Real-time indoor localization using smartphone magnetic with LSTM networks

Zhang, Mingyang; Jia, Jie; Chen, Jian; Yang, Leyou; Guo, Liang; Wang, Xingwei

doi:10.1007/s00521-021-05774-5

Cited by 25 publications

(12 citation statements)

References 41 publications

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“…Many scholars have been devoted to finding a solution to the indoor localization problem by combining recurrent neural network (RNN) models with sensor data. Zhang et al [6] modeled the magnetic-based localization as a recursive function approximation problem. Long short-term memory networks (LSTMs) were trained by the time-series magnetic feature dataset to output a smartphone's location.…”

Section: B Indoor Localizationmentioning

confidence: 99%

“…A portable smartphone has superior environmental perception due to the assistance of multiple sensors, such as cameras [5], inertial measurement units (IMUs), geomagnetic [6], Wi-Fi, and Bluetooth low energy (BLE). The sensed information reflects the spatial layout of an unknown indoor environment because a user's trajectory is related to various information, including semantics, Wi-Fi fingerprints, and payment data.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Sensor Fusion-Based Indoor Single-Track Semantic Map Construction and Localization

Chai

2023

IEEE Sensors J.

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The advancement of smartphones has been a prerequisite for multi-sensor fusion-based indoor mapping due to their high penetration and low cost. This paper constructs a sparse single-track semantic map by fusing semantic landmarks, Wi-Fi landmarks, and waypoints, which are scene-representable and navigational in unknown indoor environments. The pedestrian dead reckoning (PDR)-aided visual-inertial simultaneous localization and mapping (PDR-aided VI-SLAM) method uses the PDR velocity as an external observation to constrain the preintegration of inertial measurement unit (IMU) measurements. The semantic objects detected by the YOLO V4 are prefiltered by the proposed semantic object filtering algorithm before semantics matching. The centroid of feature points clustered by the density-based spatial clustering of applications with noise (DBSCAN) algorithm characterizes the location of a semantic landmark relative to the waypoints estimated by the PDR-aided VI-SLAM. To enhance the stability of Wi-Fi landmarks, Wi-Fi signals are processed by the proposed sliding window-based Wi-Fi fusion algorithm. By using waypoints as an intermediate quantity, the association relationships between waypoints, semantic landmarks, and Wi-Fi landmarks are established. After that, a lightweight single-track semantic map is constructed. A landmark matching-based localization method is proposed to evaluate the similarity between the local map and the constructed scene map to infer the location of a pedestrian in the scene map. Experiments are conducted in the office building and mall scenes under various illumination conditions and semantic density. Results demonstrate the high quality of the single-track semantic map and high-precision localization of a local map in the constructed scene map.

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Section: B Indoor Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion-Based Indoor Single-Track Semantic Map Construction and Localization

Chai

2023

IEEE Sensors J.

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“…Zhang et al [219] proposed an indoor magnetic localization algorithm for smartphones based on LSTMs that exploits the potential predictive power of LSTMs to solve the indoor magnetic localization problem, thus avoiding the time-consuming fingerprint matching localization. A double sliding window-based dimension expansion scheme was applied to preprocess magnetic data to overcome the low discernibility problem of magnetic signals.…”

Section: Comparison Of Different Indoor Positioning Techniquesmentioning

confidence: 99%

A Survey of Magnetic-Field-Based Indoor Localization

Ouyang

Abed-Meraim

2022

Electronics

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Magnetic fields have attracted considerable attention in indoor localization due to their ubiquitous and infrastructure-free characteristics. This survey provides a comprehensive review of magnetic-field-based indoor localization methods. We first introduce characteristics of the magnetic field, its advantages, and its challenges. We then describe the magnetometer model and the effect of ferromagnetic interference. We also present coordinate systems commonly used for magnetic field localization and describe their transformation relationships. We then compare the existing publicly available magnetic field benchmark datasets, present magnetometer calibration algorithms, and show how efficiently magnetic field maps can be built. We also summarize state-of-the-art magnetic field localization methods (e.g., magnetic landmarks, dynamic time warping, magnetic fingerprinting, filters, simultaneous localization and mapping, and neural network). The smartphone-based pedestrian dead reckoning approach is also reviewed.

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“…Scholars have proposed many sensor-based mapping solutions, such as lidar-based [7], camera-based [8], Wi-Fi-based, inertial measurement unit (IMU)-based, and magneticbased [9], to solve the mapping problem in unknown indoor environments. Due to these sensors' inherent characteristics, single-sensor-based map construction methods have limited application scopes.…”

Section: Introductionmentioning

confidence: 99%

“…Fusing multiple single-track maps is a feasible solution for constructing high-accuracy and wide-coverage venue maps, where the raw sensor data are collected by a new sensing paradigm, crowdsourcing [12]. Smartphones, which are portable smart terminals, have superior environmental perception thanks to an array of built-in sensors, including cameras [13], IMUs, Wi-Fi signal receivers, magnetometers [9], and photoelectric encoders. Due to the prevalence of smartphones, the efficiency of crowdsourcing data collection is also enhanced.…”

Section: Introductionmentioning

confidence: 99%

Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization

Chai

Yang

et al. 2022

Sensors

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The advancement of smartphones with multiple built-in sensors facilitates the development of crowdsourcing-based indoor map construction and localization. This paper proposes a crowdsourcing-based indoor semantic map construction and localization method using graph optimization. Using waypoints, semantic landmarks, and Wi-Fi landmarks as nodes and the relevance between waypoints and landmarks (i.e., waypoint–waypoint, waypoint–semantic, waypoint–Wi-Fi, semantic–semantic, and Wi-Fi–Wi-Fi) as edges, the optimization graph is constructed. Initializing the venue map is the single-track semantic map with the highest quality, as determined by a proposed map quality evaluation function. The aligned venue and candidate maps are optimized while satisfying the constraints, with the candidate map exhibiting the highest degree of similarity to the venue map. The lightweight venue map is then updated in terms of waypoint and landmark attributes, as well as the relationship between waypoints and landmarks. To determine a pedestrian’s location on a venue map, similarities between a local map and a venue map are evaluated. Experiments conducted in an office building and shopping mall scenes demonstrate that crowdsourcing-based venue maps are superior to single-track semantic maps. Additionally, the landmark matching-based localization method can achieve a mean localization error of less than 0.5 m on the venue map, compared to 0.6 m in a single-track semantic map.

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Real-time indoor localization using smartphone magnetic with LSTM networks

Cited by 25 publications

References 41 publications

Multi-Sensor Fusion-Based Indoor Single-Track Semantic Map Construction and Localization

Multi-Sensor Fusion-Based Indoor Single-Track Semantic Map Construction and Localization

A Survey of Magnetic-Field-Based Indoor Localization

Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization

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