KR20080053834A - A distortion correction method for a vehicle's rear camera - Google Patents
A distortion correction method for a vehicle's rear camera Download PDFInfo
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- KR20080053834A KR20080053834A KR1020060125853A KR20060125853A KR20080053834A KR 20080053834 A KR20080053834 A KR 20080053834A KR 1020060125853 A KR1020060125853 A KR 1020060125853A KR 20060125853 A KR20060125853 A KR 20060125853A KR 20080053834 A KR20080053834 A KR 20080053834A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R1/00—Optical viewing arrangements; Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R11/00—Arrangements for holding or mounting articles, not otherwise provided for
- B60R11/04—Mounting of cameras operative during drive; Arrangement of controls thereof relative to the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
- B60R2300/40—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the details of the power supply or the coupling to vehicle components
- B60R2300/402—Image calibration
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
- B60R2300/60—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by monitoring and displaying vehicle exterior scenes from a transformed perspective
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
- B60R2300/80—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the intended use of the viewing arrangement
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Abstract
Description
도 1은 본 발명의 후방 차량 진행 궤적 표시 상태도1 is a rear vehicle progress track display state diagram of the present invention
도 2는 본 발명의 후방 카메라 영상 왜곡 보정 상태의 사진Figure 2 is a photograph of the rear camera image distortion correction state of the present invention
도 3은 본 발명의 탑-뷰를 이용한 렌즈 왜곡 보정 결과 사진Figure 3 is a photograph of the lens distortion correction results using the top-view of the present invention
도 4는 본 발명의 렌즈 왜곡 보정 결과 비교 사진Figure 4 is a photograph comparing the lens distortion correction results of the present invention
본 발명은 차량의 후방 카메라의 영상 왜곡 변환 방법 및 주차궤적 표현 방법에 관한 것으로, 더 자세하게는 다항식 왜곡 모델과 함께 FOV 모델을 적용하여 영상 왜곡을 보정할 수 있도록 한 것에 관한 것이다.The present invention relates to an image distortion conversion method and a parking trajectory representation method of a rear camera of a vehicle. More particularly, the present invention relates to a method of correcting image distortion by applying a FOV model together with a polynomial distortion model.
일반적으로 차량의 후방 카메라는 자동차의 안전성과 편의성 향상을 위한 대표적인 장치로서, 후진이나 주차시 운전자에게 차량의 후방영상을 제공함으로써 운전자의 또 다른 눈의 역할을 하여 운전의 부하를 줄여준다.In general, the rear camera of the vehicle is a representative device for improving the safety and convenience of the vehicle, and serves as another driver's eyes by reducing the load of the driver by providing a rear image of the vehicle to the driver when reversing or parking.
상기 후방 카메라는 후방의 많은 정보를 운전자에게 제공하기 위해 큰 화각을 갖는 광각렌즈나 어안렌즈를 사용하여 카메라 왜곡 현상이 발생하는데 이런 현상을 보정하는 알고리즘을 구현하는 것이 핵심 기술이다.The rear camera generates a camera distortion phenomenon by using a wide angle lens or a fisheye lens having a large angle of view in order to provide the driver with a lot of information in the rear. The key technology is to implement an algorithm for correcting the phenomenon.
종래에 있어서 상기 카메라 왜곡 현상은 그대로 보정없이 사용하거나 Tsai's 방정식(Equation)을 이용하며, 렌즈 왜곡 모델로 다항식 왜곡 모델(Polynominal disrortion model)을 사용하여 후방 카메라의 왜곡을 보정하였다.In the related art, the camera distortion phenomenon is used without correction or using Tsai's equation, and the distortion of the rear camera is corrected by using a polynominal disrortion model as a lens distortion model.
또한, 후방 주행 시에 진행 방향 표시할 때에도 카메라 왜곡을 고려하지 않은 표시선을 디스플레이하였다.In addition, the display line which does not consider camera distortion is displayed also when displaying the direction of travel at the time of rearward driving.
종래의 Tsai's 방정식을 이용한 후방 카메라 왜곡 보정 알고리즘 적용시 중심점에서 거리에 비례적으로 오차율이 심하며, 특히 대각선 방향으로 오차율이 심하게 나타난다.When the rear camera distortion correction algorithm using the conventional Tsai's equation is applied, the error rate is severely proportional to the distance from the center point, and the error rate is particularly severe in the diagonal direction.
또한 후방 주행 시 진행 방향을 표시할 때에도 카메라 왜곡을 고려하지 않으므로 실제의 진행 예상 방향과 일치하지 않는 결과를 가져왔다.In addition, camera distortion is not taken into account when the driving direction is displayed when driving in the rear, and thus the driving direction is inconsistent with the actual driving direction.
본 발명은 상기 종래의 실정을 감안하여 안출한 것이며, 그 목적이 후방 카메라를 통해 운전자에게 제공되는 영상의 현실감을 향상시키고, 후방 영상 표현 시 차량의 진행 예상 궤적을 표현하여 쉽게 후진 및 후진 주차를 안내할 수 있도록 하는 차량의 후방 카메라의 영상 왜곡 변환 방법 및 주차궤적 표현 방법을 제공하는 데에 있는 것이다.The present invention has been made in view of the above-mentioned conventional situation, and its purpose is to improve the realism of the image provided to the driver through the rear camera, and to express the predicted trajectory of the vehicle when the rear image is expressed, to easily reverse and reverse parking. The present invention provides a method of converting image distortion of a rear camera of a vehicle and a method of expressing a parking trajectory for guiding a vehicle.
본 발명은 종래 카메라 왜곡보정 알고리즘의 카메라 왜곡함수 모델 설정시 FOV 모델(Field Of View Model)을 추가하여 카메라의 왜곡보정의 오차를 제거하여 운전자에게 제공되는 영상의 현실감을 향상시킬 수 있도록 하는 것을 특징으로 한다.The present invention is to add a field of view model (FOV) model when setting the camera distortion function model of the conventional camera distortion correction algorithm to remove the error of the camera's distortion correction to improve the reality of the image provided to the driver It is done.
아울러 본 발명은 운전자가 쉽게 후진 및 후진 주차에 용이하도록 후방 영상 표현 시 차량의 진행 예상 궤적을 후방 카메라 왜곡 보정 알고리즘을 적용하여 표현하는 것을 특징을 한다.In addition, the present invention is characterized by expressing the expected trajectory of the vehicle by applying a rear camera distortion correction algorithm when the rear image is expressed so that the driver can easily reverse and reverse parking.
카메라 모델에서 상부 조망(Top-View) 좌표계를 정리하면 다음과 같다.The top-view coordinate system in the camera model is summarized as follows.
Pt : Top-view 좌표계Pt: Top-view coordinate system
Pw : 월드좌표계로 표시한 위치Pw: Position displayed in world coordinate system
Pf : CCD 센서에 반영된 영상 좌표Pf: Image coordinate reflected in CCD sensor
Pt - Pw - PfPt-Pw-Pf
Xw = Sx * Xt + PxXw = Sx * Xt + Px
Yw = Sy * (row Yt) + PyYw = Sy * (row Yt) + Py
Sx, Sy : 변환변수 [mm/pixel], row : 탑뷰영상의 y방향의 픽셀 수, Sx, Sy: Conversion variable [mm / pixel], row: Number of pixels in y direction of the top view image,
(Px,Py) : 월드 좌표계의 원점과 관심영역 사이의 2차원 벡터(Px, Py): 2D vector between the origin of the world coordinate system and the ROI
* Top-View 영상* Top-View video
* 카메라 왜곡 영상* Camera distortion video
카메라 모델에서 왜곡 보정 좌표계를 정리하면 다음과 같다.The distortion correction coordinate system in the camera model is summarized as follows.
Pw : 월드좌표계로 표시한 위치Pw: Position displayed in world coordinate system
P : 카메라 좌표계로 표시한 위치P: location in camera coordinate system
Pu : 이상적인 투영된 영상 좌표Pu: ideal projected image coordinates
Pd : 렌즈의 왜곡이 반영된 투영 영상 좌표Pd: Projected image coordinates reflecting lens distortion
Pf : CCD 센서에 반영된 영상 좌표Pf: Image coordinate reflected in CCD sensor
Pw - P - Pu - Pd - PfPw-P-Pu-Pd-Pf
P = R * Pw + TP = R * Pw + T
Pu = P * (f / z) Pu = P * (f / z)
f : 초점거리, z : z축 거리f: focal length, z: z-axis distance
Pu = Pd + DPu = Pd + D
D : 왜곡보정, 다항식 왜곡 모델(Polynomial distortion model)과 FOV 모델D: Distortion Correction, Polynomial Distortion Model and FOV Model
Xf = sx * dx'-1 * Xd + CxXf = sx * dx ' -1 * Xd + Cx
Yf = dy'-1 * Yd + CyYf = dy ' -1 * Yd + Cy
dx' = dx * (Ncx / Nfx) dx '= dx * (Ncx / Nfx)
dy' = dy * (Ncy / Nfy)dy '= dy * (Ncy / Nfy)
Sx : Scale 변수, Nf : 컴퓨터 영상의 픽셀수, d : 센서픽셀사이의 거리, Nc : 단위 센서 수량, Cx,Cy : 영상중심점 (640x480 영상 320,240)Sx: Scale variable, Nf: Number of pixels in computer image, d: Distance between sensor pixels, Nc: Unit sensor quantity, Cx, Cy: Image center point (640x480 image 320,240)
본 발명에 있어서 왜곡 함수 모델은 다항식 왜곡 모델(Polynomial Distortion Model)과 FOV 모델 (Field of View Model)을 사용한다.In the present invention, the distortion function model uses a polynomial distortion model and a field of view model.
다항식 왜곡 모델(Polynomial Distortion Model)은 왜곡이 거의 없는 렌즈에서는 대부분 K1까지만 사용하며, 왜곡이 클 경우 높은 차수의 모델이 필요하므로 차수에 따라 P1(K1), P2(K1,K2) 등의 모델을 적용한다.The polynomial distortion model is mostly used up to K 1 in lenses with little distortion, and high distortion models are required for large distortion lenses, so P1 (K 1 ) and P2 (K 1 , K 2 ) depending on the order Apply the model of.
FOV 모델 (Field of View Model)은 이상적인 어안렌즈에 대응되는 화각(Field of view) ω를 하나의 왜곡함수로 갖는다.The FOV model has a field of view ω corresponding to an ideal fisheye lens as a distortion function.
이 FOV 모델에 있어서 상기 그림과 같이 점c(principal point)와 영상평면상의 한 점 m사이의 거리는 대응되는 3차원상의 한점 M, 초점 C와 광축(Cz)이 이루는 각도에 비례하며, 각도의 정밀도는 반지름을 따라 영상의 정밀도에 비례한다.In this FOV model, the distance between the point c (principal point) and one point m on the image plane is proportional to the angle formed by the corresponding point M, focal C, and the optical axis Cz in the corresponding three-dimensional plane, as shown in the above figure. Is proportional to the precision of the image along the radius.
왜곡이 큰 어안렌즈의 경우 다항식 왜곡 모델 또는 FOV 모델 하나로는 충분하지 않다.For fisheye lenses with high distortion, a polynomial distortion model or an FOV model is not enough.
따라서 다항식 왜곡 모델을 FOV 모델 전에 적용하며, 차수의 정도에 따라 P1(K1), P2(K1,K2), FOV1(ω), FOV2(K1,ω), FOV3(K1,K2,ω) ..을 적용한다.Therefore, the polynomial distortion model is applied before the FOV model, and P1 (K 1 ), P2 (K 1 , K 2 ), FOV1 (ω), FOV2 (K 1 , ω), FOV3 (K 1 , K) 2 , ω) .. applies.
상기 왜곡 함수 모델을 적용한 결과 렌즈 왜곡 없이 영상평면의 한 점에 투영될 때의 좌표(xu,yu) - 왜곡 중심으로부터 이 점까지의 거리를 ru라 하고, 왜곡된 영상에서 대응되는 영상평면의 좌표(xd,yd) - 왜곡 중심으로부터 이 점까지의 거리를 rd라 할 때 왜곡함수는 ru'=R-1(rd)가 되며, 왜곡모델에 의한 오차는 ru와 ru'의 차가 된다.Coordinate (xu, yu) when projected to a point on the image plane without lens distortion as a result of applying the distortion function model-the distance from the center of distortion to this point is called ru, and the coordinate of the corresponding image plane in the distorted image (xd, yd)-When the distance from the center of distortion to this point is rd, the distortion function is ru '= R -1 (rd), and the error by the distortion model is the difference between ru and ru'.
후방영상 왜곡보정 - 후방 영상에서 아래의 룩업 테이블(lookup table)을 이용하여 왜곡이 보정된 영상을 만들어 내었다.Rear image distortion correction-Distortion-corrected image was created by using the lookup table below.
왜곡보정 영상 정보계산식Distortion Correction Image Information Formula
보정된 영상정보(Pux, Puy) = a11 * 원영상정보(Pdx,Pdy) + a12 * 원영상정보(Pdx+1,Pdy) + a21 * 원영상정보(Pdx,Pdy+1) + a22 * 원영상정보(Pdx+1,Pdy+1)Corrected image information (Pux, Puy) = a11 * Original image information (Pdx, Pdy) + a12 * Original image information (Pdx + 1, Pdy) + a21 * Original image information (Pdx, Pdy + 1) + a22 * Circle Video Information (Pdx + 1, Pdy + 1)
예) 영상정보가 YCbCr로 표현된 경우, ROI가 1이면,Example) When the image information is represented by YCbCr, if the ROI is 1,
Y(Pux, Puy) = a11 * Y(Pdx, Pdy)+ a12 * Y(Pdx+1, Pdy) + a21 * Y(Pdx, Pdy+1) + a22 * Y(Pdx+1, Pdy+1)Y (Pux, Puy) = a11 * Y (Pdx, Pdy) + a12 * Y (Pdx + 1, Pdy) + a21 * Y (Pdx, Pdy + 1) + a22 * Y (Pdx + 1, Pdy + 1)
Cb(Pux, Puy) = a11 * Cb(Pdx, Pdy) + a12 * Cb(Pdx+1, Pdy) + a21 * Cb(Pdx, Pdy+1)+ a22 * Cb(Pdx+1, Pdy+1)Cb (Pux, Puy) = a11 * Cb (Pdx, Pdy) + a12 * Cb (Pdx + 1, Pdy) + a21 * Cb (Pdx, Pdy + 1) + a22 * Cb (Pdx + 1, Pdy + 1)
Cr(Pux, Puy) = a11 * Cr(Pdx, Pdy) + a12 * Cr(Pdx+1, Pdy) + a21 * Cr(Pdx, Pdy+1) + a22 * Cr(Pdx+1, Pdy+1)Cr (Pux, Puy) = a11 * Cr (Pdx, Pdy) + a12 * Cr (Pdx + 1, Pdy) + a21 * Cr (Pdx, Pdy + 1) + a22 * Cr (Pdx + 1, Pdy + 1)
본 발명에 있어서 후방 영상 차량 궤적 표현은 이미지 프로세서(Image processor)의 고유 기능을 활용하며, 제어유니트(MCU)에서 신호정보(후진기어, 좌우 방향지시 신호)를 기초로 판단하여 제어한다.In the present invention, the rear image vehicle trajectory representation utilizes an inherent function of an image processor and is controlled based on signal information (reverse gear, left and right direction indication signals) in the control unit (MCU).
본 발명에 있어서 예상 궤적 합성할 때는 이미지 프로세서(Image processor)의 OSD 기능을 활용한다.In the present invention, when synthesizing the predicted locus, the OSD function of an image processor is utilized.
제어유니트(MCU)에서는 조향각을 분석하여 조향각에 따른 오버레이(overlay) 정보를 보내주며, 거리에 따라 다른 색으로 예상 궤적을 표현한다.(1m이하 : Green, 3m이하 : Blue, 5m이하 : Red)The control unit (MCU) analyzes the steering angle and sends overlay information according to the steering angle and expresses the expected trajectories in different colors according to the distance. (1m or less: Green, 3m or less: Blue, 5m or less: Red)
메모리 사이즈(Memory size) = 조향각 수 x point 수 x point 정보량Memory size = number of steering angles x number of points x amount of information
조향각 수 : 조향각을 표시할 수 (예) 조향각 : -38˚~ 38˚, 정밀도 : 2˚, 조향각 수 = 39Steering angle: Display steering angle (example) Steering angle: -38˚ ~ 38˚, precision: 2˚, steering angle = 39
point 수 : 조향각별로 한 프레임(frame)에 표현할 포인트 그룹(point group)의 대표 point 수Number of points: Representative point number of point group to be expressed in one frame for each steering angle.
point 정보량 : x position, y position, color point amount of information: x position, y position, color
본 발명에 있어서 룩업 테이블(Lookup Table) 표현 형식은 (V, H, C)의 형식으로 각 조향각 당 Max Point 수만큼 저장하여 표현한다.In the present invention, the lookup table expression format is stored in the form of (V, H, C) and stored as many as the number of Max Points for each steering angle.
V : Vertical 좌표 값, H : Horizontal 좌표 값, C : Color 값 (1: Green, 2: Blue, 3: Red)V: Vertical coordinate value, H: Horizontal coordinate value, C: Color value (1: Green, 2: Blue, 3: Red)
도 1은 본 발명의 후방 차량 진행 궤적 표시 상태도이고, 도 2는 본 발명의 후방 카메라 렌즈 왜곡 보정 상태의 사진, 도 3은 본 발명의 탑-뷰를 이용한 렌즈 왜곡 보정 결과 사진, 도 4는 본 발명의 렌즈 왜곡 보정 결과 비교 사진이다.1 is a state diagram showing the rear vehicle progress track of the present invention, Figure 2 is a photograph of the rear camera lens distortion correction state of the present invention, Figure 3 is a lens distortion correction result photo using the top-view of the present invention, Figure 4 is It is a photograph comparing the lens distortion correction result of this invention.
이상에서와 같이 본 발명은 차량의 후방 카메라 왜곡보정 알고리즘의 카메라 왜곡함수 모델 설정시 다항식 왜곡 모델에 FOV 모델(Field Of View Model)을 추가하여 카메라의 왜곡 보정의 오차를 제거하여 운전자에게 제공되는 영상의 현실감을 향상시킬 수 있도록 하고, 운전자가 쉽게 후진 및 후진 주차에 용이하도록 후방 영상 표현 시 차량의 진행 예상 궤적을 후방 카메라 왜곡 보정 알고리즘을 적용한 것으로, 본 발명에 의하면 후방 카메라의 입력 영상에서 어안렌즈에 의해 휨 왜곡을 제거하는 왜곡보정(Distortion Correction) 알고리즘을 적용함으로써 운전자에게 제공되는 영상의 현실감을 크게 향상시킬 수 있게 되고, 후방 영상 표현 시에 차량의 진행 예상 궤적을 표현하여 쉽게 후진 및 후진 주차를 안내할 수 있게 되어 운전자 편의 증진 및 차량 안전성에 크게 기여할 수 있게 된다.As described above, the present invention adds a field of view model to the polynomial distortion model when setting the camera distortion function model of the rear camera distortion correction algorithm of the vehicle to remove the error of the distortion correction of the camera image provided to the driver The rear camera distortion correction algorithm is applied to the predicted trajectory of the vehicle when the rear image is expressed so that the driver can easily reverse and reverse the parking. By applying the distortion correction algorithm that removes the bending distortion, the realism of the image provided to the driver can be greatly improved. To improve driver comfort and vehicle safety. You can make a big contribution.
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