JP2001169310A - Distance detection device - Google Patents

Abstract

(57) [Summary] [Problem] To calculate an accurate parallax by correcting an error caused by a parallelism shift between optical axes of two imaging devices mounted on a vehicle with a small amount of calculation compared with the related art, and calculating a distance. Provided is a distance detection device capable of improving detection accuracy. SOLUTION: At times t0 and t1, a parallax δd (t0) δd (t1) of a stationary object is detected, and a traveling distance L of the vehicle from time t0 to t1 is calculated. Based on the detected parallaxes δd (t0) and δd (t1) and the traveling distance L, an error due to the parallelism deviation between the optical axes of the cameras 1R and 1L is obtained as a parallax offset amount D (φ),
The detected parallax δd is corrected by the parallax offset amount D (φ).

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to distance detection for calculating a parallax of an object from two images obtained by two image pickup devices mounted on a vehicle and detecting a distance to the object based on the parallax. Related to the device.

[0002]

2. Description of the Related Art A method for detecting a distance to an object included in an image based on two images obtained by two image pickup devices has been conventionally known. In this method, two image pickup devices are used. The parallelism of the optical axis greatly affects the distance detection accuracy. That is, when the optical axes of the two imaging devices are not parallel (when there is a parallelism shift between the optical axes), an object located at a position sufficiently far from the mounting interval of the two imaging devices (effectively at infinity) Since the parallax of the object that can be regarded as being present is not “0”, the detected parallax always includes an error.

Therefore, it is conceivable to shorten the mounting interval (base line length) between the two image pickup devices to obtain a compact and robust structure and to maintain the parallelism between the optical axes accurately. Even if the parallelism is ensured, there is a problem that the distance detection accuracy is reduced. Further, when mounted on a vehicle or the like, even if it is small and robust, there is a high possibility that the optical axis parallelism changes over time due to vibration and vehicle body distortion accompanying driving.

[0004] Accordingly, a distance measuring device that calculates a mounting angle difference between two imaging devices mounted on a vehicle based on images obtained from the respective imaging devices, and detects a distance in consideration of the calculated mounting angle difference. Has already been proposed (Japanese Unexamined Patent Application Publication No.
-126759). The mounting angle difference calculation method disclosed in this publication recognizes a white line on a road near the host vehicle, finds a position on an image corresponding to an infinity point of each imaging device based on the white line, and calculates the position of the position. The attachment angle difference is calculated from the displacement.

[0005]

However, the technique disclosed in the above publication recognizes a white line near the host vehicle and obtains a position on an image corresponding to a point at infinity based on the white line. Therefore, the amount of calculation is increased, the accuracy is hardly obtained, and it is difficult to obtain a stable result.

SUMMARY OF THE INVENTION The present invention has been made in view of the above points, and has been made to correct an error caused by a shift in parallelism between optical axes of two image pickup devices mounted on a vehicle with a smaller amount of calculation than in the related art. It is an object of the present invention to provide a distance detection device that can calculate an accurate parallax and improve the distance detection accuracy.

[0007]

According to the first aspect of the present invention, a parallax of an object is calculated based on two images obtained by two image pickup units mounted on a vehicle. A distance detection device that detects a distance from the parallax to the object, a stationary object parallax calculating unit that calculates a parallax of the stationary object at first and second times, and a second object from the first time. A vehicle moving amount calculating unit that calculates a moving amount of the vehicle during a period up to the time, and the two imaging units based on a parallax of the stationary target object and the vehicle moving amount at the first and second times. And a parallax correcting unit that calculates a parallax offset amount caused by the deviation of the parallelism between the optical axes, and corrects the parallax based on the calculated parallax offset amount.

According to this configuration, the parallax of the stationary target object is calculated at the first and second times, and the vehicle movement amount from the first time to the second time is calculated.
Based on the parallax of the stationary object and the vehicle movement amount at the first and second times, a parallax offset amount for correcting an error caused by a shift in parallelism between the optical axes of the two imaging units is calculated, and the parallax offset amount is calculated. Since the parallax is corrected by the offset amount, the error due to the deviation of the parallelism between the optical axes of the two imaging units is corrected with a smaller amount of calculation than in the past, the correct parallax is calculated, and the distance detection accuracy is improved. Can be done.

The stationary object parallax calculating means determines the stationary object based on the presence or absence of the characteristic of the object (for example, a traffic light or a telephone pole) which has a high possibility of being detected during traveling of the vehicle and is surely stationary. It is desirable to do.

[0010]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram showing a configuration of a vehicle periphery monitoring device according to an embodiment of the present invention. This device includes two infrared cameras 1R and 1 capable of detecting far infrared rays.
L, a yaw rate sensor 5 for detecting a yaw rate of the vehicle, a vehicle speed sensor 6 for detecting a traveling speed (vehicle speed) VCAR of the vehicle, a brake sensor 7 for detecting a brake operation amount, and a camera 1R for these cameras. , An image processing unit 2 that detects an object such as an animal in front of the vehicle based on the image data obtained by 1L, and issues an alarm when the possibility of collision is high, and a speaker 3 that issues an alarm by voice; A head-up display (hereinafter, referred to as "HUD") 4 for displaying an image obtained by the camera 1R or 1L and for allowing the driver to recognize an object having a high possibility of collision is provided.

As shown in FIG. 2, the cameras 1R and 1L are arranged in front of the vehicle 10 at positions substantially symmetric with respect to the center axis of the vehicle 10 in the lateral direction.
The optical axes of L are fixed so that they are parallel to each other and their heights from the road surface are equal. Infrared camera 1R,
1L has the characteristic that the higher the temperature of the object, the higher the output signal level (the higher the luminance).

The image processing unit 2 includes an A / D conversion circuit for converting an input analog signal into a digital signal, an image memory for storing digitized image signals, a CPU (Central Processing Unit) for performing various types of arithmetic processing, and a CPU for performing arithmetic operations. RAM used to store intermediate data (Ra
ROM (Read Only Memory) for storing programs, tables, maps, etc., executed by the CPU.
mory), an output circuit for outputting a drive signal for the speaker 3, a display signal for the HUD 4, and the like.
1L and the output signals of the sensors 5 to 7 are converted into digital signals and input to the CPU. As shown in FIG. 2, the HUD 4 is provided such that a screen 4a is displayed at a position in front of the driver in a front window of the vehicle 10.

FIGS. 3 and 4 are flowcharts showing the processing procedure in the image processing unit 2. FIG. First, in step S10, the parallax offset amount D (φ) corresponding to the deviation of the parallelism between the optical axes of the cameras 1R and 1L is calculated as an initial value (for example, a value set at the time of factory shipment, or calculated at the time of the previous operation, and stored in the storage device). (The stored parallax offset amount), then A / D convert the output signals of the cameras 1R and 1L and store them in the image memory (steps S11, S12,
S13). The image stored in the image memory is a gray scale image including luminance information. FIGS. 5A and 5B
Is a diagram for explaining a grayscale image (a right image is obtained by the camera 1R and a left image is obtained by the camera 1L) obtained by the cameras 1R and 1L, respectively. An area surrounded by a thick solid line, which is a grayscale (gray) area, is an area of an object having a high luminance level (at high temperature) and displayed as white on a screen (hereinafter, referred to as a “high luminance area”). . In the right image and the left image, the horizontal position of the same object on the screen is shifted, so that the distance to the object can be calculated from the shift (parallax).

In step S14 of FIG. 3, the right image is used as a reference image, and the image signal is binarized, that is, an area brighter than a luminance threshold ITH determined experimentally is set to "1".
(White) and the dark area is set to “0” (black). FIG. 6 shows an image obtained by binarizing the image shown in FIG.
This figure shows that the hatched area is black, and the high-luminance area surrounded by the thick solid line is white.

In the following step S15, a process for converting the binarized image data into run-length data is performed. FIG. 7A is a diagram for explaining this. In FIG. 7A, an area whitened due to binarization is represented by a line L1 at a pixel level.
ＬL8. Each of the lines L1 to L8 has a width of one pixel in the y direction and is actually arranged without a gap in the y direction, but is shown apart for the sake of explanation. Lines L1 to L8 are respectively 2 pixels, 2 pixels, 3 pixels, 8 pixels, 7 pixels, 8 pixels, 8 pixels in the x direction.
Pixels have a length of 8 pixels. The run-length data indicates the lines L1 to L8 by using the coordinates of the start point of each line (the left end point of each line) and the length (the number of pixels) from the start point to the end point (the right end point of each line). It is shown.
For example, the line L3 is (x3, y5), (x4, y5)
And (x5, y5), the run-length data is (x3, y5, 3).

In steps S16 and S17, FIG.
A process of extracting the target object is performed by labeling the target object as shown in FIG. That is, among the lines L1 to L8 converted into run-length data, the lines L1 to L3 having a portion overlapping in the y direction are regarded as one object 1,
The lines L4 to L8 are regarded as one object 2, and object labels 1 and 2 are added to the run-length data. By this processing, for example, the high luminance areas shown in FIG.

In step S18, as shown in FIG. 7C, the center of gravity G, the area S, and the aspect ratio ASPECT of the circumscribed rectangle indicated by the broken line are calculated. The area S is
The length of the run-length data is calculated by integrating the same object, and the coordinates of the center of gravity G are calculated as x-coordinates of a line that bisects the area S in the x-direction and y of a line that bisects the area S in the y-direction.
Calculated as coordinates, and the aspect ratio APECT is calculated as the ratio Dy / Dx between Dy and Dx shown in FIG. The position of the center of gravity G may be replaced by the position of the center of gravity of a circumscribed rectangle.

In step S19, tracking of the object between times, that is, recognition of the same object at each sampling period is performed. The time at which the time t as the analog quantity is discretized at the sampling period is k, and when the objects 1 and 2 are extracted at the time k as shown in FIG.
The identity of the objects 3 and 4 extracted in 1) and the objects 1 and 2 is determined. Specifically, the following identity determination conditions 1)
When 3) are satisfied, it is determined that the objects 1 and 2 are the same as the objects 3 and 4, and the objects 3 and 4 are respectively 1 and 2.
The time tracking is performed by changing the label to "."

1) Object i at time k (= 1, 2)
The coordinates of the position of the center of gravity on the image of (xi (k),
yi (k)) and the object j at time (k + 1)
The coordinates of the position of the center of gravity of the image of (= 3, 4) are represented by (xj (k
+1), yj (k + 1)), | xj (k + 1) −xi (k) | <Δx | yj (k + 1) −yi (k) | <Δy. Here, Δx and Δy are allowable values of the moving amount on the image in the x direction and the y direction, respectively.

2) Object i at time k (= 1, 2)
Is the area on the image Si (k), and the area of the object j (= 3, 4) on the image at time (k + 1) is Sj
When (k + 1), Sj (k + 1) / Si (k) <1 ± ΔS. Here, ΔS is an allowable value of the area change.

3) Object i at time k (= 1, 2)
Let ASPECTi (k) denote the aspect ratio of the circumscribed rectangle of ASPECTj (k + 1), and let ASPECTj (k + 1) denote the aspect ratio of the circumscribed rectangle of the object j (= 3, 4) at time (k + 1). ) <1
± ΔASPECT. However, ΔASPECT is an allowable value of the change in the aspect ratio.

8 (a) and 8 (b), each object has a larger size on the image, but the objects 1 and 3 satisfy the above-described identity determination condition, and Things 2 and 4
Satisfy the above-described identity determination condition, the objects 3 and 4 are recognized as the objects 1 and 2, respectively. The position coordinates (of the center of gravity) of each object recognized in this way are stored in a memory as time-series position data, and are used in subsequent arithmetic processing. The processes in steps S14 to S19 described above are executed for the binarized reference image (the right image in the present embodiment).

In step S20 of FIG. 3, the vehicle speed sensor 6
Speed VCAR and yaw rate sensor 5 detected by
By reading the detected yaw rate YR and integrating the yaw rate YR with time, the turning angle θr of the host vehicle 10 is obtained.
(See FIG. 16).

On the other hand, in steps S31 to S34, processing for calculating the distance z between the object and the host vehicle 10 is performed in parallel with the processing in steps S19 and S20. Since this operation requires a longer time than steps S19 and S20, a period longer than steps S19 and S20 (for example, steps S11 to S20)
(The cycle is about three times the execution cycle of S20).

In step S31, a reference image (right image)
By selecting one of the objects tracked by the binarized image of (1), the search image R1 (here, the entire region surrounded by the circumscribed rectangle is searched from the right image as shown in FIG. Image). Subsequent step S32
Then, a search area for searching for an image corresponding to the search image (hereinafter referred to as “corresponding image”) is set from the left image, and a correlation operation is executed to extract the corresponding image. Specifically, as shown in FIG. 9B, a search region R2 is set in the left image according to each vertex coordinate of the search image R1, and the height of the correlation with the search image R1 in the search region R2. Brightness difference sum C indicating
(A, b) is calculated by the following equation (1), and the sum C
An area where (a, b) is minimum is extracted as a corresponding image. Note that this correlation operation is performed using a grayscale image instead of a binary image. When there is past position data for the same object, a region R2a (shown by a broken line in FIG. 9B) smaller than the search region R2 is set as a search region based on the position data.

(Equation 1)

Here, IR (m, n) is the luminance value at the position of the coordinates (m, n) in the search image R1 shown in FIG. 10, and IL (a + m-M, b + n-N) is the search value. The luminance value at the position of the coordinates (m, n) in the local region R3 having the same shape as the search image R1 with the coordinates (a, b) in the region as a base point. The position of the corresponding image is specified by changing the coordinates (a, b) of the base point and obtaining the position where the luminance difference total value C (a, b) is minimized.

By the process of step S32, the search image R1 and the corresponding image R4 corresponding to this object are extracted as shown in FIG. 11, so in step S33, the center of gravity of the search image R1 and the center of the image are extracted. Distance d from line LCTR
R (the number of pixels) and the distance dL (the number of pixels) between the center of gravity position of the corresponding image R4 and the image center line LCTR are obtained, and the distances dR and dL are added to obtain the detected parallax δd (= dR + d).
L) is calculated, and the parallax Δd is calculated by applying the detected parallax δd and the parallax offset D (φ) to the following equation (2).
The parallax offset amount D (φ) is determined in step S27 described later.
In FIG. 4, the two cameras 1 </ b> R and 1 </ b> L are updated by a calculation using a stationary target object detected during driving, and by correcting the detected parallax δd by Expression (2).
Except for the error caused by the deviation of the parallelism between the optical axes, it is possible to obtain an accurate parallax and, consequently, an accurate distance z. Δd = δd-D (φ) (2)

As shown in FIG. 12, the optical axis 2 of the camera 1L
Assuming that the amount of parallax offset when D is parallel to the straight line 22 parallel to the optical axis 21R of the camera 1R by an angle φ (hereinafter referred to as “pan angle φ”) is D (φ). As shown in FIG. 13, regardless of the distance z, the detected parallax δd is larger than the true parallax Δd by the parallax offset amount D (φ).
Since the distance becomes larger by an amount, an accurate parallax can be obtained by the above equation (2). In the present embodiment, the pan angle φ and the parallax offset amount D (φ) when the optical axes of the two cameras are shifted in a direction in which they are open to each other (direction of FIG. 12) are positive values, and when the optical axes are shifted, the negative values are negative. Value. Also, the detected parallax δd
Is that when the pan angle φ is a negative value, that is, when the optical axes of the two cameras are shifted in a direction to close each other (a direction opposite to the direction of FIG. 12), the object image in the right image is In such a case, the detected parallax δd is set to a negative value.

In the following step S33, the parallax Δd is applied to the following equation (3), and the distance z between the host vehicle 10 and the object is calculated.
Is calculated.

(Equation 2) Here, B is the base line length, that is, the distance in the horizontal direction (x direction) between the center position of the image sensor 11R of the camera 1R and the center position of the image sensor 11L of the camera 1L as shown in FIG. F is the lens 12R, 12L
Is the pixel distance in the image sensors 11R and 11L.

In step S21 of FIG. 4, the coordinates (x, y) in the image and the distance z calculated by the equation (3) are applied to the following equation (4) to obtain the real space coordinates (X, Y, Z). Convert. Here, the real space coordinates (X, Y, Z) are shown in FIG.
As shown in (a), the position of the middle point of the mounting positions of the cameras 1R and 1L (the position fixed to the host vehicle 10) is the origin O
The coordinates in the image are determined as shown in FIG.
As shown in the figure, the horizontal direction is x,
The vertical direction is defined as y.

(Equation 3)

Here, (xc, yc) represents the coordinates (x, y) on the right image with the real space origin O based on the relative positional relationship between the mounting position of the camera 1R and the real space origin O. This is converted into coordinates in a virtual image in which the center of the image is matched. F is a ratio between the focal length F and the pixel interval p.

In step S22, a turning angle correction for correcting a positional shift on the image due to the turning of the host vehicle 10 is performed. As shown in FIG. 16, from time k (k +
During the period up to 1), the own vehicle is turned to the left, for example, by a turning angle θr
Just turning around, on the image obtained by the camera,
Since it is shifted in the x direction by Δx as shown in FIG. 17, this is a process for correcting this. Specifically, by applying the real space coordinates (X, Y, Z) to the following equation (5), the corrected coordinates (Xr,
Yr, Zr) is calculated. The calculated real space position data (Xr, Yr, Zr) is stored in the memory in association with each object. In the following description, the coordinates after the turning angle correction are displayed as (X, Y, Z).

(Equation 4)

In step S23, as shown in FIG. 18, for the same object, N real space position data (for example, about N = 10) obtained during the period of ΔT after turning angle correction, ie, time series From the data, the object and the vehicle 10
An approximate straight line LMV corresponding to the relative movement vector is obtained. Specifically, assuming that a direction vector Lu = (lx, ly, lz) (| Lu | = 1) indicating the direction of the approximate straight line LMV, a straight line represented by the following equation (6) is obtained.

(Equation 5)

Here, u is a parameter having an arbitrary value, and Xav, Yav, and Zav are the average value of the X coordinate, the average value of the Y coordinate, and the average value of the Z coordinate of the real space position data sequence, respectively. is there. The equation (6) becomes the following equation (6a) if the parameter u is eliminated. (X-Xav) / lx = (Y-Yav) / ly = (Z-Zav) / lz (6a)

FIG. 18 is a diagram for explaining the approximate straight line LMV, in which P (0), P (1), P (2),
, P (N-2) and P (N-1) indicate the time series data after the turning angle correction, and the approximate straight line LMV indicates the average position coordinates Pav (= (Xav, Yav, Za) of the time series data.
v)) is obtained as a straight line that minimizes the average value of the square of the distance from each data point. Here, the numerical value in parentheses attached to P indicating the coordinates of each data point indicates that the larger the value is, the more past the data is. For example, P
(0) indicates the latest position coordinates, P (1) indicates the position coordinates one sample cycle ago, and P (2) indicates the position coordinates two sample cycles ago. D (j), X (j), Y in the following description
The same applies to (j), Z (j), and the like.

More specifically, a vector D (j) = (DX (j), DY (j), DZ) from the average position coordinates Pav to the coordinates P (0) to P (N-1) of each data point.
(J)) = (X (j) -Xav, Y (j) -Yav, Z
The inner product s of (j) -Zav) and the direction vector Lu is calculated by the following equation (7), and the direction vector Lu = (lx, ly, lz) that maximizes the variance of the inner product s is obtained. s = lx.DX (j) + ly.DY (j) + lz.DZ (j) (7)

The variance-covariance matrix V of the coordinates of each data point is
Since the eigenvalue σ of this matrix corresponds to the variance of the inner product s, the eigenvector corresponding to the largest eigenvalue among the three eigenvalues calculated from this matrix is the direction vector Lu to be obtained. In order to calculate eigenvalues and eigenvectors from the matrix of Expression (8), a method known as the Jacobi method (for example, shown in “Numerical Calculation Handbook” (Ohm)) is used.

(Equation 6)

Next, the latest position coordinates P (0) = (X
(0), Y (0), Z (0)) and the position coordinates P (N−1) = (X (N−) before (N−1) samples (before time ΔT).
1), Y (N-1), Z (N-1)) are approximated to the straight line LMV
Correct to the upper position. Specifically, Z in the above equation (6a)
By applying the coordinates Z (0) and Z (N-1), that is, by the following equation (9), the corrected position coordinates Pv
(0) = (Xv (0), Yv (0), Zv (0)) and Pv (N-1) = (Xv (N-1), Yv (N-1),
Zv (N-1)).

(Equation 7)

The position coordinates Pv (N−N) calculated by the equation (9)
A relative movement vector is obtained as a vector from 1) toward Pv (0). In this manner, the influence of the position detection error is reduced by calculating the approximate straight line that approximates the relative movement trajectory of the target object with respect to the own vehicle 10 from the plurality (N) of data within the monitoring period ΔT. As a result, it is possible to more accurately predict the possibility of collision with the object.

Returning to FIG. 4, in step S24, it is determined whether or not a stationary object exists among the objects detected at that time. The “stationary object” referred to here is an object that is highly likely to be detected while the vehicle is traveling and is surely stationary, such as a traffic signal 101 or a utility pole 102 illustrated in FIG. Judgment is made based on the presence or absence of the feature of the object. Specifically, an object that satisfies the following conditions is determined as a “stationary object”.

1) Circular objects of the same size are arranged side by side and have a height of 4.5 m or more 2) A rectangular object having a height of 5 m or more When there is no stationary object in step S24 , The process immediately proceeds to step S28.
The parallax offset amount D (φ) is updated through 5 to S27. The parallax offset D using a stationary object is described below.
A method of calculating (φ) will be described with reference to FIG.

As shown in FIG. 20, the distance from the host vehicle to the stationary object and the detected parallax at the first time t0 are Z (t0) and δd (t0), respectively, and the first time t0
The distance and the detected parallax from the host vehicle to the stationary object at the second time t1 after a lapse of the time Tm from Z (t1)
And δd (t1), the distances Z (t0) and Z (t1) are obtained from the following equations (10) and (1) from the equations (2) and (3).
It becomes like 1). The time Tm is, for example, the vehicle speed VCA.
It is set to be shorter as R is higher.

(Equation 8)

The following equation (12) is obtained by eliminating the parallax offset D (φ) from the equations (10) and (11).

(Equation 9) Here, the variable α is defined by the following equation (13), and Z (t1)
= Z (t0) -L, the equation (12) becomes:
It is transformed into the following equation (14).

(Equation 10)

When equation (14) is solved under the condition of Z (t0)> 0, the distance Z (t0) is given by the following equation (15).

[Equation 11] On the other hand, when equation (10) is modified, the parallax offset amount D
Since (φ) is given by the following equation (10a), the equation (1)
The following equation (16) is obtained by substituting 5).

(Equation 12)

Equation (16) shows that the first and second times t0, t
By applying the detected parallaxes δd (t0) and δd (t1) at 1, and the traveling distance L of the own vehicle 10 therebetween,
The parallax offset amount D (φ) can be obtained.

When the road has a curved shape, the traveling distance L along the road shape is determined by the linear movement amount according to the turning angle θr from the first time t0 to the second time t1. The parallax offset amount D (φ) can be calculated by using the equation (16).

Returning to FIG. 4, if the answer to step S24 is affirmative (YES), that is, if there is a stationary object that satisfies the above condition, the first time t0 to the second time t1
By integrating the vehicle speed VCAR in the period up to, the traveling distance L of the host vehicle 10 is calculated, and the parallaxes δd (t0) and δd (t1) are calculated at times t0 and t1, respectively (steps S25 and S26). Equation (1)
6) The travel distance L and the detected parallax δd (t0), δd (t
By applying 1), the parallax offset amount D (φ) is updated to the latest value (step S27).

In the following step S28, based on the distance to the object and other position information of the object (horizontal and vertical coordinates perpendicular to the traveling direction of the host vehicle 10), the target The possibility of collision between the object and the host vehicle 10 is determined, and when the possibility of collision is high, the speaker 3 and the HUD
A warning is issued to the driver via 4. After executing step S28, the process returns to step S11, and the process is repeated.

As described above, in the present embodiment, the presence or absence of a stationary object is determined during traveling, and when there is a stationary object, two time points, that is, the first and second times t0 and t0.
1, the detected parallaxes δd (t0) and δd of the stationary object
(T1) is calculated, and the parallax offset D (φ) is calculated based on the detected parallaxes δd (t0) and δd (t1) and the movement amount L of the vehicle from time t0 to t1. Since the detected parallax δd is corrected by the parallax offset amount D (φ) to obtain an accurate parallax Δd, by using this parallax Δd, the parallelism deviation between the optical axes of the cameras 1R and 1L (pan angle φ ) Can be corrected to calculate an accurate parallax, thereby improving the distance detection accuracy to the target object. In addition, since inspection at a factory or confirmation at a store can be easily performed by driving the vehicle toward a stationary object, there is no need to use an object with a known distance. Special space and equipment can be eliminated.

In this embodiment, the infrared cameras 1R, 1L
Corresponds to an imaging unit, and the image processing unit 2 constitutes a distance detection device. Specifically, steps S24 and S26 in FIG. 4 correspond to the stationary object parallax calculating means, step S25 in the figure corresponds to the vehicle moving amount calculating means, and step S33 in FIG. 3 and step S27 in FIG. It corresponds to parallax correction means.

The present invention is not limited to the above-described embodiment, and various modifications are possible. For example, although an infrared camera is used as the imaging means, a television camera or the like that can detect only ordinary visible light may be used. Also, 2
The two imaging means only need to be arranged apart from each other.
However, the present invention is not limited to the arrangement shown in FIG.

[0052]

As described above in detail, according to the present invention, the parallax of the stationary object is calculated at the first and second times, and the vehicle between the first time and the second time is calculated. A movement amount is calculated, and a parallax offset amount for correcting an error caused by a parallelism shift between optical axes of the two imaging units based on the parallax of the stationary object and the vehicle movement amount at the first and second times. Is calculated, and the parallax is corrected based on the parallax offset amount. Therefore, an error due to the parallelism shift between the optical axes of the two imaging units is corrected with a smaller amount of calculation than in the related art, and an accurate parallax is calculated. In addition, the distance detection accuracy can be improved.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of a vehicle periphery monitoring device including a distance detection device according to an embodiment of the present invention.

FIG. 2 is a view for explaining a mounting position of the camera shown in FIG. 1;

FIG. 3 is a flowchart illustrating a procedure of processing in the image processing unit of FIG. 1;

FIG. 4 is a flowchart illustrating a procedure of processing in the image processing unit of FIG. 1;

FIG. 5 is a diagram showing a gray scale image obtained by an infrared camera with hatching applied to a halftone portion;

FIG. 6 is a diagram showing a black area with hatching to explain an image obtained by binarizing a grayscale image.

FIG. 7 is a diagram for explaining a conversion process to run-length data and labeling.

FIG. 8 is a diagram for explaining time tracking of an object.

FIG. 9 is a diagram for explaining a search image in a right image and a search area set in a left image.

FIG. 10 is a diagram for explaining a correlation calculation process for a search area.

FIG. 11 is a diagram illustrating a method of calculating parallax.

FIG. 12 is a diagram for explaining a parallelism shift between optical axes of two cameras.

FIG. 13 is a diagram for explaining an error in detected parallax caused by a shift in parallelism between optical axes.

FIG. 14 is a diagram for explaining a method of calculating a distance from parallax.

FIG. 15 is a diagram illustrating a coordinate system according to the present embodiment.

FIG. 16 is a diagram for explaining turning angle correction;

FIG. 17 is a diagram showing a displacement of an object position on an image caused by turning of the vehicle.

FIG. 18 is a diagram for explaining a method of calculating a relative movement vector.

FIG. 19 is a diagram illustrating an example of a stationary object.

FIG. 20 is a diagram for describing a method of calculating a parallax offset amount using a stationary object.

[Explanation of symbols]

1R, 1L infrared camera (imaging means) 2 Image processing unit (stationary object parallax calculating means, vehicle moving amount calculating means, parallax correcting means) 6 Vehicle speed sensor

 ──────────────────────────────────────────────────続 き Continuing from the front page (72) Inventor Hiroshi Hattori 1-4-1 Chuo, Wako-shi, Saitama Prefecture Inside Honda R & D Co., Ltd. (72) Inventor Masato Watanabe 1-4-1 Chuo, Wako-shi, Saitama Honda R & D Co., Ltd. GA34 GA36 HA04 9A001 JJ77 KK54

Claims (1)

[Claims] 1. A distance detection device that calculates a parallax of an object based on two images obtained by two image pickup units mounted on a vehicle and detects a distance from the parallax to the object. And a stationary object parallax calculating means for calculating a parallax of a stationary object at a second time; and a vehicle moving amount calculating means for calculating a moving amount of the vehicle during a period from the first time to the second time. And calculating, based on the parallax of the stationary object at the first and second times and the vehicle movement amount, a parallax offset amount caused by a parallelism shift between optical axes of the two imaging units. And a parallax correcting unit configured to correct parallax based on the parallax offset amount.