CN113777622B - Rail obstacle identification method and device - Google Patents
Rail obstacle identification method and device Download PDFInfo
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- CN113777622B CN113777622B CN202111011733.1A CN202111011733A CN113777622B CN 113777622 B CN113777622 B CN 113777622B CN 202111011733 A CN202111011733 A CN 202111011733A CN 113777622 B CN113777622 B CN 113777622B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- Radar, Positioning & Navigation (AREA)
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- Train Traffic Observation, Control, And Security (AREA)
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Abstract
The application provides a method and a device for identifying a track obstacle. The method comprises the following steps: based on the three-dimensional point cloud data, confirming whether an obstacle exists in the front track; if an obstacle exists, controlling to start a camera, and acquiring image information of the obstacle through the camera; and determining barrier early warning information based on the three-dimensional point cloud data and the image information fusion result. According to the application, the laser radar is utilized to scan the front of the train to acquire data, the point cloud data of the obstacle is acquired, the camera is guided to acquire a visible image of the obstacle, the processed point cloud three-dimensional data and the two-dimensional data of the graph are fused, and the information such as the azimuth, the distance and the intensity of the obstacle is acquired, so that the accurate obstacle early warning information is obtained.
Description
Technical Field
The application relates to the technical field of rail traffic control, in particular to a method and a device for identifying rail obstacles.
Background
With the rapid development of the current urban mass, urban rail transit has become the preferred mode of people going out due to the advantages of large passenger capacity, no blockage and the like. And the running speed of the train is continuously improved, and the running safety requirement on the train is also higher and higher. In order to ensure the travel safety of people, rail obstacles with the range larger than 950m need to be identified timely and accurately, and early warning is provided to inform the running train to take corresponding protection measures.
At present, the technical scheme of track obstacle discernment mainly has:
(1) The obstacle images are collected and identified through the zoom camera, and early warning is carried out according to the processed obstacle space position information and the current state information of the vehicle, so that the obstacle in 300 meters in front of the vehicle can be effectively detected;
(2) The method adopts a plurality of radars and cameras of different types, is based on machine learning and track recognition of gray projection algorithm, and acquires detection targets by fusing acquired obstacle image data and radar data, thereby achieving the purpose of active anti-collision monitoring.
The technical scheme (1) is limited in detection range only by means of a single visual technology, and cannot meet the requirements of subways on the detection range of anti-collision equipment; the algorithm of the technical scheme (2) is complex and not easy to engineer, and meanwhile, the cost of the complex hardware equipment is far higher than that of a common anti-collision system due to the fact that the complex hardware equipment is used for a plurality of sensors.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a method and a device for identifying rail obstacles.
In a first aspect, the present application provides a method for identifying a track obstacle, comprising:
based on the three-dimensional point cloud data, confirming whether an obstacle exists in the front track;
if an obstacle exists, controlling to start a camera, and acquiring image information of the obstacle through the camera;
and determining barrier early warning information based on the three-dimensional point cloud data and the image information fusion result.
Optionally, the determining the obstacle early warning information based on the three-dimensional point cloud data and the image information fusion result includes:
acquiring two-dimensional data information of the obstacle based on the image information;
based on the spatial resolution corresponding to the image information and the spatial correspondence between the image information and the three-dimensional point cloud data, projecting the two-dimensional data information to the rasterized three-dimensional point cloud data to obtain enhanced two-dimensional point cloud data;
and obtaining barrier early warning information based on the fusion result of the three-dimensional point cloud data and the enhanced two-dimensional point cloud data.
Optionally, before determining whether the front track has an obstacle based on the three-dimensional point cloud data, the method includes:
collecting first data information of a track in front of a train through a laser radar;
and carrying out abnormality removal processing on the first data information to obtain the effective three-dimensional point cloud data.
Optionally, the method further comprises:
and sending prompt information to the camera, and controlling the camera to rotate in advance so as to acquire the image information of the curve.
In a second aspect, the present application provides an apparatus for identifying a track obstacle, comprising:
the confirming module is used for confirming whether the front track is provided with an obstacle or not based on the three-dimensional point cloud data;
the control module is used for controlling to start the camera if the obstacle exists, and acquiring image information of the obstacle through the camera;
and the fusion module is used for determining barrier early warning information based on the three-dimensional point cloud data and the image information fusion result.
Optionally, the fusion module is further configured to:
acquiring two-dimensional data information of the obstacle based on the image information;
based on the spatial resolution corresponding to the image information and the spatial correspondence between the image information and the three-dimensional point cloud data, projecting the two-dimensional data information to the rasterized three-dimensional point cloud data to obtain enhanced two-dimensional point cloud data;
and obtaining barrier early warning information based on the fusion result of the three-dimensional point cloud data and the enhanced two-dimensional point cloud data.
Optionally, the apparatus further comprises a data processing module, configured to:
collecting first data information of a track in front of a train through a laser radar;
and carrying out abnormality removal processing on the first data information to obtain the effective three-dimensional point cloud data.
Optionally, the device further includes a prompt module, configured to:
and sending prompt information to the camera, and controlling the camera to rotate in advance so as to acquire the image information of the curve.
In a third aspect, the present application provides an electronic device comprising a processor and a memory storing a computer program, the processor implementing the steps of the method of track obstacle recognition of the first aspect when executing the program.
In a fourth aspect, the present application provides a processor-readable storage medium storing a computer program for causing the processor to perform the steps of the method of track obstacle recognition of the first aspect.
According to the method and the device for identifying the track obstacle, the laser radar is utilized to scan the front of the train to perform data acquisition, the point cloud data of the obstacle is obtained, the camera is guided to acquire the visible image of the obstacle, the processed point cloud three-dimensional data and the two-dimensional data of the graph are fused, and the information such as the azimuth, the distance and the intensity of the obstacle is obtained, so that accurate obstacle early warning information is obtained.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for identifying a track obstacle according to the present application;
FIG. 2 is a general flow chart of a method of identifying a rail obstacle provided by the present application;
FIG. 3 is a schematic diagram of a device for identifying rail obstacles;
fig. 4 is a schematic structural diagram of an electronic device provided by the present application;
FIG. 5 is a schematic diagram of a specific data fusion method provided by the present application;
fig. 6 is a schematic diagram of fusion of three-dimensional point cloud data and two-dimensional data information corresponding to an image acquired by a camera.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The method and apparatus for identifying rail obstructions in the present application are described below with reference to FIGS. 1-6.
Fig. 1 is a flow chart of a method for identifying a track obstacle according to the present application, as shown in fig. 1, the method for identifying a track obstacle includes:
step 101, based on three-dimensional point cloud data, confirming whether an obstacle exists in a front track;
102, if an obstacle exists, controlling to start a camera, and acquiring image information of the obstacle through the camera;
and step 103, determining barrier early warning information based on the three-dimensional point cloud data and the image information fusion result.
Specifically, the lidar is a radar system, an active sensor, and the data formed is in the form of a point cloud. The working spectrum is between infrared and ultraviolet, and is composed of a main transmitter, a receiver, a measurement control and a power supply. And the laser radar has incomparable advantages in the aspects of reliability, detection range, ranging precision and the like.
The main content of the laser radar for measuring is to collect the position information of the obstacle in the three-dimensional space and collect the distance information from the obstacle to the measuring system through laser detection; meanwhile, the encoder of the laser radar can obtain the angle information, such as azimuth angle and pitch angle.
In general, according to the concept of modern lidar, it is often divided into the following:
1. there are ultraviolet laser radar, visible laser radar and infrared laser radar according to laser wavelength bands.
2. The laser medium is classified into a gas laser radar, a solid laser radar, a semiconductor laser radar, a diode laser pumped solid laser radar, and the like.
3. There are pulse lidar, continuous wave lidar, hybrid lidar, etc. according to the lasing waveform.
4. There are analog or digital display lidar and imaging lidar according to display modes.
5. According to the carrying platform, there are foundation fixed laser radar, vehicle-mounted laser radar, airborne laser radar, ship-mounted laser radar, satellite-borne laser radar, missile-borne laser radar, handheld laser radar and the like.
6. According to functions, there are laser ranging radar, laser speed measuring radar, laser angle measuring radar, tracking radar, laser imaging radar, laser target indicator, biological laser radar and the like.
7. According to the application, there are laser range finders, range lidars, fire control lidars, tracking recognition lidars, multifunctional tactical lidars, poison detection lidars, navigation lidars, weather lidars, poison detection and atmosphere monitoring lidars and the like.
The laser radar adopted in the application is not particularly limited, and mainly utilizes the advantages of long laser radar ranging, strong angle measuring capability, high measuring precision, high response speed and no influence of light, scans the front of the train running direction, and performs data acquisition to obtain the point cloud data of all objects in the reaching range which can be detected by the laser radar in front of the train, wherein the point cloud data is three-dimensional data. And determining whether an obstacle exists in the front track of the train according to the acquired three-dimensional point cloud data. Such as whether there is a protrusion above the track, obstructing the travel of the train, whether there is an obstacle above the ground of the train, affecting the passage of the train, etc.
According to the three-dimensional point cloud data, it is determined that an object higher than a train ground or protruding above a track obviously exists, then the object is judged to exist, the train sends a control start command to a camera, after the camera receives and executes the command, the camera performs graph acquisition on the object in front of the running of the train, timeliness of image acquisition work is improved, and corresponding image information is obtained.
And fusing the three-dimensional point cloud data with image information obtained by a camera to determine more accurate information of the obstacle, thereby providing early warning information for the train.
According to the method for identifying the track obstacle, the laser radar is utilized to scan the front of the train to conduct data acquisition, point cloud data of the obstacle are obtained, a camera is guided to collect visible images of the obstacle, the processed point cloud three-dimensional data and two-dimensional data of the graph are fused, and information such as azimuth, distance and strength of the obstacle is obtained, so that accurate obstacle early warning information is obtained.
Optionally, the determining the obstacle early warning information based on the three-dimensional point cloud data and the image information fusion result includes:
acquiring two-dimensional data information of the obstacle based on the image information;
based on the spatial resolution corresponding to the image information and the spatial correspondence between the image information and the three-dimensional point cloud data, projecting the two-dimensional data information to the rasterized three-dimensional point cloud data to obtain enhanced two-dimensional point cloud data;
and obtaining barrier early warning information based on the fusion result of the three-dimensional point cloud data and the enhanced two-dimensional point cloud data.
Specifically, image data of an obstacle in front of the train running acquired by a camera is processed, and two-dimensional data information of the obstacle is obtained.
And carrying out rasterization processing on the three-dimensional point cloud data acquired by the laser radar according to the spatial resolution corresponding to the image information after preprocessing, so as to obtain a point cloud raster image.
And projecting the image information to the point cloud grid image according to the spatial corresponding relation between the image information and the three-dimensional point cloud data to obtain enhanced two-dimensional point cloud data. And determining the height value of the three-dimensional point cloud data according to the three-dimensional point cloud data, and combining the enhanced two-dimensional point cloud data to obtain a fusion result so as to obtain accurate barrier early warning information.
And, the wave bands of the two-dimensional image acquired by the camera and the three-dimensional point cloud data acquired by the laser radar are different. Two information of the obstacle are contained in the two-dimensional image: coordinates and intensity.
The specific data fusion method is shown in fig. 5, and includes: and (3) point cloud rasterization, point cloud raster image, multispectral image spatial resolution and overlap region data interception.
1. Preprocessing three-dimensional point cloud data acquired by a radar, such as outlier removal, smoothing and the like, and recording the height value of the three-dimensional point cloud data, namely, when the three-dimensional point cloud data is expressed in the form of XYZ coordinates, the corresponding Z value is rasterized according to the spatial resolution of a two-dimensional image, so as to obtain a point cloud raster image; wherein the two-dimensional image spatial resolution is determined from image information acquired by a camera.
2. And (3) projecting the preprocessed two-dimensional image data to the two-dimensional pixel data obtained in the step (1) according to the spatial correspondence relation between the preprocessed two-dimensional image data and the point cloud data, and obtaining overlapping area data of the point cloud raster image obtained in the step (1), namely the two-dimensional point cloud data with enhanced intensity.
3. And 2, combining the data obtained in the step 2 with the height value of the three-dimensional point cloud data obtained by the radar, wherein the final fusion result is corrected point cloud data, namely three-dimensional point cloud data with enhanced strength, and the data can further represent the specific characteristics of the obstacle. And integrating the XY coordinates of the two-dimensional point cloud data with the intensity enhanced and the Z coordinates of the height value of the corresponding three-dimensional point cloud data to obtain a fusion result, namely the three-dimensional point cloud data with the intensity enhanced.
And the final result of fusion of the three-dimensional point cloud data and the two-dimensional data information corresponding to the image acquired by the camera is shown in fig. 6.
If the pixels of the two-dimensional image are projected onto the three-dimensional point cloud data, the pixels falling into the projected three-dimensional interval and the three-dimensional point cloud data have a corresponding relationship, and the pixel intensity of the obstacle is improved through fusion in the mode.
Three-dimensional point cloud data acquired by a radar have limited wave bands, and two-dimensional data of an image are utilized to project the three-dimensional point cloud data, so that pixel intensities of smaller wave bands corresponding to coordinates are supplemented. The obtained fusion result enriches three-dimensional point cloud data, so that the obtained obstacle is more specific.
According to the method for identifying the track obstacle, the laser radar is utilized to scan the front of the train to conduct data acquisition, point cloud data of the obstacle are obtained, a camera is guided to collect visible images of the obstacle, the processed point cloud three-dimensional data and two-dimensional data of the graph are fused, and information such as azimuth, distance and strength of the obstacle is obtained, so that accurate obstacle early warning information is obtained.
Optionally, before determining whether the front track has an obstacle based on the three-dimensional point cloud data, the method includes:
collecting first data information of a track in front of a train through a laser radar;
and carrying out abnormality removal processing on the first data information to obtain the effective three-dimensional point cloud data.
Specifically, the laser radar on the train is used for collecting the original data information of the track in front of the train as the first data information, because when the corresponding three-dimensional point cloud data information is captured, part of the data information may be invalid, for example, flying objects appear, and invalid data information needs to be removed. There may be a case where part of the data information is a discrete point, and information not belonging to an obstacle, at which time the data information of the discrete point is deleted by the smoothing processing.
According to the method for identifying the track obstacle, the laser radar is utilized to scan the front of the train to conduct data acquisition, point cloud data of the obstacle are obtained, a camera is guided to collect visible images of the obstacle, the processed point cloud three-dimensional data and two-dimensional data of the graph are fused, and information such as azimuth, distance and strength of the obstacle is obtained, so that accurate obstacle early warning information is obtained.
Optionally, the method further comprises:
and sending prompt information to the camera, and controlling the camera to rotate in advance so as to acquire the image information of the curve.
Specifically, the rotation control of the camera can be realized through the signal sent by the train, and after the camera receives the rotation control instruction, the camera can rotate a certain angle in advance to acquire the graph of the train running from a straight line running curve, so that the curve obstacle can be identified, and the vision blind area does not exist;
according to the method for identifying the rail obstacle, provided by the application, reliable cross-line operation chart adjustment is realized by proposing an operation chart adjustment algorithm standard; and can adapt to the operation diagrams of different manufacturers according to the standard to adjust, thereby meeting the requirements of complex line-crossing operation.
Fig. 2 is an overall flowchart of a method for identifying a track obstacle according to the present application, and as shown in fig. 2, the overall flowchart of the method for identifying a track obstacle includes the following steps:
s201, collecting laser radar data;
and data acquisition is carried out on the front of the train through a laser radar.
S202, three-dimensional point cloud data processing;
and processing the data acquired by the laser radar, removing discrete points, smoothing the data, and screening to obtain effective data.
S203, determining whether an obstacle exists?
If no obstacle exists, the flow goes to S201 to continue the laser radar data acquisition;
if an obstacle is present, the flow goes to S204.
S204-1, if an obstacle exists, storing point cloud data;
s204-2, controlling the camera to be started at the same time, and collecting images;
s204-3, processing image data;
under the condition that an obstacle exists, three-dimensional point cloud data acquired by the laser radar are stored, the camera is controlled to be started, image acquisition is carried out on the obstacle in front of the train, and image data acquired by the camera are processed to obtain two-dimensional data information of the obstacle.
S205, fusing the image data and the point cloud data;
and fusing the two-dimensional data information of the obstacle with the three-dimensional point cloud data of the obstacle stored previously.
S206, generating early warning information;
and obtaining accurate barrier early warning information according to the fused data.
S207, sending the information to the train.
And finally, sending the early warning information to the train to inform the train to take measures, so that the running safety is ensured.
The following describes a device for identifying a track obstacle provided by the present application, and the device for identifying a track obstacle described below and the method for identifying a track obstacle described above can be referred to correspondingly.
Fig. 3 is a schematic structural diagram of a device for identifying a track obstacle according to the present application, as shown in fig. 3, the device for identifying a track obstacle includes:
a confirmation module 301, configured to confirm whether an obstacle exists in the front track based on the three-dimensional point cloud data;
the control module 302 is configured to control to start the camera if an obstacle exists, and acquire image information of the obstacle through the camera;
and the fusion module 303 is configured to determine obstacle early warning information based on the three-dimensional point cloud data and the image information fusion result.
Optionally, the fusion module 303 is further configured to:
acquiring two-dimensional data information of the obstacle based on the image information;
based on the spatial resolution corresponding to the image information and the spatial correspondence between the image information and the three-dimensional point cloud data, projecting the two-dimensional data information to the rasterized three-dimensional point cloud data to obtain enhanced two-dimensional point cloud data;
and obtaining barrier early warning information based on the fusion result of the three-dimensional point cloud data and the enhanced two-dimensional point cloud data.
Optionally, the apparatus further includes a data processing module 304 configured to:
collecting first data information of a track in front of a train through a laser radar;
and carrying out abnormality removal processing on the first data information to obtain the effective three-dimensional point cloud data.
The optional control module 302 is further configured to:
and sending prompt information to the camera, and controlling the camera to rotate in advance so as to acquire the image information of the curve.
It should be noted that the division of the units in the present application is illustrative, and is merely a logic function division, and other division manners may be implemented in practice. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that, the device provided by the present application can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and the parts and beneficial effects that are the same as those of the method embodiment in the present embodiment are not described in detail herein.
Fig. 4 illustrates a physical schematic diagram of an electronic device, as shown in fig. 4, which may include: processor 410, communication interface (Communication Interface) 420, memory 430 and communication bus 440, wherein processor 410, communication interface 420 and memory 430 communicate with each other via communication bus 440.
Alternatively, the processor 410 may be a central processing unit (Central Processing Unit, CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA), or a complex programmable logic device (Complex Programmable Logic Device, CPLD), and the processor may also employ a multi-core architecture.
The processor 410 may call a computer program in the memory 430 to perform the steps of the method of rail obstacle recognition, for example including:
based on the three-dimensional point cloud data, confirming whether an obstacle exists in the front track;
if an obstacle exists, controlling to start a camera, and acquiring image information of the obstacle through the camera;
and determining barrier early warning information based on the three-dimensional point cloud data and the image information fusion result.
Optionally, the determining the obstacle early warning information based on the three-dimensional point cloud data and the image information fusion result includes:
acquiring two-dimensional data information of the obstacle based on the image information;
based on the spatial resolution corresponding to the image information and the spatial correspondence between the image information and the three-dimensional point cloud data, projecting the two-dimensional data information to the rasterized three-dimensional point cloud data to obtain enhanced two-dimensional point cloud data;
and obtaining barrier early warning information based on the fusion result of the three-dimensional point cloud data and the enhanced two-dimensional point cloud data.
Optionally, before determining whether the front track has an obstacle based on the three-dimensional point cloud data, the method includes:
collecting first data information of a track in front of a train through a laser radar;
and carrying out abnormality removal processing on the first data information to obtain the effective three-dimensional point cloud data.
Optionally, the steps further include:
and sending prompt information to the camera, and controlling the camera to rotate in advance so as to acquire the image information of the curve.
Further, the logic instructions in the memory 430 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that, the electronic device provided in the embodiment of the present application can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and the details of the same parts and beneficial effects as those of the method embodiment in the embodiment are not described here.
In another aspect, the present application also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the steps of the method of rail obstacle recognition provided by the methods described above, for example comprising:
based on the three-dimensional point cloud data, confirming whether an obstacle exists in the front track;
if an obstacle exists, controlling to start a camera, and acquiring image information of the obstacle through the camera;
and determining barrier early warning information based on the three-dimensional point cloud data and the image information fusion result.
In another aspect, an embodiment of the present application further provides a processor readable storage medium, where a computer program is stored, where the computer program is configured to cause the processor to perform the steps of the method for identifying a track obstacle provided in the foregoing embodiments, for example, including:
based on the three-dimensional point cloud data, confirming whether an obstacle exists in the front track;
if an obstacle exists, controlling to start a camera, and acquiring image information of the obstacle through the camera;
and determining barrier early warning information based on the three-dimensional point cloud data and the image information fusion result.
The processor-readable storage medium may be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic storage (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), semiconductor storage (e.g., ROM, EPROM, EEPROM, nonvolatile storage (NAND FLASH), solid State Disk (SSD)), and the like.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present application without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (8)
1. A method of identifying a track obstacle, comprising:
based on the three-dimensional point cloud data, confirming whether an obstacle exists in the front track;
if an obstacle exists, controlling to start a camera, and acquiring image information of the obstacle through the camera;
determining barrier early warning information based on the three-dimensional point cloud data and the image information fusion result;
the determining obstacle early warning information based on the three-dimensional point cloud data and the image information fusion result comprises the following steps:
acquiring two-dimensional data information of the obstacle based on the image information;
based on the spatial resolution corresponding to the image information and the spatial correspondence between the image information and the three-dimensional point cloud data, projecting the two-dimensional data information to the rasterized three-dimensional point cloud data to obtain enhanced two-dimensional point cloud data;
and obtaining barrier early warning information based on the fusion result of the three-dimensional point cloud data and the enhanced two-dimensional point cloud data.
2. The method for identifying an obstacle in a track according to claim 1, wherein before confirming whether the obstacle exists in the front track based on the three-dimensional point cloud data, the method comprises:
collecting first data information of a track in front of a train through a laser radar;
and carrying out abnormality removal processing on the first data information to obtain the effective three-dimensional point cloud data.
3. The method of track obstacle identification of claim 1, further comprising:
and sending prompt information to the camera, and controlling the camera to rotate in advance so as to acquire the image information of the curve.
4. An apparatus for identifying a track obstacle, comprising:
the confirming module is used for confirming whether the front track is provided with an obstacle or not based on the three-dimensional point cloud data;
the control module is used for controlling to start the camera if the obstacle exists, and acquiring image information of the obstacle through the camera;
the fusion module is used for determining barrier early warning information based on the three-dimensional point cloud data and the image information fusion result;
the fusion module is also used for:
acquiring two-dimensional data information of the obstacle based on the image information;
based on the spatial resolution corresponding to the image information and the spatial correspondence between the image information and the three-dimensional point cloud data, projecting the two-dimensional data information to the rasterized three-dimensional point cloud data to obtain enhanced two-dimensional point cloud data;
and obtaining barrier early warning information based on the fusion result of the three-dimensional point cloud data and the enhanced two-dimensional point cloud data.
5. The apparatus for identifying an obstacle in a track as claimed in claim 4, further comprising a data processing module for:
collecting first data information of a track in front of a train through a laser radar;
and carrying out abnormality removal processing on the first data information to obtain the effective three-dimensional point cloud data.
6. The apparatus for identifying an obstacle in a track as claimed in claim 4, further comprising a prompting module for:
and sending prompt information to the camera, and controlling the camera to rotate in advance so as to acquire the image information of the curve.
7. An electronic device comprising a processor and a memory storing a computer program, characterized in that the processor, when executing the computer program, carries out the steps of the method of rail obstacle recognition according to any one of claims 1 to 3.
8. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method of rail obstacle recognition according to any one of claims 1 to 3.
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| CN114817014B (en) * | 2022-04-14 | 2024-10-15 | 西安恒歌数码科技有限责任公司 | Method for avoiding graph nodes from each other in three-dimensional scene |
| WO2024216523A1 (en) * | 2023-04-19 | 2024-10-24 | 深圳技术大学 | Method and system for sensing foreign matter within urban rail train travellng clearance, and apparatus and medium |
| CN116922448B (en) * | 2023-09-06 | 2024-01-02 | 湖南大学无锡智能控制研究院 | Environment sensing method, device and system for high-speed railway body-in-white transfer robot |
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