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

GC-Loc: A Graph Attention Based Framework for Collaborative Indoor Localization Using Infrastructure-free Signals

Tao He, Qun Niu and Ning Liu

Intro: We introduce the collaboration mechanism and propose a graph attention based collaborative indoor localization framework, termed GC-Loc, which provides another perspective for efficient indoor localization. GC-Loc utilizes multiple discrete signal fingerprints collected by several users as input for collaborative localization. Specifically, we first construct an adaptive graph representation to efficiently model the relationships among the collaborative fingerprints. Then taking state-of-the-art GAT model as basic unit, we design a deep network with the residual structure and the hierarchical attention mechanism to extract and aggregate the features from the constructed graph for collaborative localization. Finally, we further employ ensemble learning mechanism in GC-Loc and devise a location refinement strategy based on model consensus for enhancing the robustness of GC-Loc.

Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp), 2023  (CCF-A)
[Paper]

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

Fast Radio Map Construction with Domain Disentangled Learning for Wireless Localization

Weina Jiang, Lin Shi, Qun Niu and Ning Liu*

Intro: We propose an accurate and generalizable framework for efficient radio map construction, which takes advantage of readily-available fine-grained radio maps and constructs fine-grained radio maps of a new site with a small proportion of measurements in it. Specifically, we regard radio maps as domains and propose a Radio Map construction approach based on Domain Adaptation (RMDA). We first employ the domain disentanglement feature extractor to learn domain-invariant features for aligning the source domains (available radio maps) with the target domain (initial radio map) in the domain-invariant latent space. Furthermore, we propose a dynamic weighting strategy, which learns the relevancy of the source and target domain in the domain adaptation. Then, we extract the domain-specific features based on the site's floorplan and use them to constrain the super-resolution of the domain-invariant features.

Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp), 2023  (CCF-A)
[Paper]

Adaptive Radio Map Reconstruction via Adversarial Wireless Fingerprint Learning

Weina Jiang, Qun Niu, Suining He and Ning Liu*

Intro: We propose a Radio Map Reconstruction framework (RMRec), which adopts adversarial learning to efficiently reconstruct the latest radio map with new signal samples collected at a small portion of Reference Points (RPs). The reconstruction model we built reveals the inherent spatial relations of the Wi-Fi signals in a large-scale building structure and by which the coarse-grained radio map is mapped into the corresponding fine-grained one, thus reducing the cost of the site survey significantly. The adversarial mechanism in RMRec enhances the textural features of the updated radio map, consequently improving the localization service. Meanwhile, we employ the scene-constrained downsample method and the CutPaste data augmentation to improve our model’s reconstruction accuracy and transferability. Besides, we design a non-uniform sampling strategy to reduce the site survey cost by allocating different selection rates for each subarea according to its anti-noise ability for location service.

Neural Computing and Applications (NCA), 2023  (中科院 2区)
[Paper]

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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