Indoor positioning system based on sensor fusion for the Blind and Visually Impaired

Gallagher, Thomas J.; Wise, Elyse; Li, Binghao; Dempster, Andrew G.; Rizos, Chris; Ramsey-Stewart, Euan

doi:10.1109/ipin.2012.6418882

Cited by 56 publications

(35 citation statements)

References 14 publications

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“…Reference [61] presents an indoor positioning system that has been designed in that way, examining the requirements of the BVI in terms of accuracy, reliability and interface design. The system runs locally on mid-range smartphone and relies at its core on a Kalman filter that fuses the information of all the sensors available on the phone Wi-Fi chipset, accelerometers and magnetic field sensor.…”

Section: Wifi Multimodal Systemsmentioning

confidence: 99%

Blind MuseumTourer: A System for Self-Guided Tours in Museums and Blind Indoor Navigation

Μηλιώνης

Sampson

2018

Technologies

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Notably valuable efforts have focused on helping people with special needs. In this work, we build upon the experience from the BlindHelper smartphone outdoor pedestrian navigation app and present Blind MuseumTourer, a system for indoor interactive autonomous navigation for blind and visually impaired persons and groups (e.g., pupils), which has primarily addressed blind or visually impaired (BVI) accessibility and self-guided tours in museums. A pilot prototype has been developed and is currently under evaluation at the Tactual Museum with the collaboration of the Lighthouse for the Blind of Greece. This paper describes the functionality of the application and evaluates candidate indoor location determination technologies, such as wireless local area network (WLAN) and surface-mounted assistive tactile route indications combined with Bluetooth low energy (BLE) beacons and inertial dead-reckoning functionality, to come up with a reliable and highly accurate indoor positioning system adopting the latter solution. The developed concepts, including map matching, a key concept for indoor navigation, apply in a similar way to other indoor guidance use cases involving complex indoor places, such as in hospitals, shopping malls, airports, train stations, public and municipality buildings, office buildings, university buildings, hotel resorts, passenger ships, etc. The presented Android application is effectively a Blind IndoorGuide system for accurate and reliable blind indoor navigation.

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Section: Wifi Multimodal Systemsmentioning

confidence: 99%

Blind MuseumTourer: A System for Self-Guided Tours in Museums and Blind Indoor Navigation

Μηλιώνης

Sampson

2018

Technologies

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“…But their instability makes it necessary to combine it with other sources of information. A hybrid solution can be implemented based on the smartphone sensors, combining the absolute positioning, through Wi-Fi signals, and the relative positioning provided by the inertial sensors, like accelerometer and magnetic field sensor, allowing the estimation of speed and direction [17]. Other solutions added the barometer to estimate the floor level in the building [18].…”

Section: Some Solutions For the Blind And Visually Impaired Personsmentioning

confidence: 99%

An indoor navigation architecture using variable data sources for blind and visually impaired persons

Gomes

Cunha

Morais

2018

2018 13th Iberian Conference on Information Systems and Technologies (CISTI)

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-Contrary to outdoor positioning and navigation systems, there isn't a counterpart global solution for indoor environments. Usually, the deployment of an indoor positioning system must be adapted case by case, according to the infrastructure and the objective of the localization. A particularly delicate case is related with persons who are blind or visually impaired. A robust and easy to use indoor navigation solution would be extremely useful, but this would also be particularly difficult to develop, given the special requirements of the system that would have to be more accurate and user friendly than a general solution. This paper presents a contribute to this subject, by proposing a hybrid indoor positioning system adaptable to the surrounding indoor structure, and dealing with different types of signals to increase accuracy. This would permit lower the deployment costs, since it could be done gradually, beginning with the likely existing Wi-Fi infrastructure to get a fairy accuracy up to a high accuracy using visual tags and NFC tags when necessary and possible.

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“…Unlike existing sensor fusion algorithms for tracking a user indoors using a motion model and WiFi [5], [15], [24], we do not know the RF signal map in advance: our objective is to reconstruct it from the data acquired by a freely moving pedestrian wearing a commercial grade smartphone in her pocket, with limited or no human intervention.…”

Section: A Objective: Crowd-sourced Rf Mapping From the Pocketmentioning

confidence: 99%

“…Among the recently-published hybrid localization techniques using PDR on smartphones, we notice that they typically require the user to hand-held the smartphone during the walk [15], [17], [25]. For better accuracy in the estimation of the step length or even the heading direction, it is preferable to use foot-mounted sensors [37], [38].…”

Section: ) Pedestrian Dead Reckoning and Its Limitationsmentioning

confidence: 99%

SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

Mirowski¹,

Ho²,

Yi³

et al. 2013

International Conference on Indoor Positioning and Indoor Navigation

146

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Abstract-Indoor localization typically relies on measuring a collection of RF signals, such as Received Signal Strength (RSS) from WiFi, in conjunction with spatial maps of signal fingerprints. A new technology for localization could arise with the use of 4G LTE telephony small cells, with limited range but with rich signal strength information, namely Reference Signal Received Power (RSRP). In this paper, we propose to combine an ensemble of available sources of RF signals to build multi-modal signal maps that can be used for localization or for network deployment optimization. We primarily rely on Simultaneous Localization and Mapping (SLAM), which provides a solution to the challenge of building a map of observations without knowing the location of the observer. SLAM has recently been extended to incorporate signal strength from WiFi in the so-called WiFi-SLAM. In parallel to WiFi-SLAM, other localization algorithms have been developed that exploit the inertial motion sensors and a known map of either WiFi RSS or of magnetic field magnitude. In our study, we use all the measurements that can be acquired by an off-the-shelf smartphone and crowd-source the data collection from several experimenters walking freely through a building, collecting time-stamped WiFi and Bluetooth RSS, 4G LTE RSRP, magnetic field magnitude, GPS reference points when outdoors, Near-Field Communication (NFC) readings at specific landmarks and pedestrian dead reckoning based on inertial data. We resolve the location of all the users using a modified version of Graph-SLAM optimization of the users poses with a collection of absolute location and pairwise constraints that incorporates multi-modal signal similarity. We demonstrate that we can recover the user positions and thus simultaneously generate dense signal maps for each WiFi access point and 4G LTE small cell, "from the pocket". Finally, we demonstrate the localization performance using selected single modalities, such as only WiFi and the WiFi signal maps that we generated.

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Indoor positioning system based on sensor fusion for the Blind and Visually Impaired

Cited by 56 publications

References 14 publications

Blind MuseumTourer: A System for Self-Guided Tours in Museums and Blind Indoor Navigation

Blind MuseumTourer: A System for Self-Guided Tours in Museums and Blind Indoor Navigation

An indoor navigation architecture using variable data sources for blind and visually impaired persons

SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

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