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A visual-inertial fusion navigation system and method based on θ-incremental learning

A technology of incremental learning and integrated navigation, applied in the field of navigation, can solve problems such as the frequency limitation of integrated navigation, and achieve the effects of good practical performance, strong stability, and high navigation accuracy

Active Publication Date: 2022-05-13
WUHAN UNIV
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Problems solved by technology

Although VINet proposed by Clark et al. [Document 10] also regards visual-inertial fusion navigation as a sequence-to-sequence regression problem, the frequency of its fusion navigation is still limited by the low-frequency data flow

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  • A visual-inertial fusion navigation system and method based on θ-incremental learning
  • A visual-inertial fusion navigation system and method based on θ-incremental learning
  • A visual-inertial fusion navigation system and method based on θ-incremental learning

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Embodiment Construction

[0065] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0066]Aiming at the problem that existing navigation methods are difficult to achieve high precision, high frequency, strong stability and low cost at the same time, considering the complementary advantages of visual navigation and inertial navigation, the present invention discloses a novel visual-inertial fusion navigation system. The system uses high-precision visual navigation as the main navigation method, and the inertial acquisition unit with high sampling frequency is used as an auxiliary to make up for the defect of low visual navigation frequency. A...

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Abstract

The invention discloses a visual-inertial fusion navigation system and method based on θ-incremental learning. A θ-incremental learning method for networks that uses cascaded networks to estimate the increment of the temporal variable θ in a shared parameter manner. Using this θ-incremental learning method, the present invention constructs a cascaded network for incremental estimation of attitude data and a nested cascaded network for incremental estimation of position data for visual-inertial fusion navigation, and utilizes the trained network to achieve high Navigation with precision, high frequency and strong stability. The present invention uses visual navigation as a leading factor to ensure high navigation accuracy, and inertial navigation as an auxiliary, which not only helps to make up for the defect of low frequency of visual navigation, but also solves the problem of unstable visual navigation caused by occlusion.

Description

technical field [0001] The invention belongs to the field of navigation technology, and relates to a visual-inertial fusion navigation system and method, in particular to a method of using deep learning technology to construct a cascaded network based on θ-incremental learning to achieve high precision, high frequency and strong stability Fusion navigation system and method. Background technique [0002] Navigation technology can be used to guide moving targets to reach the destination safely and accurately along the selected route. This technology is one of the key technologies in the fields of mobile robots, autonomous driving, and aerospace. In the field of augmented reality, fast and accurate navigation is also an important link to connect real scenes and virtual scenes and provide an immersive interactive experience. Especially for visual and tactile interactive applications that require high real-time performance and precision, navigation with high precision, high fre...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C21/16G01C21/20
CPCG01C21/165G01C21/20
Inventor 袁志勇童倩倩李潇洒
Owner WUHAN UNIV
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