CN109784292B - Method for automatically searching parking space by intelligent automobile in indoor parking lot - Google Patents
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Abstract
The invention provides a method for an intelligent automobile to autonomously find a parking space in an indoor parking lot, which comprises the following steps: s1, calibrating a vehicle-mounted vision system; s2, acquiring image information of parking spaces on two sides of a lane by using cameras on the sides of the vehicle, and identifying and judging whether the vehicle exists or not; s3, recognizing a lane line of the parking lot by using a front camera of the vehicle, and setting a constant value of the length of the vehicle from the lane line so that the distance between the vehicle and the lane line is kept at the constant value; s4, supplementing the vehicle running track through a third-order Bezier curve without lane line positions at the curve, obtaining a plurality of discrete points on the supplemented running track, and calculating the turning angle of the steering wheel through the discrete points. According to the method for automatically searching the parking space of the intelligent automobile in the indoor parking lot, the parking space can be accurately identified through the visual detection and positioning method, and automatic parking is smoothly carried out.
Description
Technical Field
The invention belongs to the technical field of automatic driving, and particularly relates to a method for automatically searching a parking space by an intelligent automobile in an indoor parking lot.
Background
With the continuous development of automobile autopilot technology, the application of autopilot in an indoor parking lot is also beginning to get a lot of attention. In the current traditional indoor parking lot, the difficulty plagued the driver is to find an empty parking space. The existing method for automatically searching the parking space in the indoor parking lot needs high cost and workload, and the problem of automatically searching the parking space in the indoor parking lot is difficult to solve.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for automatically searching for a parking space by an intelligent automobile in an indoor parking lot, so as to solve the problem that the existing automatic driving technology is difficult to search for the parking space in the indoor parking lot.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
a method for autonomous finding of a parking space by a smart car in an indoor parking lot, comprising:
s1, calibrating a vehicle-mounted vision system;
s2, acquiring image information of parking spaces on two sides of a lane by using cameras on the sides of the vehicle, and identifying and judging whether the vehicle exists or not;
s3, recognizing a lane line of the parking lot by using a front camera of the vehicle, and setting a constant value of the length of the vehicle from the lane line so that the distance between the vehicle and the lane line is kept at the constant value;
s4, supplementing the vehicle running track through a third-order Bezier curve without lane line positions at the curve, obtaining a plurality of discrete points on the supplemented running track, and calculating the turning angle of the steering wheel through the discrete points.
In step S1, the calibration board is used to calibrate the side camera and the front camera of the vehicle, and perform distortion calibration.
Further, in the step S2, whether there is a car is determined by identifying the identification line of the parking space.
Further, in the step S2, the specific method for identifying the identification line is as follows:
s201, after an image containing parking space identification lines is acquired by a camera, preprocessing the image, setting a value range of an HSV color space of white, yellow and blue, only identifying the parking space identification lines with different colors through color matching, and filtering most of interference information in an image background;
s202, drawing the extracted parking space identification line in a picture with a blank background, and carrying out edge detection and probability Hough transformation on the extracted parking space identification line to extract a straight line segment;
s203, classifying and clustering the extracted straight line segments, combining the straight line segments which are close to collineation into a straight line segment and reserving the straight line segment, scanning the straight line segment in the vertical direction according to the characteristic of brightness in view of the fact that the parking space identification line has a certain width, judging that the parking space identification line is the parking space identification line according to the rule that the brightness value change is firstly increased and then decreased within a certain range, and reserving the parking space identification line, otherwise discarding the parking space identification line;
s204, finally drawing the border of the parking space, and solving four corner points of the parking space.
Further, in the step S3, the image collected by the front camera is binarized, and the binary image is processed through hough transform.
Further, the specific method for identifying and calculating the lane lines is as follows:
s301, regarding a parameter space as discrete, establishing and initializing a two-dimensional array, namely an accumulator, wherein the two-dimensional array is used for recording accumulation results and statistical peaks, the dimension of the accumulator is equal to the number of unknown parameters, and the linear polar coordinate equation only contains two parameters (rho, theta), so that only the two-dimensional accumulator is required to be established, the first dimension in the accumulator represents the range of linear slope in the image coordinate space, and the second dimension represents the range of linear intercept in the image coordinate space;
s302, when detecting a straight line, traversing each point (i, j) in the image, calculating rho values corresponding to all pixel points at each theta angle, and counting the occurrence times of the rho values;
s303, setting a threshold value, and considering that a straight line is detected when the number of times of occurrence of the rho value is higher than the threshold value. And a fixed value d is set as the distance between the vehicle and the lane line in the decision, and the vehicle runs at the straight line according to the lane line through the lane keeping function.
Further, the specific method of step S4 is as follows:
s401, when the vehicle automatically runs to a road intersection, namely, when a road does not have a lane line, taking a point C where the vehicle is located at the moment as a starting point, measuring the transverse and longitudinal coordinates of a point A of the lane to be run to relative to a front camera of the vehicle through a camera, and further calculating the transverse and longitudinal coordinates of a point B through implementing a fixed value d in the step S3, wherein the point B is the end point of the running of the vehicle at a turning position;
s402, obtaining the starting point and the end point of the turning point according to the step S401, selecting four control points of which two control points form a third-order Bezier curve, and obtaining an expression of the third-order Bezier curve through the abscissa information of the four control points, wherein the formula of the third-order Bezier curve is as follows:
wherein X is A The abscissa representing the starting point a in the plane rectangular coordinate system; y is Y A The ordinate representing the starting point a in the plane rectangular coordinate system; x is X B The abscissa of the control point B in the plane rectangular coordinate system; y is Y B Representing the ordinate of the control point B in the plane rectangular coordinate system; x is X C The abscissa representing the control point C in a planar rectangular coordinate system; y is Y C Representing the ordinate of the control point C in a planar rectangular coordinate system; x is X D Is shown in flatThe abscissa of the termination point D in the plane rectangular coordinate system; y is Y D Representing the ordinate of the termination point D in the plane rectangular coordinate system;
s403, as the third-order Bezier curve is an expression about time t, discrete points on the third-order Bezier curve are obtained through given time parameters, the steering wheel angle is calculated through the discrete points, and the steering wheel angle is calculated according to the vehicle parameters.
Compared with the prior art, the method for automatically searching the parking space by the intelligent automobile for the indoor parking lot has the following advantages:
(1) According to the method for automatically searching the parking space by the intelligent automobile in the indoor parking lot, when the adjacent side or two sides of the target parking space are not used for parking the automobile, the automatic parking system based on the ultrasonic radar and other sensors cannot position the parking space.
(2) According to the method for automatically searching the parking space by the intelligent automobile in the indoor parking lot, when the parking positions of the parked vehicles on the left side and the right side of the target parking space are irregular, the parking space can be accurately identified through the visual detection and positioning method, and adverse effects on automatic parking caused by the parking positions of the left parked vehicle and the right parked vehicle are eliminated.
(3) According to the method for automatically searching the parking space of the intelligent automobile for the indoor parking lot, before automatic parking, the initial parking positions of vehicles in different types of parking spaces (vertical parking spaces, horizontal parking spaces and oblique parking spaces) are different, different types of parking spaces are identified through a visual identification method, and the vehicles can be parked according to specified requirements before automatic parking, so that automatic parking is smoothly achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of autonomous parking space searching according to an embodiment of the invention;
FIG. 2 is a front-to-back comparison of visual calibration according to an embodiment of the present invention;
fig. 3 is a flowchart for identifying parking space identification lines and four corner points according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating curve driving according to an embodiment of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", etc. may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art in a specific case.
The invention will be described in detail below with reference to the drawings in connection with embodiments.
As shown in fig. 1, a method for an intelligent automobile for an indoor parking lot to autonomously find a parking space includes:
s1, calibrating a vehicle-mounted vision system;
s2, acquiring image information of parking spaces on two sides of a lane by using cameras on the sides of the vehicle, and identifying and judging whether the vehicle exists or not;
s3, recognizing a lane line of the parking lot by using a front camera of the vehicle, and setting a constant value of the length of the vehicle from the lane line so that the distance between the vehicle and the lane line is kept at the constant value;
s4, supplementing the vehicle running track through a third-order Bezier curve without lane line positions at the curve, obtaining a plurality of discrete points on the supplemented running track, and calculating the turning angle of the steering wheel through the discrete points.
As shown in fig. 2, in the step S1, the calibration board is used to calibrate the internal parameters of the side camera and the front camera of the vehicle, and perform the distortion calibration.
In the step S2, whether a car exists or not is judged by identifying the identification line and the four corner points of the parking space.
As shown in fig. 3, in the step S2, a specific method for identifying and judging whether the parking space has a car is as follows:
s201, after an image containing parking space identification lines is acquired by a camera, preprocessing the image, setting a value range of an HSV color space of white, yellow and blue, only identifying the parking space identification lines with different colors through color matching, and filtering most of interference information in an image background;
s202, drawing the extracted parking space identification line in a picture with a blank background, and carrying out edge detection and probability Hough transformation on the extracted parking space identification line to extract a straight line segment;
s203, classifying and clustering the extracted straight line segments, combining the straight line segments which are close to collineation into a straight line segment and reserving the straight line segment, scanning the straight line segment in the vertical direction according to the characteristic of brightness in view of the fact that the parking space identification line has a certain width, judging that the parking space identification line is the parking space identification line according to the rule that the brightness value change is firstly increased and then decreased within a certain range, and reserving the parking space identification line, otherwise discarding the parking space identification line;
s204, finally drawing the border of the parking space, and solving four corner points of the parking space.
In the step S3, the image collected by the front camera is binarized, and the binary image is processed through hough transform.
The specific method for identifying and calculating the lane lines is as follows:
s301, regarding a parameter space as discrete, establishing and initializing a two-dimensional array, namely an accumulator, wherein the two-dimensional array is used for recording accumulation results and statistical peaks, the dimension of the accumulator is equal to the number of unknown parameters, and the linear polar coordinate equation only contains two parameters (rho, theta), so that only the two-dimensional accumulator is required to be established, the first dimension in the accumulator represents the range of linear slope in the image coordinate space, and the second dimension represents the range of linear intercept in the image coordinate space;
s302, when detecting a straight line, traversing each point (i, j) in the image, calculating rho values corresponding to all pixel points at each theta angle, and counting the occurrence times of the rho values;
s303, setting a threshold value, and considering that a straight line is detected when the number of times of occurrence of the rho value is higher than the threshold value. And a fixed value d is set as the distance between the vehicle and the lane line in the decision, and the vehicle runs at the straight line according to the lane line through the lane keeping function.
As shown in fig. 4, the specific method of step S4 is as follows:
s401, when the vehicle automatically runs to a road intersection, namely, when a road does not have a lane line, taking a point C where the vehicle is located at the moment as a starting point, measuring the transverse and longitudinal coordinates of a point A of the lane to be run to relative to a front camera of the vehicle through a camera, and further calculating the transverse and longitudinal coordinates of a point B through implementing a fixed value d in the step S3, wherein the point B is the end point of the running of the vehicle at a turning position;
s402, obtaining the starting point and the end point of the turning point according to the step S401, selecting four control points of which two control points form a third-order Bezier curve, and obtaining an expression of the third-order Bezier curve through the abscissa information of the four control points, wherein the formula of the third-order Bezier curve is as follows:
wherein X is A The abscissa representing the starting point a in the plane rectangular coordinate system; y is Y A The ordinate representing the starting point a in the plane rectangular coordinate system; x is X B The abscissa of the control point B in the plane rectangular coordinate system; y is Y B Representing the ordinate of the control point B in the plane rectangular coordinate system; x is X C The abscissa representing the control point C in a planar rectangular coordinate system; y is Y C Representing the ordinate of the control point C in a planar rectangular coordinate system; x is X D The abscissa representing the termination point D in a planar rectangular coordinate system; y is Y D Representing the ordinate of the termination point D in the plane rectangular coordinate system;
s403, as the third-order Bezier curve is an expression about time t, discrete points on the third-order Bezier curve are obtained through given time parameters, the steering wheel angle is calculated through the discrete points, and the steering wheel angle is calculated according to the vehicle parameters.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (4)
1. A method for autonomous parking space searching by an intelligent automobile in an indoor parking lot, comprising:
s1, calibrating a vehicle-mounted vision system;
s2, acquiring image information of parking spaces on two sides of a lane by using cameras on the sides of the vehicle, and identifying and judging whether the vehicle exists or not;
s3, recognizing a lane line of the parking lot by using a front camera of the vehicle, and setting a constant value of the length of the vehicle from the lane line so that the distance between the vehicle and the lane line is kept at the constant value;
s4, supplementing a vehicle running track through a third-order Bezier curve without lane line positions at the curve, obtaining a plurality of discrete points on the supplemented running track, and calculating the turning angle of the steering wheel through the discrete points;
the specific method for identifying and calculating the lane lines is as follows:
s301, regarding a parameter space as discrete, establishing and initializing a two-dimensional array, namely an accumulator, wherein the two-dimensional array is used for recording accumulation results and statistical peaks, the dimension of the accumulator is equal to the number of unknown parameters, and the linear polar coordinate equation only contains two parameters (rho, theta), so that only the two-dimensional accumulator is required to be established, the first dimension in the accumulator represents the range of linear slope in the image coordinate space, and the second dimension represents the range of linear intercept in the image coordinate space;
s302, when detecting a straight line, traversing each point (i, j) in the image, calculating rho values corresponding to all pixel points at each theta angle, and counting the occurrence times of the rho values;
s303, setting a threshold value, considering that a straight line is detected when the number of occurrence times of the rho value is higher than the threshold value, setting a fixed value d as the distance between the vehicle and the lane line in a decision, and enabling the vehicle to run at the straight line according to the lane line through a lane keeping function;
the specific method of the step S4 is as follows:
s401, when the vehicle automatically runs to a road intersection, namely, when a road does not have a lane line, taking a point C where the vehicle is located as a starting point, measuring the transverse and longitudinal coordinates of a point A of the lane to be run to relative to a front camera of the vehicle through the front camera, and further calculating the transverse and longitudinal coordinates of a point B by implementing a fixed value d in the step S3, wherein the point B is the end point of the vehicle running at a turning position;
s402, obtaining the starting point and the end point of the turning point according to the step S401, selecting four control points of which two control points form a third-order Bezier curve, and obtaining an expression of the third-order Bezier curve through the abscissa information of the four control points, wherein the expression of the third-order Bezier curve is as follows:
wherein X is A The abscissa representing the starting point a in the plane rectangular coordinate system; y is Y A The ordinate representing the starting point a in the plane rectangular coordinate system; x is X B The abscissa of the control point B in the plane rectangular coordinate system; y is Y B Representing the ordinate of the control point B in the plane rectangular coordinate system; x is X C The abscissa representing the control point C in a planar rectangular coordinate system; y is Y C Representing the ordinate of the control point C in a planar rectangular coordinate system; x is X D The abscissa representing the termination point D in a planar rectangular coordinate system; y is Y D Representing the ordinate of the termination point D in the plane rectangular coordinate system;
s403, as the third-order Bezier curve is an expression about time t, discrete points on the third-order Bezier curve are obtained through given time parameters, the steering wheel angle is calculated through the discrete points, and the steering wheel angle is calculated according to the vehicle parameters;
in the step S2, the specific method for identifying the identification line is as follows:
s201, after an image containing parking space identification lines is acquired by a camera, preprocessing the image, setting a value range of an HSV color space of white, yellow and blue, only identifying the parking space identification lines with different colors through color matching, and filtering interference information in an image background;
s202, drawing the extracted parking space identification line in a picture with a blank background, and carrying out edge detection and probability Hough transformation on the extracted parking space identification line to extract a straight line segment;
s203, classifying and clustering the extracted straight line segments, combining the straight line segments which are close to collineation into a straight line segment and reserving the straight line segment, scanning the straight line segment in the vertical direction according to the characteristic of brightness in view of the fact that the parking space identification line has a certain width, judging that the parking space identification line is the parking space identification line according to the rule that the brightness value change is firstly increased and then decreased within a certain range, and reserving the parking space identification line, otherwise discarding the parking space identification line;
s204, finally drawing the border of the parking space, and solving four corner points of the parking space.
2. The method for autonomous parking space finding by a smart car for an indoor parking lot according to claim 1, wherein: in the step S1, the side camera and the front camera of the vehicle are calibrated by using a calibration plate, and distortion calibration is performed.
3. The method for autonomous parking space finding by a smart car for an indoor parking lot according to claim 1, wherein: in the step S2, whether a car is present or not is determined by identifying the identification line of the parking space.
4. The method for autonomous parking space finding by a smart car for an indoor parking lot according to claim 1, wherein: in the step S3, the image collected by the front camera is binarized, and the binary image is processed through hough transform.
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| CN110544386A (en) * | 2019-09-18 | 2019-12-06 | 奇瑞汽车股份有限公司 | parking space identification method and device and storage medium |
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