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

Overview of attention for article published in Sensors (14248220), October 2015
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Title
APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information
Published in
Sensors (14248220), October 2015
DOI 10.3390/s151027251
Pubmed ID
Authors

Jianga Shang, Fuqiang Gu, Xuke Hu, Allison Kealy

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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
Germany 1 3%
Unknown 35 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 27%
Student > Ph. D. Student 7 19%
Unspecified 5 14%
Researcher 5 14%
Student > Bachelor 4 11%
Other 6 16%
Readers by discipline Count As %
Computer Science 19 51%
Engineering 9 24%
Unspecified 5 14%
Business, Management and Accounting 2 5%
Psychology 1 3%
Other 1 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 October 2015.
All research outputs
#9,297,335
of 11,625,761 outputs
Outputs from Sensors (14248220)
#3,030
of 4,993 outputs
Outputs of similar age
#171,497
of 249,544 outputs
Outputs of similar age from Sensors (14248220)
#72
of 107 outputs
Altmetric has tracked 11,625,761 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,993 research outputs from this source. They receive a mean Attention Score of 2.5. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
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We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.