Small area generation device

Abstract

To provide a small region generation device that solves the problems of the prior arts, that is, can generate a small region having a shape suitable for a type of a feature.SOLUTION: A small region generation device comprises: a two-dimensional terrain model storage means, small region generation means, and feature-specific shape memory means for each feature. The small region generation means is means of storing a two-dimensional terrain model in which feature classification is granted to a feature in a target region, the small region generation means is means of generating a plurality of small regions with respect to the target area, and the feature-specific shape storage means is means of storing a feature-specific shape set according to the feature classification of the two-dimensional terrain model. The small region generation means generates a small region so as to divide the feature by the feature-specific shape of the feature classification of the two-dimensional terrain model.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a technology for assigning height information such as altitude to features, and more specifically, to a small area generation device that can generate small areas (meshes) of shapes corresponding to the classification of features in order to assign height information that is more suited to the current situation. [Background technology]

[0002] Recently, the demand for topographical information (spatial information) has been increasing, and an increasing number of managers are requesting spatial information on facilities, such as their shapes and locations, for the purpose of more advanced management of facilities installed on or along roads. At the same time, advanced maintenance and management of social infrastructure (hereinafter simply referred to as "social infrastructure") is considered an important issue for the realization of Society 5.0, which is currently being promoted by the public and private sectors together. Furthermore, as autonomous driving technology becomes more practical, various spatial information related to roads, including road edges (road boundaries), is desired from many quarters.

[0003] Traditionally, two-dimensional (2D) planar drawings (plan views) such as topographical maps have been the mainstream for showing spatial information. Plan views sometimes show height information such as contour lines and endpoint elevations, but their main focus is on showing planar positions, making it difficult to grasp the target range as a three-dimensional (3D) space. On the other hand, in recent years, advances in measurement technology have made it easy to obtain large amounts of three-dimensional measurement points (hereafter referred to as "3D point clouds"), and advances in information technology have made it easy to handle these three-dimensional point clouds.

[0004] For example, to obtain a 3D point cloud of terrain including roads, measurement methods such as aerial photogrammetry, airborne laser measurement, terrestrial laser measurement, MMS (Mobile Mapping System), etc. Among these, MMS is a system in which a moving vehicle is equipped with sensors such as a laser scanner, a camera, a satellite positioning system (GNSS: Global Navigation Satellite System) for acquiring the vehicle's own position, an IMU (Inertial Measurement Unit), and an odometry, making it possible to obtain a 3D point cloud using a laser scanner while moving on the roadway.

[0005] The 3D point cloud obtained by MMS or the like is generally used as a 3D model of the terrain to be measured (hereinafter referred to as "3D model"). This 3D model represents the target terrain in 3D coordinates, and is a terrain model represented by DSM (Digital Surface Model) or DEM (Digital Elevation Model).

[0006] Typically, a 3D model is composed of small regions that divide the target planar range. These small regions are also called meshes, and are formed, for example, by dividing the area into orthogonal grids, with each small region having a representative point. Since 3D point clouds obtained by measurement are often random data (data that is irregularly arranged on a plane), geometric calculations are often used to assign height to the representative point of the small region. Examples of calculation methods include the TIN (Triangulated Irregular Network) method, which finds height using an irregular triangular network formed from random data, the Nearest Neighbor method, which uses the nearest laser measurement point, as well as the Inverse Distance Weighting (IDW), Kriging method, and averaging method.

[0007] 3D models can grasp the target terrain in both two and three dimensions, and can therefore be used for a variety of purposes compared to floor plans. However, 3D models based on measurement results only provide spatial information based on three-dimensional coordinates, and cannot show the attributes of features. In other words, by simply looking at a 3D model, it is not possible to understand where the outer edge of an office building (the so-called edge) is, or where the edge of a road is.

[0008] Adding attribute information of features to a 3D model requires a survey of the features. In other words, a manual survey is required, such as by going to the site and recording the information seen with one's eyes on a drawing, or by extracting the attributes of features while visually examining aerial photographs. However, since roads, for example, generally have a considerable length, the amount of work required for the survey is enormous, and considering the labor and time involved, it requires a huge cost.

[0009] As mentioned above, traditionally, plan views were mainly used. In some cases, these plan views were used as raster data or vector data (i.e., digitized), and in other cases, features were converted into shapes (polylines or polygons) and given attribute information. Alternatively, recent advances in machine learning technology have made it possible to mechanically (automatically) extract features from aerial photographs and plan views and extract their shapes and attribute information. In this way, there are cases where a "2D terrain model" that does not include height information such as elevation but does include attribute information of features is prepared separately. If this 2D terrain model is used, manual surveys of features can be omitted (or significantly reduced).

[0010] Therefore, Patent Document 1 proposes a technique for generating three-dimensional feature shape lines by adding elevations to the feature shape lines shown in two-dimensional map data using a point cloud obtained by measurement. [Prior art documents] [Patent documents]

[0011] [Patent Document 1] JP 2020-013351 A Summary of the Invention [Problem to be solved by the invention]

[0012] As described above, a 3D model is composed of multiple small regions, and height information (e.g., elevation value) is assigned to each small region. Since the 3D point cloud is random data, multiple 3D measurement points may be placed within a small region, in which case the elevation value of the small region is determined by statistical processing using those 3D measurement points. For example, if there are five 3D measurement points within a small region, the average elevation value of those five points is calculated and this is determined as the elevation value of the small region. Once the elevation value of the small region is determined, elevation values ​​can also be assigned to the features. Specifically, a small region containing a feature is extracted, and the elevation value of that small region can be used to assign an elevation value to the feature.

[0013] Incidentally, the multiple small areas that make up the 3D model are all set to the same shape, such as squares divided into orthogonal grids, and all in the same direction. However, depending on the type of feature, it may not be appropriate to use 3D measurement points contained in square small areas. For example, in the case where the feature is a road, there is a significant difference in height when viewed in the road crossing direction, but there is not much change in the road extension direction. Therefore, like other features, it is considered more appropriate to use rectangular small areas that are divided "densely" in the road crossing direction and "sparsely" in the road extension direction, rather than setting the small areas as squares. In addition, it is considered that the orientation of the small areas set on the road should be aligned with the road extension direction or the road crossing direction, so that height information that is appropriate to the current situation can be given to the road.

[0014] An object of the present invention is to solve the problems associated with the conventional technology, that is, to provide a small area generating device capable of generating small areas having shapes suited to the type of feature. [Means for solving the problem]

[0015] The present invention focuses on the fact that the shape of a small area suitable for the type of feature is set in advance, and then small areas of an appropriate shape are generated as appropriate while checking the type of feature, and is an invention based on an idea that has not been seen in the past.

[0016] The small area generating device of the present invention comprises a 2D topographical model storage means, a small area generating means, and a feature shape storage means. Of these, the small area generating means is a means for storing a 2D topographical model in which feature classifications are assigned to features in a target area. The small area generating means is a means for generating a plurality of small areas for a target area, and the feature shape storage means is a means for storing feature shapes that are set according to the feature classifications of the 2D topographical model. The small area generating means generates small areas so as to divide features by the feature shapes of the feature classifications of the 2D topographical model.

[0017] The small area generating device of the present invention may generate small areas so that the small areas are substantially parallel (including parallel) to an axial direction set for a feature on the ground.

[0018] The small area generating device of the present invention can also set a "rectangle" as the feature shape for "roads" in the feature classification of the 2D terrain model. In this case, the small area generating means generates small areas so as to divide the road in the extension direction and the cross direction.

[0019] The small area generating device of the present invention may generate a small area such that the extension direction of the road is the longitudinal direction of the rectangle.

[0020] The small area generating device of the present invention may further include a point cloud storage means and a feature height calculation means. Of these, the point cloud storage means is a means for storing a plurality of 3D measurement points obtained by measuring the inside of the target area, and the feature height calculation means is a means for setting the height (feature height) of the small area generated by the small area generating means. In this case, the feature height calculation means assigns height information to the road edge based on the feature height set in the small area located at the road edge.

[0021] The small area generating device of the present invention may further include a template storage means, a template selection means, and a template arrangement means. Among these, the template storage means is a means for storing a plurality of types of cross section templates, the template selection means is a means for selecting a desired cross section template from the cross section templates stored in the template storage means, and the template arrangement means is a means for arranging the template selected by the template selection means so that it is in the cross section direction of the road. The cross section template is formed by two or more line segments set on a vertical cross section. When the difference between the height of the cross section template at the road edge and the height of the road edge assigned by the feature height calculation means exceeds a predetermined threshold value, the height of the cross section template is adopted as the height of the road edge. Effect of the Invention

[0022] The small area generating device of the present invention has the following advantages. (1) By using 3D measurement points of small areas set with shapes and orientations according to the type of feature, height information is added to the feature, making it possible to generate 3D models that are more accurate to the current situation than before. (2) Since a 3D model that reflects the current situation can be obtained, it is possible to manage road facilities, etc. in a more advanced manner and provide map information that is more useful for automated driving. (3) By using a 2D terrain model with feature classification, manual surveys of features can be omitted or significantly reduced. (4) For example, even in areas where part of the road edge is obscured by roadside trees or the like and is therefore not visible, i.e. where partial occlusion by roadside trees occurs, the road edge can be given an appropriate height by using a cross-sectional template (template height). [Brief description of the drawings]

[0023] [Figure 1] 1 is a block diagram showing the main configuration of a small area generating device according to the present invention; [Diagram 2] 1A is a plan view showing a schematic view of a portion of a road before small regions are generated, and FIG. 1B is a plan view showing a schematic view of a portion of a road after small regions have been generated. [Diagram 3] FIG. 13 is a schematic plan view showing a small area MS generated for a school, a road, and an apartment building. [Figure 4] A model diagram showing a 3D point cloud and small region groups overlaid with their planar positions aligned. [Diagram 5] (a) is a model diagram that shows a schematic model of a crossing template representing a road crossing with a bow slope, (b) is a model diagram that shows a schematic model of a crossing template representing a road crossing with a bow slope and a central reservation, and (c) is a model diagram that shows a schematic model of a crossing template representing a road crossing with a bow slope and different sidewalk heights. [Figure 6] (a) is a vertical cross-sectional view showing a schematic of a road where the difference in height between the feature height at the road edge and the template height is below a threshold, and (b) is a vertical cross-sectional view showing a schematic of a road where the difference in height between the feature height at the road edge and the template height is above a threshold. [Figure 7] FIG. 2 is a flow chart showing the flow of main processes performed by the feature height assignment device of the present invention. [Figure 8] 4 is a flowchart showing a main process flow for correcting feature heights of road edges by a feature height correction means. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] An example of the small area generating device of the present invention will be described with reference to the drawings.

[0025] 1 is a block diagram showing the main components of a small area generation device 100 of the present invention. As shown in this figure, the small area generation device 100 is configured to include a small area generation means 101, a two-dimensional topographical model storage means 108, and a feature-specific shape storage means 109, and can also be configured to include a feature height calculation means 102, a template selection means 103, a template arrangement means 104, a feature extraction means 105, a feature-specific shape acquisition means 106, a feature height correction means 107, a point cloud storage means 110, a template storage means 111, and a generation condition storage means 112.

[0026] The small area generating means 101, feature height calculation means 102, template selection means 103, template placement means 104, feature extraction means 105, feature-specific shape acquisition means 106, and feature height correction means 107 that constitute the small area generating device 100 can be manufactured as dedicated units, or a general-purpose computer device can be used. This computer device includes a processor such as a CPU, memories such as ROM and RAM, and some further include input means such as a mouse and keyboard, and a display, and can be configured, for example, by a personal computer (PC) or a server.

[0027] The 2D topographical model storage means 108, feature shape storage means 109, point cloud storage means 110, template storage means 111, and generation condition storage means 112 can use the storage device of a general-purpose computer (for example, a personal computer) or can be built in a database server. When built in a database server, they can be placed on a local network (LAN: Local Area Network) or can be a cloud server that stores data via the Internet (i.e., wireless communication).

[0028] Below, each of the main elements constituting the small area generating device 100 of the present invention will be described in detail.

[0029] (2D terrain model storage means) The two-dimensional topographical model storage means 108 stores a "two-dimensional topographical model" of a target planar range (hereinafter referred to as a "target area"). Here, the two-dimensional topographical model is two-dimensional topographical information such as a topographical map that does not include height information, and is a topographical model that includes information on features (hereinafter referred to as "feature information"). The feature information includes information on the planar position and shape of features such as polygons and polylines (hereinafter referred to as "feature figures"), and attribute information on the features. Furthermore, the feature attribute information includes the type of feature, such as roads, road edges, buildings, building edges, parks, and schools (hereinafter referred to as "feature classifications"). In other words, feature figures are arranged on a plane within the target area, and attribute information on the features (including feature classifications) is associated with these feature figures.

[0030] (Point cloud storage means) The point cloud storage means 110 stores a "three-dimensional point cloud." Here, a three-dimensional point cloud is a collection of many three-dimensional measurement points acquired as a result of measuring a target area using a method such as aerial photogrammetry, airborne laser measurement, terrestrial laser measurement, MMS, etc., and these three-dimensional measurement points can naturally be represented by three-dimensional coordinates consisting of planar position and height information.

[0031] Incidentally, a three-dimensional terrain model (3D model) such as a DSM or DEM can be generated from the above-mentioned three-dimensional point cloud and multiple small regions. Here, the small regions are divided areas (so-called meshes) formed by dividing a planar range of a target such as a feature with, for example, an orthogonal grid, and are composed of information indicating a two-dimensional position (planar position), and do not have height information such as altitude. A 3D model is usually composed of multiple small regions, and a 3D model is generated by assigning height information (e.g., altitude) to a representative point set in each small region. Since a three-dimensional point cloud obtained by measurement (e.g., laser measurement) is often random data, geometric calculation is often performed to assign an altitude to a representative point of a small region. As a calculation method, in addition to the TIN method and nearest neighbor method described above, the inverse distance weighting method, the Kriging method, the average method, etc. can be mentioned.

[0032] (Shape memory means for each feature) The feature shape storage means 109 stores feature classifications and feature shapes in association with each other. Here, the feature shape refers to the planar shape of a small area generated by the small area generation means 101, which will be described later, and is the planar shape that is most suitable for that feature classification; for example, if the feature classification is a "road", the feature shape may be a "rectangle", and if the feature classification is a "building" or "park", the feature shape may be a "square". Specifically, a table that associates feature classifications with feature shapes is created in advance, and the feature shape storage means 109 stores this "feature classification-feature shape table".

[0033] (Feature extraction means) The feature extraction means 105 is a means for extracting a predetermined feature figure (polygon, etc.) based on feature information possessed by the 2D topographical model. When extracting feature figures, it may be possible to sequentially extract all feature figures possessed by the 2D topographical model, or to extract feature figures of a feature classification designated by the operator. Alternatively, it may be possible for the operator to extract a desired feature figure by using a pointing device (mouse, touch panel, pen tablet, touch pad, track pad, track ball, etc.), a keyboard, etc. while visually checking the 2D topographical model displayed on a display means (such as a liquid crystal display).

[0034] (Method for acquiring shape of each feature) The feature-specific shape acquisition means 106 is a means for acquiring the feature-specific shape of the feature related to the feature figure extracted by the feature extraction means 105. Specifically, the feature-specific shape is acquired by querying the feature-specific shape storage means 109 with the feature classification related to the feature (FIG. 1).

[0035] (Small area generation means) The small area generating means 101 is a means for generating small areas for feature figures extracted by the feature extraction means 105. Specifically, it generates a plurality of small areas so as to divide the feature figure based on the feature shape acquired by the feature shape acquisition means 106 and the "generation conditions" read out from the generation condition storage means 112 (FIG. 1). The generation conditions here are conditions that determine the number of divisions of the feature figure, such as "divide into four in the road width direction," or the dimensions of a portion of the small area, such as "the length of the short side of the rectangle is 10 cm," and are literally conditions for generating small areas. The feature shapes are only specified as planar shapes such as squares, rectangles, or equilateral triangles, and all similar shapes are targeted. Therefore, the shape and size of the small area to be generated by the small area generating means 101 are determined based on the feature shape and the generation conditions.

[0036] FIG. 2 is a diagram showing a schematic view of a part of a road among the features of the two-dimensional terrain model, where (a) is a plan view showing the road before the small area MS is generated, and (b) is a plan view showing the road after the small area MS is generated. As described above, the road shows a significant difference in elevation when viewed in the road crossing direction, but does not change much in the road extension direction. In the example of FIG. 2, a rectangle is set as the feature-specific shape of the road (feature), that is, the feature-specific shape acquisition means 106 acquires a rectangle (feature-specific shape) from the feature-specific shape storage means 109, and the small area generation means 101 generates a plurality of small areas MS so as to divide the road based on the rectangle (feature-specific shape) and the generation conditions. In addition, the small area generation means 101 generates the small areas MS so that the longitudinal direction (direction of the long side) of the rectangle is the road extension direction, as shown in FIG. 2(b). As a result, the small areas MS are arranged "densely" in the road crossing direction, and the small areas MS are arranged "sparsely" in the road extension direction.

[0037] Usually, the multiple small regions that make up a 3D model such as a DSM or DEM are all the same shape and are all arranged in the same direction. However, depending on the feature, there are cases where it is better to arrange the small regions according to the main direction (hereinafter referred to as the "axial direction") of the feature. For example, when creating a 3D model that includes a road, it is considered that it is possible to give height information (elevation, etc.) for the road that is appropriate to the current situation by arranging the small regions along the road extension direction or road crossing direction.

[0038] FIG. 3 is a plan view showing a schematic representation of small regions MS generated for a school, a road, and an apartment house. The school shown in this figure is arranged parallel to the vertical and horizontal directions of the paper, and therefore the axis direction of the school can be set to the vertical direction (or horizontal direction). On the other hand, the road and the apartment house are arranged to be inclined with respect to the vertical direction of the paper, and therefore the axis directions of the road and the apartment house can be set to be inclined with respect to the vertical direction. The small region generating means 101 generates the small regions MS so that they are arranged approximately parallel (including parallel) to the axis directions of the school, the road, and the apartment house. That is, the small regions MS are generated so that the square small region MS is arranged approximately parallel to the vertical direction of the paper for the school, the rectangular small region MS is arranged to be inclined from the vertical direction of the paper for the road, and the square small region MS is arranged to be inclined from the vertical direction of the paper for the apartment house.

[0039] (Method of calculating feature height) The feature height calculation means 102 is a means for calculating the height of the small area MS (hereinafter referred to as "feature height") generated by the small area generation means 101. As shown in FIG. 4, when the three-dimensional point cloud and the small area MS are overlapped after aligning the planar positions, multiple three-dimensional measurement points may be placed in the small area MS. In this case, the feature height calculation means 102 calculates the feature height of the small area MS by performing statistical processing using these three-dimensional measurement points. Specifically, using the multiple three-dimensional measurement points included in the small area MS, it is possible to calculate the average value (hereinafter referred to as "average height"), minimum value (hereinafter referred to as "minimum height"), maximum value (hereinafter referred to as "maximum height"), mode (hereinafter referred to as "mode"), median (hereinafter referred to as "median height"), and the like as the feature height.

[0040] However, depending on the type of feature (i.e., feature classification), it may not necessarily be appropriate to adopt the average height of the 3D measurement points within the small area MS. For example, in cases where the feature is the edge of a road, it is considered more appropriate to adopt the minimum value among the 3D measurement points within the small area MS, and in cases where the feature is the outer edge of a building (such as an office building), it is considered more appropriate to adopt the maximum height among the 3D measurement points within the small area MS.

[0041] Therefore, the feature height calculation means 102 can be configured to calculate the feature height according to the feature classification, that is, by the statistical processing most suitable for that feature classification (hereinafter referred to as "optimum statistical processing"). Specifically, a "feature classification-optimum statistical processing table" that associates the feature classification with the optimal statistical processing is created in advance, and the feature height calculation means 102 calculates the feature height based on this feature classification-optimum statistical processing table. For example, in a case where a small area MS is generated for a road edge (feature), the feature height calculation means 102 calculates the minimum height as the feature height, in a case where a small area MS is generated for the outer edge of a building (such as an office building), the feature height calculation means 102 calculates the maximum height as the feature height, and in a case where a small area MS is generated for a park, the feature height calculation means 102 calculates the average height as the feature height.

[0042] (Template storage means) The template storage means 111 stores "crossing templates." Here, a crossing template indicates a typical cross-sectional shape of the road, and is graphic data formed by two or more line segments set on a vertical plane. For example, in FIG. 5, three types of crossing templates are set, with (a) being a crossing template representing a road crossing with a bowing gradient, (b) being a crossing template representing a road crossing with a median strip with a bowing gradient, and (c) being a crossing template representing a road crossing with a bowing gradient and different sidewalk heights. As shown in FIG. 5, it is preferable to form the crossing template to include the center of the road.

[0043] (Template selection method and template placement method) The template selection means 103 is a means for an operator to select a desired crossing template from among the crossing templates stored in the template storage means 111 using a pointing device, a keyboard, or the like. The template selection means 103 can be designed to select a desired crossing template by an operator operation, or to automatically select from the "floor feature classification" on the cross section. For example, when a roadside tree hides a sidewalk, the template selection means 103 can be designed to automatically select an appropriate crossing template by referring to the crossing templates before and after the road extension direction, or to generate a vertical section line at the sidewalk position and automatically select a crossing template by an interpolation process based on the crossing templates before and after the road extension direction. The template placement means 104 is a means for placing the selected crossing template so that it is in the crossing direction of the road. The process of placing the crossing template by the template placement means 104 will be described in detail below.

[0044] A feature height is assigned to the road in advance. Specifically, the feature extraction means 105 extracts the feature figure (polygon, etc.) of the road, and the feature height calculation means 102 calculates the feature height for the small area MS generated by the small area generation means 101. Then, based on the calculated feature height, a cross section of the road projected onto a vertical plane (hereinafter referred to as a "measured cross section") is created, and the road center (feature) and road edge (feature) of the 2D terrain model are set in the measured cross section. The measured cross section can be created at a position specified by the operator, or can be automatically generated at regular intervals (e.g., 20 m pitch) in the road extension direction. When the measured cross section is created, the road surface height at the road centerline and the road center is matched as shown in FIG. 6, and then the cross section template is placed. At this time, the cross section template is placed on the measured cross section, that is, so as to be in the cross direction of the road. Therefore, it is preferable that the 2D terrain model has the cross direction of the road as well as the road center and road edge as features.

[0045] (Feature height correction means) The object height correction means 107 is a means for correcting the object height of the road edge calculated by the object height calculation means 102 as necessary. Specifically, when the difference (hereinafter referred to as "differential height") between the object height at the road edge and the height of the cross-sectional template at the road edge (hereinafter referred to as "template height") exceeds a predetermined threshold value, the object height correction means 107 corrects the object height of the road edge. In other words, instead of the object height of the road edge calculated by the object height calculation means 102, the template height is set as a new object height of the road edge. On the other hand, when the differential height is below the threshold value, the object height correction means 107 maintains the object height of the road edge as it is.

[0046] In the example of FIG. 6(a), the difference between the feature height at the road edge and the template height is below the threshold, so the feature height correction means 107 maintains the feature height of the road edge as it is. In contrast, in the example of FIG. 6(b), the difference between the feature height at the right road edge and the template height is above the threshold, so the feature height correction means 107 sets the template height as the new feature height of the right road edge. In actual road edges, retaining walls may be installed along the way, and in that case, the retaining walls are obtained as the feature height, so it is possible that the result will not grasp the entire road edge. Therefore, when the shape is significantly different from the crossing template that represents the road crossing, the template height of the crossing template is adopted rather than the feature height of the road edge set based on the 3D point cloud, so that the entire road edge is grasped.

[0047] (Processing flow) Below, the main processing of the feature height correction means 107 will be described in detail with reference to Fig. 7. Fig. 7 is a flow chart showing the flow of the main processing of the feature height correction means 107 of the present invention. In this flow chart, the action to be performed is shown in the center column, what is necessary for that action is shown in the left column, and what results from that action is shown in the right column.

[0048] As shown in Fig. 7, first, the feature extraction means 105 extracts a feature figure from the 2D topographical model (Step 201). Once the feature figure has been extracted, the feature shape acquisition means 106 acquires the feature shape associated with that feature from the feature shape storage means 109 (Step 202). Then, the small area generation means 101 generates a plurality of small areas MS so as to divide the feature figure, based on the feature shape acquired by the feature shape acquisition means 106 and the generation conditions read from the generation condition storage means 112 (Step 203). Once the small areas MS have been generated, the feature height calculation means 102 calculates the feature height for each small area MS (Step 204). At this time, the feature height calculation means 102 can also calculate the feature height using the optimal statistical processing set for the feature classification.

[0049] Next, the process of correcting the feature height of the road edge by the feature height correcting means 107 will be described in detail with reference to Fig. 8. Fig. 8 is a flow diagram showing the main process flow when the feature height correcting means 107 corrects the feature height of the road edge. Note that in this flow diagram, the action to be performed is shown in the center column, what is necessary for that action is shown in the left column, and what results from that action is shown in the right column.

[0050] As shown in Fig. 8, when the feature height calculation means 102 calculates the feature height of the road edge (Step 204), the operator selects a desired cross section template from the template storage means 111 using a pointing device, keyboard, etc. (Step 205). Then, the template placement means 104 aligns the road centerline with the road surface height at the road center as shown in Fig. 6, and places the cross section template over the measured cross section (Step 206). At this time, the cross section template is placed so that it is in the cross direction of the road.

[0051] Once the cross section template has been placed in a prescribed position, the feature height correction means 107 calculates the difference in height between the feature height at the road edge and the cross section template (Step 207), and compares this difference in height with a threshold value (Step 208). If the difference in height exceeds the threshold value (Yes in Step 208), the feature height correction means 107 sets the template height as the new feature height of the road edge, replacing the feature height of the road edge calculated by the feature height calculation means 102 (Step 209). On the other hand, if the difference in height is below the threshold value (No in Step 208), the feature height correction means 107 maintains the feature height of the road edge as it is. [Industrial Applicability]

[0052] The small area generating device of the present invention can be particularly suitably used as map information for managing various facilities such as road facilities, and for automatic driving. In addition, the present invention can provide highly accurate step information that is useful for elderly people and wheelchair users, and can also be effectively used in disaster prevention planning. The small area generating device of the present invention is an invention that can be expected to make a great contribution to society as well as being applicable industrially. [Explanation of symbols]

[0053] 100 Small area generating device of the present invention 101 (small area generating device) small area generating means 102 (small area generating device) feature height calculation means 103 (small area generating device) template selection means 104 (small area generating device) template placement means 105 (small area generating device) feature extraction means 106 (small area generating device) feature-specific shape acquisition means 107 (Small Area Generator) Feature Height Correction Method 108 (small area generating device) 2D terrain model storage means 109 (Small area generating device) feature shape storage means 110 (small area generating device) point cloud storage means 111 (small area generating device) template storage means 112 (small area generating device) generation condition storage means MS small area

Claims

1. a two-dimensional topographical model storage means for storing a two-dimensional topographical model in which a topographical object classification is assigned to the topographical objects of a target area; A small area generating means for generating a plurality of small areas for a target area; a feature-specific shape storage means for storing feature-specific shapes set in accordance with the feature classification of the two-dimensional topographical model; a point cloud storage means for storing a plurality of three-dimensional measurement points obtained by measuring a target area; a feature height calculation means for calculating a feature height, which is the height of the small area generated by the small area generation means, the small area generating means generates the small areas so as to divide the features according to the feature shapes of the feature classification of the two-dimensional topographical model; the feature-specific shape storage means stores a rectangle as the feature-specific shape for a "road" in the feature classification of the two-dimensional topographical model; the small area generating means generates the small areas so as to divide the road into an extension direction and a cross direction for the feature classification "road"; the feature height calculation means assigns height information to a road edge based on the feature height set in the small area located on the road edge for a "road" in the feature classification; A small area generating device comprising:

2. the small area generating means generates the small area so that the road extension direction is the longitudinal direction of the rectangle.

2. The small area generating device according to claim 1.

3. A template storage means for storing a plurality of types of crossing templates; a template selection means for selecting a desired transverse template from the transverse templates stored in the template storage means; a template arrangement means for arranging the crossing template selected by the template selection means so as to be in a crossing direction of a road, the transverse template is formed by two or more line segments set on a vertical cross section; adopting the height of the cross-sectional template as the height of the road edge when a difference between the height of the cross-sectional template at the road edge and the height of the road edge assigned by the feature height calculation means exceeds a predetermined threshold value; 3. The small area generating device according to claim 1 or 2.

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