CN114863385A - Road curved surface information generation method, device, equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a road curved surface information generation method, a road curved surface information generation device, equipment and a computer readable medium. One embodiment of the method comprises: extracting key points of each road image in the pre-acquired road image sequence to generate an obstacle key point coordinate sequence set, and obtaining an obstacle key point coordinate sequence set; constructing a right-angle constraint equation based on the set of the coordinate series groups of the key points of the obstacles; updating the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation; and determining the target road surface equation as the road surface information. This embodiment may improve the accuracy of the generated road surface information.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a road curved surface information generation method, a road curved surface information generation device, road curved surface information generation equipment and a computer readable medium.
Background
The generation of the road curved surface information has important significance to the field of automatic driving. At present, when generating road curved surface information, the method generally adopted is as follows: static features (such as lane lines) are extracted from a road image, three-dimensional coordinates of the features are obtained by a triangulation method, and road surface information is generated.
However, when the road surface information is generated in the above manner, there are often the following technical problems:
firstly, the static characteristics of the road surface are easy to be shielded, so that the extracted static characteristics are not accurate enough, and the accuracy of the generated road curved surface information is reduced;
secondly, the static features of the road surface lack obvious feature points, so that the extracted static features are not accurate enough, and the accuracy of the generated road surface information is reduced.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose a road surface information generating method, apparatus, device and computer readable medium to solve one or more of the technical problems mentioned in the above background section.
In a first aspect, some embodiments of the present disclosure provide a method for generating road surface information, the method including: extracting key points of each road image in the pre-acquired road image sequence to generate an obstacle key point coordinate sequence group to obtain an obstacle key point coordinate sequence group set; constructing a right-angle constraint equation based on the set of the coordinate series groups of the key points of the obstacles; updating the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation; and determining the target road surface equation as the road surface information.
In a second aspect, some embodiments of the present disclosure provide a road surface information generating apparatus, including: the extraction unit is configured to extract key points of each road image in the pre-acquired road image sequence to generate an obstacle key point coordinate sequence group to obtain an obstacle key point coordinate sequence group set; the building unit is configured to build a right-angle constraint equation based on the set of the coordinate series groups of the key points of the obstacles; the updating unit is configured to update the initial road curved surface equation based on the right-angle constraint equation to obtain a target road curved surface equation; a determination unit configured to determine the target road surface equation as the road surface information.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The above embodiments of the present disclosure have the following advantages: by the road curved surface information generation method of some embodiments of the present disclosure, the accuracy of the generated road curved surface information can be improved. Specifically, the reason why the accuracy of the generated road surface information is reduced is that: the static features of the road surface are easily blocked, resulting in the inaccurate extracted static features. Based on this, in the road curved surface information generating method according to some embodiments of the present disclosure, first, a key point extraction is performed on each road image in a pre-acquired road image sequence to generate an obstacle key point coordinate sequence group, so as to obtain an obstacle key point coordinate sequence group set. By extracting the coordinates of the key points of the obstacles, the road surface information can be generated without depending on static characteristics. The condition that the extracted static features are not accurate enough due to the fact that the static features of the road surface are easily shielded is avoided. And then, constructing a right-angle constraint equation based on the set of the coordinate series groups of the key points of the obstacles. The right-angle constraint equation is constructed, so that the accuracy of the generated road curved surface information can be improved. And then updating the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation. By updating, the accuracy of the road surface equation can be further improved. And finally, determining the target road surface equation as the road surface information. Therefore, the road curved surface information generation method of some embodiments of the present disclosure constructs a right-angle constraint equation based on the coordinates of the key points of the obstacles, and can improve the accuracy of the generated road curved surface information.
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The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a flow diagram of some embodiments of a road surface information generation method according to the present disclosure;
FIG. 2 is a flow diagram of further embodiments of a road surface information generation method according to the present disclosure;
FIG. 3 is a schematic block diagram of some embodiments of a road surface information generating device according to the present disclosure;
FIG. 4 is a schematic block diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates a flow 100 of some embodiments of a road surface information generation method according to the present disclosure. The process 100 of the road curved surface information generating method includes the following steps:
In some embodiments, the executing entity of the road curved surface information generating method may perform key point extraction on each road image in the pre-acquired road image sequence to obtain a set of the coordinate sequence group of the key points of the obstacle. Wherein each road image in the road image sequence may be a continuous frame image captured by the onboard camera. And extracting key points of each road image in the pre-acquired road image sequence through a preset extraction algorithm to obtain a set of the coordinate sequence group of the key points of the barrier. Each obstacle keypoint coordinate sequence may correspond to an obstacle. Each set of obstacle keypoint coordinate sequences may correspond to a respective obstacle in one road image. The obstacle keypoints may characterize the outermost points of the tire where the obstacle vehicle is in contact with the ground. The coordinates of the key points of the obstacles in the coordinate sequence of the key points of the obstacles may be arranged in a preset order. In addition, the number of detection keypoint coordinates in the detection keypoint coordinate sequence may be: one, two, three or four, etc. The preset sequence may be a clockwise sequence or a counterclockwise sequence, for example, a counterclockwise sequence: right front wheel, left rear wheel, right rear wheel.
As an example, the extraction algorithm may include, but is not limited to, at least one of: a GUP (Geometry unscientific Projection, monocular three-dimensional object detection Network), a SegNet (image semantic segmentation depth Network), an FCN (full volume neural Networks) model, etc., a VGG (Visual Geometry Group Network) model, a google net (deep neural Network) model, etc. For example, three points corresponding to the right front wheel, the left front wheel, and the left rear wheel of the obstacle vehicle are detected. The order of the corresponding detection keypoint coordinates may be chronologically ordered starting from the right front wheel. Then, the sequential number of the coordinates of the obstacle key points corresponding to the right front wheel, the left front wheel, and the left rear wheel may be (1-2-3).
And 102, constructing a right-angle constraint equation based on the set of the coordinate series groups of the key points of the obstacles.
In some embodiments, the execution subject may construct the right-angle constraint equation based on the set of the coordinate series of the key points of the obstacle in various ways.
In some optional implementation manners of some embodiments, the constructing a right-angle constraint equation by the execution subject based on the set of coordinate series of key points of the obstacle may include:
firstly, screening each barrier key point coordinate sequence in the set of barrier key point coordinate sequences to obtain a set of target barrier key point coordinate sequences.
In some optional implementation manners of some embodiments, the performing main body performs a screening process on each obstacle key point coordinate sequence in the set of obstacle key point coordinate sequences to obtain a set of target obstacle key point coordinate sequences, and may include the following steps:
for each obstacle key point coordinate sequence in the set of obstacle key point coordinate sequences, performing the following screening processing steps:
and a first substep of back-projecting the coordinates of each obstacle key point in the obstacle key point coordinate sequence to obtain a three-dimensional obstacle key point coordinate sequence. The coordinates of the key points of the obstacles can be back projected from the image coordinate system to the vehicle coordinate system by an inverse perspective transformation method. Therefore, the coordinate sequence of the key points of the three-dimensional obstacle can be obtained.
And a second substep of constructing a unit vector sequence by using the coordinates of each three-dimensional obstacle key point in the three-dimensional obstacle key point coordinate sequence. Wherein, for every two adjacent three-dimensional obstacle key point coordinates, a unit vector can be constructed. The set of unit vectors can be generated by the following formula:
wherein j and p represent serial numbers. l represents a unit vector in the above-described unit vector sequence. l j Represents the jth unit vector in the unit vector sequence. And w represents the coordinates of the three-dimensional obstacle key points in the three-dimensional obstacle key point coordinate sequence. w is a p And representing the coordinates of the p-th three-dimensional obstacle key point in the three-dimensional obstacle key point coordinate sequence. w is a p+1 And (3) representing the coordinates of the p +1 th three-dimensional obstacle key point in the three-dimensional obstacle key point coordinate sequence. I | · | purple wind 2 Represents a 2-way expression.
And a third substep, in response to determining that the coordinates of each barrier key point in the barrier key point coordinate sequence meet a preset key point condition and the unit vector sequence meets a preset vector relationship condition, determining the three-dimensional barrier key point coordinate sequence as a target barrier key point coordinate sequence. Wherein, the preset key point condition may be: the number of the obstacle key points in the obstacle key point coordinate sequence is a preset number (for example, 3), and the sequence number of each obstacle key point is one of a preset sequence number set. The preset vector relation condition may be that every two adjacent unit vectors in the unit vector sequence have a vertical relation therebetween. The order of the respective unit vectors in the unit vector sequence may correspond to the order in the above-described obstacle keypoint coordinate sequence.
As an example, the above sequence number set may be: {(1-2-3),(2-3-4),(1-4-3),(2-1-4)}. For example, the sequence of the key points of each obstacle is numbered (1-2-3). It may be determined that the obstacle keypoint coordinate sequence satisfies the preset keypoint condition. Thus, there may be two unit vectors generated, respectively: the three-dimensional obstacle key point coordinates in order numbers 1 and 2 pass through the unit vector constituted by the above formula, and the three-dimensional obstacle key point coordinates in order numbers 2 and 3 pass through the unit vector constituted by the above formula. If the two unit vectors are perpendicular, it can be determined that the unit vector sequence satisfies the preset vector relation condition.
In practice, the sequential numbering of the coordinates of the key points of the obstacles can be used not only to distinguish the corresponding wheels of the obstacle vehicle, but also to facilitate the generation of the unit vector. And the sequential numbering is introduced, so that the generated unit vector can represent the boundary of the rectangle of the approximate vehicle in the overlooking angle. By introducing the preset key point condition, the coordinate sequence of the key points of the obstacle meeting the preset key point condition can be screened out to construct a right-angle constraint equation. In addition, by introducing a preset vector relation condition, the method can be used for determining that the unit vector is the boundary of the rectangular frame and is perpendicular to the unit vector. In this way, the accuracy of characterizing an actual obstacle vehicle is improved. Further, it can be used to improve the accuracy of the generated road surface information.
And secondly, constructing a right-angle constraint equation based on the set of the target obstacle key point coordinate series groups. And the unit vector sequence corresponds to the target obstacle key point coordinate sequences in the target obstacle key point coordinate sequence group set one by one. The right angle constraint equation constructed may be:
wherein e is 1 The result of the right angle constraint equation, i.e., the right angle relationship error, is expressed.
And a square error corresponding to the jth unit vector in the unit vector sequence is shown. l j Represents the jth unit vector in the unit vector sequence. l j+1 Represents the j +1 th unit vector in the unit vector sequence.
And 103, updating the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation.
In some embodiments, the executing entity updates the initial road surface equation to obtain the target road surface equation based on the right-angle constraint equation in various ways.
In some optional implementations of some embodiments, the initial road surface equation is generated by:
firstly, extracting lane line points from a preset target road image to obtain a lane line point coordinate set. The target road image may be a first frame road image acquired in advance. The extraction algorithm can be used for extracting the lane line points of the preset target road image to obtain a lane line point coordinate set.
And secondly, back projecting the coordinates of each lane line point in the lane line point coordinate set to a vehicle coordinate system to obtain a lane line point three-dimensional coordinate set. And performing back projection on each lane line point coordinate in the lane line point coordinate set from the image coordinate system to the vehicle coordinate system in an inverse perspective transformation mode to obtain a lane line point three-dimensional coordinate set.
And thirdly, generating the initial road surface equation based on the three-dimensional coordinate set of the lane line points. The three-dimensional coordinates of the lane line points in the three-dimensional coordinate set of the lane line points can be input into a preset curved surface equation, and parameters of the initial road curved surface equation are obtained through solution. From this, an initial road surface equation can be obtained. The expression of the initial surface equation may be as follows:
where P(s) represents the initial surface equation. And s represents the three-dimensional coordinates of the lane line points in the three-dimensional coordinate set of the lane line points. x represents an abscissa value of the three-dimensional coordinates of the lane line point. y represents a vertical coordinate value of the three-dimensional coordinates of the lane line point. z represents a vertical coordinate value of the three-dimensional coordinates of the lane line point. A represents the coefficient matrix of the initial surface equation. B denotes a coefficient vector of the initial surface equation. c represents a constant term. a is 1 、a 2 、a 3 Representing the data in the coefficient matrix. b 1 、b 2 Representing data in a coefficient vector. T denotes the transpose of the matrix.
Specifically, the coefficient matrix in the initial surface equation may be a zero matrix.
And step 104, determining the target road surface equation as the road surface information.
In some embodiments, the execution subject may determine the target road surface equation as the road surface information. The road surface information may be information representing a road surface.
Optionally, the executing body may further perform the following steps:
firstly, extracting characteristic points of each road image in the road image sequence to obtain a road surface characteristic point coordinate set. Feature point extraction can be performed on each road image in the road image sequence through the extraction algorithm, so that a road surface feature point coordinate set is obtained. The coordinates of each road surface feature point in the road surface feature point coordinate set can be used for representing a lane line corresponding to the road image.
And secondly, back projecting each road surface characteristic point coordinate in the road surface characteristic point coordinate set to a coordinate system where the target road surface equation is located to obtain a back projection characteristic point coordinate set. And performing back projection on each road characteristic point coordinate in the road characteristic point coordinate set from an image coordinate system to a coordinate system where the target road curved surface equation is located by using a back projection transformation method to obtain a back projection characteristic point coordinate set. The coordinate system in which the target road surface equation is located may be a vehicle coordinate system. On the basis of improving the accuracy of the target road surface equation, the accuracy of the generated back projection characteristic point coordinate set can be improved by back projecting the road surface characteristic point coordinate to the coordinate system of the target road surface equation.
And thirdly, sending the back projection feature point coordinate set and the road curved surface information to a display terminal for displaying. Wherein, the accuracy of the back projection feature point coordinate set and the road curved surface information is improved. Thus, the accuracy of the road information displayed by the display terminal can be improved. Further, it can be used to improve driving safety.
The above embodiments of the present disclosure have the following advantages: by the road curved surface information generation method of some embodiments of the present disclosure, the accuracy of the generated road curved surface information can be improved. Specifically, the reason why the accuracy of the generated road surface information is reduced is that: the static features of the road surface are easily blocked, resulting in inaccurate extracted static features. Based on this, in the road curved surface information generation method according to some embodiments of the present disclosure, first, a set of key point coordinate series groups of the obstacle is obtained by performing key point extraction on each road image in the pre-acquired road image series. By extracting the coordinates of the key points of the obstacles, the road surface information can be generated without depending on static characteristics. The condition that the extracted static features are not accurate enough due to the fact that the static features of the road surface are easily shielded is avoided. And then, constructing a right-angle constraint equation based on the set of the coordinate series groups of the key points of the obstacles. The right-angle constraint equation is constructed, so that the accuracy of the generated road curved surface information can be improved. And then updating the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation. By updating, the accuracy of the road surface equation can be further improved. And finally, determining the target road curved surface equation as the road curved surface information. Therefore, the road curved surface information generation method of some embodiments of the present disclosure constructs a right-angle constraint equation based on the coordinates of the key points of the obstacles, and can improve the accuracy of the generated road curved surface information.
With further reference to FIG. 2, a flow 200 of further embodiments of a road surface information generation method is illustrated. The process 200 of the road curved surface information generation method includes the following steps:
And 202, constructing a right-angle constraint equation based on the set of the coordinate series groups of the key points of the obstacles.
In some embodiments, the specific implementation manner and technical effects of the steps 201-202 can refer to the steps 101-102 in the embodiments corresponding to fig. 1, which are not described herein again.
And 203, generating a set of the key point projection error sequence groups based on a preset camera internal reference matrix and a coordinate transformation matrix.
In some embodiments, the executing entity may generate the set of keypoint projection error sequence groups based on a preset camera internal reference matrix and a coordinate transformation matrix. Wherein, the set of the keypoint projection error sequence groups can be generated by the following formula:
wherein i, k, n represent the numbers. e.g. of the type 2 And representing the projection errors of the key points in the set of the series of projection errors of the key points.
Representing a set of sets of keypoint projection error sequencesAnd (4) the projection errors of the key points in the ith key point projection error sequence group.
And representing the projection error of the key point in the k key point projection error sequence in the ith key point projection error sequence group in the set of key point projection error sequence groups.
And representing the nth key point projection error in the kth key point projection error sequence in the ith key point projection error sequence set in the set of key point projection error sequences. K denotes a camera internal reference matrix. R represents a coordinate rotation matrix. And m represents the coordinates of the key points of the target obstacle in the set of key point coordinate series of the target obstacle. m is i And the coordinates of the target obstacle key points in the ith target obstacle key point coordinate series group in the target obstacle key point coordinate series group set are expressed. m is i，p And the coordinates of the key point of the target obstacle in the p-th target obstacle key point coordinate sequence in the ith target obstacle key point coordinate sequence group in the target obstacle key point coordinate sequence group set are expressed. m is i，p，n And the coordinates of the nth target obstacle key point in the coordinate sequence of the pth target obstacle key point in the coordinate sequence set of the ith target obstacle key point in the coordinate sequence set of the target obstacle key point are expressed. k represents an obstacle key point coordinate corresponding to a target obstacle key point coordinate in the set of target obstacle key point coordinate series groups. k is a radical of i And the coordinates of the key points of the obstacle corresponding to the coordinates of the key points of the target obstacle in the ith group of key points of the target obstacle in the group of key points of the target obstacle. k is a radical of i，p And the coordinate of the key point of the obstacle corresponding to the coordinate of the key point of the target obstacle in the p-th key point of the target obstacle in the coordinate series group of the key point of the target obstacle in the set of the key point series groups of the target obstacle. k is a radical of i，p，n Representing the ith target obstacle in the set of the target obstacle key point coordinate series groupsAnd the coordinates of the key point of the obstacle corresponding to the coordinates of the key point of the nth target obstacle in the key point sequence of the pth target obstacle in the coordinate sequence group of the key point of the object. N represents a normal distribution sign. Sigma p Representing a preset projection error covariance matrix. () 3 The 3 rd element of the vector in parentheses is taken. () 1：2 The 1 st to 2 nd elements of the vector in parentheses are taken.
And 204, updating the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation.
In some embodiments, the executing entity may update the initial road surface equation based on the set of keypoint projection error series groups, the right-angle constraint equation, and a preset covariance matrix to obtain a target road surface equation. Wherein first an initial state vector of an initial state equation can be determined. The initial state vector may be composed of the coefficient matrix and data in the coefficient vector, and the initial surface equation constant term. For example: z ═ c, b 1 ，b 2 ，a 1 ，a 2 ，a 3 ]. Where Z may represent an initial state vector. Then, the target state vector sequence can be obtained by the following formula:
wherein Z represents the initial state vector. Z' represents a target state vector in the sequence of target state vectors. Z' i Representing the ith target state vector in the sequence of target state vectors. P (m) i，p，n ) And the result of inputting the nth target obstacle key point coordinate in the pth target obstacle key point coordinate sequence in the ith target obstacle key point coordinate sequence group in the target obstacle key point coordinate sequence group set into the initial state equation is shown.
Representing the ith target in the set of the target obstacle key point coordinate series groupsAnd (3) a result of a constraint equation corresponding to the jth target obstacle key point coordinate in the pth target obstacle key point coordinate sequence in the obstacle key point coordinate sequence group, namely, a rectangular relation error.
And the transpose matrix represents the projection error of the nth key point in the projection error sequence of the kth key point in the projection error sequence group of the ith key point in the projection error sequence group set of the key point.
An inverse matrix representing a preset projection error covariance matrix. λ represents a projection parameter in a preset projection parameter set. Indicating the ith projection parameter in the preset projection parameter set. D denotes a conversion parameter for shortening the formula length.
Finally, the last target state vector in the sequence of target state vectors may be determined as a parameter of the target road surface equation. Therefore, the initial road surface equation can be updated to obtain the target road surface equation.
In practice, the projection parameters are related to the degree of vehicle jounce: the larger the degree of jerk, the lower the data reliability of the obstacle vehicle, and the smaller the projection parameter, so that the result of the term projection parameter is of lower importance. Thereby, it can be used to reduce the influence of the degree of thrashing on the generation of the target state vector. Thus, the accuracy of the generated target state vector is improved. In addition, the above formula for generating the target state vector can be solved by a nonlinear optimization method. For example, the non-linear optimization method may include, but is not limited to, at least one of: ISAM (Incremental Smoothing And Mapping method), GTSAM (nonlinear optimization library), And the like. In the solving process, the condition that the projection error of the key point meets the Gaussian distribution needs to be met. And the inverse of the covariance matrix represents the degree of certainty of the error, and the larger the error value is, the larger the degree of certainty is, and the smaller the degree of uncertainty is. Therefore, the influence of the error in generating the coordinates of the key points of the obstacle is reduced by introducing the inverse of the coordinate covariance matrix. Therefore, various errors can be reduced, and the accuracy of generating the target surface equation is improved.
The above formulas and the related contents thereof are used as an invention point of the embodiment of the disclosure, and the technical problem mentioned in the background art that the static characteristics of the road surface lack obvious characteristic points, so that the extracted static characteristics are not accurate enough, and the accuracy of the generated road surface information is reduced is solved. Factors that cause a reduction in the accuracy of the generated road surface information tend to be as follows: the static features of the road surface lack distinct feature points, resulting in an inaccurate extraction of the static features. If the above factors are solved, the accuracy of the generated road surface information can be improved. To achieve this, first, the coordinates of the key points of the obstacle can be used to replace the static features of the road surface by extracting them. Then, by generating a formula of the unit vector, the unit vector can be generated, thereby facilitating the construction of the constraint relation. Then, through the right angle constraint equation, it can be used to generate the right angle relation error. For use in improving the accuracy of generating the target state vector. Then, by introducing the initial surface equation, the generation of the initial state vector can be facilitated. In order to generate the target state vector. Then, by generating a formula of the projection error of the key point, constraint conditions can be further increased, and the accuracy of solving the target state vector is improved. Finally, the target state vector can be generated under the condition that the conditions are met by a formula for generating the target state vector. Thus, the accuracy of the target state vector is improved. Furthermore, it can be used to improve the accuracy of the road curvature information.
In some embodiments, the specific implementation manner and technical effects of step 205 may refer to step 104 in those embodiments corresponding to fig. 1, and are not described herein again.
As can be seen from fig. 2, compared with the description of some embodiments corresponding to fig. 1, the flow 200 of the road surface information generating method in some embodiments corresponding to fig. 2 represents a step of updating the initial road surface equation. Through the formulas and the related contents, the technical problem that the extracted static characteristics are not accurate enough due to the fact that the static characteristics of the road surface lack obvious characteristic points is solved. Further, the accuracy of the generated road information is improved.
With further reference to fig. 3, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a road curvature information generating apparatus, which correspond to those shown in fig. 2, and which may be applied in various electronic devices in particular.
As shown in fig. 3, a road surface information generating apparatus 300 of some embodiments includes: an extraction unit 301, a construction unit 302, an update unit 303, and a determination unit 304. The extraction unit 301 is configured to perform key point extraction on each road image in a pre-acquired road image sequence to generate an obstacle key point coordinate sequence group, so as to obtain an obstacle key point coordinate sequence group set; a constructing unit 302 configured to construct a right-angle constraint equation based on the set of the coordinate series of the key points of the obstacle; an updating unit 303, configured to update the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation; a determination unit 304 configured to determine the above-described target road surface equation as the road surface information.
It will be understood that the units described in the apparatus 300 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 300 and the units included therein, and are not described herein again.
Referring now to fig. 4, a block diagram of an electronic device 400 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 409, or from the storage device 408, or from the ROM 402. The computer program, when executed by the processing device 401, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: extracting key points of each road image in the pre-acquired road image sequence to generate an obstacle key point coordinate sequence set, and obtaining an obstacle key point coordinate sequence set; constructing a right-angle constraint equation based on the set of the coordinate series groups of the key points of the obstacles; updating the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation; and determining the target road surface equation as the road surface information.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an extraction unit, a construction unit, an update unit, and a determination unit. Here, the names of the units do not constitute a limitation to the unit itself in some cases, and for example, the extraction unit may also be described as a "unit that extracts a set of obstacle keypoint coordinate series groups".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A road curved surface information generation method comprises the following steps:
extracting key points of each road image in the pre-acquired road image sequence to generate an obstacle key point coordinate sequence set, and obtaining an obstacle key point coordinate sequence set;
constructing a right-angle constraint equation based on the set of the coordinate series groups of the key points of the barrier;
updating the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation;
and determining the target road surface equation as the road surface information.
2. The method of claim 1, wherein the method further comprises:
extracting characteristic points of each road image in the road image sequence to obtain a road surface characteristic point coordinate set;
back projecting each road surface characteristic point coordinate in the road surface characteristic point coordinate set to a coordinate system where the target road surface equation is located to obtain a back projection characteristic point coordinate set;
and sending the back projection feature point coordinate set and the road curved surface information to a display terminal for display.
3. The method of claim 1, wherein constructing a right angle constraint equation based on the set of sets of obstacle keypoint coordinate series comprises:
screening each barrier key point coordinate sequence in the set of barrier key point coordinate sequences to obtain a set of target barrier key point coordinate sequences;
and constructing a right-angle constraint equation based on the set of the key point coordinate series group of the target obstacle.
4. The method according to claim 3, wherein the screening each barrier key point coordinate sequence in the set of barrier key point coordinate sequences to obtain a set of target barrier key point coordinate sequences comprises:
for each obstacle key point coordinate sequence in the set of obstacle key point coordinate sequences, performing the following screening process steps:
carrying out back projection on each barrier key point coordinate in the barrier key point coordinate sequence to obtain a three-dimensional barrier key point coordinate sequence;
constructing a unit vector sequence by utilizing each three-dimensional barrier key point coordinate in the three-dimensional barrier key point coordinate sequence;
and in response to the fact that the coordinates of each barrier key point in the barrier key point coordinate sequence meet the preset key point condition and the unit vector sequence meets the preset vector relation condition, determining the three-dimensional barrier key point coordinate sequence as a target barrier key point coordinate sequence.
5. The method of claim 3, wherein the initial road surface equation is generated by:
extracting lane line points of a preset target road image to obtain a lane line point coordinate set;
back projecting each lane line point coordinate in the lane line point coordinate set to a vehicle coordinate system to obtain a lane line point three-dimensional coordinate set;
and generating the initial road surface equation based on the three-dimensional coordinate set of the lane line points.
6. The method of claim 5, wherein before said updating the initial road surface equation to obtain the target road surface equation based on the right angle constraint equation, the method further comprises:
and generating a set of the key point projection error sequence groups based on a preset camera internal reference matrix and a coordinate transformation matrix.
7. The method of claim 6, wherein updating the initial road surface equation based on the right angle constraint equation to obtain the target road surface equation comprises:
and updating the initial road surface equation based on the set of the key point projection error sequence groups, the right-angle constraint equation and a preset covariance matrix to obtain a target road surface equation.
8. A road surface information generating apparatus comprising:
the extraction unit is configured to extract key points of each road image in the pre-acquired road image sequence to generate an obstacle key point coordinate sequence group to obtain an obstacle key point coordinate sequence group set;
a construction unit configured to construct a right-angle constraint equation based on the set of the barrier key point coordinate series groups;
the updating unit is configured to update the initial road surface equation based on the right-angle constraint equation to obtain a target road surface equation;
a determination unit configured to determine the target road surface equation as the road surface information.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Address after: 201, 202, 301, No. 56-4 Fenghuang South Road, Huadu District, Guangzhou City, Guangdong Province, 510806 Patentee after: Heduo Technology (Guangzhou) Co.,Ltd. Address before: 100099 101-15, 3rd floor, building 9, yard 55, zique Road, Haidian District, Beijing Patentee before: HOLOMATIC TECHNOLOGY (BEIJING) Co.,Ltd. |
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Denomination of invention: Method, device, equipment, and computer-readable medium for generating road surface information Granted publication date: 20230407 Pledgee: Bank of Shanghai Co.,Ltd. Beijing Branch Pledgor: Heduo Technology (Guangzhou) Co.,Ltd. Registration number: Y2024980009891 |