Indoor Navigation Method Based on Deep Learning

A technology of indoor navigation and deep learning, which is applied in the field of indoor navigation based on deep learning, can solve problems such as technology cannot be invested, and achieve the effects of reducing costs, improving ease of use, and avoiding investment

Active Publication Date: 2018-04-10
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, for the above three indoor navigation technologies, on the one hand, it requires a lot of investment in physical equipment, and on the other hand, it also increases the requirements for users' mobile devices. These problems prevent these technologies from being put into a large number of practical applications.

Method used

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  • Indoor Navigation Method Based on Deep Learning
  • Indoor Navigation Method Based on Deep Learning
  • Indoor Navigation Method Based on Deep Learning

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Embodiment Construction

[0027] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0028] The method for indoor navigation based on deep learning according to an embodiment of the present invention will be described below with reference to the accompanying drawings.

[0029] figure 1 It is a flow chart of an indoor navigation method based on deep learning according to an embodiment of the present invention. Such as figure 1 As shown, the method includes the following steps:

[0030] Step S1: Collect images in a preset area, and record the current location information corresponding to the collected pictures.

[0031...

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Abstract

The invention provides an indoor navigation method based on deep learning, wherein the indoor navigation method includes the steps: collecting images in a preset area, and recording current position information of the collected images; processing the images to obtain grayscale images with a predetermined size; training the grayscale images with the predetermined size, so as to obtain a deep auto-encoder; encoding all the obtained grayscale images with the predetermined size, to obtain a first encoding result; acquiring a new collected image, and processing into a grayscale image with a predetermined size; encoding the grayscale image with the predetermined size corresponding to the new collected image, to obtain a second encoding result; matching the second encoding result with the first encoding result, to obtain a target image corresponding to encoding with highest matching level, and acquiring target position information of the target image; and comparing the target position information and the current position information, and determining a user travelling route according to the comparison result. The method has the advantages of low cost and high usability.

Description

technical field [0001] The invention relates to the technical field of deep learning and positioning, in particular to an indoor navigation method based on deep learning. Background technique [0002] The rapid development of mobile Internet makes location-based services possible. The traditional positioning method is mainly through the Global Positioning System (Global Positioning System, GPS) equipped on the mobile device, or through the network of the mobile telecommunications operator, that is, the Global System for Mobile Communications (GSM) for positioning. These two positioning methods are now widely used in the field of outdoor positioning and navigation. However, the GPS signal is extremely weak inside large buildings, so the positioning effect is not ideal, and its function is almost invalid for navigation inside complex buildings; on the other hand, the positioning accuracy of the operator's network positioning itself is relatively poor , so the indoor position...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C21/20
CPCG01C21/206
Inventor 靳晓明何涛
Owner TSINGHUA UNIV
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