JP4103898B2 - Map information updating method and map updating apparatus - Google Patents

Description

  The present invention relates to a map information updating method and a map information updating device, and more specifically, using an image obtained by photographing a region, detecting continuity and change information of a graphic described in the map of the region, The present invention relates to a map information update method and a map information update device for detecting a new feature described and updating map information.

  Japanese Patent Laid-Open No. 5-181411, “Map Information Collation Update Method” creates a central projection by performing coordinate transformation on a numerical map, collates it with an aerial photograph image, and uses the map information from the collation map. Conventional techniques for extracting aging and landscape information and updating map information have been disclosed.

Japanese Patent Laid-Open No. 5-181411

  With the prior art disclosed in the above-mentioned Japanese Patent Application Laid-Open No. 5-181411, the position of a new feature not described on the map cannot be specified. In addition, the association between a plurality of features obtained by collecting adjacent features and a plurality of figures corresponding to them is not considered. Therefore, when the distance between features is smaller than the resolution of the image, adjacent multiple features are represented by one segment, but these multiple features are not associated with graphic data.

  Further, there is no mention of a technique for extracting the edge of a feature from an image, and there are cases where an edge corresponding to a figure cannot be extracted. Further, there is no mention of a method for determining a threshold value when an image is divided into regions. The area division threshold is common to all the features included in the image and cannot be changed for each feature. Therefore, the region division may fail and the segment corresponding to the graphic may not be extracted.

  Further, there is no mention of a reading value setting method when inputting or digitizing an image or a photograph using a scanner or the like. Images with different reading settings for each feature cannot be used. Further, there is no mention of a method for displaying the result of updating the graphic information.

  Accordingly, a first object of the present invention is to provide a map information updating method and a map updating apparatus capable of specifying the position of a new feature not described in a map.

  A second object of the present invention is that when a distance between features is smaller than the resolution of the image, a plurality of adjacent features are represented by one segment, and the plurality of features are associated with graphic data. Another object of the present invention is to provide a map information updating method and a map updating apparatus that can extract a plurality of features in a batch.

  A third object of the present invention is to select an appropriate edge extraction filter using graphic information collated with a feature in an image and extract an edge of the feature corresponding to the graphic to obtain a feature shape. Is to provide a map information updating method and a map updating apparatus capable of extracting the information.

  A fourth object of the present invention is to perform segmentation with an appropriate threshold value for each feature using graphic information collated with the feature in the image, and select a segment of the feature corresponding to the graphic. The present invention provides a map information updating method and a map updating apparatus that can extract a feature shape.

  A fifth object of the present invention is to provide a map information updating method and map updating apparatus that can use an image having an appropriate reading characteristic that is input and digitized for each feature when extracting features. There is to do.

  A sixth object of the present invention is to provide a map information updating method and a map updating apparatus capable of displaying an updated map with a minimum increase in data amount while preserving map information before updating. is there.

  In order to achieve the first object, a graphic in the map DB is made into a template, template matching is repeated in an area that does not correspond to the map graphic, and a matching feature is found to find a new feature not described in the map. A graphic template forming means for detecting the position of the feature and a graphic / feature matching means are provided.

  In order to achieve the second object, when the distance between adjacent graphics superimposed on the image is 1 pixel or less, these adjacent graphics are grouped together into one graphics group, and the corresponding multiple feature regions are collected. Is extracted, and adjacent graphic grouping means is provided for associating a plurality of features obtained by collecting adjacent features with a plurality of corresponding graphics.

  Further, in order to achieve the third object, the edge position, direction, and width are estimated using graphic information collated with the feature in the image, and an appropriate edge extraction filter is selected. A graphic corresponding edge extracting means for extracting an edge of a feature corresponding to the graphic by removing an inappropriate edge is provided.

  Further, in order to achieve the fourth object, the graphic information collated with the feature in the image is used to prevent the segment from straddling the edge or the superimposed graphic line segment extracted for each feature. There is provided a figure corresponding segment extracting means for extracting a feature segment corresponding to a figure by determining the region division threshold value and selecting a segment using the figure shape information.

  In addition, in order to achieve the fifth object, a plurality of images having different reading characteristics at the time of digitization are created for one photograph taken by a satellite or an aircraft, and feature extraction is performed with one image. The image input means is provided which makes it possible to use an image having an appropriate reading characteristic at the time of digitization for each feature by re-extracting with another reading setting image when it fails.

  In order to achieve the sixth object, means for displaying a map composed only of map update information in a time-series order on a map before update is provided.

  Since the present invention has the above-described configuration, it is possible to extract a new feature by specifying the position of a new feature not described in the map.

In addition, by associating a plurality of features in which adjacent features are grouped with a plurality of figures corresponding to them, a plurality of features combined for low resolution can be extracted.

  In addition, the feature shape can be extracted by selecting an appropriate edge extraction filter for extracting the feature edge and extracting the feature edge corresponding to the figure.

  Further, the feature shape can be extracted by performing region division with a threshold automatically determined for each feature and selecting a segment of the feature corresponding to the figure.

  Also, when extracting features, an image having appropriate reading characteristics at the time of input and digitization can be used for each individual feature.

  In addition, an updated map can be displayed with a minimum increase in the amount of data while pre-update map information is stored.

  Since the present invention has the above-described configuration, it is possible to specify the position of a new feature that is not described in the map by creating a template from an existing graphic in the map DB and finding a matching feature.

In addition, by grouping adjacent figures , it is possible to associate multiple grouped figures with a corresponding set of multiple features, and to collect multiple sets of features together for low resolution. It becomes possible to extract. Further, it is possible to accurately extract the edge of the feature by estimating the position, direction, and width of the edge of the feature from the graphic information collated with the feature and selecting an appropriate edge extraction filter. .

  Further, it is possible to automatically determine the image region division threshold value by using the edge of the feature or the matching graphic line segment. Furthermore, it becomes possible to extract a feature segment corresponding to a graphic by paying attention to the orthogonality and linearity of the graphic shape. By these processes, it is possible to determine an appropriate area division threshold value for each feature, instead of using the same area division threshold value for the entire image area.

Also, by creating multiple images with different reading characteristics at the time of digitization for one image and reextracting with other images when feature extraction fails with one image, for each feature the most appropriate read setting values during extraction it is possible to use the digitized image.
In addition, by displaying a map consisting only of the map update information in chronological order on the map before update, the map is updated with the smallest increase in data volume while preserving the map information before update. Can display the map.

  As described above, it is possible to automatically update the map using the image, and it is possible to reduce the burden and cost on the user for the map update.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

Figure 1 is a functional configuration example related to the map information update according to an embodiment of the present invention, FIG. 2 is an image processing example taken as an example building as a representative of a feature.

First, the input unit will be described.
A map database (DB) 101 exists in the system in which the above method operates or on a connected network. The map database 101 includes a graphic 102 (201 in FIG. 2). The figure 102 is a two-dimensional numerical map composed of (x, y) coordinate sequences or a three-dimensional numerical map (hereinafter referred to as a map) composed of (x, y, z) coordinate sequences, and has a vector data structure. It includes position, shape information, and attribute information of features in a wide area. Attribute information is information attached to figures such as buildings, roads, fields, bare land, grassland, and forests. Further, the shape information of the figure and the attribute information are collectively referred to as figure information.

Similarly, an image database (denoted as DB) 103 exists in the system in which the above method operates or on a connected network. The image database 103 includes a digital image (hereinafter referred to as an image) 104 of a feature. Here, the source image 104 is an aerial photograph or satellite image, input by using, for example a scanner, when digitized, one image is set 1, set 2, ..., set n Thus, it is read as the number of images set. Also, the image 104 includes an image (202 in FIG. 2) representing the feature 105 such as a road or a building in the area.

  Next, the processing unit will be described. The map / image positioning means 106 associates the map figure with the corresponding feature in the image by projective transforming the map and superimposing it on the image when detecting a graphic change.

  When the distance between adjacent graphics superimposed on an image is 1 pixel or less, the adjacent graphic grouping means 107 collects these adjacent graphics into a graphic group, Corresponding to the corresponding graphic group. As a result, a figure / feature image collation diagram 110 (203 in FIG. 2) is output.

  The graphic template forming means 108 forms a graphic in the map DB in order to detect a new feature such as a newly built building.

  The figure / feature matching means 109 uses the figure template to repeat template matching in an area that does not correspond to the figure on the map, and finds the matching feature, thereby determining the position of the new feature not described in the map. To detect. As a result, a figure / feature image collation diagram 110 (203 in FIG. 2) is output.

  From the figure / feature matching diagram 110, the feature peripheral area is estimated from the shape of the superimposed figure. As a result, a feature peripheral region extraction diagram (204 in FIG. 2) is output.

  The image figure corresponding edge extraction means 111 estimates the edge position, direction, and width using the figure information matched to the feature when extracting the feature shape corresponding to the figure shape, and selects an appropriate edge extraction filter. The edge of the feature corresponding to the figure is extracted by removing the inappropriate edge from the extracted edge. As a result, the feature edge extraction diagram 112 (205 in FIG. 2) is output.

  The figure-corresponding segment extracting means 113 uses the figure information collated with the feature in the image to segment the area for each figure so that the segment does not span the edge or the superimposed figure line segment extracted for each feature. A feature segment corresponding to the figure is extracted by determining a value and selecting a segment using shape information of the figure. As a result, the feature segment extraction diagram 114 (206 in FIG. 2) is output.

  When the shape of the feature cannot be extracted, the figure-use feature pixel classification means 115 classifies and extracts the pixel in the matched graphic as a pixel representing the feature. As a result, the feature pixel extraction diagram 118 (207 in FIG. 2) is output.

  The figure / feature shape correspondence determining means 117 compares the extracted feature shape with the graphic shape to determine whether or not they correspond.

  The figure / feature attribute correspondence determining means 118 compares the characteristic values (brightness, hue, saturation) of the pixel representing the extracted feature and the features of the texture with the graphic attributes, and determines whether they correspond. . As a result, graphic change information 119 and new feature information 120 are output.

Finally, the output unit will be described.
The map database 121 is the same device as the map database 101 described above, and an information update graphic 122 is newly stored based on the graphic change information 119 and the new feature information 120 output from the processing unit.

  The time-series map display means 123 displays a map composed only of the information update graphic 122 on the pre-update map in chronological order.

  FIG. 3 is a flowchart showing the procedure of the graphic change detection process in the map information update method. Hereinafter, the graphic change detection process will be described with reference to the process explanatory diagrams of FIGS. 3 and 5 to 8.

  In addition, in the process explanatory diagrams of FIGS. 5 to 8, only buildings are used as representative features, and the following processes are not limited to buildings.

  (Step 301) A map to be updated is input to the system.

  (Step 302) A remote sensing image such as a satellite image or aerial photograph taken at a newer time than the map is prepared, read by the read setting number n, and input to the system as n digital images.

  For example, FIG. 5 shows an example in which a plurality of reading settings for luminance are provided to form a plurality of digital images. In the case of FIG. 5A, as the setting changes from setting 1 (501), setting 2 (502),..., Setting n (503) to the original image, the luminance gradation of the low luminance portion of the original image becomes finer. A digital image with a rough luminance gradation in a high luminance portion of the original image is obtained. According to the reading setting, the same building also changes to a building image 504, a building image 505,.

  On the other hand, in the case of FIG. 5B, the luminance gradation of the low-luminance portion of the original image is changed as setting 1 (507), setting 2 (508),..., Setting n (509) is changed with respect to the original image. Is a digital image with a coarse luminance and a fine luminance gradation in the high luminance portion of the original image.

  Here, the luminance is taken as an example, but in the case of a color image, the same may be performed for each of the RGB components, and the same may be performed for the saturation and the hue. The reading setting curve can be set freely by the user according to the feature of the feature image to be detected.

  (Step 303) Corresponding points are selected from the map and the image, and a coordinate conversion coefficient between the map coordinates and the image coordinates is obtained, thereby superimposing and collating the map and the image.

  Here, the relationship between image coordinates and map coordinates is considered for map geometric transformation. The relationship between a map and an image is represented by projective transformation from map coordinates to image coordinates.

The relationship between the map coordinates (X, Y) and the image coordinates (u v) is

It is expressed. Map coordinate point (Xi, Yi) image coordinate point corresponding to the (ui, vi) if is known, it is possible to determine the C ₃₂ from the coordinate transformation coefficient C ₁₁ by the least squares method.

  (Step 304) The graphic grouping process (Step 305, Step 306) is performed for all the figures to be detected.

  As illustrated in FIG. 6, when the distance D601 between buildings is smaller than the resolution of the image, the adjacent building 602 and the building 603 are represented by one segment 604. The plurality of buildings 604 that become one segment cannot be individually associated with the building figures 605 and 606. In order to extract a plurality of features that are grouped for low resolution in this way, it is necessary to associate a plurality of features that are a collection of adjacent features and a plurality of figures corresponding to them.

  (Step 305) The distance d605 between the building figure 606 and the building figure 607 is compared with the resolution of the image.

  (Step 306) When the distance d605 between figures is smaller than the image resolution, these figures are grouped. A plurality of figures 608 in which the figure 606 and the figure 608 are grouped and a plurality of buildings 604 formed as one segment are associated with each other.

  Thus, when the distance between adjacent figures is 1 pixel or less, it is estimated that the adjacent feature in a corresponding image is one segment. By grouping adjacent figures, it is possible to associate a plurality of grouped figures with a corresponding group of features.

  (Step 307) Perform figure / feature alignment correction in units of multiple figure groups such as a block. Furthermore, figure / feature alignment correction is performed for each figure group unit, figure group and feature are collated, figure / feature image collation diagram in which figure and feature are aligned and superimposed (203 in FIG. 2) Create

  The alignment correction method is based on searching for the most matching position by matching the image while moving the figure superimposed on the image in parallel. After making large corrections in units of multiple graphic groups and then making small corrections in each graphic group, matching with adjacent features of similar shapes is not possible. Since the alignment is corrected, the position of the feature is shifted due to the undulation of the ground surface, and even if it does not match the figure simply by superimposition, it is possible to perform collation by accurate alignment.

  (Step 308) The change determination process (Steps 309 to 325) is performed for all the graphic groups to be changed.

  (Step 309) The features are extracted by the processing (Step 310 to Step 325) for the number of images to be read. When the feature segment corresponding to the graphic group is extracted, the processing ends (Step 322). When the feature segment extraction fails, re-processing (Step 324) is performed using another setting image. If no figure-corresponding feature is extracted from all the images, it is determined that the figure has changed, and the process ends (Step 325).

In this way, by creating a plurality of images with different reading characteristics at the time of digitization for one image and reextracting with other images when feature extraction fails with one image, individual images can be obtained. it is possible to use the digitized image in the most appropriate read set value to the feature.

  (Step 310) It is determined according to the attribute whether the shape of the change detection target figure can be extracted. When the change detection target figure is a feature having a clear shape such as a building, the figure change determination process is performed by the shape and attribute change determination of (Step 311 to Step 325). When the change detection target graphic is a feature having no clear shape such as a forest, graphic change determination processing is performed by attribute change determination in (Step 320 to Step 325). Whether or not to perform shape extraction may be determined by a user instruction.

  (Step 311) Estimate that the feature exists in the area of the matching figure range plus the range that takes into account the figure coordinate error, image resolution, and figure and feature matching error. 204) of FIG. 2 is output.

(Step 312) As illustrated in FIG. 7, a feature edge extraction diagram (205 in FIG. 2) is created by extracting corresponding edges for all line segments of the matched figure (Steps 313 to 315). .

  (Step 313) The edge corresponding to the line segment is located within the range 704 in consideration of the figure coordinate error, the image resolution, and the matching error between the figure and the feature around the line segment 703 in the building figure 702 superimposed on the building image 701. I guess. This is referred to as an edge peripheral region 704. Prior to the edge extraction process, the edge direction and width are classified in advance and an edge extraction filter corresponding to them is created. The direction of the edge is estimated from the direction of the graphic line segment 703, the size of the edge width is estimated from the graphic attribute and the image resolution, and an appropriate filter is selected from the prepared filters. In the example of FIG. 7, the edge directions are classified into eight directions, filters 705 for each direction are prepared, and the corresponding filter 706 is selected from the edge directions estimated from the direction of the line segment. To do.

  (Step 314) In the edge peripheral region 704, the edge 707 is extracted using the selected filter 706.

  (Step 315) Of the extracted edges 707, those whose length in the direction parallel to the graphic line segment is smaller than the threshold value are removed to obtain the edge 708 from which noise has been removed.

  As in (Step 313 to Step 315), by using the matching graphic information, the position, direction, and width of the edge of the feature are estimated, and further, an appropriate edge extraction filter is selected and used. It is possible to extract the edge of the feature corresponding to the line segment.

(Step 316) Oite the estimated feature peripheral region (204 in FIG. 2) in (Step 311), as illustrated in FIG. 8, the segments produced building image 801 and area division, (Step 312 Among the region division threshold values that do not cross the edge 802 extracted in Step 315), the maximum value is set as the region division threshold value. It is also possible to perform the same processing using the superimposed graphic line segment instead of the edge.

  (Step 317) In the region around the feature, region division processing is performed using the determined region division threshold value, and the region is divided into segments 803.

  (Step 318) A segment representing a feature (a building in FIG. 8) is extracted from each segment 803 generated as a result of the image region dividing process, and a feature segment extraction diagram (206 in FIG. 2) is created.

  The segment adjacent to the extracted edge inside the figure is defined segment 804, and the segment where the edge is not extracted is selected segment 805. If the collation graphic has a high orthogonality, the selected segment is selected so that the orthogonality is maximized as a whole segment combining the determined segment and the selected segment. If the linearity of the corresponding feature is high, the selected segment is selected so that the entire perimeter of the combined segment of the confirmed segment and the selected segment is the shortest. As a result, the feature segment 806 is extracted.

  As in (Step 316 to Step 318), it is possible to automatically determine the image region division threshold by using the edge of the feature or the matching graphic line segment. Furthermore, it becomes possible to extract a feature segment corresponding to a graphic by paying attention to the orthogonality and linearity of the graphic shape.

  Also, with these processes, it is possible to determine an appropriate region division threshold value for each feature, instead of using the same region division threshold value for the entire image region.

  (Step 319) It is determined whether the shape and attribute of the extracted segment area correspond to the shape and attribute of the figure.

  The attribute of the extracted segment is estimated from the texture of the segment. The correspondence determination condition depends on the area ratio between the graphic and the extracted segment, the degree of overlap, the attribute match, the barycentric position of the extracted segment, and the overlap between the extracted segment and another graphic.

  (Step 320) If the shape of the feature cannot be extracted, the feature shape is substituted with the shape of the matching figure, so that the pixel in the figure is extracted as a pixel representing the feature, and the pixel extraction diagram in the figure (FIG. 2). 207).

  (Step 321) It is determined whether or not the figure corresponds to the extracted area by determining whether the attribute of the extracted pixel area matches the attribute of the figure. The attribute of the extracted pixel area is estimated from the texture.

  (Step 322) If it is determined that the figure corresponds to the extraction area, it is determined that the figure exists because the feature corresponding to the figure exists. The processing result is stored in the map DB.

  (Step 323) If it is determined that the figure does not correspond to the extraction area, it is assumed that the corresponding feature extraction has failed or there is no feature corresponding to the figure.

  (Step 324) If there is an image with another setting, reprocessing (Step 310 and subsequent steps) is performed using the other setting image.

  (Step 325) When there is no image of another setting, it means that corresponding feature extraction has failed in all setting images. In this case, it is determined that there is no feature corresponding to the graphic, and the graphic change is determined. Then, the processing result is stored in the map DB.

  Next, the new feature detection process will be described with reference to the flowchart of FIG. 4 and the process explanatory diagram of FIG. Similarly to the description of the graphic change detection process, the process explanatory diagram of FIG. 9 merely uses a building as a representative example of the feature, and the following process is not limited to the building.

  (Step 401) The map to be updated is input to the system in the same manner as when the figure change is detected (Step 301 in FIG. 3).

  (Step 402) As in the case of detecting a graphic change (Step 302 in FIG. 3), remote sensing images such as satellite images and aerial photographs are input to the system as a plurality of digital images.

  (Step 403) Estimate the existence area of new features. The new feature existence area is assumed to be other than the existence area of the feature segment extracted by the graphic change detection process or other than the existence area of the figure collated with the target feature in the collation diagram of the map and the image.

(Step 404) Templates are created for all figures corresponding to new features in the map database (all figures having the same attributes as the new features), and feature detection processing (Steps 405 to 421) is performed. Processing shall be performed with high presence probability figure forward. If the existence probability is unknown, the processing may be performed in descending order of the graphic area. The graphics to be templated include graphics representing other typical feature shapes and attributes in addition to the existing graphics described on the map and collated with the image. Moreover, the figure which expanded, reduced, rotated, and deform | transformed these figures is also contained.

  (Step 405) As illustrated in FIG. In the case of FIG. 9, since it is a building graphic, + value and -value components are arranged so that the template outputs a luminance difference between the inside and the outside of the building graphic.

  (Step 406) In the new feature existence region 903, template matching using the graphic template 902 is performed to search for a template matching position 904. A graphic / feature matching diagram (203 in FIG. 2) is created assuming that there is a possibility that the target feature exists at the matching position.

  (Step 407) The feature extraction process of (Step 408 to Step 421) is performed for the number of matching positions of the template.

  (Step 408) Feature extraction is performed by the processing of (Step 409 to Step 421) for the number of images. When the feature segment corresponding to the figure is extracted, the processing ends (Step 418). When the feature segment extraction fails, re-processing (Step 420) is performed using another setting image. When the figure-corresponding feature is not extracted from all the images, it is determined that the feature does not exist, and the processing is ended (Step 421).

  (Step 409 to Step 417) The same feature segment extraction processing as that at the time of figure change detection (Step 311 to Step 319 in FIG. 3) is performed.

  (Step 418) If it is determined that the figure corresponds to the extracted segment, it is determined that a feature corresponding to the figure exists and a new feature appears, and the processing result is stored in the map DB.

(Step 404~Step 418) As described above, the template of existing shapes in the map DB, by finding a feature matching, we are possible to specify the position of the new feature not described in the map.

  (Step 419) If it is determined that the figure does not correspond to the extracted segment, it is assumed that the figure-corresponding feature extraction has failed or the feature corresponding to the figure does not exist.

  (Step 420) If there is an image with another setting, reprocessing (Steps 409 and after) is performed using the other setting image.

  (Step 421) When there is no image of another setting, it means that the feature extraction has failed in all the setting images. In this case, it is determined that there is no feature corresponding to the figure.

  Finally, with reference to FIG. 10, the display method of the updated map is demonstrated.

A map 1007 is a map before update, and includes road figures and building figures 1008. Other figures are omitted here. It is assumed that this map 1007 displays feature information as of December 1998.

  The map 1004 displays graphic information updated between December 1998 and January 1999, and includes an object graphic 1005 that has newly appeared and a building graphic 1006 that has disappeared during this time.

  The map 1001 displays graphic information updated between January 1999 and February 1999, and includes a newly appearing object graphic 1002 and an extinguished building graphic 1003.

  A map 1009 is a map in which maps 1004 and 1001 consisting only of update information are superimposed on a map 1007 before update and displayed in chronological order, and displays the state of features after December 1998. In this way, an updated map can be displayed with a minimum increase in the amount of data while preserving the map information before the update.

It is a functional block diagram of the map information update system concerning this invention. It is an image processing example of a map information update system. It is a flowchart which shows the flow of the figure change detection process concerning this invention. It is a flowchart which shows the flow of the novel feature detection process concerning this invention. It is explanatory drawing of input digital image reading. It is explanatory drawing of figure grouping. It is explanatory drawing of a feature edge extraction. It is explanatory drawing of feature segment extraction. It is explanatory drawing of figure template formation. It is a display example of a map update result.

Explanation of symbols

101 …… Map DB 102 …… Figure 103 …… Image DB 104 …… Image (Setting 1)… (Setting n) 105 …… Features 106 …… Map / image alignment means 107 …… Adjacent figure grouping means 108 ...... Figure template forming means 109 ...... Figure / feature matching means 110 ...... Figure / feature image collation diagram 111 ...... Figure corresponding edge extraction means 112 ...... Feature edge extraction diagram 113 ...... Figure corresponding segment extraction means 114 …… Feature segment extraction diagram 115 ...... Graphic feature feature pixel classification means 116 …… Pixel pixel extraction diagram 117 …… Graph / feature shape correspondence determining means 118 …… Graphic / feature attribute correspondence determining means 119 …… Figure change information 120 …… New feature information 121 …… Map DB 122 …… Information update figure 123 …… Time series map display Step.

Claims (6)

A database that records a planar numerical map composed of (X, Y) coordinate sequences or a three-dimensional numerical map composed of (X, Y, Z) coordinate sequences;
Alignment means for projectively converting the map and superimposing the converted map and a still image obtained by photographing the area;
Select the edge peripheral area where the edge corresponding to the line segment of the building figure of the superimposed map is located, estimate the direction of the edge from the direction of the line segment included in the edge peripheral area, and the direction of the edge Edge extracting means for extracting the edge of the building of the still image to be superimposed on the building figure of the map in the peripheral region of the edge using a filter selected based on the estimation result of
Based on the edge of the building, it means for extracting the feature segments on the still image corresponding to the building geometry of the map,
A map processing apparatus, comprising: a correspondence determination unit that compares the building figure of the map with the feature segment of the still image and determines whether or not it corresponds. The map processing apparatus according to claim 1, wherein the edge extraction unit also estimates the size of the edge width from the attribute of the building graphic and the resolution of the still image.   3. The registration unit according to claim 1 or 2, wherein the registration unit corrects registration of the still image and the map in a group unit having a plurality of graphics, and further performs registration correction for each of the graphics to perform the superposition. The map information update device described in 1. The feature segment extraction means sets a threshold value for area division that does not extend over the edge extracted by the edge extraction means for each building in the still image, and performs area division based on the threshold value. The map information updating apparatus according to claim 1, wherein a graphic corresponding segment is extracted. A figure template forming means for making a building figure in the map into a template;
5. The map processing apparatus according to claim 1, further comprising a template matching unit that detects a new building from a map in the still image and an uncorresponding region by matching using the graphic template. . Building shapes of the map, the map processor according to claim 1, characterized in that the building is determined using the attribute information.

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