer labeling, the density of the 3D point cloud position data to be sampled is insufficient, the windows 602 and tiles 603 to 605 are not labeled as surfaces, and the whole is labeled as a wall 601. The In the first layer labeling, the density of the three-dimensional point cloud position data to be sampled is higher than that in the second layer, so the window 602 is labeled as a surface. At this time, the tiles 603 to 605 are not labeled with sufficient resolution. In the 0th layer labeling, the density of the three-dimensional point cloud position data to be sampled is higher than that in the first layer. Therefore, the tiles 603 to 605 are also labeled and converted into data as surfaces.

  Further, FIG. 6 shows an example in which the upper layer label is taken over again after the transition to the lower layer labeling. First, the wall 601 is labeled as a surface by labeling in the second layer. Then, when moving to the first layer lower than the second layer, the labeling data of the wall 601 is taken over as it is, and the wall 601 recognized as a plane in the second layer is targeted as the update in the first layer hierarchy. The labeling of the fine surface is performed. Then, the window 602 is labeled as a surface in the first layer, but the data as the surface of the window 602 is carried over to the 0th layer. Further, in a state where the wall 601 and the window 602 that are labeled in the second layer and the first layer are regarded as surfaces, the finer surface labeling is performed in the zeroth layer. In this case, the tiles 603 to 605 that are recognized when the wall 601 is viewed in more detail are labeled. By performing relabeling while taking over the label in this lower layer, the waste of performing the labeling work already performed is eliminated, and the processing becomes efficient.

  If the data of the surface of the second layer in the model illustrated in FIG. 6 (data obtained by using the wall 601 as a surface) is not inherited at the time of labeling in the layer of the first layer, the wall in the layer of the first layer again A labeling process for recognizing 601 is required, and wasteful computation is performed.

  Re-labeling after moving from the second layer to the first layer or re-labeling from the first layer to the 0th layer shown in FIG. 6 is performed in steps S509 and S509 in FIG. This is an example of the process of step S510.

  Returning to FIG. 5, after the re-labeling in step S510, the processes in and after step S505 are executed again to create a three-dimensional model based on the newly labeled data, and further display the image of the three-dimensional model. (Step S506). According to this processing, for example, as shown on the right side of FIG. 6, a three-dimensional model image display in which the resolution gradually increases and details that are not expressed in the upper hierarchy are sequentially expressed. Is done.

  FIG. 7 shows an example of a three-dimensional model image when a three-dimensional model is created while lowering the hierarchy stepwise (increasing resolution). FIG. 7 shows an example of a three-dimensional model image corresponding to the second layer, the first layer, and the zeroth layer shown in FIGS. 3 and 6. FIG. 7 shows a state in which the difference in resolution is exaggerated.

  FIG. 7 shows a three-dimensional model image of a five-story apartment house. FIG. 7A shows a case where the sampling density of the three-dimensional point cloud position data is the lowest and the resolution is the lowest. In the case of FIG. 7A, since the resolution is low, the state of details is hardly expressed. In FIG. 7B, the resolution is higher than in the case of (A) and the shape of the window is not clear, but the three-dimensional model in which the state of details is clearer than in the case of (A). An image is shown. FIG. 7C shows an image of a three-dimensional model using the original of the zero-th layer, that is, the three-dimensional point cloud position data acquired by the three-dimensional laser scanner. In this case, an image of a three-dimensional model in which details such as the shape of the window are expressed is displayed.

(Superiority)
In the example described above, first, a three-dimensional model with the resolution changed stepwise is created based on the three-dimensional point cloud position data measured by the three-dimensional laser scanner. At this time, a low-resolution three-dimensional model is created and displayed, and thereafter, a high-resolution three-dimensional model is created and displayed by taking over the labeled data. Here, by displaying the low-resolution three-dimensional model, the user can grasp the stage in the middle of completing the final three-dimensional model.

  For example, in the case of the image display of the three-dimensional model of FIG. 7, the image display of the second-layer three-dimensional model is performed in the first step S506 of FIG. Here, when it is determined that the viewpoint selection error or the three-dimensional model of this viewpoint is unnecessary, the processing can be terminated. The 3D point cloud position data to be processed at this stage has a significantly smaller data amount than the original 3D point cloud position data, as is apparent from the example of FIG. 3 (in this case) 1/16). Therefore, the processing time is shortened by finishing the processing at the stage of the rough three-dimensional model having a low resolution as compared with the case where all the three-dimensional point cloud position data are processed. Further, by stopping the calculation in the middle, it is not necessary to perform useless processing.

  Further, as shown in FIG. 7, the progress of the process of creating the three-dimensional model can be visually confirmed step by step, so that the stress received by the user is reduced and the workability of the user is improved. Also, as shown in FIG. 6, when the hierarchy is lowered, the labeling data obtained in the higher hierarchy is taken over by the lower hierarchy. For this reason, useless processing is suppressed.

  In addition, since more detailed labeling is performed step by step, it is possible to display a state in which the image of the three-dimensional model becomes more detailed as shown in FIG. 7, and the user grasps the process of gradually becoming more detailed. easy. Further, when it is desired to obtain a three-dimensional model whose outline can be understood, it is possible to obtain a three-dimensional model with desired accuracy in a shorter calculation time by designating a hierarchy with reduced resolution.

2. Second Embodiment Here, an example of a method for determining the number of layers and the resolution will be described. First, the resolution of a specific hierarchy is expressed by the following Equation 3. The resolution here is a value of the smallest dimension that can be resolved, and the smaller the value, the higher the precision and the smaller the portion is expressed.

  Here, a is the scan pitch. FIG. 8 shows the relationship between the scan pitch a, the scan range L of the measurement object, the measurement distance H, and the number M of three-dimensional point group position data in the scan range L. n is a numerical value (natural number) indicating the number of layers when n = 0 is original scan data without data thinning (for example, the three-dimensional point cloud position data in FIG. 3C). . As is clear from the cases of FIGS. 3 and 6, the larger the value of n, the larger the resolution value (that is, the lower the resolution).

  The number of layers (n + 1) is called the number of layers. The number of layers is represented by the following number 4. Here, the unit processing time is the time required to process one point cloud position data, and the user analysis request time is the time required for the calculation of the corresponding hierarchy required by the user.

For example, it is assumed that the processing time required for 10,000 point cloud position data is 30 milliseconds (3 × 10 ⁻⁶ seconds per piece), and the analysis request time for one hierarchy required by the user is 10 seconds. Further, it is assumed that the number of original (0th layer) three-dimensional point cloud position data is 10 million. In this case, n = 3 is calculated based on the above equation 4, and the number of layers is 3. That is, in this case, if the hierarchy is set in three stages as in the case of FIGS. 3 and 6, the time required for the calculation for one layer can be suppressed to 10 seconds. In this case, the processing of the second layer → the first layer → the zeroth layer and each 10 seconds is performed three times.

  Further, when Equation 4 is transformed using the relational expression shown in FIG. 8, the following Equation 5 is obtained. Here, T is the unit processing time, t is the user's analysis request time for processing of one layer, and H and L are parameters shown in FIG. Θ is the angle pitch of the laser scanner used. This θ is determined by the accuracy of laser scanner angle control.

  According to Equation 5, for example, if L and H are determined due to restrictions at the measurement site, θ is determined based on the performance of the laser scanner, and T is determined based on the performance of the computer used, the analysis request time t By deciding, you can decide the number of layers. In order to view the image of the three-dimensional model as soon as possible even if the accuracy is low, it is necessary to set t short. On the other hand, if t is set short, the number of layers increases, and more layers are required.

  Although it is an example, the standard of the number of layers to be selected is 5 layers when the number of target 3D point cloud position data is 100 million or more, 4 layers when 10 million to 100 million points, 10 million to 10 million In the case of 50 million points, it is preferable to have 3 layers, in the case of 5 million points to 10 million points, 2 layers, and in the case of less than 5 million points, it is preferable to have 1 layer.

  Hereinafter, an example of processing for automatically setting the number of layers in the three-dimensional point cloud position data processing apparatus 100 shown in FIG. 1 will be described. First, the user selects the resolution a of the finally created three-dimensional model image and the processing time t required to obtain the three-dimensional model image, and inputs these items from the operation input unit 106. Here, the resolution a is set to a numerical value such as 5 cm or 10 cm. Further, preliminary measurement is performed with the three-dimensional laser scanner to be used, and the values of H and L in FIG. 8 are acquired.

  The resolution setting unit 102 calculates θ in Formula 5 based on the input resolution a. Then, the number of layers is calculated by substituting this value of θ, t specified by the user, H and L obtained in the preliminary measurement, and T determined from the performance of the computer to be used into Expression 5. By doing this, the user performs initial settings without performing troublesome operations, and the number of layers is calculated, and the image of the three-dimensional model whose resolution increases stepwise as illustrated in FIG. 7 is displayed. Settings are made.

3. Third Embodiment Hereinafter, a three-dimensional laser scanner having a function of processing three-dimensional point cloud position data will be described. In this example, the three-dimensional laser scanner irradiates the object to be measured with distance measuring light (laser light), and performs a number of measurements on the object to be measured from its own position based on the time of flight of the laser light. Measure the distance to the point. The three-dimensional laser scanner 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. The three-dimensional laser scanner acquires a two-dimensional image (RGB intensity at each measurement point) obtained by photographing the measurement object, and forms three-dimensional point group position data that combines the two-dimensional image and the three-dimensional coordinates. . Further, the three-dimensional laser scanner shown here has a function of performing the processing of the three-dimensional point cloud position data processing apparatus 100 described with reference to FIG.

(Constitution)
9 and 10 are cross-sectional views showing the configuration of the three-dimensional laser scanner 1. The three-dimensional laser scanner 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. 9 shows a state where 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. 11) 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, a rotary 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. 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 optical information processing apparatus 1 The distance from the object to be measured (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 photographing unit 25 includes an image light receiving unit 58 and functions as a camera. 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. 10, 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. 10, 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. 11 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. Position information obtained from the GPS is input to a three-dimensional point cloud position data processing unit 100 'described later.

  FIG. 12 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 three-dimensional 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 calculates the three-dimensional coordinates (orthogonal coordinates) of each measurement point with the position of the optical information processing apparatus 1 as the origin (0, 0, 0). calculate.

  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. 13 is a conceptual diagram showing three-dimensional point group position data in which the distance between points is not constant, and FIG. 14 is a conceptual diagram showing a formed grid. As shown in FIG. 13, the average horizontal interval H _1-N of each column is obtained, and 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 6 ). The vertical interval is calculated by calculating the distance ΔV _{N, H} from the vertical adjacent point 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 7 ). Then, as shown in FIG. 14, a grid with 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 three-dimensional point cloud position data processing unit 100 '. The three-dimensional point cloud position data processing unit 100 ′ is a dedicated integrated circuit that performs the same operation as the three-dimensional point cloud position data processing device 100 described in the first embodiment. The three-dimensional point cloud position data processing unit 100 ′ is configured by, for example, an FPGA. Further, in the operation of the three-dimensional point cloud position data processing unit 100 ′, an image presented to the user is displayed on the display unit 7 that is a liquid crystal display.

  The coordinate data on the earth obtained from the GPS receiving unit 82 is input to the three-dimensional point cloud position data processing unit 100 ′. According to this configuration, coordinates handled by the three-dimensional point cloud position data processing unit 100 ′ are linked to position data (for example, electronic map information) obtained from the GPS. Thereby, for example, the installation position of the three-dimensional laser scanner 1 can be displayed on the electronic map, and the coordinates of the measurement object on the electronic map can be specified.

(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. 9 and 10 uses the personal computer shown in the first embodiment. Thus, a three-dimensional laser scanner that can be used in combination with the three-dimensional point cloud position data processing apparatus 100 is obtained. Also, a configuration in which the processing performed by the three-dimensional point cloud position data processing unit 100 ′ is performed in a distributed manner is possible. For example, a configuration in which a part of the functions of the three-dimensional point cloud position data processing unit 100 ′ is performed by a server connected by a communication line is also possible. The same applies to the case of the three-dimensional point cloud position data processing apparatus 100 of FIG. These configurations are understood as an example of the three-dimensional point cloud position data processing system of the present invention.

  The present invention can also be used for processing three-dimensional point cloud position data obtained from a plurality of viewpoints. In this case, a 3D point obtained by integrating 3D point cloud position data obtained from the first viewpoint and 3D point cloud position data obtained from a second viewpoint that is a viewpoint different from the first viewpoint. Processing is performed on the group position data. The number of viewpoints may be two or more. By setting the number of viewpoints to multiple, the part that becomes a blind spot from one viewpoint is complemented by the 3D point cloud position data obtained from another viewpoint, and the blind spot when the 3D model is rotated and displayed Therefore, the occurrence of a portion where image information is missing (occurrence of occlusion) is suppressed.

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

  DESCRIPTION OF SYMBOLS 1 ... Three-dimensional laser scanner, 2 ... Three-dimensional point cloud position data, 22 ... Leveling part, 23 ... Rotation mechanism part, 24 ... Distance measuring part, 25 ... Imaging | photography part, 26 ... Control part, 27 ... Main-body part, 28 DESCRIPTION OF SYMBOLS ... Rotary irradiation part, 29 ... Base, 30 ... Lower casing, 31 ... Pin, 32 ... Adjustment screw, 33 ... Tension spring, 34 ... Leveling motor, 35 ... Leveling drive gear, 36 ... Leveling driven gear, 37 DESCRIPTION OF SYMBOLS ... Inclination sensor, 38 ... Horizontal rotation motor, 39 ... Horizontal rotation drive gear, 40 ... Horizontal rotation gear, 41 ... Rotating shaft part, 42 ... Rotary base, 43 ... Bearing member, 44 ... Horizontal angle detector, 45 ... Main body casing, 46 ... Tube, 47 ... Optical axis, 48 ... Beam splitter, 49,50 ... Optical axis, 51 ... Pulse laser light source, 52 ... Perforated mirror, 53 ... Beam waist changing optical system, 54 ... Measurement Distance light receiving part, 55 .. Rotating mirror for high and low angle 56 ... Projection optical axis, 57 ... Condensing lens, 58 ... Image receiver, 59 ... Projection casing, 60 ... Flange, 61 ... Mirror holder plate, 62 ... Rotating shaft, 63 ... High / low angle gear, 64 ... High / low angle detector, 65 ... Drive motor for high / low angle, 66 ... Drive gear, 67 ... Terumoto Terumon, 68 ... External storage device, 69 ... Horizontal drive unit, 70 ... High / low drive unit, 71 ... Leveling drive unit, 72 ... Distance data processing unit, 73 ... Image data processing unit, 81 ... GPS antenna, 82 ... GPS receiving unit, 100 '... Three-dimensional point cloud position data processing unit.

Claims (14)

A 3D point cloud position data acquisition unit for acquiring 3D point cloud position data of the measurement object;
A density changing unit for three-dimensional point group position data for changing the density of the three-dimensional point group position data acquired by the three-dimensional point group position data acquiring unit;
A non-surface region removing unit that removes points of the non-surface region from the three-dimensional point cloud position data whose density has been changed by the density changing unit;
A surface labeling unit that gives the same label to points on the same surface, with respect to points other than the points removed by the non-surface region removing unit,
A three-dimensional point cloud position data processing apparatus comprising: a three-dimensional model creating unit that creates a three-dimensional model based on the surfaces classified by the surface labeling unit.   The three-dimensional point cloud position data processing apparatus according to claim 1, wherein density change in the density changing unit is performed in two or more layers.   3. The 3D point cloud position data processing apparatus according to claim 2, wherein the density changing unit determines the number of the hierarchies based on a time required to create the 3D model.   The three-dimensional model creation unit creates a three-dimensional model with a relatively high resolution after creating a three-dimensional model with a relatively low resolution. 3D point cloud position data processing device.   The three-dimensional model creating unit creates the three-dimensional model having a relatively high resolution based on the information of the same label given when the three-dimensional model having a relatively low resolution is created. The three-dimensional point cloud position data processing device according to claim 4, A three-dimensional model image creating unit for creating an image of the three-dimensional model;
2. The 3D model image creation unit creates the 3D model image with a relatively high resolution after creating the 3D model image with a relatively low resolution. The three-dimensional point cloud position data processing device according to any one of claims 1 to 5.   The image processing apparatus according to claim 6, further comprising: an accepting unit that receives an instruction to cancel the creation of the relatively high-resolution 3D model after creating the relatively low-resolution 3D model image. 3D point cloud position data processing device. A three-dimensional edge extraction unit that extracts a three-dimensional edge at a boundary portion between the surfaces divided by the surface labeling unit;
A two-dimensional edge extraction unit that extracts a two-dimensional edge from the plane divided by the surface labeling unit;
The three-dimensional point cloud position data processing device according to claim 1, further comprising: an edge integration unit that integrates the three-dimensional edge and the two-dimensional edge. A boundary region surface forming part that forms a boundary region surface connecting between adjacent surfaces;
9. The three-dimensional point cloud position data processing apparatus according to claim 8, wherein the three-dimensional edge extraction unit extracts a three-dimensional edge based on the boundary region surface.   The non-surface area removing unit is configured to generate a non-surface area based on at least one of fitting accuracy to a plane of a local surface formed by a plurality of three-dimensional point group position data, curvature, and coplanarity with another local surface. The three-dimensional point cloud position data processing device according to claim 1, wherein the three-dimensional point cloud position data processing device is removed.   11. The apparatus according to claim 1, further comprising a label expansion unit that applies a label on the nearest surface to a point that is not provided with a label by the surface labeling unit, and expands the label. The three-dimensional point cloud position data processing device according to item. 3D point cloud position data acquisition step for acquiring 3D point cloud position data of the measurement object;
A density changing step of 3D point cloud position data for changing the density of the 3D point cloud position data acquired in the 3D point cloud position data acquiring step;
A non-surface region removal step of removing non-surface region points from the three-dimensional point cloud position data whose density has been changed in the density change step;
A surface labeling step for giving the same label to points on the same surface for points other than the points removed in the non-surface region removing step;
A three-dimensional point cloud position data processing method, comprising: a three-dimensional model creation step for creating a three-dimensional model based on the surfaces classified in the surface labeling step. 3D point cloud position data acquisition means for acquiring 3D point cloud position data of the measurement object;
3D point cloud position data density changing means for changing the density of the 3D point cloud position data acquired by the 3D point cloud position data acquiring means;
Non-surface area removing means for removing non-surface area points from the three-dimensional point cloud position data whose density has been changed by the density changing means;
A surface labeling unit that gives the same label to a point on the same surface with respect to a point other than the point removed by the non-surface region removing unit,
A three-dimensional point cloud position data processing system comprising: a three-dimensional model creating means for creating a three-dimensional model based on the surfaces classified by the surface labeling means. A program that is read and executed by a computer,
A three-dimensional point cloud position data acquisition unit for acquiring three-dimensional point cloud position data of a measurement object;
A density changing unit for three-dimensional point group position data for changing the density of the three-dimensional point group position data acquired by the three-dimensional point group position data acquiring unit;
A non-surface region removing unit that removes points of the non-surface region from the three-dimensional point cloud position data whose density has been changed by the density changing unit;
A surface labeling unit that gives the same label to points on the same surface, with respect to points other than the points removed by the non-surface region removing unit,
A program that functions as a three-dimensional model creation unit that creates a three-dimensional model based on the surfaces classified by the surface labeling unit.

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