Multimodal Indoor Localization

MAIL: Multi-Scale Attention-Guided Indoor Localization Using Geomagnetic Sequences

Intro: We propose MAIL, a multi-scale attention-guided indoor localization network. Our key contributions are as follows. First, instead of extracting a single holistic feature from an input sequence directly, we design a scale-based feature extraction unit that takes variational anomalies at different scales into consideration. Second, we propose an attention generation scheme that identifies attention values for different scales. Rather than setting fixed numbers, MAIL learns them adaptively with the input sequence, thus increasing its adaptability and generality. Third, guided by attention values, we fuse multi-scale features by paying more attention to prominent ones and estimate current location with the fused feature.

Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp), 2020

DeepNavi: A Deep Signal-Fusion Framework for Accurate and Applicable Indoor Navigation

Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp), 2019

Maxlifd: Joint Maximum Likelihood Localization Fusing Fingerprints and Mutual Distances

IEEE Transactions on Mobile Computing (TMC), 2019

Indoor Localization with Spatial and Temporal Representations of Signal Sequences

IEEE Global Communications Conference (GLOBECOM), 2019

Resource efficient and Automated Image-based Indoor Localization

ACM Transactions on Sensor Networks (TOSN), 2019
[Introduction] [Paper]

SLAC: Calibration-Free Pedometer-Fingerprint Fusion for Indoor Localization

IEEE Transactions on Mobile Computing (TMC), 2018

RecNet: A Convolutional Network for Efficient Radiomap Reconstruction

IEEE International Conference on Communications (ICC), 2018

SweepLoc: Automatic Video-based Indoor Localization by Camera Sweeping

Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp), 2018
[Introduction] [Paper]

Back to top