Abstract
The utility and adoption of indoor localization applications have been limited due to the complex nature of the physical environment combined with an increasing requirement for more robust localization performance. Existing solutions to this problem are either too expensive or too dependent on infrastructure such as Wi-Fi access points. To address this problem, we propose APFiLoc—a low cost, smartphone-based framework for indoor localization. The key idea behind this framework is to obtain landmarks within the environment and to use the augmented particle filter to fuse them with measurements from smartphone sensors and map information. A clustering method based on distance constraints is developed to detect organic landmarks in an unsupervised way, and the least square support vector machine is used to classify seed landmarks. A series of real-world experiments were conducted in complex environments including multiple floors and the results show APFiLoc can achieve 80% accuracy (phone in the hand) and around 70% accuracy (phone in the pocket) of the error less than 2 m error without the assistance of infrastructure like Wi-Fi access points.
Highlights
Location is the most crucial context in mobile and ubiquitous computing [1], and how to obtain and infer the location is the key to location-aware applications
We design a clustering method based on distance constraints to learn organic landmarks in an unsupervised way, while we use the least square support vector machine (LS-SVM) to classify seed landmarks, which has been proven as the more effective supervised classification method [30] compared to Decision Tree, Bayesian Network using the Gaussian Mixture
This paper presents an indoor positioning solution called APFiLoc that uses the Augmented Particle Filter (APF) to fuse readings of smartphone inertial sensors, map constraint, and landmarks
Summary
Location is the most crucial context in mobile and ubiquitous computing [1], and how to obtain and infer the location is the key to location-aware applications. Research on reducing the effort of collecting fingerprints has been done in [12,13] by modeling the constraints imposed by the physics of wireless propagation or by combining the signal characteristics with users’ movement Another popular technique for indoor localization is PDR (Pedestrian Dead Reckoning). To take advantage of the characteristics of complex indoor space and eliminate the reliance on Wi-Fi infrastructure, we develop an indoor localization solution called APFiLoc, which uses an augmented particle filter to integrate readings of inertial sensors, map information, and landmarks. Experimental results show that APFiLoc could achieve 80% accuracy (phone in the hand) and around 70% accuracy (phone in the pocket) of the error less than 2 m error without the assistance of Wi-Fi APs. This study postulates that a floor plan describing experimental areas is available, which we think does not need extra efforts in many cases since indoor maps are basic information to support location-based services.
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