GraMap

Abstract

Recently, several approaches have been proposed to automatically model indoor environments. Most of such efforts principally rely on the crowd to sense data such as motion traces, images, and WiFi footprints. However, large datasets are usually required to derive precise indoor models which can negatively affect the energy efficiency of the mobile devices participating in the crowd-sensing system. Furthermore, the aforementioned data types are hardly suitable for deriving 3D indoor models. To overcome these challenges, we propose GraMap, a QoS-aware automatic indoor modeling approach through crowd-sensing 3D point clouds. GraMap exploits a recently-developed sensors fusion mechanism, namely Tango technology, to cooperatively collect point clouds from the crowd. Afterward, a set of backend servers extracts the required geometrical information to derive indoor models. For the sake of improving the energy efficiency of the mobile devices, GraMap performs data quality assurance along with 3D data compression. Specifically, we propose a probabilistic quality model---implemented on the mobile devices---to ensure high-quality of the captured point clouds. In this manner, we conserve energy via sidestepping the repetition of sensing queries due to uploading low-quality point clouds. Nevertheless, the resultant indoor models may still suffer from incompleteness and inaccuracies. Therefore, GraMap leverages formal grammars which encode design-time knowledge, i.e. structural information about the building, to enhance the quality of the derived models. To demonstrate the effectiveness of GraMap, we implemented a crowd-sensing Android App to collect point clouds from volunteers. We show that GraMap derives highly-accurate models while reducing the energy costs of pre-processing and reporting the point clouds.