Next Article in Journal
In-Line Measurement of Water Contents in Ethanol Using a Zeolite-Coated Quartz Crystal Microbalance
Next Article in Special Issue
A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor Positioning
Previous Article in Journal
Step Detection Robust against the Dynamics of Smartphones
Previous Article in Special Issue
Hyperbolic Positioning with Antenna Arrays and Multi-Channel Pseudolite for Indoor Localization
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(10), 27251-27272; doi:10.3390/s151027251

APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information

1
Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China
2
National Engineering Research Center for Geographic Information System, Wuhan 430074, China
3
Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Sisi Zlatanova
Received: 3 September 2015 / Revised: 19 October 2015 / Accepted: 19 October 2015 / Published: 26 October 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
View Full-Text   |   Download PDF [1609 KB, uploaded 26 October 2015]   |  

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. View Full-Text
Keywords: indoor localization; infrastructure-free; pedestrian dead reckoning; augmented particle filter; unsupervised clustering; landmark recognition indoor localization; infrastructure-free; pedestrian dead reckoning; augmented particle filter; unsupervised clustering; landmark recognition
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Shang, J.; Gu, F.; Hu, X.; Kealy, A. APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information. Sensors 2015, 15, 27251-27272.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top