JP7750569B2 - Orthoimage creation method, orthoimage creation system, and road survey method - Google Patents

Description

Patent Law Article 30, Paragraph 2 applied. Published at the training program of the Kansai branch of Toa Road Industry Co., Ltd. on May 25, 2018.

Patent Act Article 30, Paragraph 2 applied. Published at the 4th Surveying and Geospatial Information Innovation Conference on June 20, 2018.

Patent Law Article 30, Paragraph 2 applied. Published at the 55th Kyoto City Venture Business Evaluation Committee on September 26, 2018.

Patent Law Article 30, Paragraph 2 applied. Published at the "Corporate Forest/Industry-Academia Forest" subsidy project presentation on October 18, 2018.

Article 30, Paragraph 2 of the Patent Act applied. Published on October 18, 2018, in the presentation materials for the Golden Egg Excavation Project 2018.

Article 30, Paragraph 2 of the Patent Act was applied. Published on October 24, 2018, at the presentation and exhibition of case studies of the support project for prototype development for small and medium-sized manufacturing enterprises and small-scale businesses.

Patent Law Article 30, Paragraph 2 applied. Published on November 11, 2018, at the presentation for the first screening of the Golden Egg Excavation Project 2018.

Article 30, Paragraph 2 of the Patent Act applied. Published at the Kansai Road Research Association Awards Committee on June 7, 2019.

The present invention relates to an orthoimage creation method, an orthoimage creation system, and a road survey method for creating an orthoimage based on images captured from the sky by, for example, an unmanned aerial vehicle.

Traditionally, if damage such as cracks occurs on the surface of the asphalt pavement that makes up the surface layer of a road, the road needs to be repaired.

In order to repair roads, various investigations are conducted, such as investigating the road condition at the time repair work begins and the position of planar elements, including road edges and lane markings. For example, investigations are conducted into the location and extent of cracks on the road. Traditionally, crack investigations have been carried out visually by inspectors, but the task of having each inspector inspect the road and detect cracks is extremely cumbersome. Therefore, instead of inspectors detecting cracks themselves, road conditions are sometimes investigated using a dedicated road surface property measurement vehicle (see Patent Document 1).

JP 2018-123510 A

When investigating road conditions using a dedicated road surface property measurement vehicle, the vehicle must be driven, but on narrow roads, the vehicle cannot drive, making it impossible to inspect the road conditions.

In addition, a survey is conducted to determine the locations of planar elements, including road edges and lane markings in the repair area where repairs are to be performed. Conventionally, surveys were conducted at a large number of planar locations on road edges and lane markings, and planar elements, including road edges and lane markings, were mapped based on each planar location. Therefore, in order to map planar elements, surveys must be conducted at a large number of planar locations, which is extremely cumbersome.

In addition, if there is a manhole in the repair area, an investigation will be conducted into the manhole's adjustment height. The investigation into the manhole's adjustment height involves investigating the adjustment height (the difference in elevation between the elevation at the time repair work begins and the elevation of the repair plan surface) for each position in the longitudinal and transverse directions of the manhole.

Previously, the elevation of each horizontal position around the manhole was detected based on the road cross sections and road cross sections that pass through each horizontal position, and the adjustment height was calculated from the difference in elevation between that elevation and the elevation of the repair plan surface. Therefore, it was necessary to detect the elevation of each horizontal position based on the road cross sections and road cross sections for each manhole, which was extremely cumbersome.

The present invention has been made in light of these problems, and aims to provide an orthoimage creation method, an orthoimage creation system, and a road survey method that make it possible to easily survey the road condition at the time of starting repair work when repairing a road.

The present invention takes the following measures to solve these problems:

In other words, the orthoimage creation method of the present invention comprises a coordinate acquisition step of acquiring three-dimensional coordinates for a plurality of feature points, a photographing step of taking a plurality of photographed images using an unmanned aerial vehicle or a model aircraft flying in the sky so that each of the plurality of feature points is included in at least two photographed images, and an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired by the coordinate acquisition step and the plurality of photographed images taken by the photographing step, wherein the orthoimage creation step is characterized in that an orthoimage without vehicles on the road is created by replacing the area around a vehicle on the road with an image of a road without vehicles in another photographed image.

The orthoimage creation system of the present invention comprises a coordinate storage means for storing three-dimensional coordinates for a plurality of feature points; a captured image storage means for storing a plurality of captured images for the plurality of feature points taken by an unmanned aerial vehicle or a model aircraft flying in the sky so that each of the plurality of feature points is included in at least two captured images; and an orthoimage creation means for creating an orthoimage based on the three-dimensional coordinates of each feature point stored in the coordinate storage means and the plurality of captured images stored in the captured image storage means, wherein the orthoimage creation means creates an orthoimage without vehicles on the road by replacing the area around a vehicle on the road with an image of the road without vehicles in another captured image.

As a result, the orthoimage creation method and orthoimage creation system of the present invention create orthoimages based on multiple images taken by an unmanned aerial vehicle or model aircraft flying overhead , making it possible to create orthoimages that clearly show the condition of the road surface and the location of planar elements around the road.The orthoimages created by the present invention make it possible to clearly identify areas where cracks have occurred or where patching has occurred on the road.Therefore, since there is no need to run a dedicated road surface property measurement vehicle to investigate the condition of cracks on the road surface, it is possible to investigate road conditions regardless of road width.

In addition, the orthoimages created by this invention make it possible to clearly identify the positions of planar elements, including road edges and lane markings. Therefore, since there is no need to conduct surveys at a large number of planar positions in order to map planar elements, including road edges and lane markings, it is possible to easily map planar elements based on orthoimages.

In addition, the orthoimages created by this invention make it possible to detect the longitudinal and transverse planar positions of the area surrounding the manhole. Therefore, after identifying the longitudinal and transverse planar positions of the area surrounding the manhole, the elevation of each planar position can be extracted from the point cloud data acquired by the 3D scanning device. Therefore, there is no need to create longitudinal and transverse road sections for each manhole in order to detect the elevation of each longitudinal and transverse planar position of the area surrounding the manhole. Therefore, it is possible to easily detect the adjusted manhole height.

In the orthoimage creation method of the present invention, the feature points are anti-aircraft markers installed on the ground at the time of photographing by the unmanned aerial vehicle or the model aircraft, and the coordinate storage means stores the three-dimensional coordinates of the anti-aircraft markers obtained by a total station, a satellite-based positioning system, and a three-dimensional scanning device.

In the orthoimage creation system of the present invention, the feature point is an anti-aircraft marker installed on the ground at the time of photographing in the photographing step, and the coordinate acquisition step is characterized in that the three-dimensional coordinates of the anti-aircraft marker are acquired by a total station, a satellite-based positioning system, and a three-dimensional scanning device.

As a result, the orthoimage creation method and orthoimage creation system of the present invention can create orthoimages based on images taken by an unmanned aerial vehicle or a model aircraft, making it possible to create orthoimages that clearly show the condition of the road surface and the positions of planar elements around the road.

In the orthoimage creation method of the present invention, the feature points are specified points within an image taken by the unmanned aerial vehicle or the model aircraft, and the coordinate storage means stores the three-dimensional coordinates of the specified points extracted from the three-dimensional coordinated point cloud data obtained for each point within the taken image.

In the orthoimage creation system of the present invention, the feature points are predetermined points within the images captured by the unmanned aerial vehicle or the model aircraft, and the coordinate acquisition step is characterized in that the three-dimensional coordinates of the predetermined points are acquired from the three-dimensional coordinated point cloud data acquired for each point within the captured image.

As a result, the orthoimage creation method and orthoimage creation system of the present invention can create orthoimages based on images taken by an unmanned aerial vehicle or a model aircraft, making it possible to create orthoimages that clearly show the condition of the road surface and the positions of planar elements around the road.

a photographing step of photographing a plurality of photographed images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of photographed images includes at least two of the photographed images; an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each of the feature points acquired in the coordinate acquisition step and the plurality of photographed images taken in the photographing step , and creating an orthoimage without vehicles on the road by replacing an area around a vehicle on the road with an image of the road without vehicles in another photographed image ; a display step of displaying the orthoimage on a display unit; a derivation step of dividing a survey area into a plurality of survey ranges in the orthoimage displayed on the display unit and deriving a crack rate or patching rate for each of the plurality of survey ranges; and a road condition display step of displaying the condition of the road by adding a color to the orthoimage displayed on the display unit according to the magnitude of the crack rate or patching rate of each survey range derived in the derivation step.

As a result, the road inspection method of the present invention creates an orthoimage based on multiple images taken by an unmanned aerial vehicle or model aircraft flying overhead , making it possible to create an orthoimage that clearly identifies the condition of the road surface and the positions of planar elements around the road. The orthoimage created by the present invention makes it possible to clearly identify areas where cracks have occurred or areas where patching has occurred on the road. Therefore, since there is no need to run a dedicated road surface property measurement vehicle to inspect the crack condition on the road surface, it is possible to inspect the road condition regardless of the road width.

The road survey method of the present invention is characterized by comprising: a coordinate acquisition step of acquiring three-dimensional coordinates for a plurality of feature points; a photographing step of taking a plurality of photographic images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two photographic images; an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquisition step and the plurality of photographic images taken in the photographing step, and creating an orthoimage without vehicles on the road by replacing an area around a vehicle on the road with an image of the road without vehicles in another photographic image; a display step of displaying the orthoimage on a display unit; and a planar element mapping step of tracing planar elements in the orthoimage displayed on the display unit to map the planar elements.

As a result, the road survey method of the present invention creates an orthoimage based on multiple images taken by an unmanned aerial vehicle or model aircraft flying overhead , making it possible to create an orthoimage that clearly identifies the condition of the road surface and the positions of planar elements around the road.The orthoimage created by the present invention makes it possible to clearly identify the positions of planar elements, including road edges and lane markings.Therefore, since it is not necessary to survey a large number of points to plot planar elements, including road edges and lane markings, it is possible to easily plot planar elements based on the orthoimage.

a photographing step of photographing a plurality of photographed images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two photographed images; an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquisition step and the plurality of photographed images taken in the photographing step , and creating an orthoimage without vehicles on the road by replacing an area around a vehicle on the road with an image of the road without vehicles in another photographed image; a point cloud data acquisition step of acquiring point cloud data of an area including the periphery of a manhole in the orthoimage; a display step of displaying the orthoimage on a display unit; and an elevation difference derivation step of deriving the elevation difference between the elevation of the periphery of the manhole in the orthoimage displayed on the display unit and the elevation of the periphery of the manhole on a repair planning screen.

As a result, the road inspection method of the present invention creates an orthoimage based on multiple images taken by an unmanned aerial vehicle or model aircraft flying overhead , making it possible to create an orthoimage that clearly shows the condition of the road surface and the location of planar elements around the road. The orthoimage created by the present invention makes it possible to detect the planar positions of the longitudinal and transverse directions around the manhole. Therefore, after identifying the planar positions of the longitudinal and transverse directions around the manhole, it is possible to extract the elevation of each planar position from the point cloud data of the area including the manhole. Therefore, there is no need to create road longitudinal and transverse sections for each manhole to detect the elevation of each planar position of the longitudinal and transverse directions around the manhole. Therefore, it is possible to easily detect the manhole adjustment height.

a photographing step of photographing a plurality of photographed images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two of the photographed images; an orthoimage creating step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquiring step and the plurality of photographed images taken in the photographing step , and creating an orthoimage without vehicles on the road by replacing an area around a vehicle on the road with an image of the road without vehicles in another photographed image ; a point cloud data acquiring step of acquiring point cloud data of an area including the plurality of feature points; a display step of displaying the orthoimage on a display unit; a designation step of designating two designated points spaced apart from each other in the orthoimage displayed on the display unit; and a distance display step of displaying the distance between the two designated points when the two designated points are designated in the designation step.

As a result, the road inspection method of the present invention creates an orthoimage based on multiple images taken by an unmanned aerial vehicle or model aircraft flying overhead , and by associating the orthoimage with point cloud data of the area within the orthoimage, it is possible to clearly determine the positions of planar elements including road edges and lane markings, and to display, for example, the distance between two specified points in the area surrounding the road in the orthoimage. Therefore, even if an inspector does not measure the distance between two specified points in the area surrounding the road, the distance between the two specified points can be easily detected by specifying the two specified points on the display unit displaying the orthoimage.

a photographing step of photographing a plurality of photographed images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two of the photographed images; an orthoimage creating step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquiring step and the plurality of photographed images taken in the photographing step , and creating an orthoimage without vehicles on the road by replacing an area around a vehicle on the road with an image of the road without vehicles in another photographed image ; a point cloud data acquiring step of acquiring point cloud data of an area including the plurality of feature points; a display step of displaying the orthoimage on a display unit; a designation step of designating a designated range within the orthoimage displayed on the display unit; and an area display step of displaying the area of the designated range when the designated range is designated in the designation step.

As a result, in the road inspection method according to the present invention, an orthoimage is created based on a plurality of images taken by an unmanned aerial vehicle or a model aircraft flying overhead, and the orthoimage is associated with point cloud data of an area within the orthoimage. This makes it possible to clearly determine the positions of planar elements, including road edges and lane markings and other dividing lines, while displaying the area of a specified range within the orthoimage. Therefore, even if an inspector does not measure the area of the specified range within the road surrounding area, the area of the specified range can be easily detected by specifying the specified range on the display unit displaying the orthoimage.
The orthoimage creation method of the present invention creates an orthoimage based on three-dimensional coordinates of a plurality of feature points and a plurality of images taken by an unmanned aerial vehicle or model aircraft flying in the sky so that each of the plurality of feature points is included in at least two of the images, and is characterized in that an orthoimage is created in which the area around a vehicle on a road is replaced with an image of the road without the vehicle in another image taken .

As described above, according to the present invention, by creating an orthoimage based on multiple images captured by an unmanned aerial vehicle or model aircraft flying overhead , it is possible to create an orthoimage that clearly identifies the condition of the road surface and the location of planar elements around the road. The orthoimage created by the present invention clearly identifies the locations of planar elements, including cracks and patching on the road, road edges, and lane markings, and can detect the longitudinal and transverse planar positions of the area around a manhole. The road survey method of the present invention allows for easy detection of the distance between two specified points by specifying two points on a display unit displaying an orthoimage. The road survey method of the present invention allows for easy detection of the area of a specified range by specifying a specified range on a display unit displaying an orthoimage.

1 is a diagram showing a schematic configuration of an orthoimage creation system according to an embodiment of the present invention. FIG. 10 is a diagram showing a state in which a plurality of anti-aircraft signs are installed near both ends of a road when the road is photographed from above. FIG. 10A and 10B are diagrams illustrating a state in which an anti-aircraft marker is included in two captured images. FIG. 1 is a diagram illustrating a method for creating an orthoimage in an orthoimage creation device. FIG. 10 is a diagram showing a state in which an orthoimage is displayed on a display unit. FIG. 1 is an enlarged view of a road surface on which cracks have formed. FIG. 1 is an enlarged view of a road surface on which cracks have formed. An enlarged view of the road surface where cracks have formed. A close-up view of the road surface with a manhole. FIG. 1 is a diagram illustrating the tape used to evaluate the detection of cracks formed on the road surface. FIG. 12 is a diagram showing the state in which the tape of FIG. 11 is attached to the road surface. FIG. 13 is a diagram showing an orthoimage of the road surface of FIG. 12. FIG. 1 is a diagram illustrating a method for investigating the crack condition of a road surface. FIG. 10 is a diagram showing a survey area in an orthoimage displayed on a display unit. FIG. 16 is an enlarged view of a portion A within the investigation area shown in FIG. 15. FIG. 10 is a diagram showing the results of investigating the road surface conditions for each survey area in the entire survey area. 10A and 10B are diagrams illustrating a method for investigating the positions of planar elements around a road. FIG. 10 is a diagram showing a survey area in an orthoimage displayed on a display unit. FIG. 1 is an ortho-CAD plan view of an orthoimage. FIG. 21 is a diagram showing a result of tracing planar elements around a road on the ortho-CAD plan view of FIG. 20. This is a diagram illustrating planar elements of the entire survey area. FIG. 1 is a diagram illustrating an investigation method for repairing the area around a manhole. FIG. 10 is a diagram showing a survey area in an orthoimage displayed on a display unit. FIG. 1 is a schematic diagram illustrating a longitudinal section plan. FIG. 1 is a schematic diagram illustrating a cross-sectional plan. FIG. 10 is a diagram showing the elevation of a predetermined position around a manhole displayed. FIG. 10 is a diagram showing the adjustment heights of each position around the manhole. FIG. 10 is a diagram illustrating a method for investigating the distance between specified points on the road surface. FIG. 10 is a diagram showing a state in which the distance between specified points on the road surface is displayed. FIG. 10 is a diagram illustrating a method for investigating the area of a specified range on a road surface. FIG. 10 is a diagram showing a state in which the area of a specified range on the road surface is displayed.

Embodiments of the present invention will now be described with reference to the drawings. An orthoimage creation system 1 according to an embodiment of the present invention comprises a total station 2 installed at a known point (e.g., a reference point), a UAV 3 (Unmanned Aerial Vehicle) that serves as an imaging device, a 3D scanner 4 (three-dimensional scanning device) installed at the known point, and an orthoimage creation device 10 in which the total station 2, UAV 3, and 3D scanner 4 are wirelessly connected.

The total station 2 emits distance measurement light toward each point on the road surface, receives the light reflected at each point, and acquires the three-dimensional coordinates of each point relative to known points based on the number of times the light wave oscillates between emission and reception. The three-dimensional coordinates are then supplied to the orthoimage creation system 10. In this embodiment, the total station 2 is used to acquire the three-dimensional coordinates of multiple anti-aircraft signs 6.

UAV3 is equipped with an imaging device that captures images of the road surface from the sky, acquires imaging data, and supplies the imaging data to the orthoimage creation device 10.

The 3D scanner 4 emits laser light to acquire three-dimensional coordinated point cloud data (a collection of elevations with planar position coordinates) for each point on the road surface, and supplies the point cloud data to the orthoimage creation system 10. The 3D scanner 4 emits line laser light, for example, vertically and horizontally, toward the measurement object (road surface), and measures the time it takes for the laser pulse to travel back and forth between the measurement point on the measurement object and the sensor, thereby determining the distance to the measurement point. In this embodiment, the 3D scanner 4 is used to acquire three-dimensional coordinates (point cloud data) of each point in an area including the repair site where road repair work is to be performed, at the time of repair work commencement. The point cloud data acquired by the 3D scanner 4 is data at positions spaced, for example, at intervals of 25 cm or less; in this embodiment, the 3D scanner 4 acquires point cloud data at positions spaced, for example, at intervals of 5 mm.

As shown in Figure 1, the orthoimage creation device 10 is composed of, for example, a microcomputer, and includes a CPU, a ROM that stores a program that controls the operation of the orthoimage creation device 10, and a RAM that temporarily stores data used when executing the program.

The orthoimage creation device 10 has a coordinate storage unit 11, a captured image storage unit 12, an orthoimage creation unit 13, and a display control unit 14. The orthoimage creation device 10 also has a display unit 5 such as a display screen.

The coordinate memory unit 11 separately stores the three-dimensional coordinates of feature points such as multiple anti-aircraft markers 6 acquired by the total station 2.

The captured image storage unit 12 stores multiple images of a road taken from above by a UAV 3 flying above the road at a nearly constant altitude. When taking images, the UAV 3 flies at an altitude of 20 meters or less above the ground, for example, between 5 and 20 meters, and preferably between 5 and 15 meters.

When photographing a road from the air using a UAV 3, as shown in Figure 2, multiple anti-aircraft markers 6 are installed as multiple feature points, for example near both ends of the road. The multiple anti-aircraft markers 6 are installed along the edges of the road (longitudinal direction of the road), for example at intervals of 5 to 15 meters. The multiple anti-aircraft markers 6 are installed with the aim of connecting multiple images taken from the air to create an orthoimage. The anti-aircraft markers 6 are feature points for which three-dimensional coordinates are provided, and are used as assessment points. Note that when connecting multiple images to create an orthoimage, feature points other than the anti-aircraft markers 6 that are included in multiple images but for which three-dimensional coordinates are not provided may also be used.

As shown in Figure 3, the anti-aircraft sign 6 is a square, plate-like member. The anti-aircraft sign 6 has a pattern that clearly identifies its center. The anti-aircraft sign 6 has an adhesive layer on its back and is sticker-like with a backing paper attached to cover the adhesive layer. By removing the backing paper and attaching it to the road, it can be easily fixed to the installation location. Therefore, when using the anti-aircraft sign 6, the backing paper covering the adhesive layer is removed and the back of the anti-aircraft sign 6 is attached to the road surface. The anti-aircraft sign 6 in this embodiment is, for example, a 9 cm x 9 cm square, but the type, shape, size, pattern, etc. of the anti-aircraft sign 6 are not limited to this.

As shown in Figure 4, the multiple images captured by the UAV 3 are taken so that each anti-aircraft marker 6 is included in at least two of the images. Therefore, at least one common anti-aircraft marker 6 is captured in two adjacent images. Note that Figure 4 illustrates a case where an anti-aircraft marker 6 is included in all of the images, but the multiple images captured by the UAV 3 may be taken so that either the anti-aircraft marker 6 or a feature other than the anti-aircraft marker 6 is included in at least two of the images.

The orthoimage creation unit 13 creates an orthoimage based on the three-dimensional coordinates of the anti-aircraft beacon 6 stored in the coordinate memory unit 11 and the multiple captured images stored in the captured image memory unit 12. Specifically, the orthoimage creation unit 13 performs SfM (Structure from Motion) analysis or the like on the data for the multiple captured images to connect two adjacent captured images based on the common anti-aircraft beacon 6 captured in them, thereby creating an orthoimage. If a vehicle is present on the road in the captured image used to create the orthoimage, it is possible to automatically recognize the vehicle (through automatic image recognition) and replace the area around the vehicle on the road with an image of the road without a vehicle from another captured image, thereby creating an orthoimage without the vehicle on the road.

The display control unit 14 displays the orthoimage created by the orthoimage creation unit 13 on the display unit 5. The user can perform an operation to specify a predetermined position within the image displayed on the display unit 5 by pressing the display surface 5a of the display unit 5. For example, when the orthoimage created by the orthoimage creation unit 13 is displayed on the display unit 5, the user can perform an operation to specify a predetermined position by pressing a predetermined position within the orthoimage displayed on the display surface 5a of the display unit 5.

(Creating orthoimages)
A method for creating an orthoimage in the orthoimage creation device 10 will be described with reference to FIG.

In step S1 (coordinate acquisition step), the total station 2 acquires three-dimensional coordinates, i.e., planar positions (latitude, longitude) and altitudes (height), for multiple predetermined positions around the repair site where road repairs are to be performed, i.e., predetermined positions where multiple anti-aircraft signs 6 will be installed.

In step S2 (photographing step), a UAV 3 flying at an altitude of 20 meters or less above the ground photographs the road from above. When photographing is performed, multiple anti-aircraft markers 6 are installed in advance at multiple predetermined locations surveyed in step S1. Therefore, multiple images are taken of multiple anti-aircraft markers 6 so that each anti-aircraft marker 6 is included in at least two photographed images.

In step S3 (orthoimage creation step), an orthoimage is created based on the three-dimensional coordinates acquired in step S1 and the multiple images captured in step S2.

In step S4 (display step), the orthoimage is displayed on the display unit 5, as shown in Figure 6. In this embodiment, the ground pixel size of the orthoimage is 5 millimeters or less.

Figures 7 to 9 are enlarged views of a road surface on which cracks have formed. Figure 10 is an enlarged view of a road surface on which a manhole is located. In this way, the orthoimages created by the orthoimage creation device 10 of this embodiment make it possible to clearly identify cracks formed on the road surface, and also to clearly identify the type of manhole based on the letters and symbols written on the manhole cover.

In addition, when investigating the crack condition of road surfaces, conventionally, when road conditions are surveyed using a dedicated road surface property measurement vehicle, it is possible to detect cracks on the road surface that are approximately 1 mm wide. Therefore, we evaluated whether the orthoimages created by this invention can detect cracks on the road surface that are approximately 1 mm wide, in the same way as a dedicated road surface property measurement vehicle.

For the above-mentioned evaluation, as shown in Figures 11 and 12, 1 mm, 2 mm, and 3 mm wide tapes were used and attached to the road surface to simulate cracks of 1 mm, 2 mm, and 3 mm in width on the road surface. Then, the road with the simulated cracks was photographed from above by a UAV3 flying at an altitude of less than 20 meters above the ground, and an orthoimage was created.

Figure 13 shows an orthoimage of a road with simulated cracks formed on it. It was found that the orthoimages created by this invention can detect simulated cracks of 1 mm, 2 mm, and 3 mm widths formed on the road surface. Therefore, the orthoimages created by this invention can detect cracks of approximately 1 mm width formed on the road surface.

(Road survey method using orthoimages)
The orthoimages created by the orthoimage creation device 10 as described above are used for various surveys that are carried out when road repairs are carried out.

In this embodiment, we will explain road survey methods using orthoimages created by the orthoimage creation device 10 to (1) investigate the state of cracks on the road surface, (2) investigate the location of planar elements around the road, including areas where repairs will be performed, (3) investigate the area surrounding a manhole, (4) investigate the distance between two specified points on the road surface, and (5) investigate the area of a specified range on the road surface.

(Road Survey Method 1)
The method for inspecting the crack state on the road surface will be explained with reference to FIG.

Investigations into the crack condition of road surfaces include investigating areas where cracks have formed on the road surface, including areas where road repairs will be performed, and the cracking and patching rates in those areas.

After the orthoimage is displayed in steps S1 to S4 described above, in step S5, the crack condition of the road surface is investigated based on the orthoimage displayed on the display unit 5. Specifically, the survey area of the road displayed on the display unit 5 is divided into multiple survey ranges, and the crack rate and patching rate of the road surface are investigated for each survey range.

Figure 15 shows the survey area in the orthoimage displayed on the display unit 5. In this embodiment, the survey area is divided into survey ranges of 50 cm x 50 cm, and for each survey range, a survey of the crack rate and a survey of the patching rate are conducted to investigate the crack condition. In this embodiment, the survey of the crack rate involves a survey of the crack amount (quantity of cracks) for each survey range.

Figure 16 is an enlarged view of part A within the survey area shown in Figure 15, showing the state of the area divided into multiple survey ranges. For each survey range, Figure 16 distinguishes between the following states: no cracks and a patching rate of 25% or less, a linear crack state (a state with one crack), a planar crack state (a state with two or more cracks), a patching rate of 25-75%, and a patching rate of 75% or more. Figure 16 distinguishes the road surface condition for each survey range using different patterns, but the road surface condition for each survey range may also be displayed using different colors.

In step S6 (road condition display step), as shown in Figure 17, the results of the investigation of the crack condition on the road surface for each investigation range in the entire investigation area are displayed on the display unit 5. In Figure 17, the investigation ranges for linear crack conditions, planar crack conditions, patching rates of 25 to 75%, and patching rates of 75% or more may be displayed, for example, in different colors. Furthermore, the investigation ranges for linear crack conditions and planar crack conditions may be displayed, for example, in different colors, from the investigation ranges for patching rates of 25 to 75% and patching rates of 75% or more.

(Road Survey Method 2)
A method for investigating the positions of planar elements around a road will be described with reference to FIG.

When investigating the location of planar elements around the road, the locations of planar elements including the edges of the road, including repair areas where road repairs will be carried out, road deformations, dividing lines such as white painted areas indicating lanes on the road surface, and lines indicating the location of manholes, etc. are investigated.

After creating an orthoimage through steps S1 to S4 described above, in step S8 (planar element mapping step), planar elements around the road are traced manually or automatically (auto-trace processing) based on the orthoimage displayed on the display unit 5. Specifically, planar elements are traced on the road displayed on the display unit 5, including, for example, road edges, road deformations, lane markings painted in white on the road surface, and lines indicating the location of manholes, etc.

Figure 19 illustrates the survey area in the orthoimage displayed on the display unit 5. Figure 20 is an ortho-CAD plan view of the orthoimage shown in Figure 6, and Figure 21 shows a diagram in which planar elements around the road have been traced from the ortho-CAD plan view of Figure 20. An ortho-CAD plan view is a plan view created by converting an orthoimage into 2D CAD. Therefore, in Figure 21, lines indicating the positions of planar elements around the road have been added to the ortho-CAD plan view corresponding to the orthoimage of Figure 20.

In step S8, as shown in Figure 22, a tracing process is performed on planar elements around the road throughout the entire survey area, and the resulting planar elements are displayed on the display unit 5.

(Road Survey Method 3)
The investigation method for repairing the area around the manhole will be explained with reference to FIG.

In surveys to repair the area around a manhole, the height (adjustment height) to be adjusted so that the elevation of the area around the manhole matches the elevation of the repair plan surface during road repairs is investigated. Therefore, the adjustment height of the area around the manhole is the difference in elevation between the elevation of the area around the manhole when repair work begins and the elevation of the repair plan surface. The adjustment height of the area around the manhole is investigated by examining the difference in elevation between two locations, one upstream and one downstream of the manhole in the longitudinal direction, and the difference in elevation between two locations, one upstream and one downstream of the manhole in the transverse direction.

Figure 24 illustrates the survey area in the orthoimage displayed on the display unit 5. The survey area in Figure 24 includes one manhole and the area surrounding the manhole.

After the orthoimage is displayed in steps S1 to S4 described above, longitudinal and cross-sectional planning is performed in step S9, and planning surface data showing the repair planning surface for road repairs is obtained.

The repair plan includes longitudinal and transverse plans. After a longitudinal plan is made along the longitudinal direction of the road, transverse plans are made along the transverse direction at multiple locations on the road, thereby obtaining the repair plan surface to be used when carrying out repairs. Therefore, the repair plan surface includes plan surface data showing the longitudinal plan surface and plan surface data showing multiple transverse plan surfaces.

A longitudinal plan includes a plan for the elevation of each point on a line along the longitudinal direction of the road in the center of the road. For example, Figure 25 shows a longitudinal plan surface for the elevation of each point on a line along the center of the road. In Figure 25, a repair area requiring a repair plan is located between an unrepaired area on the left and an unrepaired area on the right. The repair area in Figure 25 is shown with an elevation change based on point cloud data, along with a longitudinal plan surface.

The longitudinal plan surface in Figure 25 is obtained by connecting the elevations at each position on a line along the center of the road, after the elevations at each position on the line along the center of the road are planned taking into account factors such as the flatness of the road. The positions on the line along the center of the road are, for example, every 10 meters or every 20 meters.

In longitudinal planning, elevations at each position on a line along the center of the road are planned, followed by cross-sectional planning. Cross-sectional planning is a plan for the elevations of each point on a line along the cross-sectional direction of the road at each position on the line along the center of the road. For example, Figure 26 shows a cross-sectional planning surface for the elevations of each point on a line along the cross-sectional direction of the road at point a in Figure 25. In Figure 26, a repair location requiring a repair plan is located between the left and right ends of the road. The repair location is illustrated with elevation changes based on point cloud data, as well as a cross-sectional planning surface. Figure 26 also illustrates the slope of the road for easy understanding.

The cross-section planning surface is obtained by planning each position on the line along the center of the road shown in Figure 25, taking into account the gradient of the slope that slopes downward from the elevation of the center of the road toward both ends of the road. For example, when planning a cross-section of a road, it is generally designed so that the slope slopes downward at a specified gradient from the center of the road toward the ends of the road.

For example, in the cross-sectional plan surface of Figure 26, the elevation decreases from the elevation of point a at the center of the road on the longitudinal plan surface of Figure 25 to point a1 along a slope that slopes downward at a predetermined gradient toward both ends of the road. The elevation then decreases to the left and right ends of the road along the connecting surfaces that connect point a1 with the left and right ends of the road. Therefore, when repairs are made based on the cross-sectional plan surface, the surface layer of the asphalt pavement formed at the repair location and the concrete portions at the left and right ends of the road are connected without any steps. Note that the cross-sectional plan surface of Figure 26 is an example of a cross-sectional plan surface, and cross-sectional planning methods are not limited to this. Therefore, the cross-sectional plan surface may be designed, for example, so that slopes that slope downward at different gradients from the center of the road toward the edges of the road are connected.

By connecting the cross-sectional plan surfaces at each position on the line along the center of the road obtained as described above in the longitudinal direction, a repair plan surface for repairing the road surface can be obtained.

In step S10 (point cloud data acquisition step), the 3D scanner 4 acquires point cloud data for each point on the road surface. The point cloud data acquired by the 3D scanner 4 is converted into a three-dimensional TIN model (irregular triangular network), which is a collection of triangular planes connected at their vertices, making it possible to derive data corresponding to the latitude, longitude, and altitude of each point on the road surface. Even if point cloud data for each point in the survey area has not been acquired by the 3D scanner 4, it is possible to derive data corresponding to the latitude, longitude, and altitude of each point.

In step S11 (elevation difference derivation step), by pressing and specifying a specific position around the manhole based on the orthoimage displayed on the display surface 5a of the display unit 5, the planar position (latitude, longitude) of that specific position is displayed, as shown in Figure 27. Therefore, by changing the position specified around the manhole in the orthoimage displayed on the display surface 5a of the display unit 5, two planar positions, one upstream and one downstream in the longitudinal direction of the manhole, and two planar positions, one upstream and one downstream in the transverse direction of the manhole, are detected.

The elevation of each position around the manhole at the time repair work begins is determined by detecting the planar position of each position around the manhole, and the elevation of that planar position is derived based on the point cloud data acquired by the 3D scanner 4. In this embodiment, using the orthoimage and the point cloud data acquired by the 3D scanner 4, the elevation of all planar positions in the orthoimage can be derived based on the point cloud data acquired by the 3D scanner 4. The elevation of each position on the repair plan surface is extracted from the plan surface data indicating the repair plan surface acquired in step S9.

Then, the elevation difference between the elevation of each position around the manhole at the time of repair work commencement and the elevation of each position on the repair plan is calculated as the adjustment height. Therefore, two adjustment heights, one on the upstream side and one on the downstream side of the manhole in the longitudinal direction, and two adjustment heights, one on the upstream side and one on the downstream side of the manhole in the transverse direction, are calculated. Figure 28 shows that the adjustment heights at each position around the manhole are a ₁ cm, a ₂ cm, a ₃ cm, and a ₄ cm, respectively. Therefore, a ₁ cm is the adjustment height on the upstream side of the manhole in the transverse direction, a ₂ cm is the adjustment height on the upstream side of the manhole in the longitudinal direction, a ₃ cm is the adjustment height on the downstream side of the manhole in the transverse direction, and a ₄ cm is the adjustment height on the downstream side of the manhole in the longitudinal direction.

(Road Survey Method 4)
A method for investigating the distance between two specified points on the road surface will be described with reference to FIG.

When investigating the distance between two specified points on the road surface, if various distances are required for road repair, those distances are investigated based on the orthoimage displayed on the display unit 5. Distances required for road repair include, for example, the length of the road repair section, the width of the road, and the length of a specified area on the road surface.

In step S101 (point cloud data acquisition step), the 3D scanner 4 acquires point cloud data for each point on the road surface around the repair location where road repairs are to be performed. The point cloud data acquired by the 3D scanner 4 is converted into a three-dimensional TIN model (irregular triangular network), which is a collection of triangular planes connected at their vertices, making it possible to derive data corresponding to the latitude, longitude, and altitude of each point on the road surface. Even if point cloud data for each point in the survey area has not been acquired by the 3D scanner 4, it is possible to derive data corresponding to the latitude, longitude, and altitude of each point.

In step S102 (coordinate acquisition step), based on the point cloud data acquired in step S101, three-dimensional coordinates, i.e., planar positions (latitude, longitude) and altitude (height), are acquired for a plurality of predetermined positions, i.e., predetermined positions where a plurality of anti-aircraft signs 6 will be installed, around the repair location where road repairs are to be performed. Note that the three-dimensional coordinates of the plurality of predetermined positions may be acquired using a total station 2.

In step S103 (photographing step), a UAV3 flying at an altitude of 20 meters or less above the ground photographs the road from above. When photographing is performed, multiple anti-aircraft markers 6 are installed in advance at multiple predetermined positions whose three-dimensional coordinates were acquired in step S102. Therefore, multiple images are taken of multiple anti-aircraft markers 6 so that each anti-aircraft marker 6 is included in at least two photographed images.

In step S104 (orthoimage creation step), an orthoimage is created based on the three-dimensional coordinates acquired in step S102 and the multiple images captured in step S103. At this time, the orthoimage created in step S104 is associated with the point cloud data acquired in step S101. That is, each point in the orthoimage is associated with the three-dimensional coordinates of the point cloud data, and each point on the orthoimage corresponds to a planar position (latitude, longitude) and an elevation (height).

In step S105 (display step), the orthoimage is displayed on the display unit 5 as shown in FIG. 6. In step S106 (distance display step), two designated points on the road surface are pressed on the orthoimage displayed on the display surface 5a of the display unit 5 to specify the distance between those designated points. For example, as shown in FIG. 30, when two designated points A1 and A2 at an intersection are specified, the distance between those designated points A1 and A2 is displayed. Therefore, when photographing a road from above using a UAV 3 flying at an altitude of 20 meters or less above ground in step S103 (photographing step), even if measurements of various distances required for road repairs are not performed, the distances between various designated points within the orthoimage can be detected by changing the positions of the two designated points on the road surface in the orthoimage displayed on the display surface 5a of the display unit 5.

(Road Survey Method 5)
A method for investigating the area of a specified range on the road surface will be described with reference to FIG.

When investigating the area of a specified range of the road surface, if the areas of various regions are required for road repair, the areas of those regions are investigated based on the orthoimage displayed on the display unit 5. The areas required for road repair include, for example, the area of the road repair section.

In step S101 (point cloud data acquisition step), the 3D scanner 4 acquires point cloud data for each point on the road surface around the repair location where road repairs are to be performed. The point cloud data acquired by the 3D scanner 4 is converted into a three-dimensional TIN model (irregular triangular network), which is a collection of triangular planes connected at their vertices, making it possible to derive data corresponding to the latitude, longitude, and altitude of each point on the road surface. Even if point cloud data for each point in the survey area has not been acquired by the 3D scanner 4, it is possible to derive data corresponding to the latitude, longitude, and altitude of each point.

In step S102 (coordinate acquisition step), based on the point cloud data acquired in step S101, three-dimensional coordinates, i.e., planar positions (latitude, longitude) and altitude (height), are acquired for a plurality of predetermined positions, i.e., predetermined positions where a plurality of anti-aircraft signs 6 will be installed, around the repair location where road repairs are to be performed. Note that the three-dimensional coordinates of the plurality of predetermined positions may be acquired using a total station 2.

In step S103 (photographing step), a UAV3 flying at an altitude of 20 meters or less above the ground photographs the road from above. When photographing is performed, multiple anti-aircraft markers 6 are installed in advance at multiple predetermined positions whose three-dimensional coordinates were acquired in step S102. Therefore, multiple images are taken of multiple anti-aircraft markers 6 so that each anti-aircraft marker 6 is included in at least two photographed images.

In step S104 (orthoimage creation step), an orthoimage is created based on the three-dimensional coordinates acquired in step S102 and the multiple images captured in step S103. At this time, the orthoimage created in step S104 is associated with the point cloud data acquired in step S101. That is, each point in the orthoimage is associated with the three-dimensional coordinates of the point cloud data, and each point on the orthoimage corresponds to a planar position (latitude, longitude) and an elevation (height).

In step S105 (display step), the orthoimage is displayed on the display unit 5 as shown in FIG. 6. In step S108 (area display step), a specified range of the road surface is specified on the orthoimage displayed on the display surface 5a of the display unit 5, and the area of that specified range is displayed as shown in FIG. 32. For example, as shown in FIG. 32, when a specified range (shaded area) indicating the upper part of an intersection is specified, the area of that specified range is displayed. Therefore, when a road is photographed from above by a UAV 3 flying at an altitude of 20 meters or less above ground in step S103 (photographing step), even if measurements of various areas required for road repair are not performed, the areas of various regions within the orthoimage can be detected by changing the position of the specified range of the road surface in the orthoimage displayed on the display surface 5a of the display unit 5.

The orthoimage creation system 1 of this embodiment includes a coordinate memory unit 11 that stores the three-dimensional coordinates of multiple anti-aircraft markers 6, a captured image memory unit 12 that stores multiple captured images of multiple anti-aircraft markers 6 taken by a UAV 3 flying at an altitude of 20 meters or less above the ground, with each anti-aircraft marker 6 included in at least two captured images, and an orthoimage creation unit 13 that creates orthoimages based on the three-dimensional coordinates of each anti-aircraft marker 6 stored in the coordinate memory unit 11 and the multiple captured images stored in the captured image memory unit 11.

The orthoimage creation method of this embodiment includes a coordinate acquisition step of acquiring three-dimensional coordinates for multiple anti-aircraft markers 6, a photographing step of capturing multiple images of the multiple anti-aircraft markers 6 using a UAV 3 flying at an altitude of 20 meters or less above the ground, so that each anti-aircraft marker 6 is included in at least two photographed images, and an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquisition step and the multiple photographed images captured in the photographing step.

As a result, the orthoimage creation system 1 and orthoimage creation method of this embodiment create orthoimages based on multiple images taken by a UAV 3 flying at an altitude of 20 meters or less above the ground, making it possible to create orthoimages that clearly show the condition of the road surface and the location of planar elements around the road. The orthoimages created in this embodiment make it possible to clearly identify areas on the road where cracks have occurred or where patching has occurred. Therefore, since there is no need to run a dedicated road surface property measurement vehicle to investigate the condition of cracks on the road surface, it is possible to investigate road conditions regardless of road width.

In addition, the orthoimages created in this embodiment make it possible to clearly identify the positions of planar elements, including road edges and lane markings. Therefore, since there is no need to survey a large number of planar positions in order to plot planar elements, including road edges and lane markings, it is possible to easily plot planar elements based on orthoimages.

In addition, the orthoimage created in this embodiment makes it possible to detect each planar position in the longitudinal and transverse directions around the manhole. Therefore, after identifying each planar position in the longitudinal and transverse directions around the manhole, it is possible to extract the elevation of each planar position from the point cloud data acquired by the 3D scanning device. As a result, there is no need to create longitudinal and transverse road sections for each manhole in order to detect the elevation of each planar position in the longitudinal and transverse directions around the manhole. This makes it possible to easily detect the adjusted manhole height.

In the orthoimage creation system 1 of this embodiment, an anti-aircraft marker 6 installed on the ground is used when taking photographs using the UAV 3, and the coordinate memory unit 11 stores the three-dimensional coordinates of the anti-aircraft marker 6 acquired by the total station 2.

In the orthoimage creation method of this embodiment, an aerial marker 6 installed on the ground is used during photography in the photography step, and the three-dimensional coordinates of the aerial marker 6 are acquired by the total station 2 in the coordinate acquisition step.

As a result, the orthoimage creation system 1 and orthoimage creation method of this embodiment can provide three-dimensional coordinates to the orthoimage based on the three-dimensional coordinates of the anti-aircraft marker 6 contained in the image captured by the UAV 3.

The road inspection method of this embodiment includes a coordinate acquisition step for acquiring the three-dimensional coordinates of multiple anti-aircraft signs 6; a photographing step for capturing multiple images of the multiple anti-aircraft signs 6 using a UAV 3 flying at an altitude of 20 meters or less above the ground, such that each anti-aircraft sign 6 is included in at least two photographed images; an orthoimage creation step for creating an orthoimage based on the three-dimensional coordinates of each anti-aircraft sign 6 acquired in the coordinate acquisition step and the multiple photographed images captured in the photographing step; a display step for displaying the orthoimage on the display unit 5; a derivation step for dividing the survey area into multiple survey ranges in the orthoimage displayed on the display unit 5 and deriving a crack rate or patching rate for each of the multiple survey ranges; and a road condition display step for displaying the road condition by adding a color to the orthoimage displayed on the display unit 5 according to the magnitude of the crack rate or patching rate for each survey range derived in the derivation step.

As a result, the road inspection method of this embodiment creates an orthoimage based on multiple images taken by a UAV3 flying at an altitude of 20 meters or less above the ground, making it possible to create an orthoimage that clearly identifies the condition of the road surface and the location of planar elements around the road. The orthoimage created in this embodiment makes it possible to clearly identify areas on the road where cracks have occurred or where patching has occurred. Therefore, since there is no need to run a dedicated road surface property measurement vehicle to investigate the condition of cracks on the road surface, it is possible to investigate road conditions regardless of road width.

The road survey method of this embodiment includes a coordinate acquisition step for acquiring three-dimensional coordinates for multiple anti-aircraft signs 6; a photographing step for capturing multiple images of the multiple anti-aircraft signs 6 using a UAV 3 flying at an altitude of 20 meters or less above the ground, such that each anti-aircraft sign 6 is included in at least two photographed images; an orthoimage creation step for creating an orthoimage based on the three-dimensional coordinates of each anti-aircraft sign 6 acquired in the coordinate acquisition step and the multiple photographed images captured in the photographing step; a display step for displaying the orthoimage on the display unit 5; and a planar element mapping step for tracing planar elements in the orthoimage displayed on the display unit 5 and mapping the planar elements.

As a result, the road survey method of this embodiment creates an orthoimage based on multiple images taken by a UAV3 flying at an altitude of 20 meters or less above the ground, making it possible to create an orthoimage that clearly identifies the condition of the road surface and the position of planar elements around the road. The orthoimage created by this invention makes it possible to clearly identify the positions of planar elements, including road edges and lane markings. Therefore, since it is not necessary to survey a large number of planar positions in order to map planar elements, including road edges and lane markings, it is possible to easily map planar elements based on the orthoimage.

The road inspection method of this embodiment includes a coordinate acquisition step for acquiring three-dimensional coordinates for multiple anti-aircraft signs 6; a photographing step for capturing multiple images of the multiple anti-aircraft signs 6 using a UAV 3 flying at an altitude of 20 meters or less above ground level so that each anti-aircraft sign 6 is included in at least two photographed images; an orthoimage creation step for creating an orthoimage based on the three-dimensional coordinates of each anti-aircraft sign 6 acquired in the coordinate acquisition step and the multiple photographed images captured in the photographing step; a display step for displaying the orthoimage on the display unit 5; a point cloud data acquisition step for acquiring point cloud data for an area including the periphery of the manhole in the orthoimage displayed on the display unit 5; and an elevation difference derivation step for deriving the elevation difference between the elevation of the periphery of the manhole in the orthoimage displayed on the display unit 5 and the elevation of the periphery of the manhole on the repair plan surface.

As a result, the road survey method of this embodiment creates an orthoimage based on multiple images taken by a UAV3 flying at an altitude of 20 meters or less above ground, making it possible to create an orthoimage that clearly identifies the condition of the road surface and the location of planar elements around the road. The orthoimage created by this invention makes it possible to detect each planar position in the longitudinal and transverse directions around the manhole. Therefore, after identifying each planar position in the longitudinal and transverse directions around the manhole, the elevation of each planar position can be derived based on point cloud data for the area including the manhole periphery. Therefore, there is no need to create road longitudinal and transverse sections for each manhole in order to detect the elevation of each planar position in the longitudinal and transverse directions around the manhole. This makes it possible to easily detect the manhole adjustment height.

The road survey method according to this embodiment includes a coordinate acquisition step for acquiring three-dimensional coordinates for a plurality of anti-aircraft signs 6; a photographing step for capturing a plurality of photographed images of the plurality of anti-aircraft signs 6 using a UAV 3 flying at an altitude of 20 meters or less above the ground, such that each anti-aircraft sign 6 is included in at least two photographed images; an orthoimage creation step for creating an orthoimage based on the three-dimensional coordinates of each anti-aircraft sign 6 acquired in the coordinate acquisition step and the plurality of photographed images acquired in the photographing step; a point cloud data acquisition step for acquiring point cloud data of an area including the plurality of anti-aircraft signs 6; a display step for displaying the orthoimage on the display unit 5; a designation step for designating two designated points spaced apart from each other in the orthoimage displayed on the display unit 5; and a distance display step for displaying the distance between the two designated points when two designated points are designated in the designation step.

As a result, the road inspection method of the present invention creates an orthoimage based on multiple images taken by a UAV3 flying at an altitude of 20 meters or less above ground level, and by associating the orthoimage with point cloud data for the area within the orthoimage, it is possible to clearly determine the positions of planar elements, including road edges and lane markings and other dividing lines, while also displaying, for example, the distance between two specified points in the area surrounding the road within the orthoimage. Therefore, even if an inspector does not measure the distance between two specified points in the area surrounding the road, they can easily detect the distance between the two specified points by specifying the two specified points on the display unit 5 displaying the orthoimage.

The road survey method of the present invention is characterized by comprising a coordinate acquisition step for acquiring three-dimensional coordinates for multiple anti-aircraft signs 6; a photographing step for capturing multiple images of the multiple anti-aircraft signs 6 using a UAV 3 flying at an altitude of 20 meters or less above the ground, such that each anti-aircraft sign 6 is included in at least two photographed images; an orthoimage creation step for creating an orthoimage based on the three-dimensional coordinates of each anti-aircraft sign 6 acquired in the coordinate acquisition step and the multiple photographed images captured in the photographing step; a point cloud data acquisition step for acquiring point cloud data of an area including the multiple anti-aircraft signs 6; a display step for displaying the orthoimage on the display unit 5; a designation step for designating a designated range within the orthoimage displayed on the display unit 5; and an area display step for displaying the area of the designated range when the designated range is designated in the designation step.

As a result, the road inspection method of the present invention creates an orthoimage based on multiple images taken by a UAV3 flying at an altitude of 20 meters or less above ground level, and by associating the orthoimage with point cloud data for the area within the orthoimage, it is possible to clearly determine the positions of planar elements including road edges and lane markings and other dividing lines, while also displaying the area of a specified range within the orthoimage, for example. Therefore, even if an inspector does not measure the area of the specified range within the road surrounding area, the area of the specified range can be easily detected by specifying the range on the display unit 5 displaying the orthoimage.

The above describes an embodiment of the present invention, but the specific configuration of each part is not limited to the above-described embodiment, and various modifications are possible without departing from the spirit of the present invention.

In the above embodiment, an orthoimage is created based on images captured from above the road by a UAV 3 flying at a nearly constant altitude. However, the present invention also includes creating an orthoimage based on images captured from above the road by a UAV 3 flying at different altitudes less than 20 meters above the ground. In the above embodiment, the three-dimensional coordinates of the anti-aircraft markers 6 installed around the road are acquired using a total station 2. However, the three-dimensional coordinates of the anti-aircraft markers 6 installed around the road may also be acquired using a GNSS (Global Navigation Satellite System), a positioning system using satellites such as GPS. The three-dimensional coordinates of the anti-aircraft markers 6 installed around the road may also be acquired by scanning using a 3D scanner 4. In the above embodiment, the anti-aircraft markers 6 have a pattern that clearly identifies the central position used as the evaluation point. However, the anti-aircraft markers 6 may have a pattern that identifies a position other than the central position, and the position other than the central position may be used as the evaluation point. In the above embodiment, multiple anti-aircraft markers 6 are installed along the edge of the road (longitudinal direction of the road) at intervals of, for example, 5 to 15 meters. However, the placement of multiple anti-aircraft markers 6 is arbitrary. Therefore, multiple anti-aircraft markers 6 may be installed along the width direction of the road at intervals of, for example, 1 meter or less. In step S1 (coordinate acquisition step), the total station 2 acquires three-dimensional coordinates for predetermined positions where multiple anti-aircraft markers 6 are installed. However, if three-dimensional coordinates for the predetermined positions have already been acquired, those three-dimensional coordinates may be acquired instead. In the above embodiment, plate-shaped anti-aircraft markers 6 are installed on the road surface. However, instead of using plate-shaped anti-aircraft markers 6, a pattern similar to the anti-aircraft markers 6 may be formed on the road surface using any material, such as paint. For example, a pattern similar to the anti-aircraft markers 6 may be formed by spraying a pattern identical to the white portion of the anti-aircraft marker 6 in FIG. 3 onto the asphalt surface of the road using paint of a different color than the asphalt surface. Even when anti-aircraft signs are formed on the road surface using any material such as paint, the type, shape, size, pattern, etc. of the anti-aircraft sign are optional.

In the above embodiment, roads were photographed using an unmanned aerial vehicle (including a camera) flying at an altitude of 20 meters or less above ground level. However, roads may also be photographed using a model aircraft (including a camera) flying at an altitude of 20 meters or less above ground level. In this invention, an unmanned aerial vehicle is an airplane, rotorcraft, airship, etc. that cannot carry a person and can fly by remote control or automatic piloting, such as a drone (multicopter), radio-controlled aircraft, etc. Furthermore, a model aircraft is, for example, a multicopter, radio-controlled aircraft, etc., with a weight of less than 200 grams, which is the total weight of the aircraft body and battery.

In the above embodiment, an anti-aircraft sign 6 installed on the ground at the time of shooting is used as a feature point for connecting multiple captured images, and the three-dimensional coordinates of the anti-aircraft sign 6 are each acquired by the total station 2. However, if a specific point in an image captured by a UAV 3 is used as a feature point for connecting multiple captured images, and three-dimensional coordinated point cloud data for each point in the captured image, including the specific point, has already been acquired by scanning with a 3D scanner 4, the three-dimensional coordinates of the specific point may be acquired from that point cloud data.

In the above embodiments, examples of methods for creating orthoimages and road survey methods have been described, but in Figures 5, 14, 18, and 23, the order of steps S1 and S2 may be reversed. Therefore, it is possible to capture an image after obtaining the three-dimensional coordinates of the anti-aircraft sign 6, and it is also possible to obtain the three-dimensional coordinates of the anti-aircraft sign 6 after capturing the image.

In the above embodiments, examples of methods for creating orthoimages and road survey methods have been described, but in Figures 29 and 31, the order of steps S102 and S103 may be reversed. Therefore, it is possible to capture an image after obtaining the three-dimensional coordinates of the anti-aircraft sign 6, and it is also possible to obtain the three-dimensional coordinates of the anti-aircraft sign 6 after capturing the image. Also, in Figures 29 and 31, the order of steps S101 and S103 may be reversed. Therefore, although point cloud data of an area including multiple anti-aircraft signs 6 is acquired before capturing an image of the area including multiple anti-aircraft signs 6, point cloud data of an area including multiple anti-aircraft signs 6 may be acquired after capturing an image of the area including multiple anti-aircraft signs 6.

In the above embodiment, the following surveys were described as being conducted using orthoimages created by the orthoimage creation device 10: a survey of the crack condition on the road surface, a survey of the position of planar elements around the road, a survey to repair the area around a manhole, a survey of the distance between two specified points on the road surface, and a survey of the area of a specified range on the road surface. However, the orthoimages created by the orthoimage creation device 10 may also be used for other surveys.

1 Ortho image creation system 2 Total station 3 UAV (unmanned aerial vehicle)
4. 3D scanner (3D scanning device)
5 Display unit 6 Anti-aircraft marker 10 Orthoimage creation device 11 Photographed image storage unit (photographed image storage means)
12 Orthoimage creation unit (orthoimage creation means)
13 Coordinate storage unit (coordinate storage means)
14 Display control unit

Claims (12)

a coordinate acquisition step of acquiring three-dimensional coordinates of a plurality of feature points;
an imaging step of capturing a plurality of images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two of the captured images;
an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired by the coordinate acquisition step and the plurality of photographed images taken by the photographing step,
An orthoimage creation method characterized in that the orthoimage creation step creates an orthoimage in which there is no vehicle on the road by replacing the area around the vehicle on the road with an image of the road without the vehicle in another captured image. the feature point is an anti-aircraft sign installed on the ground at the time of photographing in the photographing step,
The orthoimage creation method according to claim 1, characterized in that in the coordinate acquisition step, the three-dimensional coordinates of the anti-aircraft sign are acquired by one of a total station, a satellite-based positioning system, and a three-dimensional scanning device. the feature points are predetermined points within an image captured by the unmanned aerial vehicle or the model aircraft,
The orthoimage creation method according to claim 1, characterized in that in the coordinate acquisition step, three-dimensional coordinates of a specified point are acquired from three-dimensional coordinated point cloud data acquired for each point in the captured image. a coordinate storage means for storing three-dimensional coordinates of a plurality of feature points;
a captured image storage means for storing a plurality of captured images taken by an unmanned aerial vehicle or a model aircraft flying in the sky such that each of the plurality of feature points is included in at least two of the captured images;
an orthoimage creation means for creating an orthoimage based on the three-dimensional coordinates of each feature point stored in the coordinate storage means and the plurality of photographed images stored in the photographed image storage means,
The orthoimage creation system is characterized in that the orthoimage creation means creates an orthoimage in which there is no vehicle on the road by replacing the area around the vehicle on the road with an image of the road without the vehicle in another captured image. the feature points are anti-aircraft markers installed on the ground at the time of photographing by the unmanned aerial vehicle or the model aircraft;
The orthoimage creation system according to claim 4, characterized in that the coordinate storage means stores the three-dimensional coordinates of the anti-aircraft markers acquired by either a total station, a satellite-based positioning system, or a three-dimensional scanning device. the feature points are predetermined points within an image captured by the unmanned aerial vehicle or the model aircraft,
The orthoimage creation system according to claim 4, characterized in that the coordinate storage means stores the three-dimensional coordinates of a specified point extracted from the three-dimensional coordinated point cloud data obtained for each point in the captured image. a coordinate acquisition step of acquiring three-dimensional coordinates of a plurality of feature points;
an imaging step of capturing a plurality of images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two of the captured images;
an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquisition step and the plurality of photographed images taken in the photographing step, and creating an orthoimage in which no vehicle is on the road by replacing an area around the vehicle on the road with an image of the road in another photographed image in which no vehicle is on the road;
a display step of displaying the orthoimage on a display unit;
A derivation step of dividing the survey area in the orthoimage displayed on the display unit into a plurality of survey ranges and deriving a crack rate or a patching rate for each of the plurality of survey ranges;
a road condition display step of displaying the road condition by adding a color to the orthoimage displayed on the display unit according to the magnitude of the crack rate or patching rate of each survey area derived in the derivation step. a coordinate acquisition step of acquiring three-dimensional coordinates of a plurality of feature points;
an imaging step of capturing a plurality of images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two of the captured images;
an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquisition step and the plurality of photographed images taken in the photographing step, and creating an orthoimage in which no vehicle is on the road by replacing an area around the vehicle on the road with an image of the road in another photographed image in which no vehicle is on the road;
a display step of displaying the orthoimage on a display unit;
a planar element mapping step of tracing planar elements in the orthoimage displayed on the display unit to map the planar elements. a coordinate acquisition step of acquiring three-dimensional coordinates of a plurality of feature points;
an imaging step of capturing a plurality of images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two of the captured images;
an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquisition step and the plurality of photographed images taken in the photographing step, and creating an orthoimage in which no vehicle is on the road by replacing an area around the vehicle on the road with an image of the road in another photographed image in which no vehicle is on the road;
a point cloud data acquisition step of acquiring point cloud data of an area including a periphery of the manhole in the orthoimage;
a display step of displaying the orthoimage on a display unit;
A road survey method characterized by comprising an elevation difference derivation step of deriving the elevation difference between the elevation of the area surrounding the manhole in the orthoimage displayed on the display unit and the elevation of the area surrounding the manhole on the repair plan surface. a coordinate acquisition step of acquiring three-dimensional coordinates of a plurality of feature points;
an imaging step of capturing a plurality of images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two of the captured images;
an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquisition step and the plurality of photographed images taken in the photographing step, and creating an orthoimage in which no vehicle is on the road by replacing an area around the vehicle on the road with an image of the road in another photographed image in which no vehicle is on the road;
a point cloud data acquisition step of acquiring point cloud data of an area including the plurality of feature points;
a display step of displaying the orthoimage on a display unit;
a designation step of designating two designated points spaced apart from each other in the orthoimage displayed on the display unit;
a distance display step of displaying the distance between the two designated points when the two designated points are designated in the designation step. a coordinate acquisition step of acquiring three-dimensional coordinates of a plurality of feature points;
an imaging step of capturing a plurality of images using an unmanned aerial vehicle or a model aerial vehicle flying in the sky so that each of the plurality of feature points is included in at least two of the captured images;
an orthoimage creation step of creating an orthoimage based on the three-dimensional coordinates of each feature point acquired in the coordinate acquisition step and the plurality of photographed images taken in the photographing step, and creating an orthoimage in which no vehicle is on the road by replacing an area around the vehicle on the road with an image of the road in another photographed image in which no vehicle is on the road;
a point cloud data acquisition step of acquiring point cloud data of an area including the plurality of feature points;
a display step of displaying the orthoimage on a display unit;
a designation step of designating a designated range in the orthoimage displayed on the display unit; and an area display step of displaying an area of the designated range when the designated range is designated in the designation step. An orthoimage creation method for creating an orthoimage based on three-dimensional coordinates of a plurality of feature points and a plurality of photographed images taken by an unmanned aerial vehicle or a model aircraft flying in the sky so that each of the plurality of feature points is included in at least two of the photographed images,
An orthoimage creation method, characterized by replacing an area around a vehicle on a road with an image of the road without the vehicle in another captured image, thereby creating an orthoimage in which the vehicle is not on the road.