MagPIE: A dataset for indoor positioning with magnetic anomalies

Hanley, David; Faustino, Alexander B.; Zelman, Scott D.; Degenhardt, David A.

doi:10.1109/ipin.2017.8115961

Cited by 39 publications

(28 citation statements)

References 16 publications

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“…To foster reproducibility and comparability in indoor positioning research, several databases have recently been made available to the public [10,[16][17][18][19][20]. More specifically, Table 1 presents public databases that we have found available on-line and which can be used to train a WiFi RSS-based IPS.…”

Section: Introductionmentioning

confidence: 99%

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Mendoza-Silva

Richter

Torres-Sospedra

et al. 2018

Data

128

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Abstract:WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.

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Section: Introductionmentioning

confidence: 99%

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Mendoza-Silva

Richter

Torres-Sospedra

et al. 2018

Data

128

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“…The following scenario is considered to test Algorithm 1 and Algorithm 2 for robustness against missing data. We consider the ground truth trajectory of a robot available for free from the MagPIE project dataset [32]. The coordinates x and y are measured by eight nodes of a WSN, x, m whose connections are depicted in Fig.…”

Section: Simulationsmentioning

confidence: 99%

Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

2019

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Wireless sensor networks (WSN) are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data. In this paper, we develop an unbiased finite impulse response (UFIR) filtering algorithm for optimal consensus on estimates in distributed WSN. Simulations are provided assuming two possible scenarios with missing data. The results show that the distributed UFIR filter is more robust than the distributed Kalman filter against missing data.

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“…The use of the magnetic field as a source of position information for indoor navigation is a promising novel approach that has gained interest in recent years [1][2][3][4][5][6][7][8]. It relies upon the spatial variation of the ambient magnetic field which is typically due to ferromagnetic material in the structures of buildings and to a lesser extent due to the presence of e.g.…”

Section: Introductionmentioning

confidence: 99%

Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps

Kok

Solin

2018

2018 21st International Conference on Information Fusion (FUSION)

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We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping (SLAM) using local anomalies in the magnetic field as a source of position information. These anomalies are due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture. We represent the magnetic field map using a Gaussian process model and take well-known physical properties of the magnetic field into account. We build local maps using three-dimensional hexagonal block tiling. To make our approach computationally tractable we use reduced-rank Gaussian process regression in combination with a Rao-Blackwellised particle filter. We show that it is possible to obtain accurate position and orientation estimates using measurements from a smartphone, and that our approach provides a scalable magnetic field SLAM algorithm in terms of both computational complexity and map storage.

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MagPIE: A dataset for indoor positioning with magnetic anomalies

Cited by 39 publications

References 16 publications

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps

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