JP2020086751A - Map generating device and map generating method - Google Patents

Abstract

To assist in generation of maps for facilities existing at locations not present in design data.SOLUTION: An as-built support map generating device 1 comprises: a facility shape search unit 13 for searching for point group data 12b associated with facilities in design data 11b from the point group data 12b as a result of measuring the facilities that actually exist in the space, the design data 11b defining the location of the facility and the shape of the facility arranged in the space; a facility unmatched shape point group evaluation unit 15 for extracting the remaining point group data 12b not associated by the facility shape search unit 13 as an unmatched shape point group; a classification evaluation unit 16 for classifying, as the facility that does not exist in the design data 11b, a partial point group shape formed from the extracted unmatched shape point group; and a display control unit 18 for displaying on a display device 2 a map that combines the facilities that exist in the design data 11b and the facilities in the partial point group shape classified as the facility that does not exist in the design data 11b.SELECTED DRAWING: Figure 1

Description

The present invention relates to a map generation device and a map generation method.

A technique is known in which a building is actually measured by laser scanning and the surface shape of the building is recognized by point cloud data, which is a set of three-dimensional position data. Furthermore, this technology takes multiple reference points for arranging laser scanners, synthesizes the acquired point cloud data, and creates large-scale, complex forms such as plants, work sites, streets, and cultural property buildings with three-dimensional information. Applied to

For example, the three-dimensional data evaluation apparatus described in Patent Document 1 includes a sparse point group data storage unit that stores sparse point group data measured with a three-dimensional measuring device at a density that is coarser than the density of measurement points of three-dimensional detailed data. A positioning unit that positions the three-dimensional detailed data and the sparse point group data in the same coordinate system, and a difference amount that calculates the amount of difference between the three-dimensional detailed data and the sparse point group data based on the result of the positioning. It has a calculation part and a change part specific part which specifies the change part from three-dimensional detailed data based on the calculation result of the amount of difference.

Japanese Unexamined Patent Publication No. 2016-217941

A difference may occur between the design data of the plant building and the point cloud data obtained by measuring the actual plant building constructed according to the design data. This difference is due to the following reasons, for example.
-When constructing a plant building, the installation process of pipes, ducts, equipment, etc. will be performed with a correction process such as actual matching.
-Even if both data are the same at the beginning of construction, there is a possibility that changes will occur due to later repairs.
-There is a possibility that there are pipes and conduits with different responsibilities other than the plant manufacturer.

Such differences cause problems in plant maintenance, subsequent removal, removal in new installation, delivery, installation plans, etc.Three-dimensional measurement of the position information of the actual product and reflection of the measurement results in the design data There is a desire to make it.
In addition, in patent document 1, when the shape of the equipment in the place which exists in design data differs from the shape registered in the design data (changed part), the mechanism which notifies that is provided. However, the equipment that is not in the design data was excluded.

Therefore, the main object of the present invention is to support the generation of a map of equipment located in a location that does not exist in the design data.

In order to solve the above-mentioned subject, the map generation device of the present invention has the following features.
The present invention, the design data that defines the position of the equipment and the shape of the equipment arranged in the space, and the point cloud data of the result of measuring the actual equipment in the space are stored in the storage unit,
From the point cloud data read from the storage unit, an equipment shape search unit that searches for the point cloud data associated with the equipment in the design data,
Remaining point cloud data not associated by the equipment shape search unit, equipment shape out point cloud evaluation unit to extract as a out point cloud,
A classification evaluation unit that classifies a partial point cloud shape formed from the extracted outlier point cloud as equipment that does not exist in the design data,
It is characterized by having a display control unit for displaying on the display device a map that combines the equipment existing in the design data and the equipment of the partial point group shape classified as non-existing equipment.
Other means will be described later.

According to the present invention, it is possible to support the generation of a map of equipment located at a location that does not exist in design data.
Note that further features, operations, and effects related to the present invention will be apparent from the description of this specification and the accompanying drawings. Aspects of the present invention are also achieved and realized by the elements and combinations of various elements, and the following detailed description and the aspects of the appended claims.

It is a schematic diagram of an azbil support map generation device concerning one embodiment of the present invention. It is a perspective view showing an example of the design equipment shape concerning one embodiment of the present invention. It is a cubic diagram showing an example of classification A concerning one embodiment of the present invention. It is a top view showing an example of classification B concerning one embodiment of the present invention. It is a top view showing an example of classification C concerning one embodiment of the present invention. It is a top view showing an example of classification D concerning one embodiment of the present invention. It is a top view of the plant which shows the example of a display of the design equipment shape by the display control part concerning one embodiment of the present invention. It is a top view of the plant which shows the example of a display which overlapped the design equipment shape and the partial point cloud shape by the display control part concerning one embodiment of the present invention. It is a flow chart which shows the processing which the equipment shape search part concerning one embodiment of the present invention searches for each design equipment shape from point cloud data. It is a flow chart which shows processing in the shape and position evaluation part concerning one embodiment of the present invention. It is the example which fitted the Gaussian function to the histogram of the distance distribution regarding one embodiment of the present invention. It is a flowchart which shows the process in the equipment shape deviation point cloud evaluation part regarding one Embodiment of this invention. It is a flow chart which shows processing of a classification evaluation part concerning one embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, functionally the same elements may be represented by the same numbers. It should be noted that the attached drawings show specific embodiments and implementation examples according to the principle of the present invention, but these are for understanding the present invention, and are not intended to limit the present invention in any way. It is not used.

Although the present embodiment has been described in detail enough for those skilled in the art to carry out the present invention, other implementations and modes are also possible without departing from the scope and spirit of the technical idea of the present invention. It is necessary to understand that the structure and structure can be changed and various elements can be replaced. Therefore, the following description should not be limited to this. In addition, in the present specification and the drawings, components having substantially the same function or configuration are denoted by the same reference numerals, and duplicate description is omitted.

FIG. 1 shows a schematic diagram of an azbil support map generation device 1 according to the present invention.
The as-built support map generation device 1 (map generation device) is configured as a computer having a CPU (Central Processing Unit), a memory, a storage unit (storage unit) such as a hard disk, and a network interface.
In this computer, a CPU executes a program (also referred to as an application or its abbreviation application) read on a memory to operate a control unit (control unit) configured by each processing unit.

The azbilt support map generation device 1 includes a design data input unit 11a, a three-dimensional measurement unit 12a, an equipment shape search unit 13, a shape/position evaluation unit 14, and an equipment shape out point group evaluation unit 15 as control units. It has a classification evaluation unit 16 and a display control unit 18.
The azbilt support map generation device 1 has a storage unit that stores design data 11b, point cloud data 12b, and classification map data 17, which are data used by the control unit.
Hereinafter, before describing the details of each component of FIG. 1, two types of shape data (design equipment shape, partial point group shape) handled by the as-built support map generation device 1 and a result of comparison between them are defined. The two classifications A to D will be described with reference to FIGS.

FIG. 2 is a three-dimensional view showing an example of the design facility shape. The piping parts 101 and 102 of the plant are examples of design equipment shapes, and are extracted from the design data 11b. This extraction process is realized by, for example, a process of converting what is represented by a cylinder or a torus into a triangular mesh.
On the other hand, the partial point cloud shape is shape data extracted from the point cloud data 12b, and is generated by connecting each point of the point cloud with a line segment, for example.
When the as-built support map generation device 1 compares the design facility shape with the partial point group shape, shape comparison and position comparison are performed separately. The shape comparison is a process of comparing whether or not both shapes themselves are similar (that is, whether or not the shape deviation amount is small). The position comparison is a process of comparing whether or not the positions arranged in both spaces are in the vicinity (that is, whether or not the amount of positional deviation is small).

Therefore, the following four classifications are defined by the combination of the comparison results of the shape comparison and the position comparison.
[Category A]=When the shape shift amount is small and the position shift amount is also small. That is, the case where a partial point cloud shape having the same (similar) shape as the design equipment shape of the design data 11b exists at a position indicating that the equipment of the design data 11b exists.
[Class B]=When the amount of shape deviation is large and the amount of position deviation is small. That is, a case where a partial point cloud shape that is different (unsimilar) from the design facility shape of the design data 11b exists at a position indicating that there is facility of the design data 11b.
[Category C]=When the shape shift amount is small and the position shift amount is large. That is, the case where the partial point cloud shape having the same (similar) shape as the design equipment shape of the design data 11b exists at another position deviating from the position indicating that the equipment of the design data 11b exists.
[Category D]=when the amount of shape deviation is large and the amount of position deviation is also large. That is, when the partial point cloud shape that is different (unsimilar) from the design facility shape of the design data 11b exists at another position deviating from the position where the facility of the design data 11b indicates that the facility exists.

FIG. 3 is a three-dimensional view showing an example of [Classification A]. Since the point cloud exists along the position of the pipe 111 which is the design equipment shape, the design equipment shape and the partial point cloud shape have a small shape deviation amount and a small position deviation amount.

FIG. 4 is a plan view showing an example of [Classification B]. The design facility shape D1 indicated by the solid line is arranged at the center of gravity position P1, and the partial point group shape D2 indicated by the broken line is arranged at the center of gravity position P2.
In the shape comparison, the partial point group shape D2 has a large amount of shape deviation with respect to the design facility shape D1 because the lower part thereof is largely projected. In the position comparison, the distance between the barycentric positions P1 and P2 is short, so the amount of positional deviation is small. Therefore, the relationship between the design facility shape D1 and the partial point group shape D2 is [Class B].

FIG. 5 is a plan view showing an example of [Category C]. The design facility shape D1 indicated by the solid line is arranged at the center of gravity position P1, and the partial point group shape D3 indicated by the broken line is arranged at the center of gravity position P3.
In the shape comparison, since the design facility shape D1 and the partial point group shape D2 are similar in shape, the amount of shape deviation is small. In the position comparison, since the distance between the center of gravity positions P1 and P2 is long, the position shift amount is large. Therefore, the relationship between the design facility shape D1 and the partial point group shape D2 is [Category C].

FIG. 6 is a plan view showing an example of [Class D]. The partial point group shape D4 indicated by the broken line has a large amount of shape deviation and a large amount of position deviation from any of the surrounding solid line design facility shapes D1, D5, D6, D7.

Returning to FIG. 1, the design data input unit 11a is a processing unit for inputting the design data 11b. The design data 11b includes, for example, a plant building shape, piping in the plant, design equipment shapes such as devices.
The three-dimensional measurement unit 12a is a processing unit for acquiring the point cloud data 12b, and is, for example, a three-dimensional space scanner by laser distance measurement.
It is assumed that the coordinate system of the point cloud data 12b and the coordinate system of the design data 11b are matched by the mutual alignment.
Note that, as described above, the azbilt support map generation device 1 handles shape data included in both coordinate systems while allowing shape deviation and positional deviation. On the other hand, the coordinate system alignment here is a rough (overall) alignment of the building shape of the plant. In a plant, for example, with respect to a building shape, it is considered that the shape data of both of them coincide with each other with high accuracy. Therefore, the positioning of both coordinate systems can be performed mainly by performing the positioning based on the building shape.

The equipment shape search unit 13 searches the point cloud data 12b for a designed equipment shape included in the design data 11b, and extracts a partial point cloud shape that is the search result. The shape/position evaluation unit 14 evaluates the shape deviation amount and the position deviation amount based on the design facility shape and the partial point group shape.
The equipment shape deviation point cloud evaluation unit 15 evaluates the point cloud data of the partial point cloud shape that is not included in any of the designed equipment shapes.
The classification evaluation unit 16 performs a classification evaluation (classification into any of [classification A] to [classification D]) between each design equipment shape and each partial point group shape based on the shape deviation amount and the positional deviation amount. The result is used as the classification map data 17.
The classification map data 17 is a set of partial point group shapes divided for each design facility shape and each classification. The display control unit 18 presents the classification map data 17 to the worker by displaying the classification map data 17 on the display device 2.

FIG. 7 is a plan view of a plant showing a display example of the design facility shape by the display control unit 18. Design facilities such as buildings, pipes, and equipment are located in the plant. However, the design data 11b including the design facility shape in FIG. 7 is old data at the time of construction of the plant, and it is assumed that various additions are currently made to the plant.

FIG. 8 is a plan view of a plant showing a display example in which the design facility shape and the partial point cloud shape are overlapped by the display control unit 18. In FIG. 8, in addition to the design equipment shape of FIG. 7, the partial point cloud shape indicated by the thick line is added as equipment installed at various places in the plant. The added partial point group shape is included in the classification map data 17 as [classification D].
By referring to the shape of the partial point cloud indicated by the thick line, the operator can grasp in what part an object that does not exist in the design data 11b exists. Therefore, the worker can update the old design data 11b of FIG. 7 to the new design data 11b of FIG.

The outline of the as-built support map generation device 1 has been described above with reference to FIGS. 1 to 8. Hereinafter, the processing of each processing unit will be described in detail using a flowchart.

FIG. 9 is a flowchart showing a process in which the equipment shape searching unit 13 searches for each designed equipment shape from the point cloud data.
As S201 (reading process of a set {m[i]} of design equipment shapes), the equipment shape searching unit 13 sets the design equipment shapes read from the storage unit as a set {m[i]}. m[i] is represented by, for example, a triangular mesh, a basic figure such as a rectangular parallelepiped, a sphere, a cylinder, or a torus, or a part of the basic figure or a combination of the basic figures.
In S202 (reading processing of point cloud data set {c[j]}), the equipment shape searching unit 13 sets the point cloud data 12b read from the storage unit into a set {c[j]}.
In S203, the equipment shape searching unit 13 calculates a distance d[j] between each point c[j] of the point group and the closest design equipment shape m[j][k]. For example, the distance between the point c[j] and all m[j] is calculated, and the smallest value is adopted.
As S204 to S206, the equipment shape searching unit 13 executes a loop for each designed equipment shape m[i].
As S205, the equipment shape searching unit 13 obtains a point group {c[i][L]} having the shortest distance from m[i] being selected in the loop. The distribution set with the shortest distance at this time is {d[i][L]}.

In the above description, the equipment shape searching unit 13 selects the design equipment shape (m[i]) in S204, and then selects the partial equipment shape (point cloud {c[c[ i][L]}) is determined in S205. On the other hand, the equipment shape searching unit 13 may first select the partial point cloud shape and then obtain the designed equipment shape having the shortest distance from the partial point cloud shape.

FIG. 10 is a flowchart showing the processing in the shape/position evaluation unit 14.
As S301 to S302, the shape/position evaluation unit 14 executes a loop for each design facility shape m[i].
In step S311, the shape/position evaluation unit 14 sets the distribution set {d[i][L]} of the set {c[i][L]} into k overlapping and missing subsets {D[1]. ,...,D[k]} are divided into clusters. The k-means method or the like can be applied to this clustering division processing. A point group corresponding to each D[k] is defined as a division point group C[k].

In S312 to S313, the shape/position evaluation unit 14 executes a loop for each divided point group C[k].
In step S321, the shape/position evaluation unit 14 sets a division point group obtained by performing ICP (Iterative Closest Points) conversion for the design facility shape m[i] from the division point group C[k] to C_tr[k]=T[k ] ・C[k].
The ICP conversion is an algorithm for updating the corresponding points of two point groups that originally have the same shape to obtain a combination of corresponding points that minimizes the distance between the closest points. The result is a transformation matrix that aligns from one point cloud to another.

Here, the shape/position evaluation unit 14 appropriately generates a point group on the surface of the design facility shape m[i], and performs ICP conversion on the point group and the division point group C[k]. To do. The moved division point group obtained by this is set as C_tr[k]=T[k]·C[k]. However, T[k] is a conversion matrix obtained by ICP conversion. The amount of movement of the point group at this time is s[k]=|T[k]·c[k][L]-c[k][L]|. However, c[k][L]εC[k].

In S322, the shape/position evaluation unit 14 obtains a distance distribution D_tr[k] between C_tr[k] and m[i].
At S323, the shape/position evaluation unit 14 fits the Gaussian function shown in Expression 1 to the histogram of the distance distribution D_tr[k].

Here, α, μ, and σ are fitting parameters, and when α=1, a probability distribution function of a normal distribution having an average value μ and a standard deviation σ is obtained. That is, if the distance distribution D_tr[k] follows a normal distribution in the range of an appropriate standard deviation, the division point group C_tr[k] indicates the shape of the design facility shape m[i].

FIG. 11 is an example in which a Gaussian function is fitted to the histogram of the distance distribution D_tr[k].
The horizontal axis of the graph 401 represents the distance x, and the vertical axis represents the frequency y of the histogram.
Reference numeral 402 is a cross-shaped marker indicating a histogram of the distance distribution D_tr[k].
A solid line 403 indicates the fitted Gaussian function.
Note that the negative distance indicates that it is behind the design facility shape. To fit a non-linear function such as a Gaussian function, an algorithm such as Levenberg-Marquardt method can be used. The average value obtained by fitting is μ_tr[k], and the standard deviation is σ_tr[k].

Returning to FIG. 10, in S314, the shape/position evaluation unit 14 acquires k_min[i] that gives σ_tr[k_min] equal to or less than the threshold value. That is, the shape/position evaluation unit 14 obtains the minimum k that is σ_tr[k] after the iterative process for all C[k] is completed. The value of k at this time is k_min[i].

FIG. 12 is a flowchart showing a process in the equipment shape deviation point group evaluation unit 15.
As S501 to S505, the equipment shape outlier point group evaluation unit 15 executes a loop for each designed equipment shape m[i].
As S502, the equipment shape deviation point cloud evaluation unit 15 extracts the point cloud C_cad[i] at the position where the designed equipment shape m[i] exists. Specifically, the equipment shape deviation point group evaluation unit 15 calculates m[i after ICP conversion for each point c of the divided point group C[k_min[i]] corresponding to k_min[i] as shown in Expression 2. The distance between the distance d_tr[c] and the average μ_tr[k_min[i]] is 3·σ_tr[k_min[i]] or less and the point cloud is stored as C_cad[i].
C_cad[i]={cεC[k_min[i]] | |d_tr[c]−μ_tr[k_min[i]]|≦3·σ_tr[k_min[i]]} (Equation 2)

According to the processing of S502, even if the shape of the partial point group is closest to the design facility shape, the point group existing at the position close to the normal distribution is extracted. Here, the threshold value is described as 3·σ_tr[k_min[i]], but it can be changed to an appropriate value in consideration of the design tolerance of the equipment, the point cloud data acquisition accuracy, and the like.

As S503, the equipment shape outlier point cloud evaluation unit 15 extracts the point group C_nocad[i] at a position where the designed equipment shape m[i] does not exist as an outlier point group. Specifically, the equipment shape outlier point group evaluation unit 15 is included in all the partial point group shapes C[k] other than k_min[i] and C[k_min[i]], as shown in Expression 3. Of the points, the point group excluded in S502 is registered as C_nocad[i].
C_nocad[i]={k≠k_min[i]|C[k]}∩{c∈C[k_min[i]] | |d_tr[c]−μ_tr[k_min[i]]|>3・σ_tr[k_min [i]]} (Equation 3)
As S504, the equipment shape outlier point group evaluation unit 15 sets σ[i]=σ_tr[k_min[i]].

FIG. 13 is a flowchart showing the processing of the classification evaluation unit 16.
As S601 to S602, the classification evaluation unit 16 executes a loop for each point group C_cad[i].
In this loop, the classification evaluation unit 16 uses a predetermined standard deviation threshold value σ_th in the shape comparison as a threshold value for determining the shape deviation amount. That is, if σ[k_min[i]]≦σ_th, the shape deviation amount is small, and if σ[k_min[i]]>σ_th, the shape deviation amount is large.
Further, in the position comparison, the classification evaluation unit 16 uses the threshold value s_th of the movement amount by the ICP conversion determined in advance as the threshold value for determining the displacement amount. That is, if s[k_min[i]]≦s_th, the amount of positional deviation is small, and if s[k_min[i]]>s_th, the amount of positional deviation is large.

When the classification evaluation unit 16 determines that the shape shift amount is small and the position shift amount is also small (S611, Yes), the point group included in C_cad[i] is classified as [Class A] (S621). The point group of [Class A] exists at the position where the design facility shape exists according to the normal distribution.

When the classification evaluation unit 16 determines that the amount of shape deviation is large and the amount of position deviation is small (S612, Yes), the point group included in C_cad[i] is classified as [Class B] (S622). The point group of [Category B] exists at the position where the design facility shape exists, but the standard deviation exceeds the threshold value when compared with the normal distribution.

When the classification evaluation unit 16 determines that the shape deviation amount is small and the position deviation amount is large (S613, Yes), the point group included in C_cad[i] is classified as [Classification C] (S623). The point group of [Category C] exists in the distance distribution along the design facility shape, but the amount of movement by ICP conversion exceeds the threshold value.

When the classification evaluation unit 16 determines that the amount of shape deviation is large and the amount of positional deviation is also large (S614, Yes), the point group included in C_cad[i] is classified as [Class D] (S624). The point group of [Class D] exists at the position where the design facility shape does not exist.
Further, in S603 to S604, the classification evaluation unit 16 executes a loop for each point group C_nocad[i], and classifies C_nocad[i] as [class D] in the loop (S615).

The as-built support map generation device 1 of the present embodiment described above compares the three-dimensional point cloud data 12b obtained by a laser scanner or the like with the three-dimensional design data 11b, and modifies or adds the design data 11b. Support the work. This support is, for example, as shown in FIG. 8, adding equipment (partial point group shape [classification C, D]) in a place where CAD (Computer Aided Design) parts (design equipment shape) do not exist to the map. It is to be.

As a result, by supporting the generation of a map of equipment in locations where CAD parts do not exist, it is possible to efficiently confirm the points that should be reflected in the design data, and how to correct the design data. It becomes possible to know. That is, it is possible to efficiently confirm the points (partial point cloud shapes of the point cloud data 12b) to be reflected in the design data 11b, and to notify the operator together with how to modify the design data 11b. Becomes

For example, the correction work of the design data 11b according to the classification can be supported as follows.
The operator confirms that the shape of the partial point group of [Class A] has already been reflected in the design data 11b, so that the operator can understand that the correction work is unnecessary.
Since the shape of the sub-group of [Class B] already exists as a different shape in the design data 11b, it is useful for the operator to consider changing the shape in the design data 11b.
The partial point cloud shape of [Category C] already exists as the same shape in the design data 11b, but it is useful for the operator to confirm that the position has been changed. Even if the position exists on the design data 11b or the position largely differs depending on the construction state, it is properly classified as [class C].
The subclass shape of [class D] is new equipment that does not exist in the design data 11b, which is useful for adding the new equipment to the design data 11b. The point cloud data 12b in places where CAD parts do not exist, such as pipes and conduits that are not in the design, are properly classified as [Class D], so even if there are other CAD parts in the vicinity, they will be different. It can be suppressed that it is handled as a part of CAD parts.

Other implementations of the invention will be apparent to those of ordinary skill in the art from consideration of the specification and embodiments of the invention disclosed herein. Various aspects and/or components of the described embodiments may be used alone or in any combination in the as-built assistance map generator 1. The specification and specific examples are merely exemplary, and the scope and spirit of the present invention are shown in the claims.

It should be noted that the present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the configurations described.
Further, a part of the configuration of a certain embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of a certain embodiment.
Further, with respect to a part of the configuration of each embodiment, other configurations can be added/deleted/replaced. Further, each of the above-described configurations, functions, processing units, processing means, etc. may be realized by hardware by designing a part or all of them with, for example, an integrated circuit.
Further, each of the above-described configurations and functions may be realized by software by a processor interpreting and executing a program that realizes each function.

Information such as programs, tables, and files that realize each function is stored in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), or an IC (Integrated Circuit) card, an SD card, a DVD (Digital Versatile Disc), etc. It can be placed on a recording medium.
In addition, the control lines and information lines shown are those that are considered necessary for explanation, and not all the control lines and information lines on the product are necessarily shown. In practice, it may be considered that almost all configurations are connected to each other.
Further, the communication means for connecting the respective devices is not limited to the wireless LAN, and may be changed to a wired LAN or other communication means.

1 Azbilt support map generator 2 Display device 11a Design data input unit 11b Design data 12a Three-dimensional measurement unit 12b Point cloud data 13 Equipment shape search unit 14 Shape/position evaluation unit 15 Equipment shape out point cloud evaluation unit 16 Classification evaluation unit 17 Classification map data 18 Display control unit

Claims (7)

Design data that defines the position and shape of the equipment placed in the space, and point cloud data that is the result of measuring the equipment that actually exists in the space are stored in the storage unit.
From the point cloud data read from the storage unit, an equipment shape search unit that searches for the point cloud data associated with the equipment in the design data,
Remaining point cloud data not associated by the equipment shape search unit, equipment shape out point cloud evaluation unit to extract as a out point cloud,
A classification evaluation unit that classifies a partial point cloud shape formed from the extracted outlier point cloud as equipment that does not exist in the design data,
A map generation apparatus, comprising: a display control unit that causes a display device to display a map that combines equipment existing in the design data and equipment having the partial point group shape classified as equipment that does not exist. The map generation device further has a shape/position evaluation unit,
The shape/position evaluation unit compares the design equipment shape indicating the equipment of the design data with the partial point cloud shape formed from the associated point cloud data to obtain the shape deviation amount and the position deviation amount. Respectively,
The said classification|category evaluation part classifies the said partial point group shape with which the said shape shift amount and the said positional shift amount are each larger than a predetermined threshold value as a facility which does not exist in the said design data, The claim 1 characterized by the above-mentioned. Map generator. The classification evaluation unit classifies the partial point group shape in which the shape deviation amount is smaller than a predetermined threshold value but the positional deviation amount is larger than a predetermined threshold value as equipment that exists in the design data but has a changed position. Then
The map generation device according to claim 2, wherein the display control unit causes the display device to display a map including a facility whose position has been changed. The shape/position evaluation unit generates one or more divided point groups by performing clustering division on the point group data, and the partial point group shape and the design formed from the generated divided point groups. The map generation device according to claim 2, wherein the shape deviation amount is obtained by performing a shape comparison with an equipment shape. The shape/position evaluation unit obtains the positional deviation amount based on the amount of movement of the point group when ICP (Iterative Closest Points) conversion is performed on the design facility shape from the partial point group shape. The map generation device according to claim 4. The equipment shape deviating point group evaluation unit, the distance distribution after performing the alignment with the design equipment shape, the point group at a position largely deviated from the average, as the deviating point group, The map generation device according to claim 2. The map generation device includes a storage unit, an equipment shape search unit, an equipment shape outlier point group evaluation unit, a classification evaluation unit, and a display control unit,
The storage unit stores design data that defines the position and shape of the equipment arranged in the space, and point cloud data that is the result of measuring the equipment that actually exists in the space,
The equipment shape search unit searches the point cloud data read from the storage unit for the point cloud data associated with the equipment in the design data,
The equipment shape outlier point cloud evaluation unit extracts the remaining point group data not associated with the equipment shape search unit as an outlier point group,
The classification evaluation unit classifies the partial point group shape formed from the extracted outlier point group as equipment that does not exist in the design data,
The map generation method, wherein the display control unit causes a display device to display a map that combines equipment existing in the design data and equipment having the partial point cloud shape classified as equipment that does not exist.

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