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

Mathieu Nowakowski 1, 2 Cyril Joly 1 Sébastien Dalibard 2 Nicolas Garcia 2 Fabien Moutarde 1
1 CAOR - Centre de Robotique
2 SoftBank Robotics Europe
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.
Keywords : data fusion topological localization kidnapped robot low- cost sensors
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Communication dans un congrès
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Soumis le : mercredi 24 janvier 2018 - 14:03:48
Dernière modification le : lundi 12 novembre 2018 - 11:03:37
Document(s) archivé(s) le : jeudi 24 mai 2018 - 18:35:28

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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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