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Topological Localization using Wi-Fi and Vision merged into FABMAP framework

Abstract : — 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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Contributor : Fabien Moutarde <>
Submitted on : Wednesday, January 24, 2018 - 2:03:48 PM
Last modification on : Thursday, September 24, 2020 - 5:04:02 PM
Long-term archiving on: : Thursday, May 24, 2018 - 6:35:28 PM

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Mathieu Nowakowski, Cyril Joly, Sébastien Dalibard, Nicolas Garcia, Fabien Moutarde. Topological Localization using Wi-Fi and Vision merged into FABMAP framework. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep 2017, Vancouver, France. ⟨10.1109/IROS.2017.8206171⟩. ⟨hal-01691920⟩

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