IFrame: Dynamic indoor map construction through automatic mobile sensing

Abstract

Many pervasive computing applications depend upon maps for navigation and support of location based services. Maps are commonly available for outdoor pervasive applications from a variety of sources. An individual can determine their location outdoors on these maps via GPS. Indoor pervasive applications may also need to know the layout of rooms, doorways and hallways of buildings, and the objects and obstacles within them, however indoor maps of buildings are less prevalent. Moreover, indoor maps may need to be dynamic and updated regularly since the layout changes when objects and obstacles are added or removed by people within the building. In this paper, we present iFrame, a dynamic approach that leverages existing mobile sensing capabilities for constructing indoor floor plans. We explore how iFrame users may collaborate and contribute to constructing 2-dimensional indoor maps by merely carrying smartphones or other mobile devices, and to allow their mobile devices to share information with other users' devices. The iFrame approach consists of four steps: 1) Abstract the unknown indoor map as a matrix; 2) Leverage collaborating mobile devices that incorporate three mobile sensing technologies — accelerometers to support dead reckoning, Bluetooth RSSI detection, and WiFi RSSI detection; 3) Combine the three methods by Curve Fit Fusion (CFF), and 4) Extend iFrame from one room to a whole building by shadow rates and anchor points analysis. We conducted a deployment study that shows iFrame is a light-weight and unattended approach that provides a skeleton map of a real building effectively and automatically. The layouts of 12 rooms are reconstructed within 5–10 minutes. Changes of layout in indoor maps can be detected and the resolution of the reconstructed indoor floor plans can be improved when there is an increase in the number of cooperating users.