CN112149572A - Road edge detection method, device and storage medium - Google Patents
Road edge detection method, device and storage medium Download PDFInfo
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- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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Abstract
The application relates to the technical field of point cloud processing, in particular to a road edge detection method, a road edge detection device and a storage medium. The method comprises the following steps: acquiring point cloud data containing roads, and extracting an interesting region in the point cloud data; determining a ground plane in the region of interest; rasterizing the region of interest; acquiring statistical information in each grid according to the ground plane, and identifying road edge points in the grids according to the statistical information; and determining the road edge in the road according to the identified road edge points. The problem of poor road edge accuracy determined in the prior art is solved; the effect that the road edge can be accurately identified and obtained when the road surface has the slope and the accuracy of the road edge identification is improved is achieved.
Description
Technical Field
The application relates to the technical field of point cloud processing, in particular to a road edge detection method, a road edge detection device and a storage medium.
Background
Road edge identification is an important research content in the context of environmental awareness in autonomous driving. After the road edge is identified, the sensing environment can be divided into a road area and a non-road area, and a more accurate road section is provided for subsequent obstacle detection and identification of a drivable area, so that the calculated amount is reduced, and the real-time performance of environment sensing is improved.
The current common road edge detection method comprises the following steps: acquiring an image based on a visual sensor, performing binarization processing on the acquired image to obtain a binarized image, and performing edge detection on the binarized image to further obtain a road edge in the image; and based on the collected point cloud data of the laser radar, extracting road edge data points by using an interval collinear point extraction algorithm, clustering the road edge data points, and then performing linear fitting on the actual road edge by adopting a least square method.
However, in the above scheme, the quality of the acquired image is unstable under the influence of ambient light, and accordingly, the accuracy of the processed road edge is also unstable. For the road edge points with errors, the linear fitting by using the least square method has larger errors.
Disclosure of Invention
In view of the above, embodiments of the present application provide a method, an apparatus, and a storage medium for detecting a road edge, so as to solve the problems in the prior art.
According to a first aspect, an embodiment of the present application provides a road edge detection method, including:
acquiring point cloud data containing roads, and extracting an interesting region in the point cloud data;
determining a ground plane in the region of interest;
rasterizing the region of interest;
acquiring statistical information in each grid according to the ground plane, and identifying road edge points in the grids according to the statistical information;
and determining the road edge in the road according to the identified road edge points.
Optionally, the extracting the region of interest in the point cloud data includes:
and performing direct filtering on the data of the two channels in the point cloud data respectively to obtain filtered point cloud data.
Optionally, the determining the ground plane in the region of interest includes:
randomly selecting 3 points from all the points of the region of interest to obtain a candidate ground plane;
calculating the distance between each point of the road edge points except the selected 3 points and the candidate ground plane;
counting the accumulated number of points with the distance to the candidate ground plane smaller than a first preset distance;
counting the times by + 1;
when the updated statistical frequency does not reach a first preset frequency, the step of randomly selecting 3 points from all the points in the region of interest is executed again to obtain a candidate ground plane;
and when the updated statistical frequency reaches the first preset frequency, determining the candidate ground plane with the maximum corresponding accumulative number as the ground plane.
Optionally, the obtaining statistical information in each grid according to the ground plane, and identifying the road edge point in the grid according to the statistical information includes:
for each grid, counting the number of point clouds in the grid, the maximum height between the point clouds and the ground plane and the minimum height between the point clouds and the ground plane;
and when the number of the point clouds reaches a number threshold, the maximum height is within a height threshold range, and the height difference between the maximum height and the minimum height is within a height difference threshold range, determining the point clouds in the grid as road edge points.
Optionally, the determining the road edge in the road according to the identified road edge points includes:
determining a left road edge and a right road edge according to the coordinates of the road edge points obtained by identification;
and for each road edge, performing straight line fitting on each road edge point through a random sampling consistency algorithm to obtain the road edge in the road.
Optionally, the performing linear fitting on each road edge point through a random sampling consistency algorithm to obtain the road edge in the road includes:
randomly selecting 2 points from the road edge points to obtain a candidate straight line;
calculating the distance between each point except the selected 2 points in each road edge point and the candidate straight line;
counting the accumulated number of points with the distance to the candidate straight line smaller than a second preset distance;
counting the times by + 1;
when the updated statistical frequency does not reach a second preset frequency, the step of randomly selecting 2 points in each road edge point is executed again to obtain a candidate straight line;
and when the updated statistical frequency reaches a second preset frequency, determining the candidate straight line with the maximum corresponding accumulated number as the route line.
Optionally, the method further includes:
and tracking and predicting the road edge obtained by fitting the previous frame through Kalman filtering to obtain a predicted value, and combining the detection result of the current frame to obtain the road edge of the current frame.
Optionally, the acquiring point cloud data including a road includes:
and acquiring the point cloud data acquired by the laser radar.
In a second aspect, there is provided a road edge detection apparatus, the apparatus comprising a memory and a processor, the memory having at least one program instruction stored therein, and the processor implementing the method according to the first aspect by loading and executing the at least one program instruction.
In a third aspect, there is provided a computer storage medium having stored therein at least one program instruction for loading and execution by a processor to implement the method of the first aspect.
Extracting an interesting region in point cloud data by acquiring the point cloud data containing a road; determining a ground plane in the region of interest; rasterizing the region of interest; acquiring statistical information in each grid according to the ground plane, and identifying road edge points in the grids according to the statistical information; and determining the road edge in the road according to the identified road edge points. The problem of poor road edge accuracy determined in the prior art is solved; the effect that the road edge can be accurately identified and obtained when the road surface has the slope and the accuracy of the road edge identification is improved is achieved.
In addition, the road edge is extracted according to the point cloud in the grid by rasterizing the region of interest, so that the problem of large data quantity of point-by-point screening in the prior art is solved, and the effect of rapidly screening the road edge point is achieved.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
Fig. 1 is a flowchart of a method of detecting a road edge according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for determining a ground plane according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for determining a road edge according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Referring to fig. 1, a flowchart of a method of a road edge detection method according to an embodiment of the present application is shown, and as shown in fig. 1, the method includes:
in actual implementation, the point cloud data acquired by the laser radar can be acquired.
After the point cloud data is obtained, straight-through filtering is respectively carried out on the data of two channels in the point cloud data, and filtered point cloud data is obtained. The interference information in the point cloud data can be filtered through the filtering, and the point cloud data in the region of interest is obtained.
in practical implementation, straight-through filtering can be performed on a Z channel of point cloud data, point clouds near the ground are selected, and then plane fitting is performed on the point clouds in the region of interest through a random sampling consistency algorithm to obtain a ground plane in the region of interest.
Optionally, referring to fig. 2, the present step includes:
(1) randomly selecting 3 points from all the points of the region of interest to obtain a candidate ground plane;
randomly selecting 3 points from the various points in the region of interest to form a plane, and using the plane as a candidate ground plane. In actual implementation, if the randomly selected 3 points are on the same straight line, the step is executed again.
(2) Calculating the distance between each point except the selected 3 points in each road edge point and the candidate ground plane;
(3) counting the accumulated number of points with the distance to the candidate ground plane smaller than a first preset distance;
the first preset distance is a default distance of the system or a self-defined distance of a user according to actual use requirements.
And when traversing each point, when the distance between the point and the candidate ground plane is less than a first preset distance, taking the point as an inner point, otherwise, if the distance is more than the first preset distance, taking the point as an outer point. After the traversal is complete, the cumulative number of inliers is calculated.
(4) Counting the times by + 1;
(5) when the updated statistical frequency does not reach a first preset frequency, the step of randomly selecting 3 points in all the points of the region of interest is executed again to obtain a candidate ground plane;
the first preset times are default times or user-defined times, and the times are times of iteratively calculating the candidate ground planes. In addition, in actual implementation, the first preset number of times may be determined according to the size of the region of interest, for example, the larger the region of interest is, the larger the first preset number of times is, and otherwise, the smaller the region of interest is, the smaller the first preset number of times is.
The first preset times are integers which are more than or equal to 2, and the larger the first preset times are, the larger the number of the obtained candidate ground planes is, the higher the accuracy of the obtained ground planes is finally determined; otherwise, the calculated amount is lower in accuracy deviation, and when the calculation is actually implemented, a numerical value of the first preset number of times may be set according to an actual requirement, which is not limited in this embodiment.
(6) And when the updated statistical frequency reaches the first preset frequency, determining the candidate ground plane with the maximum corresponding cumulative number as the ground plane.
And when the updated statistical frequency reaches a first preset frequency, the candidate ground planes with the first preset frequency are obtained, and at this time, the candidate ground plane with the largest accumulated number is selected from the plurality of candidate ground planes to be used as the finally determined ground plane.
103, rasterizing the region of interest;
after obtaining the region of interest, the region of interest may be subjected to rasterization, and the size of each grid may be the same or different, which is not limited to this, and this embodiment is exemplified by dividing the region of interest into grids with the same size of M × N.
104, acquiring statistical information in each grid according to the ground plane, and identifying road edge points in the grids according to the statistical information;
this step may include:
firstly, counting the number of point clouds in each grid, the maximum height between the point clouds and the ground plane and the minimum height between the point clouds and the ground plane;
after the ground plane is determined in step 102, the height between each point cloud in the grid and the ground plane may be calculated, resulting in a minimum height and a maximum height.
In the second place, the first place is,
and when the number of the point clouds reaches a number threshold, the maximum height is within a height threshold range, and the height difference between the maximum height and the minimum height is within a height difference threshold range, determining the point clouds in the grid as road edge points.
In actual implementation, the point with the height difference smaller than the height difference threshold is selected as the road edge point, so that the height difference of the selected road edge point is within the error range of the actual road edge height, and the accuracy of the determined road edge point is improved.
In addition, the difference value between the maximum height and the preset height can be detected during actual implementation, and if the difference value is larger than the preset threshold value, the difference value is ignored.
And 105, determining the road edge in the road according to the identified road edge points.
Since both sides of the road will have road edges, this step may include:
firstly, determining a left road edge and a right road edge according to the coordinates of each road edge point obtained by identification;
secondly, for each road edge on each side, performing straight line fitting on each road edge point through a random sampling consistency algorithm to obtain the road edge in the road.
Optionally, referring to fig. 3, the step may include:
(1) randomly selecting 2 points from the road edge points to obtain a candidate straight line;
(2) calculating the distance between each point except the selected 2 points in each road edge point and the candidate straight line;
(3) counting the accumulated number of points with the distance to the candidate straight line smaller than a second preset distance;
(4) counting the times by + 1;
(5) when the updated statistical frequency does not reach a second preset frequency, the step of randomly selecting 2 points in each road edge point is executed again to obtain a candidate straight line;
(6) and when the updated statistical frequency reaches a second preset frequency, determining the candidate straight line with the maximum corresponding cumulative number as the route line.
This step is similar to step 102 described above and will not be described again.
In addition, after the road edge is obtained, the road edge obtained by fitting the previous frame can be tracked and predicted through Kalman filtering to obtain a predicted value, and the road edge of the current frame is obtained by combining the detection result of the current frame. The problem of frame loss or overlarge error of the collected point cloud data is solved, and the effect of smoother road edge detection is achieved.
In summary, by acquiring point cloud data including a road, extracting an area of interest in the point cloud data; determining a ground plane in the region of interest; rasterizing the region of interest; acquiring statistical information in each grid according to the ground plane, and identifying road edge points in the grids according to the statistical information; and determining the road edge in the road according to the identified road edge points. The problem of poor road edge accuracy determined in the prior art is solved; the effect that the road edge can be accurately identified and obtained when the road surface has the slope and the accuracy of the road edge identification is improved is achieved.
In addition, the road edge is extracted according to the point cloud in the grid by rasterizing the region of interest, so that the problem of large data quantity of point-by-point screening in the prior art is solved, and the effect of rapidly screening the road edge point is achieved.
The present embodiment also provides a road edge detection apparatus, where the apparatus includes a memory and a processor, where the memory stores at least one program instruction, and the processor implements the method according to the foregoing embodiment by loading and executing the at least one program instruction.
The present embodiment also provides a computer storage medium, in which at least one program instruction is stored, and the at least one program instruction is used for being loaded and executed by a processor to implement the method of the above embodiment.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of road edge detection, the method comprising:
acquiring point cloud data containing roads, and extracting an interesting region in the point cloud data;
determining a ground plane in the region of interest;
rasterizing the region of interest;
acquiring statistical information in each grid according to the ground plane, and identifying road edge points in the grids according to the statistical information;
and determining the road edge in the road according to the identified road edge points.
2. The method of claim 1, wherein the extracting regions of interest in the point cloud data comprises:
and performing direct filtering on the data of the two channels in the point cloud data respectively to obtain filtered point cloud data.
3. The method of claim 1, wherein the determining the ground plane in the region of interest comprises:
randomly selecting 3 points from all the points of the region of interest to obtain a candidate ground plane;
calculating the distance between each point of the road edge points except the selected 3 points and the candidate ground plane;
counting the accumulated number of points with the distance to the candidate ground plane smaller than a first preset distance;
counting the times by + 1;
when the updated statistical frequency does not reach a first preset frequency, the step of randomly selecting 3 points from all the points in the region of interest is executed again to obtain a candidate ground plane;
and when the updated statistical frequency reaches the first preset frequency, determining the candidate ground plane with the maximum corresponding accumulative number as the ground plane.
4. The method of claim 1, wherein obtaining statistical information in each grid from the ground plane, and identifying road edge points in the grid from the statistical information comprises:
for each grid, counting the number of point clouds in the grid, the maximum height between the point clouds and the ground plane and the minimum height between the point clouds and the ground plane;
and when the number of the point clouds reaches a number threshold, the maximum height is within a height threshold range, and the height difference between the maximum height and the minimum height is within a height difference threshold range, determining the point clouds in the grid as road edge points.
5. The method of claim 1, wherein determining the road edge in the road from the identified road edge points comprises:
determining a left road edge and a right road edge according to the coordinates of the road edge points obtained by identification;
and for each road edge, performing straight line fitting on each road edge point through a random sampling consistency algorithm to obtain the road edge in the road.
6. The method of claim 5, wherein the step of fitting a straight line to each road edge point through a random sampling consistency algorithm to obtain the road edge in the road comprises:
randomly selecting 2 points from the road edge points to obtain a candidate straight line;
calculating the distance between each point except the selected 2 points in each road edge point and the candidate straight line;
counting the accumulated number of points with the distance to the candidate straight line smaller than a second preset distance;
counting the times by + 1;
when the updated statistical frequency does not reach a second preset frequency, the step of randomly selecting 2 points in each road edge point is executed again to obtain a candidate straight line;
and when the updated statistical frequency reaches a second preset frequency, determining the candidate straight line with the maximum corresponding accumulated number as the route line.
7. The method of any of claims 1 to 6, further comprising:
and tracking and predicting the road edge obtained by fitting the previous frame through Kalman filtering to obtain a predicted value, and combining the detection result of the current frame to obtain the road edge of the current frame.
8. The method of any one of claims 1 to 6, wherein the obtaining point cloud data including a road comprises:
and acquiring the point cloud data acquired by the laser radar.
9. A road edge detection device, comprising a memory having at least one program instruction stored therein and a processor that implements the method of any of claims 1 to 8 by loading and executing the at least one program instruction.
10. A computer storage medium having stored therein at least one program instruction for loading and execution by a processor to perform the method of any of claims 1 to 8.
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