Cross-terminal picture transmission method and system

A picture transmission and cross-terminal technology, applied in the field of data transmission, can solve problems such as inoperability, poor compatibility, and real-time picture acquisition, and achieve low-latency transmission, high versatility, and easy deployment.

Active Publication Date: 2021-07-23
SHANDONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The inventors found that ROS itself supports cross-terminal data transmission between nodes, but many Python3-based deep learning programs have poor compatibility with ROS, and it is difficult for Python3 deep learning programs to directly obtain real-time images from ROS data streams through ROS communication.
This is mainly due to the following reasons: (1) The Python version natively supported in the most popular ROS version is Python2, and many packages of Python3 cannot be perfectly adapted to ROS, or even cannot run directly; (2) Based on the depth of Python3 Learning general programs is complex and has many dependencies, and it is difficult to refactor into Python2 programs; (3) In the ROS framework, each Python node shares the same operating environment, which is difficult to achieve environmental protection; and deep learning programs usually require a variety of operating dependencies And the version is different, it is necessary to protect the environment of different deep learning programs
The above three points have brought great difficulties to the application of server-side deep learning programs to ROS-based robots.

Method used

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  • Cross-terminal picture transmission method and system

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Embodiment 1

[0051] Such as Figure 1-Figure 5 As shown, Embodiment 1 of the present disclosure provides a method for high-speed cross-terminal image transmission from ROS data flow to a Python3-based deep learning program. This method can realize the low-latency cross-terminal transmission of the ROS image data stream on the robot computer and the deep learning program on the server; it can protect the Python3 environment of deep learning from any conflict with the public Python2 environment of ROS, and can be applied to Various deep learning programs with different operating environments have high versatility; the deployment of this method can be completed with minor changes to the deep learning program, no code refactoring, no need to care about the startup sequence, easy to deploy and use .

[0052] The method of this embodiment adopts the mode of image storage and reading based on the hard disk to solve the data flow in ROS and the image transmission problem of the deep learning prog...

Embodiment 2

[0113]Embodiment 2 of the present disclosure provides a cross-terminal picture transmission system, including: a first terminal and a second terminal, the first terminal is equipped with a robot operating system, and the second terminal is equipped with a preset deep learning network model;

[0114] The data forwarding node on the second terminal stores pictures from the data flow of the robot operating system to the exchange space in real time according to the image dislocation storage method, and the deep learning program of the preset deep learning network model reads from the exchange space according to the image dislocation reading method picture and process it.

[0115] The working method of the system is the same as the method provided in Embodiment 1, and will not be repeated here.

[0116] Those skilled in the art should understand that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Accordingly, the presen...

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Abstract

The invention provides a cross-terminal picture transmission method and system, which are applied to a first terminal and a second terminal, wherein the first terminal carries a robot operation system, and the second terminal carries a preset deep learning network model. Comprising the following steps: a data forwarding node on a second terminal stores a picture from a data stream of a robot operating system to an exchange space in real time according to a picture dislocation storage mode, and a deep learning program of a preset deep learning network model reads and processes the picture from the exchange space according to a picture dislocation reading mode. According to the invention, a picture dislocation storage mode and a picture dislocation reading mode are provided, file read-write conflicts in the high-speed data transmission process can be avoided, and file read and write are operated in parallel in different processes, so that final low-delay transmission is ensured.

Description

technical field [0001] The present disclosure relates to the technical field of data transmission, and in particular to a method and system for cross-terminal picture transmission. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] ROS (Robot Operating System, robot operating system) has been widely used in robot development, providing communication framework and dependency packages for various programs of robots. Using the ROS framework can efficiently control various parts of the robot and run various robot algorithms. ROS supports multiple programming languages ​​and realizes efficient communication between different programming language scripts. The two best supported programming languages ​​are Python and C++, and the program runs in the robot as a ROS node. In the next few years, ROS will still be the mainstream framework for robot ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/51G06F16/535G06N3/04
CPCG06F16/51G06F16/535G06N3/04
Inventor 田国会邵旭阳张营
Owner SHANDONG UNIV
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