AMID: Accurate Magnetic Indoor Localization Using Deep Learning

Abstract

Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics. However, it must overcome the problems of ambiguity that originate with the nature of geomagnetic data. Most studies manage this problem by incorporating particle filters along with inertial sensors. However, they cannot yield reliable positioning results because the inertial sensors in smartphones cannot precisely predict the movement of users. There have been attempts to recognize the magnetic sequence pattern, but these attempts are proven only in a one-dimensional space, because magnetic intensity fluctuates severely with even a slight change of locations. This paper proposes accurate magnetic indoor localization using deep learning (AMID), an indoor positioning system that recognizes magnetic sequence patterns using a deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying the sequences by patterns that are generated by nearby magnetic landmarks. Locations are estimated by detecting the landmarks. AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. The landmark detection accuracy was over 80% in a two-dimensional environment.

Mining Minimal Map-Segments for Visual Place Classifiers

“…It is planned to explore a more general framework for combining different segmentation cues to improve robustness against noises (e.g., occlusions, limited field of view, or background clutter) and other general-purpose image/video segmentation techniques [46]. The proposed MS framework automatically finds a compact set of landmarks (i.e., minimal map segments); however, the compactness might be improved by introducing landmark selection techniques [47].…”

Learning Sequence-Based Fingerprint for Magnetic Indoor Positioning System

“…A few magnetic-based positioning techniques [11]- [21] have been proposed due to the stability and uniqueness of the magnetic field. The work in [11] proposed an energyefficient indoor positioning method based on MFS sequence with an improved Dynamic Time Warping algorithm.…”

“…In addition, the MFS is robust to indoor multipath phenomena and without LOS operating conditions. A few magnetic-based positioning methods [11]- [21] have been proposed in the literatures due to these special properties (for a review sees Section 2). However, existing magnetic-based positioning methods suffer from two challenges.…”

Calibrating Recurrent Neural Networks on Smartphone Inertial Sensors for Location Tracking

“…Motion recognition from acceleration data has been modeled with simpler classifiers [4], and its been shown to be body attachment sensitive, which is hard to model accurately [17]. Orientation is commonly performed by combining magnetometer and gyroscope data, as presented by Huyghe et al [18] using a Kalman filter and using deep learning [19]. Unlike all these systems, we leave the mechanics of motion to be automatically discovered by the LSTM from data.…”