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LightGyro: A Batteryless Orientation Measuring Scheme Based on Light Reflection.

Authors :
Guo, Qing
Xie, Lei
Lu, Xinran
Bu, Yanling
Wang, Chuyu
Ye, Baoliu
Lu, Sanglu
Source :
ACM Transactions on Sensor Networks; Jul2024, Vol. 20 Issue 4, p1-23, 23p
Publication Year :
2024

Abstract

In industrial production, the orientation of facility components can indicate whether the facility is on a regular operating track. For example, when a component gets loose, the orientation variation of the component would exceed the normal range. A common approach for orientation measurement is to attach an inertial measurement unit (IMU) to the target device. However, the IMU requires additional power maintenance. This article presents LightGyro, a cheap and efficient batteryless scheme to measure the orientation, in which we attach a reflective film to the target device and use a camera to capture the light spot on the reflective film. The basic idea of LightGyro is to extract the light spots in the captured frame and use their pixel coordinates to infer the orientation. It is difficult to recognize a single light spot, because the spot lacks distinctive features. To solve the problem, we switch light sources on and off to regulate the appearance of light spots and utilize frame subtraction to extract light spots. The depth of field of light spot is lost in the process of camera projection, which is necessary for the orientation measurement. To address the issue, we propose a light array-based reflection model to extract the depth of field from the relative positions of multiple light spots. To the best of our knowledge, this is the first work to utilize reflection to measure orientation. Experiment results show that the orientation error of LightGyro decreases with the increasing length of the reflection route and the orientation error can achieve less than 1<superscript>ˆ</superscript>. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15504859
Volume :
20
Issue :
4
Database :
Complementary Index
Journal :
ACM Transactions on Sensor Networks
Publication Type :
Academic Journal
Accession number :
178625641
Full Text :
https://doi.org/10.1145/3597934