IEEE Signal Processing Magazine · 2017 · 180 citations · 44 references
HumanitiesGeoinformaticsBuilt EnvironmentLocalizationMobile ComputingIndoor LocalizationLandmark-based LocalizationLocalization UncertaintyLocation AnalysisMap-based LocalizationRobot LocalizationIndoor 3D MappingIndoor Landmark LocalizationIndoor Localization UncertaintyIndoor NavigationLocalization Accuracy EstimationMachine Learning For LocalizationWireless Local Area Network Localization
We present the results, experiences, and lessons learned from comparing a diverse set of indoor location technologies during the Microsoft Indoor Localization Competition. Over the last four years (2014-2017), more than 100 teams from academia and industry deployed their indoor location solutions in realistic, unfamiliar environments, allowing us to directly compare their accuracies and overhead. In this article, we provide an analysis of this four-year-long evaluation study's results and discuss the current state of the art in indoor localization.
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The active badge location system
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The Cricket location-support system
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