JP2018159693A - Information processing program, information processing method and information processing device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve the accuracy of synthesizing plural three-dimensional point group data.SOLUTION: An information processing device 100 corrects, for each of combinations of first planes included in a plurality of first planes extracted from first point group data 111 and second planes included in a plurality of second planes extracted from second point group data 112, the height of the plurality of second planes so that the heights between the planes included in the combination are matched, and specifies a height that matches the height of each of the second planes after correction, of the heights of the plurality of first planes. The information processing device 100 specifies, for each of the combinations, the number of combinations, of two specified height combinations, in which a difference between the heights included in a height combination matches a characteristic distance that a difference of one distance among distances each between the plurality of first planes from the other distances is large. The information processing device 100 specifies, for each of the combinations, the amount of offset used for correction on the basis of the specified number of combinations.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing program, an information processing method, and an information processing apparatus.

  Conventionally, there is a distance sensor that measures the distance to an object by irradiating the object with a laser and measuring the time until the reflected light returns. In addition, there is a so-called 2D (2-dimensions) sensor that measures the distance to an object while performing horizontal scanning by rotating an optical system in the distance sensor. In addition, there is a technique called 3D measurement in which the position and shape of a three-dimensional object are converted into data by rotating a 2D sensor using a driving device.

  Conventionally, there is a technique related to alignment of a plurality of three-dimensional point group data in order to unite a plurality of three-dimensional point group data measured in 3D at different measurement positions. For example, there is a technique in which measurement position groups specified by respective three-dimensional point group data are divided at a certain height in the horizontal direction, and point groups included in each horizontal direction are matched in 2D. For example, in order to divide a three-dimensional point group in the horizontal direction at the same height, for example, there is a technique of matching the height reference based on a reference plane or the like. More specifically, for example, in each of a plurality of three-dimensional point cloud data, the heights of the reference surfaces are made to coincide with each other using the largest surface, the lowest surface, the highest surface, etc. as the reference surface. By correcting the height of one of the three-dimensional point group data, the horizontal positions of the plurality of three-dimensional point group data are adjusted.

  As a prior art, for example, there is a technique for calculating an overlap portion including a feature portion suitable for alignment between three-dimensional point cloud data obtained from two measurement positions (see, for example, Patent Document 1 below). Further, for example, in the position information of the moving body, the 3D point cloud data before the movement and the current 3D point cloud data are acquired, and the 3D point cloud data before the movement and the current 3D point cloud data There is a technique for correcting the position of the current three-dimensional point cloud data so that the difference in distance between the two is minimized (see, for example, Patent Document 2 below).

JP 2012-63866 A JP-A-2006-45330

  However, the same reference plane cannot be found among a plurality of three-dimensional point group data, and the synthesis accuracy of the three-dimensional point group data may decrease.

  In one aspect, an object of the present invention is to provide an information processing program, an information processing method, and an information processing apparatus capable of improving the synthesis accuracy of three-dimensional point cloud data.

  According to an aspect of the present invention, three-dimensional point cloud data based on a distance from each measurement position to an object is obtained for each measurement position of a plurality of measurement positions, and obtained for each measurement position. The point cloud data belonging to the plane area is extracted from the point cloud data, and an offset amount in which the vertical difference between the plane areas to which the extracted point cloud data belongs is matched between the plurality of measurement positions. An information processing program, an information processing method, and an information processing apparatus are proposed that perform positioning using the point cloud data that is calculated and corrected according to the calculated offset amount.

  According to one embodiment of the present invention, it is possible to improve the synthesis accuracy of three-dimensional point cloud data.

FIG. 1 is an explanatory diagram illustrating an operation example of the information processing apparatus. FIG. 2 is an explanatory diagram illustrating an example of a plan view when a staircase is measured in 3D. FIG. 3 is an explanatory diagram illustrating a usage example of the information processing apparatus 100. FIG. 4 is an explanatory diagram illustrating an occlusion example 1. FIG. 5 is an explanatory diagram showing an occlusion example 2. FIG. 6 is an explanatory diagram illustrating an example in which a plurality of combinations of planes are evaluated by the number of planes having the same height. FIG. 7 is an explanatory diagram illustrating an example of the information processing apparatus 100 for performing 3D measurement. FIG. 8 is a block diagram illustrating a hardware configuration example of the information processing apparatus 100. FIG. 9 is an explanatory diagram illustrating an example of point groups and point data on a plane. FIG. 10 is a block diagram illustrating a functional configuration example of the information processing apparatus 100. FIG. 11 is an explanatory diagram illustrating an example of a distance between planes. FIG. 12 is an explanatory diagram illustrating Evaluation Example 1 for a candidate for the offset amount Δh. FIG. 13 is an explanatory diagram illustrating Evaluation Example 2 for a candidate for the offset amount Δh. FIG. 14 is an explanatory diagram illustrating an example in which the number f ₁ is different from the number f ₂ . FIG. 15 is a flowchart illustrating an example of a processing procedure performed by the information processing apparatus 100. FIG. 16 is a flowchart (part 1) showing a detailed description of the offset amount and evaluation value calculation processing shown in FIG. FIG. 17 is a flowchart (part 2) illustrating a detailed description of the offset amount and evaluation value calculation processing illustrated in FIG. 15. FIG. 18 is a flowchart showing a detailed description of the registration process shown in FIG. FIG. 19 is an explanatory diagram of an example system.

  Hereinafter, embodiments of an information processing program, an information processing method, and an information processing apparatus according to the present invention will be described in detail with reference to the drawings.

  FIG. 1 is an explanatory diagram illustrating an operation example of the information processing apparatus. The information processing apparatus 100 is a computer that supports the synthesis of 3D point cloud data obtained by 3D measurement for each measurement position of a plurality of measurement positions. In the present embodiment, the first measurement position 101 and the second measurement position 102 will be described as examples of different measurement positions. The 3D point cloud data acquired for the first measurement position 101 is referred to as first point cloud data 111. The 3D point cloud data acquired for the second measurement position 102 is referred to as second point cloud data 112.

  Conventionally, there is a sensor that measures the distance to an object by irradiating the object with a laser and measuring the time until the reflected light returns. In addition, there is a so-called 2D sensor that measures the distance to an object while performing horizontal scanning by rotating an optical system in the sensor. 3D measurement can be performed by rotating the 2D sensor with a driving device.

  Three-dimensional point cloud data (hereinafter referred to as 3D point cloud data) is used for various simulators and applications such as CAD (Computer Aided Design). The 3D point cloud data is a set of point data. A point can be identified by point data. The point here is a point of an object irradiated with a laser by a 2D sensor. Specifically, the point data includes position information based on at least the distance from the information processing apparatus 100 to the point. The information processing apparatus 100 represents the position of each point using a local orthogonal coordinate system such as an x-axis, a y-axis, or a z-axis. More specifically, the point data includes vector data that is a coordinate value indicating the position of each point in the coordinate system C used in the information processing apparatus 100. The coordinate values are an x coordinate value, a y coordinate value, and a z coordinate value. An example of a point group and point data will be described with reference to FIG. In addition, an application such as CAD can simulate a measurement target space on a computer space based on 3D point cloud data, for example.

  Here, a series of processes from 3D measurement to generation of model data when 3D measurement is performed at a plurality of measurement positions will be briefly described. For example, model data that can be used in CAD or the like is generated by procedures such as (1) 3D measurement, (2) filtering, (3) registration, and (4) mesh modeling. The information processing apparatus 100 acquires a plurality of 3D point cloud data by performing 3D measurement at different measurement positions (1). And the information processing apparatus 100 removes the point cloud used as noise by filtering for every 3D point cloud data (2). For example, for each point specified by the 3D point cloud data, the variance is calculated between a predetermined number of points from the closest order. At this time, if the points are elements forming an article or the like, the neighboring points are also dense, so the variance is smaller than the isolated points that become noise, and if there are isolated points that become noise, there are points in the vicinity. Since it is very sparse, the dispersion is larger than the point that is a component of the article. For this reason, as an example, the information processing apparatus 100 regards and removes points whose variance is equal to or greater than a threshold as noise.

  Then, the information processing apparatus 100 integrates a plurality of 3D point cloud data as one 3D point cloud data by performing alignment processing of a plurality of 3D point cloud data after noise removal (3). The information processing apparatus 100 converts the 3D point cloud data integrated into one into model data that is three-dimensional surface data by meshing processing (4). Note that the meshing process is performed by CAD or the like.

  As a registration method for aligning 3D point groups measured at different measurement positions, there is a method using a target for alignment of 3D point groups. Specifically, a target having a known shape is placed in the space to be measured, and a plurality of 3D point cloud data is obtained by detecting the target (reference point) from the 3D point cloud data measured at each measurement position. There is a way to match one. However, this method takes time and labor to install the target, and it is left to the operator at the site to determine at which point the target should be installed.

  For this reason, there is a need for a method of automatically performing alignment from the shape of the environment represented by 3D point cloud data without using a target. However, if a plurality of features such as points, lines, and surfaces are extracted for each of a plurality of 3D point group data, and corresponding points between the plurality of 3D point group data are obtained according to an algorithm such as RANSAC (RANDOM Sample Consensus), Calculation time may increase.

  Therefore, instead of processing the 3D point cloud as it is, each 3D point cloud is sliced horizontally at a certain height, and the point cloud contained therein is matched two-dimensionally, thereby improving the calculation efficiency. There is technology to enhance.

  In such a technique, a condition is that a plurality of 3D point groups can be sliced horizontally at the same height with respect to the space to be measured. At this time, it is only necessary to perform all 3D measurements at the same height. However, if it is desired to measure the space to be measured without blind spots, the length of the legs of the information processing apparatus 100 may be changed, or the measurement may be performed on a desk or table. There is a case where the information processing apparatus 100 is placed and measurement is desired. From such a background, a method for matching the height reference of each 3D point cloud data in advance is required.

  As a method of adjusting the height reference of each 3D point cloud data before slicing the 3D point cloud in the height direction, there is a method of detecting a floor surface observable at many measurement positions and using the floor surface as a reference. is there. For example, all the planes formed by point groups whose normals are vertically upward are extracted and the plane having the lowest height is defined as the reference plane, and all planes formed by the point groups whose normals are vertically upward are all There are two methods, Method 2 in which the surface having the largest area after extraction is used as the reference surface.

  However, even if either of the above two methods is adopted, it is assumed that different point planes are selected on the point cloud data obtained at the two measurement positions to be aligned due to the occlusion problem. There are constraints on the range. An example of the measurement site is shown in FIG. 3, and an example of occlusion is shown in FIGS.

  Therefore, the information processing apparatus 100 calculates, for example, an offset amount in which the relative positional relationship between the planes in the measurement target space between the 3D point groups measured at different measurement positions, that is, the altitude difference between the planes matches. And adjust the height standard. Then, the information processing apparatus 100 executes two-dimensional registration. Thereby, the information processing apparatus 100 can increase the synthesis accuracy of the 3D point cloud data in an environment with occlusion.

  Here, two sets of data related to height values of a plurality of parallel horizontal plane regions (hereinafter abbreviated as planes) extracted from the 3D point cloud data are obtained when 3D measurement is performed between the set data. Different heights. For this reason, even if it represents the height regarding the same plane, the value of the height differs. That is, the position of the coordinate system C is different. The difference in height with respect to the same plane is the offset amount to be obtained. Here, the offset amount is represented by Δh. At this time, even in the same plane, the height value is not the same between the data. For example, if the distance is calculated using a combination of the same plane, such as the distance between the floor and the plane on the desk, the distance between the plane on the desk and the ceiling, the distance between the floor and the ceiling, etc., between the 3D point cloud data There is a high possibility that substantially the same value will be obtained even if there is some measurement error. Therefore, it is possible to search for a corresponding surface between different 3D point cloud data characterized by the distance between the planes. If the distance between corresponding planes is the same among the 3D point cloud data, the offset amount Δh, which is the correct correction amount in the height direction, is obtained, and the height value of each plane is converted. A plurality of values having the same height can be obtained.

  However, when the space to be measured is a space in which similar planes are repeated at regular intervals like a staircase, the heights of the planes are different between different 3D point cloud data at the wrong offset amount Δh. The number of may increase. An example in which the number of planes having the same height at the wrong offset amount Δh increases is shown in FIG.

  Therefore, the information processing apparatus 100 corrects the height of the second surface group obtained from the second point group data at different positions in accordance with one surface of the first plane group obtained from the first point group data. Of the distances between the first surfaces having the same height as the surface of the group, the offset amount is specified from the number of the same distances as the characteristic distance between the surfaces of the first surface group. Thereby, the synthesis accuracy of 3D measurement results at different positions can be raised.

In FIG. 1, a set of heights of a plurality of first planes extracted from the first point cloud data 111 is set as H _1, and a set of heights of a plurality of second planes extracted from the second point cloud data 112 is set as H _1. Let H ₂ . The corrected set obtained by correcting the set H ₂ with the offset amount Δh is H ′ ₂ .

  The information processing apparatus 100 uses, for each of the first measurement position 101 and the second measurement position 102, three-dimensional point group information (first point group data 111 and second point group) based on the distance from each measurement position to the object. Data 112) is obtained. Here, the information processing apparatus 100 converts the coordinate of each point data included in the point cloud data for each point cloud data at each measurement position so that the normal direction of the horizontal plane from which the positive direction of the z axis is extracted. Keep it.

  The information processing apparatus 100 extracts point group information belonging to the planar area from the acquired point group data for each measurement position. Specifically, the information processing apparatus 100 extracts a plurality of parallel planes for each measurement position based on the 3D point cloud specified by the acquired point cloud data. A plurality of parallel planes extracted from the first point group data 111 are defined as a plurality of first planes. A plurality of parallel planes extracted from the second point group data 112 are defined as a plurality of second planes. Here, detailed processing of plane extraction will be described later.

  In FIG. 1, a first measurement position 101 and a second measurement position 102 are stair landings as shown in FIG. The first measurement position 101 can measure, for example, the upstairs side as shown in FIG. The second measurement position 102 can measure, for example, the descending staircase side as shown in FIG. The plurality of first planes extracted from the first point group data 111 include, for example, a ceiling surface, a step surface of an upstairs, a floor surface of a landing, and the like. The plurality of second planes extracted from the second point group data 112 include, for example, a ceiling surface, a step surface of a descending staircase, a floor surface of a landing, and the like.

  Here, the information processing apparatus 100 can also extract a horizontal plane from the 3D point cloud data for each 3D point cloud data measured at a plurality of measurement positions.

A set related to height values of a plurality of first planes extracted from the first point cloud data 111 is H ₁ . A set related to the height values of the plurality of second planes extracted from the second point cloud data 112 is H ₂ . For example, the heights of the plurality of first planes are indicated by solid lines, and the heights of the plurality of second planes are indicated by dotted lines.

  The information processing apparatus 100 calculates an offset amount in which the vertical difference between the planar areas to which the extracted point group information belongs is the same among a plurality of measurement positions.

Specifically, the information processing apparatus 100 is a distance at which the difference between the first measurement position 101 and another distance among the distances between the first planes of the plurality of first planes is a predetermined value or more. Is identified. The plurality of first planes are mutually parallel first plane groups belonging to each of the plurality of point group data extracted from the acquired first point group data 111. Each distance between the first planes of the plurality of first planes is, for example, d ₁₁ , d ₁₂ , d ₁₃ , d ₁₄ , d ₁₅ , d ₁₆ . The predetermined value may be set by the user, for example. In the example described later, the predetermined value is represented by α. Here, the information processing apparatus 100 identifies a characteristic distance compared to other distances. The distance d ₁₅ and the distance d ₁₆ are distances between the floor surfaces of the stairs and are substantially the same. The information processing apparatus 100 does not specify a distance d ₁₅ and the distance d _16. Here, the information processing apparatus 100 specifies the distances d ₁₁ , d ₁₂ , d ₁₃ , and d ₁₄ . The identified distance is also referred to as a characteristic distance.

  The information processing apparatus 100 generates, for the second measurement position 102, a combination of a second plane included in the plurality of second planes and a first plane included in the plurality of first planes. The plurality of second planes are second plane groups that are parallel to each other and belong to each of the plurality of point group data extracted from the acquired second point group data 112. For each of the generated combinations, the information processing apparatus 100 matches the vertical direction of the second plane included in the plurality of second planes so that the positions in the vertical direction (z-axis direction) between the planes included in the combination match. Correct the position of. That is, the information processing apparatus 100 corrects the heights of the plurality of second planes so that the heights between the planes included in the combination match.

In FIG. 1, a correction example using two combinations is given. The first is an example (correct correspondence example in FIG. 1) corrected by a combination of the ceiling surface of the plurality of first planes and the ceiling surface of the plurality of second planes. The second is an example in which the height of the upper floor of the ascending stairs among the plurality of first planes and the height of the landing floor of the plurality of second planes are made to coincide (FIG. 1). Example of erroneous correspondence). A set relating to the height values of the plurality of second planes after correction is H ′ ₂ .

  Next, for each combination, the information processing apparatus 100 matches the vertical position of each corrected second plane region among the vertical positions of the first planes of the plurality of first planes. Identify the location. In other words, for each combination, the information processing apparatus 100 matches the height of each second plane of the plurality of corrected second planes out of the height of each first plane of the plurality of first planes. Specify the height.

  In the case of a correct correspondence example, the height of the ceiling surface included in the plurality of first planes and the height of the floor surface of the landing included in the plurality of first planes are specified. In the case of an erroneous correspondence example, the heights of the two floor surfaces of the ascending staircase included in the plurality of first planes and the heights of the landing floor surfaces included in the plurality of first planes are specified.

  For each combination, the information processing apparatus 100 determines that the difference between the positions (heights) included in the combination of the positions (heights) of the two combinations of the specified positions (heights) is a characteristic distance. Identify the number of matching combinations.

In the case of a correct correspondence example, the difference between the height of the ceiling surface and the height of the landing floor and the combination is the distance d ₁₃ , and the information processing apparatus 100 determines that this difference is included in the characteristic distance. In the case of a correct correspondence example, the information processing apparatus 100 specifies 1 as the number of combinations.

In the case of an erroneous correspondence example, the difference between the height of the upper floor surface of the upstairs and the height of the lower floor surface of the upstairs is the distance d ₁₅ , and the information processing apparatus 100 is characterized by this difference. It is not included in the distance. A difference distance d ₁₄ for the combination of the heights of the landing floor of the upper floor of the uplink stairs, the information processing apparatus 100 determines that the difference is included in the feature distances. The difference regarding the combination of the height of the lower floor of the upstairs and the height of the landing floor is the distance d ₁₆ , and the information processing apparatus 100 determines that this difference is included in the characteristic distance. Therefore, in the case of an erroneous correspondence example, the information processing apparatus 100 specifies 1 as the number of combinations.

  Next, the information processing apparatus 100 specifies the offset amount based on the number specified for each combination. Specifically, the information processing apparatus 100 specifies the offset amount for the combination with the largest specified number. In the example of FIG. 1, the specified number is the same for any combination of correct correspondence and incorrect correspondence. Therefore, the offset amount for both combinations may be specified as the offset amount, and other evaluation values may be used. The amount of offset may be specified by introducing.

  Then, the information processing apparatus 100 performs alignment using the point cloud information corrected according to the specified offset amount. Specifically, the information processing apparatus 100 performs alignment using the second point cloud data 112 and the first point cloud data 111 in which the second point cloud data 112 is corrected according to the specified offset amount. Do. The information processing apparatus 100 adds the specified offset amount to the z coordinate value of each point data included in the second point cloud data 112. Thereby, the corrected second point cloud data 112 is obtained. Note that the conventional technique may be used for alignment.

  Further, as will be described later with reference to FIGS. 2 and 6, when the number of planes having the same height as the corrected plurality of second planes among the plurality of first planes is used as the evaluation value, there is an error. Will be higher than the correct response. On the other hand, in the example of FIG. 1, since the evaluation of the incorrect response and the correct response is the same, it is possible to suppress the selection of the offset amount for the incorrect response. As a result, the synthesis accuracy of a plurality of 3D point cloud data can be improved.

  FIG. 2 is an explanatory diagram illustrating an example of a plan view when a staircase is measured in 3D. FIG. 2A shows a first measurement position 101 at a stair landing and a second measurement position 102 at the same landing. The information processing apparatus 100 measures the stairs going up from the first measurement position 101. The information processing apparatus 100 measures the stairs going down from the second measurement position 102.

FIG. 2 (2) shows a plurality of first planes extracted from the first point cloud data 111 measured at the first measurement position 101 and the second point cloud data 112 measured at the second measurement position 102. A plurality of second planes to be extracted are shown (the hatched portion of points). The plurality of first planes are, for example, P _{11 to} P ₁₅ . The plurality of second planes are P _{21 to} P ₂₆ .

In FIG. 2, the first plane P ₁₁ and the second plane P ₂₁ are ceiling surfaces of landings. A first plane P ₁₅ second plane P ₂₂ is the floor of the landing. First plane P ₁₄ from the first plane P ₁₂ is the tread of the uplink stairs respectively. Each of the second plane P ₂₃ to the second plane P ₂₅ is a tread of a descending staircase. The second plane P ₂₆ is a floor surface of a landing that is down the descending stairs. Thus, the first plane P ₁₄ from the first plane P _12, the second plane P ₂₃ and the second plane P _25, which are the treads of a different staircase.

Here, the evaluation value is the number of coincidence heights of the plurality of first planes for the first measurement position 101 and the plurality of second planes after the height correction for the second measurement position 102 is corrected. Will be described. Details of the evaluation value calculation method will be described with reference to FIG. A plurality of first planes P ₁₅ and second planes P ₂₅ are made to coincide with each other as compared with the case where the plurality of second planes are corrected so that the heights of the first plane P ₁₁ and the second plane P ₂₁ coincide with each other. When the second plane is corrected, the number of the first plane and the corrected second plane coincide with each other. In this way, if similar planes are continued at regular intervals like a staircase, an incorrect response may have a higher evaluation than a correct response. Then, an incorrect offset amount is specified as the optimum offset amount.

  As described with reference to FIG. 1, the information processing apparatus 100 according to the present embodiment has a high-precision offset when performing alignment using a space in which similar planes are continuous at equal intervals, such as stairs, as a measurement target. The amount can be specified.

  Here, FIG. 3 is used to show an example of measurement site, and FIGS. 4 and 5 are used to show an example of occlusion.

  FIG. 3 is an explanatory diagram illustrating a usage example of the information processing apparatus 100. FIG. 3 illustrates a site 300 </ b> A to a site 300 </ b> N as an example of a section where work is performed. In the following description, “site 300” may be referred to when the sites 300A to 300N are collectively referred to.

  The information processing apparatus 100 is mounted as a portable apparatus in which the worker 301 performs hand carry, for example. When the worker 301 performs work on the site 300 </ b> A to 300 </ b> N, it is not necessary to install one information processing device 100 per site 300, and one information processing device 100 is carried around at each site 300. Can be used. That is, each time work is completed at the site 300, the worker 301 brings the information processing apparatus 100 to the next site 300 by hand carry or the like and places the information processing device 100 at an arbitrary position on the next site 300, thereby supporting data. Can be provided.

  As described above, the information processing apparatus 100 can measure and digitize the surrounding environment with a simple operation by bringing the information processing apparatus 100 to the site 300A to 300N. Furthermore, the information processing apparatus 100 records the behavior of the person in the environment or provides information according to the state of the person by measuring the person in the environment and linking it with the digitized environment information. Various information support is possible.

  FIG. 4 is an explanatory diagram illustrating an occlusion example 1. FIG. 4 shows a scene where the height reference adjustment fails due to occlusion in the method 1 in which all the planes formed by the point groups whose normals are vertically upward are extracted and the plane having the lowest height is used as the reference plane. An example of is shown. In FIG. 4, the measurement of 3D point cloud data is performed with the information processing apparatus 100 placed at the measurement position M1, and the 3D point cloud is placed with the information processing apparatus 100 placed at the measurement position M2. The case where data measurement is carried out is shown. Further, in FIG. 4, the plane having the lowest height among the planes extracted from the 3D point cloud data measured at the measurement position M1 is indicated by a thick solid line, while the 3D points measured at the measurement position M2 are shown. The plane with the lowest height among the planes extracted from the group data is indicated by a thick broken line. On site 300A shown in FIG. 4, there is a stair landing at a position lower than the floor. In such a stair landing, the laser beam reaches from the measurement position M2, while the laser beam does not reach the measurement point M1 because the staircase is a blind spot. By this occlusion, the floor is extracted as the lowest surface from the 3D point cloud data measured at the measurement position M1, while the stair landing is extracted as the lowest surface from the 3D point cloud data measured at the measurement position M2. Is done. As a result, the reference plane is displaced between the measurement position M1 and the measurement position M2, so that the height alignment fails.

  FIG. 5 is an explanatory diagram showing an occlusion example 2. FIG. 5 shows an example of a scene in which height alignment fails due to occlusion in Method 2 in which all planes formed by point clouds whose normals are vertically upward are extracted and the plane with the largest area is used as the reference plane. Indicated. In FIG. 5, the measurement of 3D point cloud data is performed in a state where the information processing apparatus 100 is placed at the measurement position M3, and the 3D point group is displayed in a state where the information processing apparatus 100 is placed at the measurement position M4. The case where data measurement is carried out is shown. Further, in FIG. 5, the surface having the largest area among the planes extracted from the 3D point cloud data measured at the measurement position M3 is indicated by a thick solid line, while the 3D point cloud measured at the measurement position M4. The plane with the largest area among the planes extracted from the data is indicated by a thick broken line.

  On the site 300B shown in FIG. 5, a large conference table is placed in the conference room. At the measurement position M3, the information processing apparatus 100 is placed on the floor, while at the measurement position M4, the conference table is on the desk. A case where the information processing apparatus 100 is placed is shown. In this case, the floor surface can be scanned widely from the measurement position M3, while only a part of the floor surface can be scanned behind the measurement position M4 placed on the desk of the conference table while being hidden by the conference table. By such occlusion, the floor surface is extracted from the 3D point cloud data measured at the measurement position M3 as the surface having the largest area, while the 3D point cloud data measured at the measurement position M4 is extracted on the desk of the conference table. The surface is extracted as the surface having the largest area. As a result, since the reference plane is displaced between the measurement position M3 and the measurement position M4, the height alignment fails.

  As shown in FIG. 4 and FIG. 5, in the method of detecting a specific place in the environment, not limited to the floor, and using it as a reference, the reference position in occlusion is partially or entirely hidden depending on the measurement position. weak. The information processing apparatus 100 does not perform the height reference adjustment for a specific surface such as a floor surface. As described above, the information processing apparatus 100 includes the first plane included in the plurality of first planes extracted from the point cloud data 111, and the second plane included in the plurality of second planes extracted from the point cloud data 112. An offset amount that matches the heights of, is calculated, and alignment in the height direction is performed. The information processing apparatus 100 performs two-dimensional alignment.

FIG. 6 is an explanatory diagram illustrating an example in which a plurality of combinations of planes are evaluated by the number of planes having the same height. In FIG. 6, for each combination of the height included in the set H ₁ and the height included in the set H ₂ , a candidate for the offset amount Δh is calculated based on the height included in the combination. That is, a candidate for the offset amount Δh is calculated such that the plane corresponding to the height of the set H ₁ included in the combination matches the plane corresponding to the height of the set H ₂ included in the combination. The The candidate for the offset amount Δh is, for example, a value obtained by subtracting the height of the set H ₂ included in the combination from the height of the set H ₁ included in the combination.

Then, for each of the combinations, a set H _'2 corrected by correcting the set H ₂ is calculated. For each combination, the number of differences between the height of the set H _{1 and} the height of the corrected set H ′ ₂ within a predetermined range is calculated as an evaluation value. And the candidate of offset amount (DELTA) h about the combination in which the calculated evaluation value becomes the largest is specified as offset amount (DELTA) h.

  FIG. 6 shows an example of correct correspondence corrected so that the ceiling surface at the first measurement position 101 and the ceiling surface at the second measurement position 102 are aligned. FIG. 6 shows an erroneous correspondence example in which the floor surface of the ascending staircase at the first measurement position 101 and the landing floor surface at the second measurement position 102 are corrected so as to match each other. The evaluation value of the erroneous correspondence example is larger than that of the correct correspondence example, and the candidate of the offset amount Δh for the erroneous correspondence is specified as the correct offset amount.

  If the space to be measured is not a place like a staircase, the offset amount may be specified by the number of matching heights. However, if a space in which similar planes are continuous at equal intervals, such as stairs, is the measurement target, for example, when the tread surfaces of different stairs are matched, the first plane and the corrected second plane There are cases where the number of matching heights increases. In such a case, an incorrect offset amount may be specified as the optimum offset amount.

  Therefore, the information processing apparatus 100 is configured such that, for each combination of the first plane and the second plane, the height of each first plane that matches the height of each second plane corrected so that the height of the combination matches. Among the distances between one plane, the number of distances that coincide with the characteristic distance between each first plane is obtained. Then, the information processing apparatus 100 specifies the offset amount based on the number of matches obtained for each combination of the first plane and the second plane. Thereby, when similar planes continue at regular intervals like a staircase, it is possible to determine whether or not the distances between characteristic horizontal planes match. It is possible to improve the synthesis accuracy of a plurality of point cloud data measured in 3D at different positions.

  FIG. 7 is an explanatory diagram illustrating an example of the information processing apparatus 100 for performing 3D measurement. In FIG. 7, the information processing apparatus 100 includes a drive device 701, a measurement device 702, and a control device 703. The information processing apparatus 100 is installed and used on a horizontal floor, for example. In FIG. 7, the shape of the housing of the information processing apparatus 100 is, for example, a shape that has substantially the same outer shape when viewed from any direction, but is not limited thereto.

  In addition, the tripod portion of the information processing apparatus 100 can be expanded and contracted, for example. The height of the information processing apparatus 100 can be changed by expansion and contraction of the tripod portion of the information processing apparatus 100. The information processing apparatus 100 can change the height from 30 cm to 130 cm, for example, by the tripod portion.

The drive device 701 is a motor that rotates in the pan direction d1 around a rotation shaft 710 (corresponding to the z-axis in FIG. 7). In the following description, the rotation angle in the pan direction d1 (the angle with respect to the x axis in FIG. 7) may be expressed as “rotation angle θ _h (θ _h = 0 to 360 ° (degrees))”. However, it is assumed that the front direction when the driving device 701 is at the initial position matches the x-axis direction.

  The measuring device 702 is a 2D sensor that measures the distance from the own device to the object by using the time until the reflected light is received while irradiating light toward the object while scanning in the tilt direction d2. Here, the light is, for example, a laser such as infrared rays. The measuring device 702 can obtain infrared reflection intensity and color information at the same time as well as the distance from the device to the object if the light is infrared. The tilt direction d2 is a direction that rotates about the tilt axis 720. That is, the measuring apparatus 702 rotates the optical system (for example, a light emitting unit 811 and a light receiving unit 812 shown in FIG. 8 described later) around the tilt axis 720, while rotating the direction (irradiation direction) shown in FIG. ).

  In the information processing apparatus 100, the measurement device 702 is attached to the drive device 701 so that the tilt direction d2 is perpendicular to the pan direction d1. The measuring device 702 irradiates the object with light (hereinafter referred to as “laser”) while scanning in the tilt direction d2 while moving along the circular orbit about the rotation axis 710. Measure the distance.

  At this time, the measuring device 702 irradiates the laser in a range of “0 to 360 °” in the tilt direction d2. The tilt angle θv is an angle of the tilt direction d2 with respect to the rotation shaft 710. More specifically, for example, the measuring device 702 performs this measurement each time it is moved from the 0 ° to 360 ° by a predetermined angle in the tilt direction d2. For example, when the predetermined angle is 0.25 °, the measuring device 702 measures about 1440 points at 25 [msec] per round. For example, the measuring device 702 performs this measurement every time the driving device 701 moves the panning direction d1 by “0 to 360 °” by a predetermined angle. However, it is not necessary to measure from 0 ° to 360 ° in both the tilt direction and the pan direction. If one of the tilt direction and the pan direction is measured from 0 to 180 °, all directions can be measured. In addition, the measurement range may be limited by specifying an angle, not limited to all directions. Thereby, 3D measurement can be performed using the measuring device 702 which is a 2D sensor.

  The control device 703 includes a display unit 704, for example. The display unit may be a projector or a display.

(Hardware configuration example of information processing apparatus 100)
FIG. 8 is a block diagram illustrating a hardware configuration example of the information processing apparatus 100. The information processing apparatus 100 includes a CPU (Central Processing Unit) 801, a memory 802, an I / F (Interface) 803, a projector 804, a speaker 805, a driving device 701, and a measuring device 702. Each component is connected by a bus 800.

  Here, the CPU 801 governs overall control of the information processing apparatus 100. The memory 802 includes, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), a flash ROM, and the like. Specifically, for example, a flash ROM or ROM stores various programs, and a RAM is used as a work area for the CPU 801. The program stored in the memory 802 is loaded into the CPU 801 to cause the CPU 801 to execute the coded processing.

  The I / F 803 is connected to a wired or wireless network, and is connected to another computer (for example, a user's personal computer) via the network 810. The I / F 803 controls an interface between the network 810 and the own apparatus, and controls input / output of data from other computers. The projector 804 is a display unit 704, for example. The projector 804 is a device that displays an image or video by projecting it onto a large screen or the like. For example, the projector 804 may project a model indicated by the model data onto a screen. The speaker 805 emits sound by electricity. Specifically, the speaker 805 generates sound such as music and voice. The control device 703 illustrated in FIG. 8 includes, for example, a CPU 801, a memory 802, an I / F 803, a projector 804, and a speaker 805.

  The driving device 701 is a motor that rotates in the pan direction d1 about the rotation shaft 710. Measuring device 702 includes a light emitting unit 811, a light receiving unit 812, a driving unit 813, and a sensor control unit 814. The light emitting unit 811 is a light source that irradiates a laser, and is, for example, a semiconductor laser. The light receiving unit 812 receives reflected light. The drive unit 813 rotates the light emitting unit 811 in the tilt direction d2 around the tilt axis 720. The sensor control unit 814 irradiates the object with the laser while scanning in the tilt direction d2, and measures the distance from the own apparatus to the object using the time until the reflected light is received. The sensor control unit 814 outputs the coordinate value of the object in the coordinate system C to the memory 802 or the like as point coordinate data based on the distance.

  The information processing apparatus 100 may include, for example, a disk drive, a disk, an SSD (Solid State Drive), and the like in addition to the above-described components. Further, the information processing apparatus 100 may include, for example, an input device that can accept an operation input by the worker 301 or the like, a display different from the projector 804, and the like.

FIG. 9 is an explanatory diagram illustrating an example of point groups and point data on a plane. First, the plane will be described. The point group on the plane P ₁ is {p ₁₁ , p ₁₂ ,..., P _1M1 }. The point group on the plane Pu is {p _u1 , p _u2 ,..., P _uMu }.

The point data of the point p _uv is {x _uv , n _uv , c _uv , I _uv }. x _uv is a position vector of the point p _uv . x _uv includes an x coordinate value, a y coordinate value, and a z coordinate value. n _uv is a normal direction vector of the point p _uv . c _uv is a color information vector of the point p _uv . c _uv includes an R value, a G value, and a B value. I _uv is infrared reflected light intensity information of the point p _uv . Note that the point cloud data is a set of point data. For example, the area of each plane P is approximated by the point group number M _u on each plane P. For example, based on x _uv and n _uv , horizontal alignment, nearest point, and distance between points are calculated.

(Functional configuration example of information processing apparatus 100)
FIG. 10 is a block diagram illustrating a functional configuration example of the information processing apparatus 100. The information processing apparatus 100 includes an acquisition unit 1001 and a control unit 1002. The acquisition unit 1001 is realized by the drive device 701 and the measurement device 702. In addition, the control unit 1002 includes an extraction unit 1011, a distance calculation unit 1012, an offset amount calculation unit 1013, a first calculation unit 1014, a second calculation unit 1015, a specification unit 1016, an alignment unit 1017, Have

  The processing of the control unit 1002 from the extraction unit 1011 to the alignment unit 1017 is realized by the memory 802, for example. It is coded in a program stored in a memory 802 or the like accessible by the CPU 801 shown in FIG. Then, the CPU 801 reads the program from the memory 802 or the like, and executes the process coded in the program. Thereby, the processing of the control unit 1002 is realized. The processing result of the control unit 1002 is stored in, for example, the memory 802.

  The acquisition unit 1001 acquires 3D point group data based on the distance from each measurement position to the object for each measurement position of the plurality of measurement positions. Specifically, for example, the acquisition unit 1001 performs 3D scanning based on the distance from the information processing apparatus 100 to the object by scanning each measurement position while irradiating light and using reflected light reflected from the object. Get point cloud data.

  In the present embodiment, the acquisition unit 1001 acquires first point group data 111 for the first measurement position 101 and second point group data 112 for the second measurement position 102 as 3D point group data. . When there are three or more measurement positions, 3D point group data after combining 3D point group data at two of the three measurement positions may be used as the first point group data 111. The 3D point cloud data at the remaining measurement positions among the three measurement positions may be used as the second point cloud data 112. Thereby, 3D point cloud data measured at three or more measurement positions can be synthesized with high accuracy.

  For example, for each measurement position, the extraction unit 1011 extracts a plane in the space to be measured from the 3D point cloud data for each measurement position. Specifically, the extraction unit 1011 extracts a plane formed by the 3D point group specified by the 3D point group data acquired by the acquisition unit 1001, for example, according to an algorithm such as RANSAC.

  A method for extracting a plane based on an algorithm such as RANSAC will be described in more detail. For example, the extraction unit 1011 uses a 3D point group specified by 3D point group data as a sample. Then, the extraction unit 1011 extracts three points from the sample at random. Subsequently, the extraction unit 1011 further extracts a point group within a predetermined distance from a plane model determined by three points randomly extracted from the sample, among the 3D point groups specified by the 3D point group data. The predetermined distance is not particularly limited as long as it is determined in advance. Even if the points are on the same plane model, if the distance from the information processing apparatus 100 is long, the distance between the points is long. Therefore, the predetermined distance may be set by a position vector included in the point data of the point p selected as the sample, for example.

  Here, it is assumed that a point group within a predetermined distance from the planar model is a point group existing on the planar model, and the subsequent processing will be described. The extraction unit 1011 determines whether or not the number of point groups existing on the planar model is greater than or equal to a predetermined threshold value. When the point group on the planar model is greater than or equal to the threshold value, the extraction unit 1011 stores, in the memory 802, planar data in which the parameter defining the planar model is associated with the point group included in the planar model. Examples of the parameter include three-point coordinates or a plane equation. On the other hand, the extraction unit 1011 does not store the plane data regarding the plane model when the number of point groups existing on the plane model is less than the threshold. After that, the extraction unit 1011 repeatedly executes random sampling of three points from the sample and plane data storage associated therewith for a predetermined number of trials. By such a plane extraction method, a plane model in which a certain number or more of point groups are within a certain distance in the normal direction from the plane model can be obtained. The fixed distance is the minimum distance between a plurality of plane models parallel to each other. The constant distance is a minimum distance for extraction as a different plane model in the normal direction of the plane model. In other words, for example, the distance between a plurality of plane models parallel to each other is a certain distance or more. Hereinafter, a portion where a 3D point group is present at a predetermined density or higher on a plane defined by the plane model may be referred to as a “plane”.

  Here, the extraction unit 1011 can also extract a plane from the 3D point cloud data for each 3D point cloud data measured at a plurality of measurement positions. In this case, the extraction unit 1011 calculates the direction of the plane between the 3D point cloud data at each measurement position. Then, the extraction unit 1011 converts the coordinates of each point included in the 3D point group data for each 3D point group data at each measurement position so that the positive direction of the z axis is the normal direction of the plane. Then, as described above, the extraction unit 1011 extracts a plane from the 3D point group data for each 3D point group data at each measurement position according to an algorithm such as RANSAC.

  Furthermore, the extraction unit 1011 can group two or more arbitrary numbers of 3D point group data as registration execution targets according to the following criteria. For example, each time the acquisition unit 1001 acquires 3D point cloud data, the extraction unit 1011 groups the 3D point cloud data with the 3D point cloud data acquired so far, thereby performing registration execution targets. can do. Furthermore, the extraction unit 1011 may group only 3D point cloud data acquired in a predetermined period (for example, within 10 minutes) among 3D point cloud data acquired in the past. Further, the extraction unit 1011 may accept a combination of 3D point cloud data to be grouped via an input device (not shown).

  As described above, when a plane is extracted from the 3D point group data for each 3D point group data at each measurement position, the extraction unit 1011 calculates the height (z coordinate) of each plane based on the origin of the coordinate system C. To do.

Here, as described with reference to FIG. 1, a set of heights of a plurality of first planes extracted from the first point cloud data 111 measured at the first measurement position 101 is set as H _1, and the second measurement position 102 is set. in the set of measured heights of the plurality of second plane which is extracted from the second point group data 112 and H _2. The set H ₁ and the set H ₂ are expressed as follows. M and N are positive integers. M is the number of first planes. N is the number of the plurality of second planes.

H ₁ = {h ₁₁ , h ₁₂ ,..., H _1M }
H ₂ = {h ₂₁ , h ₂₂ ,..., H _2N }

Further, as will be described later, a set of the heights of the planes corrected by the offset amount Δh or the offset amount Δh candidates is defined as H ′ ₂ . The set H′2 is expressed as follows.

H ′ ₂ = {h ′ ₂₁ , h ′ ₂₂ ,..., H ′ _2N }
= {H ₂₁ + Δh, h ₂₂ + Δh,..., H _2N + Δh}

FIG. 11 is an explanatory diagram illustrating an example of a distance between planes. FIG. 11 shows a plurality of first plane height sets H ₁ and a plurality of second plane height sets H ₂ .

H ₁ {h ₁₁ , h ₁₂ , h ₁₃ , h ₁₄ } is obtained as the set H ₁ . Further, H ₂ {h ₂₁ , h ₂₂ , h ₂₃ , h ₂₄ } is obtained as the set H ₂ . In FIG. 11, the height included in the set H ₁ is indicated by a solid thick line. Height included in the set H ₂ is indicated by dashed thick line.

  Next, the distance calculation unit 1012 has a difference between the first measurement position 101 and another distance among the distances between the first planes of the plurality of first planes parallel to each other being equal to or greater than a predetermined value. Identify the distance. In addition, the distance calculation unit 1012 specifies a second distance at which the difference between the second measurement position 102 and other distances among the plurality of distances between the plurality of second planes parallel to each other is equal to or greater than a predetermined value.

  Specifically, the distance calculation unit 1012, for example, for the first measurement position 101, for each combination of two first planes included in the plurality of first planes, the distance between the two first horizontal planes included in the combination. Is calculated. Specifically, the distance calculation unit 1012 calculates, for example, the difference in height between the first horizontal planes as the distance between the first planes. The difference between the first planes is obtained, for example, by subtracting the height of the lower first plane from the height of the higher first plane of the two first planes.

  Specifically, the distance calculation unit 1012, for example, for the second measurement position 102, for each combination of two second planes included in a plurality of second planes, between the two second planes included in the combination. The distance is calculated. Specifically, the distance calculation unit 1012 calculates, for example, the difference in height between the second horizontal planes as the distance between the second planes. The difference between the second planes is obtained, for example, by subtracting the height of the lower second plane from the height of the higher second plane of the two second planes.

In the first measurement position 101, which is the difference between the height h ₁₁ and the height h ₁₂ is the distance d _11. Difference between the height h ₁₁ and height h ₁₃ is the distance d _12. The difference between the height h ₁₁ and the height h ₁₄ is the distance d ₁₃ . Difference between the height h ₁₂ and height h ₁₃ is the distance d _15. Difference between the height h ₁₂ and height h ₁₄ is the distance d _14. Difference between the height h ₁₃ and height h ₁₄ is the distance d _16.

In the second measurement position 102, which is the difference between the height h ₂₁ and height h ₂₂ is the distance d _21. The difference between the height h ₂₁ and the height h ₁₃ is the distance d ₂₂ . The difference between the height h ₂₁ and the height h ₁₄ is the distance d ₂₃ . The difference between the height h ₂₂ and the height h ₁₃ is the distance d ₂₅ . The difference between the height h ₂₂ and the height h ₁₄ is the distance d ₂₄ . The difference between the height h ₂₃ and the height h ₂₄ is the distance d ₂₆ .

  Next, for example, for each measurement position, the distance calculation unit 1012 extracts a distance where there is no distance at which the difference in distance is equal to or less than a threshold among the distances between the planes. That is, the distance calculation unit 1012 extracts a characteristic distance that does not have a similar distance.

The distance calculation unit 1012 extracts a set D ₁ as a characteristic distance for the first measurement position 101 below. Note that ε is a threshold value. ε is a value close to 0.

D ₁ : a set having d _1i as an element in which d _1j that _satisfies | d ₁₁ −d _1j | ≦ ε (i ≠ j) does not exist

In the first measurement position 101, since the difference between the distance d ₁₅ and the distance d ₁₆ near 0, D ₁ becomes _{{d 11, d 12, d} 13, d 14}.

  In addition, the distance calculation unit 1012 extracts a set D2 as a characteristic distance for the second measurement position 102 below.

D ₂ : a set having d _2i as an element in which there is no d _{2j satisfying} | d ₂₁ −d _2j | ≦ ε (i ≠ j)

Since the difference between the distance d ₂₅ and the distance d ₂₆ is close to 0 at the second measurement position 102, D ₂ becomes {d ₂₁ , d ₂₂ , d ₂₃ , d ₂₄ }.

  Next, the offset amount calculation unit 1013 generates a combination of a first plane included in the plurality of first planes and a second plane included in the plurality of second planes. The offset amount calculation unit 1013 corrects the vertical positions of the plurality of second planes so that the vertical positions between the planes included in the combinations are the same for each combination. Here, the vertical direction is the z-axis direction or the height direction.

Specifically, the offset amount calculation unit 1013 calculates, for each combination, the distance between planes included in the combination as the offset amount Δh. Specifically, the offset amount calculating section 1013, a plurality of height h ₁ in set H _1, the combination of a plurality of height h ₂ in the set H _2, the height h included in the combination The difference value between ₁ and the height h ₂ is calculated as a candidate for the offset amount Δh. For example, the difference value is a value obtained by subtracting the height h ₂ from the height h ₁ included in the combination.

  Then, the offset amount calculation unit 1013 adds the calculated offset amount Δh candidates to the heights of the plurality of second planes for each combination. As a result, the vertical positions of the plurality of second planes are corrected so that the vertical positions between the planes included in the combination match.

Next, for each combination of planes, the first calculation unit 1014 selects a position in the vertical direction of each first plane that coincides with the vertical position of each corrected second plane region. Identify. In other words, for each combination of planes, the first calculation unit 1014 has a height that matches the set H ₁ and the set H ′ ₂ corrected based on the offset amount Δh candidates for each offset amount Δh candidate. Specify the set of plane heights to be used. A set H ′ _{2 obtained} by correcting the set H ₂ in FIG. 11 based on the offset amount Δh candidates is {h ′ ₂₁ , h ′ ₂₂ , h ′ ₂₃ , h ′ ₂₄ }. The set of plane heights for which there is a matching height for the first measurement position 101 is denoted G ₁ . Set height of the plane matching the height of the second measurement position 102 is present, represents a G _2. Specifically, the first calculation unit 1014 identifies the set G ₁ and the set G ₂ as follows.

G ₁ : a set having h _1i where h ′ _{2j satisfying} | h _1i −h ′ _2j | ≦ α exists G ₂ : h including h ′ _{1j satisfying} | h _1i −h ′ _2j | ≦ α ' _2j element set

  The predetermined value α is a preset value and is not particularly limited. Here, for example, the predetermined value α is set to a value smaller than ½ of the minimum interval between horizontal planes extracted as different horizontal planes during horizontal plane extraction.

Then, the first calculating unit 1014, for each of the combinations, one of the two combinations of the specified positions for the first measurement position 101, the distance between the positions included in the two combinations of positions, included in the set D ₁ Identify the number of combinations that match the distance to be measured. The position here is a position in the height direction. For this reason, specifically, for each combination of planes, the first calculation unit 1014 calculates the difference in height included in this combination among the two combinations of heights included in the set G ₁ as the set D _1. Specify the number of combinations included in. The number specified for the first measurement position 101 is denoted as f ₁ .

The first calculation unit 1014, for each of the combinations of the plane, of the two combinations of the specified position for the second measurement position 102, the distance between the positions included in the two combinations of positions, the set D ₂ Identify the number of combinations that match the included distance. Specifically, for each combination of planes, the first calculation unit 1014 includes, in the set D ₂ , a difference in height included in this combination among two combinations of heights included in the set G _2. Specify the number of combinations. The number specified for the second measurement position 102 is represented as f _2.

  For each combination of planes, the first calculation unit 1014 uses the smaller value of the number of combinations calculated for each measurement position as an evaluation value. The higher the evaluation value, the higher the evaluation.

Here, an example in which the set G _{1, the} set G ₂ , the number f _1, and the number f ₂ are specified for two offset amount Δh candidates will be described with reference to FIGS. 11 and 12.

FIG. 12 is an explanatory diagram illustrating Evaluation Example 1 for a candidate for the offset amount Δh. FIG. 12 illustrates an example in which the offset amount Δh candidate is h ₁₁ −h ₂₁ . First, an example in which the set G ₁ and the number f ₁ are obtained for the first measurement position 101 will be described. Since the difference between the height h ₁₁ and the height h ′ ₂₂ is less than or equal to α, the first calculation unit 1014 puts the height h ₁₁ into the set G ₁ . The first calculation unit 1014 does not include the height h ₁₂ in the set G ₁ because the difference between the height h ₁₂ and each height included in the set H ′ ₂ is larger than α. The first calculation unit 1014 does not include the height h ₁₃ in the set G ₁ because the difference between the height h ₁₃ and each height included in the set H ′ ₂ is larger than α. Since the difference between the height h ₁₄ and the height h ′ ₂₄ is less than or equal to α, the first calculation unit 1014 puts the height h ₁₄ into the set G ₁ . The set G ₁ is as follows.

G ₁ = {h ₁₁ , h ₁₄ }

Then, the first calculation unit 1014 sets the number of combinations in which the difference in the combination height is included in the set D ₁ among the combinations of heights included in the set G ₁ as the number f ₁ .

There are one combination of heights included in the set G ₁ . The difference between the height h ₁₁ and the height h ₁₄ is d ₁₃ and is included in the set D ₁ . For this reason, the first calculation unit 1014 sets the number f ₁ to 1.

Next, an example in which the set G ₂ and the number f ₂ are obtained for the second measurement position 102 will be described. For example, since the difference between the height h ′ ₂₁ and the height h ₁₁ is equal to or less than α, the first calculation unit 1014 puts the height h ′ ₂₁ into the set G ₂ . The first calculation unit 1014 puts the height h ′ ₂₂ in the set G ₂ because the difference between the height h ′ ₂₂ and the height h ₁₄ is α or less. The first calculation unit 1014 does not include the height h ′ ₂₃ in the set G ₂ because the difference between the height h ′ ₂₃ and each height included in the set H ₁ is larger than α. The first calculation unit 1014 does not include the height h ′ ₂₄ in the set G ₂ because the difference between the height h ′ ₂₄ and each height included in the set H ₁ is larger than α. The set G2 is as follows.

G ₂ = {h ′ ₂₁ , h ′ ₂₂ }

The first calculation unit 1014, among the combinations of the height included in the set G _2, the number of combinations the difference in height of the combinations included in the set D ₂ and f _2.

The combination of the height included in the set G ₂ is a 1 ways. The difference between the height h ′ ₂₁ and the height h ′ ₂₂ included in the combination of heights is d ₂₁ and is included in the set D ₂ . Therefore, the first calculation unit 1014 sets the number f ₂ to 1.

The first calculation unit 1014 calculates the smaller one of the number f ₁ and the number f ₂ as an evaluation value for the offset amount candidate. Here, the evaluation value when the offset amount Δh candidate is h ₁₁ −h ₂₁ is 1.

FIG. 13 is an explanatory diagram illustrating Evaluation Example 2 for a candidate for the offset amount Δh. FIG. 13 illustrates an example in which the offset amount Δh candidate is h ₁₂ −h ₂₂ . The second measurement position 102 will be described. The first calculation unit 1014 does not include the height h ₁₁ in the set G ₁ because the difference between the height h ₁₁ and each height included in the set H ′ ₂ is larger than the predetermined value α. The first calculation unit 1014 puts the height h ₁₂ in the set G ₁ because the difference between the height h ₁₂ and the height h ′ ₂₂ is α or less. The first calculation unit 1014 puts the height h ₁₃ in the set G ₁ because the difference between the height h ₁₃ and the height h ′ ₂₃ is α or less. Since the difference between the height h ₁₄ and the height h ′ ₂₄ is equal to or less than the predetermined value α, the first calculation unit 1014 puts the height h ₁₄ into the set G ₁ .

G ₁ = {h ₁₂ , h ₁₃ , h ₁₄ }

Then, the first calculation unit 1014 specifies the number f ₁ of combinations in which the difference in height of the combinations is included in the set D ₁ among the combinations of heights included in the set G ₁ .

There are three combinations of heights included in the set G ₁ . First, the difference between the height h ₁₂ and the height h ₁₃ is d ₁₅ and is not included in the set D ₁ . The difference between the height h ₁₂ and the height h ₁₄ is d ₁₄ and is included in the set D ₁ . The difference between the height h ₁₃ and the height h ₁₄ is d ₁₆ and is not included in the set D ₁ . For this reason, the first calculation unit 1014 sets the number f ₁ to 1.

The second measurement position 102 will be described. The first calculation unit 1014 does not include the height h ′ ₂₁ in the set G ₂ because the difference between the height h ′ ₂₁ and each height included in the set H ₁ is larger than α. Since the difference between the height h ′ ₂₂ and the height h ₁₂ is less than or equal to α, the first calculation unit 1014 puts the height h ′ ₂₂ into the set G ₂ . Since the difference between the height h ′ ₂₃ and the height h ₁₃ is equal to or less than the predetermined value α, the first calculation unit 1014 puts the height h ′ ₂₃ into the set G ₂ . The first calculation unit 1014 puts the height h ′ ₂₄ in the set G ₂ because the difference between the height h ′ ₂₄ and the height h ₁₄ is α or less.

G ₂ = {h ′ ₂₂ , h ′ ₂₃ , h ′ ₂₄ }

Then, the first calculation unit 1014 specifies the number f ₂ of combinations in which the difference in the combination height is included in the set D ₂ among the combinations of heights included in the set G ₂ .

The combination of the height included in the set D ₂ is triplicate. The difference between the height h ′ ₂₂ and the height h ′ ₂₃ is d ₂₅ and is not included in the set D ₂ . The difference between the height h ′ ₂₂ and the height h ′ ₂₄ is d ₂₄ and is included in the set D ₂ . The difference between the height h ′ ₂₃ and the height h ′ ₂₄ is d ₂₆ and is not included in the set D ₂ . Therefore, the first calculation unit 1014 sets the number f ₂ to 1.

The first calculation unit 1014 calculates the smaller one of the number f ₁ and the number f ₂ as an evaluation value for the offset amount candidate. Here, the evaluation value when the offset amount Δh candidate is h ₁₂ −h ₂₂ is 1.

  Next, the specifying unit 1016 specifies the offset amount Δh based on the number specified for each combination. Specifically, the specifying unit 1016 specifies the offset amount Δh candidate for the combination having the largest evaluation value among the plane combinations as the offset amount Δh. Here, evaluation is so high that an evaluation value is large.

As described above, the combination of the plane where the offset amount Δh candidate shown in FIG. 12 is h ₁₁ −h ₂₁ and the combination of the plane where the offset amount Δh candidate shown in FIG. 13 is h ₁₂ −h ₂₂ are shown. The evaluation value is the same. Thus, unlike the example shown in FIG. 6, the evaluation values are the same. For this reason, it can suppress that the candidate of offset amount (DELTA) h regarding an incorrect response is specified.

FIG. 14 is an explanatory diagram illustrating an example in which the number f ₁ is different from the number f ₂ . In FIG. 14, the set H ₁ is {h ₁₁ , h ₁₂ }, and the set H ′ ₂ is {h ′ ₂₁ , h ′ ₂₂ , h ′ ₂₃ }. Set D ₁ includes the distance height h ₁₁ and the height h _12. When the distance between the height h ′ ₂₁ and the height h ′ ₂₂ is the same as the distance between the height h ′ ₂₂ and the height h ′ ₂₃ , nothing is included in the set D _2. Not included. The distance between the height h ₁₁ and the height h _{12 is the same as} the distance between the height h ′ ₂₁ and the height h ′ ₂₂ and the distance between the height h ′ ₂₂ and the height h ′ _23. It is.

In the case shown in FIG. 14, when the offset amount Δh candidates are h ₁₁ and h ₂₁ , the set G ₁ is {h ₁₁ , h ₁₂ }, and the set G 2 is {h ′ ₂₁ , h is' _22}. Since the distance between the height h ₁₁ and the height h ₁₂ is included in the set D ₁ , the number f ₁ is 1. Since the distance between the height h ′ ₂₁ and the height h ′ ₂₂ is not included in the set D ₂ , the number f ₂ is zero. Thus, since the number f ₁ and the number f ₂ are not necessarily the same, the first calculation unit 1014 uses the smaller number of the number f ₁ and the number f ₂ as an evaluation value. In the example of FIG. 14, the evaluation value is 0.

  In addition, there may be a plurality of offset amount candidates having the largest evaluation value calculated by the first calculation unit 1014. In such a case, the second calculation unit 1015 identifies the offset amount based on an evaluation value different from the evaluation value calculated by the first calculation unit 1014.

  Specifically, the second calculation unit 1015 calculates a different evaluation value for each offset amount candidate having the largest evaluation value calculated by the first calculation unit 1014. Although there are no particular limitations on the method for calculating different evaluation values, three methods will be exemplified here.

  First, a first method for calculating different evaluation values will be described.

  The second calculation unit 1015 obtains the number of point groups included in each extracted plane for each measurement position. The number of point groups included in each plane can be used to distinguish a relatively narrow plane such as a tread of a staircase from a wide plane such as a floor surface or a ceiling surface.

  Then, the second calculation unit 1015 selects a candidate of the offset amount Δh having the largest evaluation value calculated by the first calculation unit 1014, for the point group included in the plane having the same height between the two 3D measurement position groups. A numerical correlation value is calculated as an evaluation value.

  For example, the number of points on each plane in the first point cloud data 111 is as follows.

Plane P ₁₁ (plane with height h ₁₁ ): 300
Plane P ₁₂ (plane with height h ₁₂ ): 100
Plane P ₁₃ (plane with height h ₁₃ ): 80
Plane P ₁₄ (plane with height h ₁₄ ): 280

  For example, the number of points on each plane in the second point cloud data 112 is as follows.

Plane P ₂₁ (plane with height h ₂₁ ): 280
Plane P ₂₂ (plane with height h ₂₂ ): 300
Plane P ₂₃ (plane with height h ₂₃ ): 90
Plane P ₂₄ (plane with height h ₂₄ ): 70

The candidates of the offset amount Δh having the largest evaluation value calculated by the first calculation unit 1014 are h ₁₁ -h ₂₁ and h ₁₂ -h ₂₂ .

When the candidate for the offset amount Δh is h ₁₁ -h ₂₁ , the planes correspond to plane P ₁₁ -plane P ₂₁ and plane P ₁₄ -plane P ₂₂ . In this case, the correlation value is -1.

When the offset amount Δh is a candidate h ₁₂ -h ₂₂ , the planes correspond to plane P ₁₂ -plane P ₂₂ , plane P ₁₃ -plane P ₂₃ , plane P ₁₄ -plane P _24. . In this case, the correlation value is −0.48. Since the correlation value is stronger as the absolute value is closer to 1, the specifying unit 1016 specifies a candidate for the offset amount Δh that is h ₁₂ -h ₂₂ as the offset amount Δh.

  Next, a second method for calculating different evaluation values will be described.

  As shown in FIG. 9, at the time of measurement, infrared reflection intensity and color information may be obtained simultaneously for each point group. Therefore, in the second method, the offset amount Δh is specified based on whether or not these pieces of information are similar between planes having the same height.

  The second calculation unit 1015 calculates, for each offset amount Δh candidate, a correlation value of a reflection intensity or a histogram of color information as an evaluation value for a plane whose height matches between two 3D measurement position groups.

As described above, when the candidate for the offset amount Δh is h ₁₁ -h ₂₁ , the heights between the planes coincide, such as plane P ₁₁ -plane P ₂₁ and plane P ₁₄ -plane P ₂₂ . For example, the second calculation unit 1015 calculates a reflection intensity of the plane P ₁₁ and the plane P ₁₄ and a histogram of color information. For example, the second calculation unit 1015 calculates a histogram of reflection intensity and color information of the planes P ₂₁ and P ₂₂ . Then, the second calculation unit 1015 calculates the correlation value of the histogram when the offset amount Δh candidate is h ₁₁ -h ₂₁ .

As described above, when the candidate of the offset amount Δh is h ₁₂ -h ₂₂ , the plane is like plane P ₁₂ -plane P ₂₂ , plane P ₁₃ -plane P ₂₃ , plane P ₁₄ -plane P _24. The height between them matches. For example, the second calculation unit 1015 calculates a histogram of reflection intensity and color information for the plane P ₁₂ , the plane P _13, and the plane P ₁₄ . For example, the second calculation unit 1015 calculates a histogram of reflection intensity and color information for the plane P ₂₂ , the plane P _23, and the plane P ₂₄ .

Next, the second calculation unit 1015 calculates a correlation value between the histogram of the plane P _{12 and} the histogram of the plane P ₂₂ . Then, the second calculation unit 1015 calculates a correlation value between the histogram of the plane P _{13 and} the histogram of the plane P ₂₃ . Then, the second calculation unit 1015 calculates a correlation value between the histogram of the plane P _{14 and} the histogram of the plane P ₂₄ . The second calculation unit 1015 calculates an average value of the calculated correlation values as an evaluation value.

  Since the correlation value has a stronger correlation as the absolute value is closer to 1, the specifying unit 1016 specifies the offset amount Δh candidate having the largest evaluation value as the offset amount Δh.

  Next, a third method for calculating different evaluation values will be described.

  In the third method, the alignment result obtained by performing the 2D alignment for each of the offset amount Δh candidates is evaluated. As an evaluation of whether or not the alignment result is good, the higher the number of corresponding points between the two 3D point groups, the higher the evaluation. A method of alignment by the alignment unit 1017 will be described later.

  The alignment unit 1017 corrects the second point group data corrected by the offset amount Δh candidate for each of the offset amount Δh candidates. Then, the alignment unit 1017 aligns the first point group data 111 and the corrected second point group data 112 for each of the offset amount Δh candidates.

  The second calculation unit 1015 specifies the point group (first point group) specified by the first point group data 111 out of the 3D point group data after alignment and the second point group data 112 after alignment. The number of points corresponding to the point group (second point group) to be calculated is calculated. The evaluation value is calculated for each of the offset amount Δh candidates (each combination of the first plane and the second plane). Specifically, the second calculation unit 1015 determines, for each point in the first point group, whether or not the distance from each point until it is included in the nearest second point group is within a certain distance. judge. The second calculation unit 1015 uses the number of points determined to be within a certain distance as an evaluation value. The larger this evaluation value, the higher the evaluation. The second calculation unit 1015 sets the offset amount Δh with the highest evaluation as the offset amount Δh.

  Here, three methods are listed as other evaluation value calculation methods, but the present invention is not limited to this. For example, the offset amount may be specified by an evaluation value calculated by any one of the three methods. Alternatively, the offset amount may be specified by each evaluation value calculated by a plurality of methods. Or three methods may be performed in order until an offset amount is specified uniquely.

Next, the alignment unit 1017 adjusts the height reference between the 3D point cloud data at each measurement position according to the offset amount Δh specified by the specifying unit 1016. That is, the alignment unit 1017 applies the offset amount to each point included in the 3D point group data to which the set H ₂ is assigned among the 3D point group data measured at different measurement positions, that is, the second point group data 112. Add Δh. Thereafter, as described in Related Document 1 below, the alignment unit 1017 applies a 3D point group included in a section having a predetermined height for each 3D point group data to be aligned to a predetermined surface, for example, Two-dimensional horizontal slice data is extracted by projecting onto the floor surface. Then, the alignment unit 1017 performs two-dimensional registration on these slice data.

  [Related Literature 1] “Automatic generation of integrated point cloud model in large-scale environment (3rd report), fully automatic registration of multiple point clouds by point cloud pair alignment and match determination”, Yusuke Matsuyama, Hiroaki Date, Osamu Kanai , 2014 Precision Engineering Society Spring Meeting, Session ID: E45

  In addition, the alignment unit 1017 may align the two-dimensional point group obtained by slicing and extracting each 3D point group at the same height in a plane by the method described in Related Document 2 below. .

  [Related Literature 2] Masahiro Tomono, “Two-dimensional global scan matching method using feature invariant for Euclidean transformation, Journal of the Robotics Society of Japan, Vol. 25, No. 3, pp 390-401 (2007)”

  Explaining this, the two-dimensional point group can calculate the normal of each point from the positions of surrounding points, and the alignment unit 1017 can obtain the pointed point group. If one correspondence of a point with a direction is determined between data, in the case of two dimensions, coordinate conversion parameters (x, y, θ) for alignment are obtained. Accordingly, the alignment unit 1017 evaluates the correspondence candidates of the pointed points from the positional relationship of other points and narrows down to a predetermined number. As a result, the alignment unit 1017 narrows down the candidates for coordinate transformation. Therefore, after performing the detailed alignment process for minimizing the alignment error for each candidate, the residual is evaluated, and the residual is the smallest. Adopt the alignment result. Accordingly, the alignment unit 1017 can realize two-dimensional registration.

  The information processing apparatus 100 can also convert into 3D surface data by performing meshing processing on the 3D point cloud data integrated into one in this way. By using 3D point cloud data or three-dimensional surface data integrated into one of these, support data for supporting the worker 301 can be created and projected by projection. Further, the information processing apparatus 100 may display a three-dimensional model indicated by the three-dimensional surface data using the projector 804 or the like.

(Example of processing procedure performed by information processing apparatus 100)
FIG. 15 is a flowchart illustrating an example of a processing procedure performed by the information processing apparatus 100. The information processing apparatus 100 acquires 3D point cloud data for each measurement position (step S1501). The information processing apparatus 100 calculates the direction of the horizontal plane for the 3D point cloud data at each measurement position (step S1502).

  The information processing apparatus 100 performs coordinate conversion for each measurement position so that the z-axis positive direction is the normal direction of the horizontal plane (step S1503). The information processing apparatus 100 extracts a plane from the 3D point cloud data for each measurement position (step S1504). The information processing apparatus 100 calculates the height (z coordinate) of each plane (step S1505).

  The information processing apparatus 100 performs an offset amount and evaluation value calculation process (step S1506). The information processing apparatus 100 determines whether or not the number of plane combinations with the highest evaluation is one (step S1507). When it is determined that the number of combinations of planes with the highest evaluation is one (step S1507: Yes), the information processing apparatus 100 proceeds to step S1509.

  If it is determined that the number of combinations of planes with the highest evaluation is not one (step S1507: No), the information processing apparatus 100 calculates another evaluation value for the combination of planes with the highest evaluation (step S1508). . Next, the information processing apparatus 100 specifies the offset amount Δh based on the combination of the planes with the highest evaluation (step S1509).

  Next, the information processing apparatus 100 corrects the position of one 3D point group based on the offset amount for the combination of planes with the highest evaluation (step S1510). Then, the information processing apparatus 100 extracts 2D point cloud data sliced at the same height in the horizontal direction (step S1511). The information processing apparatus 100 performs a registration process (step S1512) and ends a series of processes.

16 and 17 are flowcharts showing detailed descriptions of the offset amount and evaluation value calculation processing shown in FIG. First, the information processing apparatus 100 calculates distances between a plurality of first planes for the first measurement position 101 (step S1601). Next, the information processing apparatus 100 extracts a set D ₁ of characteristic distances from the calculated distances (step S1602).

Then, the information processing apparatus 100 calculates the distance between the plurality of second planes for the second measurement position 102 (step S1603). The information processing apparatus 100 extracts a set D ₂ of the characteristic distance from each distance calculated (step S1604). The information processing apparatus 100 generates all combinations of the first plane included in the plurality of first planes of the first measurement position 101 and the second plane included in the plurality of second planes of the second measurement position 102 ( Step S1605). The information processing apparatus 100 determines whether there is an unevaluated combination (step S1606).

  If it is determined that there is no unevaluated combination (step S1606: No), the information processing apparatus 100 selects one combination from the unevaluated combinations as an evaluation target (step S1701). For the selected combination, the information processing apparatus 100 calculates a candidate for the offset amount Δh that matches the heights of the planes included in the combination (step S1702). The information processing apparatus 100 corrects the heights of the plurality of second planes at the second measurement position 102 by using the calculated offset amount Δh (step S1703).

Then, the information processing apparatus 100 specifies a set G ₁ of heights of the first planes whose heights coincide with the corrected second planes among the plurality of first planes (step S1704).

The information processing apparatus 100 specifies the set G ₂ of the heights of the second planes whose height matches the corrected second plane among the plurality of first planes (step S1705). The information processing apparatus 100 calculates the number f _{1 in} which the height difference (distance) is included in the set D ₁ for each combination of two heights included in the set G ₁ (step S1706). The information processing apparatus 100 calculates the number f _{2 in} which the height difference (distance) is included in the set D ₂ for each combination of the two heights included in the set G ₂ (step S1707), and the process proceeds to step S1606. Transition.

  When it is determined in step S1606 that there is an unevaluated combination (step S1606: Yes), the information processing apparatus 100 ends the series of processes.

  FIG. 18 is a flowchart showing a detailed description of the registration process shown in FIG. The information processing apparatus 100 uses one 3D point group (correction point group) specified by one corrected 3D point group data and a 3D point group (reference point group) specified by the other 3D point group data that is not corrected. The normal direction is calculated for each point (step S1801). Next, the information processing apparatus 100 extracts a plurality of point correspondence candidates between the reference point group and the correction point group (step S1802). The information processing apparatus 100 calculates a coordinate conversion parameter for each point correspondence candidate (step S1803).

  The information processing apparatus 100 clusters a plurality of coordinate conversion parameters (step S1804). The information processing apparatus 100 calculates a coordinate conversion parameter for the center of gravity of the cluster having a large number of elements (step S1805). The information processing apparatus 100 uses the positional relationship of the point group after conversion with each parameter as an initial position, and performs alignment for each cluster using the ICP algorithm (step S1806). The information processing apparatus 100 evaluates the residuals of the respective alignment results, selects the most matching alignment result (step S1807), and ends the series of processes.

  FIG. 19 is an explanatory diagram of an example system. In the present embodiment, the information processing apparatus 100 is described as an example in which the information processing apparatus 100 is the measurement device itself, but the present invention is not limited to this. Some of the functional units of the information processing apparatus 100 illustrated in FIG. 10 may be implemented in another device.

  A system 1900 includes an information processing apparatus 100 that is a measuring device, an apparatus 1901, and an apparatus 1902. The information processing apparatus 100, the apparatus 1901, and the apparatus 1902 are connected via a network 810, for example. In the example of FIG. 19, the device 1901 is a PC, and the device 1902 is a portable terminal device, but is not limited thereto. Examples of the device 1901 and the device 1902 include a PC, a server, and a portable terminal device. For example, the acquisition unit 1001 may be realized by the information processing apparatus 100 which is a measuring device, and the control unit 1002 may be realized by the apparatus 1901. Further, some functions of the control unit 1002 may be realized by the information processing apparatus 100, and the remaining functions of the control unit 1002 may be realized by the apparatus 1901.

  Further, for example, the apparatus 1902 may notify the information processing apparatus 100 that is a measurement apparatus of a measurement start instruction received by an operation input by a user. Thereby, the worker 301 can perform 3D measurement without directly operating the measuring device. The apparatus 1901 may display a three-dimensional model indicated by the three-dimensional surface data on a display or the like.

  The devices 1901 and 1902 are not particularly limited as long as they have a hardware configuration such as a CPU, a disk, a memory, an I / F, an input device, and an output device such as a display. Various 3D point cloud data and various model information may be stored and managed in a database server (not shown).

  As described above, the information processing apparatus 100 corrects the point cloud data in accordance with the offset amount such that the heights of the reference planes match between the point cloud data measured at different measurement positions, and performs two-dimensional correction. Perform position alignment. Thereby, the information processing apparatus 100 can improve the synthesis accuracy of the 3D point cloud data in an environment where there is occlusion.

  The information processing apparatus 100 adjusts the height and height of the second plane group obtained by correcting the height of the second plane group obtained from the second point group data in accordance with one surface of the first plane group obtained from the first point group data. Among the distances between the same first planes, the offset amount is specified from the number of the same distances as the characteristic distance between the surfaces of the first plane group. Thus, the information processing apparatus 100 compares the offset amount with the number of first planes having the same height as the corrected second planes among the plurality of first planes. Thus, it is possible to improve the accuracy of alignment in the height direction.

  Further, the information processing apparatus 100 has a characteristic between the second planes among the distances between the corrected second planes whose heights coincide with the first planes among the corrected second planes. The offset amount is specified based on the second number of the distances included in the short distance and the specified first number. Thereby, since the first number may be different from the second number, the smaller number of the two types of numbers can be used as the evaluation value. As a result, the information processing apparatus 100 can perform correction in the height direction with a more accurate evaluation value.

  Note that the information processing method described in this embodiment can be realized by executing an information processing program prepared in advance on a computer such as a personal computer or a workstation. The information processing program is recorded on a computer-readable recording medium such as a magnetic disk, an optical disk, or a USB (Universal Serial Bus) flash memory, and is executed by being read from the recording medium by the computer. The information processing program may be distributed via a network such as the Internet.

  In addition, the information processing apparatus 100 described in this embodiment includes an application-specific IC (hereinafter simply referred to as “ASIC”) such as a standard cell or a structured ASIC (Application Specific Integrated Circuit), or an FPGA (Field Programmable Gate Array). ) And other PLDs (Programmable Logic Devices). Specifically, for example, the function of the control unit 1002 of the information processing apparatus 100 described above is defined by HDL description, and the HDL (Hardware Description Language) description is logically synthesized and given to the ASIC or PLD, thereby processing the information. The control unit 1002 of the apparatus 100 can be manufactured.

_{d 11, d 12, d 13} , d 14, d 15, d 16, d 21, d 22, d 23, d 24, d 25, d 26 distance p _1M1, p _11, p ₁₂ points P _11, P ₁₂ , P ₁₃ , P ₁₄ , P ₁₅ first plane P ₂₁ , P ₂₂ , P ₂₃ , P ₂₄ , P ₂₅ , P ₂₆ second plane h ₁₁ , h ₁₂ , h ₁₃ , h ₁₄ height of the first plane h ₂₁ , h ₂₂ , h ₂₃ , h ₂₄ Height of second plane 100 Information processing device 101 First measurement position 102 Second measurement position 111 First point cloud data 112 Second point cloud data 300 On-site 301 Worker 701 Drive device 702 Measuring device 703 Control device 1001 Acquisition unit 1002 Control unit 1011 Extraction unit 1012 Distance calculation unit 1013 Offset amount calculation unit 1014 First calculation unit 1015 Second calculation unit 1016 Identification unit 1900 System 1901, 1902 Device

Claims (6)

For each measurement position of a plurality of measurement positions, obtain three-dimensional point cloud data based on the distance from each measurement position to the object,
For each measurement position, extract the point cloud data belonging to the plane area from the acquired point cloud data,
Calculating an offset amount in which a vertical difference between the planar areas to which the extracted point cloud data belongs is the same among the plurality of measurement positions;
Align using the point cloud data corrected according to the calculated offset amount,
An information processing program for causing a computer to execute processing. The process for calculating the offset amount includes:
Between the first plane areas of the plurality of first plane areas parallel to each other belonging to each of the plurality of point group data extracted from the acquired point group data for the first measurement position of the plurality of measurement positions. A distance in which the difference between each of the distances and other distances is equal to or greater than a predetermined value,
A second plane region included in a plurality of parallel second plane regions belonging to each of a plurality of point group data extracted from the point group data acquired for the second measurement position of the plurality of measurement positions; For each combination of the first planar regions included in the plurality of first planar regions, the vertical positions between the planar regions included in the combination are matched to each other. Correct the vertical position of the second plane region,
For each of the combinations, the position in the vertical direction of each of the first plane regions is identified as the position that matches the position in the vertical direction of each of the second plane regions after correction.
For each of the combinations, among the two combinations of the specified positions, specify the number of combinations in which the distance between the positions included in the two combinations of the positions matches the specified distance,
Identifying the offset amount based on the number identified for each of the combinations;
The process of performing the alignment is as follows.
The point cloud data for the second measurement position is aligned using the point cloud data corrected according to the specified offset amount and the point cloud data for the first measurement position.
The information processing program according to claim 1. The process for calculating the offset amount includes:
Further, for the second measurement position, a second distance in which a difference between each distance and another distance among the distances between the respective second plane regions is equal to or greater than the predetermined value,
Further, for each of the combinations, a second number of combinations in which the distance between the positions included in the two combinations of the positions matches the second distance specified among the two combinations of the specified positions is specified. ,
Of the processes for calculating the offset amount, the process for specifying the offset amount is:
Identifying the offset amount based on the identified number and the identified second number for each of the combinations;
The information processing program according to claim 2. Of the processes for calculating the offset amount, the process for specifying the offset amount is:
Identifying the offset amount based on the smaller number of the number identified for each of the combinations and the identified second number;
The information processing program according to claim 3. For each measurement position of a plurality of measurement positions, obtain three-dimensional point cloud data based on the distance from each measurement position to the object,
For each measurement position, extract the point cloud data belonging to the plane area from the acquired point cloud data,
Calculating an offset amount in which a vertical difference between the planar areas to which the extracted point cloud data belongs is the same among the plurality of measurement positions;
Align using the point cloud data corrected according to the calculated offset amount,
An information processing method, wherein a computer executes a process. An acquisition unit that acquires three-dimensional point cloud data based on a distance from each measurement position to an object for each of the plurality of measurement positions;
For each measurement position, point cloud data belonging to a plane area is extracted from the point cloud data acquired by the acquisition unit, and the plane area to which the extracted point cloud data belongs is extracted between the plurality of measurement positions. A control unit that calculates an offset amount in which a vertical difference between them coincides, and performs alignment using the point cloud data corrected according to the calculated offset amount;
An information processing apparatus comprising:

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