CN112650300A - Unmanned aerial vehicle obstacle avoidance method and device - Google Patents
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Abstract
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle obstacle avoidance method and device, wherein the method comprises the following steps: acquiring path information and speed information of the unmanned aerial vehicle during flying and acquired multi-path image data in a cloud end; extracting image data in the same direction as the flight path according to the path information; calling an image recognition algorithm to judge whether the extracted image data has an obstacle target or not; if the obstacle target exists, calling depth information of the image depth algorithm to the obstacle target, wherein the depth information comprises space position information, relative distance information and shape information of the obstacle; and calculating to obtain obstacle touching time data according to the relative distance information and the speed information, and transmitting the obstacle touching time data back to the cloud end, so that the unmanned aerial vehicle can be prevented from colliding with obstacles in advance.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle obstacle avoidance method.
Background
With the rapid development of the unmanned aerial vehicle industry, the unmanned aerial vehicle is more and more widely applied with the rapid development of the unmanned aerial vehicle industry, and the research on the unmanned aerial vehicle obstacle avoidance system and method is particularly important in order to improve the safety of the unmanned aerial vehicle.
The existing obstacle avoidance technologies mainly comprise ultrasonic waves, laser radars and visual obstacle avoidance. Ultrasonic waves and laser radars are active barrier detection technologies, and have the advantages that the technologies are mature, and the detection distance precision is high; the defects are that the load is large, the power consumption is high, a detection blind area exists, a plurality of probes are needed for range scanning, however, visual obstacle avoidance mainly depends on analysis of each acquired frame image, the image needs to be transmitted back to a server for calculation and analysis, and the obstacle avoidance effectiveness is poor.
Disclosure of Invention
In view of the above, embodiments of the present invention are proposed to provide an obstacle avoidance method and apparatus for a drone, which overcome the above problems or at least partially solve the above problems.
The invention provides an unmanned aerial vehicle obstacle avoidance method, which comprises the following steps:
acquiring path information and speed information of the unmanned aerial vehicle during flying and acquired multi-channel image data;
extracting image data in the same direction as the flight path according to the path information;
calling an image recognition algorithm to judge whether the extracted image data has an obstacle target or not;
if the obstacle target exists, calling depth information of the image depth algorithm to the obstacle target, wherein the depth information comprises space position information, relative distance information and shape information of the obstacle;
and calculating to obtain obstacle touching time data according to the relative distance information and the speed information, and sending the obstacle touching time data.
Further, the extracting image data in the same direction as the flight path according to the path information includes:
acquiring the space coordinates of each positioning point in the path information, and determining the path direction formed by a plurality of continuous adjacent positioning points;
and calculating an included angle between the path direction and the image center in each path of image data, and taking one path of image data with the minimum included angle as the extracted image data.
Further, the step of calling an image recognition algorithm to judge whether the extracted image data has an obstacle target includes:
acquiring an obstacle sample pattern;
conveying the sample pattern into a neural network for training;
and constructing an image recognition algorithm of the obstacle target.
Further, the calculating according to the relative distance information and the speed information to obtain obstacle touching time data, and transmitting the obstacle touching time data back to the cloud, and then, the method further includes:
acquiring first path image data and second path image data, and calling the image recognition algorithm to judge whether barrier targets exist in the first path image data and the second path image data;
if both the first path of image data and the second path of image data exist, calling the image depth algorithm to calculate the obstacle touching time of the obstacle target in the first path of image data and the second path of image data respectively to obtain first obstacle touching time data and second obstacle touching time data;
and sending the first barrier time data or the second barrier time data with the maximum time value.
Still provide an unmanned aerial vehicle and keep away barrier device, its characterized in that includes:
the image acquisition module is used for acquiring path information and speed information of the unmanned aerial vehicle during flying and acquired multi-path image data;
the image extraction module is used for extracting image data in the same direction as the flight path according to the path information;
the obstacle target identification module is used for calling an image identification algorithm to judge whether the extracted image data has an obstacle target or not;
the depth calculation module is used for calling depth information of the image depth algorithm to the obstacle target if the obstacle target exists, wherein the depth information comprises space position information, relative distance information and shape information of the obstacle;
and the obstacle touching time calculation module is used for calculating obstacle touching time data according to the relative distance information and the speed information and sending the obstacle touching time data.
Further, the image extraction module further includes:
the positioning point obtaining submodule is used for obtaining the space coordinates of each positioning point in the path information and determining the path direction formed by a plurality of continuous adjacent positioning points;
and the calculation submodule is used for calculating an included angle between the path direction and the image center in each path of image data, and one path of image data with the smallest included angle is taken as the extracted image data.
Further, the obstacle target identification module further includes:
the sample pattern acquisition submodule is used for acquiring a barrier sample pattern;
the training submodule is used for transmitting the sample pattern to a neural network for training;
and the construction submodule is used for constructing an image recognition algorithm of the obstacle target.
Further, the barrier time calculation module further includes:
the image acquisition submodule is used for acquiring a first path of image data and a second path of image data and calling the image recognition algorithm to judge whether an obstacle target exists in the first path of image data and the second path of image data;
the obstacle touching time submodule is used for calling the image depth algorithm to calculate obstacle touching time from the first path of image data to an obstacle target in the second path of image data if both the first path of image data and the second path of image data exist, and obtaining first obstacle touching time data and second obstacle touching time data;
a sending submodule for sending the first or second barrier time data with the largest time value
The embodiment of the invention has the following advantages:
according to the method, whether an obstacle target exists in the image data is judged by acquiring the image data in the same direction as the flight path, the obstacle touching time of the unmanned aerial vehicle is calculated through an image depth algorithm, meanwhile, the obstacle touching time in other image data can be calculated, and the better obstacle touching time is selected and sent to the cloud, so that the unmanned aerial vehicle is triggered to execute a matched flight instruction within the obstacle touching time, and the obstacle is avoided being touched.
Drawings
Fig. 1 is a flowchart illustrating steps of an unmanned aerial vehicle obstacle avoidance method according to the present invention;
fig. 2 is a structural block diagram of an unmanned aerial vehicle obstacle avoidance device of the present invention.
Fig. 3 is a block diagram of a structure of a computer device for obstacle avoidance of an unmanned aerial vehicle according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and detailed description, in order to make the objects, features and advantages thereof more comprehensible.
As shown in fig. 1, a flow chart of steps of an unmanned aerial vehicle obstacle avoidance method of the present invention is shown, where the method includes:
s100, acquiring path information and speed information of the unmanned aerial vehicle during flying and acquired multi-channel image data;
s110, extracting image data in the same direction as the flight path according to the path information;
s120, calling an image recognition algorithm to judge whether the extracted image data has an obstacle target or not;
s130, if the obstacle target exists, calling depth information of the image depth algorithm to the obstacle target, wherein the depth information comprises space position information, relative distance information and shape information of the obstacle;
and S140, calculating to obtain obstacle touching time data according to the relative distance information and the speed information, and sending the obstacle touching time data.
The invention mainly applies 5G communication technology, establishes a cloud server, and receives video data and digital data transmitted by a communication module on an unmanned aerial vehicle, wherein the video data comprises continuous frame image data and the like acquired by a multi-directional camera on the unmanned aerial vehicle, and the digital data comprises flight path information, flight speed information and the like of the unmanned aerial vehicle; the method and the device have the main point that the advantages of large bandwidth and low time delay of the 5G communication technology are utilized, and the acquired and uploaded image data are subjected to obstacle analysis, so that the obstacle is prevented from being touched along the original flight path. After the steps are combined, the most basic obstacle avoidance scheme is that the original flight path is used, the flight time is set according to the calculated obstacle touching time, and when the flight time is reached or close to the flight time, the unmanned aerial vehicle is controlled to hover or descend so as to avoid touching obstacles in advance.
When the unmanned aerial vehicle flies along a preset flying path, a main optical axis in the multi-path cameras for collecting image data and the camera with the minimum included angle between the main optical axis and the path are main camera lenses. Extracting image data collected by a main shooting lens from image data uploaded to each direction of the cloud, namely extracting image data in the same direction as a flight path according to the path information, wherein the step further comprises the following steps:
acquiring the space coordinates of each positioning point in the path information, and determining the path direction formed by a plurality of continuous adjacent positioning points;
it should be noted that the multiple cameras carried by the unmanned aerial vehicle provide panoramic image acquisition support for a user, and the unmanned aerial vehicle can determine image data in the direction of a flight path no matter along which flight path in the non-vertical flight process. The path information is composed of a plurality of connected positioning points, and the direction of the path can be determined through the space coordinate information of each adjacent positioning point.
And calculating an included angle between the path direction and the image center in each path of image data, and taking one path of image data with the minimum included angle as the extracted image data.
In the above technical solution, the purpose of determining the extracted image data is to determine an obstacle to be touched in the approximate direction of the flight path, and it can be understood that an included angle between the path direction and the center of each path of image data is calculated, that is, an included angle between the path direction and the center of each frame of image in each path of image data is calculated, that is, an included angle between the path direction and the main optical axis of each path of camera is calculated, and the extracted image data is determined.
In this embodiment, the called image recognition algorithm is pre-constructed by the following steps:
acquiring an obstacle sample pattern;
conveying the sample pattern into a neural network for training;
and constructing an image recognition algorithm of the obstacle target.
It should be noted that, by using the image recognition algorithm, whether an obstacle target exists in the extracted image data can be quickly recognized, and after the obstacle target exists in the image data is confirmed, the called image depth algorithm is also constructed in advance, so as to estimate the distance of touching the obstacle along the flight path direction.
In another embodiment, in addition to the basic hovering or descending obstacle avoidance manner, it may be determined whether other obstacle targets exist in other road image data by further extracting other road image data, and the specific steps are as follows:
acquiring first path image data and second path image data, and calling the image recognition algorithm to judge whether barrier targets exist in the first path image data and the second path image data;
if both the first path of image data and the second path of image data exist, calling the image depth algorithm to calculate the obstacle touching time of the obstacle target in the first path of image data and the second path of image data respectively to obtain first obstacle touching time data and second obstacle touching time data;
and sending the first barrier time data or the second barrier time data with the maximum time value.
It can be understood that the image data extracted before is collected by the main camera, and the first path of image data and the second path of image data may be image data collected by adjacent cameras of the main camera.
The unmanned aerial vehicle can receive the obstacle touching time data in the cloud, the received obstacle touching time data can be subjected to data numbering, for example, if the number of the basic obstacle touching time data is 0, a hovering/descending scheme matched with the basic obstacle touching time data in advance is triggered to be executed within the duration of the number 0; if the first barrier time data number is 1, triggering to execute a left-turn scheme matched with the first barrier time data number in advance within the duration of the number 1; if the second barrier time data number is 2, triggering to execute a right turn scheme matched with the second barrier time data number in advance within the duration of the number 2;
it should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Referring to fig. 2, a structural block diagram of an unmanned aerial vehicle obstacle avoidance device of the present invention is shown, which may specifically include the following modules:
the image acquisition module 100 is used for acquiring path information and speed information of the unmanned aerial vehicle during flying and acquired multi-path image data;
an image extraction module 200, configured to extract image data in the same direction as the flight path according to the path information;
an obstacle target identification module 300, configured to invoke an image identification algorithm to determine whether an obstacle target exists in the extracted image data;
a depth calculation module 400, configured to invoke depth information of the image depth algorithm to the obstacle target if the obstacle target exists, where the depth information includes spatial position information, relative distance information, and shape information of the obstacle;
and the obstacle touching time calculating module 500 is configured to calculate obstacle touching time data according to the relative distance information and the speed information, and send the obstacle touching time data.
In this embodiment, the image extraction module 200 further includes:
the positioning point obtaining submodule is used for obtaining the space coordinates of each positioning point in the path information and determining the path direction formed by a plurality of continuous adjacent positioning points;
and the calculation submodule is used for calculating an included angle between the path direction and the image center in each path of image data, and one path of image data with the smallest included angle is taken as the extracted image data.
In this embodiment, the obstacle target identification module 300 further includes:
the sample pattern acquisition submodule is used for acquiring a barrier sample pattern;
the training submodule is used for transmitting the sample pattern to a neural network for training;
and the construction submodule is used for constructing an image recognition algorithm of the obstacle target.
In this embodiment, the barrier time calculation module 400 further includes:
the image acquisition submodule is used for acquiring a first path of image data and a second path of image data and calling the image recognition algorithm to judge whether an obstacle target exists in the first path of image data and the second path of image data;
the obstacle touching time submodule is used for calling the image depth algorithm to calculate obstacle touching time from the first path of image data to an obstacle target in the second path of image data if both the first path of image data and the second path of image data exist, and obtaining first obstacle touching time data and second obstacle touching time data;
a sending submodule for sending the first or second barrier time data with the largest time value
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
As shown in fig. 3, a computer device of the unmanned aerial vehicle obstacle avoidance method of the present invention is shown, which may specifically include the following:
in an embodiment of the present invention, the present invention further provides a computer device, where the computer device 12 is represented in a general computing device, and the components of the computer device 12 may include but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)31 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (commonly referred to as "hard drives"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, with the program modules 42 configured to carry out the functions of embodiments of the invention.
A program/utility 41 having a set (at least one) of program modules 42 may be stored, for example, in memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing an obstacle avoidance method for the unmanned aerial vehicle provided by the embodiment of the present invention.
That is, the processing unit 16 implements, when executing the program: acquiring path information and speed information of the unmanned aerial vehicle during flying and acquired multi-path image data in a cloud end; extracting image data in the same direction as the flight path according to the path information; calling an image recognition algorithm to judge whether the extracted image data has an obstacle target or not; if the obstacle target exists, calling depth information of the image depth algorithm to the obstacle target, wherein the depth information comprises space position information, relative distance information and shape information of the obstacle; and calculating to obtain obstacle touching time data according to the relative distance information and the speed information, and transmitting the obstacle touching time data back to the cloud.
In an embodiment of the present invention, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements an unmanned aerial vehicle obstacle avoidance method as provided in all embodiments of the present application.
That is, the program when executed by the processor implements: acquiring path information and speed information of the unmanned aerial vehicle during flying and acquired multi-path image data in a cloud end; extracting image data in the same direction as the flight path according to the path information; calling an image recognition algorithm to judge whether the extracted image data has an obstacle target or not; if the obstacle target exists, calling depth information of the image depth algorithm to the obstacle target, wherein the depth information comprises space position information, relative distance information and shape information of the obstacle; and calculating to obtain obstacle touching time data according to the relative distance information and the speed information, and transmitting the obstacle touching time data back to the cloud.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer-readable storage medium or a computer-readable signal medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPOM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The unmanned aerial vehicle obstacle avoidance method and the unmanned aerial vehicle obstacle avoidance device provided by the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the unmanned aerial vehicle obstacle avoidance method, and the description of the embodiment is only used for helping to understand the method and the core idea of the unmanned aerial vehicle obstacle avoidance method; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. An unmanned aerial vehicle obstacle avoidance method is characterized by comprising the following steps:
acquiring path information and speed information of the unmanned aerial vehicle during flying and acquired multi-channel image data;
extracting image data in the same direction as the flight path according to the path information;
calling an image recognition algorithm to judge whether the extracted image data has an obstacle target or not;
if the obstacle target exists, calling depth information of the image depth algorithm to the obstacle target, wherein the depth information comprises space position information, relative distance information and shape information of the obstacle;
and calculating to obtain obstacle touching time data according to the relative distance information and the speed information, and sending the obstacle touching time data.
2. The method of claim 1, wherein said extracting image data co-directional with a flight path from said path information comprises:
acquiring the space coordinates of each positioning point in the path information, and determining the path direction formed by a plurality of continuous adjacent positioning points;
and calculating an included angle between the path direction and the image center in each path of image data, and taking one path of image data with the minimum included angle as the extracted image data.
3. The method of claim 1, wherein said invoking an image recognition algorithm prior to determining whether an obstacle target is present in the extracted image data comprises:
acquiring an obstacle sample pattern;
conveying the sample pattern into a neural network for training;
and constructing an image recognition algorithm of the obstacle target.
4. The method of claim 1, wherein the calculating of the obstacle time data from the relative distance information and the speed information, the sending of the obstacle time data, and thereafter further comprises:
acquiring first path image data and second path image data, and calling the image recognition algorithm to judge whether barrier targets exist in the first path image data and the second path image data;
if both the first path of image data and the second path of image data exist, calling the image depth algorithm to calculate the obstacle touching time of the obstacle target in the first path of image data and the second path of image data respectively to obtain first obstacle touching time data and second obstacle touching time data;
and sending the first barrier time data or the second barrier time data with the maximum time value.
5. The utility model provides an unmanned aerial vehicle keeps away barrier device which characterized in that includes:
the image acquisition module is used for acquiring path information and speed information of the unmanned aerial vehicle during flying and acquired multi-path image data;
the image extraction module is used for extracting image data in the same direction as the flight path according to the path information;
the obstacle target identification module is used for calling an image identification algorithm to judge whether the extracted image data has an obstacle target or not;
the depth calculation module is used for calling depth information of the image depth algorithm to the obstacle target if the obstacle target exists, wherein the depth information comprises space position information, relative distance information and shape information of the obstacle;
and the obstacle touching time calculation module is used for calculating obstacle touching time data according to the relative distance information and the speed information and sending the obstacle touching time data.
6. The apparatus of claim 5, wherein the image extraction module further comprises:
the positioning point obtaining submodule is used for obtaining the space coordinates of each positioning point in the path information and determining the path direction formed by a plurality of continuous adjacent positioning points;
and the calculation submodule is used for calculating an included angle between the path direction and the image center in each path of image data, and one path of image data with the smallest included angle is taken as the extracted image data.
7. The apparatus of claim 5, wherein the obstacle target identification module further comprises:
the sample pattern acquisition submodule is used for acquiring a barrier sample pattern;
the training submodule is used for transmitting the sample pattern to a neural network for training;
and the construction submodule is used for constructing an image recognition algorithm of the obstacle target.
8. The apparatus of claim 5, wherein the barrier time calculation module further comprises:
the image acquisition submodule is used for acquiring a first path of image data and a second path of image data and calling the image recognition algorithm to judge whether an obstacle target exists in the first path of image data and the second path of image data;
the obstacle touching time submodule is used for calling the image depth algorithm to calculate obstacle touching time from the first path of image data to an obstacle target in the second path of image data if both the first path of image data and the second path of image data exist, and obtaining first obstacle touching time data and second obstacle touching time data;
and the sending submodule is used for sending the first barrier time data or the second barrier time data with the maximum time value.
9. Electronic device, characterized in that it comprises a processor, a memory and a computer program stored on said memory and capable of running on said processor, said computer program, when executed by said processor, implementing the method according to any one of claims 1 to 4.
10. Computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method according to any one of claims 1 to 4.
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