CN114419190A - Grid map visual guiding line generation method and device - Google Patents
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Abstract
The invention discloses a grid map visual guiding line generation method and a device, wherein the method comprises the following steps: s01, acquiring a road environment image acquired in the driving process of a vehicle, and extracting road area outline information; s02, extracting point sets of left and right boundaries of the road from the contour information of the road area; s03, calculating coordinates of a plurality of road intermediate points according to the point sets of the left and right boundaries of the road; and S04, generating a road guiding line according to the coordinates of the road intermediate points. The method can realize automatic generation of the road guiding line, and has the advantages of simple realization method, high efficiency and precision, wide application range, no need of depending on GPS information and the like.
Description
Technical Field
The invention relates to the technical field of vehicle trajectory planning, in particular to a grid map visual guiding line generation method and device.
Background
The road guiding line is provided for a planning layer of a vehicle system to plan a track by acquiring a center line of a road area as the guiding line in the driving process of the vehicle. For road guiding lines, in the prior art, vehicles carrying high-precision GPS (global positioning system) positioning are usually driven to travel on a target road segment by manual driving, and tracks generated by GPS signals of corresponding vehicle positions are recorded during the driving process, so that corresponding road guiding lines are generated and provided for a planning layer of an unmanned vehicle system as reference; or the center line of the road is further marked manually by using the lane line of the road on the manually made high-precision map to be used as the road guiding line.
However, the above road guiding line obtaining method in the prior art has the following problems:
1. the method is required to depend on manual implementation, is time-consuming and labor-consuming, and is easy to generate errors.
2. The acquired global road guiding lines are all global road guiding lines, when unmanned track planning is carried out by using the global road guiding lines, primary positioning must be carried out on the global road guiding lines by depending on GPS signals, and then the local guiding lines of targets are positioned, namely the acquisition of the road guiding lines must depend on the GPS signals, and the method is not suitable for scenes that the GPS signals cannot be acquired or are weak in GPS signals. For example, in a scene such as a tunnel or a road with many buildings, the above method cannot be applied to obtain the road guiding line because the GPS signal is weak or even signal interruption is easy to occur.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides a grid map visual guiding line generation method and device which are simple in implementation method, high in efficiency and precision and wide in application range and do not need to depend on GPS information.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a grid map visual guiding line generation method comprises the following steps:
s01, acquiring a road environment image acquired in the driving process of a vehicle, and extracting road area outline information;
s02, extracting point sets of left and right boundaries of the road from the contour information of the road area;
s03, calculating coordinates of a plurality of road intermediate points according to the point sets of the left and right boundaries of the road;
and S04, generating a road guiding line according to the coordinates of the road intermediate points.
Further, the step S01 includes:
s101, performing semantic segmentation on the road environment image, and projecting the road environment image to a vehicle body coordinate system to obtain a grid map;
s102, processing the grid map obtained in the step S101 to obtain all image outlines;
and S103, taking the outline surrounding area with the largest area in all the image outlines as the road area outline.
Further, the step S101 further includes performing a closed operation on the projected grid map to obtain a final grid map output.
Further, in step S01, the extracted information of the road area contour is sequentially stored in the array of positions of the road area contour points, where a starting point is an upper left corner point of the road area contour, and the remaining points in the contour are obtained along the contour in a counterclockwise sequence and sequentially stored in the array of positions of the road area contour points; in step S02, a plurality of data points in the array of the positions of the contour points in the road area are sequentially taken as the right boundary of the road, and a plurality of data points are taken in an index reverse order to obtain a point set of the left boundary of the road, so as to obtain a point set of the left and right boundaries of the road.
Further, a front data point in the road area contour point position array is specifically taken as a right boundary of the road, and a plurality of data points at the nearest end of the road are removed.
Further, the step S03 includes:
s301, carrying out interval sampling on the point set of the left and right boundaries of the road to obtain a plurality of left and right boundary point pairs;
s302, calculating coordinate values of intermediate points between each left and right boundary point pair to obtain coordinate values of corresponding road intermediate points.
Further, in step S04, the road guiding line is generated by inserting a parametric cubic spline between the road intermediate points.
Further, after the step S04, the method further includes smoothing the generated road guiding line by using interval sampling and interpolation.
A grid map visual guideline generation apparatus, comprising:
the contour extraction module is used for acquiring a road environment image acquired in the driving process of a vehicle and extracting road area contour information;
the boundary extraction module is used for extracting point sets of left and right boundaries of the road from the contour information of the road area;
the middle point calculating module is used for calculating the coordinates of the road middle points according to the point sets of the left and right boundaries of the road;
and the guiding line generating module is used for generating a road guiding line according to the coordinates of the road intermediate point.
A computer device comprising a processor and a memory, the memory being adapted to store a computer program, the processor being adapted to execute the computer program, and the processor being adapted to execute the computer program to perform the method as described above.
Compared with the prior art, the invention has the advantages that: the invention utilizes a visual perception method, extracts road region outline information in an image to be processed, then extracts point sets of left and right boundaries of a road, calculates coordinates of a plurality of road intermediate points according to the left and right boundary point sets, and further generates a road guiding line according to the coordinates of each road intermediate point, can directly, automatically and efficiently generate a local road guiding line, does not need to depend on GPS signals, can be flexibly suitable for various scenes with weak GPS signals, such as tunnels, roads with more buildings and the like, and further can provide the generated road guiding line for an automatic driving planning layer to carry out track planning.
Drawings
Fig. 1 is a schematic flow chart of an implementation of the grid map visual guiding line generation method according to the embodiment.
Fig. 2 is a detailed flow chart of the grid map visual guiding line generation in the embodiment of the invention.
Fig. 3 is a schematic diagram of a grid map including a road area according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a road region profile obtained in an embodiment of the present invention.
FIG. 5 is a diagram illustrating road guiding line effects generated in an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
As shown in fig. 1, the steps of the grid map visual guiding line generating method of the present embodiment include:
s01, acquiring a road environment image acquired in the driving process of a vehicle, and extracting road area outline information;
s02, extracting point sets of left and right boundaries of a road from contour information of a road area;
s03, calculating coordinates of a plurality of road intermediate points according to the point sets of the left and right boundaries of the road;
and S04, generating a road guiding line according to the coordinates of the middle points of the roads.
According to the embodiment, by means of a visual perception method, the contour information of the road area in the image to be processed is extracted, then the point sets of the left and right boundaries of the road are extracted, the coordinates of a plurality of road intermediate points are calculated according to the left and right boundary point sets, and then the road guiding lines are generated according to the coordinates of each road intermediate point, so that local road guiding lines can be directly, automatically and efficiently generated, the GPS signals are not needed, the method can be flexibly suitable for various scenes with weak GPS signals, such as tunnels, roads with more buildings and the like, and the generated road guiding lines can be further provided for an automatic driving planning layer to carry out track planning.
In this embodiment, the step S01 specifically includes:
s101, semantic segmentation is carried out on the road environment image, and the road environment image is projected to a vehicle body coordinate system to obtain a grid map;
s102, processing the grid map obtained in the step S101 to obtain all image outlines;
and S103, taking the outline surrounding area with the largest area in all the image outlines as the road area outline.
In the embodiment, a camera is arranged on a vehicle in advance, and the camera acquires images of a road area in front of the vehicle in the driving process in real time in the driving process of the vehicle; after the road area image is acquired, the road area image is firstly subjected to semantic segmentation, the outlines of all targets in the image can be segmented, the segmented targets can be interfered by other vehicles, pedestrians, trees on the road and the like except the road area, and compared with other types of targets, the occupied area of the road area outline in the whole image is the largest.
In this embodiment, the step S101 further includes performing an image closing operation on the projected grid map to obtain a final grid map output. By carrying out closed operation on the grid image, the connectivity of the road area in the grid map can be better, so that the road area outline can be more accurately extracted.
It is understood that the above method for extracting the road region contour may also be implemented by other methods according to actual requirements.
In step S01, sequentially storing the extracted information of the road area contour in a position array of a road area contour point, where a starting point is an upper left corner point of the road area contour, and obtaining the rest points in the contour along a counterclockwise sequence of the contour and sequentially storing the rest points in the position array of the road area contour point; in step S02, a plurality of data points in the array of the positions of the contour points in the road area are sequentially taken as the right boundary of the road, and a plurality of data points are taken in the index reverse order to obtain a point set of the left boundary of the road, so as to obtain a point set of the left and right boundaries of the road. The key point of road guiding line generation is to find the position of the center line of the road, and the embodiment utilizes the left and right boundaries of the road to indirectly calculate the point on the center line of the road. In the road area contour point position array, front data points in the road area contour point position array are specifically taken as the right boundary of the road, a plurality of data points at the most proximal end of the road are removed, then a plurality of data points are taken according to the index reverse order to obtain a point set of the left boundary of the road, and then the point set of the left and right boundaries of the road can be obtained.
The present embodiment specifically extracts road region contour information by a findContours (contour extraction) function in opencv, and the road region contour information extracted by the findContours function is stored in a road region contour point position array. opencv is a cross-platform computer vision and machine learning software library based on apache2.0 open source, a findContours function is a function for contour extraction, the starting point of a road area contour point position array output by the findContours function is the upper left corner point of a contour, and subsequent points are stored in the array along the contour in a counterclockwise sequence. As the near end of the road from the center of the vehicle body is arranged on the upper part of the image, the front appointed number (such as 400-500) of points of the array are sequentially taken as the right boundary of the road, a series of points at the nearest end of the road correspond to the uppermost transverse boundary line point in the image contour, and the points need to be deleted, so that the points (such as 40-60) arranged on the last appointed number in the contour point position array of the road area are removed, and the points with the same number as the right boundary are taken in the reverse order of the index, so that the point set of the left boundary of the road can be obtained.
In this embodiment, the step S03 specifically includes:
s301, carrying out interval sampling on the point set of the left and right boundaries of the road to obtain a plurality of left and right boundary point pairs;
s302, calculating coordinate values of intermediate points between each left and right boundary point pair to obtain coordinate values of corresponding road intermediate points.
In this embodiment, specifically, according to the point sets of the left and right boundaries extracted in step S02, one point is taken at every specified number of points in the point sets of the left and right boundaries, that is, one point is taken at every N points in the left boundary set, and at the same time, one point is taken at every N points in the right boundary set, so as to obtain a series of left and right boundary point pairs, where the left and right boundary points correspond to left and right boundary points in a road area on the same horizontal line, and then the position of the middle point can be determined according to the left and right boundary points.
In step S04, a parametric cubic spline (cubic parametric spline) is inserted between the road intermediate points, that is, the road intermediate points are formed into a continuous smooth curve by using the cubic spline, so as to generate the final road guiding line. Furthermore, because the road boundary is not smooth, the guiding lines obtained by directly obtaining all the intermediate points of the left and right road boundaries are also not smooth and cannot meet the requirements of vehicle kinematics. The cubic spline curve is continuous and smooth at the junction of the points and the points, and the first derivative and the second derivative of the cubic spline curve are also continuous, thereby meeting the relevant requirements of the automatic driving kinematics.
After the step S04, smoothing the generated road guiding line by using an interval sampling and interpolation method, i.e., performing interval sampling and then performing interpolation to make the generated road guiding line smoother.
It will be appreciated that the generation of road guiding lines from road intermediate points described above may also be implemented in other ways than cubic spline curves.
As shown in fig. 2, the detailed steps of the method for generating the road guiding line in the embodiment of the present invention are as follows:
step 1: semantic segmentation is carried out on a visual image acquired in the vehicle running process, the visual image is projected to a vehicle body coordinate system to obtain a grid map, and closed operation of the image is carried out once to enable the connectivity of a road area in the grid map to be better. The obtained road area grid map is shown in fig. 3, wherein the black filling area in the map is the area from the top to the bottom of the road area from the near to the far;
step 2, processing the grid map image in the step S01 through a findContours function in opencv to obtain image contour information, taking a series of generated contours, taking a contour of the maximum area of a contour surrounding area as a road area contour, and obtaining the road area contour as shown in fig. 4 specifically;
step 3, according to the position array of the outline point of the road area obtained in the step 2, as the findContours function outputs the initial point of the array as the upper left corner point of the outline, the subsequent points are stored in the array along the outline in a counterclockwise sequence; because the near end of the road from the center of the vehicle body is positioned at the upper part of the image, the first 400 points of the array are sequentially taken as the right boundary of the road, a series of points (corresponding to the top horizontal point in FIG. 4) at the most proximal end of the road are removed, namely the last about 40 points in the array are removed, and 400 values are taken according to the index reverse order to obtain a point set of the left boundary of the road;
step 4, according to the point sets of the left and right boundaries obtained in the step 3, respectively taking one point at intervals of 10 points in the point sets of the left and right boundaries to obtain a series of left and right boundary point pairs;
step 5, solving the coordinate values of intermediate points between the point pairs according to the point pairs obtained in the step 4 to obtain a series of coordinate values of road intermediate points;
step 6: and (3) inserting a parameter cubic spline curve between the intermediate points obtained in the step (5) as shown in fig. 5 to form a road guiding line, and further smoothing the road center guiding line by adopting an interval sampling and interpolation method so as to enable the guiding line to meet the requirements of vehicle kinematics.
The grid map visual guiding line generating device of the embodiment comprises:
the contour extraction module is used for acquiring a to-be-processed grid map image and extracting road area contour information;
the boundary extraction module is used for extracting point sets of left and right boundaries of the road from the contour information of the road area;
the middle point calculating module is used for calculating the coordinates of the road middle points according to the point sets of the left and right boundaries of the road;
and the guiding line generating module is used for generating a road guiding line according to the coordinates of the road middle point.
In this embodiment, the contour extraction module includes:
the grid map conversion unit is used for performing semantic segmentation on the road environment image and projecting the road environment image to a vehicle body coordinate system to obtain a grid map;
the image contour extraction unit is used for processing the grid map obtained by the grid map conversion unit to obtain all image contours;
and the road area wheel screening unit is used for taking the outline surrounding area with the largest area in all the image outlines as the road area outline.
In this embodiment, a closed operation unit is further disposed between the grid map conversion unit and the image contour extraction unit, and is configured to perform a closed operation on the projected grid map once to obtain a final grid map output.
In this embodiment, the contour extraction module specifically stores the extracted information of the road area contour in the position array of the road area contour point in sequence, where a starting point is an upper left corner point of the road area contour, and obtains the rest points in the contour along the counterclockwise sequence of the contour and stores the rest points in the position array of the road area contour point in sequence; the boundary extraction module specifically sequentially takes a plurality of data points in the road area contour point position array as the right boundary of the road, and takes the plurality of data points according to the index reverse order to obtain a point set of the left boundary of the road and obtain a point set of the left and right boundaries of the road.
In this embodiment, the intermediate point calculating module includes:
the interval sampling unit is used for carrying out interval sampling on the point set of the left and right boundaries of the road to obtain a plurality of left and right boundary point pairs;
and a middle point calculating unit for calculating the coordinate values of the middle points between the left and right boundary point pairs to obtain the coordinate values of the corresponding road middle points.
The grid map visual guiding line generating device of the present embodiment corresponds to the grid map visual guiding line generating method one by one, and is not described herein again one by one.
The present embodiment also includes a computer device comprising a processor and a memory, the memory being configured to store a computer program, the processor being configured to execute the computer program, wherein the processor is configured to execute the computer program to perform the method as described above. Those skilled in the art will appreciate that the above description of a computer device is by way of example only and is not intended to limit the computer device, and that many more or less components than those described above may be included, or some of the components may be combined, or different components may be included, such as input output devices, network access devices, buses, etc. The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the computer device and the various interfaces and lines connecting the various parts of the overall computer device.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash memory Card (FlashCard), at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
The computer device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can be executed by a processor to implement the steps of the embodiments of the template tagging-based distributed crawler method described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, electrical signals, software distribution medium, and the like.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (10)
1. A grid map visual guiding line generation method is characterized by comprising the following steps:
s01, acquiring a road environment image acquired in the driving process of a vehicle, and extracting road area outline information;
s02, extracting point sets of left and right boundaries of the road from the contour information of the road area;
s03, calculating coordinates of a plurality of road intermediate points according to the point sets of the left and right boundaries of the road;
and S04, generating a road guiding line according to the coordinates of the road intermediate points.
2. The grid map visual guiding line generating method according to claim 1, wherein said step S01 includes:
s101, performing semantic segmentation on the road environment image, and projecting the road environment image to a vehicle body coordinate system to obtain a grid map;
s102, processing the grid map obtained in the step S101 to obtain all image outlines;
and S103, taking the outline surrounding area with the largest area in all the image outlines as the road area outline.
3. The grid map visual guiding line generating method of claim 2, wherein the step S101 further comprises performing an image closing operation on the projected grid map to obtain a final grid map output.
4. The grid map visual guiding line generating method according to claim 1, wherein in step S01, the extracted road region contour information is sequentially stored in a road region contour point position array, wherein a starting point is an upper left corner point of the road region contour, and the remaining points in the contour are obtained along the contour in a counterclockwise sequence and sequentially stored in the road region contour point position array; in step S02, a plurality of data points in the array of the positions of the contour points in the road area are sequentially taken as the right boundary of the road, and a plurality of data points are taken in an index reverse order to obtain a point set of the left boundary of the road, so as to obtain a point set of the left and right boundaries of the road.
5. The grid map visual guideline generation method of claim 4, wherein a front portion data point in the array of road region contour point locations is specifically taken as a right boundary of the road, and a plurality of data points at a nearest end of the road are removed.
6. The grid map visual guidance line generation method according to any one of claims 1 to 5, wherein the step S03 includes:
s301, carrying out interval sampling on the point set of the left and right boundaries of the road to obtain a plurality of left and right boundary point pairs;
s302, calculating coordinate values of intermediate points between each left and right boundary point pair to obtain coordinate values of corresponding road intermediate points.
7. The grid map visual guiding line generating method according to any one of claims 1 to 5, wherein in step S04, the road guiding line is generated by inserting a parametric cubic spline curve between each of the road intermediate points.
8. The grid map visual guiding line generating method of claim 7, wherein said step S04 is followed by smoothing said generated road guiding line by interval sampling and interpolation.
9. A grid map visual guideline generation apparatus, comprising:
the contour extraction module is used for acquiring a road environment image acquired in the driving process of a vehicle and extracting road area contour information;
the boundary extraction module is used for extracting point sets of left and right boundaries of the road from the contour information of the road area;
the middle point calculating module is used for calculating the coordinates of the road middle points according to the point sets of the left and right boundaries of the road;
and the guiding line generating module is used for generating a road guiding line according to the coordinates of the road intermediate point.
10. A computer device comprising a processor and a memory, the memory being adapted to store a computer program, the processor being adapted to execute the computer program, wherein the processor is adapted to execute the computer program to perform the method of any of claims 1 to 8.
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