Camera-based positioning system using learning

Abstract

A device, system, and methods are described to perform machine-learning camera-based indoor mobile positioning. The indoor mobile positioning may utilize inexact computing, wherein a small decrease in accuracy is used to obtain significant computational efficiency. Hence, the positioning may be performed using a smaller memory overhead at a faster rate and with lower energy cost than previous implementations. The positioning may not involve any communication (or data transfer) with any other device or the cloud, providing privacy and security to the device. A hashing-based image matching algorithm may be used which is cheaper, both in energy and computation cost, over existing state-of-the-art matching techniques. This significant reduction allows end-to-end computation to be performed locally on the mobile device. The ability to run the complete algorithm on the mobile device may eliminate the need for the cloud, resulting in a privacy-preserving localization algorithm by design since network communication with other devices may not be required.

Description

GOVERNMENT RIGHTS IN INVENTION[0001]This invention was made with government support under Grant No. FA8750-16-2-0004 awarded by the Department of Defense-Air Force Research Laboratory (DoD-AFRL). The government has certain rights in the invention.FIELD OF THE INVENTION[0002]The present invention relates to the field of wireless devices, and more particularly to camera-based positioning systems for wireless devices.DESCRIPTION OF THE RELATED ART[0003]As mobile electronic devices become increasingly interwoven in a user's life, there have arisen a multitude of situations in which it may be desirable to locate the position of the device. Localization technology to accomplish this task is an active area of research. Many existing implementations rely on communication using satellite-based positioning technology (e.g., GPS or other technology). However, in some indoor situations communication with a remote satellite may prove difficult. Camera-based localization typically requires substa...

AI Technical Summary

Benefits of technology

[0004]A device, system, and methods are described to perform machine-learning camera-based indoor mobile positioning. The indoor mobile positioning may utilize inexact computing, wherein a small decrease in accuracy is used to obtain significant computational efficiency. Hence, the positioning may be performed using a smaller memory overhead at a faster rate and with lower energy cost than previous implementations. The positioning may not involve any communication (or data transfer) with any other device or the cloud, providing privacy and security to the device. A hashing-based image matching algorithm may be used which is cheaper, both in energy and computation cost, over existing state-of-the-art matching techniques. This significant reduction allows end-to-end computation to be performed locally on the mobile device. The ability to run the complete algorithm on the mobile device may eliminate the need for the cloud, resulting in a privacy-preserving localization algorithm by design since network communication with other devices may not be required.

Problems solved by technology

However, in some indoor situations communication with a remote satellite may prove difficult.

Camera-based localization typically requires substantial time and computational resources to perform effectively, and may further require that the computation be performed remotely (e.g., in the cloud).

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