CN112069899A - Road shoulder detection method and device and storage medium - Google Patents
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Abstract
The application discloses a road shoulder detection method, a road shoulder detection device and a storage medium, relates to the technical field of image processing, and is used for providing a reasonable road shoulder detection method. The method comprises the following steps: acquiring point cloud data to be detected; carrying out data classification on the point cloud data to be detected to obtain at least two groups of single line data; wherein the single line data is composed of a plurality of point cloud data; denoising each group of single line data respectively, and integrating the denoised single line data; and performing curve fitting on the integrated point cloud data to obtain a road shoulder curve. Therefore, the method for detecting the road shoulder is finally provided through extraction and fitting of the point cloud data, the road shoulder data obtained through the method is high in accuracy, and a road shoulder curve can be detected in real time during automatic driving, so that the shape of a road is obtained, and a traveling route is planned for automatic driving.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to a road shoulder detection method, apparatus, and storage medium.
Background
Along with the continuous deep development of artificial intelligence, the research for automatic driving is more and more deep, and automatic driving not only can avoid driving the potential safety hazard of going out, also can be for the user plans reasonable trip route, brings convenient service for people's life.
However, prior art research on autopilot is not mature, and there is no reasonable method for shoulder detection, so a reasonable travel route cannot be planned in an autopilot scenario.
Disclosure of Invention
The embodiment of the application provides a road shoulder detection method, a road shoulder detection device and a storage medium, which are used for solving the problem that a reasonable travelling route cannot be planned in an automatic driving scene because no reasonable method for road shoulder detection exists in the prior art.
In a first aspect, an embodiment of the present application provides a road shoulder detection method, including:
acquiring point cloud data to be detected;
carrying out data classification on the point cloud data to be detected to obtain at least two groups of single line data; wherein the single line data is composed of a plurality of point cloud data;
denoising each group of single line data respectively, and integrating the denoised single line data;
and performing curve fitting on the integrated point cloud data to obtain a road shoulder curve.
In a possible implementation manner, the acquiring point cloud data to be detected includes:
acquiring original point cloud data through a laser radar;
detecting the original point cloud data, and determining road surface point cloud data in the original point cloud data;
and determining the road surface height according to the road surface point cloud data, and taking the original point cloud data which is higher than the road surface height and within a first preset height as the point cloud data to be detected.
In one possible embodiment, the single-line data is acquired by:
grouping the point cloud data to be detected with the same identification according to the identification of the point cloud data to be detected;
and taking the group of the point cloud data to be detected in the group meeting the preset condition as single line data.
In one possible implementation, the denoising processing is performed on each group of single line data, and includes:
removing the point cloud data to be detected which is higher than the second preset height of the point cloud data to be detected in the group of single line data aiming at each group of single line data; the minimum point cloud data to be detected is the point cloud data to be detected with the minimum height value in the single line data; and/or;
removing the point cloud data to be detected in the center meeting the removing condition aiming at each group of single line data; the elimination condition is that the number of the point cloud data to be detected in a range formed by taking the point cloud data to be detected at the center as the center and taking the preset distance as the radius is smaller than the preset number.
In a possible embodiment, the performing curve fitting on the integrated point cloud data to obtain a road shoulder curve includes:
dividing the integrated point cloud data into a plurality of parts;
aiming at any part of point cloud data, performing straight line fitting on the part of point cloud data to obtain a fitting line segment of the part of point cloud data;
and fitting the fitting line segments of the point cloud data of each part to obtain a road shoulder curve.
In a second aspect, an embodiment of the present application provides a road shoulder detecting device, including:
the acquisition module is used for acquiring point cloud data to be detected;
the classification module is used for carrying out data classification on the point cloud data to be detected to obtain at least two groups of single line data; wherein the single line data is composed of a plurality of point cloud data;
the integration module is used for respectively carrying out denoising processing on each group of single-line data and carrying out data integration on each group of single-line data after denoising;
and the fitting module is used for performing curve fitting on the integrated point cloud data to obtain a road shoulder curve.
In one possible implementation, the obtaining module includes:
the system comprises an original point cloud data acquisition unit, a data acquisition unit and a data acquisition unit, wherein the original point cloud data acquisition unit is used for acquiring original point cloud data through a laser radar;
a road surface point cloud data determining unit for detecting the original point cloud data and determining the road surface point cloud data in the original point cloud data;
and the point cloud data determining unit is used for determining the road surface height according to the road surface point cloud data and taking the original point cloud data which is higher than the road surface height and is within a first preset height as the point cloud data to be detected.
In one possible embodiment, the single-line data is acquired by:
the grouping module is used for grouping the point cloud data to be detected with the same identification according to the identification of the point cloud data to be detected;
and determining single line data, and using the groups of the point cloud data to be detected in the groups meeting the preset conditions as the single line data.
In one possible embodiment, the integration module includes:
the first eliminating unit is used for eliminating the point cloud data to be detected, which is higher than the minimum point cloud data to be detected by a second preset height, in the group of single line data aiming at each group of single line data; the minimum point cloud data to be detected is the point cloud data to be detected with the minimum height value in the single line data;
the second removing unit is used for removing the point cloud data to be detected in the center, which meet removing conditions, from each group of single line data; the elimination condition is that the number of the point cloud data to be detected in a range formed by taking the point cloud data to be detected at the center as the center and taking the preset distance as the radius is smaller than the preset number.
In one possible embodiment, the fitting module comprises:
a dividing unit for dividing the integrated point cloud data into a plurality of parts;
the first fitting unit is used for performing straight line fitting on any part of point cloud data to obtain a fitting line segment of the part of point cloud data;
and the second fitting unit is used for fitting the fitting line segments of the point cloud data of each part to obtain a road shoulder curve.
In a third aspect, an embodiment of the present application provides a computing device, including at least one processing unit and at least one storage unit, where the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit is caused to execute any one of the steps of the game protocol testing method.
In one embodiment, the computing device may be a server or a terminal device.
In a fourth aspect, embodiments of the present application provide a computer-readable medium storing a computer program executable by a terminal device, when the program is run on the terminal device, the program causing the terminal device to perform any one of the steps of the game protocol testing method described above.
According to the road shoulder detection method, the road shoulder detection device and the storage medium, cloud data of points to be detected are classified to obtain a plurality of single line arrays, single line data are denoised, the denoised plurality of single line data are integrated together, and finally the integrated data are fitted to obtain a road shoulder curve. Therefore, the method for detecting the road shoulder is finally provided through extraction and fitting of the point cloud data, the road shoulder data obtained through the method is high in accuracy, and a road shoulder curve can be detected in real time during automatic driving, so that the shape of a road is obtained, and a traveling route is planned for automatic driving.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a road shoulder detection method in an embodiment of the present application;
fig. 2 is a schematic diagram of road shoulder characteristic data after integration in the embodiment of the present application;
FIG. 3 is a schematic diagram of fitting a shoulder curve in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a road shoulder detecting device in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a terminal device in an embodiment of the present application.
Detailed Description
In order to solve the problem that a reasonable method for road shoulder detection is not available in the prior art, and therefore a reasonable traveling route cannot be planned in an automatic driving scene, the embodiment of the application provides a road shoulder detection method, a road shoulder detection device and a storage medium. In order to better understand the technical solution provided by the embodiments of the present application, the following brief description is made on the basic principle of the solution:
the existing automatic driving mainly adopts a road surface detection algorithm of a laser radar and utilizes a deeply learned network model to carry out network training on a large amount of real road information so as to realize road prediction of a driving path, or adopts a global plane detection algorithm to detect a road plane. However, when planning a route for an autonomous vehicle, it is necessary to detect not only a road plane but also to know the shoulder of the road. However, the prior art does not provide a specific detection method for road shoulders.
Based on this, the embodiment of the application provides a road shoulder detection method, a road shoulder detection device and a storage medium, wherein a plurality of single line arrays are obtained by classifying cloud data of points to be detected, denoising is performed on the single line data, the denoised plurality of single line data are integrated together, and finally the integrated data are fitted to obtain a road shoulder curve. Therefore, the method for detecting the road shoulder is finally provided through extraction and fitting of the point cloud data, the road shoulder data obtained through the method is high in accuracy, and a road shoulder curve can be detected in real time during automatic driving, so that the shape of a road is obtained, and a traveling route is planned for automatic driving.
The preferred embodiments of the present application will be described below with reference to the accompanying drawings of the specification, it should be understood that the preferred embodiments described herein are merely for illustrating and explaining the present application, and are not intended to limit the present application, and that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The road shoulder detection method provided in the embodiments of the present application is further explained below. As shown in fig. 1, the method comprises the following steps:
s101: and acquiring point cloud data to be detected.
In the embodiment of the present application, point cloud data to be detected is acquired from original point cloud data, and specifically includes steps a 1-A3:
step A1: and acquiring original point cloud data through a laser radar.
The original point cloud data is obtained by scanning through a laser radar.
It should be noted that the default road surface during automatic driving is one plane or a combination of multiple planes (where the slope, the potholes on the road surface, and the ramps vertical to the road surface on both sides all belong to different planes). Therefore, the point cloud data of the vehicle travelling direction and two sides of the travelling direction can be obtained by scanning the planes through the laser radar. The laser radar is arranged on the top of the motor vehicle through the laser radar fixing support, and the laser radar can be arranged at the position horizontal to the driving direction of the motor vehicle in order to obtain better original point cloud data.
Step A2: and detecting the original point cloud data, and determining the road surface point cloud data in the original point cloud data.
Step A3: and determining the road surface height according to the road surface point cloud data, and taking the original point cloud data which is higher than the road surface height and within a first preset height as the point cloud data to be detected.
In the embodiment of the application, in order to avoid unnecessary point cloud information interference, the road surface point cloud containing the road shoulder information is segmented from the original point cloud data to perform road shoulder feature detection, so that the data volume is reduced, and meanwhile, the algorithm detection efficiency can be increased. Specifically, the road surface point cloud data can be detected from the original point cloud data through a road surface detection function, and the subsequent road shoulder feature extraction operation is performed by taking the point cloud data from the road surface and the point cloud data at a preset height from the road surface.
And acquiring the road surface point cloud data from the original point cloud data through a road surface detection function, determining the height of the road surface point cloud data, and taking the original point cloud data within the preset height as the data to be detected. For example: and determining that the height of the road surface point cloud data is 0, and the first preset height is 20 cm, and taking the original point cloud data between 0 and 20 cm as the point cloud data to be detected.
Therefore, the point cloud data to be detected is obtained by processing the original point cloud data, so that the algorithm detection efficiency can be improved while the data volume is reduced.
S102: carrying out data classification on the point cloud data to be detected to obtain at least two groups of single line data; wherein the single line data is composed of a plurality of point cloud data.
Wherein, a group of single line data is data scanned by a laser line.
In the embodiment of the application, the point cloud data is obtained by scanning the laser radar, so that the point cloud data to be detected is divided according to the data scanned by each laser line. The method specifically comprises the following steps B1-B2:
step B1: and grouping the point cloud data to be detected with the same identification according to the identification of the point cloud data to be detected.
Each point cloud data to be detected is provided with an identification, and the identifications of the point cloud data obtained by scanning the same laser line are the same.
Step B2: and taking the group of the point cloud data to be detected in the group meeting the preset condition as single line data.
In the embodiment of the present application, in order to make the quality of the extracted single-line data high, only the packets satisfying the preset condition can be regarded as a group of single-line data. The preset conditions include continuity of point cloud data to be detected, curvature change of the point cloud data to be detected on a plane, height difference between adjacent point cloud data to be detected and the like.
The continuity of the point cloud data to be detected is used for judging whether the point cloud data to be detected in the single line data is complete or not; the curvature change of the point cloud data to be detected on the plane and the height difference between the adjacent point cloud data to be detected are used for judging whether the single line data meet the requirements or not. Therefore, the method can keep the determined single-line data more complete and improve the accuracy of road shoulder detection.
S103: and respectively carrying out denoising processing on each group of single line data, and carrying out data integration on each group of single line data subjected to denoising.
In the embodiment of the application, the primarily extracted road shoulder feature point cloud data contains more noise, and in order to further improve the accuracy of road shoulder detection, denoising processing is performed on each group of single line data after multiple groups of single line data are obtained. The method specifically comprises the following steps of C1-C2:
step C1: removing the point cloud data to be detected which is higher than the second preset height of the point cloud data to be detected in the group of single line data aiming at each group of single line data; and the minimum point cloud data to be detected is the point cloud data to be detected with the minimum height value in the single line data.
For example: and (4) determining that the point cloud data with the minimum height value in the group of single line data is 5, and the second preset height is 10 cm, and removing the point cloud data to be detected, which are not 15 cm.
Step C2: removing the point cloud data to be detected in the center meeting the removing condition aiming at each group of single line data; the elimination condition is that the number of the point cloud data to be detected in a range formed by taking the point cloud data to be detected at the center as the center and taking the preset distance as the radius is smaller than the preset number.
For example: if a point cloud data is taken as a center and the radius is 10 cm, a sphere is formed. And counting the number of other point cloud data contained in the sphere, if the number is lower than a threshold value, judging that the point cloud data is an invalid discrete point, and rejecting the point cloud data.
Therefore, the accuracy of road shoulder detection can be further improved by carrying out denoising processing on each single line data.
In the embodiment of the application, after the single line data are denoised, the processed single line data are integrated into a coordinate system, so that point cloud data about road shoulders, namely road shoulder characteristic data, are obtained. As shown in fig. 2, it is a schematic diagram of integrated road shoulder characteristic data. Wherein each point in the graph represents a road shoulder feature data.
S104: and performing curve fitting on the integrated point cloud data to obtain a road shoulder curve.
In the embodiment of the application, because after integration, the road shoulder characteristic data are more, and fitting the road shoulder characteristic data directly can result in a poor detection result, in order to improve the accuracy of road shoulder detection, the point cloud data after integration is fitted by a piecewise fitting method, which specifically includes steps D1-D3:
step D1: and dividing the integrated point cloud data into a plurality of parts.
In the embodiment of the application, the point cloud data can be divided according to the coordinates of the point cloud data, or a sliding window with a fixed size can be used for moving from the point cloud data, and the point cloud data in the sliding window is taken as a part. This is not limited in this application.
Step D2: and aiming at any part of point cloud data, performing straight line fitting on the part of point cloud data to obtain a fitting line segment of the part of point cloud data.
Step D3: and fitting the fitting line segments of the point cloud data of each part to obtain a road shoulder curve.
As shown in fig. 3, which is a schematic diagram of fitting a road shoulder curve. The left part fits the integrated point cloud data with line segments, and each part can obtain one line segment. The right part is a road shoulder curve obtained by fitting each line segment.
Therefore, the method for detecting the road shoulder is finally provided through extraction and fitting of the point cloud data, the road shoulder data obtained through the method is high in accuracy, and a road shoulder curve can be detected in real time during automatic driving, so that the shape of a road is obtained, and a traveling route is planned for automatic driving.
Based on the same inventive concept, the embodiment of the application also provides a road shoulder detection device. As shown in fig. 4, the apparatus includes:
an obtaining module 401, configured to obtain point cloud data to be detected;
a classification module 402, configured to perform data classification on the point cloud data to be detected to obtain at least two sets of single line data; wherein the single line data is composed of a plurality of point cloud data;
an integration module 403, configured to perform denoising processing on each group of single line data, and perform data integration on each group of single line data after denoising;
and a fitting module 404, configured to perform curve fitting on the integrated point cloud data to obtain a road shoulder curve.
In one possible implementation, the obtaining module 401 includes:
the system comprises an original point cloud data acquisition unit, a data acquisition unit and a data acquisition unit, wherein the original point cloud data acquisition unit is used for acquiring original point cloud data through a laser radar;
a road surface point cloud data determining unit for detecting the original point cloud data and determining the road surface point cloud data in the original point cloud data;
and the point cloud data determining unit is used for determining the road surface height according to the road surface point cloud data and taking the original point cloud data which is higher than the road surface height and is within a first preset height as the point cloud data to be detected.
In one possible embodiment, the single-line data is acquired by:
the grouping module is used for grouping the point cloud data to be detected with the same identification according to the identification of the point cloud data to be detected;
and determining single line data, and using the groups of the point cloud data to be detected in the groups meeting the preset conditions as the single line data.
In one possible implementation, the integration module 403 includes:
the first eliminating unit is used for eliminating the point cloud data to be detected, which is higher than the minimum point cloud data to be detected by a second preset height, in the group of single line data aiming at each group of single line data; the minimum point cloud data to be detected is the point cloud data to be detected with the minimum height value in the single line data;
the second removing unit is used for removing the point cloud data to be detected in the center, which meet removing conditions, from each group of single line data; the elimination condition is that the number of the point cloud data to be detected in a range formed by taking the point cloud data to be detected at the center as the center and taking the preset distance as the radius is smaller than the preset number.
In one possible implementation, the fitting module 404 includes:
a dividing unit for dividing the integrated point cloud data into a plurality of parts;
the first fitting unit is used for performing straight line fitting on any part of point cloud data to obtain a fitting line segment of the part of point cloud data;
and the second fitting unit is used for fitting the fitting line segments of the point cloud data of each part to obtain a road shoulder curve.
For convenience of description, the above parts are separately described as modules (or units) according to functional division. Of course, the functionality of the various modules (or units) may be implemented in the same one or more pieces of software or hardware when implementing the present application.
Based on the same technical concept, the present application further provides a terminal device 500, and referring to fig. 5, the terminal device 500 is configured to implement the methods described in the above various method embodiments, for example, implement the embodiment shown in fig. 2, and the terminal device 500 may include a memory 501, a processor 502, an input unit 503, and a display panel 504.
A memory 501 for storing computer programs executed by the processor 502. The memory 501 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 for at least one function, and the like; the storage data area may store data created according to the use of the terminal device 500, and the like. The processor 502 may be a Central Processing Unit (CPU), a digital processing unit, or the like. The input unit 503 may be used to obtain a user instruction input by a user. The display panel 504 is configured to display information input by a user or information provided to the user, and in this embodiment of the present application, the display panel 504 is mainly used to display a display interface of each application program in the terminal device and a control entity displayed in each display interface. Alternatively, the display panel 504 may be configured in the form of a Liquid Crystal Display (LCD) or an organic light-emitting diode (OLED), and the like.
The embodiment of the present application does not limit the specific connection medium among the memory 501, the processor 502, the input unit 503, and the display panel 504. In the embodiment of the present application, the memory 501, the processor 502, the input unit 503, and the display panel 504 are connected by the bus 505 in fig. 5, the bus 505 is represented by a thick line in fig. 5, and the connection manner between other components is merely illustrative and not limited thereto. The bus 505 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
The memory 501 may be a volatile memory (volatile memory), such as a random-access memory (RAM); the memory 501 may also be a non-volatile memory (non-volatile memory) such as, but not limited to, a read-only memory (rom), a flash memory (flash memory), a Hard Disk Drive (HDD) or a solid-state drive (SSD), or any other medium which can be used to carry or store desired program code in the form of instructions or data structures and which can be accessed by a computer. The memory 501 may be a combination of the above memories.
The processor 502, for implementing the embodiment shown in fig. 2, includes:
a processor 502 for invoking a computer program stored in the memory 501 to perform the embodiment shown in fig. 2.
The embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions required to be executed by the processor, and includes a program required to be executed by the processor.
In some possible embodiments, aspects of a road shoulder detection method provided by the present application may also be implemented in the form of a program product including program code for causing a terminal device to perform the steps of a road shoulder detection method according to various exemplary embodiments of the present application described above in this specification when the program product is run on the terminal device. For example, the terminal device may perform the embodiment as shown in fig. 3.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A 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 (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
A shoulder detection program product for an embodiment of the present application may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be executable on a computing device. However, the program product of the present application is not limited thereto, and in this document, a 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.
A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a 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 readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including a physical programming language such as Java, C + + or the like 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 computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device over any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., over the internet using an internet service provider).
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units described above may be embodied in one unit, according to embodiments of the application. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.
Further, while the operations of the methods of the present application are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable shoulder detection apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable shoulder detection apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable shoulder detection apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable shoulder detection apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (12)
1. A road shoulder detection method, characterized in that the method comprises:
acquiring point cloud data to be detected;
carrying out data classification on the point cloud data to be detected to obtain at least two groups of single line data; wherein the single line data is composed of a plurality of point cloud data;
denoising each group of single line data respectively, and integrating the denoised single line data;
and performing curve fitting on the integrated point cloud data to obtain a road shoulder curve.
2. The method according to claim 1, wherein the acquiring point cloud data to be detected comprises:
acquiring original point cloud data through a laser radar;
detecting the original point cloud data, and determining road surface point cloud data in the original point cloud data;
and determining the road surface height according to the road surface point cloud data, and taking the original point cloud data which is higher than the road surface height and within a first preset height as the point cloud data to be detected.
3. The method of claim 1, wherein the single line data is obtained by:
grouping the point cloud data to be detected with the same identification according to the identification of the point cloud data to be detected;
and taking the group of the point cloud data to be detected in the group meeting the preset condition as single line data.
4. The method of claim 1, wherein the denoising each group of single-line data comprises:
removing the point cloud data to be detected which is higher than the second preset height of the point cloud data to be detected in the group of single line data aiming at each group of single line data; the minimum point cloud data to be detected is the point cloud data to be detected with the minimum height value in the single line data; and/or;
removing the point cloud data to be detected in the center meeting the removing condition aiming at each group of single line data; the elimination condition is that the number of the point cloud data to be detected in a range formed by taking the point cloud data to be detected at the center as the center and taking the preset distance as the radius is smaller than the preset number.
5. The method according to any one of claims 1 to 4, wherein the step of performing curve fitting on the integrated point cloud data to obtain a road shoulder curve comprises:
dividing the integrated point cloud data into a plurality of parts;
aiming at any part of point cloud data, performing straight line fitting on the part of point cloud data to obtain a fitting line segment of the part of point cloud data;
and fitting the fitting line segments of the point cloud data of each part to obtain a road shoulder curve.
6. A road shoulder detection apparatus, the apparatus comprising:
the acquisition module is used for acquiring point cloud data to be detected;
the classification module is used for carrying out data classification on the point cloud data to be detected to obtain at least two groups of single line data; wherein the single line data is composed of a plurality of point cloud data;
the integration module is used for respectively carrying out denoising processing on each group of single-line data and carrying out data integration on each group of single-line data after denoising;
and the fitting module is used for performing curve fitting on the integrated point cloud data to obtain a road shoulder curve.
7. The apparatus of claim 6, wherein the means for obtaining comprises:
the system comprises an original point cloud data acquisition unit, a data acquisition unit and a data acquisition unit, wherein the original point cloud data acquisition unit is used for acquiring original point cloud data through a laser radar;
a road surface point cloud data determining unit for detecting the original point cloud data and determining the road surface point cloud data in the original point cloud data;
and the point cloud data determining unit is used for determining the road surface height according to the road surface point cloud data and taking the original point cloud data which is higher than the road surface height and is within a first preset height as the point cloud data to be detected.
8. The apparatus of claim 6, wherein the single line data is obtained by:
the grouping module is used for grouping the point cloud data to be detected with the same identification according to the identification of the point cloud data to be detected;
and determining single line data, and using the groups of the point cloud data to be detected in the groups meeting the preset conditions as the single line data.
9. The apparatus of claim 6, wherein the integration module comprises:
the first eliminating unit is used for eliminating the point cloud data to be detected, which is higher than the minimum point cloud data to be detected by a second preset height, in the group of single line data aiming at each group of single line data; the minimum point cloud data to be detected is the point cloud data to be detected with the minimum height value in the single line data;
the second removing unit is used for removing the point cloud data to be detected in the center, which meet removing conditions, from each group of single line data; the elimination condition is that the number of the point cloud data to be detected in a range formed by taking the point cloud data to be detected at the center as the center and taking the preset distance as the radius is smaller than the preset number.
10. The apparatus of any one of claims 6 to 9, wherein the fitting module comprises:
a dividing unit for dividing the integrated point cloud data into a plurality of parts;
the first fitting unit is used for performing straight line fitting on any part of point cloud data to obtain a fitting line segment of the part of point cloud data;
and the second fitting unit is used for fitting the fitting line segments of the point cloud data of each part to obtain a road shoulder curve.
11. An electronic device, characterized in that it comprises a processor and a memory, wherein the memory stores program code which, when executed by the processor, causes the processor to carry out the steps of the method of any one of claims 1 to 5.
12. Computer-readable storage medium, characterized in that it comprises program code for causing an electronic device to carry out the steps of the method of any one of claims 1 to 5, when said program product is run on said electronic device.
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