CN111427349A - Vehicle navigation obstacle avoidance method and system based on laser and vision - Google Patents
Vehicle navigation obstacle avoidance method and system based on laser and vision Download PDFInfo
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- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
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- G05D1/02—Control of position or course in two dimensions
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- G05D1/0253—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow
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Abstract
The invention discloses a vehicle navigation obstacle avoidance method and system based on laser and vision, comprising the following steps: the method comprises the steps of scanning a vehicle driving area through laser to obtain point cloud data, and realizing laser feature matching based on a global feature matching algorithm of laser point cloud; recognizing objects around the vehicle by adopting a vision-based target recognition algorithm; and fusing the laser characteristic matching result and the object recognition result, and constructing an image of the road around the vehicle in real time to realize vehicle navigation and obstacle avoidance reminding. The invention integrates laser and a vision sensor, realizes the accurate positioning of the scooter and reduces the error in a single sensor mode; meanwhile, the real-time performance of data can be guaranteed, the navigation and positioning functions of the scooter can be achieved, the safety factor is improved, on the basis of algorithm, the information is more perfect based on global feature matching of laser point cloud, the operation time of the algorithm is shortened due to the introduction of the FPFH operator, and the real-time performance of the algorithm is accelerated.
Description
Technical Field
The invention relates to the technical field of navigation and obstacle avoidance, in particular to a vehicle navigation and obstacle avoidance algorithm and system based on laser and vision.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the development of science and technology, the mobility scooter gradually moves to the sight of the public, but due to the increase of traffic route vehicles and the increase of traffic accidents, the society urgently needs the mobility scooter capable of following the current research social situation.
The inventor finds that most of vehicles are manually driven and operated, and for the scooter, the single manual autonomous driving is assisted by other instruments, and the manual autonomous driving is not integrated into a complete system, so that the vehicle owner is very easy to fatigue when driving due to the complexity of a traffic route, and accidents occur. Meanwhile, in the prior art, only a single function of vehicle navigation is often researched, an unmanned obstacle avoidance technology and a safety alarm technology are not considered, and the vehicle navigation system is lack of comprehensiveness and integrity and is not beneficial to safe driving of the vehicle.
Disclosure of Invention
The invention aims to solve the defects of the prior art, and provides a vehicle navigation obstacle avoidance method and system based on laser and vision.
In some embodiments, the following technical scheme is adopted:
the vehicle navigation obstacle avoidance method based on laser and vision comprises the following steps:
the method comprises the steps of scanning a vehicle driving area through laser to obtain point cloud data, and realizing laser feature matching based on a global feature matching algorithm of laser point cloud;
recognizing objects around the vehicle by adopting a vision-based target recognition algorithm;
and fusing the laser characteristic matching result and the object recognition result, and constructing an image of the road around the vehicle in real time to realize vehicle navigation and obstacle avoidance reminding.
In other embodiments, the following technical solutions are adopted:
a vehicle navigation obstacle avoidance system based on laser and vision comprises:
the device is used for scanning a vehicle driving area through laser to obtain point cloud data and realizing laser feature matching based on a global feature matching algorithm of laser point cloud;
means for identifying objects surrounding the vehicle using a vision-based target identification algorithm;
and the device is used for fusing the laser characteristic matching result and the object recognition result, constructing images of roads around the vehicle in real time, and realizing vehicle navigation and obstacle avoidance reminding.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the vehicle navigation obstacle avoidance method based on the laser and the vision.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium, wherein a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the vehicle navigation obstacle avoidance method based on laser and vision.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention integrates laser and a vision sensor, realizes the accurate positioning of the scooter and reduces the error in a single sensor mode; meanwhile, the real-time performance of data can be guaranteed, the navigation and positioning functions of the scooter can be realized, the safety factor is improved, information is more perfect in algorithm based on laser point cloud global feature matching, and due to the introduction of a FPFH (fast point weather modification) operator, the running time of the algorithm is reduced, and the real-time performance of the algorithm is accelerated.
(2) The invention combines the standard ICP algorithm to realize the high-precision matching of the T L S point cloud, and because the matching result precision of the FGR algorithm is limited, the result obtained by simple point cloud clusters is not enough to perfectly match the obstacles during the vehicle moving, the standard ICP algorithm is used for optimization on the basis of the FGR algorithm, and the accuracy is improved;
(3) the intelligent service function of the invention gives the owner a humanized service function, and can carry out intelligent anti-fatigue service according to the fatigue state of the driver, thereby not only ensuring the driving safety, but also relieving the fatigue of the owner.
Drawings
FIG. 1 is a flow chart of a vehicle navigation obstacle avoidance method based on laser and vision in an embodiment of the present invention;
FIG. 2 is a global feature matching algorithm based on laser point cloud in an embodiment of the present invention;
FIG. 3 is a flow chart of a vision-based target recognition algorithm in an embodiment of the present invention;
FIG. 4 is a flow chart of a multi-sensor fusion obstacle avoidance algorithm in an embodiment of the present invention;
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
In one or more embodiments, a vehicle navigation obstacle avoidance method based on laser and vision is disclosed, and with reference to fig. 1, the method includes the following steps:
step S1: realizing laser feature matching based on a global feature matching algorithm of laser point cloud;
step S2: the object recognition is realized based on a vision-based target recognition algorithm;
step S3: the laser vision fusion realizes the instant positioning and map building, and realizes the navigation and obstacle avoidance functions of the scooter;
step S4: the vehicle alarm device and the vehicle seat service function realize personalized service;
specifically, referring to fig. 2, the vehicle driving area is scanned by laser to obtain point cloud data, data preprocessing is performed firstly, including filtering of regions of no interest, positioning of driving areas and classification of point transportation information, region information is screened according to the size of the classification areas, class features meeting conditions are extracted, a target is obtained by a template matching method, and feature matching is achieved.
In this embodiment, the laser-point-cloud-based global feature matching algorithm implements feature matching of laser, and the specific method includes:
step S11: the method comprises the following steps of carrying out initial search elimination and down-sampling on point clouds, reducing the complexity of collected point cloud information, filtering out regions of no interest, reducing information data and accelerating the operation rate of an algorithm;
step S12: extracting feature points of the DoG (Difference-of-Gaussian), and subtracting point clouds under different parameters by Gaussian filtering calculation to obtain different DoG images, wherein because the contrast of the corner points is obvious, the corner point information is retained to obtain the feature point corner point information;
step S13: performing FPFH (fast point feature histogram) description, and performing bidirectional consistency matching through fast feature point histogram description;
step S14: optimizing the obtained laser point cloud data by using an FGR operator, preprocessing the data to obtain an independent point cloud cluster, and solving to obtain matching high-precision parameters by removing the noise of the point cloud cluster by using an FGR algorithm;
step S15, realizing high-precision matching of the point cloud of T L S by combining a standard ICP algorithm;
the high-precision matching of the T L S point cloud is realized by combining a standard ICP algorithm, and because the matching result precision of the FGR algorithm is limited, the result obtained by a simple point cloud cluster in the process of vehicle traveling is not enough to perfectly match the obstacle, the standard ICP algorithm is used for optimization on the basis of the FGR algorithm, and the accuracy is improved.
Referring to fig. 3, the object recognition is realized by a vision-based target recognition algorithm, specifically:
step S21: the vision sensor adopts a (chargeable) CCD camera, the shot image is an image with 256 gray levels and 1628 by 1236 pixels, and the traffic condition image data in the driving direction of the vehicle is shot;
step S22: extracting dynamic ROI (region of interest) information of the collected image, segmenting samples in different areas on a road, and providing data for next template matching; by adopting dynamic ROI information extraction, the operation amount of image processing can be avoided, and the real-time performance is improved.
Step S23: self-adaptive binarization processing and median filtering are carried out, an Ostu method is used for automatically selecting a global threshold T from a histogram of an image, if the gray value of a certain pixel in the image is smaller than T, the gray value of the pixel is set to be 0, otherwise, the gray value is set to be a maximum value 255, and the change expression is
Removing extra noise by using a 3-by-3 template median filter on the obtained image;
step S24, edge processing is carried out by using a second-order L OG (L alias of Gaussian) operator:
wherein, sigma is the Gaussian standard deviation,
and performing Gaussian convolution, and enhancing the edge information of the image by using L OG operator to obtain a clear edge image and an enhanced image on the vehicle driving road.
Step S25: and (3) feature point identification, namely identifying feature points of objects on the vehicle travelling path, matching the obtained image corner point information with a template library, identifying the obstacle form and judging the obstacle category.
Referring to fig. 4, the vision-based target identification and navigation positioning includes fusing laser and vision-acquired information to ensure real-time performance; specifically, a laser feature matching result and an object recognition result are fused, the laser recognizes the actual moving obstacles on the road surface, the point cloud information is matched with a template in a template library, the types of the obstacles, such as a running vehicle and a pedestrian on the roadside, the information collected by a visual camera assists in recognizing the road surface condition, traffic lights and the like, are recognized, and the image of the road around the vehicle is constructed in real time.
The method specifically comprises the following steps:
step S31: combining the information obtained by the laser sensor and the vision sensor;
step S32: constructing images of roads around the scooter in real time to realize a navigation function;
step S33: performing obstacle labeling and lane reminding according to the navigation image;
step S34: and realizing the obstacle avoidance function according to the navigation information.
And the massage service is provided when the user is tired, the fatigue is relieved, and the real-time broadcast is realized based on the navigation service prompt when the user is moving. The method specifically comprises the following steps:
step S41: starting a fatigue mode;
step S42: starting a seat heating mode;
step S43: starting a seat massage function;
step S44: voice prompt and addiction prevention.
The vehicle warns the vehicle owner according to the information of laser visual feedback, prevents fatigue driving to give massage service when it is tired, remove fatigue, carry out the service suggestion based on the navigation when marcing, report road surface condition in real time, can keep away the barrier according to navigation information.
It should be noted that, the present embodiment can implement two modes of manned driving and unmanned driving, and can select the driving mode by itself. Under the unmanned driving mode, the vehicle can carry out autonomous obstacle avoidance driving according to the navigation information.
Example two
In one or more embodiments, a vehicle navigation obstacle avoidance system based on laser and vision is disclosed, comprising:
the device is used for scanning a vehicle driving area through laser to obtain point cloud data and realizing laser feature matching based on a global feature matching algorithm of laser point cloud;
means for identifying objects surrounding the vehicle using a vision-based target identification algorithm;
and the device is used for fusing the laser characteristic matching result and the object recognition result, constructing images of roads around the vehicle in real time, and realizing vehicle navigation and obstacle avoidance reminding.
Also comprises a device for preventing fatigue prompt and a fatigue-preventing massage chair.
The specific implementation of the device is realized by referring to the method disclosed in the first embodiment.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed, which includes a server including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the laser and vision-based vehicle navigation obstacle avoidance method in the first embodiment. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The vehicle navigation obstacle avoidance method based on laser and vision in the first embodiment may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. The vehicle navigation obstacle avoidance method based on laser and vision is characterized by comprising the following steps:
the method comprises the steps of scanning a vehicle driving area through laser to obtain point cloud data, and realizing laser feature matching based on a global feature matching algorithm of laser point cloud;
recognizing objects around the vehicle by adopting a vision-based target recognition algorithm;
and fusing the laser characteristic matching result and the object recognition result, and constructing an image of the road around the vehicle in real time to realize vehicle navigation and obstacle avoidance reminding.
2. The vehicle navigation obstacle avoidance method based on laser and vision as claimed in claim 1, wherein the laser feature matching is realized by a global feature matching algorithm based on laser point cloud, specifically:
carrying out initial inspection rejection and down-sampling on the point cloud;
extracting feature points of the DoG (Difference-of-Gaussian), and subtracting point clouds under different parameters through Gaussian filtering calculation to obtain different DoG images and obtain feature point angle point information;
performing FPFH (fast point feature histogram) description, and performing bidirectional consistency matching through fast feature point histogram description;
aiming at the matched laser point cloud data, obtaining an independent point cloud cluster through data preprocessing, optimizing the point cloud cluster by using an FGR operator, and removing point cloud cluster noise to obtain matched high-precision parameters;
and (3) realizing high-precision matching of the point cloud of T L S by adopting an ICP (inductively coupled plasma) algorithm.
3. The vehicle navigation obstacle avoidance method based on laser and vision as claimed in claim 1, characterized in that the recognition of the objects around the vehicle is realized by adopting a vision-based target recognition algorithm, specifically:
adopting a vision sensor to obtain traffic condition image data in the vehicle driving direction;
carrying out dynamic region-of-interest operation on the traffic condition image data, segmenting different region samples on a road, and providing data for next template matching;
performing self-adaptive binarization processing and median filtering on the obtained result to obtain binarized image data;
performing edge processing on the binarized image data by using a second-order L OG operator to obtain an edge image and an enhanced image on a vehicle driving road;
and identifying the characteristic points of the object on the vehicle travelling path, matching the obtained image characteristic point information with the template library, identifying the object form, and judging the object type.
4. The vehicle navigation obstacle avoidance method based on laser and vision as claimed in claim 3, wherein the adaptive binarization processing process specifically comprises:
and automatically selecting a global threshold T according to the histogram, wherein if the gray value of a certain pixel in the image is smaller than T, the gray value of the pixel is set to be 0, and otherwise, the gray value is set to be the maximum value.
5. The vehicle navigation obstacle avoidance method based on laser and vision as claimed in claim 1, characterized in that the laser feature matching result is fused with the result of object recognition, the actual moving obstacle on the road surface is recognized by laser, and the obstacle category is recognized by matching point cloud information with templates in a template library; the traffic condition in the driving direction of the vehicle is identified in an auxiliary way through image information acquired by a vision sensor; an image of the road around the vehicle is constructed in real time.
6. The vehicle navigation obstacle avoidance method based on laser and vision as claimed in claim 1, characterized in that, according to the navigation image, the obstacle labeling and lane reminding are performed, according to the laser point cloud matching, the obstacles on the road are labeled according to the distance degree, the dangerous interval is divided, according to the image information acquired by vision, the road traffic condition is labeled, the current vehicle driving state is judged, and the obstacle avoidance in the driving process is realized according to the navigation information.
7. The laser and vision based vehicle navigation obstacle avoidance method of claim 1, further comprising: judging whether fatigue-preventing service needs to be started or not according to the acquired state information of the driver;
if the anti-fatigue service is started, the seat massage function is started; meanwhile, road conditions and the current driving position are prompted through intelligent voice.
8. The utility model provides a vehicle navigation keeps away barrier system based on laser and vision which characterized in that includes:
the device is used for scanning a vehicle driving area through laser to obtain point cloud data and realizing laser feature matching based on a global feature matching algorithm of laser point cloud;
means for identifying objects surrounding the vehicle using a vision-based target identification algorithm;
and the device is used for fusing the laser characteristic matching result and the object recognition result, constructing images of roads around the vehicle in real time, and realizing vehicle navigation and obstacle avoidance reminding.
9. A terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the laser and vision based vehicle navigation obstacle avoidance method of any one of claims 1-7.
10. A computer readable storage medium having stored thereon a plurality of instructions, wherein the instructions are adapted to be loaded by a processor of a terminal device and to perform the laser and vision based vehicle navigation obstacle avoidance method of any one of claims 1 to 7.
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