Indoor localization using smartphone inertial sensors

Liu, Yang; Dashti, Marzieh; Rahman, Mohd Amiruddin Abd; Zhang, Jie

doi:10.1109/wpnc.2014.6843288

Cited by 31 publications

(13 citation statements)

References 6 publications

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“…It is important to note that to minimize the energy consumption associated to the algorithm computational cost, an estimate of the distance covered by the user is provided only when a significant change in direction is detected or when the user enters in the static state. This is different with respect to the approach adopted both in [19] and [18] where instead an event is generated each time a step is detected in order to provide a continuous update of the user location. A step counter essentially provides a measure of the distance by counting the number of steps that the user employs for covering the considered distance.…”

Section: A Step Countermentioning

confidence: 99%

“…However, since they are based on an inertial navigation and therefore the localization process is recursive, their accuracy is very poor and they need to be integrated with other solutions whenever it is possible. Efficient methods for improving the accuracy of IMU-based systems have been discussed in [18], where results obtained in a computer simulated environment have been analysed and also in [19] where instead experimental results have been reported. Methods proposed in these papers are based on a particle filter algorithm for interpolating information provided by the IMU with constraints from the floor map, that needs to be known apriori.…”

Section: Introductionmentioning

confidence: 99%

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A smartphone-based indoor localization system for visually impaired people

Basso

Frigo

Giorgi

2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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The World Health Organization assesses the number of visually impaired people to be nearly 285 million in August 2014, of whom 39 million are blind. One of the most important discomfort factors for these persons is known to be the difficulty in moving and orienting by themselves in unfamiliar surroundings. Nowadays, several devices are currently available for supporting these persons in there everyday life. To this end, the attention in this paper is mainly focused on the localization and navigation in indoor environment. The solution adopted in this paper consists in populating a database of virtual maps, that the user itself contributes to create by exploring the surrounding environment. An inertial platform, represented by a set of sensors (basically accelerometer, gyroscope, electronic compass) placed in a device worn by user, is used at the purpose as sensing system. This approach does not require the installation of external equipments since it relies on a smartphone, which is used both as measurement platform and user interface, and, in particular, does not require any a-priori knowledge of the indoor environment. The application will be described in the paper where some experimental preliminary results will also be discussed.This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.978-1-4799-6477-2/15/$31.00 ©2015 IEEE

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Section: A Step Countermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A smartphone-based indoor localization system for visually impaired people

Basso

Frigo

Giorgi

2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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“…In this regard, an inertial navigation system (INS) is a navigation aid that uses motion sensors and rotation sensors to continuously calculate the position, orientation, and velocity (i.e., direction and speed of movement) of a moving object without the need for external references [17]. Modern smartphones are equipped with inertial sensors (i.e., accelerometers and gyroscopes) and this makes possible the realization of low-cost indoor localization systems [18].…”

Section: G Other Technologiesmentioning

confidence: 99%

A survey on indoor positioning systems

Mainetti

Patrono

Sergi

2014

2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

264

136

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This paper aims to provide the reader with a review of the main technologies explored in the literature to solve the indoor localization issue. Furthermore, some systems that use these enabling technologies in real-world scenarios are presented and discussed. This could deliver a better understanding of the state-of-the-art and motivate new research efforts in this promising field. Finally, focusing on one of the major challenges in the indoor localization field, i.e., the indoor animal tracking, existing indoor tracking systems have been reviewed and compared by analyzing advantages and drawbacks.

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“…estimation have been investigated, including methods based on cooperation and learning [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Enhanced indoor localization through crowd sensing

Arias-de-Reyna

Dardari

Closas

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In localization tasks, one typically assumes a statistical model of the observations, where the model quantifies the observations by exploiting interrelationships based on geometry. These models might incorporate unknown parameters that, in general, are functions of space. In this article, we propose a crowd sensing method for estimating a spatial field of a quantity (e.g., ranging biases due to line-of-sight/non-line-of-sight or path-loss parameter) allowing for improved indoor localization. Our method takes advantage of the information provided by various users that navigate the area of interest. The proposed learning approach is based on Gaussian processes and its computational cost does not increase with the number of measurements. We present numerical results that show how the proposed method estimates a spatial field of biases and how these estimates lead to much improved performance in estimation of user positions.

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Indoor localization using smartphone inertial sensors

Cited by 31 publications

References 6 publications

A smartphone-based indoor localization system for visually impaired people

A smartphone-based indoor localization system for visually impaired people

A survey on indoor positioning systems

Enhanced indoor localization through crowd sensing

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