Characterization of the indoor magnetic field for applications in Localization and Mapping

Angermann, Michael; Frassl, Martin; Doniec, Marek; Julian, Brian J.; Robertson, Patrick

doi:10.1109/ipin.2012.6418864

Cited by 108 publications

(106 citation statements)

References 10 publications

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“…We fit a nominal field strength by dividing the area into grid cells with size 30 cm and taking the empirical mean of all of the magnetic field readings taken within that cell. Angermann et al [23] create a similar map of the ambient indoor magnetic field, using the local structure in order to localize robots within a workspace.…”

Section: A Sensor Characterizationmentioning

confidence: 99%

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Dames

Schwager

Rus

et al. 2016

IEEE Robot. Autom. Lett.

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Abstract-Magnetic Anomaly Detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.

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Section: A Sensor Characterizationmentioning

confidence: 99%

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Dames

Schwager

Rus

et al. 2016

IEEE Robot. Autom. Lett.

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“…Similarly, a particle-filtering-based engine was designed in [21] to localize and track users with a given geomagnetic fingerprint map. In general, these techniques require special hardware [13,[24][25][26][27] or dense samples of magnetic data to build a fingerprint map with high training overhead [13,21,23,42]. In contrast, FOLLOWME utilizes the features of geomagnetic field without customized hardware and avoids the time-consuming map construction process.…”

Section: Related Workmentioning

confidence: 99%

Last-Mile Navigation Using Smartphones

Shu

Shin

et al. 2015

Proceedings of the 21st Annual International Conference on Mobile Computing and Networking

154

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Although GPS has become a standard component of smartphones, providing accurate navigation during the last portion of a trip remains an important but unsolved problem. Despite extensive research on localization, the limited resolution of a map imposes restrictions on the navigation engine in both indoor and outdoor environments. To bridge the gap between the end position obtained from legacy navigation services and the real destination, we propose FOLLOWME, a "last-mile" navigation system to enable plugand-play navigation in indoor and semi-outdoor environments. FOLLOWME exploits the ubiquitous, stable geomagnetic field and natural walking patterns to navigate the users to the same destination taken by an earlier traveler. Unlike existing localization and navigation systems, FOLLOWME is infrastructure-free, energyefficient and cost-saving. We implemented FOLLOWME on smartphones, and evaluated it in a four-story campus building with a testing area of 2000m 2 . Our experimental results with 5 users show that 95% of spatial errors during navigation were 2m or less with at least 50% energy savings over a benchmark system.

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“…Indoors however the observed magnetic field will be perturbed by elements of the structure of the building, including steel beams, metal reinforcement in concrete and electrical currents [15], [16].…”

Section: Magnetic Field Based Positioningmentioning

confidence: 99%

Magnetic Field Swimmer Positioning

Marshall

2015

IEEE Sensors J.

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Abstract-Several projects have tracked the movement of swimmers in pools using body worn inertial measurement units. In swimming, inertial sensing is subject to large amounts of drift and accumulated error which can only be corrected for after a complete length has been swum. In this article, we present a new method for tracking swimmers by detecting variations in the magnetic field caused by the structure of pools. This method is complementary to inertial positioning, as it allows the direct extraction of position without requiring post-processing, and unlike inertial sensing which loses accuracy over time, magnetic field tracking becomes increasingly accurate towards the end of a length.

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Characterization of the indoor magnetic field for applications in Localization and Mapping

Cited by 108 publications

References 10 publications

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Last-Mile Navigation Using Smartphones

Magnetic Field Swimmer Positioning

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