DE102010009620B4 - Method and device for detecting at least one obstacle in a vehicle environment - Google Patents

Abstract

Method for detecting at least one obstacle (10) in a vehicle environment, the method comprising the following method steps: - acquiring a first image (B) by means of a monocular image capture system, - detecting feature points of the first image (B) in the image area of a carriageway (9) in the first image (B), - acquiring a second image (B) by means of the monocular image acquisition system, wherein the second image (B) is detected after the first image (B), - detection of feature points of the second image (B) in the image region the roadway (9) in the second image (B), - determination of corresponding feature points from the feature points of the first and second image (BB), - estimation of at least one parameter of a projective transformation from the corresponding feature points, - transformation of at least one pixel of the first image (B) by means of the projective transformation into a transformed pixel of the first image (B), - Bere a difference of an intensity of the transformed pixel of the first image (B) and an intensity of the pixel of the second image (B) corresponding to the transformed pixel of the first image (B), - comparison of the difference with a predetermined threshold (Thr) and- Detecting an obstacle (10) if the difference is greater than the predetermined threshold (Thr).

Description

The invention relates to a method and a device for detecting at least one obstacle in a vehicle environment.

In modern motor vehicles, it is customary to monitor a vehicle environment, in particular a front vehicle corridor with respect to the vehicle and / or a rear vehicle corridor, and possibly detect existing static or dynamic obstacles. This will u.a. Supports a collision avoidance of the vehicle with such obstacles.

A monitoring of the vehicle environment is usually done with the help of radar, lidar and ultrasound systems. Further known are image acquisition systems, such as camera systems, which are arranged in or on the vehicle. They are e.g. stereo-based camera systems known that allow a three-dimensional detection of obstacles.

Further, so-called monocular camera systems are known which allow only two-dimensional detection of a vehicle environment. These monocular camera systems allow imaging of the vehicle environment into a two-dimensional camera image. To detect static obstacles in the vehicle environment here u.a. so-called "structure from motion" method (SfM method) used. These require a high computational effort. In addition, a detection of dynamic obstacles in the vehicle environment with such methods is currently not possible.

In the writing by C. Demonceaux; A. Potelle; D. Kachi-Akkouche: Obstacle detection in a road scene based motion analysis. In: IEEE Transactions on Vehicular Technology, Volume 53, 2004, Issue 6, pages 1649-1656 is described an obstacle detection system with a vehicle camera, wherein first the road motion is determined by a wavelets analysis. Subsequently, a detection of different movement by means of a Bayesian modulation. With the help of the system obstacles on the street are to be recognized in different recorded image states.

The EP 1 830 321 A2 discloses an obstacle detection system including a vehicle camera in which a first image and a second image generated at a different time are linked. The characteristic shape of the road surface is detected and the images are aligned on the basis of the characteristic shape of the road surface. In this case, the area is judged as an area containing an obstacle in which a difference occurs.

The US 2010/0002078 A1 discloses an environmental monitoring system by means of a camera, wherein an obstacle is detected on the basis of a difference between image portions corresponding to the same detected area.

The DE 10 2006 060 893 A1 discloses an apparatus for determining a clearance in front of a vehicle. In this case, an imaging sensor is designed such that it receives a first image at a first time and a second image of an observation area in front of the vehicle at a second time after the first time. Further, an image processing unit is configured to obtain a difference image having motion edges by forming a difference of image data of the first image and image data of the second image. The movement edges are defined here by differing pixel values of the image data of the first image with respect to the image data of the second image. Furthermore, the difference image is provided as a processing image. A pattern recognition unit is designed in such a way that it determines, as a free space in front of the vehicle, that part of the observation area which corresponds to a region of the difference image free of movement edges, starting from a lower image edge of the difference image.

The technical problem arises of providing a method and a device which enable an improved detection of obstacles by means of a monocular image acquisition system, in particular a detection that is favorable in terms of computational effort.

The solution of the technical problem results from the objects with the features of claims 1 and 10. Further advantageous embodiments of the invention will become apparent from the dependent claims.

Proposed is a method for detecting at least one obstacle in a vehicle environment. In particular, the method is used to detect at least one obstacle in a rear vehicle corridor or in a vehicle corridor in front of the vehicle. The proposed method comprises the following method steps.

In a first step, a first image of the vehicle surroundings is acquired by means of a monocular image acquisition system. Here, a detection area of the monocular image sensing system includes a lane on which the vehicle is traveling. The monocular imaging system may be, for example, a camera system. The Monocular imaging system allows this to record at a certain time an image of the vehicle environment. The monocular imaging system does not allow stereoscopic detection of the vehicle environment. It is conceivable, however, that a camera of a stereoscopic camera system, which is present for example in the vehicle, is used to carry out the method.

An image of the vehicle environment is determined by the so-called intrinsic and extrinsic parameters of the image acquisition system, in particular the camera system. Here, extrinsic parameters describe a position and orientation of a coordinate system of the image acquisition system to a coordinate system of the vehicle. This is u.a. Also a viewing direction of the image acquisition system in relation to the coordinate system of the vehicle determined. Intrinsic parameters include, for example, parameters of distortion, scaling and focal length of the imaging system. By means of the image acquisition system, an image of the vehicle environment is made into a two-dimensional image. Pixels of this image can be assigned image coordinates. For example, the imaging of the vehicle environment via one or more lenses on a CMOS or CCD chip.

The extrinsic parameters of the imaging system are preferably previously known. That is, the position and orientation of the coordinate system of the image acquisition system in the coordinate system of the vehicle is known.

From a previously known vehicle geometry, a position of a road surface in the coordinate system of the vehicle can be determined. In this case, it can be assumed that the roadway plane is a plane plane whose normal vector is parallel to a vertical axis (also referred to as yaw axis or vertical axis) of the vehicle. Furthermore, a height, that is to say a distance of a coordinate origin of the coordinate system of the vehicle from the road plane along the vertical axis, can be known via a previously known vehicle geometry. Thus, therefore, a position of a so-called ideal road surface in the coordinate system of the vehicle can be determined. If the extrinsic and intrinsic parameters of the image acquisition system are already known, an image area of the first image can be determined in which the ideal roadway plane is imaged. This image area can be, for example, a trapezoidal image area. The image area of the roadway in the first image thus comprises those two-dimensional image points on which points of the roadway plane can be imaged.

In a second step, a detection of so-called feature points of the first image in the image area of the roadway takes place. The detection of feature points may be e.g. by means of an image processing unit. In this case, image data of the image acquisition system can be transferred to the data processing unit for image processing. Characteristic points here designate characteristic points on the road surface, which are imaged in the image area of the roadway in the first image. These can be given in particular by road markings or special structures on the road. The detection of these feature points hereby includes the identification of the feature points or feature surfaces and the determination of image coordinates of these feature points or faces. Identification of feature points or areas here may be e.g. pixel-based, area-based, intensity-based, edge-based or texture-based. Other methods for identifying suitable feature points can also be used for this purpose. For this purpose, the skilled person can refer to known methods for the identification of feature points (also referred to as landmarks) in the field of image processing. In the first step, therefore, a set of image coordinates of feature points is determined. This can e.g. stored in a memory unit.

In a third step, a second image is acquired by means of the image acquisition system, wherein the second image is recorded temporally after the first image. In this case, the vehicle may have moved between the first and second times. In this case, the second image can be acquired with a predetermined period of time after the first image. Also, the second image may be acquired with a speed-dependent period of time after the first image. As in the first image, feature points of the second image are detected in the image area of the roadway in the second image. In the third step, therefore, a set of feature points of the second image is generated. This can e.g. also be stored in a memory unit.

In a fourth step, a determination of corresponding feature points from the sets of feature points of the first and the second image takes place. The determination can take place here by means of a calculation unit. Preferably, point-point correspondences are evaluated here. Here, for example, spatial relations between the feature points of the first image and spatial relations between the feature points of the second image can be exploited. Alternatively or cumulatively, so-called invariant descriptors, which represent for example scaling and / or distortion-invariant feature descriptions of the points, can be exploited. By means of Determination of corresponding feature points is therefore generated a set of corresponding feature points.

In a fifth step, at least one parameter of a projective transformation is estimated by means of the corresponding feature points determined in the fourth step. Here, the projective transformation describes a general mapping of points of a first plane to points of a second plane, wherein the first plane is spatially offset from the second plane, or, with respect to the image capture system, has been imaged from different layers and / or orientations of the image capture system , As a rule, in a projective transformation 8th estimate unknown parameters of the projective transformation. For this purpose, the person skilled in the art can apply a previously known estimation method, eg a method based on the least squares method. Iterative estimation methods can also be used to estimate the parameters of the projective transformation. Starting values for such an iterative estimation method result, for example, from the previously known image of the roadway level in an image region of the roadway in the first and in the second image. This takes into account the condition that points which were in the image area of the road in the first image must also be located in the image area of the roadway in the second image.

In a sixth step, at least one pixel of the first image is transformed by means of the projective transformation into a transformed pixel of the first image. In this case, the parameters estimated in the fifth step are used to carry out the transformation. Preferably, a plurality of pixels of the first image are transformed. Preferably, all pixels in the image area of the roadway of the first image are transformed. Alternatively, all pixels of the first image can be transformed. By the transformation of the pixels of the first image, a computational, realized by the transformation, compensation of a movement of the image acquisition system is performed. The transformed pixels of the first image should also be referred to as motion-compensated pixels. If the vehicle has moved between the time of acquisition of the first image and the time of detection of the second image, it is possible to detect static and dynamic obstacles. If the vehicle has not moved between the time of acquisition of the first image and the time of detection of the second image, only dynamic obstacle detection is possible. The method thus advantageously enables a simultaneous detection of static and dynamic obstacles when the vehicle has moved between the time of detection of the first image and the time of detection of the second image. This is an essential difference to the cited prior art.

In a seventh step, a difference between an intensity of the transformed pixel of the first image and an intensity of the pixel of the second image corresponding to the transformed pixel of the first image is calculated. Here, the pixel of the second image corresponding to the transformed pixel of the first image has the same image coordinates as the pixel of the first image. The calculation of a difference preferably takes place for a plurality of transformed pixels of the first image, in particular for all pixels of the image region of the roadway of the first image. The pixels for which a difference in intensity was calculated define a so-called difference image. An intensity of a pixel here denotes, for example, a gray scale intensity, for example in a range from 0 to 255, or an RGB or CYMK-based intensity.

The projective transformation in this case a motion compensation for points in the road level is performed. Consequently, a difference of the intensities of transformed pixels which map points of the road plane and pixels corresponding to these transformed pixels, which also depict points of the road plane, will be small, ideally 0. Points that are not in the road level generate an intensity difference. Thus, each element that protrudes from the roadway plane produces an intensity difference. This applies to static and dynamic obstacles that protrude from the road surface. Such an intensity difference can also be described as a so-called compensation error. This follows from the fact that the projective transformation only ensures compensation for vehicle movement for pixels onto which points of the road surface are mapped. A transformation of pixels of the first image that map points outside the roadway to corresponding pixels of the second image that map points outside the roadway plane is done in accordance with a transformation that is different from the particular projective transformation.

In an eighth step, the determined difference or the determined differences are compared with a predetermined threshold. The predetermined threshold value can be stored, for example, in a memory unit. It is also conceivable that the threshold value is adaptive, for example as a function of an average value of a gray value distribution in the picture, it is determined. An obstacle is detected if the difference or differences are greater than the predetermined threshold.

In the above-described method, only the first and second images and the predetermined threshold value serve as an input. An output variable here is a signal which indicates whether an obstacle has been detected or not.

The proposed method is thus based on a motion compensation of image contents with subsequent evaluation of the compensation error. Here, it is assumed that the lane is a plane, whereby the displacement of pixels caused by a vehicle movement can be described and compensated by means of a projective transformation. Possible obstacles are not in the road level and thus lead to a compensation error, which is used to detect the obstacle. This results in an advantageously easy to implement, robust and less computationally expensive method for the detection of a static or dynamic obstacle in the vehicle environment.

In a further embodiment, in a determination of image coordinates of feature points, a compensation of image distortions of the monocular image capture system takes place. In particular, a compensation of distortions of the image acquisition system takes place. If intrinsic or internal parameters of the image acquisition system are known, so-called equalized image coordinates can be calculated with the aid of an equalization rule, which is dependent on the intrinsic parameters of the image acquisition system. Besides the distortion, e.g. Also a scaling of the image acquisition system can be compensated. This results in an advantageous manner that image distortions of the image acquisition system does not distort the image coordinates of the feature points and thus lead to a less accurate method. This advantageously results in an improved sensitivity of the method for the detection of obstacles and greater robustness.

In a further embodiment, the transformation of at least one pixel of the first image takes place on the basis of an unadulterated, in particular equalized, pixel of the first image. For this purpose, it is assumed that the parameters of the projective transformation were estimated on the basis of unadulterated, corresponding feature points in the first and in the second image. Then, before the transformation of at least one pixel of the first image by means of the projective transformation, an unadulterated pixel of the first image is calculated. Analogous to the compensation of image distortions, this unadulterated pixel of the first image can be calculated by means of an equalization rule, which depends on intrinsic parameters of the image acquisition system. This unadulterated pixel of the first image is then transformed by means of the projective transformation into a transformed, unadulterated pixel of the first image. Subsequently, this transformed, unadulterated pixel of the first image is again falsified. Falsification takes place for example by means of a so-called distortion rule, which in turn is dependent on the intrinsic parameters of the image acquisition system. Here, the distortion rule may be an inverse function to the equalization rule. This results in an advantageous manner that the motion compensation takes place on the basis of unadulterated image coordinates. This achieves improved sensitivity of the method for detecting an obstacle and improved robustness in the detection.

In a further embodiment, a difference image is subdivided into at least two partial areas, wherein an obstacle in a partial area is detected if a predetermined number of differences in the partial area is greater than the predetermined threshold value. Subareas may be, for example, rectangular or circular image areas. In this case, the geometric shape of a partial area can be dependent on the shape of the obstacle to be detected. An obstacle is thus detected only if, for a predetermined number of pixels in the subregion of the difference image, a difference is greater than the predetermined threshold value. As a result, a noise susceptibility of the method is advantageously reduced or a robustness of the method is increased. In this case, it is assumed that an obstacle does not generate a difference in the intensity exclusively for one pixel of the difference image, but rather for a plurality of pixels of the difference image which lie in the difference image within a partial area of the difference image.

In a further embodiment, the at least two partial regions are horizontal partial regions of the differential image. For this purpose, the difference image can be divided into horizontal subregions, each comprising one or more image lines of the difference image. This results in an advantageous manner that even flat obstacles can be detected with sufficient sensitivity and robustness.

In a further embodiment, image coordinates of at least one foot point of an obstacle are determined. In this case, a lower edge of a detected obstacle in image coordinates is determined as footing. A lower edge of a In this case, the obstacle is the subarea of the obstacle in the difference image, which has the smallest distance from a lower image edge of the difference image. Further, it is possible to perform the detection of at least one obstacle only in an upper portion of the image, whereby a compensation error in the lower image area, which is caused for example by a bumper of the vehicle is ignored. In this case, the upper subarea includes a subarea of the image that does not contain any imaging of vehicle parts.

Further, depending on the image coordinates of the at least one foot point of the obstacle, a distance of the obstacle from the vehicle can be estimated. This results in an advantageous manner that in addition to the pure detection of an obstacle and its distance from the vehicle can be estimated. Here, the input variables of the method are the first and the second image, the predetermined threshold value, the extrinsic and intrinsic parameters of the image acquisition system and the position and orientation of the coordinate system of the image acquisition system in relation to the coordinate system of the vehicle. Here, it is assumed that a position and orientation (extrinsic parameters) and intrinsic parameters of the image acquisition system are previously known. These parameters can be used to calculate an intersection of a line of sight with the road surface. The line of sight here denotes the line along which the foot point of the obstacle is projected into an image plane of the image acquisition system. When the position and orientation of a coordinate system of the image acquisition system with respect to a coordinate system of the vehicle is assumed to be known, a distance of the obstacle from the vehicle can also be estimated. However, the estimation of the distance of the obstacle can be falsified by various factors. For example, For example, different loading conditions of the vehicle and an uneven roadway, such as e.g. a ramp, distort the estimate of the distance of the obstacle.

In a preferred embodiment, only selected, corresponding feature points are used from the set of corresponding feature points to determine the parameters of the projective transformation. Here, a method for point selection can be applied. In this way, it is advantageously achieved that the estimation of the parameters of the projective transformation is based only on the basis of reliable feature points, in particular feature points corresponding to characteristic points in the roadway plane. For example, the so-called RANSAC method can be used to estimate the parameters of the projective transformation. The RANSAC method is a method for robust parameter estimation. This reduces the influence of so-called outliers in the set of corresponding feature points, in particular feature points that map points outside the roadway plane. A model required for the RANSAC method is given, for example, by the previously known position of the ideal roadway plane. In this case, the model of the ideal roadway plane requires that feature points in the first and in the second image must satisfy the condition of being in the previously known roadway plane. In this case, as explained above, it can be utilized that the imaging of an ideal roadway plane into the image area of the image acquisition system is known as a function of previously known extrinsic and optionally intrinsic parameters. Defining e.g. Feature points of the first image, a plane that deviates more than a predetermined amount from imaging the ideal roadway plane into the image area of the image acquisition system, one or more of these feature points may be discarded. Of course, other, known in the art, methods for robust parameter estimation can be applied.

In a further embodiment, the proposed method is carried out for at least two subareas of the image area of the roadway. This advantageously makes it possible to obtain an e.g. subdividing non-flat roadway into quasi-planar parts of the roadway and performing a separate obstacle detection for each of these subareas.

Also, e.g. at a previously known curvature or prior art inclination of the road, the curvature or inclination of the road are computationally compensated. Also, a curvature or recession of the roadway can be estimated by means of sensors. Analogously to the deletion or equalization, this makes it possible in an advantageous manner to compensate for a curvature of the road in the proposed method.

Further proposed is a device for detecting at least one obstacle. The device comprises at least one monocular camera system, at least one unit for image processing, at least one calculation unit and at least one comparison unit. By means of this device, the proposed method can be carried out. It is of course possible for individual method steps to be performed by only one unit, for example the calculation unit, or by separate units.

• 1 ein schematisches Blockschaltbild einer Vorrichtung zur Detektion mindestens eines Hindernisses,
• 2 ein schematisches Flussdiagramm eines Verfahrens zur Detektion mindestens eines Hindernisses und
• 3 eine schematische Bestimmung einer Entfernung eines Fußpunkts eines Hindernisses von einem Fahrzeug.

The invention will be explained in more detail with reference to an embodiment. The figures show:

• 1 1 is a schematic block diagram of a device for detecting at least one obstacle;
• 2 a schematic flow diagram of a method for detecting at least one obstacle and
• 3 a schematic determination of a distance of a foot point of an obstacle from a vehicle.

Hereinafter, like reference numerals designate elements having the same or similar technical characteristics.

In 1 is a schematic block diagram of a device 1 to detect at least one obstacle 10 shown. The device 1 includes a monocular camera 2 , one unity 3 for image processing, a storage unit 4 and a calculation unit 5 , The camera 2 is data technology with the unit 3 connected to image processing. The unit 3 for image processing is data technology with the storage unit 4 and the calculation unit 5 connected. The storage unit 4 is data technology with the calculation unit 5 connected. The camera is hereby arranged in or on a vehicle, not shown. By means of the camera 2 a vehicle environment is monitored. This captures the camera 2 also a roadway 9 on which the vehicle drives. Also captures the camera 2 an obstacle 10 , which is on the road 9 located and sticking out of this. One from the camera 2 recorded first picture _Bk-1 (please refer 2 ) of the vehicle environment, in particular the roadway 9 and the obstacle 10 , is data technically to the unit 3 transferred for image processing. The unit 3 For image processing this leads to a detection of feature points in the camera 2 transmitted first image _Bk-1 by. Also, the unit can 3 For image processing in a pre-processing, for example, perform a noise filtering. Upon detection of feature points, the unit identifies 3 characteristic for the image processing points of the road 9 that are in an image area of the roadway 9 of the first picture _Bk-1 be imaged. It is possible that the unit 3 For image processing as feature points also points detected, the characteristic points of the obstacle 10 correspond. The feature points thus detected by the image of the roadway 9 and the obstacle 10 are generated in the storage unit 4 saved. The camera 2 captures a second image at a later time B _k (please refer 2 ) of the vehicle environment. It is assumed that the vehicle is during the recording of the first image _Bk-1 and the second picture B _k on the roadway 9 has moved. In an analogous way, the unit detects 3 for image processing feature points in the second image B _k the camera 2 , The feature points of the second image B _k are data-technically to the calculation unit 5 transfer. The calculation unit 5 determined from the set of feature points of the first image _Bk-1 , the calculation unit 5 from the storage unit 4 data transfer, and the set of feature points of the second image B _k a set of corresponding feature points of the first and second images _Bk-1 . B _k , Next estimates the calculation unit 5 Parameters of a projective transformation from the set of corresponding feature points. The calculation unit further transforms 5 all pixels of the image area of the roadway 9 of the first picture _Bk-1 by means of the projective transformation, which is parameterized with the previously estimated parameters. After that, the calculation unit calculates 5 a difference image from a difference of intensities of pixels of the image area of the roadway 9 of the second picture B _k and transformed pixels of the image area of the roadway 9 of the first picture _Bk-1 , Next transfers the calculation unit 5 this difference image to the comparison unit 6 , The comparison unit 6 compares individual subareas of the difference image, for example, horizontal subregions of the difference image, with a predetermined threshold, in a further memory unit 7 stored and data technology to the comparison unit 6 is transmitted. Has a predetermined number, for example, also in the further memory unit 7 is stored, a higher difference than the predetermined threshold, the comparison unit generates 6 an activation signal for a warning unit 8th , The warning unit 8th serves the warning of a driver and is activated if an obstacle was detected.

In 2 1 is a schematic flowchart of a method for detecting at least one obstacle in a vehicle environment. Hereby form a first picture _Bk-1 and a second picture B _k as well as intrinsic camera parameters pint , extrinsic camera parameters P _ext and a predetermined threshold Thr the input variables of the method. In a first step 11 become in the first and in the second picture _Bk-1 . B _k Feature points detected as well as corresponding feature points of the first image _Bk-1 and the second picture B _k certainly. Depending on the intrinsic camera parameters pint will be in a second step 12 a compensation of a lens distortion of eg in 1 illustrated camera 2 carried out. In this case, so-called equalized pixels of the corresponding feature points from the pixels in the first step 11 certain corresponding feature points depending on the intrinsic camera parameters pint certainly. In a third step 13 An estimation of parameters of a projective transformation is carried out from the equalized, corresponding feature points. In a fourth step 14 is a Compensation of the camera movement. This will be the first picture _Bk-1 depending on the intrinsic camera parameters pint equalized, by means of the third step 13 estimated projective transformation and in turn depending on the intrinsic camera parameters pint distorted. As a result, a motion-compensated, distorted first image is calculated. From this and the second picture B _k will be in a fifth step 15 generates a difference image. By comparing intensities of the pixels of the difference image with a predetermined threshold value Thr becomes an obstacle 10 detected if the intensities of the pixels of the difference image is greater than the predetermined threshold value Thr is (sixth step 16 ). In a seventh step 17 an estimate of the distance of the detected obstacle is made 10 from the vehicle depending on the intrinsic camera parameters pint and the extrinsic camera parameter P _ext , where the extrinsic camera parameters P _ext a location and orientation of a coordinate system of the camera 2 indicate in the vehicle. The seventh step 17 estimated distance of the obstacle 10 For example, the driver may be on a display unit 18 being represented.

In 3 schematically is the estimation of a distance of an obstacle 10 represented by a vehicle. Here are the axes y _F and z _F the axes of a coordinate system of the vehicle. Next designate y _K and z _K the axes of a coordinate system of eg in 1 illustrated camera 2 , The location and orientation of the coordinate system of the camera 2 in the coordinate system of the vehicle is known here. It is further assumed that the obstacle 10 has already been detected by means of the previously described method. Next, suppose that in the difference image is a foot of the obstacle 10 was determined. Next then becomes a line of sight 19 determines on which the base of the obstacle 10 into the picture plane of the camera 2 is shown. The line of sight 19 closes an angle here α with an optical axis 20 the camera 2 on. The optical axis 20 closes an angle θ with the axis y _K of the coordinate system of the camera 2 on. Next, suppose that a height H _F of the vehicle over a carriageway 9 is known. From the known position and orientation of the coordinate system of the camera 2 to the coordinate system of the vehicle and the height H _F of the vehicle above the carriageway 9 can be removed in a first step z _{H, K} of the obstacle 10 from the camera 2 along the axis z _{k of} the camera 2 determine. By means of the known position of the coordinate system of the camera 2 to the coordinate system of the vehicle can be removed in a second step, a removal of the obstacle 10 from the coordinate system of the vehicle along the axis z _F determine.

LIST OF REFERENCE NUMBERS

1

Device for detecting at least one obstacle

2

camera

3

Image processing unit

4

storage unit

5

calculation unit

6

comparing unit

7

additional storage unit

8th

warning unit

9

roadway

10

obstacle

11

first step of the procedure

12

second step of the procedure

13

third step of the procedure

14

fourth step of the procedure

15

fifth step of the procedure

16

sixth step of the procedure

17

seventh step of the procedure

18

display unit

_Bk-1

first picture

B _k

second picture

pint

intrinsic camera parameters

P _ext

extrinsic camera parameters

Thr

predetermined threshold

y _K

Axis of a coordinate system of a camera

z _K

Axis of the coordinate system of a camera

y _F

Axis of a coordinate system of a vehicle

z _F

Axis of a coordinate system of a vehicle

H _F

Height of the coordinate system of the vehicle above the roadway

z _{H, K}

Removal of an obstacle from a camera

19

line of sight

20

optical axis

α.

corner

θ

corner

Claims (10)

Method for detecting at least one obstacle (10) in a vehicle environment, the method comprising the following method steps: - acquiring a first image (B _k-1 ) by means of a monocular image acquisition system, - detecting feature points of the first image (B _k-1 ) in FIG Image area of a carriageway (9) in the first image (B _k-1 ), - acquiring a second image (B _k ) by means of the monocular image acquisition system, wherein the second image (B _k ) detects time after the first image (B _k-1 ) - Detection of feature points of the second image (B _k ) in the image area of the carriageway (9) in the second image (B _k ), - Determination of corresponding feature points from the feature points of the first and the second image (B _k-1, B _k ), - Estimation of at least one parameter of a projective transformation from the corresponding feature points, - Transformation of at least one pixel of the first image (B _k-1 ) by means of the projective transformation into a t ransformierten pixel of the first image (B _k-1 ), - calculating a difference of an intensity of the transformed pixel of the first image (B _k-1 ) and an intensity of the transformed pixel of the first image (B _k-1 ) corresponding pixel of the second image (B _k ), - comparing the difference with a predetermined threshold (Thr) and - detecting an obstacle (10) if the difference is greater than the predetermined threshold (Thr). Method according to Claim 1 , characterized in that in a determination of image coordinates of feature points, a compensation of image distortions of the monocular imaging system takes place. Method according to Claim 2 , characterized in that before the transformation of at least one pixel of the first image (B _k-1 ) by means of the projective transformation into a transformed pixel of the first image (B _k-1 ) at least one unadulterated pixel of the first image (B _k-1 ), wherein imaging distortions of the monocular imaging system are compensated in imaging the at least one pixel of the first image (B _k-1 ), transforming the at least one unadorned pixel of the first image (B _k-1 ) into a transformed, unadulterated pixel of the first image (B _k-1 ) and after transformation from the at least one transformed, unadulterated pixel of the first image (B _k-1 ) a transformed, corrupted pixel of the first image (B _k-1 ) is calculated, wherein image distortions of the monocular imaging system., Method according to one of the preceding claims, characterized in that a difference image is divided into at least two subregions, wherein an obstacle is detected in a subregion, if a predetermined number of differences in the subregion is greater than the predetermined threshold value (Thr). Method according to Claim 4 , characterized in that the at least two subregions are horizontal subregions. Method according to one of the preceding claims, characterized in that image coordinates of at least one base point of an obstacle (10) are determined. Method according to Claim 6 , characterized in that depending on the image coordinates of the at least one foot of the obstacle (10), a distance of the obstacle (10) is estimated by the vehicle. Method according to one of the preceding claims, characterized in that the estimation of at least one parameter of a projective transformation from selected, corresponding feature points takes place, wherein selected, corresponding feature points are selected by means of a method for point selection. Method according to one of the preceding claims, characterized in that the method for detecting at least one obstacle (10) for at least two subregions of the image area of the roadway (9) is performed. Device for detecting at least one obstacle (10), wherein the device comprises at least one monocular imaging system, at least one unit (3) for image processing, at least one calculation unit (5) and at least one comparison unit (6), wherein a first and a second imaging system are provided by the monocular imaging system at least one second image (B _k-1 , B _k ) can be detected, wherein the second image (B _k ) is detectable in time after the first image (B _k-1 ), wherein by means of the unit (3) for image processing feature points of the first Image (B _k-1 ) in the image area of a roadway (9) in the first image (B _k-1 ) and feature points of the second image (B _k ) in the image area of the roadway (9) in the second image (B _k ) are detectable, wherein by means of the calculation unit (5) corresponding feature points from the feature points of the first and the second image (B _k-1 , B _k ) are determinable, at least one parameter of a projective transformation of the corresponding feature points can be estimated, at least one pixel of the first image (B _k-1 ) is transformed by the projective transformation into a transformed pixel of the first image (B _k-1 ), a difference of an intensity of the transformed pixel of the first image (B _{k -1} ) and an intensity of the image point of the second image (B _k ) corresponding to the transformed pixel of the first image (B _k-1 ) can be calculated, the difference being comparable to a predetermined threshold value (Thr) by means of the comparison unit (6) and an obstacle (10) is detectable if the difference is greater than the predetermined threshold (Thr)

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