CN111046735B - Lane line point cloud extraction method, electronic device and storage medium - Google Patents
Lane line point cloud extraction method, electronic device and storage medium Download PDFInfo
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- CN111046735B CN111046735B CN201911108643.7A CN201911108643A CN111046735B CN 111046735 B CN111046735 B CN 111046735B CN 201911108643 A CN201911108643 A CN 201911108643A CN 111046735 B CN111046735 B CN 111046735B
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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 invention provides a lane line point cloud extraction method, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring pavement marking point clouds, and extracting lane line point clouds from the pavement marking point clouds according to shape features of the pavement marking point cloud clusters; after segmenting the lane line point cloud, counting the frequency distribution of the data intensity of each segment of lane line point cloud to generate a frequency distribution histogram; calculating a segmentation threshold value of the point cloud intensity according to the frequency distribution histogram and an Otsu algorithm; and performing two-segmentation on the lane line point cloud data of each segment by using the segmentation threshold value of the point cloud intensity so as to extract highlighted lane line point cloud. By the scheme, the problems that the conventional method for extracting the point cloud of the lane line is time-consuming and accuracy is difficult to guarantee are solved, the point cloud of the lane line can be accurately and quickly extracted, and the data of the edge points on two sides of the lane line are reserved to the maximum extent.
Description
Technical Field
The invention relates to the field of electronic maps, in particular to a lane line point cloud extraction method, electronic equipment and a storage medium.
Background
In a high-precision map, the drawing of a lane line often needs to be performed by means of laser point cloud data, and the accuracy of the lane line point cloud and the actual boundary before the vectorization of the lane line point cloud needs to be guaranteed within 5cm, so that the maximum boundary information needs to be reserved in the extraction of the lane line point cloud, and the accurate acquisition of the lane line point cloud boundary is very important.
At present, a common lane line point cloud extraction method is to extract the lane line point cloud based on semantic segmentation, although boundary information can be retained to the maximum extent, a deep learning model is needed, and training of the model needs to provide a large number of labeled samples, which is time-consuming. The lane line point cloud boundary obtained based on the traditional classification is easily influenced by the abrasion of the lane line, and is narrower than the actual width, and the boundary point cloud extraction is not accurate.
Therefore, it is necessary to provide a simple and time-consuming method for accurately extracting the point cloud of the lane line.
Disclosure of Invention
In view of this, embodiments of the present invention provide a lane line point cloud extraction method, an electronic device, and a storage medium, so as to solve the problems that the existing extraction method is time-consuming and is difficult to ensure accuracy.
In a first aspect of the embodiments of the present invention, a lane line point cloud extraction method is provided, including:
acquiring pavement marking point clouds, and extracting lane line point clouds from the pavement marking point clouds according to shape features of the pavement marking point cloud clusters;
after segmenting the lane line point cloud, counting the frequency distribution of the data intensity of each segment of lane line point cloud to generate a frequency distribution histogram;
calculating a segmentation threshold value of the point cloud intensity according to the frequency distribution histogram and the Otsu algorithm;
and performing secondary segmentation on the lane line point cloud data of each segment by using the segmentation threshold value of the point cloud intensity so as to extract highlighted lane line point clouds.
In a second aspect of an embodiment of the present invention, there is provided an electronic device for lane line point cloud extraction, including:
the extraction module is used for acquiring pavement marking point clouds and extracting lane line point clouds from the pavement marking point clouds according to the shape features of the pavement marking point clouds;
the statistical module is used for counting the frequency distribution of the intensity of the point cloud data of each section of lane line after segmenting the point cloud of the lane line to generate a frequency distribution histogram;
the calculation module is used for calculating a segmentation threshold value of the point cloud intensity according to the frequency distribution histogram and the Otsu algorithm;
and the segmentation module is used for carrying out two-segmentation on the point cloud data of the lane line of each segment by utilizing the segmentation threshold value of the point cloud intensity so as to extract the highlighted point cloud of the lane line.
In a third aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, where the processor executes the computer program to implement the steps of the method according to the first aspect of the embodiments of the present invention.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, which stores a computer program, which when executed by a processor implements the steps of the method provided by the first aspect of the embodiments of the present invention.
In the embodiment of the invention, the initial lane line point cloud is extracted from the road surface marking line point cloud, after the initial lane line point cloud is segmented, the intensity frequency distribution histogram of each segmented lane line point cloud is counted, the intensity segmentation threshold is calculated according to the intensity frequency distribution histogram and the robust algorithm to extract the highlight point cloud data, so that the accurate extraction of the lane line boundary point cloud data is effectively ensured, the complete lane line information is retained, the problems that the extraction is time-consuming due to the fact that a large number of marking samples are needed in the prior art and the classification extraction is inaccurate are solved, the lane line point cloud extraction can be simply and quickly carried out, meanwhile, the problem that the lane line point cloud and the road noise points are difficult to segment is effectively solved, the influence of the lane line noise on the extraction of the lane line point cloud is reduced, the left and right edge point information of the lane line point cloud is retained to the maximum extent, especially, under the condition that the lane line is worn and even the lane line point cloud is fuzzy, most of the left and right edge points of the lane line point cloud can be retained, and the accuracy and reliability of the lane line point cloud extraction are ensured.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a lane line point cloud extraction method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a system for extracting a point cloud of a lane line according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for lane line point cloud extraction according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The terms "comprises" and "comprising," when used in this specification and claims, and in the accompanying drawings and figures, are intended to cover non-exclusive inclusions, such that a process, method or system, or apparatus that comprises a list of steps or elements is not limited to the listed steps or elements. In addition, "first" and "second" are used to distinguish different objects, and are not used to describe a specific order.
Referring to fig. 1, a schematic flow chart of a method for extracting a point cloud of a lane line according to an embodiment of the present invention includes:
s101, acquiring pavement marking point clouds, and extracting lane line point clouds from the pavement marking point clouds according to shape features of the pavement marking point cloud clustering;
the road surface marking point cloud also comprises point cloud data of other markings, such as a left-turn marking, a straight marking, a deceleration marking and the like, besides the road marking point cloud. Because different marking lines have different shape characteristics, such as arrow shapes and rectangles, different marking lines can be distinguished based on the point cloud shape of the pavement marking lines, and then the point cloud of the lane lines can be preliminarily extracted.
S102, after segmenting the lane line point cloud, counting the frequency distribution of the data intensity of each segment of lane line point cloud to generate a frequency distribution histogram;
in practice, the lane line is generally a long rectangle or a line, and the lane line marking line is segmented according to a certain length, that is, the point cloud of the lane line is segmented, for example, every 3 meters, and each segment is processed independently.
Optionally, after segmenting the point cloud of the lane line, taking the point cloud of the lane line of each segment as a center, and expanding a predetermined width range to two sides of the lane line.
The point cloud of the lane line is used as the center part and is expanded to a certain range, such as 10cm, the point cloud data after the outward expansion possibly comprises other point cloud data, the outward expansion based on the lane line can ensure that the point cloud data of the lane line is comprehensively obtained, omission is avoided, and the accuracy of the accurate extraction of the point cloud of the lane line is further ensured.
The point cloud data intensity is used for reflecting the reflection intensity information of the laser, and the clearer the general lane line is, the higher the point cloud reflection intensity is. And counting the intensity of the midpoint of the point cloud of the initial lane line to obtain a frequency distribution histogram of the intensity of the point cloud. The frequency distribution histogram is used for representing the intensity distribution condition of the point cloud, and the point cloud segmentation intensity value can be conveniently determined according to the frequency distribution histogram.
S103, calculating a segmentation threshold of the point cloud intensity according to the frequency distribution histogram and an Otsu algorithm;
based on the distribution histogram of the point cloud intensity and the Otsu algorithm, the intensity threshold value of the extracted point cloud can be determined, and the frequency segmentation threshold value can be more accurately determined by combining the peak-valley method of the frequency distribution histogram and the Otsu algorithm.
Specifically, the method comprises the following steps:
s1, acquiring coordinates corresponding to peak values and valley values in the frequency distribution histogram, and matching according to the distribution of the peak values and the valley values and a preset peak-valley matching rule;
s2, calculating the area of a polygon formed by peaks and valleys after peak-valley pairing, comparing the area of the polygon with a preset threshold, removing initial valleys smaller than the preset threshold, and determining the type of peak-valley pairing;
and S3, merging the peaks and the valleys of the same pairing type according to the sequence of the peaks and the valleys.
The coordinates of all the valley bottoms and the peaks in the frequency distribution histogram are detected and obtained, adjacent valleys and peaks are paired according to a group of peak valley peaks or valley peak valleys, and the coordinates of the valley bottoms and the peak top are taken as vertexes to form a polygon. Calculating three polygon areas, removing smaller valleys based on the polygon areas compared to a preset threshold, and determining an initial peak-valley shape type: left singlet, right singlet, and bimodal valley. The initial peaks and valleys of the three shape types are combined and the final frequency distribution histogram can be represented by one single left peak, several peaks and valleys and one single right peak resulting from the combination. And weighting the related valley values according to a certain weight in the merging process to obtain the merged valley value.
Further, on the basis of obtaining the combined initial peak-valley in S3, the method further includes:
s4, carrying out secondary segmentation on the intensity value of the point cloud data of the lane line of each segment through an Otsu algorithm to obtain a first intensity segmentation threshold value;
and S5, correspondingly combining the positions of the intensity segmentation threshold values in the frequency distribution histogram to form peak-valley values, and taking the corresponding valley values as first initial segmentation threshold values.
A segmentation threshold can be obtained through an algorithm, but the segmentation threshold is not accurate, and a valley bottom value corresponding to a position of the segmentation threshold in the initial peak-valley can be used as the initial segmentation threshold, namely the first initial segmentation threshold.
Further, the method also comprises the following steps:
s6, intercepting point clouds with intensity ranges between [ v-I, v + I ], and generating a second frequency distribution histogram according to the intensity interval of I/2, wherein v is an initial segmentation threshold value, and I is the intensity interval of the frequency distribution histogram;
s7, performing secondary segmentation on the intensity value of the intercepted point cloud data through an Otsu algorithm to obtain a second intensity segmentation threshold, and taking a corresponding valley value of the second intensity segmentation threshold in a second frequency histogram as a second initial segmentation threshold;
and S8, when the first initial segmentation threshold and the second initial segmentation threshold are smaller than a preset value, taking the second initial segmentation threshold as a final intensity segmentation threshold.
Intercepting the point cloud with the intensity range between [ v-I, v + I ] to regenerate a frequency distribution histogram, obtaining a second segmentation threshold value through the processing flow of the steps S1-S5, when the first intensity segmentation threshold value and the second intensity segmentation threshold value are smaller than a preset value, if the preset value is set as 10, taking the second initial segmentation threshold value as a final intensity segmentation threshold value, otherwise, repeating the steps S6 and S7 to obtain a new initial segmentation threshold value, namely, taking the second initial segmentation threshold value as the first initial segmentation threshold value, taking the newly generated initial segmentation threshold value as the second initial segmentation threshold value, judging whether the value is smaller than the preset value again, and if the value is not smaller than the preset value, continuing repeating the process until the value is smaller than the preset value.
The frequency distribution histogram and the Otsu algorithm are repeatedly utilized, and the determination of the segmentation threshold of the point cloud intensity can be accurately performed step by step.
And S104, performing secondary segmentation on the lane line point cloud data of each segment by using the segmentation threshold of the point cloud intensity to extract highlighted lane line point clouds.
And when the intensity of the point cloud of the lane line is higher than the segmentation threshold, the point cloud of the lane line is retained, otherwise, the point cloud of the lane line is deleted. Based on point cloud segmentation, most point cloud edge points can be reserved, and meanwhile road noise point clouds can be reduced.
According to the method provided by the embodiment, the point cloud range is expanded, the statistical histogram and the Otsu algorithm are combined to accurately segment the threshold value in strength, the point cloud data at the edge of the lane line are reserved to the maximum extent, and the abrasion influence of the lane line is reduced.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by functions and internal logic of the process, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 2 is a schematic structural diagram of an electronic device for lane line point cloud extraction according to a second embodiment of the present invention, where the electronic device includes:
the extraction module 210 is configured to obtain a road marking point cloud, and extract a lane line point cloud from the road marking point cloud according to a shape feature of the road marking point cloud cluster;
optionally, the extracting module 210 includes:
and the external expansion unit is used for expanding the preset width range towards the two sides of the lane line by taking the point cloud of the lane line of each segment as the center after the point cloud of the lane line is segmented.
The statistical module 220 is configured to perform statistics on frequency distribution of the intensity of each section of lane line point cloud data after segmenting the lane line point cloud, and generate a frequency distribution histogram;
a calculating module 230, configured to calculate a segmentation threshold of the point cloud intensity according to the frequency distribution histogram and the greater body fluid algorithm;
optionally, the calculating module 230 includes:
the matching unit is used for acquiring coordinates corresponding to peak values and valley values in the frequency distribution histogram and matching according to the distribution of the peak values and the valley values and a preset peak-valley matching rule;
the comparison unit is used for calculating the area of a polygon formed by peaks and valleys after peak-valley pairing, comparing the area of the polygon with a preset threshold, removing initial valleys smaller than the preset threshold, and determining the type of peak-valley pairing;
and the merging unit merges the peaks and the valleys of the same pairing type according to the appearance sequence of the peaks and the valleys.
Further, the method also comprises the following steps:
the segmentation unit is used for carrying out secondary segmentation on the intensity value of the point cloud data of the lane line of each segment through an Otsu algorithm to obtain a first intensity segmentation threshold;
and the first determining unit is used for taking the corresponding valley value as a first initial segmentation threshold value according to the position of the intensity segmentation threshold value in the peak valley formed by correspondingly combining the frequency distribution histograms.
Further, also comprises
The generating unit intercepts point clouds with the intensity ranges between [ v-I, v + I ], and generates a second frequency distribution histogram according to the intensity interval of I/2, wherein v is an initial segmentation threshold value, and I is the intensity interval of the frequency distribution histogram;
the second determining unit is used for carrying out secondary segmentation on the intensity value of the intercepted point cloud data through an Otsu algorithm to obtain a second intensity segmentation threshold value, and taking a corresponding valley value of the second intensity segmentation threshold value in a second frequency histogram as a second initial segmentation threshold value;
and the judging unit is used for taking the second intensity segmentation threshold value as a final intensity segmentation threshold value when the first initial segmentation threshold value and the second initial segmentation threshold value are smaller than the preset value.
And a segmentation module 240, configured to perform binary segmentation on the lane line point cloud data of each segment by using a segmentation threshold of the point cloud intensity to extract a highlighted lane line point cloud.
Through the electronic equipment of the embodiment, the road surface marking line point cloud data can be accurately segmented and extracted, and meanwhile, the speed is high and the efficiency is high.
Fig. 3 is a schematic structural diagram of an electronic device for lane line point cloud extraction according to an embodiment of the present invention, where the electronic device is a device that provides computing services, including but not limited to a smart phone, a tablet, a notebook, and the like, and as shown in fig. 3, the electronic device 3 of the embodiment includes: memory 310, processor 320, said memory 310 comprising executable program 3101 stored thereon, it being understood by those skilled in the art that the electronic device structure shown in fig. 3 does not constitute a limitation of the electronic terminal device or apparatus, may comprise more or less components than shown, or may combine certain components, or a different arrangement of components.
The following describes each component of the electronic device in detail with reference to fig. 3:
the memory 310 may be used to store software programs and modules, and the processor 320 executes various functional applications and data processing of the electronic terminal device by operating the software programs and modules stored in the memory 310. The memory 310 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 electronic terminal device, and the like. Further, the memory 310 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
An executable program 3101 for a method of project engineering initialization is contained on the memory 310, the executable program 3101 may be divided into one or more modules/units, which are stored in the memory 310 and executed by the processor 320 to implement lane line point cloud data extraction, and may be a series of computer program instruction segments for describing the execution process of the computer program 3101 in the electronic terminal device, which can perform specific functions. For example, the computer program 3101 may be partitioned into a computation module, a definition module, a traversal module, and a squaring module.
The processor 320 is a control center of the electronic terminal device, connects various parts of the whole electronic terminal device by using various interfaces and lines, and performs various functions of the electronic terminal device and processes data by running or executing software programs and/or modules stored in the memory 310 and calling data stored in the memory 310, thereby performing overall monitoring of the electronic terminal device. Optionally, processor 620 may include one or more processing units; preferably, the processor 320 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 320.
The electronic device may also include at least one sensor, such as light sensors, motion sensors, and other sensors, an input device, such as a touch screen, keyboard, and other, an output device, such as a speaker, display, and other. Other constituent elements are not described in detail herein.
In this embodiment of the present invention, the executable program executed by the processor 320 included in the electronic device is specifically:
acquiring pavement marking point clouds, and extracting lane line point clouds from the pavement marking point clouds according to shape features of the pavement marking point cloud clusters;
after segmenting the lane line point cloud, counting the frequency distribution of the data intensity of each segment of lane line point cloud to generate a frequency distribution histogram;
calculating a segmentation threshold value of the point cloud intensity according to the frequency distribution histogram and an Otsu algorithm;
and performing two-segmentation on the lane line point cloud data of each segment by using the segmentation threshold value of the point cloud intensity so as to extract highlighted lane line point cloud.
Further, after segmenting the lane line point cloud, counting the frequency distribution of the intensity of each segment of lane line point cloud data, and generating a frequency distribution histogram further includes:
and after segmenting the lane line point clouds, expanding a preset width range outwards from two sides of the lane line by taking the lane line point cloud of each segment as a center.
Further, the calculating a segmentation threshold of the point cloud intensity according to the frequency distribution histogram and the oxford algorithm includes:
acquiring coordinates corresponding to peak values and valley values in the frequency distribution histogram, and matching according to the distribution of the peak values and the valley values and a preset peak-valley matching rule;
calculating the area of a polygon formed by peaks and valleys after peak-valley pairing, comparing the area of the polygon with a preset threshold, removing initial valleys smaller than the preset threshold, and determining the type of peak-valley pairing;
and merging the peaks and the valleys of the same pairing type according to the sequence of the peaks and the valleys.
Further, the calculating a segmentation threshold of the point cloud intensity according to the frequency distribution histogram and the greater fluid algorithm further includes:
performing two segmentation on the intensity value of the point cloud data of the lane line of each segment through an Otsu algorithm to obtain a first intensity segmentation threshold;
and correspondingly combining the positions of the intensity segmentation threshold values in the frequency distribution histogram to form peak-valley values, and taking the corresponding valley values as first initial segmentation threshold values.
Further, the taking a corresponding valley value as an initial segmentation threshold value according to the position of the intensity segmentation threshold value in the peak valley of the frequency distribution histogram includes:
intercepting point clouds with intensity ranges between [ v-I, v + I ], and generating a second frequency distribution histogram according to the intensity interval of I/2, wherein v is an initial segmentation threshold value, and I is the intensity interval of the frequency distribution histogram;
performing secondary segmentation on the intensity value of the intercepted point cloud data through an Otsu algorithm to obtain a second intensity segmentation threshold value, and taking a corresponding valley value of the second intensity segmentation threshold value in a second frequency histogram as a second initial segmentation threshold value;
and when the first initial segmentation threshold and the second initial segmentation threshold are smaller than a preset value, taking the second intensity segmentation threshold as a final intensity segmentation threshold.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (5)
1. A lane line point cloud extraction method is characterized by comprising the following steps:
acquiring pavement marking point clouds, and extracting lane line point clouds from the pavement marking point clouds according to shape features of the pavement marking point cloud clusters;
after segmenting the lane line point cloud, counting the frequency distribution of the data intensity of each segment of lane line point cloud to generate a frequency distribution histogram;
after the point clouds of the lane lines are segmented, taking the point clouds of the lane lines of each segment as a center, and expanding a preset width range to the two sides of the lane lines;
calculating a segmentation threshold value of the point cloud intensity according to the frequency distribution histogram and an Otsu algorithm;
wherein, according to the frequency distribution histogram and the Otsu algorithm, calculating the segmentation threshold of the point cloud intensity comprises:
acquiring corresponding coordinates of peak values and valley values in the frequency distribution histogram, and matching according to the distribution of the peak values and the valley values and a preset peak-valley matching rule;
calculating the area of a polygon formed by peaks and valleys after peak-valley pairing, comparing the area of the polygon with a preset threshold, removing initial valleys smaller than the preset threshold, and determining the type of peak-valley pairing;
merging the peaks and the valleys of the same pairing type according to the sequence of the peaks and the valleys;
performing secondary segmentation on the intensity value of the point cloud data of the lane line of each segment through an Otsu algorithm to obtain a first intensity segmentation threshold value;
according to the position of the intensity segmentation threshold value in a peak valley formed by merging the frequency distribution histograms, taking a corresponding valley value as a first initial segmentation threshold value;
and performing secondary segmentation on the lane line point cloud data of each segment by using the segmentation threshold value of the point cloud intensity so as to extract highlighted lane line point clouds.
2. The method of claim 1, wherein the corresponding merging of the frequency distribution histograms according to the intensity segmentation threshold to form a position in a peak-valley, using the corresponding valley value as a first initial segmentation threshold further comprises:
intercepting point clouds with intensity ranges between [ v-I, v + I ], and generating a second frequency distribution histogram according to the intensity interval of I/2, wherein v is an initial segmentation threshold value, and I is the intensity interval of the frequency distribution histogram;
performing secondary segmentation on the intensity value of the intercepted point cloud data through an Otsu algorithm to obtain a second intensity segmentation threshold value, and taking a corresponding valley value of the second intensity segmentation threshold value in a second frequency histogram as a second initial segmentation threshold value;
and when the first initial segmentation threshold and the second initial segmentation threshold are smaller than a preset value, taking the second intensity segmentation threshold as a final intensity segmentation threshold.
3. An electronic device for lane line point cloud extraction, comprising:
the extraction module is used for acquiring pavement marking point clouds and extracting lane line point clouds from the pavement marking point clouds according to shape features of the pavement marking point cloud clusters;
wherein the extraction module comprises:
the outward expansion unit is used for expanding a preset width range outwards towards two sides of the lane line by taking the lane line point cloud of each segment as the center after segmenting the lane line point cloud;
the statistical module is used for counting the frequency distribution of the intensity of the point cloud data of each section of lane line after segmenting the point cloud of the lane line to generate a frequency distribution histogram;
the calculation module is used for calculating a segmentation threshold of the point cloud intensity according to the frequency distribution histogram and the Otsu algorithm;
wherein the calculation module comprises:
the matching unit is used for acquiring coordinates corresponding to peak values and valley values in the frequency distribution histogram and matching according to the distribution of the peak values and the valley values and a preset peak-valley matching rule;
the comparison unit is used for calculating the area of a polygon formed by peaks and valleys after peak-valley pairing, comparing the area of the polygon with a preset threshold, removing initial valleys smaller than the preset threshold, and determining the type of peak-valley pairing;
the merging unit merges the peaks and the valleys of the same pairing type according to the appearance sequence of the peaks and the valleys;
the segmentation unit is used for carrying out secondary segmentation on the intensity value of the point cloud data of the lane line of each segment through an Otsu algorithm to obtain a first intensity segmentation threshold;
a first determining unit, which takes the corresponding valley value as a first initial segmentation threshold value according to the position of the intensity segmentation threshold value in the peak valley formed by merging the frequency distribution histograms;
and the segmentation module is used for carrying out two-segmentation on the point cloud data of the lane line of each segment by utilizing the segmentation threshold value of the point cloud intensity so as to extract the highlighted point cloud of the lane line.
4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the lane line point cloud extraction method of any of claims 1-2.
5. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the lane line point cloud extraction method according to any one of claims 1 to 2.
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| CN111462141B (en) * | 2020-05-19 | 2024-01-12 | 北京爱笔科技有限公司 | Point cloud plane acquisition method and device, equipment and computer readable storage medium |
| CN112508111B (en) * | 2020-12-12 | 2022-05-31 | 武汉中海庭数据技术有限公司 | Rod fusion method and system based on road structure, server and medium |
| CN113238209B (en) * | 2021-04-06 | 2024-01-16 | 宁波吉利汽车研究开发有限公司 | Road perception method, system, equipment and storage medium based on millimeter wave radar |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106503678A (en) * | 2016-10-27 | 2017-03-15 | 厦门大学 | Roadmarking automatic detection and sorting technique based on mobile laser scanning point cloud |
| CN107463918A (en) * | 2017-08-17 | 2017-12-12 | 武汉大学 | Lane line extracting method based on laser point cloud and image data fusion |
| CN109165549A (en) * | 2018-07-09 | 2019-01-08 | 厦门大学 | Road markings acquisition methods, terminal device and device based on three dimensional point cloud |
| CN110349260A (en) * | 2019-07-11 | 2019-10-18 | 武汉中海庭数据技术有限公司 | A kind of pavement strip extraction method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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-
2019
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106503678A (en) * | 2016-10-27 | 2017-03-15 | 厦门大学 | Roadmarking automatic detection and sorting technique based on mobile laser scanning point cloud |
| CN107463918A (en) * | 2017-08-17 | 2017-12-12 | 武汉大学 | Lane line extracting method based on laser point cloud and image data fusion |
| CN109165549A (en) * | 2018-07-09 | 2019-01-08 | 厦门大学 | Road markings acquisition methods, terminal device and device based on three dimensional point cloud |
| CN110349260A (en) * | 2019-07-11 | 2019-10-18 | 武汉中海庭数据技术有限公司 | A kind of pavement strip extraction method and device |
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