Fusion of Magnetic and Visual Sensors for Indoor Localization: Infrastructure-Free and More Effective

By Zhenguang Liu, Luming Zhang, Qi Liu, Yifang Yin, Li Cheng, Roger Zimmermann. 2017

114 citations recorded by the source corpus.

Accurate and infrastructure-free indoor positioning can be very useful in a variety of applications. However, most existing approaches (e.g., WiFi and infrared-based methods) for indoor localization heavily rely on infrastructure, which is neither scalable nor pervasively available. In this paper, we propose a novel indoor localization and tracking approach, termed VMag, that does not require any infrastructure assistance. The user can be localized while simply holding a smartphone. To the best of our knowledge, the proposed method is the first exploration of fusing geomagnetic and visual sensing for indoor localization. More specifically, we conduct an in-depth study on both the advantageous properties and the challenges in leveraging the geomagnetic field and visual images for indoor localization. Based on these studies, we design a context-aware particle filtering framework to track the user with the goal of maximizing the positioning accuracy. We also introduce a neural-network-based method to extract deep features for the purpose of indoor positioning. We have conducted extensive experiments on four different indoor settings including a laboratory, a garage, a canteen, and an office building. Experimental results demonstrate the superior performance of VMag over the state of the art with these four indoor settings.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Ala Al-Fuqaha
2. Mohsen Guizani

In Electrical and Electronic Engineering

1. Ala Al-Fuqaha
2. M. Mohammadi
3. Mohsen Guizani
4. Sameh Sorour

In Ocean Engineering

1. Ala Al-Fuqaha
2. Mohsen Guizani
3. M. Mohammadi

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “In [95], a CNN is used for indoor localization by fusion of both magnetic and visual sensing data.”