WO2019181342A1 - Region specification information generation device - Google Patents

Region specification information generation device Download PDF

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Publication number
WO2019181342A1
WO2019181342A1 PCT/JP2019/006339 JP2019006339W WO2019181342A1 WO 2019181342 A1 WO2019181342 A1 WO 2019181342A1 JP 2019006339 W JP2019006339 W JP 2019006339W WO 2019181342 A1 WO2019181342 A1 WO 2019181342A1
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Prior art keywords
feature point
point candidates
information
specifying information
peaks
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PCT/JP2019/006339
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French (fr)
Japanese (ja)
Inventor
英樹 三谷
昌紀 奥山
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ヤンマー株式会社
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Application filed by ヤンマー株式会社 filed Critical ヤンマー株式会社
Priority to KR1020207007665A priority Critical patent/KR20200131802A/en
Priority to CN201980005681.1A priority patent/CN111868476A/en
Publication of WO2019181342A1 publication Critical patent/WO2019181342A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C15/00—Surveying instruments or accessories not provided for in groups G01C1/00 - G01C13/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3833—Creation or updating of map data characterised by the source of data
    • G01C21/3841—Data obtained from two or more sources, e.g. probe vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles

Definitions

  • the present invention relates to a region specifying information generating device that generates region specifying information for specifying a specific target region based on position information of a plurality of sampling points around the specific target region.
  • Patent Document 1 discloses a region in which the shape of the field is obtained by sequentially moving a work vehicle capable of obtaining position information by a satellite positioning system along the periphery of the field in the field and sequentially obtaining the position information.
  • a shape acquisition device is disclosed. Specifically, the area shape acquisition device described in Patent Document 1 first makes a round antenna path (a movement path of the antenna of the satellite positioning system) based on position information of the work vehicle when the work vehicle is made to travel around. Is identified. Next, the region shape acquisition device corrects the orbital antenna path based on the circling direction of the work vehicle in the orbital traveling, the orbiting antenna path and the predetermined offset, and generates an outer peripheral side end path. Then, the area shape acquisition device acquires the shape of the work area from the outer peripheral side end path.
  • a round antenna path a movement path of the antenna of the satellite positioning system
  • An object of the present invention is to provide an area specifying information generation device capable of generating area specifying information for specifying a specific target area by a novel method based on position information of a plurality of sampling points around the specific target area. Is to provide.
  • An area specifying information generating apparatus is an area specifying information generating apparatus that generates area specifying information for specifying the specified target area based on position information of a plurality of sampling points around the specified target area.
  • a distance calculation unit for calculating a distance from an internal reference point in the specific target region to each sampling point, and the plurality of sampling points arranged in an order lined up in one direction around the specific target region
  • a peak detection unit that detects a plurality of peaks of data representing the distance from the reference point, and an information generation unit that generates the region specifying information based on position information corresponding to the plurality of peaks.
  • the information generation unit includes, among the plurality of peaks detected by the peak detection unit, a plurality of peaks having a relatively large peak width or a relatively large peak height. Each region is selected as a feature point candidate, and the region specifying information is generated based on position information corresponding to the plurality of selected feature point candidates.
  • the information generation unit includes, among the plurality of peaks detected by the peak detection unit, a plurality of peaks having a relatively large peak width or a relatively large peak height.
  • the absolute value of the difference between the selection unit for selecting each as a feature point candidate, the circumference of the polygon defined by the plurality of feature point candidates, and the circumference of the polygon defined by the plurality of sampling points is predetermined.
  • a determination unit that determines whether or not the difference value is within the threshold value, and when the difference absolute value is determined to be within the threshold value, position information corresponding to the plurality of feature point candidates is generated as the region specifying information.
  • a polygon defined by a plurality of feature point candidates is referred to as a target polygon
  • a polygon defined by a plurality of sampling points is referred to as a basic polygon.
  • At least one new feature point candidate is added between at least one set of two adjacent feature point candidates. This makes it possible to generate area specifying information suitable for specifying the specific target area.
  • the second position information generation unit supports, for each combination of two adjacent feature point candidates, among the polygonal contour lines defined by the plurality of sampling points. 2 corresponding to a combination having a relatively large difference between the length of the section between the two feature point candidates and the distance between the two feature point candidates corresponding to the combination. At least one new feature point candidate is added between the two feature point candidates.
  • FIG. 1 is a schematic diagram showing a configuration of an area specifying information generating apparatus according to an embodiment of the present invention.
  • FIG. 2 is a schematic diagram for acquiring a method for acquiring a plurality of sampling points around the field.
  • FIG. 3 is a schematic diagram showing sampling points acquired for the field of FIG.
  • FIG. 4 is a functional block diagram for explaining the functions of the PC.
  • FIG. 5 is a flowchart showing the procedure of the area specifying information generating process executed by the PC when the area specifying information generating program is started.
  • FIG. 6 is a schematic diagram for explaining the operation of the distance calculation unit.
  • FIG. 7 is a graph for explaining the operation of the peak detector.
  • FIG. 8 is a schematic diagram for explaining the process of step S6 of FIG. FIG.
  • FIG. 9 is a schematic diagram for explaining the process of step S7 of FIG.
  • FIG. 10 is a schematic diagram for explaining the process of step S7 of FIG.
  • FIG. 11A is a flowchart illustrating a part of a procedure of another example of the area specifying information generating process executed by the PC when the area specifying information generating program is activated.
  • FIG. 11B is a flowchart illustrating a part of a procedure of another example of the area specifying information generating process executed by the PC when the area specifying information generating program is activated.
  • FIG. 1 is a schematic diagram showing a configuration of an area specifying information generating apparatus 1 according to an embodiment of the present invention.
  • the region specifying information generating device 1 generates information for specifying, for example, a field where sugarcane is cultivated. That is, in this embodiment, the specific target region for which the region is to be specified is a farm field.
  • the area specifying information generating apparatus 1 is realized by a personal computer (PC) 10.
  • a display 21, a mouse 22 and a keyboard 23 are connected to the PC 10.
  • the PC 10 includes a CPU 11, a memory 12, a hard disk 13, and the like. Further, the PC 10 is provided with a USB (Universal Serial Bus) port (not shown).
  • USB Universal Serial Bus
  • the hard disk 13 stores an area identification information generation program in addition to an OS (operation system) and the like.
  • the area specifying information generation program is acquired from, for example, a storage medium such as a USB memory in which the area specifying information generation program is stored, or acquired from the website providing the area specifying information generation program via the Internet. Is possible.
  • the hard disk 13 stores position information of a plurality of sampling points around a field (specific target region) to be specified.
  • a plurality of sampling points around the field corresponds to a plurality of points (contour constituent points) on the contour of the field.
  • the hard disk 13 stores a maximum value (the maximum value of the number of feature points) M of the total number of feature points for specifying the field.
  • the feature point maximum value M is set by the user and stored in the hard disk 13. The feature point maximum value M can be changed.
  • FIG. 2 is a schematic diagram for acquiring a method for acquiring a plurality of sampling points around the field.
  • FIG. 3 is a schematic diagram showing sampling points acquired for the field of FIG.
  • a plurality of sampling points around the field 51 can be acquired using, for example, the position measuring device 2 that measures the self position using a positioning satellite.
  • the user carries the position measuring device 2 and inputs a measurement start command for starting the position measurement to the position measuring device 2, and then, as shown by a broken line 52 in FIG. Next, it travels along the periphery of the field 51.
  • the broken line 52 is drawn away from the contour line of the farm field 51 in order to display the broken line 52 so as to be identifiable with respect to the contour line (surrounding) of the farm field 51. Walk as close as possible to the 51 contour line.
  • the position measuring device 2 measures its own position, for example, every predetermined time and stores it in its own memory (for example, a non-volatile memory).
  • the user goes around the field 51, the user inputs a measurement end command to the position measuring device 2 to end the measurement and store the position information.
  • the position measuring device 2 stores the position information stored in the memory from the measurement start command to the measurement end command in the memory as current measurement result data.
  • sampling points S 1 a plurality of sampling points S 1 around the field 51, S 2, S 3, ... S N-1, time-series data consisting of the position information of S N is the position measuring device 2 Saved in the memory.
  • the subscripts 1 to N of each sampling point S are numbers indicating the order in which the sampling points are acquired. In this embodiment, they are used as identifiers (hereinafter referred to as sampling numbers) for identifying the sampling points.
  • sampling numbers hereinafter referred to as sampling numbers
  • the location information includes, for example, latitude / longitude information, altitude information, and time information.
  • the position information includes latitude / longitude information and time information.
  • the user connects the position measuring device 2 to the USB port of the PC 10 and operates the PC 10 to store the time series data for the field 51 stored in the memory in the position measuring device 2 in the hard disk 13.
  • the operation of the PC 10 when generating region specifying information for specifying the field 51 based on the time series data for the field 51 stored in the hard disk 13 will be described.
  • FIG. 4 is a functional block diagram for explaining the functions of the PC 10.
  • the PC 10 functions as a plurality of function processing units by executing the area specifying information generation program.
  • This function processing unit includes a distance calculation unit 31, a peak detection unit 32, and an information generation unit 33.
  • the distance calculation unit 31 calculates distances from a reference point inside the field 51 to a plurality of sampling points S around the field 51 based on time-series data for the field 51 stored in the hard disk 13.
  • the peak detection unit 32 detects a plurality of peaks of data representing the distance from the reference point with respect to the plurality of sampling points S arranged in the order of being arranged in one direction around the field 51.
  • the information generating unit 33 generates region specifying information for specifying the farm field 51 based on the position information corresponding to the plurality of peaks detected by the peak detecting unit 32.
  • the information generation unit 33 includes a selection unit 41, a determination unit 42, a first information generation unit 43, and a second information generation unit 44.
  • FIG. 5 is a flowchart showing the procedure of the area identification information generation process executed by the PC 10 when the area identification information generation program is started.
  • the distance calculation unit 31 calculates the distance from the internal reference point Q in the field 51 to each sampling point S as shown in FIG. 6 (step S1).
  • the reference point Q is set at the barycentric position of the field 51.
  • the position of the center of gravity of the field 51 can be calculated from the position information of the sampling point S by a method similar to a known method for obtaining the center of gravity of the figure from the contour constituent points of the figure, for example.
  • the reference point Q may be set to a point other than the gravity center position of the field 51 as long as it is inside the field 51.
  • the peak detection unit 32 first creates a graph (line graph) representing the distance from the reference point Q with respect to a plurality of sampling points S arranged in an order lined up in one direction around the field 51 (step). S2). Specifically, the peak detection unit 32 has a plurality of sampling points in a coordinate system in which the horizontal axis represents sampling numbers (identifiers) of the plurality of sampling points S and the vertical axis represents the distance from the reference point Q of the sampling points S. A graph (line graph) is created by plotting the distance corresponding to S and connecting the plotted points. On the horizontal axis, sampling numbers of a plurality of sampling points S are arranged in the order in which the position information is acquired. The sampling numbers of the plurality of sampling points S may be arranged on the horizontal axis in the order opposite to the order in which the position information is acquired.
  • FIG. 7 is not a graph created based on the distance from the reference point Q to each sampling point S shown in FIG. Therefore, there is no correlation between FIG. 7 and FIG.
  • the peak detection unit 32 obtains an average value of the graph created in step S2, and creates a peak detection graph by folding a portion below the average value of the graph up and down around the average value. (Step S3).
  • the peak detection graph is U2 in FIG.
  • the average value (folding position) of the graph U1 is indicated by a one-dot chain line.
  • this peak detection graph U2 is obtained by folding a portion below the average value of the graph U1 in FIG. 7 up and down centering on the average value, and then turning the graph back to the average in the ⁇ Y direction. It is created by shifting by the value.
  • the peak detector 32 detects the position of the maximum value of the peak detection graph obtained in step S3 as the peak of the graph created in step S2 (step S4).
  • the parameter for detecting the maximum value is adjusted so that the peak of a smooth mountain whose slope near the top is not more than a predetermined value is not detected as the maximum value.
  • the selection unit 41 in the information generation unit 33 executes the process of step S5. That is, the selection unit 41 first selects, from the plurality of peaks detected by the peak detection unit 32, a plurality of peaks having a relatively large peak width or a relatively large peak height as feature point candidate peaks. . Then, the selection unit 41 stores a plurality of sampling points corresponding to the plurality of feature point candidate peaks in the memory 12 as feature point candidates. As a result, a peak that is unlikely to be an important feature point for specifying the farm field 51, such as a portion whose direction changes in small increments, in the contour line of the farm field 51 is a feature point candidate peak. Can be suppressed.
  • the selection unit 41 calculates, for example, the half-value widths of a plurality of peaks detected by the peak detection unit 32, and selects sampling points corresponding to the upper predetermined number of peaks having a large half-value width as feature point candidates. To do.
  • the predetermined number is set to 5, for example.
  • the selection unit 41 may calculate, for example, prominences of a plurality of peaks detected by the peak detection unit 32, and select sampling points corresponding to the upper predetermined number of peaks with large prominences as feature point candidates.
  • the predetermined number is set to 5, for example.
  • the determination unit 42 in the information generation unit 33 includes a polygonal perimeter L1 defined by a plurality of feature point candidates stored in the memory 12 and a multiplicity defined by a plurality of original sampling points S. It is determined whether or not the difference absolute value
  • a polygon defined by a plurality of feature point candidates stored in the memory 12 is referred to as a “target polygon”, and a polygon defined by a plurality of original sampling points S is referred to as a “basic polygon”. There is a case.
  • step S6 The shape of the field 51 and a plurality of original sampling points S acquired from the field 51 have a shape as shown in FIG. 8, and a plurality of feature point candidates stored in the memory 12 are shown in FIG. Are five points.
  • the selection unit 41 calculates the circumference of the target polygon (in the example of FIG. 8, the polygon defined by the plurality of feature point candidates A to E) as the first circumference L1.
  • the selection unit 41 calculates the circumference of the basic polygon as the second circumference L2. Then, the selection unit 41 determines whether or not the absolute value
  • step S6 If it is determined in step S6 that the absolute value
  • step S6 If it is determined in step S6 that the absolute value
  • the second information generation unit 44 calculates, for each side of the target polygon, the absolute value of the difference between the length of the side and the length of the section corresponding to the side of the outline of the basic polygon.
  • the section of the outline of the basic polygon corresponding to any one side of the target polygon is characterized by the middle of the two sections sandwiched between the points on both sides of the outline of the basic polygon The section where no point candidate is set.
  • the section of the basic polygon outline corresponding to the side AB having both ends A and B in the target polygon is divided between two A and B on the outline of the basic polygon.
  • the section Rab is a section in which no feature point candidate is set in the middle of the section.
  • the second information generation unit 44 additionally arranges a new feature point candidate at the midpoint (center position of the section) having the largest absolute difference value in the basic polygon outline.
  • the new feature point candidate may be a point different from the original sampling point S, or may be the original sampling point S closest to the midpoint of the section.
  • the position information of the new feature point candidate is specified based on, for example, the position information of the two sampling points S on both sides of the new feature point candidate.
  • the second information generation unit 44 adds the new feature point candidate additionally arranged at the midpoint of the section having the largest difference absolute value to the feature point candidates stored in the memory 12. Thereby, the feature point candidates in the memory 12 are updated.
  • the absolute difference value corresponding to the side AB among the absolute difference values corresponding to each side of the target polygon is the largest, and therefore, in the section Sab of the outline of the basic polygon corresponding to the side AB.
  • a new feature point candidate F is added to the point.
  • the shape of the attention polygon changes.
  • the second information generating unit 44 determines whether or not the total number T of feature point candidates in the memory 12 has reached the maximum number M of feature points (step S8).
  • the maximum number of feature points is set to 15, for example.
  • step S8 NO
  • the second information generation unit 44 returns to step S7. And the process of step S7 is performed again.
  • a new feature point candidate G is added to the midpoint of the section Rbc of the outline of the basic polygon corresponding to the side BC. Thereby, as shown in FIG. 10, the shape of the target polygon changes.
  • step S8 When it is determined in step S8 that the total number T of feature point candidates in the memory 12 has reached the maximum number M of feature points (step S8: YES), the second information generation unit 44 proceeds to step S9. To do.
  • step S9 the second information generation unit 44 uses the position information corresponding to the plurality of feature point candidates stored in the memory 12 as the final feature point information (region specifying information) of the region specifying target field. 13. And the information generation part 33 complete
  • information (region specifying information) for specifying the field 51 can be generated by a novel method.
  • feature points important for specifying the shape of the farm field 51 can be generated as area specifying information for specifying the farm field 51.
  • FIGS. 11A and 11B are flowcharts showing another example of the area specifying information generating process executed by the PC 10 when the area specifying information generating program is started.
  • step S1 to S4 Since the processing from step S1 to S4 is the same as the processing from step S1 to S4 in FIG.
  • step S5A the information generation unit 33 first has a relatively large peak width or a relatively high peak height from the plurality of peaks detected by the peak detection unit 32, as in step S5 of FIG. A plurality of large peaks are selected as feature point candidate peaks. Then, the information generation unit 33 stores a plurality of sampling points corresponding to the plurality of feature point candidate peaks as initial feature point candidates in the initial candidate storage area in the memory 12. This point is different from step S5 in FIG.
  • the information generation unit 33 is defined by the polygonal perimeter L1 defined by the plurality of initial feature point candidates stored in the initial candidate storage area in the memory 12 and the plurality of original sampling points S. Whether or not the difference absolute value
  • a polygon defined by a plurality of initial feature point candidates stored in the initial candidate storage area in the memory 12 is referred to as an “initial polygon”, and a polygon defined by a plurality of original sampling points S. May be referred to as a “basic polygon”.
  • step S6A If it is determined in step S6 that the absolute value
  • step S6A If it is determined in step S6A that the absolute value
  • the information generation unit 33 randomly arranges new feature point candidates different from the initial feature point candidates stored in the initial candidate storage area of the memory 12 on the outline of the basic polygon (step S9A).
  • the number of new feature point candidates to be additionally arranged is set to a number obtained by subtracting the total number of initial feature point candidates extracted in step S5A from the maximum value M of feature points.
  • the information generation unit 33 calculates, for each side of the initial polygon, the absolute value of the difference between the length of the side and the length of the section corresponding to the side of the outline of the basic polygon as the degree of divergence.
  • a section in which new feature point candidates are additionally arranged and the number of distributions may be determined.
  • the new feature point candidates are preferentially arranged in a section with a large degree of deviation, and it is preferable that many new feature point candidates are arranged in a section with a large degree of separation.
  • the information generation unit 33 stores a candidate set including the initial feature point candidate and the new feature point candidate added this time in a predetermined candidate set storage area in the memory 12 (step S10A).
  • the candidate set storage area is provided with a predetermined value N or more to be described later, and every time the process of step S9A is performed, the candidate point storage area in which no feature point candidate set is stored in the process of step S10A is performed.
  • the current feature point candidate set is stored.
  • the information generation unit 33 calculates the absolute difference
  • the information generation unit 33 determines whether or not the count value K has reached a predetermined value N (step S12A).
  • the predetermined value N can be set to an arbitrary number.
  • step S12A If the count value K is less than the predetermined value N (step S12A: NO), the information generating unit 33 increments the count value K by 1 (step S13A). Then, the information generation unit 33 returns to step S9A. Thereby, the process after step S9A is performed again.
  • step S12A If the processing from step S9A to step S11A is performed N times, an affirmative determination is made in step S12A, so the information generating unit 33 proceeds to step 14A.
  • step 14A the information generation unit 33 first selects a candidate set having the smallest divergence ⁇ from the candidate sets stored for each candidate set storage area in the memory 12. Then, the information generation unit 33 stores the position information of the plurality of feature point candidates included in the selected candidate set on the hard disk 13 as final feature point information (region specifying information). And the information generation part 33 complete
  • information (region specifying information) for specifying the field 51 can be generated by a novel method. Also in this modified example, feature points important for specifying the shape of the farm field 51 can be generated as region specifying information for specifying the farm field 51.
  • the position information of the feature point candidate selected in step S5 of FIG. 5 may always be generated as final feature point information (region specifying information). In this case, the processes in steps S6 to S8 in FIG. 5 are omitted.
  • the measurer carrying the position measuring device 2 travels along the periphery of the farm field 51 to acquire position information of a plurality of sampling points around the farm field 51.
  • the position measuring device 2 is mounted on a moving body such as a vehicle, and the position information of a plurality of sampling points around the field 51 is acquired by moving the moving body along the periphery of the field 51. Good.

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  • Radar, Positioning & Navigation (AREA)
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Abstract

This region specification information generation device includes: a distance calculation unit 31 that calculates the distance to each sampling point from a reference point of an interior section in a region to be specified; a peak detection unit 32 that, for the plurality of sampling points that have been arranged in a sequence along one direction of the perimeter of region to be specified, detects a plurality of peaks in data representing the distances from the reference point; and an information generation unit 33 that generates region specification information on the basis of location information corresponding to the plurality of peaks.

Description

領域特定情報生成装置Region identification information generation device
　この発明は、特定対象領域の周囲の複数のサンプリング点の位置情報に基づいて、特定対象領域を特定するための領域特定情報を生成する領域特定情報生成装置に関する。 The present invention relates to a region specifying information generating device that generates region specifying information for specifying a specific target region based on position information of a plurality of sampling points around the specific target region.
　特許文献１には、衛星測位システムによって位置情報を取得可能な作業車両を、圃場内において、圃場の周縁に沿って周回移動させ、逐次位置情報を取得することによって、圃場の形状を取得する領域形状取得装置が開示されている。具体的には、特許文献１に記載の領域形状取得装置は、まず、作業車両を周回走行させた際の作業車両の位置情報に基づいて、周回アンテナ経路（衛星測位システムのアンテナの移動経路）を特定する。次に、領域形状取得装置は、周回走行における作業車両の周回方向と、周回アンテナ経路と所定のオフセットとに基づいて、周回アンテナ経路を修正して、外周側端部経路を生成する。そして、領域形状取得装置は、外周側端部経路から作業領域の形状を取得する。 Patent Document 1 discloses a region in which the shape of the field is obtained by sequentially moving a work vehicle capable of obtaining position information by a satellite positioning system along the periphery of the field in the field and sequentially obtaining the position information. A shape acquisition device is disclosed. Specifically, the area shape acquisition device described in Patent Document 1 first makes a round antenna path (a movement path of the antenna of the satellite positioning system) based on position information of the work vehicle when the work vehicle is made to travel around. Is identified. Next, the region shape acquisition device corrects the orbital antenna path based on the circling direction of the work vehicle in the orbital traveling, the orbiting antenna path and the predetermined offset, and generates an outer peripheral side end path. Then, the area shape acquisition device acquires the shape of the work area from the outer peripheral side end path.
特開第２０１７－１２７２９１号公報Japanese Patent Laid-Open No. 2017-127291
　この発明の目的は、特定対象領域の周囲の複数のサンプリング点の位置情報に基づいて、特定対象領域を特定するための領域特定情報を新規な方法で生成することができる領域特定情報生成装置を提供することである。 An object of the present invention is to provide an area specifying information generation device capable of generating area specifying information for specifying a specific target area by a novel method based on position information of a plurality of sampling points around the specific target area. Is to provide.
　この発明による領域特定情報生成装置は、特定対象領域の周囲の複数のサンプリング点の位置情報に基づいて、前記特定対象領域を特定するための領域特定情報を生成する領域特定情報生成装置であって、前記特定対象領域内の内部の基準点から、前記各サンプリング点までの距離を演算する距離演算部と、前記特定対象領域の周囲の一方向に並ぶ順番で配置された前記複数のサンプリング点に対する、前記基準点からの距離を表すデータの複数のピークを検出するピーク検出部と、前記複数のピークに対応する位置情報に基づいて、前記領域特定情報を生成する情報生成部とを含む。 An area specifying information generating apparatus according to the present invention is an area specifying information generating apparatus that generates area specifying information for specifying the specified target area based on position information of a plurality of sampling points around the specified target area. A distance calculation unit for calculating a distance from an internal reference point in the specific target region to each sampling point, and the plurality of sampling points arranged in an order lined up in one direction around the specific target region A peak detection unit that detects a plurality of peaks of data representing the distance from the reference point, and an information generation unit that generates the region specifying information based on position information corresponding to the plurality of peaks.
　この構成では、特定対象領域の周囲の一方向に並ぶ順番で配置された複数のサンプリング点に対する、基準点からの距離を表すデータの複数のピークが検出される。そして、検出された複数のピークに対応する位置情報に基づいて、領域特定情報が生成される。したがって、この構成によれば、特定対象領域を特定するための領域特定情報を新規な方法で生成することができる領域特定情報生成装置が得られる。 In this configuration, a plurality of peaks of data representing distances from the reference point are detected with respect to a plurality of sampling points arranged in an order arranged in one direction around the specific target region. And area | region specific information is produced | generated based on the positional information corresponding to the detected several peak. Therefore, according to this configuration, it is possible to obtain an area specifying information generating apparatus capable of generating area specifying information for specifying the specifying target area by a novel method.
　この発明の一実施形態では、前記情報生成部は、前記ピーク検出部によって検出された複数のピークのうち、ピーク幅が相対的に大きいかまたはピーク高さが相対的に大きい複数のピークを、それぞれ特徴点候補として選択し、選択された複数の特徴点候補に対応する位置情報に基づいて、前記領域特定情報を生成するように構成されている。 In one embodiment of the present invention, the information generation unit includes, among the plurality of peaks detected by the peak detection unit, a plurality of peaks having a relatively large peak width or a relatively large peak height. Each region is selected as a feature point candidate, and the region specifying information is generated based on position information corresponding to the plurality of selected feature point candidates.
　この構成では、特定対象領域の周囲のうち、例えば、小刻みに方向が変化している部分のピークのように、特定対象領域を特定するための重要な特徴点となる可能性の低いピークが、特徴点候補として選択されるのを抑制することができる。 In this configuration, a peak that is unlikely to be an important feature point for specifying the specific target region, such as a peak of a portion whose direction changes in small increments, around the specific target region, Selection as a feature point candidate can be suppressed.
　この発明の一実施形態では、前記情報生成部は、前記ピーク検出部によって検出された複数のピークのうち、ピーク幅が相対的に大きいかまたはピーク高さが相対的に大きい複数のピークを、それぞれ特徴点候補として選択する選択部と、前記複数の特徴点候補によって規定される多角形の周長と、前記複数のサンプリング点によって規定される多角形の周長との差分絶対値が、所定の閾値以内である否かを判別する判別部と、前記差分絶対値が前記閾値以内であると判別されたときには、前記複数の特徴点候補に対応する位置情報を、前記領域特定情報として生成する第１情報生成部と、前記前記差分絶対値が前記閾値よりも大きいと判別されたときには、前記複数の特徴点候補のうち、少なくとも１組の隣接する２つの特徴点候補の間に、少なくとも１つの新特徴点候補を追加し、前記複数の特徴点候補および前記新特徴点候補に対応する位置情報を、前記領域特定情報として生成する第２情報生成部とを含む。 In one embodiment of the present invention, the information generation unit includes, among the plurality of peaks detected by the peak detection unit, a plurality of peaks having a relatively large peak width or a relatively large peak height. The absolute value of the difference between the selection unit for selecting each as a feature point candidate, the circumference of the polygon defined by the plurality of feature point candidates, and the circumference of the polygon defined by the plurality of sampling points is predetermined. A determination unit that determines whether or not the difference value is within the threshold value, and when the difference absolute value is determined to be within the threshold value, position information corresponding to the plurality of feature point candidates is generated as the region specifying information. When it is determined that the absolute value of the difference is larger than the threshold value between the first information generation unit and at least one set of two adjacent feature point candidates among the plurality of feature point candidates , Add at least one new feature point candidate, the position information corresponding to the plurality of feature point candidate and the new feature point candidate, and a second information generating unit that generates, as the region specifying information.
　複数の特徴点候補によって規定される多角形を注目多角形といい、複数のサンプリング点によって規定される多角形を基本多角形ということにする。この構成では、注目多角形の全周長さと基本多角形の全周長さとの差分絶対値が、所定の閾値よりも大きい場合には、選択部によって選択された複数の特徴点候補のうち、少なくとも１組の隣接する２つの特徴点候補の間に、少なくとも１つの新特徴点候補が追加される。これにより、特定対象領域の特定に適した領域特定情報を生成することが可能となる。 A polygon defined by a plurality of feature point candidates is referred to as a target polygon, and a polygon defined by a plurality of sampling points is referred to as a basic polygon. In this configuration, when the absolute difference between the perimeter of the target polygon and the perimeter of the basic polygon is larger than a predetermined threshold, among the plurality of feature point candidates selected by the selection unit, At least one new feature point candidate is added between at least one set of two adjacent feature point candidates. This makes it possible to generate area specifying information suitable for specifying the specific target area.
　この発明の一実施形態では、前記第２位置情報生成部は、隣接する２つの特徴点候補の組み合わせ毎に、前記複数のサンプリング点によって規定される多角形の輪郭線のうち、当該組み合わせに対応する２つの特徴点候補の間の区間の長さと、当該組み合わせに対応する２つの特徴点候補間の距離との乖離度を演算し、得られた乖離度が相対的に大きい組み合わせに対応する２つの特徴点候補間に、少なくとも１つの新特徴点候補を追加するように構成されている。 In one embodiment of the present invention, the second position information generation unit supports, for each combination of two adjacent feature point candidates, among the polygonal contour lines defined by the plurality of sampling points. 2 corresponding to a combination having a relatively large difference between the length of the section between the two feature point candidates and the distance between the two feature point candidates corresponding to the combination. At least one new feature point candidate is added between the two feature point candidates.
　この構成では、乖離度が相対的に大きい組み合わせに対応する２つの特徴点候補間に、少なくとも１つの新特徴点候補が追加されるので、特定対象領域の特定により適した領域特定情報を生成することが可能となる。 In this configuration, since at least one new feature point candidate is added between two feature point candidates corresponding to a combination having a relatively high degree of divergence, region specifying information more suitable for specifying the specific target region is generated. It becomes possible.
　本発明における上述の、またはさらに他の目的、特徴および効果は、添付図面を参照して次に述べる実施形態の説明により明らかにされる。 The above-described or other objects, features, and effects of the present invention will be clarified by the following description of embodiments with reference to the accompanying drawings.
図１は、この発明の一実施形態に係る領域特定情報生成装置の構成を示す模式図である。FIG. 1 is a schematic diagram showing a configuration of an area specifying information generating apparatus according to an embodiment of the present invention. 図２は、圃場の周囲の複数のサンプリング点の取得方法を取得するための模式図である。FIG. 2 is a schematic diagram for acquiring a method for acquiring a plurality of sampling points around the field. 図３は、図２の圃場に対して取得されたサンプリング点を示す模式図である。FIG. 3 is a schematic diagram showing sampling points acquired for the field of FIG. 図４は、ＰＣの機能を説明するための機能ブロック図である。FIG. 4 is a functional block diagram for explaining the functions of the PC. 図５は、領域特定情報生成プログラムが起動されたときに、ＰＣによって実行される領域特定情報生成処理の手順を示すフローチャートである。FIG. 5 is a flowchart showing the procedure of the area specifying information generating process executed by the PC when the area specifying information generating program is started. 図６は、距離演算部の動作を説明するための模式図である。FIG. 6 is a schematic diagram for explaining the operation of the distance calculation unit. 図７はピーク検出部の動作を説明するためのグラフである。FIG. 7 is a graph for explaining the operation of the peak detector. 図８は、図５のステップＳ６の処理を説明するための模式図である。FIG. 8 is a schematic diagram for explaining the process of step S6 of FIG. 図９は、図５のステップＳ７の処理を説明するための模式図である。FIG. 9 is a schematic diagram for explaining the process of step S7 of FIG. 図１０は、図５のステップＳ７の処理を説明するための模式図である。FIG. 10 is a schematic diagram for explaining the process of step S7 of FIG. 図１１Ａは、領域特定情報生成プログラムが起動されたときに、ＰＣによって実行される領域特定情報生成処理の他の例の手順の一部を示すフローチャートである。FIG. 11A is a flowchart illustrating a part of a procedure of another example of the area specifying information generating process executed by the PC when the area specifying information generating program is activated. 図１１Ｂは、領域特定情報生成プログラムが起動されたときに、ＰＣによって実行される領域特定情報生成処理の他の例の手順の一部を示すフローチャートである。FIG. 11B is a flowchart illustrating a part of a procedure of another example of the area specifying information generating process executed by the PC when the area specifying information generating program is activated.
　図１は、この発明の一実施形態に係る領域特定情報生成装置１の構成を示す模式図である。 FIG. 1 is a schematic diagram showing a configuration of an area specifying information generating apparatus 1 according to an embodiment of the present invention.
　この実施形態では、領域特定情報生成装置１は、例えばサトウキビを栽培する圃場を特定するための情報を生成するものとする。つまり、この実施形態では、領域を特定しようとする特定対象領域は、圃場である。 In this embodiment, the region specifying information generating device 1 generates information for specifying, for example, a field where sugarcane is cultivated. That is, in this embodiment, the specific target region for which the region is to be specified is a farm field.
　領域特定情報生成装置１は、パーソナルコンピュータ（ＰＣ）１０によって実現される。ＰＣ１０には、ディスプレイ２１、マウス２２およびキーボード２３が接続されている。ＰＣ１０は、ＣＰＵ１１、メモリ１２、ハードディスク１３等を含む。また、ＰＣ１０には、図示しないが、ＵＳＢ（Universal Serial Bus）ポートが設けられている。 The area specifying information generating apparatus 1 is realized by a personal computer (PC) 10. A display 21, a mouse 22 and a keyboard 23 are connected to the PC 10. The PC 10 includes a CPU 11, a memory 12, a hard disk 13, and the like. Further, the PC 10 is provided with a USB (Universal Serial Bus) port (not shown).
　ハードディスク１３には、ＯＳ（オペレーションシステム）等の他、領域特定情報生成プログラムが格納されている。領域特定情報生成プログラムは、例えば、領域特定情報生成プログラムが格納されたＵＳＢメモリ等の記憶媒体から取得したり、領域特定情報生成プログラムを提供しているウエブサイトからインタネット経由で取得したりすることが可能である。 The hard disk 13 stores an area identification information generation program in addition to an OS (operation system) and the like. The area specifying information generation program is acquired from, for example, a storage medium such as a USB memory in which the area specifying information generation program is stored, or acquired from the website providing the area specifying information generation program via the Internet. Is possible.
　また、ハードディスク１３には、領域を特定しようとする圃場（特定対象領域）の周囲の複数のサンプリング点の位置情報が記憶されているものとする。圃場の周囲の複数のサンプリング点は、圃場の輪郭上の複数の点（輪郭構成点）に相当する。また、ハードディスク１３には、圃場を特定するための特徴点の総数の最大値（特徴点数最大値）Ｍが記憶されている。特徴点数最大値Ｍは、ユーザによって設定されてハードディスク１３に記憶される。特徴点数最大値Ｍは、設定変更可能である。 Further, it is assumed that the hard disk 13 stores position information of a plurality of sampling points around a field (specific target region) to be specified. A plurality of sampling points around the field corresponds to a plurality of points (contour constituent points) on the contour of the field. Further, the hard disk 13 stores a maximum value (the maximum value of the number of feature points) M of the total number of feature points for specifying the field. The feature point maximum value M is set by the user and stored in the hard disk 13. The feature point maximum value M can be changed.
　図２は、圃場の周囲の複数のサンプリング点の取得方法を取得するための模式図である。図３は、図２の圃場に対して取得されたサンプリング点を示す模式図である。 FIG. 2 is a schematic diagram for acquiring a method for acquiring a plurality of sampling points around the field. FIG. 3 is a schematic diagram showing sampling points acquired for the field of FIG.
　図２および図３を参照して、圃場の周囲の複数のサンプリング点の取得方法について説明する。 Referring to FIGS. 2 and 3, a method for acquiring a plurality of sampling points around the field will be described.
　圃場５１の周囲の複数のサンプリング点は、例えば、測位衛星を利用して自己位置を測定する位置測定器２を用いて取得することができる。具体的には、例えば、ユーザ（測定者）は、位置測定器２を携帯し、位置測定を開始させるための測定開始指令を位置測定器２に入力した後、図２に破線５２で示すように、圃場５１の周囲に沿って走行する。図２では、圃場５１の輪郭線（周囲）に対して破線５２を識別可能に表示するために、破線５２は圃場５１の輪郭線上から離れて描かれているが、実際には、ユーザは圃場５１の輪郭線の真上にできるだけ近い位置を歩行する。 A plurality of sampling points around the field 51 can be acquired using, for example, the position measuring device 2 that measures the self position using a positioning satellite. Specifically, for example, the user (measurer) carries the position measuring device 2 and inputs a measurement start command for starting the position measurement to the position measuring device 2, and then, as shown by a broken line 52 in FIG. Next, it travels along the periphery of the field 51. In FIG. 2, the broken line 52 is drawn away from the contour line of the farm field 51 in order to display the broken line 52 so as to be identifiable with respect to the contour line (surrounding) of the farm field 51. Walk as close as possible to the 51 contour line.
　測定開始指令が入力されると、位置測定器２は、例えば所定時間毎に自己位置を測定して自己内のメモリ（例えば、不揮発性メモリ）に記憶する。そして、圃場５１の周囲を一周すると、ユーザは測定を終了して位置情報を保存させるための測定終了指令を位置測定器２に入力する。測定終了指令が入力されると、位置測定器２は、測定開始指令から測定終了指令までにメモリに記憶された位置情報を、今回の測定結果データとしてメモリに保存する。 When a measurement start command is input, the position measuring device 2 measures its own position, for example, every predetermined time and stores it in its own memory (for example, a non-volatile memory). When the user goes around the field 51, the user inputs a measurement end command to the position measuring device 2 to end the measurement and store the position information. When the measurement end command is input, the position measuring device 2 stores the position information stored in the memory from the measurement start command to the measurement end command in the memory as current measurement result data.
　これにより、図３に示すように、圃場５１の周囲の複数のサンプリング点Ｓ１，Ｓ２，Ｓ３，…ＳＮ－１，ＳＮの位置情報からなる時系列データが、位置測定器２内のメモリに保存される。各サンプリング点Ｓの添字１～Ｎは、当該サンプリング点が取得された順番を示す数字であり、この実施形態では、当該サンプリング点を識別するための識別子（以下、サンプリング番号という」として用いられる。以下において、全てのサンプリング点を総称するときには、サンプリング点Ｓという場合がある。 Thus, as shown in FIG. 3, a plurality of sampling points S 1 around the field 51, S 2, S 3, ... S N-1, time-series data consisting of the position information of S N is the position measuring device 2 Saved in the memory. The subscripts 1 to N of each sampling point S are numbers indicating the order in which the sampling points are acquired. In this embodiment, they are used as identifiers (hereinafter referred to as sampling numbers) for identifying the sampling points. Hereinafter, when all sampling points are collectively referred to, they may be referred to as sampling points S.
　位置情報は、例えば、緯度経度情報と高度情報と時刻情報とからなる。この実施形態では、説明の便宜上、位置情報は、緯度経度情報と時刻情報とからなるものとする。 The location information includes, for example, latitude / longitude information, altitude information, and time information. In this embodiment, for convenience of explanation, it is assumed that the position information includes latitude / longitude information and time information.
　ユーザは、例えば、ＰＣ１０のＵＳＢポートに位置測定器２を接続し、ＰＣ１０を操作することにより、位置測定器２内のメモリに保存されている圃場５１に対する時系列データをハードディスク１３に格納する。以下、ハードディスク１３に格納された圃場５１に対する時系列データに基づいて、圃場５１を特定するための領域特定情報を生成する場合のＰＣ１０の動作について、説明する。 For example, the user connects the position measuring device 2 to the USB port of the PC 10 and operates the PC 10 to store the time series data for the field 51 stored in the memory in the position measuring device 2 in the hard disk 13. Hereinafter, the operation of the PC 10 when generating region specifying information for specifying the field 51 based on the time series data for the field 51 stored in the hard disk 13 will be described.
　図４は、ＰＣ１０の機能を説明するための機能ブロック図である。 FIG. 4 is a functional block diagram for explaining the functions of the PC 10.
　ＰＣ１０は、領域特定情報生成プログラムを実行することによって、複数の機能処理部として機能する。この機能処理部には、距離演算部３１と、ピーク検出部３２と、情報生成部３３とが含まれる。 The PC 10 functions as a plurality of function processing units by executing the area specifying information generation program. This function processing unit includes a distance calculation unit 31, a peak detection unit 32, and an information generation unit 33.
　距離演算部３１は、ハードディスク１３に格納されている圃場５１に対する時系列データに基づいて、圃場５１内の内部の基準点から、圃場５１の周囲の複数のサンプリング点Ｓまでの距離を演算する。 The distance calculation unit 31 calculates distances from a reference point inside the field 51 to a plurality of sampling points S around the field 51 based on time-series data for the field 51 stored in the hard disk 13.
　ピーク検出部３２は、圃場５１の周囲の一方向に並ぶ順番で配置された複数のサンプリング点Ｓに対する、基準点からの距離を表すデータの複数のピークを検出する。 The peak detection unit 32 detects a plurality of peaks of data representing the distance from the reference point with respect to the plurality of sampling points S arranged in the order of being arranged in one direction around the field 51.
　情報生成部３３は、ピーク検出部３２によって検出された複数のピークに対応する位置情報に基づいて、圃場５１を特定するための領域特定情報を生成する。情報生成部３３は、選択部４１と、判別部４２と、第１情報生成部４３と、第２情報生成部４４とを含む。 The information generating unit 33 generates region specifying information for specifying the farm field 51 based on the position information corresponding to the plurality of peaks detected by the peak detecting unit 32. The information generation unit 33 includes a selection unit 41, a determination unit 42, a first information generation unit 43, and a second information generation unit 44.
　以下、距離演算部３１、ピーク検出部３２および情報生成部３３内の各部４１，４２，４３，４４の動作の詳細について説明する。 Hereinafter, details of operations of the units 41, 42, 43, and 44 in the distance calculation unit 31, the peak detection unit 32, and the information generation unit 33 will be described.
　図５は、領域特定情報生成プログラムが起動されたときに、ＰＣ１０によって実行される領域特定情報生成処理の手順を示すフローチャートである。 FIG. 5 is a flowchart showing the procedure of the area identification information generation process executed by the PC 10 when the area identification information generation program is started.
　領域特定情報生成プログラムが起動されると、距離演算部３１は、図６に示すように、圃場５１内の内部の基準点Ｑから、各サンプリング点Ｓまでの距離を演算する（ステップＳ１）。基準点Ｑは、圃場５１の重心位置に設定される。圃場５１の重心位置は、例えば、図形の輪郭構成点から当該図形の重心を求める公知方法と同様な方法によって、サンプリング点Ｓの位置情報から算出することができる。なお、基準点Ｑは、圃場５１内の内部であれば、圃場５１の重心位置以外の点に設定してもよい。 When the region specifying information generation program is activated, the distance calculation unit 31 calculates the distance from the internal reference point Q in the field 51 to each sampling point S as shown in FIG. 6 (step S1). The reference point Q is set at the barycentric position of the field 51. The position of the center of gravity of the field 51 can be calculated from the position information of the sampling point S by a method similar to a known method for obtaining the center of gravity of the figure from the contour constituent points of the figure, for example. Note that the reference point Q may be set to a point other than the gravity center position of the field 51 as long as it is inside the field 51.
　次に、ピーク検出部３２は、まず、圃場５１の周囲の一方向に並ぶ順番で配置された複数のサンプリング点Ｓに対する、基準点Ｑからの距離を表すグラフ（折れ線グラフ）を作成する（ステップＳ２）。具体的には、ピーク検出部３２は、複数のサンプリング点Ｓのサンプリング番号（識別子）を横軸とし、サンプリング点Ｓの基準点Ｑからの距離を縦軸とする座標系に、複数のサンプリング点Ｓに対応する距離をプロットし、プロットされた点を結ぶことによってグラフ（折れ線グラフ）を作成する。横軸には、複数のサンプリング点Ｓのサンプリング番号が、位置情報が取得された順番で配置される。複数のサンプリング点Ｓのサンプリング番号を、位置情報が取得された順とは反対の順番に横軸に配置してもよい。 Next, the peak detection unit 32 first creates a graph (line graph) representing the distance from the reference point Q with respect to a plurality of sampling points S arranged in an order lined up in one direction around the field 51 (step). S2). Specifically, the peak detection unit 32 has a plurality of sampling points in a coordinate system in which the horizontal axis represents sampling numbers (identifiers) of the plurality of sampling points S and the vertical axis represents the distance from the reference point Q of the sampling points S. A graph (line graph) is created by plotting the distance corresponding to S and connecting the plotted points. On the horizontal axis, sampling numbers of a plurality of sampling points S are arranged in the order in which the position information is acquired. The sampling numbers of the plurality of sampling points S may be arranged on the horizontal axis in the order opposite to the order in which the position information is acquired.
　ステップＳ２で作成されるグラフの一例を図７にＵ１で示す。ただし、図７は、図６に示される基準点Ｑから各サンプリング点Ｓまでの距離に基づいて作成されたグラフではない。したがって、図７と図６との間に相関性はない。 An example of the graph created in step S2 is indicated by U1 in FIG. However, FIG. 7 is not a graph created based on the distance from the reference point Q to each sampling point S shown in FIG. Therefore, there is no correlation between FIG. 7 and FIG.
　次に、ピーク検出部３２は、ステップＳ２で作成されたグラフの平均値を求め、グラフのうち平均値以下の部分を、当該平均値を中心として上下に折り返すことによってピーク検出用グラフを作成する（ステップＳ３）。 Next, the peak detection unit 32 obtains an average value of the graph created in step S2, and creates a peak detection graph by folding a portion below the average value of the graph up and down around the average value. (Step S3).
　ステップＳ２で作成されたグラフが図７のＵ１である場合には、ピーク検出用グラフは図７のＵ２となる。図７には、グラフＵ１の平均値（折り返し位置）を一点鎖線で示している。ただし、このピーク検出用グラフＵ２は、図７のグラフＵ１のうち、その平均値以下の部分を、当該平均値を中心として上下に折り返した後、折り返し後のグラフを、－Ｙ方向に前記平均値分だけシフトさせることにより作成されている。 When the graph created in step S2 is U1 in FIG. 7, the peak detection graph is U2 in FIG. In FIG. 7, the average value (folding position) of the graph U1 is indicated by a one-dot chain line. However, this peak detection graph U2 is obtained by folding a portion below the average value of the graph U1 in FIG. 7 up and down centering on the average value, and then turning the graph back to the average in the −Y direction. It is created by shifting by the value.
　次に、ピーク検出部３２は、ステップＳ３で得られたピーク検出用グラフの極大値の位置を、ステップＳ２で作成されたグラフのピークとして検出する（ステップＳ４）。なお、この際、頂上付近の斜度が所定値以下のなだらかな山の頂点が極大値として検出されないように極大値検出のためのパラメータが調整される。 Next, the peak detector 32 detects the position of the maximum value of the peak detection graph obtained in step S3 as the peak of the graph created in step S2 (step S4). At this time, the parameter for detecting the maximum value is adjusted so that the peak of a smooth mountain whose slope near the top is not more than a predetermined value is not detected as the maximum value.
　次に、情報生成部３３内の選択部４１は、ステップＳ５の処理を実行する。つまり、選択部４１は、まず、ピーク検出部３２によって検出された複数のピークから、ピーク幅が相対的に大きいかまたはピーク高さが相対的に大きい複数のピークを特徴点候補ピークとして選択する。そして、選択部４１は、複数の特徴点候補ピークに対応する複数のサンプリング点を特徴点候補として、メモリ１２に記憶する。これにより、圃場５１の輪郭線のうち、例えば、小刻みに方向が変化している部分のように、圃場５１を特定するための重要な特徴点となる可能性の低いピークが、特徴点候補ピークとして選択されるのを抑制することができる。 Next, the selection unit 41 in the information generation unit 33 executes the process of step S5. That is, the selection unit 41 first selects, from the plurality of peaks detected by the peak detection unit 32, a plurality of peaks having a relatively large peak width or a relatively large peak height as feature point candidate peaks. . Then, the selection unit 41 stores a plurality of sampling points corresponding to the plurality of feature point candidate peaks in the memory 12 as feature point candidates. As a result, a peak that is unlikely to be an important feature point for specifying the farm field 51, such as a portion whose direction changes in small increments, in the contour line of the farm field 51 is a feature point candidate peak. Can be suppressed.
　具体的には、選択部４１は、例えば、ピーク検出部３２によって検出された複数のピークの半値幅を算出し、半値幅の大きい上位所定数のピークに対応するサンプリング点を特徴点候補として選択する。所定数は、例えば５に設定される。 Specifically, the selection unit 41 calculates, for example, the half-value widths of a plurality of peaks detected by the peak detection unit 32, and selects sampling points corresponding to the upper predetermined number of peaks having a large half-value width as feature point candidates. To do. The predetermined number is set to 5, for example.
　選択部４１は、例えば、ピーク検出部３２によって検出された複数のピークのプロミネンスを算出し、プロミネンスの大きい上位所定数のピークに対応するサンプリング点を特徴点候補として選択してもよい。所定数は、例えば５に設定される。 The selection unit 41 may calculate, for example, prominences of a plurality of peaks detected by the peak detection unit 32, and select sampling points corresponding to the upper predetermined number of peaks with large prominences as feature point candidates. The predetermined number is set to 5, for example.
　次に、情報生成部３３内の判別部４２は、メモリ１２に記憶されている複数の特徴点候補によって規定される多角形の周長Ｌ１と、オリジナルの複数のサンプリング点Ｓによって規定される多角形の周長Ｌ２との差分絶対値｜Ｌ２－Ｌ１｜が、閾値α以内である否かを判別する（ステップＳ６）。 Next, the determination unit 42 in the information generation unit 33 includes a polygonal perimeter L1 defined by a plurality of feature point candidates stored in the memory 12 and a multiplicity defined by a plurality of original sampling points S. It is determined whether or not the difference absolute value | L2-L1 | with respect to the square circumference L2 is within the threshold value α (step S6).
　以下において、メモリ１２に記憶されている複数の特徴点候補によって規定される多角形を「注目多角形」といい、オリジナルの複数のサンプリング点Ｓによって規定される多角形を「基本多角形」という場合がある。 Hereinafter, a polygon defined by a plurality of feature point candidates stored in the memory 12 is referred to as a “target polygon”, and a polygon defined by a plurality of original sampling points S is referred to as a “basic polygon”. There is a case.
　ステップＳ６の処理を、より具体的に説明する。圃場５１の形状および圃場５１から取得されたオリジナルの複数のサンプリング点Ｓが、図８に示すような形状であり、メモリ１２に記憶されている複数の特徴点候補が、図８にＡ～Ｅで示される５つの点であるとする。選択部４１は、注目多角形（図８の例では、複数の特徴点候補Ａ～Ｅによって規定される多角形）の周長を第１周長Ｌ１として算出する。また、選択部４１は、基本多角形の周長を第２周長Ｌ２として算出する。そして、選択部４１は、第１周長Ｌ１と第２周長Ｌ２の差の絶対値｜Ｌ２－Ｌ１｜が、閾値α以内である否かを判別する。 The process of step S6 will be described more specifically. The shape of the field 51 and a plurality of original sampling points S acquired from the field 51 have a shape as shown in FIG. 8, and a plurality of feature point candidates stored in the memory 12 are shown in FIG. Are five points. The selection unit 41 calculates the circumference of the target polygon (in the example of FIG. 8, the polygon defined by the plurality of feature point candidates A to E) as the first circumference L1. The selection unit 41 calculates the circumference of the basic polygon as the second circumference L2. Then, the selection unit 41 determines whether or not the absolute value | L2−L1 | of the difference between the first circumference L1 and the second circumference L2 is within the threshold value α.
　ステップＳ６において、絶対値｜Ｌ２－Ｌ１｜が閾値α以内であると判別された場合には（ステップＳ６：ＹＥＳ）、情報生成部３３内の第１情報生成部４３は、ステップＳ９の処理を実行する。つまり、第１情報生成部４３は、メモリ１２に記憶されている複数の特徴点候補に対応する位置情報を、圃場５１の最終的な特徴点情報（領域特定情報）としてハードディスク１３に格納する。そして、情報生成部３３は、今回の処理を終了する。 If it is determined in step S6 that the absolute value | L2-L1 | is within the threshold α (step S6: YES), the first information generation unit 43 in the information generation unit 33 performs the process of step S9. Execute. That is, the first information generation unit 43 stores the position information corresponding to the plurality of feature point candidates stored in the memory 12 in the hard disk 13 as final feature point information (region specifying information) of the field 51. And the information generation part 33 complete | finishes this process.
　ステップＳ６において、絶対値｜Ｌ２－Ｌ１｜が第１閾値αよりも大きいと判別された場合には（ステップＳ６：ＮＯ）、情報生成部３３内の第２情報生成部４４は、特徴点候補を追加するための候補追加処理を行う（ステップＳ７）。候補追加処理について説明する。 If it is determined in step S6 that the absolute value | L2-L1 | is greater than the first threshold value α (step S6: NO), the second information generation unit 44 in the information generation unit 33 determines the feature point candidate. A candidate addition process is performed for adding (step S7). The candidate addition process will be described.
　第２情報生成部４４は、まず、注目多角形の辺毎に、当該辺の長さと、基本多角形の輪郭線の当該辺に対応する区間の長さとの差分の絶対値を算出する。注目多角形の任意の一辺に対応する基本多角形の輪郭線の区間とは、基本多角形の輪郭線における当該一辺の両端の点に挟まれた２つの区間のうち、その区間の中間に特徴点候補が設定されていない方の区間をいう。図８の例では、例えば、注目多角形における両端がＡ，Ｂである辺ＡＢに対応する基本多角形の輪郭線の区間は、基本多角形の輪郭線上のＡとＢに挟まれた２つの区間のうち、その区間の中間に特徴点候補が設定されていない方の区間Ｒａｂとなる。 First, the second information generation unit 44 calculates, for each side of the target polygon, the absolute value of the difference between the length of the side and the length of the section corresponding to the side of the outline of the basic polygon. The section of the outline of the basic polygon corresponding to any one side of the target polygon is characterized by the middle of the two sections sandwiched between the points on both sides of the outline of the basic polygon The section where no point candidate is set. In the example of FIG. 8, for example, the section of the basic polygon outline corresponding to the side AB having both ends A and B in the target polygon is divided between two A and B on the outline of the basic polygon. Among the sections, the section Rab is a section in which no feature point candidate is set in the middle of the section.
　次に、第２情報生成部４４は、基本多角形の輪郭線において、差分絶対値が最も大きな区間の中点（区間の中央位置）に新たな特徴点候補を追加配置する。新たな特徴点候補は、オリジナルのサンプリング点Ｓとは異なる点であってもよいし、前記区間の中点に最も近いオリジナルサンプリング点Ｓであってもよい。前者の場合には、新たな特徴点候補の位置情報は、例えば、新たな特徴点候補の両隣にある２つのサンプリング点Ｓの位置情報に基づいて特定される。 Next, the second information generation unit 44 additionally arranges a new feature point candidate at the midpoint (center position of the section) having the largest absolute difference value in the basic polygon outline. The new feature point candidate may be a point different from the original sampling point S, or may be the original sampling point S closest to the midpoint of the section. In the former case, the position information of the new feature point candidate is specified based on, for example, the position information of the two sampling points S on both sides of the new feature point candidate.
　そして、第２情報生成部４４は、差分絶対値が最も大きな区間の中点に追加配置された新たな特徴点候補を、メモリ１２に記憶されている特徴点候補に加える。これにより、メモリ１２内の特徴点候補が更新される。 Then, the second information generation unit 44 adds the new feature point candidate additionally arranged at the midpoint of the section having the largest difference absolute value to the feature point candidates stored in the memory 12. Thereby, the feature point candidates in the memory 12 are updated.
　図８の例では、注目多角形の各辺に対応する差分絶対値のうち、辺ＡＢに対応する差分絶対値か最も大きくなるので、辺ＡＢに対応する基本多角形の輪郭の区間Ｓａｂの中点に新たな特徴点候補Ｆが追加される。これにより、図９に示すように、注目多角形の形状が変化する。 In the example of FIG. 8, the absolute difference value corresponding to the side AB among the absolute difference values corresponding to each side of the target polygon is the largest, and therefore, in the section Sab of the outline of the basic polygon corresponding to the side AB. A new feature point candidate F is added to the point. Thereby, as shown in FIG. 9, the shape of the attention polygon changes.
　次に、第２情報生成部４４は、メモリ１２内の特徴点候補の総数Ｔが特徴点数最大値Ｍに達したか否かを判別する（ステップＳ８）。特徴点数最大値は、例えば１５に設定される。 Next, the second information generating unit 44 determines whether or not the total number T of feature point candidates in the memory 12 has reached the maximum number M of feature points (step S8). The maximum number of feature points is set to 15, for example.
　メモリ１２内の特徴点候補の総数Ｔが特徴点数最大値Ｍに達していなければ（ステップＳ８：ＮＯ）、第２情報生成部４４は、ステップＳ７に戻る。そして、ステップＳ７の処理が再度行われる。なお、二回目のステップＳ７では、例えば、図９に示すように、辺ＢＣに対応する基本多角形の輪郭の区間Ｒｂｃの中点に新たな特徴点候補Ｇが追加される。これにより、図１０に示すように、注目多角形の形状が変化する。 If the total number T of feature point candidates in the memory 12 has not reached the maximum number M of feature points (step S8: NO), the second information generation unit 44 returns to step S7. And the process of step S7 is performed again. In the second step S7, for example, as shown in FIG. 9, a new feature point candidate G is added to the midpoint of the section Rbc of the outline of the basic polygon corresponding to the side BC. Thereby, as shown in FIG. 10, the shape of the target polygon changes.
　ステップＳ８において、メモリ１２内の特徴点候補の総数Ｔが特徴点数最大値Ｍに達していると判別された場合には（ステップＳ８：ＹＥＳ）、第２情報生成部４４は、ステップＳ９に移行する。ステップＳ９では、第２情報生成部４４は、メモリ１２に記憶されている複数の特徴点候補に対応する位置情報を、領域特定対象の圃場の最終的な特徴点情報（領域特定情報）としてハードディスク１３に格納する。そして、情報生成部３３は、今回の処理を終了する。 When it is determined in step S8 that the total number T of feature point candidates in the memory 12 has reached the maximum number M of feature points (step S8: YES), the second information generation unit 44 proceeds to step S9. To do. In step S9, the second information generation unit 44 uses the position information corresponding to the plurality of feature point candidates stored in the memory 12 as the final feature point information (region specifying information) of the region specifying target field. 13. And the information generation part 33 complete | finishes this process.
　前述の実施形態では、圃場５１を特定するための情報（領域特定情報）を新規な方法で生成することができる。また、前述の実施形態では、圃場５１の形状を特定するのに重要な特徴点を、圃場５１を特定するための領域特定情報として生成することができる。 In the above-described embodiment, information (region specifying information) for specifying the field 51 can be generated by a novel method. In the above-described embodiment, feature points important for specifying the shape of the farm field 51 can be generated as area specifying information for specifying the farm field 51.
　図１１Ａおよび１１Ｂは、領域特定情報生成プログラムが起動されたときに、ＰＣ１０によって実行される領域特定情報生成処理の他の例を示すフローチャートである。 FIGS. 11A and 11B are flowcharts showing another example of the area specifying information generating process executed by the PC 10 when the area specifying information generating program is started.
　ステップＳ１からＳ４までの処理は、図５のステップＳ１からＳ４までの処理と同様なのでその説明を省略する。 Since the processing from step S1 to S4 is the same as the processing from step S1 to S4 in FIG.
　ステップＳ４の処理が終了すると、情報生成部３３は、ステップＳ５Ａに移行する。ステップＳ５Ａでは、情報生成部３３は、まず、図５のステップＳ５と同様に、ピーク検出部３２によって検出された複数のピークから、ピーク幅が相対的に大きいかまたはピーク高さが相対的に大きい複数のピークを特徴点候補ピークとして選択する。そして、情報生成部３３は、複数の特徴点候補ピークに対応する複数のサンプリング点を初期特徴点候補として、メモリ１２内の初期候補記憶エリアに記憶する。この点が、図５のステップＳ５と異なる。 When the process of step S4 is completed, the information generating unit 33 proceeds to step S5A. In step S5A, the information generation unit 33 first has a relatively large peak width or a relatively high peak height from the plurality of peaks detected by the peak detection unit 32, as in step S5 of FIG. A plurality of large peaks are selected as feature point candidate peaks. Then, the information generation unit 33 stores a plurality of sampling points corresponding to the plurality of feature point candidate peaks as initial feature point candidates in the initial candidate storage area in the memory 12. This point is different from step S5 in FIG.
　次に、情報生成部３３は、メモリ１２内の初期候補記憶エリアに記憶されている複数の初期特徴点候補によって規定される多角形の周長Ｌ１と、オリジナルの複数のサンプリング点Ｓによって規定される多角形の周長Ｌ２との差分絶対値｜Ｌ２－Ｌ１｜が、閾値α以内である否かを判別する（ステップＳ６Ａ）。 Next, the information generation unit 33 is defined by the polygonal perimeter L1 defined by the plurality of initial feature point candidates stored in the initial candidate storage area in the memory 12 and the plurality of original sampling points S. Whether or not the difference absolute value | L2−L1 | with respect to the circumference L2 of the polygon is within the threshold α is determined (step S6A).
　以下において、メモリ１２内の初期候補記憶エリアに記憶されている複数の初期特徴点候補によって規定される多角形を「初期多角形」といい、オリジナルの複数のサンプリング点Ｓによって規定される多角形を「基本多角形」という場合がある。 Hereinafter, a polygon defined by a plurality of initial feature point candidates stored in the initial candidate storage area in the memory 12 is referred to as an “initial polygon”, and a polygon defined by a plurality of original sampling points S. May be referred to as a “basic polygon”.
　ステップＳ６において、絶対値｜Ｌ２－Ｌ１｜が閾値α以内であると判別された場合には（ステップＳ６Ａ：ＹＥＳ）、情報生成部３３は、ステップＳ７Ａの処理を実行する。つまり、情報生成部３３は、メモリ１２内の初期候補記憶エリアに記憶されている複数の初期特徴点候補に対応する位置情報を、圃場５１の最終的な特徴点情報（領域特定情報）としてハードディスク１３に格納する。そして、情報生成部３３は、今回の処理を終了する。 If it is determined in step S6 that the absolute value | L2-L1 | is within the threshold value α (step S6A: YES), the information generation unit 33 executes the process of step S7A. That is, the information generating unit 33 uses the position information corresponding to the plurality of initial feature point candidates stored in the initial candidate storage area in the memory 12 as the final feature point information (region specifying information) of the field 51 as a hard disk. 13. And the information generation part 33 complete | finishes this process.
　ステップＳ６Ａにおいて、絶対値｜Ｌ２－Ｌ１｜が第１閾値αよりも大きいと判別された場合には（ステップＳ６Ａ：ＮＯ）、情報生成部３３は、ソフトカウンタのカウント値Ｋを１に設定する（ステップＳ８Ａ）。 If it is determined in step S6A that the absolute value | L2-L1 | is greater than the first threshold value α (step S6A: NO), the information generating unit 33 sets the count value K of the soft counter to 1 (Step S8A).
　次に、情報生成部３３は、基本多角形の輪郭線上に、メモリ１２の初期候補記憶エリアに記憶されている初期特徴点候補とは異なる新特徴点候補をランダムに追加配置する（ステップＳ９Ａ）。追加配置される新特徴点候補の数は、特徴点数最大値Ｍから、ステップＳ５Ａで抽出された初期特徴点候補の総数を差し引いた数に設定される。 Next, the information generation unit 33 randomly arranges new feature point candidates different from the initial feature point candidates stored in the initial candidate storage area of the memory 12 on the outline of the basic polygon (step S9A). . The number of new feature point candidates to be additionally arranged is set to a number obtained by subtracting the total number of initial feature point candidates extracted in step S5A from the maximum value M of feature points.
　この際、情報生成部３３は、初期多角形の辺毎に、当該辺の長さと、基本多角形の輪郭線の当該辺に対応する区間の長さとの差分の絶対値を乖離度として算出し、乖離度に応じて、新たな特徴点候補を追加配置する区間および分配数を決定してもよい。具体的には、乖離度が大きい区間ほど優先的に新特徴点候補が配置されることが好ましく、離度が大きい区間ほど新特徴点候補が多く配置されることが好ましい。 At this time, the information generation unit 33 calculates, for each side of the initial polygon, the absolute value of the difference between the length of the side and the length of the section corresponding to the side of the outline of the basic polygon as the degree of divergence. Depending on the degree of divergence, a section in which new feature point candidates are additionally arranged and the number of distributions may be determined. Specifically, it is preferable that the new feature point candidates are preferentially arranged in a section with a large degree of deviation, and it is preferable that many new feature point candidates are arranged in a section with a large degree of separation.
　次に、情報生成部３３は、初期特徴点候補と今回追加された新特徴点候補とからなる候補セットを、メモリ１２内の所定の候補セット記憶エリアに記憶する（ステップＳ１０Ａ）。候補セット記憶エリアは、後述する所定値Ｎ以上設けられており、ステップＳ９Ａの処理が行われる毎に、それまでのステップＳ１０Ａの処理において、特徴点候補セットが記憶されていない候補セット記憶エリアに、今回の特徴点候補セットが記憶される。 Next, the information generation unit 33 stores a candidate set including the initial feature point candidate and the new feature point candidate added this time in a predetermined candidate set storage area in the memory 12 (step S10A). The candidate set storage area is provided with a predetermined value N or more to be described later, and every time the process of step S9A is performed, the candidate point storage area in which no feature point candidate set is stored in the process of step S10A is performed. The current feature point candidate set is stored.
　次に、情報生成部３３は、ステップＳ１０Ａで所定の候補セット記憶エリアに記憶された候補セットによって規定される多角形の周長Ｌ３と、基本多角形の周長Ｌ２との差分絶対値｜Ｌ２－Ｌ３｜を乖離度γとして算出し、算出された乖離度γを当該候補セットに関連付けて記憶する（ステップＳ１１Ａ）。 Next, the information generation unit 33 calculates the absolute difference | L2 between the polygonal perimeter L3 defined by the candidate set stored in the predetermined candidate set storage area in step S10A and the perimeter L2 of the basic polygon. -L3 | is calculated as the deviation degree γ, and the calculated deviation degree γ is stored in association with the candidate set (step S11A).
　次に、情報生成部３３は、カウント値Ｋが所定値Ｎに達したか否かを判別する（ステップＳ１２Ａ）。所定値Ｎは、任意の数に設定することができる。 Next, the information generation unit 33 determines whether or not the count value K has reached a predetermined value N (step S12A). The predetermined value N can be set to an arbitrary number.
　カウント値Ｋが所定値Ｎ未満であれば（ステップＳ１２Ａ：ＮＯ）、情報生成部３３は、カウント値Ｋを１だけインクリメントする（ステップＳ１３Ａ）。そして、情報生成部３３は、ステップＳ９Ａに戻る。これにより、ステップＳ９Ａ以降の処理が再度実行される。 If the count value K is less than the predetermined value N (step S12A: NO), the information generating unit 33 increments the count value K by 1 (step S13A). Then, the information generation unit 33 returns to step S9A. Thereby, the process after step S9A is performed again.
　ステップＳ９Ａ～ステップＳ１１Ａの処理がＮ回行われると、ステップＳ１２Ａで肯定判定となるので、情報生成部３３は、ステップ１４Ａに移行する。ステップ１４Ａでは、情報生成部３３は、まず、メモリ１２内の候補セット記憶エリア別に記憶されている候補セットのうち、乖離度γが最も小さい候補セットを選択する。そして、情報生成部３３は、選択された候補セットに含まれる複数の特徴点候補の位置情報を、最終的な特徴点情報（領域特定情報）として、ハードディスク１３に格納する。そして、情報生成部３３は、今回の処理を終了する。 If the processing from step S9A to step S11A is performed N times, an affirmative determination is made in step S12A, so the information generating unit 33 proceeds to step 14A. In step 14A, the information generation unit 33 first selects a candidate set having the smallest divergence γ from the candidate sets stored for each candidate set storage area in the memory 12. Then, the information generation unit 33 stores the position information of the plurality of feature point candidates included in the selected candidate set on the hard disk 13 as final feature point information (region specifying information). And the information generation part 33 complete | finishes this process.
　図１１Ａおよび１１Ｂに示される領域特定情報生成処理の変形例においても、圃場５１を特定するための情報（領域特定情報）を新規な方法で生成することができる。また、この変形例でも、圃場５１の形状を特定するのに重要な特徴点を、圃場５１を特定するための領域特定情報として生成することができる。 11A and 11B also in the modification example of the region specifying information generation process, information (region specifying information) for specifying the field 51 can be generated by a novel method. Also in this modified example, feature points important for specifying the shape of the farm field 51 can be generated as region specifying information for specifying the farm field 51.
　以上、この発明の実施形態について説明したが、この発明はさらに他の形態で実施することもできる。例えば、図５のステップＳ５によって選択された特徴点候補の位置情報を、常に、最終的な特徴点情報（領域特定情報）として生成するようにしてもよい。この場合には、図５のステップＳ６～Ｓ８の処理は省略される。 As mentioned above, although embodiment of this invention was described, this invention can also be implemented with another form. For example, the position information of the feature point candidate selected in step S5 of FIG. 5 may always be generated as final feature point information (region specifying information). In this case, the processes in steps S6 to S8 in FIG. 5 are omitted.
　また、前述の実施形態では、位置測定器２を携帯した測定者が、圃場５１の周囲に沿って走行することによって、圃場５１の周囲の複数のサンプリング点の位置情報を取得している。しかし、位置測定器２を車両等の移動体に搭載し、移動体を圃場５１の周囲に沿って移動させることによって、圃場５１の周囲の複数のサンプリング点の位置情報を取得するようにしてもよい。 Further, in the above-described embodiment, the measurer carrying the position measuring device 2 travels along the periphery of the farm field 51 to acquire position information of a plurality of sampling points around the farm field 51. However, the position measuring device 2 is mounted on a moving body such as a vehicle, and the position information of a plurality of sampling points around the field 51 is acquired by moving the moving body along the periphery of the field 51. Good.
　本発明の実施形態について詳細に説明してきたが、これらは本発明の技術的内容を明らかにするために用いられた具体例に過ぎず、本発明はこれらの具体例に限定して解釈されるべきではなく、本発明の範囲は添付の請求の範囲によってのみ限定される。 Although the embodiments of the present invention have been described in detail, these are merely specific examples used to clarify the technical contents of the present invention, and the present invention is construed to be limited to these specific examples. Rather, the scope of the present invention is limited only by the accompanying claims.
　この出願は、２０１８年３月２２日に日本国特許庁に提出された特願２０１８－５４７６３号に対応しており、その出願の全開示はここに引用により組み込まれるものとする。 This application corresponds to Japanese Patent Application No. 2018-54763 filed with the Japan Patent Office on March 22, 2018, the entire disclosure of which is incorporated herein by reference.
　１　領域特定情報生成装置
　２　位置測定器
　１０　パーソナルコンピュータ（ＰＣ）
　３１　距離演算部
　３２　ピーク検出部
　３３　情報生成部
　４１　選択部
　４２　判別部
　４３　第１情報生成部
　４４　第２情報生成部
　５１　圃場 DESCRIPTION OF SYMBOLS 1 Area | region specific information generator 2 Position measuring device 10 Personal computer (PC)
DESCRIPTION OF SYMBOLS 31 Distance calculating part 32 Peak detection part 33 Information generation part 41 Selection part 42 Discriminating part 43 1st information generation part 44 2nd information generation part 51 Farm

Claims (4)

  1. 　特定対象領域の周囲の複数のサンプリング点の位置情報に基づいて、前記特定対象領域を特定するための領域特定情報を生成する領域特定情報生成装置であって、
    　前記特定対象領域内の内部の基準点から、前記各サンプリング点までの距離を演算する距離演算部と、
    　前記特定対象領域の周囲の一方向に並ぶ順番で配置された前記複数のサンプリング点に対する、前記基準点からの距離を表すデータの複数のピークを検出するピーク検出部と、
    　前記複数のピークに対応する位置情報に基づいて、前記領域特定情報を生成する情報生成部とを含む、領域特定情報生成装置。     An area specifying information generating device that generates area specifying information for specifying the specific target area based on position information of a plurality of sampling points around the specific target area,
    A distance calculation unit for calculating a distance from an internal reference point in the specific target area to each sampling point;
    A peak detection unit for detecting a plurality of peaks of data representing distances from the reference point with respect to the plurality of sampling points arranged in an order arranged in one direction around the specific target region;
    An area specifying information generating apparatus including an information generating unit that generates the area specifying information based on position information corresponding to the plurality of peaks.
  2. 　前記情報生成部は、前記ピーク検出部によって検出された複数のピークのうち、ピーク幅が相対的に大きいかまたはピーク高さが相対的に大きい複数のピークを、それぞれ特徴点候補として選択し、選択された複数の特徴点候補に対応する位置情報に基づいて、前記領域特定情報を生成するように構成されている、請求項１に記載の領域特定情報生成装置。 The information generation unit selects a plurality of peaks having a relatively large peak width or a relatively large peak height among the plurality of peaks detected by the peak detection unit, as feature point candidates, The area specifying information generating apparatus according to claim 1, configured to generate the area specifying information based on position information corresponding to a plurality of selected feature point candidates.
  3. 　前記情報生成部は、
    　前記ピーク検出部によって検出された複数のピークのうち、ピーク幅が相対的に大きいかまたはピーク高さが相対的に大きい複数のピークを、それぞれ特徴点候補として選択する選択部と、
    　前記複数の特徴点候補によって規定される多角形の周長と、前記複数のサンプリング点によって規定される多角形の周長との差分絶対値が、所定の閾値以内である否かを判別する判別部と、
    　前記差分絶対値が前記閾値以内であると判別されたときには、前記複数の特徴点候補に対応する位置情報を、前記領域特定情報として生成する第１情報生成部と、
    　前記差分絶対値が前記閾値よりも大きいと判別されたときには、前記複数の特徴点候補のうち、少なくとも１組の隣接する２つの特徴点候補の間に、少なくとも１つの新特徴点候補を追加し、前記複数の特徴点候補および前記新特徴点候補に対応する位置情報を、前記領域特定情報として生成する第２情報生成部とを含む、請求項１に記載の領域特定情報生成装置。     The information generator is
    Among the plurality of peaks detected by the peak detection unit, a selection unit that selects a plurality of peaks having a relatively large peak width or a relatively large peak height as feature point candidates,
    Discrimination to determine whether or not the absolute difference between the perimeter of the polygon defined by the plurality of feature point candidates and the perimeter of the polygon defined by the plurality of sampling points is within a predetermined threshold. And
    A first information generating unit that generates position information corresponding to the plurality of feature point candidates as the region specifying information when the difference absolute value is determined to be within the threshold;
    When it is determined that the difference absolute value is larger than the threshold value, at least one new feature point candidate is added between at least one pair of two adjacent feature point candidates among the plurality of feature point candidates. The area specifying information generating apparatus according to claim 1, further comprising: a second information generating unit that generates position information corresponding to the plurality of feature point candidates and the new feature point candidate as the area specifying information.
  4. 　前記第２情報生成部は、隣接する２つの特徴点候補の組み合わせ毎に、前記複数のサンプリング点によって規定される多角形の輪郭線のうち、当該組み合わせに対応する２つの特徴点候補の間の区間の長さと、当該組み合わせに対応する２つの特徴点候補間の距離との乖離度を演算し、得られた乖離度が相対的に大きい組み合わせに対応する２つの特徴点候補間に、少なくとも１つの新特徴点候補を追加するように構成されている、請求項３に記載の領域特定情報生成装置。 The second information generation unit, for each combination of two adjacent feature point candidates, among the polygonal contour lines defined by the plurality of sampling points, between the two feature point candidates corresponding to the combination. The degree of divergence between the length of the section and the distance between the two feature point candidates corresponding to the combination is calculated, and at least 1 is obtained between the two feature point candidates corresponding to the combination having a relatively large divergence degree. The region specifying information generation device according to claim 3, configured to add one new feature point candidate.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH096961A (en) * 1995-06-16 1997-01-10 Sony Corp Processing device and method for dividing area
US20040193349A1 (en) * 2003-03-31 2004-09-30 Flann Nicholas Simon Method and system for determining an efficient vehicle path
JP2015206647A (en) * 2014-04-18 2015-11-19 井関農機株式会社 Farm field shape determination device
JP2017127289A (en) * 2016-01-22 2017-07-27 ヤンマー株式会社 Agricultural working vehicle
JP2017127291A (en) * 2016-01-22 2017-07-27 ヤンマー株式会社 Agricultural working vehicle

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH096961A (en) * 1995-06-16 1997-01-10 Sony Corp Processing device and method for dividing area
US20040193349A1 (en) * 2003-03-31 2004-09-30 Flann Nicholas Simon Method and system for determining an efficient vehicle path
JP2015206647A (en) * 2014-04-18 2015-11-19 井関農機株式会社 Farm field shape determination device
JP2017127289A (en) * 2016-01-22 2017-07-27 ヤンマー株式会社 Agricultural working vehicle
JP2017127291A (en) * 2016-01-22 2017-07-27 ヤンマー株式会社 Agricultural working vehicle

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