JP2023174728A - Information processing device - Google Patents

Abstract

To provide an information processing device with which it is possible to generate point group data that is easily utilizable.SOLUTION: A degree of contribution of constituent points is determined on the basis of flag information added to constituent points corresponding to a shadow, and overall point group data is generated on the basis of a plurality of individual point group data, it is thereby made possible to disable the constituent points corresponding to the shadow from being utilized or lower the degree of utilization of overall point group data. As the constituent points corresponding to the shadow can change due to the date and time or weather, it is possible to cause the overall point group data to be easily utilizable.SELECTED DRAWING: Figure 1

Description

The present invention relates to an information processing device.

BACKGROUND ART Conventionally, a method (SLAM; Simultaneous Localization and Mapping) is known in which a mobile object simultaneously performs self-position estimation and environmental mapping. As a type of SLAM, a method (VSLAM; Visual SLAM) has been proposed that uses a video camera instead of a laser device (for example, see Patent Document 1). VSLAM as described in Patent Document 1 aims to reduce costs by using a video camera.

Japanese Patent Application Publication No. 2014-222550

In VSLAM as described in Patent Document 1, feature points are extracted from each image (frame) captured by a video camera, and self-position estimation and environment map creation are performed based on changes in feature points between each image. conduct. The point cloud data included in the environmental map created in this way may be used for self-position estimation, etc.

The plurality of constituent points that make up the point cloud data may include constituent points that change depending on, for example, the date and time or the weather. In such a case, the generation timing of point cloud data and the timing of self-position estimation are different, resulting in a discrepancy between the constituent points that make up the point cloud data and the features detected during self-position estimation. This makes it difficult to utilize point cloud data.

Therefore, an example of an object of the present invention is to provide an information processing device that can generate point cloud data that is easy to utilize.

In order to solve the above-mentioned problems and achieve the purpose, an information processing device of the present invention according to claim 1 includes: an imaging information acquisition section that acquires imaging information from an imaging section mounted on a moving body; a generation unit that generates point cloud data based on the point cloud data; and an extraction unit that extracts constituent points corresponding to shadows from among the constituent points constituting the point cloud data; The feature is that predetermined information is given to the constituent points.

The information processing device of the present invention according to claim 4 includes: an imaging information acquisition unit that acquires imaging information from an imaging unit mounted on a moving object; and an extraction unit that extracts feature points corresponding to shadows in the imaging information. , a generation unit that generates point cloud data based on the imaging information, and the generation unit generates the point cloud data by removing feature points extracted by the extraction unit.

The information processing method of the present invention according to claim 8 includes: an imaging information acquisition step of acquiring imaging information from an imaging unit mounted on a moving object; a generation step of generating point cloud data based on the imaging information; an extraction step of extracting component points corresponding to shadows from among the component points constituting the point cloud data, and in the generation step, adding predetermined information to the component points extracted by the extraction unit. It is a feature.

The information processing method of the present invention according to claim 9 includes: an imaging information acquisition step of acquiring imaging information from an imaging unit mounted on a moving body; and an extraction step of extracting feature points corresponding to shadows in the imaging information. , a generation step of generating point cloud data based on the imaging information, and in the generation step, feature points extracted by the extraction unit are removed to generate the point cloud data.

1 is a block diagram schematically showing an information processing apparatus according to an embodiment of the present invention. FIG. 3 is a plan view showing an example of a frame of imaging information acquired by the information processing device. FIG. 3 is a plan view showing an example of point cloud data generated by the information processing device.

Embodiments of the present invention will be described below. An information processing device according to an embodiment of the present invention includes: an imaging information acquisition unit that acquires imaging information from an imaging unit mounted on a moving object; a generation unit that generates point cloud data based on the imaging information; an extraction unit that extracts constituent points corresponding to the shadow from among the constituent points constituting the shadow. The generation unit adds predetermined information to the constituent points extracted by the extraction unit.

According to the information processing device of this embodiment, by adding predetermined information to the constituent points corresponding to the shadow, when using point cloud data for self-position estimation etc., the constituent points corresponding to the shadow can be added to the constituent points corresponding to the shadow. You can prevent it from being used or reduce its usage. Since the constituent points corresponding to shadows can change depending on the date and time and the weather, point cloud data can be easily utilized.

Note that the predetermined information given to a constituent point may be, for example, information that it is a shadow, information that there is a possibility of a shadow, or information that lowers the reliability of the constituent point. (including setting it to 0). Moreover, the point cloud data generated by the information processing device may be utilized as is, or may be utilized after being integrated with other point cloud data. Further, the imaging information acquisition section, the generation section, and the extraction section do not need to be installed in the same object, and may be installed in mutually different devices or devices. That is, each configuration of the information processing device may span multiple physically separated devices.

The extraction unit may extract constituent points corresponding to the shadow based on the solar altitude information, the solar azimuth information, and the imaging azimuth information. The direction in which the edge of the feature that extends along the vertical direction forms a shadow can be estimated based on the solar altitude information, the solar azimuth information, and the imaging azimuth information.

The extraction unit may extract constituent points corresponding to the shadow based on the complexity of the shape represented by the plurality of constituent points. For example, shadows formed by vegetation foliage may be identified by estimating the fractal dimension.

An information processing device according to another embodiment of the present invention includes: an imaging information acquisition unit that acquires imaging information from an imaging unit mounted on a moving object; an extraction unit that extracts feature points corresponding to shadows in the imaging information; A generation unit that generates point cloud data based on imaging information. The generation unit generates point cloud data by removing the feature points extracted by the extraction unit.

According to the information processing device of this embodiment, by removing feature points corresponding to shadows and generating point cloud data, the influence of shadows can be avoided when using point cloud data for self-position estimation, etc. You can avoid receiving it. Since the constituent points corresponding to shadows can change depending on the date and time and the weather, point cloud data can be easily utilized. Note that the timing of extracting and removing feature points is arbitrary; feature points may be extracted and removed in each frame included in the imaging information, or point cloud data may be extracted by VSLAM based on multiple frames. At the time of generation, feature points may be extracted and removed. Further, information indicating that the feature points have been removed may be added to the point cloud data.

The extraction unit may extract feature points corresponding to the shadow based on the solar altitude information, the solar azimuth information, and the imaging azimuth information. The direction in which the edge of the feature that extends along the vertical direction forms a shadow can be estimated based on the solar altitude information, the solar azimuth information, and the imaging azimuth information.
You may.

The extraction unit may extract feature points corresponding to the shadow based on the complexity of the shape represented by the plurality of feature points. For example, shadows formed by vegetation foliage may be identified by estimating the fractal dimension.

It is preferable that the information processing apparatus of any of the embodiments further includes an output unit that outputs point cloud data. This allows the point cloud data to be utilized at the output destination.

An information processing method according to an embodiment of the present invention includes an imaging information acquisition step of acquiring imaging information from an imaging unit mounted on a moving object, a generation step of generating point cloud data based on the imaging information, and a point cloud data generation step of generating point cloud data based on the imaging information. and an extraction step of extracting constituent points corresponding to the shadow from among the constituent points constituting the shadow. In the generation step, predetermined information is given to the constituent points extracted by the extraction unit. According to such an information processing method, point cloud data can be made easier to utilize by adding predetermined information to constituent points corresponding to shadows.

An information processing method according to another embodiment of the present invention includes: an imaging information acquisition step of acquiring imaging information from an imaging unit mounted on a moving body; an extraction step of extracting feature points corresponding to shadows in the imaging information; The method includes a generation step of generating point cloud data based on the imaging information. In the generation step, the feature points extracted by the extraction unit are removed to generate point cloud data. According to such an information processing method, point cloud data can be easily utilized by removing feature points corresponding to shadows to generate point cloud data.

Further, the information processing method described above may be implemented as an information processing program that causes a computer to execute the information processing method. By doing so, it is possible to generate point cloud data that is easy to utilize using a computer.

Further, the information processing program described above may be stored in a computer-readable recording medium. By doing so, the program can be distributed as a standalone program in addition to being incorporated into a device, and version upgrades can be easily performed.

Examples of the present invention will be specifically described below. As shown in FIG. 1, the information processing unit 1 of this embodiment includes a first information processing device 1A, which is a VSLAM device mounted on a mobile object 10, and a second information processing device mounted on an external server 100. 1B. The first information processing device 1A includes an information acquisition section 2, a generation section 3, and an output section 4. The moving body 10 is, for example, a vehicle, and is equipped with an imaging section 20, a map information storage section 30, a self-position estimation section 40, a travel information sensor 50, and a date and time information management section 60.

The second information processing device 1B includes a collection section 11 and a generation section 12. The external server 100 is equipped with a communication section 101 and a storage section main body 102. Note that the external server 100 is configured to communicate with a plurality of first information processing devices 1A.

The imaging unit 20 is, for example, a video camera, and is arranged to take an image of the front side of the moving body 10 in the direction of movement. The moving image captured by the imaging unit 20 is composed of a plurality of imaging information (frames), and is transmitted to the information acquisition unit 2. Note that the imaging unit may be capable of continuously capturing still images at predetermined time intervals.

The map information storage unit 30 is composed of, for example, a hard disk or a nonvolatile memory, and stores existing map information. The map information stored in the map information storage unit 30 only needs to include at least information about terrestrial features. Information about features includes not only numerical values about the location and size of features, but also information about the attributes and types of features (distinction between residential and commercial facilities, industry type of commercial facilities, etc.). shall be included.

The self-position estimating unit 40 estimates the current position (absolute position) of the mobile object 10, and may be any GPS receiving unit that receives radio waves transmitted from a plurality of GPS (Global Positioning System) satellites, for example. . The current position estimated by the self-position estimation unit 40 is transmitted to the information acquisition unit 2 as position information indicating the imaging position of the imaging information by the imaging unit 20.

The travel information sensor 50 is a sensor for measuring the amount of displacement of the moving body 10, and includes, for example, a vehicle speed pulse acquisition section for acquiring a vehicle speed pulse of the moving body 10, and a sensor for measuring the amount of azimuth displacement of the moving body 10. A gyro sensor and an acceleration sensor for acquiring the acceleration of the moving body 10. The traveling direction of the mobile object 10 is estimated based on the amount of displacement acquired by the traveling information sensor 50. Imaging orientation information regarding the orientation of the imaging section 20 during imaging is determined by the traveling direction of the moving object 10 and the mounting direction of the imaging section 20 on the moving object 10. When the imaging unit 20 is directed toward the front of the moving body, the traveling direction and the imaging direction substantially match. Therefore, the imaging direction information is transmitted from the driving information sensor 50 to the information acquisition section 2. Note that the traveling information sensor 50 may be any sensor that can at least estimate the traveling direction of the moving body 10.

The date and time information management section 60 has a calendar function and a clock function. Note that the clock function is preferably a radio-controlled clock function that receives standard radio waves and automatically corrects errors. Date and time information is transmitted from the date and time information management section 60 to the information acquisition section 2. Note that based on the date information and time information, solar altitude information and solar azimuth information can be specified. Therefore, the information acquisition unit 2 that acquires date information and time information can be considered to acquire solar altitude information and solar azimuth information.

The information acquisition unit 2 functions as an imaging information acquisition unit by acquiring imaging information from the imaging unit 20 as described above, and functions as a position information acquisition unit by acquiring position information from the self-position estimation unit 40 as described above. It functions as a map information acquisition unit by acquiring map information from the map information storage unit 30, and functions as an imaging azimuth information acquisition unit by acquiring imaging azimuth information from the driving information sensor 50 as described above. However, by acquiring solar altitude information and solar azimuth information from the date and time information management section 60 as described above, it functions as a solar information acquisition section.

The generation unit 3 extracts feature points from the imaging information acquired by the information acquisition unit 2 to generate a two-dimensional feature point group, and further generates point cloud data based on the plurality of feature point groups. Note that the point cloud data generated by the generation unit 3 may be, for example, three-dimensional point cloud data. Note that, for example, an ORB (Oriented fast and Rotated Brief) algorithm may be used to extract feature points, or other algorithms may be used.

The output unit 4 is composed of a circuit, an antenna, etc. for communicating with networks such as the Internet and public lines, and outputs the output unit 3 by communicating with the communication unit 101 of the external server 100.
Sends the information (point cloud data) generated by. As a result, information is stored in the storage main body 102 of the external server 100.

The collection unit 11 of the second information processing device 1B acquires point cloud data (individual point cloud data) generated by the first information processing device 1A via the communication unit 101. The collection unit 11 collects individual point cloud data by acquiring individual point cloud data from a plurality of moving objects or by acquiring individual point cloud data from one moving object multiple times.

The generation unit 12 generates comprehensive point cloud data based on the plurality of individual point cloud data collected by the collection unit 11. The comprehensive point cloud data may be obtained by superimposing a plurality of individual point cloud data, or may indicate a positional average of constituent points of a plurality of individual point cloud data.

The comprehensive point cloud data generated in this way is used, for example, to estimate the self-position of a moving object. That is, when a terrestrial feature is detected by an optical sensor (so-called LIDAR; Laser Imaging Detection and Ranging) mounted on a moving object, the self-position of this moving object can be estimated by comparing it with comprehensive point cloud data.

[Identification of shadow in first information processing device]
When generating point cloud data, the generation unit 3 may identify and extract constituent points corresponding to shadows among the constituent points forming the point cloud data (that is, may function as an extraction unit). . An example of a detailed procedure at this time will be described below. First, the generation unit 3 identifies the shadow generation direction based on the solar altitude information, the solar azimuth information, and the imaging azimuth information. That is, once the position of the sun and the orientation of the imaging unit 20 are determined, it is possible to estimate the direction in which an edge of a feature such as a building that extends along the vertical direction forms a shadow (shadow generation direction) at the time of imaging. I can do it. That is, when there are constituent points arranged in the shadow generation direction, it can be estimated that these constituent points correspond to the shadow of the edge of the feature extending in the vertical direction.

In addition, whether or not the constituent points arranged in the shadow generation direction correspond to the shadow is determined based on, for example, the number of constituent points arranged in a row, the length of the straight line formed by the plurality of constituent points, etc. do it. In other words, when the number of constituent points lined up in the shadow generation direction is small, or when the length of a straight line formed by a plurality of constituent points is short, these constituent points simply coincidentally line up in the shadow generation direction. There is a high possibility that it is not compatible with shadows.

The generation unit 3 extracts constituent points arranged in the shadow generation direction, and when it is determined that these constituent points correspond to a shadow, removes these constituent points from the point group data.

Here, a specific example of a method for extracting constituent points arranged in the shadow generation direction will be described with reference to FIGS. 2 and 3. In addition, in FIGS. 2 and 3, parts other than the targeted roads and terrestrial features are omitted as appropriate.

As shown in FIG. 2, when a building B exists along a road R, a shadow S is formed by the building B on the ground according to the position of the sun and the direction of the imaging unit. The building B has an edge B1 extending in the vertical direction, and the shadow S has a straight boundary line S1 corresponding to the edge B1. Feature points on the boundary line S1 are extracted in the frame of FIG. 2, and feature points on the boundary line S1 are also extracted in the frames before and after this. Therefore, when the generation unit 3 generates the point cloud data as shown in FIG. 3, the point cloud data includes the constituent points PB corresponding to the building B and the constituent points P1 to P4 corresponding to the boundary line S1. included.

The generation unit 3 identifies the shadow generation direction L1 based on the solar altitude information, the solar azimuth information, and the imaging azimuth information. In the illustrated example, the sun is located in the southwest, and the imaging unit 20 faces north-northwest. By specifying the shadow generation direction L1, the generation unit 3 can extract the constituent points P1 to P4 lined up in this direction.

Note that when the generation unit 3 extracts constituent points arranged in the shadow generation direction and determines that these constituent points correspond to a shadow, the generation unit 3 extracts constituent points arranged in the shadow generation direction, and when it is determined that these constituent points correspond to a shadow, a predetermined shape is generated without removing these constituent points in the point cloud data. Information may be added as a flag. The predetermined information given to a constituent point may be, for example, information indicating that it is a shadow, information that there is a possibility of a shadow, or reducing the reliability of the constituent point (0 It may also be information such as:

Furthermore, when extracting constituent points arranged in the shadow generation direction as described above, the generation unit 3 uses the position information acquired from the self-position estimation unit 40 and the map information acquired from the map information storage unit 30. , the shape of the shadow of a feature included in the imaging range may be estimated. The shape of the formed shadow is determined by the shape of the edge of the feature that extends in the vertical direction. For example, if the feature along the road is a natural object or a building with a complex shape, it is difficult to form a linear shadow due to edges that extend along the vertical direction, so the constituent points arranged in the direction of shadow generation are difficult to form. Even if a point exists, it can be determined that the point does not correspond to the shadow of this feature.

Further, if the map information includes height information of a feature, the generation unit 3 may use this height information. That is, based on the height information, solar altitude information, solar azimuth information, and imaging azimuth information, it is possible to estimate not only the shadow generation direction but also the length of the boundary line of the shadow. Therefore, even if the constituent points are lined up along the shadow generation direction, it can be determined that a region that exceeds the length of the boundary line does not correspond to a shadow.

In the above example, constituent points corresponding to the shadow of the edge extending in the vertical direction of the feature are extracted, but constituent points corresponding to other shadows may be extracted. For example, constituent points corresponding to a shadow may be extracted based on the complexity of a shape represented by a plurality of constituent points. An example of such a method is a method of identifying shadows formed by leaves of vegetation by estimating the fractal dimension.

Furthermore, the generation unit 3 may extract feature points corresponding to shadows from the feature points of each frame of imaging information before generating point cloud data using VSLAM. At this time, the generation unit 3 may generate point cloud data by removing feature points corresponding to shadows. Further, information indicating that the feature points have been removed may be added to the point cloud data.

[Data structure of point cloud data]
When the generation unit 3 assigns predetermined information as a flag to the constituent points corresponding to the shadow as described above, this point cloud data has a data structure as described in detail below. The predetermined information given to a constituent point may be information indicating that it is a shadow, information that there is a possibility of a shadow, or information that indicates the reliability of the constituent point, as described above. It may be information that the value is lowered (including setting it to 0).

The generation unit (processing unit) 12 of the second information processing device 1B generates the integrated point cloud data based on the plurality of individual point cloud data collected by the collection unit 11. processing can be performed by lowering the contribution of the corresponding) constituent points. For example, when generating comprehensive point cloud data by overlapping multiple individual point cloud data, if a constituent point included in other point cloud data is not located at the position of a flagged constituent point included in one point cloud data. , it can be treated as if there were no constituent points at this position.

In addition, the information given to the constituent points includes information about the features that generate the shadows that the constituent points correspond to (for example, information that the constituent points correspond to the shadows generated by buildings, (information indicating that the shadow corresponds to the generated shadow) may also be included. Table 1 shows an example of the data structure of such point cloud data.

That is, each constituent point is assigned an ID, and each constituent point has location information, shadow flag information (i.e. whether information is given or not), and the feature that generates the shadow to which the constituent point corresponds. Information about and is given. In the example of Table 1, the position information is two-dimensional coordinate information, but the position information may be three-dimensional (in xyz space) coordinate information.

When the shadow flag information is "0", it indicates that no information is attached, and when it is "1", it indicates that information is attached. The information about the feature that generates the shadow to which the constituent points correspond is blank if the shadow flag information is 0, and if it is 1, it becomes information specifying the attributes of the feature. In the example of Table 1, the constituent points with ID=2 and the constituent points with ID=3 correspond to shadows generated by different buildings. Note that if it has been determined that the constituent points correspond to a shadow, but the feature that generates the shadow has not been identified, the information about the feature may be left blank.

[Generation of comprehensive point cloud data]
As described above, in the second information processing device 1B, the collection unit 11 collects individual point cloud data, and the generation unit 12 generates comprehensive point cloud data based on a plurality of individual point cloud data. At this time, the collecting unit 11 collects not only the individual point cloud data but also the attached information (the above-mentioned shadow flag information and (information about the feature that produces the corresponding shadow).

The generation unit 12 determines the degree of contribution of the constituent points based on the acquired assignment information, and generates comprehensive point cloud data. The degree of contribution may be any value that is used for weighting, for example, when superimposing a plurality of individual point cloud data or calculating a positional average when generating comprehensive point cloud data.

When the generation unit 12 acquires the shadow flag information, the generation unit 12 may set the degree of contribution of the constituent points to 0, that is, remove the constituent points to generate the comprehensive point group data.

In addition, when flag information is attached to constituent points at the same position in a plurality of individual point cloud data with different imaging conditions, the generation unit 12 generates the comprehensive point cloud data without reducing the contribution of these constituent points. may be generated. Component points determined to be shadows in individual point cloud data generated based on daytime imaging information and component points determined to be shadows in individual point cloud data generated based on nighttime imaging information If and exist at the same position, these constituent points may not be shadows. Therefore, even if a certain constituent point is erroneously determined to be a shadow and additional information is assigned, it is possible to prevent the degree of contribution of this constituent point from decreasing.

Further, when determining the degree of contribution of the constituent points, the generation unit 12 may refer not only to the shadow flag information but also to information about the feature that generates the shadow. The clearer the shape of the feature, the easier it is to accurately determine whether the constituent points correspond to shadows. That is, even for constituent points to which shadow flag information is attached, the determination accuracy differs depending on the feature that generates the shadow. Therefore, for example, by performing a process in which the degree of contribution of the constituent points is reduced as the shape of the feature that generates the shadow is clearer, it is possible to generate comprehensive point cloud data in consideration of the shadow determination accuracy.

With the above configuration, by adding predetermined information to the constituent points corresponding to shadows among the constituent points constituting point cloud data, when using point cloud data for self-position estimation, etc., the constituent points corresponding to shadows can be used. You can prevent it from being used or reduce its usage. Since the constituent points corresponding to shadows can change depending on the date and time and the weather, point cloud data can be easily utilized.

In addition, if point cloud data is generated by extracting and removing feature points corresponding to shadows from imaging information, it is possible to avoid being affected by shadows when using point cloud data for self-position estimation, etc. can. Since the constituent points corresponding to shadows can change depending on the date and time and the weather, point cloud data can be easily utilized.

Further, by outputting the point cloud data to the external server 100 by the output unit 4, the point cloud data can be utilized in the external server 100 as the output destination.

In addition, in the data structure of point cloud data, predetermined information is attached to the constituent points corresponding to shadows, and by processing the constituent points corresponding to shadows with a lower degree of contribution, it is possible to When using the point cloud data (comprehensive point cloud data) for self-position estimation etc., it is possible to not use constituent points corresponding to shadows or to reduce the degree of use. Since the constituent points corresponding to the shadow can change depending on the date and time and the weather, the processed point cloud data can be easily utilized.

In addition, by determining the contribution of the constituent points based on the flag information given to the constituent points corresponding to the shadow and generating the comprehensive point cloud data based on multiple individual point cloud data, , it is possible to not use the constituent points corresponding to the shadow or to lower the degree of use. Since the constituent points corresponding to shadows can change depending on the date and time and the weather, comprehensive point cloud data can be easily utilized.

Note that the present invention is not limited to the above-mentioned embodiments, but includes other configurations that can achieve the object of the present invention, and the present invention also includes the following modifications.

For example, in the above embodiment, the information acquisition section 2 and the generation section 3 are mounted on the moving object 10, but the imaging information acquisition section, the generation section, and the extraction section that constitute the first information processing device This does not necessarily have to be installed on a moving body, and may be installed on different devices or devices. That is, each configuration of the information processing device may span multiple physically separated devices.

Furthermore, in the above embodiment, the collection unit 11 and the generation unit 12 are installed in the external server 100, but the collection unit and the generation unit that constitute the second information processing device are installed in the external server. They may be installed in different devices or devices. That is, each configuration of the information processing device may span multiple physically separated devices.

In addition, the best configuration, method, etc. for carrying out the present invention have been disclosed in the above description, but the present invention is not limited thereto. That is, although the present invention has been specifically illustrated and described primarily with respect to specific embodiments, modifications may be made to the embodiments described above without departing from the spirit and scope of the invention. , materials, quantities, and other detailed configurations, those skilled in the art can make various modifications. Therefore, the descriptions that limit the shapes, materials, etc. disclosed above are provided as examples to facilitate understanding of the present invention, and do not limit the present invention. Descriptions of names of members that exclude some or all of the limitations such as these are included in the present invention.

1A First information processing device 2 Information acquisition section (imaging information acquisition section)
3 Generation section (extraction section)
4 Output unit 10 Mobile object 20 Imaging unit 1B Second information processing device 11 Collection unit 12 Generation unit

Claims (6)

a collection unit that collects individual point cloud data generated based on the imaging information;
a generation unit that generates comprehensive point cloud data based on the plurality of individual point cloud data,
The collection unit acquires attached information attached to a constituent point corresponding to a shadow among the constituent points constituting the individual point cloud data,
The information processing device is characterized in that the generation unit generates the comprehensive point group data by determining the degree of contribution of the constituent points based on the attached information. The information processing apparatus according to claim 1, wherein the generation unit generates the comprehensive point cloud data by removing constituent points to which the attached information is attached. When the attached information is attached to constituent points at the same position in a plurality of the individual point cloud data having different imaging conditions, the generation unit generates the integrated point cloud data without reducing the degree of contribution of the constituent points. 3. The information processing apparatus according to claim 1, wherein the information processing apparatus generates the information processing apparatus. a collection step of collecting individual point cloud data generated based on the imaging information;
a generation step of generating comprehensive point cloud data based on the plurality of individual point cloud data,
In the collecting step, acquiring information given to a constituent point corresponding to a shadow among the constituent points forming the individual point cloud data;
An information processing method characterized in that, in the generation step, the degree of contribution of the constituent points is determined based on the assigned information to generate the comprehensive point group data. An information processing program that causes a computer to execute the information processing method according to claim 4. A computer-readable recording medium storing the information processing program according to claim 5.

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