Introduction

Visual Indoor Localization

Resource efficient and Automated Image-based Indoor Localization

Qun Niu, Mingkuan Li, Suining He, Chengying Gao*, S.-H. Gary Chan and Xiaonan Luo

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

SweepLoc: Automatic Video-based Indoor Localization by Camera Sweeping

Mingkuan Li, Ning Liu*, Qun Niu, Chang Liu, S.-H. Gary Chan, Chengying Gao

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

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Geomagnetic Indoor Localization

Efficient Indoor Localization with Multiple Consecutive Geomagnetic Sequences

Hui Zhuang, Tao He, Qun Niu and Ning Liu*

Intro: We propose an efficient localization approach for continuous positioning scenes (most common in reality), which first utilize short geomagnetic sequences as input, alleviating high response time, and propose an efficient single position estimation model, taking advantage of modified transformer to estimate position for each independent short sequence. Noticing the temporal dependency and the spatial consistency constraint during continuous positioning, we further propose a joint position estimation model to capture the correlations among consecutive short sequences, achieving higher accuracy with multiple short sequences.

International Conference on Computer Communications and Networks (ICCCN, 2022)  (CCF-C)
[Paper]

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

Qun Niu, Tao He, Ning Liu, Suining He, Xiaonan Luo, Fan Zhou

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  (CCF-A)
[Paper]

Indoor Localization with Spatial and Temporal Representations of Signal Sequences

Tao He, Qun Niu, Suining He and Ning Liu

IEEE Global Communications Conference (GLOBECOM), 2019  (CCF-C)
[Paper]

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Wi-Fi Fingerprint-based Indoor Localization

Maxlifd: Joint Maximum Likelihood Localization Fusing Fingerprints and Mutual Distances

Suining He, S.-H. Gary Chan, Lei Yu and Ning Liu*

IEEE Transactions on Mobile Computing (TMC), 2019  (CCF-A)
[Paper]

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

Suining He, S.-H. Gary Chan, Lei Yu and Ning Liu*

IEEE Transactions on Mobile Computing (TMC), 2018  (CCF-A)
[Paper]

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Multi-modal Fusion-based Indoor Localization

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

Qun Niu, Ning Liu*, Jianjun Huang, Yangze Luo, Suining He, Tao He, S.-H. Chan and Xiaonan Luo

Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp), 2019  (CCF-A)
[Paper]

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Incrementive Reconstruction of Fingerprint Database

RecNet: A Convolutional Network for Efficient Radiomap Reconstruction

Q. Niu, Y. Nie, S. He, N. Liu and X. Luo

IEEE International Conference on Communications (ICC), 2018  (CCF-C)
[Paper]

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