Robust Indoor Localization on a Commercial Smart Phone

Kothari, Nisarg; Kannan, Balajee; Glasgwow, Evan D.; Dias, M. Bernardine

doi:10.1016/j.procs.2012.06.158

Cited by 73 publications

(53 citation statements)

References 7 publications

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“…Nisarg Kothari et al [19] contributed a methodology for Indoor localization using commercial smart phones comprising dead reckoning and WI-FI signal strength fingerprinting to reliably track their own location. The system works by combination of WI-FI with accelerometers, magnetometers and gyroscopes for fast and accurate estimation for localization.The system has a drawback that the performance of WI-FI is restricted by extended database.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of New Application for Mobile Tracking

Bhatia¹

2016

IJITCS

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Abstract-The steady rise of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. Consequently, the location based services has now become one of the most exiting features of the next generation wireless systems. This paper presents the work related to both person oriented and device oriented location based services, where the focus is not only to find the location of person but also to find the location of object (mobile phone).This work is valuable to a user to find the location in cases of kidnapping, loss of phone, security of girl child, location of elder members of the family having weak memory. Also, this type of work is also considered as an active location based service where the user is aware of being tracked with the agreement.

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Section: Related Workmentioning

confidence: 99%

Design and Development of New Application for Mobile Tracking

Bhatia¹

2016

IJITCS

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“…Remarkable accuracy is achieved by Woodman and Harle [32] using a combination of foot-mounted inertial sensors and WiFi positioning, and Quigley et al [26] who rely on WiFi and visual information. Li et al [22] set the state of the art for pedestrian tracking on hand-held smartphones using only built-in inertial sensors and map knowledge, whereas combinations of WiFi and pedestrian dead reckoning are described in [18,27,34].…”

Section: Sensor Fusion For Localization and Trackingmentioning

confidence: 99%

Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning

Hilsenbeck

Bobkov

Schroth

et al. 2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

119

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We propose a graph-based, low-complexity sensor fusion approach for ubiquitous pedestrian indoor positioning using mobile devices. We employ our fusion technique to combine relative motion information based on step detection with WiFi signal strength measurements. The method is based on the well-known particle filter methodology. In contrast to previous work, we provide a probabilistic model for location estimation that is formulated directly on a fully discretized, graph-based representation of the indoor environment. We generate this graph by adaptive quantization of the indoor space, removing irrelevant degrees of freedom from the estimation problem. We evaluate the proposed method in two realistic indoor environments using real data collected from smartphones. In total, our dataset spans about 20 kilometers in distance walked and includes 13 users and four different mobile device types. Our results demonstrate that the filter requires an order of magnitude less particles than state-of-theart approaches while maintaining an accuracy of a few meters. The proposed low-complexity solution not only enables indoor positioning on less powerful mobile devices, but also saves much-needed resources for location-based end-user applications which run on top of a localization service.

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“…The dead reckoning is performed in a pre-processing step, and all the particles in the filter are periodically updated based on a model of the variance of the dead reckoning estimate. The details of the localization technique are outlined in earlier work [13].…”

Section: A Indoor Localizationmentioning

confidence: 99%

Predictive indoor navigation using commercial smart-phones

Kannan

Meneguzzi

Dias

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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Abstract-Low-cost navigation solutions for indoor environments have a variety of real-world applications ranging from emergency evacuation to mobility aids for people with disabilities. Challenges for indoor navigation include robust localization in the absence of GPS, intuitive recognition of user navigation goals, and efficient route-planning and replanning techniques. In this paper, we present an architecture for indoor navigation that integrates observed behavior for recognizing user navigation goals and estimating future paths without direct input from the user. Our architecture comprises of three core components: effective localization, map representation and route planning, and plan recognition. The outlined architecture is unique in its integration of the core navigation and prediction components. To evaluate the feasibility of the proposed architecture, we develop a prototype application on a commercial smart-phone. The developed application was tested in an indoor environment and was found to accurately predict intended destination and to provide effective navigation guidance to the user.

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Robust Indoor Localization on a Commercial Smart Phone

Cited by 73 publications

References 7 publications

Design and Development of New Application for Mobile Tracking

Design and Development of New Application for Mobile Tracking

Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning

Predictive indoor navigation using commercial smart-phones

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