Abstract
Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Fusing spatial information is an effective way to achieve accurate indoor localization with little or with no need for extra hardware. However, existing indoor localization methods that make use of spatial information are either too computationally expensive or too sensitive to the completeness of landmark detection. In this paper, we solve this problem by using the proposed landmark graph. The landmark graph is a directed graph where nodes are landmarks (e.g., doors, staircases, and turns) and edges are accessible paths with heading information. We compared the proposed method with two common Dead Reckoning (DR)-based methods (namely, Compass + Accelerometer + Landmarks and Gyroscope + Accelerometer + Landmarks) by a series of experiments. Experimental results show that the proposed method can achieve 73% accuracy with a positioning error less than 2.5 meters, which outperforms the other two DR-based methods.
Highlights
The advent of sensor-equipped smartphones has enabled a wide range of applications such as museum and shopping guides (Bihler et al, 2011; Shang et al, 2011), emergency response (Renau et al, 2007), personal task reminder (Lin & Hung, 2014), asset tracking (Boustani et al, 2011), search and rescue (Zorn et al, 2010), advertising (Dhar & Varshney, 2011; Dao et al, 2012), and location-enabled social networks (Cho et al, 2011)
The proposed indoor localization solution was evaluated by a series of experiments conducted within the Infrastructure Engineering building located at the Parkville campus of the University of Melbourne and the surroundings of this building
Virtual Markers by Interpolating Timeline of Markers Timeline of Estimated Locations pi+1 p'i-3 p'i-2 p'i-1 p'i p'i+1. It is a prerequisite for Dead Reckoning (DR)-based localization methods to know the initial location, which can be done by manual input of users or by using other localization systems like WiFi fingerprinting-based systems
Summary
The advent of sensor-equipped smartphones has enabled a wide range of applications such as museum and shopping guides (Bihler et al, 2011; Shang et al, 2011), emergency response (Renau et al, 2007), personal task reminder (Lin & Hung, 2014), asset tracking (Boustani et al, 2011), search and rescue (Zorn et al, 2010), advertising (Dhar & Varshney, 2011; Dao et al, 2012), and location-enabled social networks (Cho et al, 2011). The popularity of smart devices equipped with inertial sensors enables DR to be widely used It is especially useful for localization and tracking in the wireless signal denied areas. Combining DR with other absolute positioning techniques such as WiFi (Jin et al, 2013) and UWB (De Angelis et al, 2010) can eliminate both the accumulated location error of DR and the jumping estimations by absolute positioning techniques for a short time. These absolute localization techniques are not always available and often need to spend extra cost on the deployment and maintenance
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