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Pose estimation method and device based on deep neural network

A deep neural network and pose estimation technology, applied in the field of artificial intelligence, can solve problems such as high sampling rate and small displacement of the measured object, and achieve the effect of reducing computational complexity

Inactive Publication Date: 2019-11-19
BEIJING UNIV OF POSTS & TELECOMM
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  • Application Information

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Problems solved by technology

However, this method requires a higher sampling rate, that is, the displacement of the measured object is required to be small

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  • Pose estimation method and device based on deep neural network
  • Pose estimation method and device based on deep neural network
  • Pose estimation method and device based on deep neural network

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Embodiment Construction

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0048] In order to solve the technical problem of high computational complexity in the field of visual odometry, embodiments of the present invention provide a pose estimation method, device, electronic device, and computer-readable storage medium based on a deep neural network. The following first introduces the pose estimation method based on the deep neural network provided by the embodiment of the present invention.

[0049] See figure...

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Abstract

The embodiment of the invention provides a pose estimation method and device based on a deep neural network, and the method comprises the steps: obtaining a target image sequence which is continuouslyphotographed by a camera in movement; inputting the target image sequence into a pose estimation network model to obtain pose information when the camera shoots each image in the target image sequence; wherein the pose estimation network model is trained in advance according to a training set, and the training set comprises a sample image sequence and sample pose information. Since the deep neural network model is adopted to estimate the pose information of the camera, key point extraction and descriptor calculation processes are not needed, the calculation complexity is reduced, and in addition, the method has no strict requirements on the measured object and can be suitable for a scene with large displacement of the measured object.

Description

Technical field [0001] The present invention relates to the field of artificial intelligence technology, in particular to a method and device for posture estimation based on a deep neural network. Background technique [0002] With the development of artificial intelligence, visual odometry has also been widely used in robotics and autonomous driving technology. In layman's terms, a camera is rigidly connected to a moving object, such as a robot, and a series of continuous image sequences taken by the camera to infer the pose information of the camera is the visual odometer. It is easy to understand that because the camera and the robot are rigidly connected, the pose information of the camera can also reflect the pose information of the robot. [0003] If only one camera is used, it is called a monocular vision odometer, and if multiple cameras are used, it is called a stereo vision odometer. [0004] At present, in the field of visual odometry, there are mainly two methods for vi...

Claims

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Application Information

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IPC IPC(8): G06T7/70
CPCG06T2207/10016G06T2207/20081G06T2207/20084G06T2207/30244G06T7/70
Inventor 焦继超焦剑邓中亮莫耀凯刘炜伦袁华宇邱德武
Owner BEIJING UNIV OF POSTS & TELECOMM