Online deep learning SLAM based image cloud computing method and system

A deep learning and cloud computing technology, applied in the field of image processing research, can solve problems such as unsatisfactory effect, time-consuming, and incompleteness

Active Publication Date: 2018-11-30
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

In the existing technology, due to problems such as low sensor accuracy and large calculation load, it will take a lot of time, and it ...

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  • Online deep learning SLAM based image cloud computing method and system
  • Online deep learning SLAM based image cloud computing method and system
  • Online deep learning SLAM based image cloud computing method and system

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Embodiment

[0055] The flow of an image cloud computing method based on online deep learning SLAM is as follows: figure 1 shown, including the following steps:

[0056] The first step: the image data acquisition layer obtains the RGBD image and the depth image through the RGBD camera, collects the image data, and uses the image stream of the streaming media server to store the image data in the memory;

[0057] Step 2: extract key frames from the image data in the memory, and upload the key frames to the cloud computing platform;

[0058] Step 3: Build a data set from the historical data on the cloud computing platform, use MapReduce to train the convolutional neural network to train the data set, and obtain the optimal convolutional neural network parameters;

[0059] The MapReduce training convolutional neural network is trained on the data set, specifically: the input stage: the data to be processed is divided into fixed-size fragments, and each fragment is further decomposed into key...

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Abstract

The invention discloses an online deep learning SLAM based image cloud computing method. The image cloud computing method comprises the following steps: acquiring image data and storing the image data; extracting a key frame and uploading the key frame; using the image data to construct a data set and training the data set to obtain optimal convolutional neural network parameters; extracting real-time image feature points and performing recognition, and performing feature point matching on adjacent frame images; iterating the image feature points to obtain the best matching transformation matrix, performing correction by using position and pose information, and obtaining the camera pose transformation; obtaining the optimal pose estimation through registration of point cloud data and the position and pose information; transforming the pose information into a coordinate system through matrix transformation, and obtaining map information; repeating the previous steps in regions with insufficient precision; and allowing a client to display the result and performing online adjustment at the same time. The invention parallelizes image processing, deep learning training and SLAM by usingthe cloud computing technology to improve the efficiency and accuracy of image processing, positioning and mapping.

Description

technical field [0001] The invention relates to the field of image processing research, in particular to an image cloud computing method and system based on online deep learning SLAM. Background technique [0002] At present, with the development of mobile robots, people's demand for them is gradually increasing, such as: unmanned driving, sweeping robots, 3D printing, criminal investigation scene records, etc., which greatly facilitate people's lives, but at the same time there are Some new questions. In the existing technology, due to problems such as low sensor accuracy and large calculation load, it will take a lot of time, and it is not perfect, and the effect is not very ideal. The development of SLAM based on three-dimensional vision has encountered certain resistance. [0003] In recent years, deep learning has developed rapidly, and has achieved good results in chess games and some simulation games. The emergence of cloud computing has made it possible to collect ...

Claims

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Application Information

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IPC IPC(8): G06T7/73G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06T7/73G06T2207/20081G06T2207/20084G06N3/045G06F18/24
Inventor 李迪楚英王世勇杨啸
Owner SOUTH CHINA UNIV OF TECH
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