Indoor WIFI localization on mobile devices

Boonsriwai, Sujittra; Apavatjrut, Anya

doi:10.1109/ecticon.2013.6559592

Cited by 44 publications

(20 citation statements)

References 5 publications

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“…In recent years, several indoor localization algorithms based on Wi-Fi sensors have been introduced [6], [7], [8]. This popularity can be explained by two reasons.…”

Section: B Related Workmentioning

confidence: 99%

Topological localization using Wi-Fi and vision merged into FABMAP framework

Nowakowski¹,

Joly²,

Dalibard³

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper introduces a topological localization algorithm that uses visual and Wi-Fi data. Its main contribution is a novel way of merging data from these sensors. By making Wi-Fi signature suited to FABMAP algorithm, it develops an early-fusion framework that solves global localization and kidnapped robot problem. The resulting algorithm is tested and compared to FABMAP visual localization, over data acquired by a Pepper robot in an office building. Several constraints were applied during acquisition to make the experiment fitted to real-life scenarios. Without any tuning, early-fusion surpasses the performances of visual localization by a significant margin: 94% of estimated localizations are less than 5m away from ground truth compared to 81% with visual localization.

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“…In recent years, several indoor localization algorithms based on Wi-Fi sensors have been introduced [6], [7], [8]. This popularity can be explained by two reasons.…”

Section: B Related Workmentioning

confidence: 99%

Topological localization using Wi-Fi and vision merged into FABMAP framework

Nowakowski¹,

Joly²,

Dalibard³

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Such devices are also equipped with multiple sensors such as: accelerometer, gyroscope and magnetometer. Usage of the mentioned sensors can help eliminate localization errors occurring in the pattern method RSSI (Radio Signal Strength Index) based methods are very convenient to implement and such method is presented in [7]. The method has two steps, at first RSSI fingerprint map of specified area is created and then trilateration (or multitrilateration) is applied at the second stage.…”

Section: Introductionmentioning

confidence: 99%

Indoor localization of objects based on RSSI and MEMS sensors

Grzechca

Wróbel

Bielecki

2014

2014 14th International Symposium on Communications and Information Technologies (ISCIT)

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There is an increasing demand for indoor localization and tracking objects with high accuracy. The GPS system is not efficient for positioning in such areas. Rising number of smartphones allow for their utilization in indoor positioning system. RSSI (Radio Signal Strength Index) is commonly applied but it is limited to pattern matching methods. A map called fingerprint is created at the first stage. However its accuracy is low and very often not satisfying. Authors propose a method for indoor localization based on smartphone available sensors and RSSI system. First results shows positioning improvement and better accuracy with respect to commonly used RSSI map. The method introduces phone orientation with respect to AP (access point), direction of the object move. Results describe relatively low error of our method comparing to RSSI system.

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“…For personal navigation systems, the low-precision localization result would likely guide users to an incorrect destination. Technically, the large error of WiFi-based localization results from the fact that different locations with similar WiFi signal strength do exist in typical indoor environments [2] [3].…”

Section: Introductionmentioning

confidence: 99%

HILL: A Hybrid Indoor Localization Scheme

Kharidia

Sampalli

et al. 2014

2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks

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Localization is a fundamental operation in wireless networks. Location determination is normally accomplished using the Global Positioning System (GPS) for outdoor applications. For indoor localization, GPS does not work due to the lack of the line of sight to satellites. High-precision indoor localization is critical to many personal and business applications. WiFi-based indoor localization was proposed to be a practical method to locate WiFi-enabled devices due to the popularity of WiFi networks. However, it suffers from large localization errors. Our experimental results indicate that this scheme consistently leads to an average error around 3 meters. The existence of different locations with similar WiFi signal strength is the reason behind the large errors. To improve the localization precision, a hybrid indoor localization scheme, HILL, is proposed in this paper. Inspired by the fact that a large number of WiFi-enabled mobile devices have been deployed, HILL uses 3 phases to improve the precision ofWiFi-based localization. First of all, it measures the distances between each pair of peer devices through acoustic ranging. Secondly, the Classical Metric Multidimensional Scaling (MDS) method is applied to the collected distances, which results in a graph consistent with the distances. Finally, the graph generated by MDS is embedded onto the graph corresponding to WiFi-based localization in order to achieve high localization precision. Our experimental results indicate that the average localization error of HILL is about 1 meter.

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Indoor WIFI localization on mobile devices

Cited by 44 publications

References 5 publications

Topological localization using Wi-Fi and vision merged into FABMAP framework

Topological localization using Wi-Fi and vision merged into FABMAP framework

Indoor localization of objects based on RSSI and MEMS sensors

HILL: A Hybrid Indoor Localization Scheme

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