SweepLoc

Abstract

Indoor localization based on visual landmarks has received much attention in commercial sites with rich features (e.g., shopping malls, museums) recently because landmarks are relatively stable over a long time. Prior arts often require a user to take multiple independent images around his/her location, and manually confirm shortlisted landmarks. The process is sophisticated, inconvenient, slow, unnatural and error-prone. To overcome these limitations, we propose SweepLoc, a novel, efficient and automatic video-based indoor localization system. SweepLoc mimics our natural scanning around to identify nearby landmarks in an unfamiliar site to localize. In SweepLoc, a user simply takes a short video clip (about 6 to 8 seconds) of his/her surroundings by sweeping the camera. Using correlation and scene continuity between successive video frames, it automatically and efficiently selects key frames (where potential landmarks are centered) and subsequently reduces the decision error on landmarks. With identified landmarks, SweepLoc formulates an optimization problem to locate the user, taking compass noise and floor map constraint into account. We have implemented SweepLoc in Android platform. Our extensive experimental results in a food plaza and a premium mall demonstrate that SweepLoc is fast (less than 1 second to localize), and achieves substantially better accuracy as compared with the state-of-the-art approaches (reducing the localization error by 30%).