SISE: Self-Updating of Indoor Semantic Floorplans for General Entities

Abstract

Indoor semantic floorplan is important for a range of location based service (LBS) applications, attracting many research efforts in several years. In many cases, the out-of-date indoor semantic floorplans would gradually deteriorate and even break down the LBS performance. Thus, it is important to automatically update changed semantics of indoor floorplans caused by environmental variation. However, few research has been focused on the continuous semantic updating problem. This paper presents SISE as a mobile crowdsourcing system that uses a new abstraction for indoor general entities and their semantics, enGraph, to automatically update changed semantics of indoor floorplans using images and inertial data. We first propose efficient methods to generate enGraph. Thus, an image can be associated with an indoor semantic floorplan. Accordingly, we formulate the enGraph matching problem and then propose a quality-based maximum common subgraph matching algorithm so that entities extracted from an image can be corresponded to entities in the indoor semantic floorplan. Furthermore, we propose a quadrant comparison algorithm and a region shrink based localization algorithm to detect and localize changed entities. Thus, the new semantics can be labeled and out-of-date semantics can be removed. Extensive experiments have been conducted on real and synthetic data. Experimental results show that 80 percent of out-of-date semantics of indoor general entities can be updated by SISE.