SemanticSLAM: Using Environment Landmarks for Unsupervised Indoor Localization

Abdelnasser, Heba; Mohamed, Reham Hussein; Elgohary, Ahmed; Alzantot, Moustafa; Wang, He; Sen, Souvik; Choudhury, Romit Roy; Youssef, Moustafa

doi:10.1109/tmc.2015.2478451

Cited by 158 publications

(85 citation statements)

References 47 publications

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“…They analyzed the characteristics of geomagnetic data and proposed geomagnetic maps and geomagnetic matching methods based on position and orientation. In reference [83], 90% of the positioning accuracy could reach 1.64 m and 50% could reach 0.71 m.…”

Section: Geomagnetic-based Indoor Positioning Technologymentioning

confidence: 99%

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Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

197

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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Section: Geomagnetic-based Indoor Positioning Technologymentioning

confidence: 99%

“…Chung et al [83] used geomagnetic signals as fingerprint data to locate indoor fingerprints. They analyzed the characteristics of geomagnetic data and proposed geomagnetic maps and geomagnetic matching methods based on position and orientation.…”

Section: Geomagnetic-based Indoor Positioning Technologymentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

197

125

View full text Add to dashboard Cite

show abstract

“…In the first level, we firstly distinguish the elevator from the other motion states by exploiting the unique acceleration pattern of an elevator [23]. For the whole period of taking an elevator, the process includes standing still to wait for the elevator, entering the elevator, standing inside, and finally walking out of it.…”

Section: Landmark Identification Modulementioning

confidence: 99%

Heading estimation fusing inertial sensors and landmarks for indoor navigation using a smartphone in the pocket

Deng

et al. 2017

J Wireless Com Network

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Principal component analysis (PCA)-based approach for user heading estimation using a smartphone in the pocket suffers from an inaccurate estimation of device attitude, which plays a central role in both obtaining acceleration signals in the horizontal plane and the ultimate global walking direction extraction. To solve this problem, we propose a novel heading estimation approach based on two unscented Kalman filters (UKFs) fusing inertial sensors and landmarks. The first UKF is developed for the recalibration of device attitude estimation. We mathematically derive the measurement equation connecting observed user heading from landmarks with the quaternion vector representing device attitude. To decrease the nonlinearity of the measurement equation and make the filter more robust, we deploy the difference between user heading derived from the landmark and estimation result of PCA-based approach as the observation variable. The second UKF is developed for user heading estimation fusing estimation results of PCA-based approach and observed user headings from landmarks. Besides, we develop a robust landmark identification method by exploiting the acceleration and device pitch patterns, while noisy barometers are no longer required as previous methods. Experiments show that the proposed landmark-aided user heading estimation approach may improve accuracy performance significantly, which is very useful for continuous indoor navigation.

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“…SemanticSLAM [32] extracts indoor magnetic field anomalies using an unsupervised-learning approach to identify locations. Another study designed a system that uses ambient magnetic field anomalies to build magnetic maps for indoor localization in [33].…”

Section: Introductionmentioning

confidence: 99%

An Infrastructure-Free Indoor Localization Algorithm for Smartphones

Wang

Luo

Men

et al. 2018

Sensors

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Accurate indoor positioning technology provides location-based service for a variety of applications. However, most existing indoor localization approaches (e.g., Wi-Fi and Bluetooth-based methods) rely heavily on positioning infrastructure, which prevents their large-scale deployment and limits the range at which they are applicable. Here, we proposed an infrastructure-free indoor positioning and tracking approach, termed LiMag, which used ubiquitous magnetic field and ambient lights (e.g., fluorescent, incandescent, and light-emitting diodes (LEDs)) without containing modulated information. We conducted an in-depth study on both the advantages and the challenges in leveraging magnetic field and ambient light intensity for indoor localization. Based on the insights from this study, we established a hybrid observation model that took full advantage of both the magnetic field and ambient light signals. To address the low discernibility of the hybrid observation model, LiMag first generated a single-step fingerprint model by vectorizing consecutive hybrid observations within each step. In order to accurately track users, a lightweight single-step tracking algorithm based on the single-step fingerprints and the particle filter framework was designed. LiMag leveraged the walking information of users and several single-step fingerprints to generate long trajectory fingerprints that exhibited much higher location differentiation ability than the single-step fingerprint. To accelerate particle convergence and eliminate the accumulative error of single-step tracking algorithm, a long trajectory calibration scheme based on long trajectory fingerprints was also introduced. An undirected weighted graph model was constructed to decrease the computational overhead resulting from this long trajectory matching. In addition to typical indoor scenarios including offices, shopping malls and parking lots, we also conducted experiments in more challenging scenarios, including large open-plan areas as well as environments characterized by strong sunlight. Our proposed algorithm achieved a 75th percentile localization accuracy of 1.8 m and 2.2 m, respectively, in the office and shopping mall tested. In conclusion, our LiMag algorithm provided location-based service of infrastructure-free with significantly improved localization accuracy and coverage, as well as satisfactory robustness inside complex indoor environments.

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SemanticSLAM: Using Environment Landmarks for Unsupervised Indoor Localization

Cited by 158 publications

References 47 publications

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Heading estimation fusing inertial sensors and landmarks for indoor navigation using a smartphone in the pocket

An Infrastructure-Free Indoor Localization Algorithm for Smartphones

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