{"title":"Luxapose: indoor positioning with mobile phones and visible light","authors":"Ye-Sheng Kuo, P. Pannuto, Ko-Jen Hsiao, P. Dutta","doi":"10.1145/2639108.2639109","DOIUrl":null,"url":null,"abstract":"We explore the indoor positioning problem with unmodified smartphones and slightly-modified commercial LED luminaires. The luminaires-modified to allow rapid, on-off keying-transmit their identifiers and/or locations encoded in human-imperceptible optical pulses. A camera-equipped smartphone, using just a single image frame capture, can detect the presence of the luminaires in the image, decode their transmitted identifiers and/or locations, and determine the smartphone's location and orientation relative to the luminaires. Continuous image capture and processing enables continuous position updates. The key insights underlying this work are (i) the driver circuits of emerging LED lighting systems can be easily modified to transmit data through on-off keying; (ii) the rolling shutter effect of CMOS imagers can be leveraged to receive many bits of data encoded in the optical transmissions with just a single frame capture, (iii) a camera is intrinsically an angle-of-arrival sensor, so the projection of multiple nearby light sources with known positions onto a camera's image plane can be framed as an instance of a sufficiently-constrained angle-of-arrival localization problem, and (iv) this problem can be solved with optimization techniques. We explore the feasibility of the design through an analytical model, demonstrate the viability of the design through a prototype system, discuss the challenges to a practical deployment including usability and scalability, and demonstrate decimeter-level accuracy in both carefully controlled and more realistic human mobility scenarios.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"584","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 20th annual international conference on Mobile computing and networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2639108.2639109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 584
Abstract
We explore the indoor positioning problem with unmodified smartphones and slightly-modified commercial LED luminaires. The luminaires-modified to allow rapid, on-off keying-transmit their identifiers and/or locations encoded in human-imperceptible optical pulses. A camera-equipped smartphone, using just a single image frame capture, can detect the presence of the luminaires in the image, decode their transmitted identifiers and/or locations, and determine the smartphone's location and orientation relative to the luminaires. Continuous image capture and processing enables continuous position updates. The key insights underlying this work are (i) the driver circuits of emerging LED lighting systems can be easily modified to transmit data through on-off keying; (ii) the rolling shutter effect of CMOS imagers can be leveraged to receive many bits of data encoded in the optical transmissions with just a single frame capture, (iii) a camera is intrinsically an angle-of-arrival sensor, so the projection of multiple nearby light sources with known positions onto a camera's image plane can be framed as an instance of a sufficiently-constrained angle-of-arrival localization problem, and (iv) this problem can be solved with optimization techniques. We explore the feasibility of the design through an analytical model, demonstrate the viability of the design through a prototype system, discuss the challenges to a practical deployment including usability and scalability, and demonstrate decimeter-level accuracy in both carefully controlled and more realistic human mobility scenarios.