Abstract
Lacking of floor plans is a fundamental obstacle to ubiquitous indoor location-based services. Recent work have made significant progress to accuracy, but they largely rely on slow crowdsensing that may take weeks or even months to collect enough data. In this chapter, we propose Knitter that can generate accurate floor maps by a single random user’s one-hour data collection efforts, and demonstrate how such maps can be used for indoor navigation. Knitter extracts high-quality floor layout information from single images, calibrates user trajectories, and filters outliers. It uses a multi-hypothesis map fusion framework that updates landmark positions/orientations and accessible areas incrementally according to evidences from each measurement. Our experiments on three different large buildings and 30+ users show that Knitter produces correct map topology, and 90-percentile landmark location and orientation errors of \(3\sim 5\,\mathrm{m}\) and \(4\sim 6^\circ \), comparable to the state of the art at more than \(20\times \) speed up: data collection can finish in about one hour even by a novice user trained just a few minutes.
Published Version
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