LiPro: light-based indoor positioning with rotating handheld devices

Xie, Bin; Gong, Shimin; Tan, Guang

doi:10.1007/s11276-016-1312-1

Cited by 18 publications

(13 citation statements)

References 27 publications

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“…With the help of the guided hand motion for the user and recorded inertial measurements, more than three RSS measurements can be collected by moving the light sensor and enable the usage of classic trilateration. The same design thought is adopted by LIPS system [2] and LiPro system [6]. Obviously, this method suffers from the high inertial measurement error and requires dedicated calibration.…”

Section: Vlp Using Less Than Three Led Luminairesmentioning

confidence: 99%

“…Most existing work requires the user to manually calibrate the IMU measurements [6] or customize a dedicated hardware with multiple sensors [11]. In our paper, we exploit the projective geometry for angle calibration.…”

Section: Angle Calibrationmentioning

confidence: 99%

“…Therefore, recently work has focused on VLP using a single luminaire. However, the existing work mainly depends on dedicated hardware [2]- [5] or the user's hand gestures [2], [6]. These limitations bring in big difficulties to deploy practical VLP systems in the indoor environments in which the users are more willing to use their own smartphones for localization.…”

Section: Introductionmentioning

confidence: 99%

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Visible Light Positioning Using A Single LED Luminaire

2019

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Visible light positioning (VLP) has developed rapidly with the ubiquitous lighting infrastructure in recent years. Based on classic trilateration or triangulation, VLP usually requires at least three light sources which makes it infeasible in some scenarios where sparse LED luminaries are mounted. In this paper, we focus on VLP in such scenarios and propose a VLP method based on a single LED luminaire as the light transmitter and commercial off-the-shelf smartphone as the receiver. A critical problem that needs to be addressed is to derive the accurate orientation of the smartphone. Although the inertial measurement unit of the smartphone can be used to obtain the orientation, the measurement error severely degrades the positioning accuracy. To address the measurement error, this paper innovates in exploiting projective geometry to calibrate the orientation measurements. We build an experimental platform to evaluate the proposed VLP method. The experimental results show that the proposed VLP method can achieve a submeter accuracy and the indoor positioning errors are within 0.16 m by 90%.

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Section: Vlp Using Less Than Three Led Luminairesmentioning

confidence: 99%

Section: Angle Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visible Light Positioning Using A Single LED Luminaire

2019

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“…At present, indoor positioning technology [1], [2] has been applied in many industries, such as navigation, medical, pension, industrial manufacturing, warehouse, and logistics, and the improvement in service level and management efficiency is obvious. In the aspect of intelligent navigation based on navigation technology, it can be used not only to guide users to their destination, but also to realize the interaction between the service side and the end-users, to achieve independent navigation, or to achieve more accurate marketing management.…”

Section: Introductionmentioning

confidence: 99%

A New Indoor Location Algorithm Based on Radio Frequency Fingerprint Matching

Lu¹

2020

IEEE Access

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Indoor positioning technology plays an important role in the production and life of today's society. Modern people's clothing, food, housing, and transportation are increasingly closely related to Location Based Service. In this paper, an indoor location algorithm based on the radio frequency fingerprint matching is offered. In the offline phase, by deploying a small number of radio signal intensity location regions, the location selection mechanism is used to predict the location of mobile terminals, which can avoid the instability of radio frequency signals caused by time-lapse and the impact of changes in Access Point (AP) facilities. In the online stage, the location engine receives real-time Radio Frequency fingerprints and updates the fingerprint location by mapping time and equipment. Finally, several main localization algorithms are simulated and compared to the aspect of location accuracy. The proposed new method reduces the location error, improves the location accuracy by 1 to 2 meters, and achieves 100% within 2.5 meters.

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“…Moreover, visible light is used for illumination while allowing for indoor positioning. Therefore, visible light positioning (VLP) is becoming a promising positioning technology [10,11,12,13]. …”

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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LiPro: light-based indoor positioning with rotating handheld devices

Cited by 18 publications

References 27 publications

Visible Light Positioning Using A Single LED Luminaire

Visible Light Positioning Using A Single LED Luminaire

A New Indoor Location Algorithm Based on Radio Frequency Fingerprint Matching

An Infrastructure-Free Indoor Localization Algorithm for Smartphones

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