Abstract

Abstract Context awareness is crucial for ubiquitous computing, and position is an important aspect of context. In an ideal world, every stationary object or entity in the built environment would be associated with position, so that applications can have precise spatial context about the environment surrounding a human. In this paper, we take a step towards this ideal: by analyzing images from Google Street View that cover different perspectives of a given object and triangulating the location of the object, our system, ALPS, can discover and localize common landmarks at the scale of a city accurately and with high coverage. ALPS contains several novel techniques that help improve the accuracy, coverage, and scalability of localization. Evaluations of ALPS on many cities in the United States show that it can localize storefronts with a coverage higher than 90% and a median error of 5 meters.