JP2002081941A - System and method of measuring three-dimensional shape of road - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately determine the three-dimensional shape of a road. SOLUTION: During running of an observation car 10 on a road, a car-carried hybrid position measuring unit 64 measures the latitude, the longitude and the height of a running trace of the car 10. In parallel, a car-carried video camera 20 takes images of the road and a picture processor 66 extracts white lines at both sides of the road to obtain the distance between the position of the car 10 and the center point of the road, based on the white lines, and calculates the latitude, the longitude and the height of the load center point, using the latitude, the longitude and the height of the travel trace, the distance between the observation car position and the road center point, and the yaw angle and the roll angle from a car-carried three-axis angle sensor 40. It obtains a three- dimensional shape of the road center line from the result and obtains a gradient in the road width direction from the roll angle to determine the latitude, the longitude and the height of an optional position on the road.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to a system and a method for measuring the three-dimensional shape of a road.

[0002]

2. Description of the Related Art In recent years, in intelligent transportation systems (ITS), advanced intelligence and computerization of vehicles have become increasingly important. Correspondingly, high-precision road 3 to control various electronic devices such as steering and brakes
There is a demand for maintenance of three-dimensional map data (= digital road map for car navigation).

Conventionally, as a method of measuring the three-dimensional shape of a road by three-dimensional positioning, a vehicle equipped with a hybrid positioning device that uses both GPS positioning and self-contained navigation is driven on a road, and the latitude and longitude of the vehicle are measured. A method for obtaining a three-dimensional shape of a road from a height is known. For example, Patent No. 3013
Invention of No. 309 and Yoshiki Yamano "Development of kinematic photogrammetry system using mobile mapping system"
The Japan Survey and Survey Technology Association, The 22nd Technical Presentation, Transactions, P.1 to P.8, and those described on June 28, 2000.

[0004] However, these conventional methods do not consider the width of the road, and do not consider the latitude, longitude, and the trajectory of the vehicle traveling on the road.
The height is a three-dimensional shape of the road. Therefore, there is a problem that a three-dimensional road shape along the center line of the road cannot be obtained. In addition, it is impossible to detect inclination in the width direction of the road (this is particularly important for speed control in a curved portion of the road).

On the other hand, as a method of measuring the road shape including the width of the road, there is an invention disclosed in Japanese Patent Application Laid-Open No. Hei 5-297941. In this type of related art, a planar shape of a road is measured by performing image processing on a white line image on the side of the road captured by a vehicle-mounted camera. However, since the height of the road is not taken into account, it is impossible to obtain an accurate three-dimensional shape of the road.

[0006]

SUMMARY OF THE INVENTION It is an object of the present invention to enable an accurate three-dimensional shape of a road, that is, a latitude, longitude and height at an arbitrary position on the road to be determined.

Another object of the present invention is to be able to determine the curvature of the road, the gradient in the vertical direction of the road, and the gradient in the lateral (width) direction of the road.

It is another object of the present invention to be able to determine the three-dimensional shape of a road along the center line of the road.

[0009]

A system for measuring the three-dimensional shape of a road according to the present invention comprises a positioning device for measuring the latitude, longitude and altitude of an observation vehicle, and an image of the road mounted on the observation vehicle. A camera, a roll angle sensor and a yaw angle sensor mounted on the observation vehicle, the latitude, longitude, and altitude of the observation vehicle from the positioning device, a road image from the camera, and a roll angle from the roll angle sensor. And an index calculator for calculating and outputting a road three-dimensional shape index enabling determination of the latitude, longitude and altitude of an arbitrary position on the road using the yaw angle from the yaw angle sensor.

[0010] The system of the present invention uses the latitude, longitude and altitude data of the traveling locus of the observation vehicle obtained from the positioning device,
Further, information obtained from a road image obtained by photographing the road from the observation vehicle, and the roll angle and the yaw angle of the observation vehicle are taken into account. This makes it possible to obtain a road three-dimensional shape index from which the latitude, longitude and altitude of an arbitrary position on the road can be determined, regardless of the position of the observation vehicle in the horizontal (width) direction on the road.

In a preferred embodiment, the road three-dimensional shape index is calculated as follows. That is, a predetermined vertical line extending on the road in the vertical direction of the road is obtained from the road image, and a relative positional relationship between the vertical line and the observation vehicle is obtained. And the relative positional relationship between the vertical line and the observation vehicle,
Using the latitude, longitude and altitude of the observation vehicle, the roll angle and the yaw angle, a three-dimensional shape such as the latitude, longitude and altitude of the vertical line is calculated. Also, the lateral gradient of the road is determined from the roll angle. Based on the three-dimensional shape of the vertical line thus obtained and the gradient in the lateral direction of the road, it is possible to determine the latitude, longitude and altitude of all positions on the road.

In a preferred embodiment, the center line of the road is used as the vertical line of the road. In addition, white lines drawn on the left and right sides of the road and lane boundaries,
It can be used as the vertical line.

In a preferred embodiment, the road three-dimensional shape index includes data of the latitude, longitude and altitude of the road center line, and data of the gradient in the lateral direction of the road. Instead of or in addition to these data, data on the curvature of the center line of the road, data on the gradient of the center line of the road (that is, the vertical direction of the road), and data on the gradient of the horizontal direction of the road are included. You may go out. Alternatively, data other than these can be used as a road three-dimensional shape index if the latitude, longitude, and altitude of an arbitrary position on the road can be determined based on the data.

In a preferred embodiment, a hybrid positioning device that combines a GPS positioning method and a self-contained navigation positioning method is used as a positioning device that measures the latitude, longitude, and altitude of an observation vehicle. However, if either the GPS positioning method or the self-contained navigation positioning method can perform positioning with sufficient accuracy, only one of them may be used. If there is another alternative positioning method, these may be used.

[0015]

FIG. 1 shows the configuration of a road three-dimensional shape measuring system according to an embodiment of the present invention.

In this embodiment, as shown in FIG.
All the components of the dimensional shape measurement system are mounted on an automobile (observation vehicle) 10 for observing a road, but this is not always necessary. The element to be mounted on the vehicle is GP
S antenna 20, video camera 30, 3 axis angle sensor 4
0 and the vehicle speed sensor 50, and the other elements may be placed in a different place from the observation vehicle 10, in which case,
What is necessary is that data can be transmitted from a vehicle-mounted device to a device that is not mounted on a vehicle offline or online.

As shown in FIG. 1, the road three-dimensional shape measuring system includes a GPS antenna 20, a video camera (a monitor (not shown) is also connected) 30, a three-axis angle sensor 40, a vehicle speed sensor 50, an arithmetic processing unit. 60, a display device 70, a printing device 80, and a storage device 90.

The GPS antenna 20 receives GPS signals for measuring the latitude, longitude and altitude of the current position of the observation vehicle 10 from a plurality of artificial satellites. The video camera 30 captures an image of a road in front of the observation vehicle 10 (when the road is divided into a plurality of lanes and is considerably wide, it may be one traveling lane) with the video camera 30. The photographed road image is displayed on an attached monitor (not shown). The field of view and direction of the video camera 30 are set so that white lines drawn on the left and right sides of the road immediately before the observation vehicle 10 can be completely captured. The white line image of the road taken by the video camera 30 is used to determine the center line of the road 10 (the center line between the white lines at both ends) and the distance between the current position of the observation vehicle 10 and the center line of the road. You.

The three-axis angle sensor 40 detects the current yaw angle, roll angle and pitch angle of the observation vehicle 10 respectively.
It has various types of angle sensors (a yaw angle sensor, a roll angle sensor, and a pitch sensor). Here, the "yaw angle" referred to in this specification is a rotation angle when the observation vehicle 10 makes a rotational movement about its vertical axis (basically, an angle obtained by changing the traveling direction to left and right). . The “roll angle” is a rotation angle (basically, the inclination of the vehicle in the horizontal direction due to the gradient in the lateral (width) direction of the road) when the observation vehicle 10 makes a rotational movement about its longitudinal axis. is there. The “pitch angle” is a rotation angle when the observation vehicle 10 makes a rotational movement around its left-right axis (basically, the inclination of the vehicle in the longitudinal direction due to the vertical gradient of the road). The vehicle speed sensor 50
The traveling speed of the observation vehicle 10 is detected. These sensors 4
The detection signals from 0 and 50 are used to correct the latitude, longitude and altitude of the observation vehicle 10 calculated from the GPS signal from the GPS antenna 20 to obtain more precise latitude, longitude and altitude (hybrid. Positioning method). In addition,
The roll angle from the three-axis angle sensor 40 is also used for calculating the gradient in the width (lateral) direction of the road where the observation vehicle 10 is currently located,
The yaw angle is used to determine the current traveling direction of the observation vehicle 10 (typically, the angle between the traveling direction and the north direction).

The arithmetic processing unit 60 calculates a road three-dimensional shape index representing the three-dimensional shape of the road using signals from the various sensors 20, 30, 40, and 50 described above, as will be described in detail later. . Here, the road three-dimensional shape index includes:
For example, the latitude, longitude and altitude of the road centerline, the curvature of the road,
The vertical gradient of the road, the horizontal gradient of the road, and the like are included. The arithmetic processing unit 60 includes, for example, a programmed computer and an attached input / output interface circuit.

The functional elements of the arithmetic processing unit 60 are as follows. First, the above-described various sensors 20, 30, 4
Signal input units 61, 62 for inputting signals from 0, 50,
There are 63 and 65. Next, the hybrid positioning calculation unit 64
This calculates the latitude, longitude and altitude of the observation vehicle using GPS signals, yaw, roll and pitch angle signals, and vehicle speed signals. The next element is an image processing unit 66, which extracts white line images on both sides of the road from a captured image of the road from the video camera 30 and detects the center point of the road from the white line image. Calculate the relative positional relationship (distance) between the road center point and the observation vehicle. The next element is the road three-dimensional shape index calculation unit 67, and the hybrid positioning calculation unit 6
4 and the output of the image processing unit 66, the above-mentioned various road three-dimensional shape indices are calculated. Finally, the road three-dimensional shape index output unit 68 displays the calculated various road three-dimensional shape indices on the printing device 80 and the storage device 90.
Output to

The display device 70 has a printing device 80 and a storage device 90 for displaying, printing, and recording the road three-dimensional shape index output from the arithmetic processing device 60, respectively.

FIG. 2 shows a flow of processing performed by the road three-dimensional shape measuring system (in particular, the arithmetic processing unit 60).
In FIG. 2, square blocks indicate processing steps performed by the system, and oval blocks indicate signal or data information obtained in each processing step.

In step 110, the on-board video camera 30 captures an image of the road ahead of the observation vehicle 10. As a result, a road image 120 having white lines on both sides is obtained. In the following step 130, the image processing unit 66 takes in the road image 120 through the image input unit 65.
Then, the image processing unit 66 extracts white lines on both sides of the road from the road image 120 in step 131 of this step 130, and in step 132, the midpoint between the extracted white lines, that is, the center point of the road. Is calculated, and the distance between the center point of the road and the position of the observation vehicle 10 is calculated (data 1
40).

In step 150, which is performed in parallel with the above steps 110 and 130, the hybrid positioning calculation section 64 transmits the signal through the signal input sections 61, 62 and 63.
A GPS signal from the GPS antenna 20, a yaw, a roll, and a pitch from the three-axis angle sensor 40;
In addition, a vehicle speed signal from the vehicle speed sensor 50 is taken in, and a hybrid positioning method combining the GPS positioning method and the self-contained navigation positioning method is used by using those signals (for example, Japanese Patent No.
The present latitude, longitude and altitude of the observation vehicle 10 are calculated by the method described in No. 3013309) (data 160).

In step 190 performed in parallel with step 150, the hybrid positioning calculation unit 64
Using the roll angle signal from the shaft angle sensor 40, the current roll angle of the observation vehicle 10 (that is, the gradient in the lateral direction of the road) is obtained (data 200). In step 210 performed in parallel with this, the hybrid positioning calculation unit 64
Using the yaw angle signal from the shaft angle sensor 40, the observation vehicle 1
Current yaw angle of 0 (ie, gradient in the direction of travel of the road)
(Data 220).

Steps 170, 230, and 250 are performed by the road three-dimensional shape index calculator 67.

In step 170, the road three-dimensional shape index calculation section 67 calculates data 140 on the relative positional relationship between the observation vehicle 10 and the road center point obtained in step 130.
And the latitude, longitude, and altitude data 160 of the observation vehicle 10 obtained in the above-described step 150, and the above-described step 1
The altitude of the road center point is calculated using the roll angle data 200 obtained at 90 (data 180).

In the following step 230, the road three-dimensional shape index calculating section 67 calculates the altitude data 180 of the road center point obtained in the above-mentioned step 170 and the above-mentioned step 150.
Latitude, longitude and altitude data 16 of the observation vehicle 10 obtained by
Using 0, the roll angle data 200 obtained in step 190 described above, and the yaw angle data 220 obtained in step 210, the latitude and longitude of the road center point are calculated (data 240). .

In the following step 250, the road three-dimensional shape index calculating section 67 calculates the latitude and longitude data 240 of the road center point obtained in the above step 230 and the above-mentioned step 14.
Using the data 140 of the relative positional relationship between the observation vehicle 10 and the road center point obtained at 0 and the roll angle data 200 obtained at the above step 190, the latitude, longitude and altitude of the road center line, The road three-dimensional shape index such as the curvature of the road, the vertical gradient of the road, and the horizontal gradient of the road is calculated (data 260).

Since the road three-dimensional shape index includes the data of the latitude, longitude and altitude of the road center line, the data of the curvature of the road, and the data of the horizontal gradient of the road,
Based on this, the latitude, longitude and altitude of an arbitrary position on the road can be determined. That is, a complete three-dimensional shape of the road can be represented.

Of the above-described processing, in particular, step 13
The method for obtaining the road center point of 0, the method for obtaining the height of the road center point in step 170, the method for obtaining the latitude and longitude of the road center point in step 230, and the method for obtaining the road three-dimensional shape index in step 250 This will be described in detail below.

1. Determination of the road center point and the distance between the observation vehicle position and the road center point (FIG. 2, step 130)

(1) The road center point and the distance between the own vehicle position and the road center point are obtained by performing image processing on a continuous road image including white lines on both sides in front of the vehicle taken by the on-board video camera 30. Therefore, the target road is a road with white lines drawn on both sides. Even if the white line is discontinuous or part of the white line is broken due to dirt, noise, or the like, the white line can be recognized by the Hough transform described later in (4). Even if the white line is a color other than white, for example, a yellow line, it can be recognized from the road surface by image processing and can be recognized.

Hereinafter, taking a substantially straight road of one lane as an example, white line position recognition on a road image by template matching (shown in step 131 of FIG. 2) and back projection transformation (= white line position on the image Road centerline points by image processing such as backprojection transformation into a three-dimensional space) and Hough transformation (= method of straightening a white line point sequence of the backprojection transformation result);
The estimation of the distance between the own vehicle position and the center of the road will be described.

(2) White line position recognition by template matching (FIG. 2, step 131)

Determination of Initial Template In order to determine the first template, first, a stripe range 400 that crosses the image (ie, crosses the road 300) is set below the photographed road image as shown in FIG. White line recognition is performed within this stripe range 400 (in the figure, two hatched lines 310 and 31).
0 is the white line on both sides of the road). A density value histogram is obtained by using the density values of all the pixels in the stripe range 400, a density threshold suitable for extracting only the white line is determined from the density value histogram, and the density threshold is used to determine the density of all the pixels in the stripe range 400. Binarize the density value. as a result,
As shown in FIG. 4, an area 4 binarized to a density value “0”
Reference numeral 10 denotes left and right white line areas. Subsequently, as shown in FIG. 5, for the extracted left and right white line regions 410, a rectangular initial template 420 in which the regions are expanded on both sides with the center of gravity as the center point is determined. The left and right initial templates 420 are the initial templates 42
It has information on the density values (original density values or binarized density values) of all the pixels in 0.

Template Matching Using the generated template 420, white line position recognition is performed on the target image. As shown in FIG. 5, in the target image, a rectangular search range 440 extending on both sides larger than the template 420 is set, and the search range 44 is set.
The area where the similarity with the template 420 is maximized within 0 is searched (FIG. 5 shows only the search range 440 for the white line on the left side, but the search area is similarly set for the white line on the right side). The white line 310 is basically a substantially continuous area extending vertically in the image toward the center of the horizontal line height of the image. Based on this fact, the position of the white line is predicted, and the search range 440 can be set above (or above) the vicinity of the first template 420. The amount of calculation is reduced by reducing the size of the search range 440 so as not to reach a range where there is no possibility that a white line exists. Template 4 within search range 440
20 and a density value pattern matching. As a result, the region 4 having the highest matching degree with the template 420
50 are extracted, and the density values of all the pixels in the extracted match area 450 are binarized in the same manner as described above. And
The area binarized to the density value “0” in the match area 450 is recognized as a new white line area, and the center of gravity of the new white line area is recognized as the center point of the new white line.

Update of Template The white line 310 on the image becomes narrower as it approaches the center of the image (farther away).
If the matching is continued while using 0, it becomes difficult to perform matching with the white line area near the center of the image. Therefore, here, a new template centered on the center point of the new white line recognized above (a template having information on the density values of all pixels inside the new template) is set, and the new template is used by using the new template. Repeat template matching. In this way, by sequentially creating and using new templates 420 to 428 as shown in FIG. 6 (only the template on the left is shown, the same applies to the right), the template becomes thinner toward the center of the image. It is possible to track the white line that extends as it becomes. The update of the template starts from the initial template 420 of the line of the observation vehicle in the image, and starts at the position 4 at a predetermined height in the image (that is, a predetermined distance ahead of the observation vehicle on a real road).
Perform up to 29. Position 429 from initial template 420
With this distance, a white line having a length necessary and sufficient to determine the center point of the road in the subsequent processing can be detected. Further, the distance from the initial template 420 to the position 429 is short enough that the road can be regarded as a substantially straight line.

By sequentially performing the matching and template updating processes on the left and right white lines, the position information of the center point of the white lines in the image is obtained. In this way, an image showing the position of the center point 500 of the white line on the target image as shown in FIG. 7 is obtained.

(3) Backprojection transformation (FIG. 2, step 13)
1,).

Projection Relationship Between Vehicle Coordinate System and Image Coordinate System Next, by performing backprojection transformation, the white line center point position on the image recognized in the above (2) is converted into a three-dimensional space position. Hereinafter, the backprojection transformation will be described.

The relationship between the position on the image obtained by photographing from the video camera 30 and the actual position in the three-dimensional space is based on the center of the lens 600 of the video camera 30 as shown in FIG. It can be represented by a relationship between two coordinate systems, a vehicle coordinate system XYZ and an image coordinate system xy with the origin at the center of the image 610 obtained by shooting. In the vehicle coordinate system XYZ, the XZ plane is a plane parallel to the road surface of the road 300. That is, the traveling direction of the observation vehicle is defined as the Z-axis, and a direction (horizontal direction of the observation vehicle) orthogonal to the Z-axis on the same horizontal plane as the Z-axis is defined as the X-axis. Let H be the height from the road surface of the road 300 to the center of the lens.

The position P in the three-dimensional space of the vehicle coordinate system XYZ
(X, Y, Z) is the position P ′ on the image by the following equation (1) (projection conversion equation)
Projected to (x, y).

(Equation 1)

A backprojection transformation from a position P ′ (x, y) on an image to a three-dimensional space position P (X, Y, Z) is represented by the following equation (1) derived from the above equation (1), where Using 2), the white line center point 50 on the captured image as shown in FIG.
By backprojecting 0 into a three-dimensional space of the vehicle coordinate system XYZ, a plan view showing the position of the white line center point 500 on the XZ plane of the vehicle coordinate system XYZ as shown in FIG. 9 is obtained. The center point 510 at the lower end of this plan view is the current position of the observation vehicle.

(Equation 2)

(4) Hough transform (FIG. 2, step 131,
)

The Hough transform, which is a general method for extracting a line segment from a sequence of points, is performed by using the white line center point 500 in the plan view obtained in the above (3).
, And a white line 520 is extracted as shown in FIG.

(5) Estimation of the road center point and the distance between the observation vehicle position and the road center point (FIG. 2, step 133)

The estimation of the road center point and the distance between the observation vehicle position and the road center point will be described. In the plan view of the white line 520 shown in FIG. 11 obtained by the Hough transform, a road existing in a direction perpendicular to the traveling direction 700 of the observation vehicle from the position 510 of the observation vehicle (that is, on the X axis of the vehicle coordinate system). Is obtained as follows. Here, the observation vehicle position 510 (= the center point at the lower end of the image) is a point lowered vertically from the center of the camera lens to the road surface.
0 is the origin on the XZ plane of the vehicle coordinate system.

First, the line at the midpoint between the two white lines 520 is determined as the road center line 720. Next, the road width d _line ,
Using a distance D1 and D2 between each white line 520 from a certain reference point 730 (for example, the upper left point of the plan view) on this plan view, it is obtained by the following equation. d _line = D2-D1 (3) The distance from the reference point 730 to the road center line 720 is
D ₁ + d _line / 2, and the inclination of the road center line 720 is θho
Since it is obtained from ugh, the road center line 720 can be expressed by a straight line equation.

Next, a point at which the road center line 720 intersects the X axis is determined as a road center point 710. Then, since the position 510 of the observation wheel is the origin, X-coordinate value x _c road central point 710, as it is self-observation wheel position 510 and the road center point 7
This is the distance d between the ten. When the observation vehicle position 510 is on the left side of the road center point 710, the distance d is positive, and when it is on the right side, the distance d is negative.

2. Measurement of latitude / longitude / height of road center point (Fig. 2, step 170)

Observation vehicle position data (latitude, longitude and altitude) obtained from the hybrid positioning in step 150 of FIG.
And the distance d between the observation vehicle position 510 and the road center point 710 described with reference to FIG. 11, and the roll angle (rolling angle in the traveling direction = gradient in the road lateral (width) direction) obtained from the three-axis angle sensor. ) And yaw angle (traveling direction angle) data,
The latitude, longitude and altitude of the road center point 710 are calculated as follows.

(1) Measurement of the height of the center point of the road (FIG. 2, step 180)

FIG. 12 shows the positional relationship between the position 510 of the observation vehicle 10 on the road 300 and the center point 710 of the road. The coordinates of the observation vehicle position 510 and O (lat, lot, alt) are set, and the coordinates of the road center point 710 are set as C (lat ', lot', alt '). The altitude alt 'of the road center point 710 is the altitude alt of the observation vehicle position 510, the observation vehicle position 510 on the road 300 and the road center point 710.
Using the distance d (inclined from the horizontal by the roll angle θ _role ) and the roll angle θ _role , the following equation can be used. alt '= alt-d · sinθ _role …… (4)

(2) Measurement of the latitude and longitude of the center point of the road (FIG. 2, step 230)

As shown in FIG. 12, the distance d between the observation vehicle position 510 and the road center point 710 is data on the road surface of the road 300 inclined by the roll angle θ _role. To determine the longitude, the distance d on the inclined road surface must be converted to a distance d 'on a horizontal map plane. Therefore, a distance d ′ on the map plane between the observation vehicle position 510 and the road center point 710 is obtained by the following equation. d '= d · cosθ _role …… (5)

FIG. 13 shows the observation vehicle 10 on the map plane.
The relationship between the traveling direction 700 and the latitude and longitude is shown.
As shown in FIG. 13, the observation vehicle 1 obtained based on the yaw angle
Assuming that the angle between the traveling direction 700 of 0 and the north direction (latitude direction) 800 is θ _dir , the distance change component Δlatm in the latitude direction 800 from the observation vehicle position 510 to the road center point 710 and the distance change in the longitude direction 810 The component Δlotm is as follows. Δlatm = d ′ · sin (−θ _dir ) Δlotm = d ′ · cos (−θ _dir ) (6)

The relationship between the distance change component in the latitude and longitude directions and the change in the latitude and longitude is 1 minute of latitude = 1848.39 m and 1 minute of longitude = 1560.48 m from the data of the earth ellipsoid GRS-80. Is an example of Kumamoto City, and specific figures vary depending on locations on the earth.) Therefore, the latitude change Δlat and the longitude change Δlot from the observation vehicle position 510 to the road center point 710
Is obtained by the following equation. Δlat = Δlatm ÷ 1848.39 Δlot = Δlotm ÷ 1560.48 (7)

Therefore, the latitude and longitude of the observation vehicle position 510 obtained by the hybrid positioning are defined as lat and lot, respectively, and the latitude lat 'and longitude lot' of the road center point 710 are calculated by the following equations. lat '= lat + Δlat lot' = lot + Δlot …… (8)

3. Calculation of road three-dimensional shape indices such as road curvature, road vertical gradient, and road horizontal gradient (FIG. 2, step 250)

The above-described road three-dimensional shape index is an index expressing the three-dimensional shape of the entire road using the three-dimensional shape of the road center line 720 as a representative value. By connecting the data of the latitude, longitude, and altitude of many continuous road center points 710, three-dimensional shape data of the road center line 720 can be obtained.
From the three-dimensional shape data of the road center line 720, the road curvature and the gradient in the vertical direction of the road are obtained. Further, the gradient in the lateral (width) direction of the road is obtained from the roll angle measured at each point in time when the observation vehicle 10 is traveling.

The road three-dimensional shape index obtained in this way is:
It represents the complete three-dimensional shape of the entire road. That is,
From the road 3D shape index, the latitude of any position on the road,
Longitude and altitude can be determined. In addition, the lateral gradient of the road greatly contributes to the optimal control of the speed and the steering, as well as the curvature of the road, for example in automatic driving of a motor vehicle.

The embodiment of the present invention has been described above.
The above embodiment is merely an example for describing the present invention, and is not intended to limit the present invention to only the above embodiment. Therefore, the present invention can be implemented in various modes other than the above-described embodiment. For example, in the above embodiment, the three-dimensional shape of the center line of the road is used as the representative value of the road. Instead of the road center line, an arbitrary position that can be specified on the road, such as one or both of the left and right white lines of the road, may be used as the representative.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of a road three-dimensional shape measuring system according to an embodiment of the present invention.

FIG. 2 is a flowchart showing a processing flow of the road three-dimensional shape measuring system.

FIG. 3 is a diagram of a road image illustrating a process of setting an initial template.

FIG. 4 is a diagram of a road image illustrating a process of setting an initial template.

FIG. 5 is a diagram of a road image for explaining template matching processing.

FIG. 6 is a diagram of a road image for explaining a template update process.

FIG. 7 is a diagram showing an image of a center point of an extracted white line.

FIG. 8 is a view for explaining the principle of back projection conversion.

FIG. 9 is a plan view of a center point of a white line obtained by back projection transformation.

FIG. 10 is a plan view of a white line obtained after the Hough transform.

FIG. 11 is a view for explaining a method of obtaining a distance between an observation vehicle position and a road center point.

FIG. 12 is a diagram showing a positional relationship between an observation vehicle position and a road center point.

FIG. 13 is a diagram showing a relationship between a traveling direction of an observation vehicle and latitude and longitude on a map plane.

[Explanation of symbols]

 Reference Signs List 10 observation vehicle 20 GPS antenna 30 video camera 40 three-axis angle sensor 50 vehicle speed sensor 60 arithmetic processing unit 70 display device 80 printing device 90 storage device 300 road 310 white line 410 white line region 420 to 428 template 440 search region 500 center point of white line 510 Observation car position 520 White line 600 In-vehicle video camera lens 610 Image taken 700 Observation car traveling direction 710 Road center point 720 Road center line 800 North direction (latitude direction) 810 Longitude direction

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Claims (5)

[Claims] A positioning device for measuring the latitude, longitude and altitude of the observation vehicle; a camera mounted on the observation vehicle for photographing a road; a roll angle sensor and a yaw angle sensor mounted on the observation vehicle; Using the latitude, longitude and altitude of the observation vehicle from the positioning device, the road image from the camera, the roll angle from the roll angle sensor, and the yaw angle from the yaw angle sensor, A system for measuring the three-dimensional shape of a road, comprising: an index calculating unit that calculates and outputs a road three-dimensional shape index enabling determination of latitude, longitude, and altitude at an arbitrary position. 2. The index calculating unit obtains a predetermined vertical line extending in the vertical direction of the road on the road from the road image, and obtains a relative positional relationship between the vertical line and the observation vehicle. Image processing unit, relative positional relationship between the longitudinal line from the image processing unit and the observation vehicle, latitude, longitude and altitude of the observation vehicle, using the roll angle and the yaw angle A vertical line shape calculation unit that calculates a three-dimensional shape of the vertical line; and a horizontal gradient calculation unit that calculates a horizontal gradient of the road from the roll angle. The system of claim 1, wherein the dimensional shape indicator includes data based on a three-dimensional shape of the vertical line and a lateral gradient of the road. 3. The system according to claim 1, wherein the road three-dimensional shape index includes data of a three-dimensional shape of a center line of the road and data of a lateral gradient of the road. 4. The road three-dimensional shape index includes: data of a curvature of the road; data of a gradient in a vertical direction of the road;
2. The method according to claim 1, further comprising data of a lateral gradient of the road.
The described system. 5. A step of measuring the latitude, longitude and altitude of the observation vehicle; a step of photographing a road from the observation vehicle; a step of detecting a roll angle and a yaw angle of the observation vehicle; Using the latitude, longitude and altitude of the road, the photographed road image, the detected roll angle and the detected yaw angle, the three-dimensional shape of the road on which the latitude, longitude and altitude of the arbitrary position on the road can be determined. Calculating and outputting the indices.